From 5b4f904d725757f77232774621b829d6563b3960 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 30 Sep 2026 07:02:22 -0400
Subject: [PATCH 01/70] Calibrate pension-age Housing Benefit to DWP's GB
 figures

Replace the OBR table 4.9 Housing Benefit target, which is GB DWP-funded
spending but was compared with UK-wide modelled Housing Benefit, with DWP
Spring 2026 targets for Housing Benefit spending and claims over Pension
Credit qualifying age in Great Britain. Targets can now name the countries
they cover.

The figures under Pension Credit qualifying age are kept but not
calibrated: the model pays working-age Housing Benefit to too few records
for a target to be met without concentrating it on a handful of them.

Stop drawing would_claim_uc for benefit units whose adults have all
reached State Pension age, keeping every other unit's draw unchanged.

Require policyengine-uk 2.102.5, which lets pension-age families make new
Housing Benefit claims (PolicyEngine/policyengine-uk#1901).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .gitignore                                    |   2 +
 changelog.d/hb-dwp-age-targets.fixed.md       |   1 +
 policyengine_uk_data/datasets/frs.py          |  42 +++
 .../targets/build_loss_matrix.py              |  14 +-
 policyengine_uk_data/targets/schema.py        |   9 +
 .../targets/sources/dwp_housing_benefit.py    | 190 ++++++++++++
 policyengine_uk_data/targets/sources/obr.py   |   7 +-
 .../tests/test_dwp_housing_benefit_targets.py | 277 ++++++++++++++++++
 .../tests/test_would_claim_uc_pension_age.py  |  56 ++++
 pyproject.toml                                |   3 +-
 uv.lock                                       |  87 +++++-
 11 files changed, 674 insertions(+), 14 deletions(-)
 create mode 100644 changelog.d/hb-dwp-age-targets.fixed.md
 create mode 100644 policyengine_uk_data/targets/sources/dwp_housing_benefit.py
 create mode 100644 policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
 create mode 100644 policyengine_uk_data/tests/test_would_claim_uc_pension_age.py

diff --git a/.gitignore b/.gitignore
index 9a741bc49..04136e33e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,5 @@
 **/_build
 !policyengine_uk_data/storage/*.csv
 **/version.json
+# Build output: household weights by local area, derived from FRS microdata
+policyengine_uk_data/storage/local_geography_weights.csv.gz
diff --git a/changelog.d/hb-dwp-age-targets.fixed.md b/changelog.d/hb-dwp-age-targets.fixed.md
new file mode 100644
index 000000000..2c4c56708
--- /dev/null
+++ b/changelog.d/hb-dwp-age-targets.fixed.md
@@ -0,0 +1 @@
+Calibrate pension-age Housing Benefit to DWP's Great Britain spending and claims over Pension Credit qualifying age, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..4a95842bb 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -392,6 +392,39 @@ def derive_is_parent_from_frs_microdata(
     return is_adult_record & has_dependent_children
 
 
+def derive_every_adult_over_state_pension_age(
+    person_benunit_ids,
+    is_adult,
+    is_over_state_pension_age,
+    benunit_ids,
+) -> np.ndarray:
+    """Identify benefit units with an adult, all of whose adults have reached
+    State Pension age.
+
+    Such a unit cannot claim Universal Credit (Welfare Reform Act 2012
+    s.4(1)(b)). The inputs are policyengine-uk's ``is_adult`` and
+    ``is_SP_age``, as in the pension-age route of its
+    ``housing_benefit_eligible``, so an 18 or 19 year old dependant makes the
+    unit working-age here as it does there.
+    """
+
+    is_adult = np.asarray(is_adult, dtype=bool)
+    over = is_adult & np.asarray(is_over_state_pension_age, dtype=bool)
+    counts = (
+        pd.DataFrame(
+            {
+                "benunit": np.asarray(person_benunit_ids),
+                "adults": is_adult.astype(int),
+                "over": over.astype(int),
+            }
+        )
+        .groupby("benunit")[["adults", "over"]]
+        .sum()
+        .reindex(np.asarray(benunit_ids), fill_value=0)
+    )
+    return ((counts.adults > 0) & (counts.over == counts.adults)).to_numpy()
+
+
 def _as_non_negative_array(values) -> np.ndarray:
     values = np.asarray(values, dtype=float)
     return np.maximum(np.nan_to_num(values, nan=0.0), 0.0)
@@ -1537,10 +1570,19 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
         pension_credit_rate,
         reported_mask=_reported_benunit_mask("pension_credit_reported"),
     )
+    # A benefit unit whose adults have all reached State Pension age cannot
+    # claim Universal Credit, so it never gets would_claim_uc, even if it
+    # reports UC. The draw still covers every unit, so the random stream and
+    # every other unit's value are unchanged.
     pe_benunit["would_claim_uc"] = assign_takeup_with_reported_anchors(
         generator.random(len(pe_benunit)),
         universal_credit_rate,
         reported_mask=_reported_benunit_mask("universal_credit_reported"),
+    ) & ~derive_every_adult_over_state_pension_age(
+        person_benunit_ids=sim.calculate("person_benunit_id", year).values,
+        is_adult=sim.calculate("is_adult", year).values,
+        is_over_state_pension_age=sim.calculate("is_SP_age", year).values,
+        benunit_ids=pe_benunit.benunit_id,
     )
     pe_benunit["would_claim_tfc"] = generator.random(len(pe_benunit)) < tfc_rate
 
diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 9552aeea2..44b34272c 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -113,7 +113,7 @@ def create_target_matrix(
             col = _compute_column(target, ctx, year)
             if col is None:
                 continue
-            df[target.name] = col
+            df[target.name] = restrict_to_countries(col, ctx.country, target.countries)
             target_names.append(target.name)
             target_values.append(val)
         except Exception as e:
@@ -122,6 +122,18 @@ def create_target_matrix(
     return df, pd.Series(target_values, index=target_names)
 
 
+def restrict_to_countries(column, household_country, countries):
+    """Zero a household column outside the countries a target covers.
+
+    ``countries`` of None means the target covers the whole UK, and the
+    column is returned unchanged.
+    """
+    if countries is None:
+        return column
+    in_scope = np.isin(np.asarray(household_country), countries)
+    return np.asarray(column, dtype=float) * in_scope
+
+
 def _resolve_value(target: Target, year: int) -> float | None:
     """Get the target value for a year, falling back to nearest year.
 
diff --git a/policyengine_uk_data/targets/schema.py b/policyengine_uk_data/targets/schema.py
index 97b814678..0a678a228 100644
--- a/policyengine_uk_data/targets/schema.py
+++ b/policyengine_uk_data/targets/schema.py
@@ -19,6 +19,11 @@ class Unit(str, Enum):
     RATE = "rate"
 
 
+# DWP statistics cover Great Britain: benefits for Northern Ireland residents
+# are the Northern Ireland Executive's responsibility.
+GREAT_BRITAIN = ("ENGLAND", "SCOTLAND", "WALES")
+
+
 class Target(BaseModel):
     """A single calibration target from an official statistical source.
 
@@ -41,6 +46,10 @@ class Target(BaseModel):
     is_count: bool = False
     reference_url: str | None = None
     forecast_vintage: str | None = None
+    # Countries a national source covers when that is less than the UK, as
+    # values of the model's `country` variable. The loss matrix column only
+    # counts households in these countries. None means the whole UK.
+    countries: tuple[str, ...] | None = None
 
     # For targets needing custom simulation logic (UC splits,
     # counterfactuals). Excluded from serialisation.
diff --git a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
new file mode 100644
index 000000000..69d17f9b6
--- /dev/null
+++ b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
@@ -0,0 +1,190 @@
+"""DWP Housing Benefit targets by age group.
+
+Housing Benefit spending and caseload over and under Pension Credit
+qualifying age, from DWP's benefit expenditure and caseload tables for the
+Spring Forecast 2026 (Housing benefits sheet, nominal £ million and
+thousands of claims, 2022-23 to 2024-25 outturn, forecast after). Financial
+year 2025-26 is stored as 2025.
+
+Coverage is Great Britain: Housing Benefit for Northern Ireland residents is
+paid under Northern Ireland legislation and sits outside DWP's figures, so
+the model columns count GB households only.
+
+These targets replace OBR EFO table 4.9 "Housing benefit (not on JSA)".
+That line is DWP-funded spending only, while DWP's tables count all
+Housing Benefit paid, including the part local authorities fund (£0.79bn
+in 2025-26). The age split sums to that full amount, so targeting both
+would ask for two different GB totals.
+
+DWP splits claims by benefit rules rather than by age alone (Notes, note
+5): the two lines equal its Pension Credit plus State Pension benefit
+groups and its ESA plus other working-age groups. Under regulation 5 of
+both Housing Benefit Regulations 2006 (SI 2006/213 and 2006/214), the
+pension-age rules apply when the claimant or partner has reached the
+qualifying age for Pension Credit, unless either is on Universal Credit,
+Income Support, income-based JSA or income-related ESA. A benefit unit is
+therefore over Pension Credit qualifying age here when one of its adults
+has reached State Pension age and it gets none of those benefits. Mixed-age
+couples who kept pension-age Housing Benefit after May 2019 fall in the
+older group, as they do in DWP's benefit groups.
+
+Only the older group is calibrated. policyengine-uk pays working-age
+Housing Benefit only as a continuing award to families that report it and
+do not claim Universal Credit: 267 of the 770 working-age records that
+report it, about 24,000 weighted claims in 2025-26 against DWP's 460,000.
+DWP's working-age figure also includes temporary and supported
+accommodation (together £5.2bn of Housing Benefit in 2025-26, not split by
+age), which the FRS barely samples. A test build on 2026-09-30 that also
+targeted the younger group reached DWP's £5.8bn by loading it onto about
+three effective records, and fitted the other targets no better. The
+younger group's figures stay here for diagnostics and tests.
+
+Source: https://www.gov.uk/government/publications/benefit-expenditure-and-caseload-tables-2026
+"""
+
+import numpy as np
+
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Target, Unit
+
+_REFERENCE_URL = (
+    "https://www.gov.uk/government/publications/"
+    "benefit-expenditure-and-caseload-tables-2026"
+)
+_VINTAGE = "spring_2026"
+
+# Benefits that keep a claim under the working-age Housing Benefit rules
+# when the claimant or partner has reached Pension Credit qualifying age.
+_WORKING_AGE_BENEFITS = (
+    "universal_credit",
+    "income_support",
+    "jsa_income",
+    "esa_income",
+)
+
+# Housing benefits sheet rows "Housing Benefit over/under Pension Credit
+# qualifying age": expenditure in £ million (nominal) and caseload in
+# thousands (annual average, rounded to the nearest thousand by DWP).
+_SPENDING_GBP_M = {
+    "over": {
+        2022: 5_890.1,
+        2023: 6_248.1,
+        2024: 6_851.0,
+        2025: 7_114.7,
+        2026: 7_268.9,
+        2027: 7_340.4,
+        2028: 7_464.4,
+        2029: 7_747.9,
+        2030: 7_941.3,
+    },
+    "under": {
+        2022: 9_689.1,
+        2023: 9_524.5,
+        2024: 8_603.5,
+        2025: 5_778.1,
+        2026: 5_205.3,
+        2027: 5_493.8,
+        2028: 5_788.5,
+        2029: 6_151.5,
+        2030: 6_405.4,
+    },
+}
+_CASELOAD_THOUSANDS = {
+    "over": {
+        2022: 1_121,
+        2023: 1_108,
+        2024: 1_104,
+        2025: 1_109,
+        2026: 1_082,
+        2027: 1_057,
+        2028: 1_040,
+        2029: 1_036,
+        2030: 1_042,
+    },
+    "under": {
+        2022: 1_388,
+        2023: 1_243,
+        2024: 968,
+        2025: 460,
+        2026: 328,
+        2027: 339,
+        2028: 349,
+        2029: 361,
+        2030: 372,
+    },
+}
+
+
+def _over_pension_credit_age(ctx) -> np.ndarray:
+    """Benefit units assessed under the pension-age Housing Benefit rules."""
+    adult = np.asarray(ctx.pe_person("is_adult"), dtype=bool)
+    over = adult & np.asarray(ctx.pe_person("is_SP_age"), dtype=bool)
+    any_over = (
+        np.asarray(ctx.sim.map_result(over.astype(float), "person", "benunit")) > 0
+    )
+    on_working_age_benefit = np.zeros_like(any_over)
+    for benefit in _WORKING_AGE_BENEFITS:
+        on_working_age_benefit |= np.asarray(ctx.sim.calculate(benefit).values) > 0
+    return any_over & ~on_working_age_benefit
+
+
+# Age groups the calibration targets; see the module docstring.
+_CALIBRATED_AGE_GROUPS = ("over",)
+
+
+def _make_compute(age_group: str, count: bool):
+    def compute(ctx, target: Target, year: int) -> np.ndarray:
+        housing_benefit = np.asarray(
+            ctx.sim.calculate("housing_benefit").values, dtype=float
+        )
+        in_group = _over_pension_credit_age(ctx)
+        if age_group == "under":
+            in_group = ~in_group
+        value = (housing_benefit > 0) if count else housing_benefit
+        return np.asarray(ctx.household_from_family(value * in_group), dtype=float)
+
+    return compute
+
+
+def get_targets() -> list[Target]:
+    return build_targets(_CALIBRATED_AGE_GROUPS)
+
+
+def build_targets(age_groups=("over", "under")) -> list[Target]:
+    """Housing Benefit spending and claims targets for these age groups."""
+    targets = []
+    for age_group in age_groups:
+        name = f"dwp/housing_benefit/{age_group}_pension_credit_age"
+        targets.append(
+            Target(
+                name=name,
+                variable="housing_benefit",
+                source="dwp",
+                unit=Unit.GBP,
+                values={
+                    year: value * 1e6
+                    for year, value in _SPENDING_GBP_M[age_group].items()
+                },
+                reference_url=_REFERENCE_URL,
+                forecast_vintage=_VINTAGE,
+                countries=GREAT_BRITAIN,
+                custom_compute=_make_compute(age_group, count=False),
+            )
+        )
+        targets.append(
+            Target(
+                name=f"{name}_claims",
+                variable="housing_benefit",
+                source="dwp",
+                unit=Unit.COUNT,
+                values={
+                    year: value * 1e3
+                    for year, value in _CASELOAD_THOUSANDS[age_group].items()
+                },
+                is_count=True,
+                reference_url=_REFERENCE_URL,
+                forecast_vintage=_VINTAGE,
+                countries=GREAT_BRITAIN,
+                custom_compute=_make_compute(age_group, count=True),
+            )
+        )
+    return targets
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index ad4e3cf17..bb4fc0ce6 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -482,11 +482,10 @@ def read_49(row_num: int) -> dict[int, float]:
                 result[fy[col]] = float(val) * 1e9
         return result
 
+    # "Housing benefit (not on JSA)" is not targeted: it is DWP-funded GB
+    # spending only, and dwp_housing_benefit.py targets total GB Housing
+    # Benefit split by age instead.
     benefit_rows = {
-        "housing_benefit": (
-            "Housing benefit (not on JSA)",
-            "housing_benefit",
-        ),
         "pip": (
             "Disability living allowance and personal independence p",
             "pip",
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
new file mode 100644
index 000000000..f92c2976c
--- /dev/null
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -0,0 +1,277 @@
+"""Tests for the DWP Housing Benefit targets by age group and GB scope."""
+
+from types import SimpleNamespace
+
+import numpy as np
+import openpyxl
+import pandas as pd
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.targets import get_all_targets
+from policyengine_uk_data.targets.build_loss_matrix import restrict_to_countries
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Unit
+from policyengine_uk_data.targets.sources import dwp_housing_benefit, obr
+
+# Other rows of the same DWP Housing benefits sheet, transcribed separately:
+# total Housing Benefit is AME within the welfare cap + AME outside it + LA
+# funded (£ million), and the total caseload (thousands).
+_DWP_TOTAL_GBP_M = {
+    2022: 14_839.6 + 160.8 + 578.7,
+    2023: 14_946.4 + 111.0 + 715.2,
+    2024: 14_593.4 + 57.2 + 804.0,
+    2025: 12_103.3 + 0.0 + 789.6,
+    2026: 11_643.4 + 0.0 + 830.8,
+    2027: 11_969.5 + 0.0 + 864.6,
+    2028: 12_352.1 + 0.0 + 900.8,
+    2029: 12_950.1 + 0.0 + 949.2,
+    2030: 13_368.2 + 0.0 + 978.5,
+}
+_DWP_TOTAL_CASELOAD_K = {
+    2022: 2_509,
+    2023: 2_351,
+    2024: 2_072,
+    2025: 1_569,
+    2026: 1_410,
+    2027: 1_396,
+    2028: 1_390,
+    2029: 1_397,
+    2030: 1_414,
+}
+
+_CALIBRATED = {
+    "dwp/housing_benefit/over_pension_credit_age",
+    "dwp/housing_benefit/over_pension_credit_age_claims",
+}
+_ALL = _CALIBRATED | {
+    "dwp/housing_benefit/under_pension_credit_age",
+    "dwp/housing_benefit/under_pension_credit_age_claims",
+}
+
+
+def _targets():
+    """Both age groups, including the one calibration leaves out."""
+    return {t.name: t for t in dwp_housing_benefit.build_targets()}
+
+
+def test_targets_cover_great_britain_in_pounds_and_claims():
+    targets = _targets()
+    assert set(targets) == _ALL
+    for name, target in targets.items():
+        assert target.countries == GREAT_BRITAIN
+        assert target.variable == "housing_benefit"
+        assert target.is_count == name.endswith("_claims")
+        assert target.unit == (Unit.COUNT if target.is_count else Unit.GBP)
+    over = targets["dwp/housing_benefit/over_pension_credit_age"]
+    under = targets["dwp/housing_benefit/under_pension_credit_age_claims"]
+    assert over.values[2025] == 7_114.7e6
+    assert under.values[2025] == 460e3
+
+
+def test_age_split_reconciles_with_dwp_totals():
+    """Over + under matches DWP's total, including LA-funded spending."""
+    targets = _targets()
+    over = targets["dwp/housing_benefit/over_pension_credit_age"].values
+    under = targets["dwp/housing_benefit/under_pension_credit_age"].values
+    over_k = targets["dwp/housing_benefit/over_pension_credit_age_claims"].values
+    under_k = targets["dwp/housing_benefit/under_pension_credit_age_claims"].values
+    for year, total in _DWP_TOTAL_GBP_M.items():
+        assert abs((over[year] + under[year]) / 1e6 - total) < 0.25, year
+    for year, total in _DWP_TOTAL_CASELOAD_K.items():
+        # DWP rounds each caseload to the nearest thousand.
+        assert abs((over_k[year] + under_k[year]) / 1e3 - total) <= 1, year
+
+
+def test_only_pension_age_housing_benefit_is_calibrated():
+    housing_benefit_targets = {
+        t.name for t in get_all_targets() if t.variable == "housing_benefit"
+    }
+    assert housing_benefit_targets == _CALIBRATED
+
+
+def test_obr_housing_benefit_row_is_not_parsed():
+    wb = openpyxl.Workbook()
+    ws = wb.active
+    ws.title = "4.9"
+    ws["B2"] = "Housing benefit (not on JSA)1"
+    ws["B3"] = "Pension credit"
+    for row in (2, 3):
+        for col in "CDEFGHI":
+            ws[f"{col}{row}"] = 6.0
+    names = {t.name for t in obr._parse_welfare(wb)}
+    assert "obr/pension_credit" in names
+    assert not any("housing_benefit" in name for name in names)
+
+
+def _ctx(
+    benunit_of_person,
+    is_adult,
+    is_sp_age,
+    benunit_hb,
+    household_of_benunit,
+    benunit_benefits=None,
+):
+    """Minimal loss-matrix context over explicit entity mappings.
+
+    ``benunit_benefits`` maps benefit-unit variables such as
+    ``universal_credit`` to amounts; any other benefit is zero.
+    """
+    benunit_of_person = np.asarray(benunit_of_person)
+    household_of_benunit = np.asarray(household_of_benunit)
+    n_benunits = len(benunit_hb)
+    n_households = household_of_benunit.max() + 1 if n_benunits else 0
+
+    def map_result(values, source, target):
+        values = np.asarray(values, dtype=float)
+        if (source, target) == ("person", "benunit"):
+            return np.bincount(benunit_of_person, values, minlength=n_benunits)
+        if (source, target) == ("benunit", "household"):
+            return np.bincount(household_of_benunit, values, minlength=n_households)
+        raise AssertionError((source, target))
+
+    person = {"is_adult": np.asarray(is_adult), "is_SP_age": np.asarray(is_sp_age)}
+    benunit_values = {"housing_benefit": benunit_hb, **(benunit_benefits or {})}
+    sim = SimpleNamespace(
+        map_result=map_result,
+        calculate=lambda variable: SimpleNamespace(
+            values=np.asarray(
+                benunit_values.get(variable, np.zeros(n_benunits)), dtype=float
+            )
+        ),
+    )
+    return SimpleNamespace(
+        sim=sim,
+        pe_person=lambda variable: person[variable],
+        household_from_family=lambda values: map_result(values, "benunit", "household"),
+    )
+
+
+def _column(ctx, name):
+    target = _targets()[name]
+    return target.custom_compute(ctx, target, 2025)
+
+
+def test_benefit_rules_assign_mixed_age_couples():
+    # Benefit units, one household each: pensioner couple; mixed-age couple
+    # on pension-age rules; working-age single; pensioner with an
+    # 18-year-old dependant; mixed-age couple whose younger partner gets
+    # income-related ESA; mixed-age couple on Universal Credit.
+    ctx = _ctx(
+        benunit_of_person=[0, 0, 1, 1, 2, 3, 3, 4, 4, 5, 5],
+        is_adult=[1] * 11,
+        is_sp_age=[1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0],
+        benunit_hb=[5_000, 4_000, 3_000, 2_000, 1_000, 500],
+        household_of_benunit=[0, 1, 2, 3, 4, 5],
+        benunit_benefits={
+            "esa_income": [0, 0, 0, 0, 4_000, 0],
+            "universal_credit": [0, 0, 0, 0, 0, 6_000],
+        },
+    )
+    over = _column(ctx, "dwp/housing_benefit/over_pension_credit_age")
+    under = _column(ctx, "dwp/housing_benefit/under_pension_credit_age")
+    np.testing.assert_array_equal(over, [5_000, 4_000, 0, 2_000, 0, 0])
+    np.testing.assert_array_equal(under, [0, 0, 3_000, 0, 1_000, 500])
+
+
+@st.composite
+def _population(draw):
+    n_benunits = draw(st.integers(1, 12))
+    n_households = draw(st.integers(1, n_benunits))
+    household_of_benunit = draw(
+        st.lists(
+            st.integers(0, n_households - 1),
+            min_size=n_benunits,
+            max_size=n_benunits,
+        )
+    )
+    n_people = draw(st.integers(n_benunits, 4 * n_benunits))
+    # Every benefit unit has at least one member.
+    benunit_of_person = list(range(n_benunits)) + draw(
+        st.lists(
+            st.integers(0, n_benunits - 1),
+            min_size=n_people - n_benunits,
+            max_size=n_people - n_benunits,
+        )
+    )
+    flags = st.lists(st.booleans(), min_size=n_people, max_size=n_people)
+    benunit_hb = draw(
+        st.lists(
+            st.one_of(st.just(0.0), st.floats(1, 50_000)),
+            min_size=n_benunits,
+            max_size=n_benunits,
+        )
+    )
+    benefit = st.lists(
+        st.sampled_from([0.0, 100.0]), min_size=n_benunits, max_size=n_benunits
+    )
+    return dict(
+        benunit_of_person=benunit_of_person,
+        is_adult=draw(flags),
+        is_sp_age=draw(flags),
+        benunit_hb=benunit_hb,
+        household_of_benunit=household_of_benunit,
+        benunit_benefits={
+            name: draw(benefit) for name in dwp_housing_benefit._WORKING_AGE_BENEFITS
+        },
+    )
+
+
+@settings(max_examples=200, deadline=None)
+@given(_population())
+def test_age_split_partitions_housing_benefit(population):
+    """Over + under is all Housing Benefit, in pounds and in claims, and
+    the older group is exactly the units with an adult over SPA and no
+    working-age income benefit."""
+    ctx = _ctx(**population)
+    household = np.asarray(population["household_of_benunit"])
+    hb = np.asarray(population["benunit_hb"])
+    n_households = household.max() + 1
+    over = _column(ctx, "dwp/housing_benefit/over_pension_credit_age")
+    under = _column(ctx, "dwp/housing_benefit/under_pension_credit_age")
+    over_k = _column(ctx, "dwp/housing_benefit/over_pension_credit_age_claims")
+    under_k = _column(ctx, "dwp/housing_benefit/under_pension_credit_age_claims")
+    np.testing.assert_allclose(
+        over + under, np.bincount(household, hb, minlength=n_households)
+    )
+    np.testing.assert_array_equal(
+        over_k + under_k, np.bincount(household, hb > 0, minlength=n_households)
+    )
+    people = pd.DataFrame(
+        {
+            "benunit": population["benunit_of_person"],
+            "older": np.asarray(population["is_adult"])
+            & np.asarray(population["is_sp_age"]),
+        }
+    )
+    older_unit = (
+        people.groupby("benunit").older.any().reindex(range(len(hb)), fill_value=False)
+    ).to_numpy()
+    on_working_age_benefit = np.any(
+        [np.asarray(v) > 0 for v in population["benunit_benefits"].values()], axis=0
+    )
+    expected_over = np.bincount(
+        household, hb * (older_unit & ~on_working_age_benefit), minlength=n_households
+    )
+    np.testing.assert_allclose(over, expected_over)
+
+
+_COUNTRIES = ["ENGLAND", "SCOTLAND", "WALES", "NORTHERN_IRELAND"]
+
+
+@settings(max_examples=200, deadline=None)
+@given(
+    st.lists(
+        st.tuples(st.floats(-1e9, 1e9), st.sampled_from(_COUNTRIES)),
+        min_size=1,
+        max_size=50,
+    )
+)
+def test_country_restriction_partitions_the_uk(rows):
+    column = np.array([value for value, _ in rows])
+    country = np.array([c for _, c in rows])
+    gb = restrict_to_countries(column, country, GREAT_BRITAIN)
+    ni = restrict_to_countries(column, country, ("NORTHERN_IRELAND",))
+    np.testing.assert_allclose(gb + ni, column)
+    np.testing.assert_array_equal(gb[country == "NORTHERN_IRELAND"], 0)
+    np.testing.assert_array_equal(restrict_to_countries(gb, country, GREAT_BRITAIN), gb)
+    assert restrict_to_countries(column, country, None) is column
diff --git a/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py b/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py
new file mode 100644
index 000000000..251848335
--- /dev/null
+++ b/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py
@@ -0,0 +1,56 @@
+"""Wholly pension-age benefit units never get would_claim_uc."""
+
+import numpy as np
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.datasets.frs import (
+    derive_every_adult_over_state_pension_age,
+)
+
+
+def test_examples():
+    # Units: pensioner couple, mixed-age couple, working-age single,
+    # pensioner with an 18-year-old dependant, a unit with no adult, and a
+    # unit with no members at all.
+    result = derive_every_adult_over_state_pension_age(
+        person_benunit_ids=[10, 10, 20, 20, 30, 40, 40, 50],
+        is_adult=[1, 1, 1, 1, 1, 1, 1, 0],
+        is_over_state_pension_age=[1, 1, 1, 0, 0, 1, 0, 0],
+        benunit_ids=[10, 20, 30, 40, 50, 60],
+    )
+    np.testing.assert_array_equal(result, [True, False, False, False, False, False])
+
+
+@settings(max_examples=300, deadline=None)
+@given(
+    st.lists(
+        st.tuples(st.integers(0, 6), st.booleans(), st.booleans()),
+        max_size=30,
+    ),
+    st.permutations(list(range(8))),
+)
+def test_matches_a_direct_definition(people, benunit_order):
+    """Differential check against a per-unit loop, for any unit order."""
+    benunit_ids = np.array(benunit_order)
+    result = derive_every_adult_over_state_pension_age(
+        person_benunit_ids=[p[0] for p in people],
+        is_adult=[p[1] for p in people],
+        is_over_state_pension_age=[p[2] for p in people],
+        benunit_ids=benunit_ids,
+    )
+    for i, unit in enumerate(benunit_ids):
+        adults = [over for b, adult, over in people if b == unit and adult]
+        assert result[i] == (len(adults) > 0 and all(adults))
+
+
+def test_built_dataset_has_no_pension_age_uc_claimants(baseline):
+    year = 2025
+    adult = baseline.calculate("is_adult", year).values.astype(bool)
+    over = adult & baseline.calculate("is_SP_age", year).values.astype(bool)
+    adults = baseline.map_result(adult.astype(float), "person", "benunit")
+    adults_over = baseline.map_result(over.astype(float), "person", "benunit")
+    wholly_pension_age = (adults > 0) & (adults_over == adults)
+    would_claim_uc = baseline.calculate("would_claim_uc", year).values.astype(bool)
+    assert wholly_pension_age.any()
+    assert not (would_claim_uc & wholly_pension_age).any()
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..e46cfa9a6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "policyengine",
     "google-cloud-storage",
     "google-auth",
-    "policyengine-uk>=2.93.0",
+    "policyengine-uk>=2.102.5",
     "microcalibrate>=0.18.0",
     "microimpute>=1.0.1",
     "ruff>=0.9.0",
@@ -38,6 +38,7 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
+    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..370de706e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/b7/13118bbc45d6d8b9d04e2de779e2a4ff23145ea39efa692b994fb874ca72/hypothesis-6.168.3.tar.gz", hash = "sha256:a43388f9067678fef6e13bdff325b6cfa6961a590498bb37f7ff31589c83bc75", size = 511022, upload-time = "2026-09-28T05:20:58.499Z" }
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 version = "3.11"
@@ -1322,7 +1391,7 @@ wheels = [
 
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-version = "2.93.0"
+version = "2.102.5"
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 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },
@@ -1419,7 +1490,7 @@ requires-dist = [
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     { name = "policyengine" },
     { name = "policyengine-core", specifier = ">=3.19.4" },
-    { name = "policyengine-uk", specifier = ">=2.93.0" },
+    { name = "policyengine-uk", specifier = ">=2.102.5" },
     { name = "pydantic", specifier = ">=2.0" },
     { name = "pytest", marker = "extra == 'dev'" },
     { name = "pyyaml" },

From 25971022f8f7384f8c9436e2e5734fb2fb40d0f6 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 30 Sep 2026 08:05:20 -0400
Subject: [PATCH 02/70] Address review: release note, docstring precision,
 loss-matrix test
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

State in the release note that modelled GB Housing Benefit falls to about
£8.0bn. Mark the mixed-age classification as an assumption, date the
benefit-group equality from 2024-25, and quote the calibrated build's
working-age claims. Add a test that the calibrated columns build on the
dataset, since the loss matrix skips a target whose column raises, and note
that would_claim_uc uses survey-year State Pension age.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/hb-dwp-age-targets.changed.md      |  1 +
 changelog.d/hb-dwp-age-targets.fixed.md        |  1 -
 policyengine_uk_data/datasets/frs.py           |  4 +++-
 .../targets/sources/dwp_housing_benefit.py     |  9 +++++----
 .../tests/test_dwp_housing_benefit_targets.py  | 18 ++++++++++++++++++
 5 files changed, 27 insertions(+), 6 deletions(-)
 create mode 100644 changelog.d/hb-dwp-age-targets.changed.md
 delete mode 100644 changelog.d/hb-dwp-age-targets.fixed.md

diff --git a/changelog.d/hb-dwp-age-targets.changed.md b/changelog.d/hb-dwp-age-targets.changed.md
new file mode 100644
index 000000000..759b2a2ff
--- /dev/null
+++ b/changelog.d/hb-dwp-age-targets.changed.md
@@ -0,0 +1 @@
+Calibrate pension-age Housing Benefit to DWP's Great Britain spending and claims over Pension Credit qualifying age, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Modelled GB Housing Benefit for 2025-26 falls from about £11.7bn to £8.0bn (DWP: £12.9bn), because pension-age Housing Benefit was about 1.6 times DWP's figure while working-age, temporary and supported-accommodation Housing Benefit remain largely unmodelled. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
diff --git a/changelog.d/hb-dwp-age-targets.fixed.md b/changelog.d/hb-dwp-age-targets.fixed.md
deleted file mode 100644
index 2c4c56708..000000000
--- a/changelog.d/hb-dwp-age-targets.fixed.md
+++ /dev/null
@@ -1 +0,0 @@
-Calibrate pension-age Housing Benefit to DWP's Great Britain spending and claims over Pension Credit qualifying age, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 4a95842bb..94e40c2a6 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -1573,7 +1573,9 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
     # A benefit unit whose adults have all reached State Pension age cannot
     # claim Universal Credit, so it never gets would_claim_uc, even if it
     # reports UC. The draw still covers every unit, so the random stream and
-    # every other unit's value are unchanged.
+    # every other unit's value are unchanged. Ages are not rolled forward, so
+    # if State Pension age rises above an adult's survey age in a later year,
+    # that unit stays without would_claim_uc there.
     pe_benunit["would_claim_uc"] = assign_takeup_with_reported_anchors(
         generator.random(len(pe_benunit)),
         universal_credit_rate,
diff --git a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
index 69d17f9b6..79870d13d 100644
--- a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
+++ b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
@@ -17,8 +17,8 @@
 would ask for two different GB totals.
 
 DWP splits claims by benefit rules rather than by age alone (Notes, note
-5): the two lines equal its Pension Credit plus State Pension benefit
-groups and its ESA plus other working-age groups. Under regulation 5 of
+5): from 2024-25 the two lines equal its Pension Credit plus State Pension
+benefit groups and its ESA plus other working-age groups. Under regulation 5 of
 both Housing Benefit Regulations 2006 (SI 2006/213 and 2006/214), the
 pension-age rules apply when the claimant or partner has reached the
 qualifying age for Pension Credit, unless either is on Universal Credit,
@@ -26,12 +26,13 @@
 therefore over Pension Credit qualifying age here when one of its adults
 has reached State Pension age and it gets none of those benefits. Mixed-age
 couples who kept pension-age Housing Benefit after May 2019 fall in the
-older group, as they do in DWP's benefit groups.
+older group; DWP does not publish its rule for them, and this assumes they
+sit in its Pension Credit and State Pension groups.
 
 Only the older group is calibrated. policyengine-uk pays working-age
 Housing Benefit only as a continuing award to families that report it and
 do not claim Universal Credit: 267 of the 770 working-age records that
-report it, about 24,000 weighted claims in 2025-26 against DWP's 460,000.
+report it, about 17,000 weighted claims in 2025-26 against DWP's 460,000.
 DWP's working-age figure also includes temporary and supported
 accommodation (together £5.2bn of Housing Benefit in 2025-26, not split by
 age), which the FRS barely samples. A test build on 2026-09-30 that also
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index f92c2976c..549006b42 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -275,3 +275,21 @@ def test_country_restriction_partitions_the_uk(rows):
     np.testing.assert_array_equal(gb[country == "NORTHERN_IRELAND"], 0)
     np.testing.assert_array_equal(restrict_to_countries(gb, country, GREAT_BRITAIN), gb)
     assert restrict_to_countries(column, country, None) is column
+
+
+def test_calibrated_columns_build_on_the_dataset(baseline, enhanced_frs):
+    """The loss matrix skips a target whose column raises, so check that the
+    calibrated columns build on a real simulation and land near DWP."""
+    from policyengine_uk_data.targets.build_loss_matrix import _SimContext
+
+    year = 2025
+    baseline.default_calculation_period = str(year)
+    ctx = _SimContext(baseline, str(year), enhanced_frs, None)
+    weight = baseline.calculate("household_weight", year).values
+    for target in dwp_housing_benefit.get_targets():
+        column = restrict_to_countries(
+            target.custom_compute(ctx, target, year), ctx.country, target.countries
+        )
+        assert np.isfinite(column).all(), target.name
+        ratio = (column * weight).sum() / target.values[year]
+        assert 0.5 < ratio < 2, (target.name, ratio)

From db089cef73ea130ae678dd4355d1e8aad25cceea Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 30 Sep 2026 23:10:22 -0400
Subject: [PATCH 03/70] Stop counting rent paid by boarders and lodgers as
 their property income

FRS CVPAY is the rent a boarder or lodger pays the householder, after
deducting any state benefits to help with rent (FRS question CvPay, asked
about each person not related to the HRP in the second and later benefit
units of a conventional household). frs.py added it to the payer's own
property_income. Move the property income calculation into a tested
helper, frs_property_income, that keeps SUBRENT and ROYYR1 and drops CVPAY.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-cvpay-not-property-income.fixed.md    |   1 +
 policyengine_uk_data/datasets/frs.py          |  48 ++++----
 .../tests/test_frs_property_income.py         | 106 ++++++++++++++++++
 3 files changed, 136 insertions(+), 19 deletions(-)
 create mode 100644 changelog.d/frs-cvpay-not-property-income.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_frs_property_income.py

diff --git a/changelog.d/frs-cvpay-not-property-income.fixed.md b/changelog.d/frs-cvpay-not-property-income.fixed.md
new file mode 100644
index 000000000..555464200
--- /dev/null
+++ b/changelog.d/frs-cvpay-not-property-income.fixed.md
@@ -0,0 +1 @@
+Stop counting the rent a boarder or lodger pays (FRS CVPAY) as that boarder's or lodger's own property income.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..6ae20a90b 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -220,6 +220,34 @@ def derive_receives_benefits_in_own_right(pe_person: pd.DataFrame) -> pd.Series:
     )
 
 
+def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.ndarray:
+    """Annual property income each person reports in the FRS.
+
+    Two FRS amounts, both weekly in the released data:
+
+    - SUBRENT, rent the household received for letting part of its home to
+      someone outside the household. It goes to the household reference
+      person, and only in owner-occupied households (TENTYP2 5 or 6).
+      ``household`` must be indexed by ``household_id``.
+    - ROYYR1, the person's rent from other property, before tax and after
+      allowable expenses.
+
+    CVPAY is not included. It is the rent that a boarder or lodger pays the
+    householder, and it sits on the boarder's or lodger's own adult record.
+    The FRS question (CvPay) asks how much rent [name] paid for board and
+    lodging, after deducting any state benefits to help with rent.
+    """
+    is_head = person.hrpid == 1
+    household_property_income = household.tentyp2.isin((5, 6)) * household.subrent
+    persons_household_property_income = (
+        household_property_income.reindex(person.household_id).fillna(0).values
+    )
+    return (
+        np.maximum(0, is_head * persons_household_property_income + person.royyr1)
+        * WEEKS_IN_YEAR
+    ).values
+
+
 def derive_is_in_non_advanced_education(
     current_education,
     is_apprentice=None,
@@ -1083,25 +1111,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         )
         * 52,
     )
-    is_head = person.hrpid == 1
-    household_property_income = (
-        household.tentyp2.isin((5, 6)) * household.subrent
-    )  # Owned and subletting
-    persons_household_property_income = (
-        pd.Series(
-            household_property_income[person.household_id].values,
-            index=person.person_id,
-        )
-        .fillna(0)
-        .values
-    )
-    pe_person["property_income"] = (
-        np.maximum(
-            0,
-            is_head * persons_household_property_income + person.cvpay + person.royyr1,
-        )
-        * WEEKS_IN_YEAR
-    )
+    pe_person["property_income"] = frs_property_income(person, household)
     maintenance_to_self = np.maximum(
         pd.Series(np.where(person.mntus1 == 2, person.mntusam1, person.mntamt1)).fillna(
             0
diff --git a/policyengine_uk_data/tests/test_frs_property_income.py b/policyengine_uk_data/tests/test_frs_property_income.py
new file mode 100644
index 000000000..f7e5e7b57
--- /dev/null
+++ b/policyengine_uk_data/tests/test_frs_property_income.py
@@ -0,0 +1,106 @@
+import numpy as np
+import pandas as pd
+import pytest
+
+from policyengine_uk_data.datasets.frs import WEEKS_IN_YEAR, frs_property_income
+
+HRP, NOT_HRP = 1, 2
+OWNED_WITH_MORTGAGE, OWNED_OUTRIGHT = 5, 6
+COUNCIL_RENTED, PRIVATE_RENTED_FURNISHED = 1, 4
+
+
+def make_tables(people, households):
+    person = pd.DataFrame(
+        people, columns=["household_id", "person_id", "hrpid", "royyr1", "cvpay"]
+    )
+    household = pd.DataFrame(
+        households, columns=["household_id", "tentyp2", "subrent"]
+    ).set_index("household_id")
+    return person, household
+
+
+def test_rent_paid_by_a_lodger_is_not_their_property_income():
+    # Owner-occupier household with a lodger who pays £100 a week (CVPAY).
+    person, household = make_tables(
+        [(1, 1_001, HRP, 0, 0), (1, 1_002, NOT_HRP, 0, 100)],
+        [(1, OWNED_OUTRIGHT, 0)],
+    )
+    assert frs_property_income(person, household).tolist() == [0, 0]
+
+
+def test_rent_from_other_property_counts_for_any_adult():
+    person, household = make_tables(
+        [(1, 1_001, HRP, 0, 0), (1, 1_002, NOT_HRP, 50, 0)],
+        [(1, COUNCIL_RENTED, 0)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [0, 50 * WEEKS_IN_YEAR]
+    )
+
+
+def test_subletting_rent_goes_to_the_owner_household_reference_person():
+    person, household = make_tables(
+        [(1, 1_001, NOT_HRP, 0, 0), (1, 1_002, HRP, 0, 0)],
+        [(1, OWNED_WITH_MORTGAGE, 80)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [0, 80 * WEEKS_IN_YEAR]
+    )
+
+
+def random_tables(seed: int):
+    """Random households of one to four adults; the first is the HRP."""
+    rng = np.random.default_rng(seed)
+    people, households = [], []
+    for household_id in range(1, rng.integers(1, 30) + 1):
+        tenure = int(rng.integers(1, 9))
+        subrent = float(rng.choice([0, rng.uniform(0, 500)]))
+        households.append((household_id, tenure, subrent))
+        for person in range(1, rng.integers(1, 5) + 1):
+            people.append(
+                (
+                    household_id,
+                    household_id * 1_000 + person,
+                    HRP if person == 1 else NOT_HRP,
+                    float(rng.choice([0, rng.uniform(0, 2_000)])),
+                    float(rng.choice([0, rng.uniform(0, 400)])),
+                )
+            )
+    return make_tables(people, households)
+
+
+SEEDS = range(200)
+
+
+@pytest.mark.parametrize("seed", SEEDS)
+def test_property_income_does_not_depend_on_cvpay(seed):
+    person, household = random_tables(seed)
+    without_cvpay = person.assign(cvpay=0.0)
+    np.testing.assert_array_equal(
+        frs_property_income(person, household),
+        frs_property_income(without_cvpay, household),
+    )
+
+
+@pytest.mark.parametrize("seed", SEEDS)
+def test_property_income_conserves_reported_rent(seed):
+    # Each owner household's SUBRENT is counted once (on its HRP) and every
+    # ROYYR1 is counted on its own record, so the totals match.
+    person, household = random_tables(seed)
+    result = frs_property_income(person, household)
+    assert (result >= 0).all()
+    owner = household.tentyp2.isin((OWNED_WITH_MORTGAGE, OWNED_OUTRIGHT))
+    expected = (household.subrent[owner].sum() + person.royyr1.sum()) * WEEKS_IN_YEAR
+    assert result.sum() == pytest.approx(expected)
+
+
+@pytest.mark.parametrize("seed", SEEDS)
+def test_extra_rent_from_other_property_moves_only_that_person(seed):
+    person, household = random_tables(seed)
+    before = frs_property_income(person, household)
+    row = seed % len(person)
+    person.loc[row, "royyr1"] += 10
+    after = frs_property_income(person, household)
+    change = np.zeros(len(person))
+    change[row] = 10 * WEEKS_IN_YEAR
+    np.testing.assert_allclose(after - before, change)

From 0812cd1b3c5952a2b4b259ae046baf44dfced8a3 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 30 Sep 2026 23:21:36 -0400
Subject: [PATCH 04/70] Test the non-negative floor and the stacked adult and
 child index

Review of #503: the seeded cases only draw non-negative amounts, so the
floor at zero was never active, and every fixture had a unique index
while create_frs stacks the adult and child tables.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_frs_property_income.py         | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/policyengine_uk_data/tests/test_frs_property_income.py b/policyengine_uk_data/tests/test_frs_property_income.py
index f7e5e7b57..4aacfab71 100644
--- a/policyengine_uk_data/tests/test_frs_property_income.py
+++ b/policyengine_uk_data/tests/test_frs_property_income.py
@@ -48,6 +48,29 @@ def test_subletting_rent_goes_to_the_owner_household_reference_person():
     )
 
 
+def test_negative_amounts_do_not_become_negative_income():
+    person, household = make_tables(
+        [(1, 1_001, HRP, -30, 0), (1, 1_002, NOT_HRP, -5, 0)],
+        [(1, OWNED_OUTRIGHT, 10)],
+    )
+    assert frs_property_income(person, household).tolist() == [0, 0]
+
+
+def test_adult_and_child_rows_sharing_index_labels():
+    # create_frs stacks the adult and child tables, so index labels repeat.
+    adults, household = make_tables(
+        [(1, 1_001, HRP, 20, 0), (2, 2_001, HRP, 0, 60)],
+        [(1, OWNED_OUTRIGHT, 80), (2, PRIVATE_RENTED_FURNISHED, 0)],
+    )
+    children, _ = make_tables([(1, 1_002, 0, 0, 0)], [])
+    person = pd.concat([adults, children]).sort_index(kind="stable")
+    assert person.index.tolist() == [0, 0, 1]
+    np.testing.assert_allclose(
+        frs_property_income(person, household),
+        [100 * WEEKS_IN_YEAR, 0, 0],
+    )
+
+
 def random_tables(seed: int):
     """Random households of one to four adults; the first is the HRP."""
     rng = np.random.default_rng(seed)

From be4d9356c80369029c8882bcb738105a4b4b0fb5 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 09:24:00 -0400
Subject: [PATCH 05/70] Stop counting property losses as income; count sub-let
 rent for every tenure

ROYYR1 holds a property loss as a positive amount flagged by RENTPROF = 2,
and the build counted it as income. A loss now counts as zero.

SUBRENT was counted only for owner-occupiers, although the FRS asks every
household about sub-letting. The tenure restriction is removed.

SUBRENT stays as reported whether SUBALLOW says it is before or after
allowable expenses; the FRS collects no expense amount.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-property-losses-subrent.fixed.md      |   1 +
 policyengine_uk_data/datasets/frs.py          |  29 ++-
 .../tests/test_frs_property_income.py         | 209 +++++++++++++++---
 .../tests/test_legacy_benefit_proxies.py      |   1 +
 4 files changed, 205 insertions(+), 35 deletions(-)
 create mode 100644 changelog.d/frs-property-losses-subrent.fixed.md

diff --git a/changelog.d/frs-property-losses-subrent.fixed.md b/changelog.d/frs-property-losses-subrent.fixed.md
new file mode 100644
index 000000000..097b15f2c
--- /dev/null
+++ b/changelog.d/frs-property-losses-subrent.fixed.md
@@ -0,0 +1 @@
+Stop counting FRS property losses (ROYYR1 with RENTPROF = 2) as property income, and count rent from sub-letting part of the home (SUBRENT) for every tenure, not only owner-occupiers.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 6ae20a90b..abd521401 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -84,6 +84,8 @@
 # FRS government-training question variants use 10 or 13 for "None of these".
 FRS_APPROVED_TRAINING_CODES = tuple(range(1, 10))
 UNKNOWN_QUALIFYING_EDUCATION_OR_TRAINING_ENTRY_AGE = 1000
+# FRS RENTPROF: whether ROYYR1 is a profit (1) or a loss (2).
+FRS_RENTPROF_LOSS = 2
 
 
 @lru_cache(maxsize=None)
@@ -226,11 +228,20 @@ def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.nda
     Two FRS amounts, both weekly in the released data:
 
     - SUBRENT, rent the household received for letting part of its home to
-      someone outside the household. It goes to the household reference
-      person, and only in owner-occupied households (TENTYP2 5 or 6).
-      ``household`` must be indexed by ``household_id``.
+      someone outside the household. The FRS asks every household (SubLet),
+      whatever its tenure, so renting and rent-free households count too. It
+      goes to the household reference person. ``household`` must be indexed
+      by ``household_id``.
     - ROYYR1, the person's rent from other property, before tax and after
-      allowable expenses.
+      allowable expenses. The questionnaire cannot take a negative amount,
+      so a loss is entered as a positive amount with RENTPROF = 2 (question
+      RentProf, "Is that a profit or a loss from the property?"). A loss
+      counts as zero: it is not income, policyengine-uk has no property loss
+      input, and it is not set against the household's SUBRENT.
+
+    SUBRENT is used as reported. SUBALLOW records whether it is before (1)
+    or after (2) allowable expenses, but the FRS collects no expense amount
+    to take off the before-expenses answers.
 
     CVPAY is not included. It is the rent that a boarder or lodger pays the
     householder, and it sits on the boarder's or lodger's own adult record.
@@ -238,12 +249,14 @@ def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.nda
     lodging, after deducting any state benefits to help with rent.
     """
     is_head = person.hrpid == 1
-    household_property_income = household.tentyp2.isin((5, 6)) * household.subrent
-    persons_household_property_income = (
-        household_property_income.reindex(person.household_id).fillna(0).values
+    persons_household_subrent = (
+        household.subrent.reindex(person.household_id).fillna(0).values
+    )
+    rent_from_other_property = person.royyr1.where(
+        person.rentprof != FRS_RENTPROF_LOSS, 0
     )
     return (
-        np.maximum(0, is_head * persons_household_property_income + person.royyr1)
+        np.maximum(0, is_head * persons_household_subrent + rent_from_other_property)
         * WEEKS_IN_YEAR
     ).values
 
diff --git a/policyengine_uk_data/tests/test_frs_property_income.py b/policyengine_uk_data/tests/test_frs_property_income.py
index 4aacfab71..1abcf931a 100644
--- a/policyengine_uk_data/tests/test_frs_property_income.py
+++ b/policyengine_uk_data/tests/test_frs_property_income.py
@@ -2,19 +2,29 @@
 import pandas as pd
 import pytest
 
-from policyengine_uk_data.datasets.frs import WEEKS_IN_YEAR, frs_property_income
+from policyengine_uk_data.datasets.frs import (
+    FRS_RENTPROF_LOSS,
+    WEEKS_IN_YEAR,
+    frs_property_income,
+)
+from policyengine_uk_data.datasets.frs_release import CURRENT_FRS_RELEASE
+from policyengine_uk_data.storage import STORAGE_FOLDER
 
 HRP, NOT_HRP = 1, 2
+NOT_ASKED, PROFIT, LOSS = 0, 1, FRS_RENTPROF_LOSS
 OWNED_WITH_MORTGAGE, OWNED_OUTRIGHT = 5, 6
 COUNCIL_RENTED, PRIVATE_RENTED_FURNISHED = 1, 4
+TENURES = range(1, 9)
+BEFORE_EXPENSES, AFTER_EXPENSES = 1, 2
 
 
 def make_tables(people, households):
     person = pd.DataFrame(
-        people, columns=["household_id", "person_id", "hrpid", "royyr1", "cvpay"]
+        people,
+        columns=["household_id", "person_id", "hrpid", "royyr1", "rentprof", "cvpay"],
     )
     household = pd.DataFrame(
-        households, columns=["household_id", "tentyp2", "subrent"]
+        households, columns=["household_id", "tentyp2", "subrent", "suballow"]
     ).set_index("household_id")
     return person, household
 
@@ -22,36 +32,80 @@ def make_tables(people, households):
 def test_rent_paid_by_a_lodger_is_not_their_property_income():
     # Owner-occupier household with a lodger who pays £100 a week (CVPAY).
     person, household = make_tables(
-        [(1, 1_001, HRP, 0, 0), (1, 1_002, NOT_HRP, 0, 100)],
-        [(1, OWNED_OUTRIGHT, 0)],
+        [(1, 1_001, HRP, 0, NOT_ASKED, 0), (1, 1_002, NOT_HRP, 0, NOT_ASKED, 100)],
+        [(1, OWNED_OUTRIGHT, 0, 0)],
     )
     assert frs_property_income(person, household).tolist() == [0, 0]
 
 
 def test_rent_from_other_property_counts_for_any_adult():
     person, household = make_tables(
-        [(1, 1_001, HRP, 0, 0), (1, 1_002, NOT_HRP, 50, 0)],
-        [(1, COUNCIL_RENTED, 0)],
+        [(1, 1_001, HRP, 0, NOT_ASKED, 0), (1, 1_002, NOT_HRP, 50, PROFIT, 0)],
+        [(1, COUNCIL_RENTED, 0, 0)],
     )
     np.testing.assert_allclose(
         frs_property_income(person, household), [0, 50 * WEEKS_IN_YEAR]
     )
 
 
-def test_subletting_rent_goes_to_the_owner_household_reference_person():
+def test_a_loss_on_other_property_is_not_income():
+    # ROYYR1 holds the size of the loss as a positive amount; RENTPROF flags it.
     person, household = make_tables(
-        [(1, 1_001, NOT_HRP, 0, 0), (1, 1_002, HRP, 0, 0)],
-        [(1, OWNED_WITH_MORTGAGE, 80)],
+        [(1, 1_001, HRP, 90, LOSS, 0), (1, 1_002, NOT_HRP, 50, PROFIT, 0)],
+        [(1, OWNED_WITH_MORTGAGE, 0, 0)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [0, 50 * WEEKS_IN_YEAR]
+    )
+
+
+def test_a_loss_on_other_property_does_not_reduce_subletting_rent():
+    person, household = make_tables(
+        [(1, 1_001, HRP, 30, LOSS, 0)],
+        [(1, OWNED_OUTRIGHT, 80, AFTER_EXPENSES)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [80 * WEEKS_IN_YEAR]
+    )
+
+
+def test_rent_with_no_profit_or_loss_answer_still_counts():
+    # Only an explicit loss removes the amount.
+    person, household = make_tables(
+        [(1, 1_001, HRP, 50, NOT_ASKED, 0)], [(1, OWNED_OUTRIGHT, 0, 0)]
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [50 * WEEKS_IN_YEAR]
+    )
+
+
+@pytest.mark.parametrize("tenure", TENURES)
+def test_subletting_rent_goes_to_the_household_reference_person(tenure):
+    # Every tenure is asked SubLet, so renting households count too.
+    person, household = make_tables(
+        [(1, 1_001, NOT_HRP, 0, NOT_ASKED, 0), (1, 1_002, HRP, 0, NOT_ASKED, 0)],
+        [(1, tenure, 80, BEFORE_EXPENSES)],
     )
     np.testing.assert_allclose(
         frs_property_income(person, household), [0, 80 * WEEKS_IN_YEAR]
     )
 
 
+@pytest.mark.parametrize("suballow", [0, BEFORE_EXPENSES, AFTER_EXPENSES])
+def test_subletting_rent_is_used_as_reported_whatever_its_expenses_basis(suballow):
+    person, household = make_tables(
+        [(1, 1_001, HRP, 0, NOT_ASKED, 0)],
+        [(1, PRIVATE_RENTED_FURNISHED, 80, suballow)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household), [80 * WEEKS_IN_YEAR]
+    )
+
+
 def test_negative_amounts_do_not_become_negative_income():
     person, household = make_tables(
-        [(1, 1_001, HRP, -30, 0), (1, 1_002, NOT_HRP, -5, 0)],
-        [(1, OWNED_OUTRIGHT, 10)],
+        [(1, 1_001, HRP, -30, PROFIT, 0), (1, 1_002, NOT_HRP, -5, PROFIT, 0)],
+        [(1, OWNED_OUTRIGHT, 10, AFTER_EXPENSES)],
     )
     assert frs_property_income(person, household).tolist() == [0, 0]
 
@@ -59,15 +113,18 @@ def test_negative_amounts_do_not_become_negative_income():
 def test_adult_and_child_rows_sharing_index_labels():
     # create_frs stacks the adult and child tables, so index labels repeat.
     adults, household = make_tables(
-        [(1, 1_001, HRP, 20, 0), (2, 2_001, HRP, 0, 60)],
-        [(1, OWNED_OUTRIGHT, 80), (2, PRIVATE_RENTED_FURNISHED, 0)],
+        [(1, 1_001, HRP, 20, PROFIT, 0), (2, 2_001, HRP, 40, LOSS, 60)],
+        [
+            (1, OWNED_OUTRIGHT, 80, BEFORE_EXPENSES),
+            (2, PRIVATE_RENTED_FURNISHED, 30, AFTER_EXPENSES),
+        ],
     )
-    children, _ = make_tables([(1, 1_002, 0, 0, 0)], [])
+    children, _ = make_tables([(1, 1_002, 0, 0, NOT_ASKED, 0)], [])
     person = pd.concat([adults, children]).sort_index(kind="stable")
     assert person.index.tolist() == [0, 0, 1]
     np.testing.assert_allclose(
         frs_property_income(person, household),
-        [100 * WEEKS_IN_YEAR, 0, 0],
+        [100 * WEEKS_IN_YEAR, 0, 30 * WEEKS_IN_YEAR],
     )
 
 
@@ -78,45 +135,88 @@ def random_tables(seed: int):
     for household_id in range(1, rng.integers(1, 30) + 1):
         tenure = int(rng.integers(1, 9))
         subrent = float(rng.choice([0, rng.uniform(0, 500)]))
-        households.append((household_id, tenure, subrent))
+        suballow = int(rng.integers(1, 3)) if subrent else 0
+        households.append((household_id, tenure, subrent, suballow))
         for person in range(1, rng.integers(1, 5) + 1):
+            royyr1 = float(rng.choice([0, rng.uniform(0, 2_000)]))
             people.append(
                 (
                     household_id,
                     household_id * 1_000 + person,
                     HRP if person == 1 else NOT_HRP,
-                    float(rng.choice([0, rng.uniform(0, 2_000)])),
+                    royyr1,
+                    int(rng.choice([PROFIT, PROFIT, LOSS])) if royyr1 else NOT_ASKED,
                     float(rng.choice([0, rng.uniform(0, 400)])),
                 )
             )
     return make_tables(people, households)
 
 
+def property_income_one_person_at_a_time(person, household):
+    """The same rules written as a loop, to check the vectorised helper."""
+    result = []
+    for row in person.itertuples():
+        weekly = 0.0
+        if row.rentprof != LOSS:
+            weekly += row.royyr1
+        if row.hrpid == HRP and row.household_id in household.index:
+            weekly += household.subrent[row.household_id]
+        result.append(max(0.0, weekly) * WEEKS_IN_YEAR)
+    return np.array(result)
+
+
 SEEDS = range(200)
 
 
 @pytest.mark.parametrize("seed", SEEDS)
-def test_property_income_does_not_depend_on_cvpay(seed):
+def test_property_income_matches_the_loop_version(seed):
     person, household = random_tables(seed)
-    without_cvpay = person.assign(cvpay=0.0)
+    np.testing.assert_allclose(
+        frs_property_income(person, household),
+        property_income_one_person_at_a_time(person, household),
+    )
+
+
+@pytest.mark.parametrize("seed", SEEDS)
+def test_property_income_does_not_depend_on_cvpay_tenure_or_expenses_basis(seed):
+    person, household = random_tables(seed)
+    rng = np.random.default_rng(seed)
+    other_person = person.assign(cvpay=rng.uniform(0, 400, len(person)))
+    other_household = household.assign(
+        tentyp2=rng.integers(1, 9, len(household)),
+        suballow=rng.integers(1, 3, len(household)),
+    )
     np.testing.assert_array_equal(
         frs_property_income(person, household),
-        frs_property_income(without_cvpay, household),
+        frs_property_income(other_person, other_household),
     )
 
 
 @pytest.mark.parametrize("seed", SEEDS)
 def test_property_income_conserves_reported_rent(seed):
-    # Each owner household's SUBRENT is counted once (on its HRP) and every
-    # ROYYR1 is counted on its own record, so the totals match.
+    # Each household's SUBRENT is counted once (on its HRP) and every ROYYR1
+    # that is not a loss is counted on its own record, so the totals match.
     person, household = random_tables(seed)
     result = frs_property_income(person, household)
     assert (result >= 0).all()
-    owner = household.tentyp2.isin((OWNED_WITH_MORTGAGE, OWNED_OUTRIGHT))
-    expected = (household.subrent[owner].sum() + person.royyr1.sum()) * WEEKS_IN_YEAR
+    profits = person.royyr1[person.rentprof != LOSS]
+    expected = (household.subrent.sum() + profits.sum()) * WEEKS_IN_YEAR
     assert result.sum() == pytest.approx(expected)
 
 
+@pytest.mark.parametrize("seed", SEEDS)
+def test_marking_rent_as_a_loss_removes_it_from_that_person_only(seed):
+    person, household = random_tables(seed)
+    before = frs_property_income(person, household)
+    row = seed % len(person)
+    was_counted = person.loc[row, "rentprof"] != LOSS
+    person.loc[row, "rentprof"] = LOSS
+    after = frs_property_income(person, household)
+    change = np.zeros(len(person))
+    change[row] = -person.loc[row, "royyr1"] * WEEKS_IN_YEAR * was_counted
+    np.testing.assert_allclose(after - before, change, atol=1e-6)
+
+
 @pytest.mark.parametrize("seed", SEEDS)
 def test_extra_rent_from_other_property_moves_only_that_person(seed):
     person, household = random_tables(seed)
@@ -125,5 +225,60 @@ def test_extra_rent_from_other_property_moves_only_that_person(seed):
     person.loc[row, "royyr1"] += 10
     after = frs_property_income(person, household)
     change = np.zeros(len(person))
-    change[row] = 10 * WEEKS_IN_YEAR
-    np.testing.assert_allclose(after - before, change)
+    change[row] = 10 * WEEKS_IN_YEAR * (person.loc[row, "rentprof"] != LOSS)
+    np.testing.assert_allclose(after - before, change, atol=1e-6)
+
+
+@pytest.mark.parametrize("seed", SEEDS)
+def test_extra_subletting_rent_moves_only_that_households_reference_person(seed):
+    person, household = random_tables(seed)
+    before = frs_property_income(person, household)
+    household_id = household.index[seed % len(household)]
+    household.loc[household_id, "subrent"] += 10
+    after = frs_property_income(person, household)
+    is_its_hrp = (person.household_id == household_id) & (person.hrpid == HRP)
+    np.testing.assert_allclose(
+        after - before, is_its_hrp * 10 * WEEKS_IN_YEAR, atol=1e-6
+    )
+
+
+def test_built_frs_matches_the_raw_tables(frs):
+    """The built base FRS against a merge-based reading of the raw tables.
+
+    Covers the call site in ``create_frs`` and the raw column names and codes.
+    The assertion carries no values, because the dataset is licensed microdata.
+    """
+    raw_folder = STORAGE_FOLDER / CURRENT_FRS_RELEASE.name
+    if not (raw_folder / "adult.tab").exists():
+        pytest.skip("Raw FRS tables not available")
+    adult = pd.read_csv(
+        raw_folder / "adult.tab",
+        sep="\t",
+        usecols=lambda c: (
+            c.upper() in ("SERNUM", "PERSON", "HRPID", "ROYYR1", "RENTPROF")
+        ),
+    )
+    househol = pd.read_csv(
+        raw_folder / "househol.tab",
+        sep="\t",
+        usecols=lambda c: c.upper() in ("SERNUM", "SUBRENT"),
+    )
+    adult.columns = adult.columns.str.upper()
+    househol.columns = househol.columns.str.upper()
+    raw = adult.merge(househol, on="SERNUM", how="left").apply(
+        pd.to_numeric, errors="coerce"
+    )
+    profit = raw.ROYYR1.where(raw.RENTPROF != 2, 0).fillna(0)
+    subrent = raw.SUBRENT.where(raw.HRPID == 1, 0).fillna(0)
+    expected = pd.Series(
+        ((profit + subrent).clip(lower=0) * WEEKS_IN_YEAR).values,
+        index=(raw.SERNUM * 1_000 + raw.PERSON).astype(int),
+    )
+    built = pd.Series(
+        frs.person.property_income.values, index=frs.person.person_id.values
+    )
+    adults_match = np.allclose(built.reindex(expected.index), expected, atol=0.01)
+    children_have_none = (built.drop(expected.index) == 0).all()
+    assert adults_match and children_have_none, (
+        "property_income in the built FRS differs from the raw FRS tables"
+    )
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index 5f1acd85f..5beb95ab3 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -442,6 +442,7 @@ def fake_read_csv(path, *args, **kwargs):
                 "mntus1": 0,
                 "mntusam1": 0,
                 "redamt": 0,
+                "rentprof": 0,
                 "royyr1": 0,
                 "seincam2": 0,
                 "sex": 1,

From cc937e0555d7ead58fc980b41b1959510709b1db Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 09:54:34 -0400
Subject: [PATCH 06/70] Floor each property amount at zero before adding them

Negative FRS values are missing-value codes (-1 to -9), not amounts. Flooring
the sum let a missing ROYYR1 code cancel the household reference person's
SUBRENT, which now matters for renting households too. Floor each amount
instead. FRS 2024-25 has no negative codes in either field, so the build is
unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py          | 10 ++--
 .../tests/test_frs_property_income.py         | 55 ++++++++++++-------
 2 files changed, 42 insertions(+), 23 deletions(-)

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index abd521401..c7f8e1795 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -243,6 +243,9 @@ def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.nda
     or after (2) allowable expenses, but the FRS collects no expense amount
     to take off the before-expenses answers.
 
+    Negative values are FRS missing-value codes (-1 to -9), not amounts, so
+    each amount is floored at zero before the two are added.
+
     CVPAY is not included. It is the rent that a boarder or lodger pays the
     householder, and it sits on the boarder's or lodger's own adult record.
     The FRS question (CvPay) asks how much rent [name] paid for board and
@@ -250,14 +253,13 @@ def frs_property_income(person: pd.DataFrame, household: pd.DataFrame) -> np.nda
     """
     is_head = person.hrpid == 1
     persons_household_subrent = (
-        household.subrent.reindex(person.household_id).fillna(0).values
+        household.subrent.clip(lower=0).reindex(person.household_id).fillna(0).values
     )
-    rent_from_other_property = person.royyr1.where(
+    rent_from_other_property = person.royyr1.clip(lower=0).where(
         person.rentprof != FRS_RENTPROF_LOSS, 0
     )
     return (
-        np.maximum(0, is_head * persons_household_subrent + rent_from_other_property)
-        * WEEKS_IN_YEAR
+        (is_head * persons_household_subrent + rent_from_other_property) * WEEKS_IN_YEAR
     ).values
 
 
diff --git a/policyengine_uk_data/tests/test_frs_property_income.py b/policyengine_uk_data/tests/test_frs_property_income.py
index 1abcf931a..5fb2982c5 100644
--- a/policyengine_uk_data/tests/test_frs_property_income.py
+++ b/policyengine_uk_data/tests/test_frs_property_income.py
@@ -12,6 +12,8 @@
 
 HRP, NOT_HRP = 1, 2
 NOT_ASKED, PROFIT, LOSS = 0, 1, FRS_RENTPROF_LOSS
+# FRS missing-value codes run from -1 to -9.
+MISSING, REFUSED = -1, -9
 OWNED_WITH_MORTGAGE, OWNED_OUTRIGHT = 5, 6
 COUNCIL_RENTED, PRIVATE_RENTED_FURNISHED = 1, 4
 TENURES = range(1, 9)
@@ -102,12 +104,20 @@ def test_subletting_rent_is_used_as_reported_whatever_its_expenses_basis(suballo
     )
 
 
-def test_negative_amounts_do_not_become_negative_income():
+def test_missing_value_codes_count_as_zero():
+    # A missing code in one amount neither becomes income nor cancels the other.
     person, household = make_tables(
-        [(1, 1_001, HRP, -30, PROFIT, 0), (1, 1_002, NOT_HRP, -5, PROFIT, 0)],
-        [(1, OWNED_OUTRIGHT, 10, AFTER_EXPENSES)],
+        [
+            (1, 1_001, HRP, MISSING, MISSING, 0),
+            (1, 1_002, NOT_HRP, REFUSED, MISSING, 0),
+            (2, 2_001, HRP, 50, PROFIT, 0),
+        ],
+        [(1, COUNCIL_RENTED, 10, AFTER_EXPENSES), (2, OWNED_OUTRIGHT, MISSING, 0)],
+    )
+    np.testing.assert_allclose(
+        frs_property_income(person, household),
+        [10 * WEEKS_IN_YEAR, 0, 50 * WEEKS_IN_YEAR],
     )
-    assert frs_property_income(person, household).tolist() == [0, 0]
 
 
 def test_adult_and_child_rows_sharing_index_labels():
@@ -134,18 +144,22 @@ def random_tables(seed: int):
     people, households = [], []
     for household_id in range(1, rng.integers(1, 30) + 1):
         tenure = int(rng.integers(1, 9))
-        subrent = float(rng.choice([0, rng.uniform(0, 500)]))
-        suballow = int(rng.integers(1, 3)) if subrent else 0
+        subrent = float(rng.choice([0, MISSING, rng.uniform(0, 500)]))
+        suballow = int(rng.integers(1, 3)) if subrent > 0 else 0
         households.append((household_id, tenure, subrent, suballow))
         for person in range(1, rng.integers(1, 5) + 1):
-            royyr1 = float(rng.choice([0, rng.uniform(0, 2_000)]))
+            royyr1 = float(rng.choice([0, MISSING, rng.uniform(0, 2_000)]))
+            if royyr1 > 0:
+                rentprof = int(rng.choice([PROFIT, PROFIT, LOSS]))
+            else:
+                rentprof = MISSING if royyr1 < 0 else NOT_ASKED
             people.append(
                 (
                     household_id,
                     household_id * 1_000 + person,
                     HRP if person == 1 else NOT_HRP,
                     royyr1,
-                    int(rng.choice([PROFIT, PROFIT, LOSS])) if royyr1 else NOT_ASKED,
+                    rentprof,
                     float(rng.choice([0, rng.uniform(0, 400)])),
                 )
             )
@@ -158,10 +172,10 @@ def property_income_one_person_at_a_time(person, household):
     for row in person.itertuples():
         weekly = 0.0
         if row.rentprof != LOSS:
-            weekly += row.royyr1
+            weekly += max(0.0, row.royyr1)
         if row.hrpid == HRP and row.household_id in household.index:
-            weekly += household.subrent[row.household_id]
-        result.append(max(0.0, weekly) * WEEKS_IN_YEAR)
+            weekly += max(0.0, household.subrent[row.household_id])
+        result.append(weekly * WEEKS_IN_YEAR)
     return np.array(result)
 
 
@@ -199,8 +213,9 @@ def test_property_income_conserves_reported_rent(seed):
     person, household = random_tables(seed)
     result = frs_property_income(person, household)
     assert (result >= 0).all()
-    profits = person.royyr1[person.rentprof != LOSS]
-    expected = (household.subrent.sum() + profits.sum()) * WEEKS_IN_YEAR
+    profits = person.royyr1.clip(lower=0)[person.rentprof != LOSS]
+    subrent = household.subrent.clip(lower=0)
+    expected = (subrent.sum() + profits.sum()) * WEEKS_IN_YEAR
     assert result.sum() == pytest.approx(expected)
 
 
@@ -213,7 +228,7 @@ def test_marking_rent_as_a_loss_removes_it_from_that_person_only(seed):
     person.loc[row, "rentprof"] = LOSS
     after = frs_property_income(person, household)
     change = np.zeros(len(person))
-    change[row] = -person.loc[row, "royyr1"] * WEEKS_IN_YEAR * was_counted
+    change[row] = -max(0, person.loc[row, "royyr1"]) * WEEKS_IN_YEAR * was_counted
     np.testing.assert_allclose(after - before, change, atol=1e-6)
 
 
@@ -222,7 +237,7 @@ def test_extra_rent_from_other_property_moves_only_that_person(seed):
     person, household = random_tables(seed)
     before = frs_property_income(person, household)
     row = seed % len(person)
-    person.loc[row, "royyr1"] += 10
+    person.loc[row, "royyr1"] = max(0, person.loc[row, "royyr1"]) + 10
     after = frs_property_income(person, household)
     change = np.zeros(len(person))
     change[row] = 10 * WEEKS_IN_YEAR * (person.loc[row, "rentprof"] != LOSS)
@@ -234,7 +249,9 @@ def test_extra_subletting_rent_moves_only_that_households_reference_person(seed)
     person, household = random_tables(seed)
     before = frs_property_income(person, household)
     household_id = household.index[seed % len(household)]
-    household.loc[household_id, "subrent"] += 10
+    household.loc[household_id, "subrent"] = (
+        max(0, household.loc[household_id, "subrent"]) + 10
+    )
     after = frs_property_income(person, household)
     is_its_hrp = (person.household_id == household_id) & (person.hrpid == HRP)
     np.testing.assert_allclose(
@@ -268,10 +285,10 @@ def test_built_frs_matches_the_raw_tables(frs):
     raw = adult.merge(househol, on="SERNUM", how="left").apply(
         pd.to_numeric, errors="coerce"
     )
-    profit = raw.ROYYR1.where(raw.RENTPROF != 2, 0).fillna(0)
-    subrent = raw.SUBRENT.where(raw.HRPID == 1, 0).fillna(0)
+    profit = raw.ROYYR1.clip(lower=0).where(raw.RENTPROF != 2, 0).fillna(0)
+    subrent = raw.SUBRENT.clip(lower=0).where(raw.HRPID == 1, 0).fillna(0)
     expected = pd.Series(
-        ((profit + subrent).clip(lower=0) * WEEKS_IN_YEAR).values,
+        ((profit + subrent) * WEEKS_IN_YEAR).values,
         index=(raw.SERNUM * 1_000 + raw.PERSON).astype(int),
     )
     built = pd.Series(

From e2e37ba72166d23e0de1a3ade1fb12990182bd55 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 09:57:29 -0400
Subject: [PATCH 07/70] Fix create_frs smoke test; use policyengine-uk's
 claimant-or-partner flag

The create_frs smoke test's fake simulation did not answer the variables
the would_claim_uc rule now reads (person_benunit_id, is_adult, is_SP_age),
which failed CI with KeyError: 'person_benunit_id'.

Pick the claimant-and-partner flag the way the locked policyengine-uk does
for Housing Benefit's pension-age route: is_claimant_or_partner where the
release defines it, otherwise is_adult as in 2.102.5. Both the
would_claim_uc rule and the age-group targets use it. Under 2.102.5 the
base FRS is identical to the previous commit's in every column.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py          | 42 ++++++++++---------
 .../targets/sources/dwp_housing_benefit.py    | 14 +++++--
 .../tests/test_dwp_housing_benefit_targets.py |  1 +
 .../tests/test_legacy_benefit_proxies.py      |  9 +++-
 .../tests/test_would_claim_uc_pension_age.py  | 28 +++++++++----
 policyengine_uk_data/utils/benefit_units.py   | 15 +++++++
 6 files changed, 75 insertions(+), 34 deletions(-)
 create mode 100644 policyengine_uk_data/utils/benefit_units.py

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 94e40c2a6..6b5b13b27 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -24,6 +24,7 @@
     add_disability_benefit_flags_from_reported_amounts,
     drop_internal_disability_reported_amounts,
 )
+from policyengine_uk_data.utils.benefit_units import claimant_or_partner_variable
 from policyengine_uk_data.utils.datasets import (
     sum_to_entity,
     categorical,
@@ -392,37 +393,36 @@ def derive_is_parent_from_frs_microdata(
     return is_adult_record & has_dependent_children
 
 
-def derive_every_adult_over_state_pension_age(
+def derive_all_claimants_over_state_pension_age(
     person_benunit_ids,
-    is_adult,
+    is_claimant_or_partner,
     is_over_state_pension_age,
     benunit_ids,
 ) -> np.ndarray:
-    """Identify benefit units with an adult, all of whose adults have reached
+    """Identify benefit units whose claimant and any partner have all reached
     State Pension age.
 
     Such a unit cannot claim Universal Credit (Welfare Reform Act 2012
-    s.4(1)(b)). The inputs are policyengine-uk's ``is_adult`` and
-    ``is_SP_age``, as in the pension-age route of its
-    ``housing_benefit_eligible``, so an 18 or 19 year old dependant makes the
-    unit working-age here as it does there.
+    s.4(1)(b)). ``is_claimant_or_partner`` should be the variable that
+    ``claimant_or_partner_variable`` names, so the rule matches the
+    pension-age route of policyengine-uk's ``housing_benefit_eligible``.
     """
 
-    is_adult = np.asarray(is_adult, dtype=bool)
-    over = is_adult & np.asarray(is_over_state_pension_age, dtype=bool)
+    claimant = np.asarray(is_claimant_or_partner, dtype=bool)
+    over = claimant & np.asarray(is_over_state_pension_age, dtype=bool)
     counts = (
         pd.DataFrame(
             {
                 "benunit": np.asarray(person_benunit_ids),
-                "adults": is_adult.astype(int),
+                "claimants": claimant.astype(int),
                 "over": over.astype(int),
             }
         )
-        .groupby("benunit")[["adults", "over"]]
+        .groupby("benunit")[["claimants", "over"]]
         .sum()
         .reindex(np.asarray(benunit_ids), fill_value=0)
     )
-    return ((counts.adults > 0) & (counts.over == counts.adults)).to_numpy()
+    return ((counts.claimants > 0) & (counts.over == counts.claimants)).to_numpy()
 
 
 def _as_non_negative_array(values) -> np.ndarray:
@@ -1570,19 +1570,21 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
         pension_credit_rate,
         reported_mask=_reported_benunit_mask("pension_credit_reported"),
     )
-    # A benefit unit whose adults have all reached State Pension age cannot
-    # claim Universal Credit, so it never gets would_claim_uc, even if it
-    # reports UC. The draw still covers every unit, so the random stream and
-    # every other unit's value are unchanged. Ages are not rolled forward, so
-    # if State Pension age rises above an adult's survey age in a later year,
-    # that unit stays without would_claim_uc there.
+    # A benefit unit whose claimant and any partner have all reached State
+    # Pension age cannot claim Universal Credit, so it never gets
+    # would_claim_uc, even if it reports UC. The draw still covers every unit,
+    # so the random stream and every other unit's value are unchanged. Ages
+    # are not rolled forward, so if State Pension age rises above a claimant's
+    # survey age in a later year, that unit stays without would_claim_uc there.
     pe_benunit["would_claim_uc"] = assign_takeup_with_reported_anchors(
         generator.random(len(pe_benunit)),
         universal_credit_rate,
         reported_mask=_reported_benunit_mask("universal_credit_reported"),
-    ) & ~derive_every_adult_over_state_pension_age(
+    ) & ~derive_all_claimants_over_state_pension_age(
         person_benunit_ids=sim.calculate("person_benunit_id", year).values,
-        is_adult=sim.calculate("is_adult", year).values,
+        is_claimant_or_partner=sim.calculate(
+            claimant_or_partner_variable(sim.tax_benefit_system.variables), year
+        ).values,
         is_over_state_pension_age=sim.calculate("is_SP_age", year).values,
         benunit_ids=pe_benunit.benunit_id,
     )
diff --git a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
index 79870d13d..eff5309f8 100644
--- a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
+++ b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
@@ -23,8 +23,8 @@
 pension-age rules apply when the claimant or partner has reached the
 qualifying age for Pension Credit, unless either is on Universal Credit,
 Income Support, income-based JSA or income-related ESA. A benefit unit is
-therefore over Pension Credit qualifying age here when one of its adults
-has reached State Pension age and it gets none of those benefits. Mixed-age
+therefore over Pension Credit qualifying age here when its claimant or
+partner has reached State Pension age and it gets none of those benefits. Mixed-age
 couples who kept pension-age Housing Benefit after May 2019 fall in the
 older group; DWP does not publish its rule for them, and this assumes they
 sit in its Pension Credit and State Pension groups.
@@ -46,6 +46,7 @@
 import numpy as np
 
 from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Target, Unit
+from policyengine_uk_data.utils.benefit_units import claimant_or_partner_variable
 
 _REFERENCE_URL = (
     "https://www.gov.uk/government/publications/"
@@ -117,8 +118,13 @@
 
 def _over_pension_credit_age(ctx) -> np.ndarray:
     """Benefit units assessed under the pension-age Housing Benefit rules."""
-    adult = np.asarray(ctx.pe_person("is_adult"), dtype=bool)
-    over = adult & np.asarray(ctx.pe_person("is_SP_age"), dtype=bool)
+    claimant = np.asarray(
+        ctx.pe_person(
+            claimant_or_partner_variable(ctx.sim.tax_benefit_system.variables)
+        ),
+        dtype=bool,
+    )
+    over = claimant & np.asarray(ctx.pe_person("is_SP_age"), dtype=bool)
     any_over = (
         np.asarray(ctx.sim.map_result(over.astype(float), "person", "benunit")) > 0
     )
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index 549006b42..d9a805f14 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -132,6 +132,7 @@ def map_result(values, source, target):
     person = {"is_adult": np.asarray(is_adult), "is_SP_age": np.asarray(is_sp_age)}
     benunit_values = {"housing_benefit": benunit_hb, **(benunit_benefits or {})}
     sim = SimpleNamespace(
+        tax_benefit_system=SimpleNamespace(variables={"is_adult": None}),
         map_result=map_result,
         calculate=lambda variable: SimpleNamespace(
             values=np.asarray(
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index 5f1acd85f..a6af94e3f 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -299,6 +299,7 @@ def __init__(self, dataset):
             "FakeTaxBenefitSystem",
             (),
             {
+                "variables": {"is_adult": None},
                 "parameters": lambda self, year: type(
                     "FakeParametersRoot",
                     (),
@@ -349,7 +350,7 @@ def __init__(self, dataset):
                             },
                         )()
                     },
-                )()
+                )(),
             },
         )()
 
@@ -360,6 +361,12 @@ def calculate(self, variable, year=None):
             return np.array([100])
         if variable == "state_pension_age":
             return pd.Series([66])
+        if variable == "person_benunit_id":
+            return pd.Series(self.dataset.person.person_benunit_id.values)
+        if variable == "is_adult":
+            return pd.Series(self.dataset.person.age.values >= 18)
+        if variable == "is_SP_age":
+            return pd.Series(self.dataset.person.age.values >= 66)
         if variable in (
             "childcare_grant",
             "parents_learning_allowance",
diff --git a/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py b/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py
index 251848335..b6a4f634d 100644
--- a/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py
+++ b/policyengine_uk_data/tests/test_would_claim_uc_pension_age.py
@@ -5,17 +5,18 @@
 from hypothesis import strategies as st
 
 from policyengine_uk_data.datasets.frs import (
-    derive_every_adult_over_state_pension_age,
+    derive_all_claimants_over_state_pension_age,
 )
+from policyengine_uk_data.utils.benefit_units import claimant_or_partner_variable
 
 
 def test_examples():
-    # Units: pensioner couple, mixed-age couple, working-age single,
-    # pensioner with an 18-year-old dependant, a unit with no adult, and a
-    # unit with no members at all.
-    result = derive_every_adult_over_state_pension_age(
+    # Units: pensioner couple, mixed-age couple, working-age single, a
+    # pensioner and a younger person flagged as claimant or partner, a unit
+    # with no claimant, and a unit with no members at all.
+    result = derive_all_claimants_over_state_pension_age(
         person_benunit_ids=[10, 10, 20, 20, 30, 40, 40, 50],
-        is_adult=[1, 1, 1, 1, 1, 1, 1, 0],
+        is_claimant_or_partner=[1, 1, 1, 1, 1, 1, 1, 0],
         is_over_state_pension_age=[1, 1, 1, 0, 0, 1, 0, 0],
         benunit_ids=[10, 20, 30, 40, 50, 60],
     )
@@ -33,9 +34,9 @@ def test_examples():
 def test_matches_a_direct_definition(people, benunit_order):
     """Differential check against a per-unit loop, for any unit order."""
     benunit_ids = np.array(benunit_order)
-    result = derive_every_adult_over_state_pension_age(
+    result = derive_all_claimants_over_state_pension_age(
         person_benunit_ids=[p[0] for p in people],
-        is_adult=[p[1] for p in people],
+        is_claimant_or_partner=[p[1] for p in people],
         is_over_state_pension_age=[p[2] for p in people],
         benunit_ids=benunit_ids,
     )
@@ -44,9 +45,18 @@ def test_matches_a_direct_definition(people, benunit_order):
         assert result[i] == (len(adults) > 0 and all(adults))
 
 
+def test_claimant_or_partner_variable_prefers_the_legal_flag():
+    assert claimant_or_partner_variable({"is_adult": 0}) == "is_adult"
+    assert (
+        claimant_or_partner_variable({"is_adult": 0, "is_claimant_or_partner": 0})
+        == "is_claimant_or_partner"
+    )
+
+
 def test_built_dataset_has_no_pension_age_uc_claimants(baseline):
     year = 2025
-    adult = baseline.calculate("is_adult", year).values.astype(bool)
+    claimant = claimant_or_partner_variable(baseline.tax_benefit_system.variables)
+    adult = baseline.calculate(claimant, year).values.astype(bool)
     over = adult & baseline.calculate("is_SP_age", year).values.astype(bool)
     adults = baseline.map_result(adult.astype(float), "person", "benunit")
     adults_over = baseline.map_result(over.astype(float), "person", "benunit")
diff --git a/policyengine_uk_data/utils/benefit_units.py b/policyengine_uk_data/utils/benefit_units.py
new file mode 100644
index 000000000..fd098770b
--- /dev/null
+++ b/policyengine_uk_data/utils/benefit_units.py
@@ -0,0 +1,15 @@
+"""Benefit-unit membership helpers shared by the dataset build and targets."""
+
+
+def claimant_or_partner_variable(variables) -> str:
+    """Name of the policyengine-uk variable marking a benefit unit's claimant
+    and any partner.
+
+    Releases that define ``is_claimant_or_partner`` use it for Housing
+    Benefit's pension-age route. Earlier releases, such as 2.102.5, use
+    ``is_adult`` (age 18 or over) there, which also counts an 18 or 19 year
+    old dependant.
+    """
+    if "is_claimant_or_partner" in variables:
+        return "is_claimant_or_partner"
+    return "is_adult"

From 0b45992696e29137fb986ab0957db1621eb717fa Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 15:38:59 -0400
Subject: [PATCH 08/70] Carry the FRS benefit-unit capital into
 pension_credit_reported_capital

TOTCAPB3 (DWP's derived benefit-unit total of savings and investments) for
every FRS benefit unit; -1 where missing or negative. Pension Credit counts
the claimant's and partner's capital (SPCA 2002 s.5); the WAS household
imputation gives FRS Pension Credit reporters a weighted median of 74,300
pounds against the survey's own 300 for those the model then finds not
entitled.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-pc-reported-capital.changed.md        |  1 +
 policyengine_uk_data/datasets/frs.py          | 31 +++++++++++++
 .../test_pension_credit_reported_capital.py   | 44 +++++++++++++++++++
 3 files changed, 76 insertions(+)
 create mode 100644 changelog.d/frs-pc-reported-capital.changed.md
 create mode 100644 policyengine_uk_data/tests/test_pension_credit_reported_capital.py

diff --git a/changelog.d/frs-pc-reported-capital.changed.md b/changelog.d/frs-pc-reported-capital.changed.md
new file mode 100644
index 000000000..7986dc739
--- /dev/null
+++ b/changelog.d/frs-pc-reported-capital.changed.md
@@ -0,0 +1 @@
+Carry each benefit unit's FRS total capital (TOTCAPB3) into policyengine-uk's `pension_credit_reported_capital`, so Pension Credit's capital test uses the survey's own benefit-unit capital instead of imputed household wealth.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..5cdb760c0 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -570,6 +570,30 @@ def validate_frs_survey_year(raw_frs_folder, year: int) -> None:
         )
 
 
+def derive_pension_credit_reported_capital(benunit: pd.DataFrame) -> np.ndarray:
+    """Each benefit unit's capital as the FRS records it, for Pension Credit.
+
+    ``TOTCAPB3`` is DWP's derived benefit-unit total of the adults' savings and
+    investments (current, savings and NS&I accounts, gilts, unit and investment
+    trusts, shares and bonds, ISAs, credit unions), the measure its FRS-based
+    National Statistics use. Pension Credit counts the claimant's capital and,
+    under the State Pension Credit Act 2002 s. 5, the partner's, which is what
+    this records. The household wealth imputation instead draws a household's
+    wealth from Wealth and Assets Survey households with similar income,
+    composition, tenure and region, with no information on means-tested
+    receipt, and policyengine-uk spreads it over the household's pension-age
+    adults.
+
+    ``TOTCAPB3`` covers financial assets only: second homes and land, which
+    Pension Credit also counts, are not in it. A missing or negative value gives
+    -1, so policyengine-uk falls back to the household proxy.
+    """
+    if "totcapb3" not in benunit.columns:
+        return np.full(len(benunit), -1.0)
+    capital = pd.to_numeric(benunit["totcapb3"], errors="coerce").to_numpy(dtype=float)
+    return np.where(np.isfinite(capital) & (capital >= 0), capital, -1.0)
+
+
 def create_frs(
     raw_frs_folder: str,
     year: int,
@@ -1627,6 +1651,13 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
 
     pe_benunit["is_married"] = benunit.famtypb2.isin([5, 7])
 
+    # Pension Credit capital as the FRS records it for the benefit unit, in
+    # place of the household wealth proxy (policyengine-uk
+    # `pension_credit_reported_capital`).
+    pe_benunit["pension_credit_reported_capital"] = (
+        derive_pension_credit_reported_capital(benunit)
+    )
+
     # Assign property_purchased to a share of households matching the UK
     # housing transaction rate, so only genuine purchasers are charged SDLT.
     #
diff --git a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
new file mode 100644
index 000000000..ca42cf438
--- /dev/null
+++ b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
@@ -0,0 +1,44 @@
+"""`pension_credit_reported_capital` from the FRS benefit-unit capital
+measure (TOTCAPB3).
+
+Invariants:
+1. A finite, non-negative TOTCAPB3 is carried over unchanged.
+2. A missing, non-numeric or negative value gives -1 (policyengine-uk's
+   "none recorded" sentinel), so the household proxy applies.
+3. Without a TOTCAPB3 column every benefit unit gets -1.
+4. The output is always -1 or a non-negative number, one per benefit unit.
+"""
+
+import numpy as np
+import pandas as pd
+
+from policyengine_uk_data.datasets.frs import derive_pension_credit_reported_capital
+
+
+def test_values_carry_over_and_invalid_values_fall_back():
+    benunit = pd.DataFrame(
+        {"totcapb3": [0.0, 300.0, 2_900.0, 1_250_000.0, np.nan, -5.0, "x"]}
+    )
+    result = derive_pension_credit_reported_capital(benunit)
+    assert result.tolist() == [0.0, 300.0, 2_900.0, 1_250_000.0, -1.0, -1.0, -1.0]
+
+
+def test_missing_column_gives_sentinel():
+    benunit = pd.DataFrame({"benunit_id": [101, 102, 201]})
+    assert derive_pension_credit_reported_capital(benunit).tolist() == [-1.0] * 3
+
+
+def test_output_is_sentinel_or_non_negative_for_random_inputs():
+    rng = np.random.default_rng(1_792)
+    for _ in range(50):
+        n = int(rng.integers(1, 200))
+        values = rng.normal(5_000, 20_000, n)
+        values[rng.random(n) < 0.1] = np.nan
+        result = derive_pension_credit_reported_capital(
+            pd.DataFrame({"totcapb3": values})
+        )
+        assert len(result) == n
+        assert np.all((result == -1) | (result >= 0))
+        keep = np.isfinite(values) & (values >= 0)
+        np.testing.assert_array_equal(result[keep], values[keep])
+        assert np.all(result[~keep] == -1)

From 96672af5f52586a279d40d37cf83816c3b2ab07e Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 01:16:36 -0400
Subject: [PATCH 09/70] Stop SPI-synthetic rows carrying benefit claims nobody
 observed

The second-stage QRF draws each SPI-donor person's benefit reports from
age, gender, region and incomes, with no view of the benefit unit or of
health. Those reports then act as existing claims in policyengine-uk. The
take-up anchors, receives_benefits_in_own_right and ssmg_reported were
also left as the FRS donor's.

On SPI-donor rows, this zeroes the income-related awards (UC, Pension
Credit, Housing Benefit, CTR, IS, tax credits, income-related ESA and
JSA, SSMG), the out-of-work benefits (contributory ESA and JSA,
incapacity benefit, SDA) and Child Benefit, whose only use is the
take-up anchor. It then rebuilds the anchors and
receives_benefits_in_own_right from the rows' own reports. The zeroed
columns stay in the QRF chain, so the values of the reports kept do not
change.

Also adds hypothesis as a dev extra, for the property tests.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .gitignore                                    |   3 +
 .../spi-synthetic-reported-benefits.fixed.md  |   1 +
 policyengine_uk_data/datasets/frs.py          |  47 +++--
 .../datasets/imputations/frs_only.py          | 102 +++++++++-
 .../tests/test_frs_only_imputation.py         |  25 +--
 .../tests/test_spi_donor_benefit_rules.py     | 187 ++++++++++++++++++
 pyproject.toml                                |   5 +-
 uv.lock                                       |  73 ++++++-
 8 files changed, 409 insertions(+), 34 deletions(-)
 create mode 100644 changelog.d/spi-synthetic-reported-benefits.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_spi_donor_benefit_rules.py

diff --git a/.gitignore b/.gitignore
index 9a741bc49..5c1ab2385 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,6 @@
 **/_build
 !policyengine_uk_data/storage/*.csv
 **/version.json
+
+# Hypothesis example database (property-based tests)
+.hypothesis/
diff --git a/changelog.d/spi-synthetic-reported-benefits.fixed.md b/changelog.d/spi-synthetic-reported-benefits.fixed.md
new file mode 100644
index 000000000..f315e0118
--- /dev/null
+++ b/changelog.d/spi-synthetic-reported-benefits.fixed.md
@@ -0,0 +1 @@
+SPI-synthetic rows no longer report income-related, out-of-work or Child Benefit receipt, and their take-up flags and `receives_benefits_in_own_right` come from their own reports instead of the FRS donor's.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..82f31fe55 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -74,6 +74,14 @@
     "esa_contrib_reported",
     "esa_income_reported",
 )
+# Take-up flags anchored on reported receipt: flag -> (take-up rate
+# parameter, person-level report column). A benefit unit with any member
+# reporting receipt claims with certainty; the rest are filled at random.
+REPORTED_TAKEUP_ANCHORS = {
+    "would_claim_child_benefit": ("child_benefit", "child_benefit_reported"),
+    "would_claim_pc": ("pension_credit", "pension_credit_reported"),
+    "would_claim_uc": ("universal_credit", "universal_credit_reported"),
+}
 NON_ADVANCED_EDUCATION_LEVELS = (
     "PRE_PRIMARY",
     "PRIMARY",
@@ -211,6 +219,16 @@ def derive_esa_support_group_proxy(
     )
 
 
+def reported_benunit_mask(
+    person: pd.DataFrame, benunit: pd.DataFrame, person_column: str
+) -> np.ndarray:
+    """Benefit units with any member reporting a positive ``person_column``."""
+    reporter_benunits = set(
+        person.loc[person[person_column] > 0, "person_benunit_id"].values
+    )
+    return benunit["benunit_id"].isin(reporter_benunits).values
+
+
 def derive_receives_benefits_in_own_right(pe_person: pd.DataFrame) -> pd.Series:
     """Identify people reporting adult benefits that end QYP status."""
 
@@ -1511,11 +1529,14 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         assign_takeup_with_reported_anchors,
     )
 
-    def _reported_benunit_mask(person_column: str) -> np.ndarray:
-        reporter_benunits = set(
-            pe_person.loc[pe_person[person_column] > 0, "person_benunit_id"].values
+    def _anchored_takeup(flag: str, rate: float) -> np.ndarray:
+        return assign_takeup_with_reported_anchors(
+            generator.random(len(pe_benunit)),
+            rate,
+            reported_mask=reported_benunit_mask(
+                pe_person, pe_benunit, REPORTED_TAKEUP_ANCHORS[flag][1]
+            ),
         )
-        return pe_benunit["benunit_id"].isin(reporter_benunits).values
 
     # Person-level
     pe_person["would_claim_marriage_allowance"] = (
@@ -1524,23 +1545,17 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
 
     # Benefit unit-level — anchor on any adult in the benefit unit having
     # reported positive receipt in the FRS benefits table.
-    pe_benunit["would_claim_child_benefit"] = assign_takeup_with_reported_anchors(
-        generator.random(len(pe_benunit)),
-        child_benefit_rate,
-        reported_mask=_reported_benunit_mask("child_benefit_reported"),
+    pe_benunit["would_claim_child_benefit"] = _anchored_takeup(
+        "would_claim_child_benefit", child_benefit_rate
     )
     pe_benunit["child_benefit_opts_out"] = (
         generator.random(len(pe_benunit)) < child_benefit_opts_out_rate
     )
-    pe_benunit["would_claim_pc"] = assign_takeup_with_reported_anchors(
-        generator.random(len(pe_benunit)),
-        pension_credit_rate,
-        reported_mask=_reported_benunit_mask("pension_credit_reported"),
+    pe_benunit["would_claim_pc"] = _anchored_takeup(
+        "would_claim_pc", pension_credit_rate
     )
-    pe_benunit["would_claim_uc"] = assign_takeup_with_reported_anchors(
-        generator.random(len(pe_benunit)),
-        universal_credit_rate,
-        reported_mask=_reported_benunit_mask("universal_credit_reported"),
+    pe_benunit["would_claim_uc"] = _anchored_takeup(
+        "would_claim_uc", universal_credit_rate
     )
     pe_benunit["would_claim_tfc"] = generator.random(len(pe_benunit)) < tfc_rate
 
diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py
index 242fbf247..036215650 100644
--- a/policyengine_uk_data/datasets/imputations/frs_only.py
+++ b/policyengine_uk_data/datasets/imputations/frs_only.py
@@ -40,6 +40,13 @@
     add_disability_benefit_categories_from_reported_amounts,
     add_disability_benefit_flags_from_reported_amounts,
 )
+from policyengine_uk_data.datasets.frs import (
+    BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS,
+    REPORTED_TAKEUP_ANCHORS,
+    reported_benunit_mask,
+)
+from policyengine_uk_data.parameters import load_take_up_rate
+from policyengine_uk_data.utils.takeup import assign_takeup_with_reported_anchors
 
 logger = logging.getLogger(__name__)
 
@@ -102,6 +109,91 @@
     "esa_income_reported",
 ]
 
+# Benefit reports set to zero on SPI-donor rows once the QRF has drawn them.
+# The QRF draws each person's reports from their age, gender, region and
+# incomes; it sees nothing of their benefit unit (partner, children, rent,
+# capital) or of their health. A report says the person is an existing
+# claimant, and policyengine-uk acts on that, so these are zeroed:
+#
+# - Income-related awards. Entitlement turns on the benefit unit's joint
+#   means and make-up, which on these rows come from the imputed incomes, so
+#   the model's own means test is the only coherent source. A report would
+#   instead be a continuing-award gate (housing benefit, income support, tax
+#   credits, income-related ESA and JSA), the paid amount (income-related
+#   ESA and JSA; council tax reduction where the model has no scheme) or a
+#   certain-claim anchor (UC, Pension Credit). Sure Start Maternity Grant
+#   needs one of these awards and is copied from the donor, not drawn.
+# - Benefits paid only to people out of work or incapable of it: ESA and
+#   JSA (contributory), incapacity benefit and severe disablement allowance.
+#   On the 2024-25 build, 76% of SPI-row ESA (contributory) reporters earned
+#   more than ESA's permitted-work limit, against 2% of FRS reporters.
+# - Child Benefit, whose only use is the take-up anchor. The draw ignores
+#   the children: on the 2024-25 build, 44% of SPI-row reports were in
+#   benefit units without a child under 16 (FRS rows: 8%).
+#
+# State pension (driven by age), winter fuel payment (not read by the
+# model), the disability benefits, carer's allowance (the draws respect its
+# earnings limit) and IIDB, AFCS and bereavement support (payable at any
+# income) keep their drawn values. The zeroed columns stay in the QRF chain
+# above so that the values kept do not change.
+SPI_DONOR_ZEROED_PERSON_VARIABLES = [
+    "universal_credit_reported",
+    "pension_credit_reported",
+    "housing_benefit_reported",
+    "council_tax_benefit_reported",
+    "income_support_reported",
+    "working_tax_credit_reported",
+    "child_tax_credit_reported",
+    "jsa_income_reported",
+    "esa_income_reported",
+    "ssmg_reported",
+    "jsa_contrib_reported",
+    "esa_contrib_reported",
+    "incapacity_benefit_reported",
+    "sda_reported",
+    "child_benefit_reported",
+]
+
+# Seed for the take-up draws on SPI-donor rows; create_frs uses 100.
+SPI_DONOR_TAKEUP_SEED = 101
+
+
+def apply_spi_donor_benefit_rules(
+    dataset: UKSingleYearDataset,
+) -> UKSingleYearDataset:
+    """Zero SPI-donor benefit reports and re-derive the flags built from them.
+
+    ``create_frs`` sets ``receives_benefits_in_own_right`` and the
+    report-anchored take-up flags from the donor's own reports, before the
+    SPI rows exist. Here they are rebuilt from the rows' own reports: a unit
+    reporting receipt claims, and the rest draw at the take-up rate. With the
+    anchoring reports zeroed, every SPI-donor unit draws at the rate, since
+    whether a synthetic family claims is unobserved.
+    """
+    dataset = dataset.copy()
+    person, benunit = dataset.person, dataset.benunit
+    for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
+        if column in person.columns:
+            person[column] = 0.0
+
+    own_right = [c for c in BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS if c in person]
+    if "receives_benefits_in_own_right" in person.columns:
+        person["receives_benefits_in_own_right"] = (
+            person[own_right].fillna(0).sum(axis=1) > 0
+        )
+
+    year = int(str(dataset.time_period)[:4])
+    generator = np.random.default_rng(seed=SPI_DONOR_TAKEUP_SEED)
+    for flag, (rate_name, column) in REPORTED_TAKEUP_ANCHORS.items():
+        if flag not in benunit.columns or column not in person.columns:
+            continue
+        benunit[flag] = assign_takeup_with_reported_anchors(
+            generator.random(len(benunit)),
+            load_take_up_rate(rate_name, year),
+            reported_mask=reported_benunit_mask(person, benunit, column),
+        )
+    return dataset
+
 
 def _one_hot_encode(df: pd.DataFrame, columns: list[str]) -> pd.DataFrame:
     """Return ``df`` with object-typed ``columns`` one-hot encoded.
@@ -177,7 +269,10 @@ def impute_frs_only_variables(
     to predict values for every row of ``target_dataset``; predictions
     replace the existing (donor-leaked) values in
     ``FRS_ONLY_PERSON_VARIABLES`` only. Variables absent from either
-    frame are skipped silently.
+    frame are skipped silently. ``apply_spi_donor_benefit_rules`` then
+    zeroes ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` and rebuilds the flags
+    derived from reports, before the disability categories and flags are
+    derived from the remaining reports.
     """
     from policyengine_uk_data.utils.qrf import QRF
 
@@ -203,9 +298,9 @@ def impute_frs_only_variables(
     if not outputs:
         logger.warning(
             "Stage-2 FRS-only imputation: no output variables available; "
-            "returning target_dataset unchanged."
+            "applying only the SPI-donor benefit rules."
         )
-        return target_dataset
+        return apply_spi_donor_benefit_rules(target_dataset)
 
     train_inputs_raw = _build_predictor_frame(train_dataset)
     target_inputs_raw = _build_predictor_frame(target_dataset)
@@ -241,6 +336,7 @@ def impute_frs_only_variables(
         values = np.maximum(predictions[column].values, 0.0)
         target_dataset.person[column] = values
 
+    target_dataset = apply_spi_donor_benefit_rules(target_dataset)
     target_dataset.person = add_disability_benefit_categories_from_reported_amounts(
         target_dataset.person,
         int(str(target_dataset.time_period)[:4]),
diff --git a/policyengine_uk_data/tests/test_frs_only_imputation.py b/policyengine_uk_data/tests/test_frs_only_imputation.py
index 1961274e4..17ba59fd1 100644
--- a/policyengine_uk_data/tests/test_frs_only_imputation.py
+++ b/policyengine_uk_data/tests/test_frs_only_imputation.py
@@ -178,25 +178,26 @@ def test_frs_only_skips_missing_output_columns():
 
 
 def test_frs_only_reported_values_correlate_with_training_pattern():
-    """UC ``_reported`` predictions should respect the training-data pattern.
+    """Drawn ``_reported`` values should respect the training-data pattern.
 
     The stage-1 QRF-imputed income on the SPI-donor side gets fed back
-    as a stage-2 predictor. If the training data only has non-zero UC
-    for low-income respondents, the QRF should preferentially draw
-    near-zero values when predicting for high-income target rows,
-    compared with low-income target rows.
+    as a stage-2 predictor. If the training data only has non-zero
+    carer's allowance for low earners (it has an earnings limit), the QRF
+    should preferentially draw near-zero values when predicting for
+    high-income target rows, compared with low-income target rows. (UC
+    would be the obvious case, but SPI-donor rows have it zeroed.)
     """
     from policyengine_uk_data.datasets.imputations.frs_only import (
         impute_frs_only_variables,
     )
 
-    # Train set with a clean employment-income → UC relationship:
-    # low-income respondents sometimes claim UC, high-income never do.
+    # Train set with a clean employment-income → carer's allowance relationship:
+    # low earners sometimes receive it, high earners never do.
     rng = np.random.default_rng(42)
     train = _fake_dataset(person_rows=2_000, seed=0)
     low_income_mask = train.person["employment_income"] < 20_000
-    train.person["universal_credit_reported"] = 0.0
-    train.person.loc[low_income_mask, "universal_credit_reported"] = rng.gamma(
+    train.person["carers_allowance_reported"] = 0.0
+    train.person.loc[low_income_mask, "carers_allowance_reported"] = rng.gamma(
         2, 4_000, size=int(low_income_mask.sum())
     )
 
@@ -215,10 +216,10 @@ def test_frs_only_reported_values_correlate_with_training_pattern():
         target_dataset=low_target,
     )
 
-    high_mean = high_result.person["universal_credit_reported"].mean()
-    low_mean = low_result.person["universal_credit_reported"].mean()
+    high_mean = high_result.person["carers_allowance_reported"].mean()
+    low_mean = low_result.person["carers_allowance_reported"].mean()
     assert high_mean < low_mean, (
-        "Stage-2 QRF should produce lower UC-receipt predictions for high-"
+        "Stage-2 QRF should produce lower carer's allowance predictions for high-"
         f"income target rows (got high={high_mean:.2f} vs low={low_mean:.2f})."
     )
 
diff --git a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
new file mode 100644
index 000000000..c221e54ed
--- /dev/null
+++ b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
@@ -0,0 +1,187 @@
+"""Benefit reports and the flags built from them on SPI-donor rows.
+
+Properties of ``apply_spi_donor_benefit_rules`` for any input:
+
+1. Every column in ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` is zero afterwards.
+2. Nothing else changes except ``receives_benefits_in_own_right`` and the
+   report-anchored take-up flags; the input is not mutated.
+3. A benefit unit with a member reporting an anchoring benefit claims it.
+4. ``receives_benefits_in_own_right`` is true exactly when the person
+   reports one of ``BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS``.
+5. The rules are deterministic and idempotent.
+
+The fixtures are synthetic, not survey records.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import HealthCheck, given, settings
+from hypothesis import strategies as st
+from policyengine_uk.data import UKSingleYearDataset
+
+from policyengine_uk_data.datasets.disability_benefits import (
+    add_disability_benefit_flags_from_reported_amounts,
+)
+from policyengine_uk_data.datasets.frs import (
+    BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS,
+    REPORTED_TAKEUP_ANCHORS,
+)
+from policyengine_uk_data.datasets.imputations import frs_only
+from policyengine_uk_data.datasets.imputations.frs_only import (
+    FRS_ONLY_PERSON_VARIABLES,
+    SPI_DONOR_ZEROED_PERSON_VARIABLES,
+    apply_spi_donor_benefit_rules,
+)
+from policyengine_uk_data.parameters import load_take_up_rate
+
+YEAR = 2024
+REPORT_COLUMNS = sorted(
+    set(FRS_ONLY_PERSON_VARIABLES) | set(SPI_DONOR_ZEROED_PERSON_VARIABLES)
+)
+DERIVED = {"receives_benefits_in_own_right", *REPORTED_TAKEUP_ANCHORS}
+
+
+def _dataset(benunit_sizes, reports, flags) -> UKSingleYearDataset:
+    n_benunits, n_people = len(benunit_sizes), sum(benunit_sizes)
+    benunit_of_person = np.repeat(np.arange(n_benunits), benunit_sizes)
+    person = pd.DataFrame(
+        {
+            "person_id": np.arange(n_people),
+            "person_benunit_id": benunit_of_person,
+            "person_household_id": benunit_of_person,
+            "age": np.full(n_people, 40),
+            "receives_benefits_in_own_right": np.asarray(flags[:n_people]),
+        }
+    )
+    for i, column in enumerate(REPORT_COLUMNS):
+        person[column] = np.asarray(reports[i][:n_people], dtype=float)
+    benunit = pd.DataFrame({"benunit_id": np.arange(n_benunits)})
+    for j, flag in enumerate(REPORTED_TAKEUP_ANCHORS):
+        benunit[flag] = np.roll(np.asarray(flags[:n_benunits]), j)
+    household = pd.DataFrame(
+        {"household_id": np.arange(n_benunits), "household_weight": 0.0}
+    )
+    return UKSingleYearDataset(
+        person=person, benunit=benunit, household=household, fiscal_year=YEAR
+    )
+
+
+@st.composite
+def datasets(draw):
+    sizes = draw(st.lists(st.integers(1, 4), min_size=1, max_size=12))
+    n = sum(sizes)
+    amount = st.one_of(st.just(0.0), st.floats(0.01, 50_000))
+    reports = [draw(st.lists(amount, min_size=n, max_size=n)) for _ in REPORT_COLUMNS]
+    flags = draw(st.lists(st.booleans(), min_size=n, max_size=n))
+    return _dataset(sizes, reports, flags)
+
+
+def _reporting_benunits(person, benunit, column):
+    reporters = person.loc[person[column] > 0, "person_benunit_id"]
+    return benunit.benunit_id.isin(set(reporters)).values
+
+
+@settings(max_examples=60, deadline=None)
+@given(datasets())
+def test_rules_zero_listed_reports_and_touch_nothing_else(dataset):
+    before = dataset.copy()
+    after = apply_spi_donor_benefit_rules(dataset)
+
+    for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
+        assert (after.person[column] == 0).all(), column
+    kept = [c for c in after.person.columns if c not in DERIVED]
+    unchanged = [c for c in kept if c not in SPI_DONOR_ZEROED_PERSON_VARIABLES]
+    pd.testing.assert_frame_equal(after.person[unchanged], before.person[unchanged])
+    pd.testing.assert_frame_equal(after.household, before.household)
+    pd.testing.assert_frame_equal(dataset.person, before.person)
+    pd.testing.assert_frame_equal(dataset.benunit, before.benunit)
+
+
+@settings(max_examples=60, deadline=None)
+@given(datasets())
+def test_rules_rebuild_own_right_flag_from_own_reports(dataset):
+    after = apply_spi_donor_benefit_rules(dataset).person
+    own = after[list(BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS)].sum(axis=1) > 0
+    assert (after.receives_benefits_in_own_right == own).all()
+
+
+@settings(
+    max_examples=60,
+    deadline=None,
+    suppress_health_check=[HealthCheck.function_scoped_fixture],
+)
+@given(dataset=datasets())
+def test_reporters_claim_whatever_is_zeroed(dataset, monkeypatch):
+    # With no anchoring report zeroed, reporters must still claim: the
+    # anchors are rebuilt from the rows' own reports, not left as copies.
+    anchoring = {column for _, column in REPORTED_TAKEUP_ANCHORS.values()}
+    monkeypatch.setattr(
+        frs_only,
+        "SPI_DONOR_ZEROED_PERSON_VARIABLES",
+        [c for c in SPI_DONOR_ZEROED_PERSON_VARIABLES if c not in anchoring],
+    )
+    after = apply_spi_donor_benefit_rules(dataset)
+    for flag, (_, column) in REPORTED_TAKEUP_ANCHORS.items():
+        reports = _reporting_benunits(after.person, after.benunit, column)
+        assert after.benunit[flag].values[reports].all(), flag
+
+
+@settings(max_examples=40, deadline=None)
+@given(datasets())
+def test_rules_are_deterministic_and_idempotent(dataset):
+    once = apply_spi_donor_benefit_rules(dataset)
+    again = apply_spi_donor_benefit_rules(dataset)
+    twice = apply_spi_donor_benefit_rules(once)
+    for frame in ("person", "benunit", "household"):
+        pd.testing.assert_frame_equal(getattr(once, frame), getattr(again, frame))
+        pd.testing.assert_frame_equal(getattr(once, frame), getattr(twice, frame))
+
+
+def test_unreported_units_claim_at_the_take_up_rate():
+    n = 40_000
+    rng = np.random.default_rng(0)
+    reports = [rng.gamma(2, 1_000, n) for _ in REPORT_COLUMNS]
+    dataset = _dataset([1] * n, reports, np.ones(n, dtype=bool))
+    after = apply_spi_donor_benefit_rules(dataset).benunit
+    for flag, (rate_name, _) in REPORTED_TAKEUP_ANCHORS.items():
+        rate = load_take_up_rate(rate_name, YEAR)
+        assert after[flag].mean() == pytest.approx(rate, abs=0.01), flag
+
+
+def test_stage_two_keeps_drawn_values_of_kept_reports(monkeypatch):
+    """Zeroing does not change the QRF draws of the reports that are kept."""
+    from policyengine_uk_data.tests.test_frs_only_imputation import _fake_dataset
+
+    train = _fake_dataset(person_rows=400, seed=0)
+    for column in ("esa_contrib_reported", "state_pension_reported", "ssmg_reported"):
+        train.person[column] = np.where(
+            np.random.default_rng(1).random(400) < 0.3, 5_000.0, 0.0
+        )
+    target = _fake_dataset(person_rows=80, seed=1)
+    target.person["ssmg_reported"] = 600.0
+    target.person["receives_benefits_in_own_right"] = True
+    target.benunit["would_claim_uc"] = True
+
+    ruled = frs_only.impute_frs_only_variables(train, target)
+    monkeypatch.setattr(frs_only, "SPI_DONOR_ZEROED_PERSON_VARIABLES", [])
+    unruled = frs_only.impute_frs_only_variables(train, target)
+
+    for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
+        assert (ruled.person[column] == 0).all(), column
+    kept = [
+        c
+        for c in FRS_ONLY_PERSON_VARIABLES
+        if c not in SPI_DONOR_ZEROED_PERSON_VARIABLES
+    ]
+    pd.testing.assert_frame_equal(ruled.person[kept], unruled.person[kept])
+    assert not ruled.person.receives_benefits_in_own_right.any()
+
+    # Disability flags come from the zeroed reports: ESA (contributory) was
+    # drawn for some people but no longer marks them disabled.
+    assert (unruled.person.esa_contrib_reported > 0).any()
+    flags = ["is_disabled_for_benefits", "is_severely_disabled_for_benefits"]
+    recomputed = add_disability_benefit_flags_from_reported_amounts(
+        ruled.person.drop(columns=flags), int(str(ruled.time_period)[:4])
+    )
+    pd.testing.assert_frame_equal(ruled.person[flags], recomputed[flags])
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..7973343cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,8 +45,9 @@ dev = [
     "yaml-changelog>=0.1.7",
     "itables",
     "quantile-forest",
-    "build",    "towncrier>=24.8.0",
-
+    "build",
+    "towncrier>=24.8.0",
+    "hypothesis>=6.168.3",
 ]
 
 [tool.setuptools]
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..672737ea1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/b7/13118bbc45d6d8b9d04e2de779e2a4ff23145ea39efa692b994fb874ca72/hypothesis-6.168.3.tar.gz", hash = "sha256:a43388f9067678fef6e13bdff325b6cfa6961a590498bb37f7ff31589c83bc75", size = 511022, upload-time = "2026-09-28T05:20:58.499Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/cd/746a2fc1e5e5ba34f07ea6ee1ad07525a3dde5ff4836db3a7980af49a891/hypothesis-6.168.3-cp310-abi3-macosx_10_12_x86_64.whl", hash = "sha256:d20972ca134e652e9928ecd200966a8a2857adc2812c1d74b12a872e5cd503eb", size = 791536, upload-time = "2026-09-28T05:18:57.162Z" },
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+    { url = "https://files.pythonhosted.org/packages/d8/d2/1de6a2ad100e44817f2e9a8e8ba3eeaf4769621f4f87f2d4966c4971fcc4/hypothesis-6.168.3-cp315-abi3.abi3t-win_amd64.whl", hash = "sha256:7b9638789548361a57d984f56619ac694a914c328181d911271618409261ff4a", size = 681699, upload-time = "2026-09-28T05:19:09.957Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/79/be3370fd02734d6b1d950183ae9224580d19fa8d356b95348d60f1e3eeb7/hypothesis-6.168.3-cp315-abi3.abi3t-win_arm64.whl", hash = "sha256:65d78e4357ec48ed2c67825f06740ee3599be4cfe770a6092bed07108d679ac5", size = 679849, upload-time = "2026-09-28T05:19:06.884Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.168.3" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From e0d4b2417e1a72c042022967ac0274bea221abf9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 02:35:58 -0400
Subject: [PATCH 10/70] Weight FRS BRMA draws by census private-rented
 households

The FRS build drew each benefit unit's Broad Rental Market Area from rows
of lha_list_of_rents.csv.gz within region and LHA category. That file's
Scottish, Welsh and Northern Ireland lists are byte-identical copies of
English BRMAs' lists, each matched on the April 2019 rate, so those
nations' draws ignored their rental markets (#515).

Draw instead in proportion to census private-rented households by region,
BRMA and the bedroom band matching each LHA category (England and Wales
2021, Scotland 2022, Northern Ireland 2021; Northern Ireland has no
bedrooms question, so its weights cover every category). Move the draw to
datasets/brma.py, add property tests for it, document sources and
validation in storage/BRMA_DATA_SOURCES.md, and remove the list of rents.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/brma-census-weights.fixed.md      |   1 +
 docs/imputations.md                           |  13 +
 policyengine_uk_data/datasets/brma.py         |  90 ++
 policyengine_uk_data/datasets/frs.py          |  25 +-
 .../storage/BRMA_DATA_SOURCES.md              |  45 +
 .../brma_private_rented_households.csv        | 937 ++++++++++++++++++
 .../storage/lha_list_of_rents.csv.gz          | Bin 853159 -> 0 bytes
 .../tests/test_brma_assignment.py             | 170 ++++
 pyproject.toml                                |   5 +-
 uv.lock                                       |  73 +-
 10 files changed, 1338 insertions(+), 21 deletions(-)
 create mode 100644 changelog.d/brma-census-weights.fixed.md
 create mode 100644 policyengine_uk_data/datasets/brma.py
 create mode 100644 policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
 create mode 100644 policyengine_uk_data/storage/brma_private_rented_households.csv
 delete mode 100644 policyengine_uk_data/storage/lha_list_of_rents.csv.gz
 create mode 100644 policyengine_uk_data/tests/test_brma_assignment.py

diff --git a/changelog.d/brma-census-weights.fixed.md b/changelog.d/brma-census-weights.fixed.md
new file mode 100644
index 000000000..ca02ec8f4
--- /dev/null
+++ b/changelog.d/brma-census-weights.fixed.md
@@ -0,0 +1 @@
+- Draw FRS households' Broad Rental Market Areas in proportion to census private-rented households by BRMA and bedrooms, instead of the row counts of the 2019-20 LHA list of rents, whose Scottish, Welsh and Northern Ireland lists were copies of English BRMAs' lists (uk-data#515). Remove `lha_list_of_rents.csv.gz`.
diff --git a/docs/imputations.md b/docs/imputations.md
index 9e6964ed3..75fd1a56d 100644
--- a/docs/imputations.md
+++ b/docs/imputations.md
@@ -211,6 +211,19 @@ Assigns student loan plan type based on age and reported repayments.
 
 ---
 
+## Broad Rental Market Area Assignment
+
+**Source:** Census private-rented households by BRMA and bedrooms (not QRF; applied when the base FRS dataset is built)
+
+The FRS identifies only the region, but Local Housing Allowance rates vary by Broad Rental Market Area (BRMA). `datasets/brma.py` draws each benefit unit's BRMA within its region, in proportion to the private-rented households in each BRMA:
+- shared-accommodation and one-bedroom LHA categories use one-bedroom homes;
+- the two-, three- and four-or-more-bedroom categories use homes with that many bedrooms;
+- Northern Ireland's census has no bedrooms, so its weights are the same for every category.
+
+A household takes one of its benefit units' BRMAs, chosen at random. Sources, method and validation are in `storage/BRMA_DATA_SOURCES.md`.
+
+---
+
 ## Calibration Targets
 
 After imputation, household weights are calibrated to match aggregate statistics from:
diff --git a/policyengine_uk_data/datasets/brma.py b/policyengine_uk_data/datasets/brma.py
new file mode 100644
index 000000000..18566c43d
--- /dev/null
+++ b/policyengine_uk_data/datasets/brma.py
@@ -0,0 +1,90 @@
+"""Broad Rental Market Area (BRMA) assignment for FRS benefit units.
+
+The FRS identifies only a household's region, but Local Housing Allowance
+rates vary by BRMA. Each benefit unit is given a BRMA drawn within its
+region, in proportion to the number of private-rented households in each
+BRMA with the number of bedrooms matching its LHA category (shared
+accommodation and one-bedroom categories both use one-bedroom homes).
+
+The counts come from the censuses (England and Wales 2021, Scotland 2022,
+Northern Ireland 2021) mapped to BRMAs; ``storage/BRMA_DATA_SOURCES.md``
+gives the sources, method and validation. A BRMA that crosses a region
+boundary appears once per region, holding the households on that side.
+
+These weights replace the row counts of the 2019-20 LHA list of rents. Its
+Scottish, Welsh and Northern Ireland rows were copies of English BRMAs'
+lists, so their counts said nothing about those nations' rental markets.
+"""
+
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from policyengine_uk_data.storage import STORAGE_FOLDER
+
+BRMA_HOUSEHOLDS_PATH = STORAGE_FOLDER / "brma_private_rented_households.csv"
+
+# Census bedroom band whose private-rented households weight each LHA category.
+LHA_CATEGORY_BEDROOMS = {"A": "1", "B": "1", "C": "2", "D": "3", "E": "4+"}
+
+
+def load_brma_weights(path: Path = BRMA_HOUSEHOLDS_PATH) -> pd.DataFrame:
+    """Return BRMA sampling weights by region and LHA category.
+
+    Columns: ``region``, ``lha_category``, ``brma``, ``weight`` (private-rented
+    households). Every row has a positive weight. Northern Ireland's census
+    has no bedrooms question, so its rows (bedrooms ``all``) weight every
+    category.
+    """
+    households = pd.read_csv(path, dtype={"bedrooms": str})
+    bands = pd.DataFrame(
+        LHA_CATEGORY_BEDROOMS.items(), columns=["lha_category", "bedrooms"]
+    )
+    by_band = bands.merge(households, on="bedrooms")
+    any_band = households[households.bedrooms == "all"].merge(
+        bands[["lha_category"]], how="cross"
+    )
+    weights = pd.concat([by_band, any_band], ignore_index=True)
+    weights = weights[weights.households > 0]
+    return weights.rename(columns={"households": "weight"})[
+        ["region", "lha_category", "brma", "weight"]
+    ].reset_index(drop=True)
+
+
+def assign_brmas(
+    region: np.ndarray,
+    lha_category: np.ndarray,
+    rng: np.random.Generator,
+    weights: pd.DataFrame | None = None,
+) -> np.ndarray:
+    """Draw a BRMA for each benefit unit from its region × LHA category cell.
+
+    Args:
+        region: Region name of each benefit unit's household.
+        lha_category: LHA category (A-E) of each benefit unit.
+        rng: Generator for the draws.
+        weights: Output of ``load_brma_weights``; loaded from storage if omitted.
+
+    Raises:
+        ValueError: If a benefit unit's cell has no BRMA with a positive weight.
+    """
+    if weights is None:
+        weights = load_brma_weights()
+    region = np.asarray(region).astype(str)
+    lha_category = np.asarray(lha_category).astype(str)
+    brma = np.empty(len(region), dtype=object)
+    for (cell_region, category), cell in weights.groupby(
+        ["region", "lha_category"], sort=True
+    ):
+        mask = (region == cell_region) & (lha_category == category)
+        if mask.any():
+            p = cell.weight.to_numpy(dtype=float)
+            brma[mask] = rng.choice(
+                cell.brma.to_numpy(), size=mask.sum(), p=p / p.sum()
+            )
+    missing = pd.isna(brma)
+    if missing.any():
+        cells = sorted(set(zip(region[missing], lha_category[missing])))
+        raise ValueError(f"No BRMA weights for region × LHA category cells {cells}.")
+    return brma
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..4e612323c 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -19,6 +19,7 @@
 from policyengine_uk.variables.household.income.employment_status import (
     EmploymentStatus,
 )
+from policyengine_uk_data.datasets.brma import assign_brmas
 from policyengine_uk_data.datasets.disability_benefits import (
     add_disability_benefit_categories_from_reported_amounts,
     add_disability_benefit_flags_from_reported_amounts,
@@ -1409,25 +1410,13 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     sim = Microsimulation(dataset=dataset)
     region = sim.populations["benunit"].household("region", dataset.time_period)
-    lha_category = sim.calculate("LHA_category", year)
-    brma = np.empty(len(region), dtype=object)
-
-    # Sample from a random BRMA in the region, weighted by the number of observations in each BRMA.
-    # Use a seeded generator so the assignment is reproducible across builds;
-    # pandas .sample() otherwise draws from the unseeded global numpy RNG.
-    lha_list_of_rents = pd.read_csv(STORAGE_FOLDER / "lha_list_of_rents.csv.gz")
-    lha_list_of_rents = lha_list_of_rents.copy()
-    brma_rng = np.random.default_rng(0)
+    lha_category = sim.calculate("LHA_category", year).values
 
-    for possible_region in lha_list_of_rents.region.unique():
-        for possible_lha_category in lha_list_of_rents.lha_category.unique():
-            lor_mask = (lha_list_of_rents.region == possible_region) & (
-                lha_list_of_rents.lha_category == possible_lha_category
-            )
-            mask = (region == possible_region) & (lha_category == possible_lha_category)
-            brma[mask] = lha_list_of_rents[lor_mask].brma.sample(
-                n=len(region[mask]), replace=True, random_state=brma_rng
-            )
+    # Draw each benefit unit's BRMA in proportion to the private-rented
+    # households in each of its region's BRMAs with the matching number of
+    # bedrooms. Use a seeded generator so the assignment is reproducible.
+    brma_rng = np.random.default_rng(0)
+    brma = assign_brmas(region, lha_category, brma_rng)
 
     # Convert benunit-level BRMAs to household-level BRMAs (pick a random one)
 
diff --git a/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md b/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
new file mode 100644
index 000000000..1a2547d45
--- /dev/null
+++ b/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
@@ -0,0 +1,45 @@
+# BRMA private-rented households
+
+File: `brma_private_rented_households.csv`, with columns `region, brma, bedrooms, households`. It holds private-rented households (private landlord or letting agency, plus other private rented) by region, Broad Rental Market Area (BRMA) and number of bedrooms (`1`, `2`, `3`, `4+`). Northern Ireland's 2021 census has no bedrooms question, so its rows have `bedrooms = all`. A BRMA that crosses a region boundary has one row per region, for the households on each side.
+
+`datasets/brma.py` uses it to draw each FRS benefit unit's BRMA within its region. LHA categories A and B (shared and one-bedroom) use one-bedroom homes, C uses two-bedroom, D three-bedroom and E four-or-more.
+
+## Sources
+
+| Nation | Households | BRMA geography |
+|---|---|---|
+| England | Census 2021 (ONS). Private-rented households per LSOA from TS054, split by bedrooms using the LSOA's mix for "private rented or lives rent free" (custom table `hh_tenure_5a` × `number_bedrooms_5a`). Rent-free households are 0.3-1.2% of that category. | VOA BRMA boundary layer, May 2020. Each LSOA is assigned by its population-weighted centroid. Five border LSOAs fall outside the English layer and take the Welsh BRMA that VOA's LHA lookup returns at their centroids. |
+| Wales | As for England. | Rent Officers Wales BRMA layer. 11 LSOAs fall in West Cheshire. |
+| Scotland | Scotland's Census 2022 (NRS), tenure by bedrooms by 2022 electoral ward. This is the finest geography at which the table is not suppressed. | Scottish Government BRMA polygons. Each ward is split by its output areas' household-weighted centroids, corrected by Rent Service Scotland's postcode-to-BRMA lookup (FOI 202300368850). |
+| Northern Ireland | NISRA Census 2021, tenure by Data Zone 2021. | NIHE postcode-district definition of BRMAs. Data Zones that span BRMAs are split by Census 2021 postcode household counts, with suppressed postcodes given NISRA's published district averages. Postcode-to-Data Zone links come from the ONS Postcode Directory, whose Northern Ireland records are under the LPS end user licence; only these BRMA aggregates are published. |
+
+Totals reconcile with the published national private-rented counts to within 0.03%:
+
+| Nation | Private-rented households in this file | Published |
+|---|---|---|
+| England | 4,795,158 | 4,794,889 |
+| Wales | 228,601 | 228,642 |
+| Scotland | 323,001 | 323,042 |
+| Northern Ireland | 132,466 | 132,436 |
+
+Small-area census counts are perturbed for disclosure control, so their sums differ slightly from national tables.
+
+## Validation
+
+The check below correlates, within each region, the BRMA shares of each candidate weight with DWP's count of Universal Credit households whose housing costs are assessed under LHA ("LHA covers rent" plus "does not cover rent"). The DWP figures are the mean of April 2019 to November 2020 (UC statistics supplementary table 3.2, February 2021).
+
+| Nation | Census (this file) | `lha_list_of_rents.csv.gz` row counts (previous weights) |
+|---|---|---|
+| England | 0.94 | 0.82 |
+| Wales | 0.95 | 0.23 |
+| Scotland | 0.82 | 0.21 |
+
+The previous weights' Scottish, Welsh and Northern Ireland lists were copies of English BRMAs' lists (issue #515).
+
+Against the genuine list-of-rents category counts, the census bedroom bands correlate as follows (VOA 2019-20 for England; Scottish Government FOI 202200303624 for Scotland):
+- categories B-E: 0.82 to 0.94 in England, 0.95 to 0.98 in Scotland;
+- category A, shared accommodation: 0.51 in England, 0.95 in Scotland. The census has no measure of room lets, and no bedroom band does better than 0.59.
+
+## Rebuilding
+
+`tools/brma_households/` (from the repository root) downloads every source, checks each one against its pinned sha256 and rebuilds this file byte for byte; see its `README.md`.
diff --git a/policyengine_uk_data/storage/brma_private_rented_households.csv b/policyengine_uk_data/storage/brma_private_rented_households.csv
new file mode 100644
index 000000000..d07a7fbc9
--- /dev/null
+++ b/policyengine_uk_data/storage/brma_private_rented_households.csv
@@ -0,0 +1,937 @@
+region,brma,bedrooms,households
+EAST_MIDLANDS,CHERWELL_VALLEY,1,356
+EAST_MIDLANDS,CHERWELL_VALLEY,2,878
+EAST_MIDLANDS,CHERWELL_VALLEY,3,812
+EAST_MIDLANDS,CHERWELL_VALLEY,4+,339
+EAST_MIDLANDS,CHESTERFIELD,1,1745
+EAST_MIDLANDS,CHESTERFIELD,2,5822
+EAST_MIDLANDS,CHESTERFIELD,3,4064
+EAST_MIDLANDS,CHESTERFIELD,4+,724
+EAST_MIDLANDS,COVENTRY,1,12
+EAST_MIDLANDS,COVENTRY,2,49
+EAST_MIDLANDS,COVENTRY,3,54
+EAST_MIDLANDS,COVENTRY,4+,23
+EAST_MIDLANDS,DERBY,1,5006
+EAST_MIDLANDS,DERBY,2,14355
+EAST_MIDLANDS,DERBY,3,10808
+EAST_MIDLANDS,DERBY,4+,2805
+EAST_MIDLANDS,DONCASTER,1,60
+EAST_MIDLANDS,DONCASTER,2,129
+EAST_MIDLANDS,DONCASTER,3,342
+EAST_MIDLANDS,DONCASTER,4+,43
+EAST_MIDLANDS,EASTERN_STAFFORDSHIRE,1,577
+EAST_MIDLANDS,EASTERN_STAFFORDSHIRE,2,2367
+EAST_MIDLANDS,EASTERN_STAFFORDSHIRE,3,2099
+EAST_MIDLANDS,EASTERN_STAFFORDSHIRE,4+,492
+EAST_MIDLANDS,GRANTHAM_NEWARK,1,1613
+EAST_MIDLANDS,GRANTHAM_NEWARK,2,5029
+EAST_MIDLANDS,GRANTHAM_NEWARK,3,4791
+EAST_MIDLANDS,GRANTHAM_NEWARK,4+,1266
+EAST_MIDLANDS,GRIMSBY,1,71
+EAST_MIDLANDS,GRIMSBY,2,556
+EAST_MIDLANDS,GRIMSBY,3,596
+EAST_MIDLANDS,GRIMSBY,4+,186
+EAST_MIDLANDS,KINGS_LYNN,1,9
+EAST_MIDLANDS,KINGS_LYNN,2,38
+EAST_MIDLANDS,KINGS_LYNN,3,43
+EAST_MIDLANDS,KINGS_LYNN,4+,9
+EAST_MIDLANDS,LEICESTER,1,14418
+EAST_MIDLANDS,LEICESTER,2,30198
+EAST_MIDLANDS,LEICESTER,3,28635
+EAST_MIDLANDS,LEICESTER,4+,9210
+EAST_MIDLANDS,LINCOLN,1,2702
+EAST_MIDLANDS,LINCOLN,2,10111
+EAST_MIDLANDS,LINCOLN,3,9875
+EAST_MIDLANDS,LINCOLN,4+,3753
+EAST_MIDLANDS,LINCOLNSHIRE_FENS,1,1705
+EAST_MIDLANDS,LINCOLNSHIRE_FENS,2,4998
+EAST_MIDLANDS,LINCOLNSHIRE_FENS,3,5108
+EAST_MIDLANDS,LINCOLNSHIRE_FENS,4+,1279
+EAST_MIDLANDS,MILTON_KEYNES,1,69
+EAST_MIDLANDS,MILTON_KEYNES,2,174
+EAST_MIDLANDS,MILTON_KEYNES,3,211
+EAST_MIDLANDS,MILTON_KEYNES,4+,88
+EAST_MIDLANDS,NORTHAMPTON,1,4425
+EAST_MIDLANDS,NORTHAMPTON,2,10324
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diff --git a/policyengine_uk_data/storage/lha_list_of_rents.csv.gz b/policyengine_uk_data/storage/lha_list_of_rents.csv.gz
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diff --git a/policyengine_uk_data/tests/test_brma_assignment.py b/policyengine_uk_data/tests/test_brma_assignment.py
new file mode 100644
index 000000000..e6fa57aa1
--- /dev/null
+++ b/policyengine_uk_data/tests/test_brma_assignment.py
@@ -0,0 +1,170 @@
+"""BRMA assignment: census private-rented weights and the draw.
+
+Invariants of ``assign_brmas``, for any weights table and any benefit units
+whose region × LHA category cell has a positive weight:
+
+1. Support: each benefit unit's BRMA has a positive weight in its cell.
+2. Determinism: the same generator state gives the same BRMAs.
+3. Fail closed: a benefit unit in a cell with no positive weight raises.
+4. Proportionality: draws converge on the cell's weights.
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+from policyengine_uk.variables.household.demographic.locations import BRMAName
+
+from policyengine_uk_data.datasets.brma import (
+    BRMA_HOUSEHOLDS_PATH,
+    LHA_CATEGORY_BEDROOMS,
+    assign_brmas,
+    load_brma_weights,
+)
+
+REGIONS = [
+    "NORTH_EAST",
+    "NORTH_WEST",
+    "YORKSHIRE",
+    "EAST_MIDLANDS",
+    "WEST_MIDLANDS",
+    "EAST_OF_ENGLAND",
+    "LONDON",
+    "SOUTH_EAST",
+    "SOUTH_WEST",
+    "WALES",
+    "SCOTLAND",
+    "NORTHERN_IRELAND",
+]
+CATEGORIES = list(LHA_CATEGORY_BEDROOMS)
+
+
+@pytest.fixture(scope="module")
+def households():
+    return pd.read_csv(BRMA_HOUSEHOLDS_PATH, dtype={"bedrooms": str})
+
+
+@pytest.fixture(scope="module")
+def weights():
+    return load_brma_weights()
+
+
+def test_table_covers_every_region_band_and_brma(households):
+    assert list(households.columns) == ["region", "brma", "bedrooms", "households"]
+    assert (households.households > 0).all()
+    assert not households.duplicated(["region", "brma", "bedrooms"]).any()
+    assert set(households.region) == set(REGIONS)
+    assert set(households.brma) == set(BRMAName.__members__)
+    bands = households.groupby("region").bedrooms.agg(set)
+    # Northern Ireland's 2021 census has no bedrooms question.
+    assert bands.pop("NORTHERN_IRELAND") == {"all"}
+    assert all(b == {"1", "2", "3", "4+"} for b in bands)
+
+
+# Published private-rented households (private landlord or letting agency plus
+# other private rented): Census 2021 TS054 (England, Wales); Scotland's Census
+# 2022 tenure by bedrooms, national cells; NISRA Census 2021 tenure, 7 categories.
+CENSUS_PRIVATE_RENTED = {
+    "ENGLAND": 4_794_889,
+    "WALES": 228_642,
+    "SCOTLAND": 323_042,
+    "NORTHERN_IRELAND": 132_436,
+}
+
+
+def test_nations_reconcile_to_published_census_totals(households):
+    nation = households.region.where(
+        households.region.isin(["WALES", "SCOTLAND", "NORTHERN_IRELAND"]), "ENGLAND"
+    )
+    totals = households.groupby(nation).households.sum()
+    for name, published in CENSUS_PRIVATE_RENTED.items():
+        # Small-area cells are perturbed for disclosure control, so sums
+        # differ slightly from national tables.
+        assert abs(totals[name] / published - 1) < 0.001, name
+
+
+def test_edinburgh_and_glasgow_hold_the_most_scottish_private_renters(households):
+    # Census 2022: Lothian 19.3%, Greater Glasgow 15.9%. The 2019-20 list of
+    # rents gave Greater Glasgow the fewest entries of any Scottish BRMA.
+    scotland = (
+        households[households.region == "SCOTLAND"].groupby("brma").households.sum()
+    )
+    assert set(scotland.nlargest(2).index) == {"LOTHIAN", "GREATER_GLASGOW"}
+
+
+def test_every_region_category_cell_has_weights(weights):
+    cells = weights.groupby(["region", "lha_category"]).weight.sum()
+    assert len(cells) == len(REGIONS) * len(CATEGORIES)
+    assert (cells > 0).all()
+
+
+def test_shared_and_one_bedroom_categories_use_the_same_weights(weights):
+    a = weights[weights.lha_category == "A"].drop(columns="lha_category")
+    b = weights[weights.lha_category == "B"].drop(columns="lha_category")
+    pd.testing.assert_frame_equal(a.reset_index(drop=True), b.reset_index(drop=True))
+
+
+def test_large_draw_matches_scottish_two_bedroom_weights(weights):
+    cell = weights[(weights.region == "SCOTLAND") & (weights.lha_category == "C")]
+    n = 400_000
+    drawn = assign_brmas(
+        np.full(n, "SCOTLAND"), np.full(n, "C"), np.random.default_rng(1), weights
+    )
+    observed = pd.Series(drawn).value_counts(normalize=True)
+    expected = cell.set_index("brma").weight / cell.weight.sum()
+    assert observed.index.isin(expected.index).all()
+    assert (
+        observed.reindex(expected.index, fill_value=0) - expected
+    ).abs().max() < 0.004
+
+
+@st.composite
+def weights_and_units(draw):
+    regions = draw(
+        st.lists(st.sampled_from(REGIONS), min_size=1, max_size=3, unique=True)
+    )
+    brmas = [f"B{i}" for i in range(draw(st.integers(1, 5)))]
+    rows = [
+        (region, category, brma, draw(st.sampled_from([0, 0.5, 1, 7, 1000])))
+        for region in regions
+        for category in CATEGORIES
+        for brma in brmas
+    ]
+    table = pd.DataFrame(rows, columns=["region", "lha_category", "brma", "weight"])
+    table = table[table.weight > 0]
+    cells = sorted(set(zip(table.region, table.lha_category)))
+    units = draw(st.lists(st.sampled_from(cells), max_size=40)) if cells else []
+    region = np.array([u[0] for u in units], dtype=str)
+    category = np.array([u[1] for u in units], dtype=str)
+    return table, region, category, draw(st.integers(0, 2**32 - 1))
+
+
+@settings(max_examples=200, deadline=None, derandomize=True)
+@given(weights_and_units())
+def test_draws_stay_on_positive_weights_and_are_deterministic(case):
+    table, region, category, seed = case
+    first = assign_brmas(region, category, np.random.default_rng(seed), table)
+    second = assign_brmas(region, category, np.random.default_rng(seed), table)
+    assert list(first) == list(second)
+    positive = set(zip(table.region, table.lha_category, table.brma))
+    assert all((r, c, b) in positive for r, c, b in zip(region, category, first))
+
+
+@settings(max_examples=100, deadline=None, derandomize=True)
+@given(weights_and_units(), st.sampled_from(REGIONS), st.sampled_from(CATEGORIES))
+def test_cells_without_positive_weight_fail_closed(case, region, category):
+    table, *_ = case
+    if ((table.region == region) & (table.lha_category == category)).any():
+        return
+    with pytest.raises(ValueError, match="No BRMA weights"):
+        assign_brmas(
+            np.array([region]), np.array([category]), np.random.default_rng(0), table
+        )
+
+
+def test_built_dataset_brmas_lie_in_their_regions_weights(enhanced_frs, weights):
+    household = enhanced_frs.household
+    supported = set(zip(weights.region, weights.brma))
+    pairs = zip(household.region.astype(str), household.brma.astype(str))
+    assert all(pair in supported for pair in pairs)
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..7973343cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,8 +45,9 @@ dev = [
     "yaml-changelog>=0.1.7",
     "itables",
     "quantile-forest",
-    "build",    "towncrier>=24.8.0",
-
+    "build",
+    "towncrier>=24.8.0",
+    "hypothesis>=6.168.3",
 ]
 
 [tool.setuptools]
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..672737ea1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
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 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+]
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+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.168.3" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From eb14fd07907cbf19dbc4cb968d0248a0e35cfde6 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 04:13:03 -0400
Subject: [PATCH 11/70] Restore donor status benefits on SPI rows, keep the
 donor's Child Benefit flag

Review of 96672af (subfleet 20261002-011737-spi-514-review):
- IIDB, AFCS and bereavement support follow an injury, service or a
  death, not income, and the QRF drew them at 6.2, 2.4 and 4.6 times the
  FRS rate by weight. SPI rows now take the donor's own values.
- The Child Benefit take-up flag keeps the donor's value: the award does
  not depend on the replaced incomes and the claim is for the same
  children. Only the UC and Pension Credit flags are redrawn.
- The comment no longer says the model's means test replaces the zeroed
  income-related reports: in policyengine-uk 2.93.0 housing benefit, CTR,
  IS, tax credits and income-related ESA and JSA need a report, so SPI
  rows no longer get them. Evidence is now cited by weight, and Child
  Benefit counts 16-19 qualifying young people.
- One assign_reported_takeup helper serves create_frs and the SPI rows,
  so the rate and the anchoring rule have one source. The SPI draws no
  longer depend on which columns are present.
- Tests pin the three rule sets, use shuffled, gapped ids and check the
  rebuilt flags against reports that are not zeroed.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../spi-synthetic-reported-benefits.fixed.md  |   2 +-
 policyengine_uk_data/datasets/frs.py          |  51 +++---
 .../datasets/imputations/frs_only.py          | 156 +++++++++-------
 .../tests/test_spi_donor_benefit_rules.py     | 172 +++++++++++++-----
 4 files changed, 246 insertions(+), 135 deletions(-)

diff --git a/changelog.d/spi-synthetic-reported-benefits.fixed.md b/changelog.d/spi-synthetic-reported-benefits.fixed.md
index f315e0118..1310079b8 100644
--- a/changelog.d/spi-synthetic-reported-benefits.fixed.md
+++ b/changelog.d/spi-synthetic-reported-benefits.fixed.md
@@ -1 +1 @@
-SPI-synthetic rows no longer report income-related, out-of-work or Child Benefit receipt, and their take-up flags and `receives_benefits_in_own_right` come from their own reports instead of the FRS donor's.
+SPI-synthetic rows no longer report income-related, out-of-work or Child Benefit receipt, take their industrial injuries, armed forces compensation and bereavement support from the FRS donor, and get UC, Pension Credit and `receives_benefits_in_own_right` flags from their own reports instead of the donor's.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 82f31fe55..1e2e9b8bf 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -33,6 +33,7 @@
     STORAGE_FOLDER,
 )
 from policyengine_uk_data.parameters import load_take_up_rate, load_parameter
+from policyengine_uk_data.utils.takeup import assign_takeup_with_reported_anchors
 from policyengine_uk_data.datasets.childcare.assumptions import (
     EXTENDED_HOURS_MEAN,
     EXTENDED_HOURS_SD,
@@ -229,6 +230,24 @@ def reported_benunit_mask(
     return benunit["benunit_id"].isin(reporter_benunits).values
 
 
+def assign_reported_takeup(
+    person: pd.DataFrame,
+    benunit: pd.DataFrame,
+    flag: str,
+    year: int,
+    draws: np.ndarray,
+) -> np.ndarray:
+    """Take-up for one ``REPORTED_TAKEUP_ANCHORS`` flag: benefit units
+    reporting receipt claim, and the rest are filled at random so the share
+    claiming matches the take-up rate (see ``utils/takeup.py``)."""
+    rate_name, report_column = REPORTED_TAKEUP_ANCHORS[flag]
+    return assign_takeup_with_reported_anchors(
+        draws,
+        load_take_up_rate(rate_name, year),
+        reported_mask=reported_benunit_mask(person, benunit, report_column),
+    )
+
+
 def derive_receives_benefits_in_own_right(pe_person: pd.DataFrame) -> pd.Series:
     """Identify people reporting adult benefits that end QYP status."""
 
@@ -1505,9 +1524,6 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     generator = np.random.default_rng(seed=100)
 
     # Load take-up rates from parameter files
-    child_benefit_rate = load_take_up_rate("child_benefit", year)
-    pension_credit_rate = load_take_up_rate("pension_credit", year)
-    universal_credit_rate = load_take_up_rate("universal_credit", year)
     marriage_allowance_rate = load_take_up_rate("marriage_allowance", year)
     child_benefit_opts_out_rate = load_take_up_rate("child_benefit_opts_out_rate", year)
     tfc_rate = load_take_up_rate("tax_free_childcare", year)
@@ -1525,19 +1541,6 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     # who report positive receipt of a benefit are assigned takeup=True with
     # certainty; the remaining non-reporters are filled probabilistically to
     # hit the aggregate target rate. See policyengine_uk_data/utils/takeup.py.
-    from policyengine_uk_data.utils.takeup import (
-        assign_takeup_with_reported_anchors,
-    )
-
-    def _anchored_takeup(flag: str, rate: float) -> np.ndarray:
-        return assign_takeup_with_reported_anchors(
-            generator.random(len(pe_benunit)),
-            rate,
-            reported_mask=reported_benunit_mask(
-                pe_person, pe_benunit, REPORTED_TAKEUP_ANCHORS[flag][1]
-            ),
-        )
-
     # Person-level
     pe_person["would_claim_marriage_allowance"] = (
         generator.random(len(pe_person)) < marriage_allowance_rate
@@ -1545,17 +1548,21 @@ def _anchored_takeup(flag: str, rate: float) -> np.ndarray:
 
     # Benefit unit-level — anchor on any adult in the benefit unit having
     # reported positive receipt in the FRS benefits table.
-    pe_benunit["would_claim_child_benefit"] = _anchored_takeup(
-        "would_claim_child_benefit", child_benefit_rate
+    pe_benunit["would_claim_child_benefit"] = assign_reported_takeup(
+        pe_person,
+        pe_benunit,
+        "would_claim_child_benefit",
+        year,
+        generator.random(len(pe_benunit)),
     )
     pe_benunit["child_benefit_opts_out"] = (
         generator.random(len(pe_benunit)) < child_benefit_opts_out_rate
     )
-    pe_benunit["would_claim_pc"] = _anchored_takeup(
-        "would_claim_pc", pension_credit_rate
+    pe_benunit["would_claim_pc"] = assign_reported_takeup(
+        pe_person, pe_benunit, "would_claim_pc", year, generator.random(len(pe_benunit))
     )
-    pe_benunit["would_claim_uc"] = _anchored_takeup(
-        "would_claim_uc", universal_credit_rate
+    pe_benunit["would_claim_uc"] = assign_reported_takeup(
+        pe_person, pe_benunit, "would_claim_uc", year, generator.random(len(pe_benunit))
     )
     pe_benunit["would_claim_tfc"] = generator.random(len(pe_benunit)) < tfc_rate
 
diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py
index 036215650..26a39532f 100644
--- a/policyengine_uk_data/datasets/imputations/frs_only.py
+++ b/policyengine_uk_data/datasets/imputations/frs_only.py
@@ -43,10 +43,9 @@
 from policyengine_uk_data.datasets.frs import (
     BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS,
     REPORTED_TAKEUP_ANCHORS,
-    reported_benunit_mask,
+    assign_reported_takeup,
+    derive_receives_benefits_in_own_right,
 )
-from policyengine_uk_data.parameters import load_take_up_rate
-from policyengine_uk_data.utils.takeup import assign_takeup_with_reported_anchors
 
 logger = logging.getLogger(__name__)
 
@@ -109,33 +108,39 @@
     "esa_income_reported",
 ]
 
-# Benefit reports set to zero on SPI-donor rows once the QRF has drawn them.
-# The QRF draws each person's reports from their age, gender, region and
-# incomes; it sees nothing of their benefit unit (partner, children, rent,
-# capital) or of their health. A report says the person is an existing
-# claimant, and policyengine-uk acts on that, so these are zeroed:
+# The QRF draws each person's benefit reports from their age, gender, region
+# and incomes. It sees nothing of their benefit unit (partner, children,
+# rent, capital), their health or their history, and policyengine-uk reads a
+# positive report as an existing claim. On SPI-donor rows, after the draw:
 #
-# - Income-related awards. Entitlement turns on the benefit unit's joint
-#   means and make-up, which on these rows come from the imputed incomes, so
-#   the model's own means test is the only coherent source. A report would
-#   instead be a continuing-award gate (housing benefit, income support, tax
-#   credits, income-related ESA and JSA), the paid amount (income-related
-#   ESA and JSA; council tax reduction where the model has no scheme) or a
-#   certain-claim anchor (UC, Pension Credit). Sure Start Maternity Grant
-#   needs one of these awards and is copied from the donor, not drawn.
-# - Benefits paid only to people out of work or incapable of it: ESA and
-#   JSA (contributory), incapacity benefit and severe disablement allowance.
-#   On the 2024-25 build, 76% of SPI-row ESA (contributory) reporters earned
-#   more than ESA's permitted-work limit, against 2% of FRS reporters.
-# - Child Benefit, whose only use is the take-up anchor. The draw ignores
-#   the children: on the 2024-25 build, 44% of SPI-row reports were in
-#   benefit units without a child under 16 (FRS rows: 8%).
+# Zeroed (SPI_DONOR_ZEROED_PERSON_VARIABLES):
+# - Income-related awards. Entitlement turns on the unit's joint means and
+#   make-up, which here come from the imputed incomes. UC and Pension Credit
+#   keep a route: their take-up flags are redrawn below. In
+#   policyengine-uk 2.93.0 the others (housing benefit, council tax
+#   reduction, income support, tax credits, income-related ESA and JSA) can
+#   only be claimed with a report, so these rows no longer receive them.
+#   Sure Start Maternity Grant needs one of these awards.
+# - Benefits paid only to people out of work or incapable of it: ESA and JSA
+#   (contributory), incapacity benefit and severe disablement allowance. On
+#   the 2024-25 build, 41% of SPI-row ESA (contributory) reporters by weight
+#   earned more than ESA's permitted-work limit, against 0.4% on FRS rows.
+# - Child Benefit, which the model reads only through the take-up flag. The
+#   draw ignores the children: 34% of SPI-row reports by weight were in
+#   benefit units with no child or qualifying young person (FRS rows: 0%).
 #
-# State pension (driven by age), winter fuel payment (not read by the
-# model), the disability benefits, carer's allowance (the draws respect its
-# earnings limit) and IIDB, AFCS and bereavement support (payable at any
-# income) keep their drawn values. The zeroed columns stay in the QRF chain
-# above so that the values kept do not change.
+# Restored to the donor's own value (SPI_DONOR_RESTORED_PERSON_VARIABLES):
+# industrial injuries, armed forces compensation and bereavement support.
+# These follow from an injury, service or a death, not income, and the QRF
+# drew them at 6.2, 2.4 and 4.6 times the FRS rate by weight.
+#
+# Kept as drawn: state pension (paid as reported once over pension age),
+# winter fuel payment (not read by the model), the disability benefits and
+# carer's allowance, whose drawn rates sit below the FRS rates as the income
+# gradient implies.
+#
+# Every column stays in the QRF chain above, so the values kept do not
+# change. They were drawn alongside the values later zeroed or restored.
 SPI_DONOR_ZEROED_PERSON_VARIABLES = [
     "universal_credit_reported",
     "pension_credit_reported",
@@ -153,45 +158,59 @@
     "sda_reported",
     "child_benefit_reported",
 ]
+SPI_DONOR_RESTORED_PERSON_VARIABLES = [
+    "iidb_reported",
+    "afcs_reported",
+    "bsp_reported",
+]
 
-# Seed for the take-up draws on SPI-donor rows; create_frs uses 100.
+# Take-up flags redrawn on SPI-donor rows. Whether a synthetic family claims
+# a means-tested benefit at its imputed income is unobserved, so these units
+# draw at the take-up rate. The Child Benefit flag keeps the donor's value:
+# the award doesn't depend on the replaced incomes, and the donor's claim is
+# for the same children.
+SPI_DONOR_REDRAWN_TAKEUP_FLAGS = ("would_claim_uc", "would_claim_pc")
+# Seed for those draws; create_frs uses 100.
 SPI_DONOR_TAKEUP_SEED = 101
 
 
 def apply_spi_donor_benefit_rules(
     dataset: UKSingleYearDataset,
+    donor_person: pd.DataFrame | None = None,
 ) -> UKSingleYearDataset:
-    """Zero SPI-donor benefit reports and re-derive the flags built from them.
-
-    ``create_frs`` sets ``receives_benefits_in_own_right`` and the
-    report-anchored take-up flags from the donor's own reports, before the
-    SPI rows exist. Here they are rebuilt from the rows' own reports: a unit
-    reporting receipt claims, and the rest draw at the take-up rate. With the
-    anchoring reports zeroed, every SPI-donor unit draws at the rate, since
-    whether a synthetic family claims is unobserved.
+    """Apply the SPI-donor benefit rules above to ``dataset``.
+
+    ``donor_person`` is the person table before the QRF draw, holding the
+    donor's own reports; without it the restored columns are left alone.
+    ``receives_benefits_in_own_right`` and the redrawn take-up flags, which
+    ``create_frs`` built from the donor's reports, are rebuilt from the rows'
+    own reports.
     """
     dataset = dataset.copy()
     person, benunit = dataset.person, dataset.benunit
     for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
         if column in person.columns:
             person[column] = 0.0
+    if donor_person is not None:
+        for column in SPI_DONOR_RESTORED_PERSON_VARIABLES:
+            if column in person.columns and column in donor_person.columns:
+                person[column] = donor_person[column].values
 
-    own_right = [c for c in BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS if c in person]
     if "receives_benefits_in_own_right" in person.columns:
+        own_right = person.reindex(
+            columns=list(BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS), fill_value=0.0
+        )
         person["receives_benefits_in_own_right"] = (
-            person[own_right].fillna(0).sum(axis=1) > 0
+            derive_receives_benefits_in_own_right(own_right).values
         )
 
     year = int(str(dataset.time_period)[:4])
     generator = np.random.default_rng(seed=SPI_DONOR_TAKEUP_SEED)
-    for flag, (rate_name, column) in REPORTED_TAKEUP_ANCHORS.items():
-        if flag not in benunit.columns or column not in person.columns:
-            continue
-        benunit[flag] = assign_takeup_with_reported_anchors(
-            generator.random(len(benunit)),
-            load_take_up_rate(rate_name, year),
-            reported_mask=reported_benunit_mask(person, benunit, column),
-        )
+    for flag in SPI_DONOR_REDRAWN_TAKEUP_FLAGS:
+        draws = generator.random(len(benunit))
+        report_column = REPORTED_TAKEUP_ANCHORS[flag][1]
+        if flag in benunit.columns and report_column in person.columns:
+            benunit[flag] = assign_reported_takeup(person, benunit, flag, year, draws)
     return dataset
 
 
@@ -270,13 +289,12 @@ def impute_frs_only_variables(
     replace the existing (donor-leaked) values in
     ``FRS_ONLY_PERSON_VARIABLES`` only. Variables absent from either
     frame are skipped silently. ``apply_spi_donor_benefit_rules`` then
-    zeroes ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` and rebuilds the flags
-    derived from reports, before the disability categories and flags are
-    derived from the remaining reports.
+    zeroes or restores some reports and rebuilds the flags derived from
+    them, before the disability categories and flags are derived from the
+    final reports.
     """
-    from policyengine_uk_data.utils.qrf import QRF
-
     target_dataset = target_dataset.copy()
+    donor_person = target_dataset.person.copy()
 
     train_person = train_dataset.person
     target_person = target_dataset.person
@@ -295,13 +313,32 @@ def impute_frs_only_variables(
             len(missing),
             sorted(missing),
         )
-    if not outputs:
+    if outputs:
+        target_dataset = _impute_outputs(train_dataset, target_dataset, outputs)
+    else:
         logger.warning(
             "Stage-2 FRS-only imputation: no output variables available; "
             "applying only the SPI-donor benefit rules."
         )
-        return apply_spi_donor_benefit_rules(target_dataset)
 
+    target_dataset = apply_spi_donor_benefit_rules(target_dataset, donor_person)
+    target_dataset.person = add_disability_benefit_categories_from_reported_amounts(
+        target_dataset.person,
+        int(str(target_dataset.time_period)[:4]),
+    )
+    target_dataset.person = add_disability_benefit_flags_from_reported_amounts(
+        target_dataset.person,
+        int(str(target_dataset.time_period)[:4]),
+    )
+
+    return target_dataset
+
+
+def _impute_outputs(train_dataset, target_dataset, outputs):
+    """Fit the stage-2 QRF and write its draws of ``outputs`` to the target."""
+    from policyengine_uk_data.utils.qrf import QRF
+
+    train_person = train_dataset.person
     train_inputs_raw = _build_predictor_frame(train_dataset)
     target_inputs_raw = _build_predictor_frame(target_dataset)
 
@@ -335,15 +372,4 @@ def impute_frs_only_variables(
         # amounts or contributions and are non-negative by construction.
         values = np.maximum(predictions[column].values, 0.0)
         target_dataset.person[column] = values
-
-    target_dataset = apply_spi_donor_benefit_rules(target_dataset)
-    target_dataset.person = add_disability_benefit_categories_from_reported_amounts(
-        target_dataset.person,
-        int(str(target_dataset.time_period)[:4]),
-    )
-    target_dataset.person = add_disability_benefit_flags_from_reported_amounts(
-        target_dataset.person,
-        int(str(target_dataset.time_period)[:4]),
-    )
-
     return target_dataset
diff --git a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
index c221e54ed..36b87a7bc 100644
--- a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
+++ b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
@@ -2,13 +2,16 @@
 
 Properties of ``apply_spi_donor_benefit_rules`` for any input:
 
-1. Every column in ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` is zero afterwards.
+1. Every column in ``SPI_DONOR_ZEROED_PERSON_VARIABLES`` is zero afterwards,
+   and every column in ``SPI_DONOR_RESTORED_PERSON_VARIABLES`` equals the
+   donor's value.
 2. Nothing else changes except ``receives_benefits_in_own_right`` and the
-   report-anchored take-up flags; the input is not mutated.
-3. A benefit unit with a member reporting an anchoring benefit claims it.
-4. ``receives_benefits_in_own_right`` is true exactly when the person
-   reports one of ``BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS``.
-5. The rules are deterministic and idempotent.
+   redrawn take-up flags, and the input is not mutated.
+3. A benefit unit with a member reporting UC or Pension Credit claims it.
+4. ``receives_benefits_in_own_right`` holds exactly when the person reports
+   one of ``BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS``.
+5. The rules are deterministic and idempotent, and no flag's draws depend on
+   which other columns are present.
 
 The fixtures are synthetic, not survey records.
 """
@@ -30,6 +33,8 @@
 from policyengine_uk_data.datasets.imputations import frs_only
 from policyengine_uk_data.datasets.imputations.frs_only import (
     FRS_ONLY_PERSON_VARIABLES,
+    SPI_DONOR_REDRAWN_TAKEUP_FLAGS,
+    SPI_DONOR_RESTORED_PERSON_VARIABLES,
     SPI_DONOR_ZEROED_PERSON_VARIABLES,
     apply_spi_donor_benefit_rules,
 )
@@ -39,15 +44,54 @@
 REPORT_COLUMNS = sorted(
     set(FRS_ONLY_PERSON_VARIABLES) | set(SPI_DONOR_ZEROED_PERSON_VARIABLES)
 )
-DERIVED = {"receives_benefits_in_own_right", *REPORTED_TAKEUP_ANCHORS}
+CHANGED = {"receives_benefits_in_own_right", *SPI_DONOR_REDRAWN_TAKEUP_FLAGS}
+RULED = set(SPI_DONOR_ZEROED_PERSON_VARIABLES) | set(
+    SPI_DONOR_RESTORED_PERSON_VARIABLES
+)
+ANCHORING_REPORTS = {
+    REPORTED_TAKEUP_ANCHORS[flag][1] for flag in SPI_DONOR_REDRAWN_TAKEUP_FLAGS
+}
+
+
+def test_rule_sets_are_pinned():
+    """Each column's treatment is a decision; changing one must be deliberate."""
+    assert set(SPI_DONOR_ZEROED_PERSON_VARIABLES) == {
+        "universal_credit_reported",
+        "pension_credit_reported",
+        "housing_benefit_reported",
+        "council_tax_benefit_reported",
+        "income_support_reported",
+        "working_tax_credit_reported",
+        "child_tax_credit_reported",
+        "jsa_income_reported",
+        "esa_income_reported",
+        "ssmg_reported",
+        "jsa_contrib_reported",
+        "esa_contrib_reported",
+        "incapacity_benefit_reported",
+        "sda_reported",
+        "child_benefit_reported",
+    }
+    assert set(SPI_DONOR_RESTORED_PERSON_VARIABLES) == {
+        "iidb_reported",
+        "afcs_reported",
+        "bsp_reported",
+    }
+    assert set(SPI_DONOR_REDRAWN_TAKEUP_FLAGS) == {"would_claim_uc", "would_claim_pc"}
+    assert not set(SPI_DONOR_ZEROED_PERSON_VARIABLES) & set(
+        SPI_DONOR_RESTORED_PERSON_VARIABLES
+    )
 
 
-def _dataset(benunit_sizes, reports, flags) -> UKSingleYearDataset:
+def _dataset(benunit_sizes, reports, flags, id_seed=0) -> UKSingleYearDataset:
+    """Benefit units of the given sizes, with shuffled, gapped ids."""
+    rng = np.random.default_rng(id_seed)
     n_benunits, n_people = len(benunit_sizes), sum(benunit_sizes)
-    benunit_of_person = np.repeat(np.arange(n_benunits), benunit_sizes)
+    benunit_ids = rng.permutation(n_benunits) * 7 + 3
+    benunit_of_person = np.repeat(benunit_ids, benunit_sizes)
     person = pd.DataFrame(
         {
-            "person_id": np.arange(n_people),
+            "person_id": rng.permutation(n_people) * 5 + 11,
             "person_benunit_id": benunit_of_person,
             "person_household_id": benunit_of_person,
             "age": np.full(n_people, 40),
@@ -56,12 +100,11 @@ def _dataset(benunit_sizes, reports, flags) -> UKSingleYearDataset:
     )
     for i, column in enumerate(REPORT_COLUMNS):
         person[column] = np.asarray(reports[i][:n_people], dtype=float)
-    benunit = pd.DataFrame({"benunit_id": np.arange(n_benunits)})
+    person = person.sample(frac=1, random_state=id_seed).reset_index(drop=True)
+    benunit = pd.DataFrame({"benunit_id": benunit_ids})
     for j, flag in enumerate(REPORTED_TAKEUP_ANCHORS):
         benunit[flag] = np.roll(np.asarray(flags[:n_benunits]), j)
-    household = pd.DataFrame(
-        {"household_id": np.arange(n_benunits), "household_weight": 0.0}
-    )
+    household = pd.DataFrame({"household_id": benunit_ids, "household_weight": 0.0})
     return UKSingleYearDataset(
         person=person, benunit=benunit, household=household, fiscal_year=YEAR
     )
@@ -74,7 +117,16 @@ def datasets(draw):
     amount = st.one_of(st.just(0.0), st.floats(0.01, 50_000))
     reports = [draw(st.lists(amount, min_size=n, max_size=n)) for _ in REPORT_COLUMNS]
     flags = draw(st.lists(st.booleans(), min_size=n, max_size=n))
-    return _dataset(sizes, reports, flags)
+    return _dataset(sizes, reports, flags, id_seed=draw(st.integers(0, 10**6)))
+
+
+def _donor(dataset, seed=1):
+    """A donor person table: the same people with different report values."""
+    donor = dataset.person.copy()
+    rng = np.random.default_rng(seed)
+    for column in SPI_DONOR_RESTORED_PERSON_VARIABLES:
+        donor[column] = np.where(rng.random(len(donor)) < 0.5, 0.0, 1_234.0)
+    return donor
 
 
 def _reporting_benunits(person, benunit, column):
@@ -84,26 +136,30 @@ def _reporting_benunits(person, benunit, column):
 
 @settings(max_examples=60, deadline=None)
 @given(datasets())
-def test_rules_zero_listed_reports_and_touch_nothing_else(dataset):
+def test_rules_zero_restore_and_touch_nothing_else(dataset):
     before = dataset.copy()
-    after = apply_spi_donor_benefit_rules(dataset)
+    donor = _donor(dataset)
+    after = apply_spi_donor_benefit_rules(dataset, donor)
 
     for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
         assert (after.person[column] == 0).all(), column
-    kept = [c for c in after.person.columns if c not in DERIVED]
-    unchanged = [c for c in kept if c not in SPI_DONOR_ZEROED_PERSON_VARIABLES]
+    for column in SPI_DONOR_RESTORED_PERSON_VARIABLES:
+        np.testing.assert_array_equal(after.person[column], donor[column])
+    unchanged = [c for c in after.person.columns if c not in CHANGED | RULED]
     pd.testing.assert_frame_equal(after.person[unchanged], before.person[unchanged])
+    kept_flags = [c for c in after.benunit.columns if c not in CHANGED]
+    pd.testing.assert_frame_equal(after.benunit[kept_flags], before.benunit[kept_flags])
     pd.testing.assert_frame_equal(after.household, before.household)
     pd.testing.assert_frame_equal(dataset.person, before.person)
     pd.testing.assert_frame_equal(dataset.benunit, before.benunit)
 
 
-@settings(max_examples=60, deadline=None)
+@settings(max_examples=30, deadline=None)
 @given(datasets())
-def test_rules_rebuild_own_right_flag_from_own_reports(dataset):
-    after = apply_spi_donor_benefit_rules(dataset).person
-    own = after[list(BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS)].sum(axis=1) > 0
-    assert (after.receives_benefits_in_own_right == own).all()
+def test_restored_columns_untouched_without_donor(dataset):
+    after = apply_spi_donor_benefit_rules(dataset)
+    restored = list(SPI_DONOR_RESTORED_PERSON_VARIABLES)
+    pd.testing.assert_frame_equal(after.person[restored], dataset.person[restored])
 
 
 @settings(
@@ -112,17 +168,23 @@ def test_rules_rebuild_own_right_flag_from_own_reports(dataset):
     suppress_health_check=[HealthCheck.function_scoped_fixture],
 )
 @given(dataset=datasets())
-def test_reporters_claim_whatever_is_zeroed(dataset, monkeypatch):
-    # With no anchoring report zeroed, reporters must still claim: the
-    # anchors are rebuilt from the rows' own reports, not left as copies.
-    anchoring = {column for _, column in REPORTED_TAKEUP_ANCHORS.values()}
+def test_flags_follow_own_reports_whatever_is_zeroed(dataset, monkeypatch):
+    # With the own-right and anchoring reports left in place, the rebuilt
+    # flags must follow them, not the donor's flags.
     monkeypatch.setattr(
         frs_only,
         "SPI_DONOR_ZEROED_PERSON_VARIABLES",
-        [c for c in SPI_DONOR_ZEROED_PERSON_VARIABLES if c not in anchoring],
+        [
+            c
+            for c in SPI_DONOR_ZEROED_PERSON_VARIABLES
+            if c not in ANCHORING_REPORTS | set(BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS)
+        ],
     )
     after = apply_spi_donor_benefit_rules(dataset)
-    for flag, (_, column) in REPORTED_TAKEUP_ANCHORS.items():
+    own = after.person[list(BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS)].sum(axis=1) > 0
+    assert (after.person.receives_benefits_in_own_right == own).all()
+    for flag in SPI_DONOR_REDRAWN_TAKEUP_FLAGS:
+        column = REPORTED_TAKEUP_ANCHORS[flag][1]
         reports = _reporting_benunits(after.person, after.benunit, column)
         assert after.benunit[flag].values[reports].all(), flag
 
@@ -130,54 +192,70 @@ def test_reporters_claim_whatever_is_zeroed(dataset, monkeypatch):
 @settings(max_examples=40, deadline=None)
 @given(datasets())
 def test_rules_are_deterministic_and_idempotent(dataset):
-    once = apply_spi_donor_benefit_rules(dataset)
-    again = apply_spi_donor_benefit_rules(dataset)
-    twice = apply_spi_donor_benefit_rules(once)
+    donor = _donor(dataset)
+    once = apply_spi_donor_benefit_rules(dataset, donor)
+    again = apply_spi_donor_benefit_rules(dataset, donor)
+    twice = apply_spi_donor_benefit_rules(once, donor)
     for frame in ("person", "benunit", "household"):
         pd.testing.assert_frame_equal(getattr(once, frame), getattr(again, frame))
         pd.testing.assert_frame_equal(getattr(once, frame), getattr(twice, frame))
 
 
+@settings(max_examples=30, deadline=None)
+@given(datasets())
+def test_draws_do_not_depend_on_other_columns(dataset):
+    full = apply_spi_donor_benefit_rules(dataset)
+    dataset.benunit = dataset.benunit.drop(columns=["would_claim_uc"])
+    partial = apply_spi_donor_benefit_rules(dataset)
+    pd.testing.assert_series_equal(
+        full.benunit.would_claim_pc, partial.benunit.would_claim_pc
+    )
+
+
 def test_unreported_units_claim_at_the_take_up_rate():
     n = 40_000
     rng = np.random.default_rng(0)
     reports = [rng.gamma(2, 1_000, n) for _ in REPORT_COLUMNS]
     dataset = _dataset([1] * n, reports, np.ones(n, dtype=bool))
     after = apply_spi_donor_benefit_rules(dataset).benunit
-    for flag, (rate_name, _) in REPORTED_TAKEUP_ANCHORS.items():
-        rate = load_take_up_rate(rate_name, YEAR)
+    for flag in SPI_DONOR_REDRAWN_TAKEUP_FLAGS:
+        rate = load_take_up_rate(REPORTED_TAKEUP_ANCHORS[flag][0], YEAR)
         assert after[flag].mean() == pytest.approx(rate, abs=0.01), flag
+    assert after.would_claim_child_benefit.all()
 
 
-def test_stage_two_keeps_drawn_values_of_kept_reports(monkeypatch):
-    """Zeroing does not change the QRF draws of the reports that are kept."""
+def test_stage_two_applies_the_rules_and_keeps_drawn_values(monkeypatch):
+    """Through ``impute_frs_only_variables``: zeroed, restored, kept and flags."""
     from policyengine_uk_data.tests.test_frs_only_imputation import _fake_dataset
 
     train = _fake_dataset(person_rows=400, seed=0)
-    for column in ("esa_contrib_reported", "state_pension_reported", "ssmg_reported"):
-        train.person[column] = np.where(
-            np.random.default_rng(1).random(400) < 0.3, 5_000.0, 0.0
-        )
+    rng = np.random.default_rng(1)
+    for column in ("esa_contrib_reported", "state_pension_reported", "iidb_reported"):
+        train.person[column] = np.where(rng.random(400) < 0.3, 5_000.0, 0.0)
     target = _fake_dataset(person_rows=80, seed=1)
     target.person["ssmg_reported"] = 600.0
+    target.person["iidb_reported"] = np.where(rng.random(80) < 0.5, 0.0, 777.0)
     target.person["receives_benefits_in_own_right"] = True
     target.benunit["would_claim_uc"] = True
+    target.benunit["would_claim_child_benefit"] = False
 
     ruled = frs_only.impute_frs_only_variables(train, target)
     monkeypatch.setattr(frs_only, "SPI_DONOR_ZEROED_PERSON_VARIABLES", [])
+    monkeypatch.setattr(frs_only, "SPI_DONOR_RESTORED_PERSON_VARIABLES", [])
     unruled = frs_only.impute_frs_only_variables(train, target)
 
     for column in SPI_DONOR_ZEROED_PERSON_VARIABLES:
         assert (ruled.person[column] == 0).all(), column
-    kept = [
-        c
-        for c in FRS_ONLY_PERSON_VARIABLES
-        if c not in SPI_DONOR_ZEROED_PERSON_VARIABLES
-    ]
+    np.testing.assert_array_equal(
+        ruled.person.iidb_reported, target.person.iidb_reported
+    )
+    assert not np.array_equal(unruled.person.iidb_reported, target.person.iidb_reported)
+    kept = [c for c in FRS_ONLY_PERSON_VARIABLES if c not in RULED]
     pd.testing.assert_frame_equal(ruled.person[kept], unruled.person[kept])
     assert not ruled.person.receives_benefits_in_own_right.any()
+    assert not ruled.benunit.would_claim_child_benefit.any()
 
-    # Disability flags come from the zeroed reports: ESA (contributory) was
+    # Disability flags come from the final reports: ESA (contributory) was
     # drawn for some people but no longer marks them disabled.
     assert (unruled.person.esa_contrib_reported > 0).any()
     flags = ["is_disabled_for_benefits", "is_severely_disabled_for_benefits"]

From 90377e06575cd8f057ac3d449a8df25c875957a9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 05:14:11 -0400
Subject: [PATCH 12/70] Drop a small-cell comparator from the SPI rules
 comment; test the rate year and the UC redraw

Round-2 review of eb14fd0 (subfleet 20261002-041419-spi-514-review-r2,
APPROVE): the FRS comparison for ESA (contributory) rested on under 10
records; the take-up rates' year and the stage-two UC redraw had no
direct test.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/imputations/frs_only.py          |  2 +-
 .../tests/test_spi_donor_benefit_rules.py     | 22 +++++++++++++++++++
 2 files changed, 23 insertions(+), 1 deletion(-)

diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py
index 26a39532f..06b9b78b0 100644
--- a/policyengine_uk_data/datasets/imputations/frs_only.py
+++ b/policyengine_uk_data/datasets/imputations/frs_only.py
@@ -124,7 +124,7 @@
 # - Benefits paid only to people out of work or incapable of it: ESA and JSA
 #   (contributory), incapacity benefit and severe disablement allowance. On
 #   the 2024-25 build, 41% of SPI-row ESA (contributory) reporters by weight
-#   earned more than ESA's permitted-work limit, against 0.4% on FRS rows.
+#   earned more than ESA's permitted-work limit.
 # - Child Benefit, which the model reads only through the take-up flag. The
 #   draw ignores the children: 34% of SPI-row reports by weight were in
 #   benefit units with no child or qualifying young person (FRS rows: 0%).
diff --git a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
index 36b87a7bc..799439038 100644
--- a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
+++ b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
@@ -224,6 +224,25 @@ def test_unreported_units_claim_at_the_take_up_rate():
     assert after.would_claim_child_benefit.all()
 
 
+def test_take_up_rates_are_read_for_the_dataset_year(monkeypatch):
+    years = []
+
+    def rate(name, year):
+        years.append(year)
+        return 0.5
+
+    monkeypatch.setattr("policyengine_uk_data.datasets.frs.load_take_up_rate", rate)
+    dataset = _dataset([1, 2], [[1.0] * 3 for _ in REPORT_COLUMNS], [True] * 3)
+    dataset = UKSingleYearDataset(
+        person=dataset.person,
+        benunit=dataset.benunit,
+        household=dataset.household,
+        fiscal_year=2031,
+    )
+    apply_spi_donor_benefit_rules(dataset)
+    assert years == [2031] * len(SPI_DONOR_REDRAWN_TAKEUP_FLAGS)
+
+
 def test_stage_two_applies_the_rules_and_keeps_drawn_values(monkeypatch):
     """Through ``impute_frs_only_variables``: zeroed, restored, kept and flags."""
     from policyengine_uk_data.tests.test_frs_only_imputation import _fake_dataset
@@ -254,6 +273,9 @@ def test_stage_two_applies_the_rules_and_keeps_drawn_values(monkeypatch):
     pd.testing.assert_frame_equal(ruled.person[kept], unruled.person[kept])
     assert not ruled.person.receives_benefits_in_own_right.any()
     assert not ruled.benunit.would_claim_child_benefit.any()
+    # Every unit entered claiming UC as the donor; with the reports zeroed,
+    # the redraw at the 55% rate leaves some 80 units out.
+    assert not ruled.benunit.would_claim_uc.all()
 
     # Disability flags come from the final reports: ESA (contributory) was
     # drawn for some people but no longer marks them disabled.

From 61f93ba3384be16587336cc290e1d58146dc1af3 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 07:50:06 -0400
Subject: [PATCH 13/70] Supply is_claimant_or_partner from FRS adult-table
 membership

An FRS benefit unit is one adult or a couple plus dependent children. The
adult table holds the head (UPERSON 1) and any partner (UPERSON 2); any
other adult forms and heads their own benefit unit. So adult-table
membership is policyengine-uk's is_claimant_or_partner (#1896), which the
model otherwise infers from ages and cannot get right for unflagged
large-age-gap households (policyengine-uk#2039).

- frs.py derives it next to is_parent and rejects a benefit unit without
  one or two adult records (none in FRS 2020-21, 2022-23, 2023-24 or 2024-25).
- SPI records are one-person benefit units, so each is its claimant.
- The transfer builder marks the tax-unit head and a joint filer's spouse;
  the checked-in enhanced_cps_2025.h5 gains the builder's column in place
  (every other column unchanged).
- stack_datasets refuses to stack a person table without the role onto one
  with it. Cloning, subsampling, uprating and the SPI and CGT donor copies
  carry whole person tables, so the column follows them.
- Tests: examples, Hypothesis properties over generated FRS-shaped
  households, stacking, cloning, SPI, transfer builder vs checked-in H5,
  and built-dataset invariants.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/frs-claimant-or-partner.added.md  |   1 +
 policyengine_uk_data/datasets/enhanced_cps.py |  10 +
 policyengine_uk_data/datasets/frs.py          |  33 ++++
 policyengine_uk_data/datasets/spi.py          |   2 +
 .../storage/enhanced_cps_2025.h5              | Bin 23390386 -> 23164435 bytes
 .../tests/test_claimant_or_partner.py         | 178 ++++++++++++++++++
 .../tests/test_clone_and_assign.py            |  14 ++
 .../tests/test_policybench_transfer.py        |  48 ++++-
 policyengine_uk_data/tests/test_spi_build.py  |  12 ++
 policyengine_uk_data/utils/stack.py           |   7 +
 pyproject.toml                                |   5 +-
 uv.lock                                       |  73 ++++++-
 12 files changed, 376 insertions(+), 7 deletions(-)
 create mode 100644 changelog.d/frs-claimant-or-partner.added.md
 create mode 100644 policyengine_uk_data/tests/test_claimant_or_partner.py

diff --git a/changelog.d/frs-claimant-or-partner.added.md b/changelog.d/frs-claimant-or-partner.added.md
new file mode 100644
index 000000000..f98eb1dac
--- /dev/null
+++ b/changelog.d/frs-claimant-or-partner.added.md
@@ -0,0 +1 @@
+Supply `is_claimant_or_partner`, each benefit unit's single adult or couple, from FRS adult-table membership, so policyengine-uk reads the survey's roles instead of inferring them from ages. SPI records and the public transfer dataset (`enhanced_cps_2025.h5`, column added in place) carry it too, and stacking refuses a person table without it.
diff --git a/policyengine_uk_data/datasets/enhanced_cps.py b/policyengine_uk_data/datasets/enhanced_cps.py
index 490f89cae..b4e038e3c 100644
--- a/policyengine_uk_data/datasets/enhanced_cps.py
+++ b/policyengine_uk_data/datasets/enhanced_cps.py
@@ -559,6 +559,15 @@ def _build_base_dataset(
         adults = scenario["adults"]
         children = scenario["children"]
         people = adults + children
+        # Each source household is one tax unit with its head listed first and,
+        # for joint filers, the spouse second: the benefit unit's claimant and
+        # partner.
+        claimants_or_partners = 2 if row["filing_status"] == "joint" else 1
+        if len(adults) < claimants_or_partners:
+            raise ValueError(
+                f"Source household {source_household_id} files "
+                f"{row['filing_status']} but lists {len(adults)} adult(s)."
+            )
 
         has_mortgage = any(
             "deductible_mortgage_interest" in person.get("inputs", {})
@@ -663,6 +672,7 @@ def _build_base_dataset(
                     "person_id": person_id,
                     "person_household_id": household_id,
                     "person_benunit_id": benunit_id,
+                    "is_claimant_or_partner": person_index <= claimants_or_partners,
                     "age": int(person["age"]),
                     "gender": "MALE" if (household_id + person_index) % 2 else "FEMALE",
                     "employment_income_before_lsr": _gbp(
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..01f2435e3 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -392,6 +392,32 @@ def derive_is_parent_from_frs_microdata(
     return is_adult_record & has_dependent_children
 
 
+def derive_is_claimant_or_partner_from_frs_microdata(
+    person_ids,
+    person_benunit_ids,
+    adult_person_ids,
+) -> np.ndarray:
+    """Identify each FRS benefit unit's single adult or couple.
+
+    An FRS benefit unit is one adult or a couple plus any dependent children.
+    The adult table holds the head (UPERSON 1) and any partner (UPERSON 2);
+    the child table holds the dependent children. Any other adult in the
+    household, such as a grown-up son or daughter, forms and heads their own
+    benefit unit. So adult-table membership is policyengine-uk's
+    `is_claimant_or_partner`, which the model otherwise infers from ages.
+    """
+
+    is_adult_record = np.isin(np.asarray(person_ids), np.asarray(adult_person_ids))
+    per_benunit = pd.Series(is_adult_record).groupby(np.asarray(person_benunit_ids))
+    counts = per_benunit.sum()
+    if not counts.between(1, 2).all():
+        raise ValueError(
+            "Every FRS benefit unit needs one or two adult-table records; "
+            f"{int((~counts.between(1, 2)).sum())} benefit units do not."
+        )
+    return is_adult_record
+
+
 def _as_non_negative_array(values) -> np.ndarray:
     values = np.asarray(values, dtype=float)
     return np.maximum(np.nan_to_num(values, nan=0.0), 0.0)
@@ -728,6 +754,13 @@ def create_frs(
         benunit_ids=pe_benunit.benunit_id,
         dependent_children=dependent_children,
     )
+    pe_person["is_claimant_or_partner"] = (
+        derive_is_claimant_or_partner_from_frs_microdata(
+            person_ids=pe_person.person_id,
+            person_benunit_ids=pe_person.person_benunit_id,
+            adult_person_ids=frs["adult"].person_id,
+        )
+    )
     MARITAL = [
         "MARRIED",
         "SINGLE",
diff --git a/policyengine_uk_data/datasets/spi.py b/policyengine_uk_data/datasets/spi.py
index e22be675c..41de96410 100644
--- a/policyengine_uk_data/datasets/spi.py
+++ b/policyengine_uk_data/datasets/spi.py
@@ -112,6 +112,8 @@ def create_spi(
     person["person_id"] = df.SREF
     person["person_household_id"] = df.SREF
     person["person_benunit_id"] = df.SREF
+    # Each SPI taxpayer is their own one-person benefit unit, so its claimant.
+    person["is_claimant_or_partner"] = np.ones(len(df), dtype=bool)
     benunit["benunit_id"] = df.SREF
     household["household_id"] = df.SREF
 
diff --git a/policyengine_uk_data/storage/enhanced_cps_2025.h5 b/policyengine_uk_data/storage/enhanced_cps_2025.h5
index 77bf31c5606021562b61aff5b265f483bcfa83b3..633cff57f50f10e89c390bdc940a9d81d879f0fa 100644
GIT binary patch
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diff --git a/policyengine_uk_data/tests/test_claimant_or_partner.py b/policyengine_uk_data/tests/test_claimant_or_partner.py
new file mode 100644
index 000000000..31edefbe7
--- /dev/null
+++ b/policyengine_uk_data/tests/test_claimant_or_partner.py
@@ -0,0 +1,178 @@
+"""The claimant-or-partner role: FRS derivation, stacking, and built datasets."""
+
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+from policyengine_uk.data import UKSingleYearDataset
+
+from policyengine_uk_data.datasets.frs import (
+    derive_is_claimant_or_partner_from_frs_microdata,
+    derive_is_parent_from_frs_microdata,
+)
+from policyengine_uk_data.utils.stack import stack_datasets
+
+
+def test_grown_up_child_heads_their_own_benefit_unit():
+    # Lone parent and dependent child in one benefit unit; grown-up son in his own.
+    result = derive_is_claimant_or_partner_from_frs_microdata(
+        person_ids=np.array([1_001, 1_002, 1_003]),
+        person_benunit_ids=np.array([101, 102, 101]),
+        adult_person_ids=np.array([1_001, 1_002]),
+    )
+    assert result.tolist() == [True, True, False]
+
+
+def test_couple_with_children():
+    result = derive_is_claimant_or_partner_from_frs_microdata(
+        person_ids=np.array([2_003, 2_001, 2_002]),
+        person_benunit_ids=np.array([201, 201, 201]),
+        adult_person_ids=np.array([2_002, 2_001]),
+    )
+    assert result.tolist() == [False, True, True]
+
+
+@pytest.mark.parametrize("adults", [[], [3_001, 3_002, 3_003]])
+def test_benefit_unit_without_one_or_two_adults_is_rejected(adults):
+    with pytest.raises(ValueError, match="one or two adult-table records"):
+        derive_is_claimant_or_partner_from_frs_microdata(
+            person_ids=np.array([3_001, 3_002, 3_003]),
+            person_benunit_ids=np.array([301, 301, 301]),
+            adult_person_ids=np.array(adults, dtype=int),
+        )
+
+
+@st.composite
+def frs_households(draw):
+    """FRS-shaped records: benefit units of one or two adults plus children."""
+    rows = []
+    for household in range(1, draw(st.integers(1, 6)) + 1):
+        person = 0
+        for benunit in range(1, draw(st.integers(1, 3)) + 1):
+            n_adults = draw(st.integers(1, 2))
+            n_children = draw(st.integers(0, 4))
+            for index in range(n_adults + n_children):
+                person += 1
+                rows.append(
+                    (
+                        household * 1_000 + person,
+                        household * 100 + benunit,
+                        index < n_adults,
+                    )
+                )
+    order = draw(st.permutations(range(len(rows))))
+    frame = pd.DataFrame(rows, columns=["person_id", "benunit_id", "adult"])
+    return frame.iloc[list(order)].reset_index(drop=True)
+
+
+def _derive(frame, adult_mask=None):
+    adult = frame.adult if adult_mask is None else adult_mask
+    return derive_is_claimant_or_partner_from_frs_microdata(
+        person_ids=frame.person_id,
+        person_benunit_ids=frame.benunit_id,
+        adult_person_ids=frame.person_id[adult],
+    )
+
+
+@settings(max_examples=300, deadline=None)
+@given(frs_households())
+def test_every_benefit_unit_has_a_claimant_and_at_most_one_partner(frame):
+    result = _derive(frame)
+    assert (result == frame.adult.to_numpy()).all()
+    counts = pd.Series(result).groupby(frame.benunit_id.to_numpy()).sum()
+    assert counts.between(1, 2).all()
+
+
+@settings(max_examples=200, deadline=None)
+@given(frs_households(), st.randoms(use_true_random=False))
+def test_record_order_does_not_change_anyones_role(frame, rng):
+    order = list(range(len(frame)))
+    rng.shuffle(order)
+    shuffled = frame.iloc[order].reset_index(drop=True)
+    by_person = dict(zip(frame.person_id, _derive(frame)))
+    assert [by_person[p] for p in shuffled.person_id] == _derive(shuffled).tolist()
+
+
+@settings(max_examples=200, deadline=None)
+@given(frs_households(), st.data())
+def test_parents_are_always_claimant_or_partner(frame, data):
+    benunits = np.sort(frame.benunit_id.unique())
+    children = data.draw(
+        st.lists(st.integers(0, 4), min_size=len(benunits), max_size=len(benunits))
+    )
+    is_parent = derive_is_parent_from_frs_microdata(
+        person_ids=frame.person_id,
+        person_benunit_ids=frame.benunit_id,
+        adult_person_ids=frame.person_id[frame.adult],
+        benunit_ids=benunits,
+        dependent_children=np.array(children),
+    )
+    assert not (is_parent & ~_derive(frame)).any()
+
+
+@settings(max_examples=200, deadline=None)
+@given(frs_households(), st.data())
+def test_a_benefit_unit_with_no_adult_or_a_third_adult_is_rejected(frame, data):
+    benunit = data.draw(st.sampled_from(sorted(frame.benunit_id.unique())))
+    members = frame.benunit_id == benunit
+    if data.draw(st.booleans()):
+        adult = frame.adult & ~members
+    else:
+        n_extra = 3 - int(frame.adult[members].sum())
+        start = frame.person_id.max() + 1
+        extra = pd.DataFrame(
+            {
+                "person_id": range(start, start + n_extra),
+                "benunit_id": benunit,
+                "adult": True,
+            }
+        )
+        frame = pd.concat([frame, extra], ignore_index=True)
+        adult = frame.adult
+    with pytest.raises(ValueError, match="one or two adult-table records"):
+        _derive(frame, adult)
+
+
+def _tiny_dataset(roles=True):
+    person = pd.DataFrame(
+        {
+            "person_id": [1, 2, 3],
+            "person_benunit_id": [1, 1, 1],
+            "person_household_id": [1, 1, 1],
+            "age": [40, 38, 10],
+        }
+    )
+    if roles:
+        person["is_claimant_or_partner"] = [True, True, False]
+    return UKSingleYearDataset(
+        person=person,
+        benunit=pd.DataFrame({"benunit_id": [1]}),
+        household=pd.DataFrame({"household_id": [1], "household_weight": [1.0]}),
+        fiscal_year=2024,
+    )
+
+
+def test_stacking_keeps_roles():
+    stacked = stack_datasets(_tiny_dataset(), _tiny_dataset())
+    stacked.validate()
+    assert stacked.person.is_claimant_or_partner.dtype == bool
+    assert stacked.person.is_claimant_or_partner.tolist() == [True, True, False] * 2
+
+
+@pytest.mark.parametrize("roles", [(True, False), (False, True)])
+def test_stacking_refuses_a_table_without_roles(roles):
+    with pytest.raises(ValueError, match="is_claimant_or_partner"):
+        stack_datasets(_tiny_dataset(roles[0]), _tiny_dataset(roles[1]))
+
+
+@pytest.mark.parametrize("fixture", ["frs", "enhanced_frs"])
+def test_built_dataset_roles(fixture, request):
+    person = request.getfixturevalue(fixture).person
+    role = person.is_claimant_or_partner
+    assert role.dtype == bool
+    counts = role.groupby(person.person_benunit_id).sum()
+    assert counts.between(1, 2).all()
+    assert not (person.is_benunit_head & ~role).any()
+    assert not (person.is_parent & ~role).any()
+    assert (person.age[role] >= 16).all()
diff --git a/policyengine_uk_data/tests/test_clone_and_assign.py b/policyengine_uk_data/tests/test_clone_and_assign.py
index a6cf15637..a3c8c0e16 100644
--- a/policyengine_uk_data/tests/test_clone_and_assign.py
+++ b/policyengine_uk_data/tests/test_clone_and_assign.py
@@ -256,6 +256,20 @@ def test_data_preserved_across_clones(self, toy_dataset, small_crosswalk):
             original_regions = toy_dataset.household["region"].values
             np.testing.assert_array_equal(regions, original_regions)
 
+    def test_claimant_or_partner_follows_each_clone(self, toy_dataset, small_crosswalk):
+        roles = np.tile([True, False], len(toy_dataset.household))
+        toy_dataset.person["is_claimant_or_partner"] = roles
+        result = clone_and_assign(
+            toy_dataset,
+            n_clones=3,
+            crosswalk_path=str(small_crosswalk),
+        )
+
+        assert result.person["is_claimant_or_partner"].dtype == bool
+        np.testing.assert_array_equal(
+            result.person["is_claimant_or_partner"].values, np.tile(roles, 3)
+        )
+
     def test_single_clone_is_near_identity(self, toy_dataset, small_crosswalk):
         """With n_clones=1, output should match input dimensions."""
         result = clone_and_assign(
diff --git a/policyengine_uk_data/tests/test_policybench_transfer.py b/policyengine_uk_data/tests/test_policybench_transfer.py
index 900b73d1f..2f966bcc1 100644
--- a/policyengine_uk_data/tests/test_policybench_transfer.py
+++ b/policyengine_uk_data/tests/test_policybench_transfer.py
@@ -26,6 +26,10 @@
     "pip_dl_category",
     "pip_m_category",
 }
+# Benefit-unit roles the source records, which policyengine-uk infers by
+# formula when a dataset leaves them out (is_claimant_or_partner from
+# policyengine-uk#1896). Releases that predate a role ignore its column.
+DATASET_SUPPLIED_ROLES = {"is_claimant_or_partner"}
 
 
 def _subset_source(tmp_path: Path, rows: int) -> Path:
@@ -81,11 +85,15 @@ def test_policybench_transfer_writes_only_valid_leaf_inputs(tmp_path: Path):
         invalid_columns = [
             column
             for column in frame.columns
-            if column not in system.variables
-            or system.variables[column].entity.key != entity
+            if (column not in system.variables and column not in DATASET_SUPPLIED_ROLES)
             or (
-                not system.variables[column].is_input_variable()
-                and column not in ALLOWED_COMPATIBILITY_INPUTS
+                column in system.variables
+                and system.variables[column].entity.key != entity
+            )
+            or (
+                column in system.variables
+                and not system.variables[column].is_input_variable()
+                and column not in ALLOWED_COMPATIBILITY_INPUTS | DATASET_SUPPLIED_ROLES
             )
         ]
         assert invalid_columns == []
@@ -228,6 +236,38 @@ def test_policybench_transfer_family_structure_matches_person_membership(
         assert observed == expected
 
 
+def test_policybench_transfer_marks_head_and_joint_spouse_as_claimant_or_partner(
+    tmp_path: Path,
+):
+    source_file_path = _subset_source(tmp_path, 200)
+    source = pd.read_csv(source_file_path)
+    dataset = create_enhanced_cps(source_file_path=source_file_path, calibrate=False)
+    person = dataset.person
+
+    counts = person.groupby("person_benunit_id").is_claimant_or_partner.sum()
+    expected = np.where(source.filing_status == "joint", 2, 1)
+    np.testing.assert_array_equal(counts.sort_index().to_numpy(), expected)
+    # The head and any spouse are listed before every other member.
+    first = person.groupby(
+        "person_benunit_id"
+    ).cumcount() < person.person_benunit_id.map(counts)
+    assert (person.is_claimant_or_partner == first).all()
+
+
+def test_checked_in_enhanced_cps_h5_roles_match_the_builder(tmp_path: Path):
+    checked_in = UKSingleYearDataset(file_path=str(ENHANCED_CPS_FILE)).person
+    built = create_enhanced_cps(
+        source_file_path=_subset_source(tmp_path, 500),
+        calibrate=False,
+    ).person
+    head = checked_in.iloc[: len(built)]
+
+    for column in ("person_id", "person_benunit_id", "age", "is_claimant_or_partner"):
+        np.testing.assert_array_equal(head[column].to_numpy(), built[column].to_numpy())
+    counts = checked_in.groupby("person_benunit_id").is_claimant_or_partner.sum()
+    assert counts.between(1, 2).all()
+
+
 def test_assign_council_tax_bands_handles_upper_percentile_edge():
     households = pd.DataFrame(
         {
diff --git a/policyengine_uk_data/tests/test_spi_build.py b/policyengine_uk_data/tests/test_spi_build.py
index efb37f810..31d3c9790 100644
--- a/policyengine_uk_data/tests/test_spi_build.py
+++ b/policyengine_uk_data/tests/test_spi_build.py
@@ -467,3 +467,15 @@ def test_income_model_cache_accepts_current_spi_release(tmp_path, monkeypatch):
     )
 
     assert income_module.create_income_model().metadata == current_metadata
+
+
+def test_create_spi_marks_every_taxpayer_as_their_benefit_units_claimant(tmp_path):
+    from policyengine_uk_data.datasets.spi import create_spi
+
+    tab = tmp_path / "spi.tab"
+    _write_fake_spi(tab)
+
+    person = create_spi(tab, 2020, seed=0).person
+    assert person["is_claimant_or_partner"].dtype == bool
+    assert person["is_claimant_or_partner"].all()
+    assert person["person_benunit_id"].is_unique
diff --git a/policyengine_uk_data/utils/stack.py b/policyengine_uk_data/utils/stack.py
index 2fe9df828..d722d347e 100644
--- a/policyengine_uk_data/utils/stack.py
+++ b/policyengine_uk_data/utils/stack.py
@@ -1,10 +1,17 @@
 from policyengine_uk.data import UKSingleYearDataset
 import pandas as pd
 
+# Benefit-unit roles taken from the survey, which policyengine-uk otherwise
+# infers from ages. A stacked table without them would leave missing roles.
+SURVEY_ROLE_COLUMNS = ("is_claimant_or_partner",)
+
 
 def stack_datasets(
     data_1: UKSingleYearDataset, data_2: UKSingleYearDataset
 ) -> UKSingleYearDataset:
+    for column in SURVEY_ROLE_COLUMNS:
+        if (column in data_1.person.columns) != (column in data_2.person.columns):
+            raise ValueError(f"Only one of the stacked datasets has {column}.")
     person_id_offset = data_1.person.person_id.max() + 1
     benunit_id_offset = data_1.benunit.benunit_id.max() + 1
     household_id_offset = data_1.household.household_id.max() + 1
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..7973343cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,8 +45,9 @@ dev = [
     "yaml-changelog>=0.1.7",
     "itables",
     "quantile-forest",
-    "build",    "towncrier>=24.8.0",
-
+    "build",
+    "towncrier>=24.8.0",
+    "hypothesis>=6.168.3",
 ]
 
 [tool.setuptools]
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..672737ea1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/b7/13118bbc45d6d8b9d04e2de779e2a4ff23145ea39efa692b994fb874ca72/hypothesis-6.168.3.tar.gz", hash = "sha256:a43388f9067678fef6e13bdff325b6cfa6961a590498bb37f7ff31589c83bc75", size = 511022, upload-time = "2026-09-28T05:20:58.499Z" }
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+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.168.3" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From df530b7804326d7c8c11cc405b86c251c2bfc20f Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 03:11:56 -0400
Subject: [PATCH 14/70] Read LHA_category as an array and drop the
 list-of-rents test stub

create_frs's smoke test stubs Microsimulation.calculate with plain arrays,
so take np.asarray rather than .values, and remove the stub of the
deleted lha_list_of_rents.csv.gz.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .gitignore                                             |  3 +++
 policyengine_uk_data/datasets/frs.py                   |  2 +-
 .../tests/test_legacy_benefit_proxies.py               | 10 ----------
 3 files changed, 4 insertions(+), 11 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9a741bc49..04d54863f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,6 @@
 **/_build
 !policyengine_uk_data/storage/*.csv
 **/version.json
+
+# Build outputs with household-level weights (licensed FRS derivatives); never commit.
+policyengine_uk_data/storage/local_geography_weights.csv.gz
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 4e612323c..bbe037a95 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -1410,7 +1410,7 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     sim = Microsimulation(dataset=dataset)
     region = sim.populations["benunit"].household("region", dataset.time_period)
-    lha_category = sim.calculate("LHA_category", year).values
+    lha_category = np.asarray(sim.calculate("LHA_category", year))
 
     # Draw each benefit unit's BRMA in proportion to the private-rented
     # households in each of its region's BRMAs with the matching number of
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index 5f1acd85f..a2bc23dd2 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -374,17 +374,7 @@ def calculate(self, variable, year=None):
 
 
 def test_create_frs_smoke_includes_legacy_proxy_columns(tmp_path, monkeypatch):
-    original_read_csv = frs_module.pd.read_csv
-
-    def fake_read_csv(path, *args, **kwargs):
-        if str(path).endswith("lha_list_of_rents.csv.gz"):
-            return pd.DataFrame(
-                {"region": ["LONDON"], "lha_category": ["A"], "brma": ["BRMA1"]}
-            )
-        return original_read_csv(path, *args, **kwargs)
-
     monkeypatch.setattr(policyengine_uk, "Microsimulation", FakeMicrosimulation)
-    monkeypatch.setattr(frs_module.pd, "read_csv", fake_read_csv)
     monkeypatch.setattr(frs_module, "load_take_up_rate", lambda *args, **kwargs: 0.0)
     monkeypatch.setattr(frs_module, "load_parameter", lambda *args, **kwargs: 0.0)
     monkeypatch.setattr(

From 48517d2a8a0ecbc020f357cc9e4409b6f6f39f5b Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 03:16:38 -0400
Subject: [PATCH 15/70] Add the pinned pipeline that rebuilds the BRMA
 households table

tools/brma_households downloads every census and BRMA geography input,
checks each against a pinned sha256 (decoded-content hashes for the ONS
custom-API batches), and rebuilds
storage/brma_private_rented_households.csv byte for byte. Scotland's
census ward table is the one manual download, with instructions in the
manifest. Area overrides established from official lookups carry dated
evidence and are asserted against the polygons so a boundary change
fails loudly.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .gitignore                         |    1 +
 tools/brma_households/README.md    |   79 +++
 tools/brma_households/build.py     |  509 +++++++++++++
 tools/brma_households/fetch.py     |  126 ++++
 tools/brma_households/sources.yaml | 1063 ++++++++++++++++++++++++++++
 5 files changed, 1778 insertions(+)
 create mode 100644 tools/brma_households/README.md
 create mode 100644 tools/brma_households/build.py
 create mode 100644 tools/brma_households/fetch.py
 create mode 100644 tools/brma_households/sources.yaml

diff --git a/.gitignore b/.gitignore
index 04d54863f..13b54d8e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,3 +22,4 @@
 
 # Build outputs with household-level weights (licensed FRS derivatives); never commit.
 policyengine_uk_data/storage/local_geography_weights.csv.gz
+.brma-cache/
diff --git a/tools/brma_households/README.md b/tools/brma_households/README.md
new file mode 100644
index 000000000..0a2462144
--- /dev/null
+++ b/tools/brma_households/README.md
@@ -0,0 +1,79 @@
+# BRMA private-rented households
+
+`policyengine_uk_data/storage/brma_private_rented_households.csv` (the default `--output`) contains 936 rows: `region,brma,bedrooms,households`.
+It counts private landlord/letting agency and other private-rented households by
+Broad Rental Market Area (BRMA), retaining each area's census region. Bedrooms are
+`1`, `2`, `3`, `4+`; Northern Ireland uses `all` because its census did not ask bedrooms.
+
+## Sources and method
+
+- **England, Census 2021:** ONS/Nomis TS054 LSOA private-rented totals multiplied
+  by each LSOA's ONS custom-API tenure5a code 3 bedroom proportions. ONS V4
+  population-weighted centroids intersect VOA May 2020 BRMA polygons; regions
+  come from ONS OA-to-LSOA and OA-to-region lookups. GML is parsed directly to
+  preserve nine incorrectly declared MultiPolygons; `make_valid` repairs seven
+  self-intersections. Five border LSOAs use documented official VOA lookup overrides:
+  E01014018 → Brecon and Radnor; E01022272–E01022275 → Monmouthshire.
+- **Wales, Census 2021:** the same TS054 × tenure5a method and Welsh ONS V4
+  centroids, with the archived Rent Officers Wales polygons (May 2012 geometry,
+  September 2014 release). Invalid geometry is repaired. Cardiff Bay W01002024
+  uses a fixed override established by 13 official postcode replies on 2 October 2026.
+  Eleven Welsh LSOAs in West Cheshire retain region `WALES`.
+- **Scotland, Census 2022:** NRS ward tenure × bedrooms, allocated using OA
+  household-weighted centroid shares in Scottish Government BRMA polygons.
+  Official postcode candidates supply nine missing assignments and correct 61
+  Balloch OAs; two ambiguous candidates use West Dunbartonshire council membership.
+  Four and five-or-more bedrooms combine into `4+`.
+- **Northern Ireland, Census 2021:** NISRA Data Zone tenure codes 5 and 6,
+  allocated using NIHE postcode-district BRMAs and ONSPD postcode-to-zone records.
+  Single-BRMA zones receive share 1; mixed zones use census postcode household
+  counts, substituting NISRA district averages after stripping district-key whitespace.
+
+BRMA cells are rounded to whole households with pandas; zero rows are dropped.
+No national balancing adjustment is made. Each of 89 inputs has its exact URL and cache filename in `sources.yaml`.
+The 72 ONS custom-API batches pin decoded JSON bytes with `content_sha256`;
+other inputs pin original bytes with `sha256`. Gzip and plain JSON are accepted
+without parsing or reserialising for verification. Live reply pages and postcode
+extracts used only to establish overrides are excluded, as are research checks.
+All eight overrides have dated evidence comments in `build.py`; exact unresolved
+area sets are asserted before assignment to guard against boundary changes.
+
+## Rebuild
+
+Run from the repository root. The sole browser/session download is Scotland's
+ward export; follow its `manual: true` manifest instructions and place it in the
+cache. Scripts never log in or accept terms. Both commands verify all input pins;
+the builder uses only cached originals and checks the expected output digest.
+
+```sh
+nice -n 10 uvx --with pandas --with geopandas --with shapely --with pyogrio --with openpyxl --with pyyaml python tools/brma_households/fetch.py .brma-cache
+nice -n 10 uvx --with pandas --with geopandas --with shapely --with pyogrio --with openpyxl --with pyyaml python tools/brma_households/build.py .brma-cache
+```
+
+`fetch.py --only SOURCE_ID ...` supports selected download checks; `build.py
+--output PATH` chooses the output destination. Missing files or changed hashes fail.
+
+## Licences and limits
+
+Census and most geography inputs use the Open Government Licence; retain ONS,
+NRS, Scottish/Welsh Government and Ordnance Survey attributions in the manifest.
+NIHE's archived page has no stated open reuse licence; live VOA replies are not inputs.
+**NI postcode geography comes from ONSPD records under the LPS Northern Ireland
+End User Licence; only aggregated BRMA counts are published, never raw BT records.**
+Centroids and ward/postcode shares approximate household locations. England/Wales
+bedroom proportions include rent-free households; census cells are perturbed.
+Geography vintages differ and bedrooms describe accommodation, not LHA entitlement.
+The 34 postcode records without Data Zone codes do not contribute to mixed-zone shares.
+
+## Verification
+
+On 2 October 2026, all 89 inputs copied from retained originals passed `fetch.py`
+verification with no downloads, including all 72 decoded-content hashes.
+Fresh `fetch.py` downloads of eight originals covered all seven publishers;
+both an English and a Welsh custom-API batch passed decoded-content verification.
+The corrected full rebuild was byte-identical to the updated reference, 936 rows:
+`3ba714d4f5267656f19cd60785da379804420f633aa4c5745bdd0fba20473e72`.
+The NI key fix changes Belfast by +1 and South East NI by −1 household.
+Changed-content, missing-manual and override-set mismatch checks were rejected.
+Scotland's manual export was verified from its retained original, not re-exported.
+`nice -n 10` was invoked; the sandbox refused priority changes and Git index writes.
diff --git a/tools/brma_households/build.py b/tools/brma_households/build.py
new file mode 100644
index 000000000..b5172a61e
--- /dev/null
+++ b/tools/brma_households/build.py
@@ -0,0 +1,509 @@
+#!/usr/bin/env python3
+"""Rebuild the BRMA private-rented household table from verified cached originals."""
+
+import argparse
+import csv
+import hashlib
+import html
+import io
+import json
+from pathlib import Path
+import re
+import xml.etree.ElementTree as ET
+import zipfile
+
+import geopandas as gpd
+import pandas as pd
+from shapely import make_valid
+from shapely.geometry import MultiPolygon, Polygon
+
+from fetch import decoded_content, sources, verify_cache
+
+
+# Overrides established on 2026-10-02; live reply pages are evidence, not inputs.
+LSOA_OVERRIDES = {
+    "E01014018": "BRECON_AND_RADNOR",  # Official VOA postcode lookup HR3 5TA, 2026-10-02.
+    "E01022272": "MONMOUTHSHIRE",  # Official VOA postcode lookup NP16 7AG, 2026-10-02.
+    "E01022273": "MONMOUTHSHIRE",  # Official VOA postcode lookup NP16 7RB, 2026-10-02.
+    "E01022274": "MONMOUTHSHIRE",  # Official VOA postcode lookup NP16 7QB, 2026-10-02.
+    "E01022275": "MONMOUTHSHIRE",  # Official VOA postcode lookup NP16 7EN, 2026-10-02.
+    "W01002024": "CARDIFF",  # Official VOA lookup CF11 0AS (and 12 other postcodes), 2026-10-02.
+}
+SCOTTISH_OVERRIDES = {
+    "S00179382": "WEST_DUNBARTONSHIRE",  # G83 8NQ; official VOA West Dunbartonshire council lookup, 2026-10-02.
+    "S00179383": "WEST_DUNBARTONSHIRE",  # G83 8SD; official VOA West Dunbartonshire council lookup, 2026-10-02.
+}
+
+
+def read_json(path):
+    return json.loads(decoded_content(path))
+
+
+def brma_key(name):
+    return re.sub(r"\W+", "_", name.replace("&", "").upper()).strip("_")
+
+
+def voa_polygons(path):
+    """Parse actual GML types: its XSD wrongly declares nine MultiPolygons."""
+    ns = {"gml": "http://www.opengis.net/gml", "ogr": "http://ogr.maptools.org/"}
+
+    def ring(node):
+        return [tuple(map(float, xy.split(","))) for xy in node.text.split()]
+
+    def polygon(node):
+        shell = ring(
+            node.find("gml:outerBoundaryIs/gml:LinearRing/gml:coordinates", ns)
+        )
+        holes = [
+            ring(n)
+            for n in node.findall(
+                "gml:innerBoundaryIs/gml:LinearRing/gml:coordinates", ns
+            )
+        ]
+        return Polygon(shell, holes)
+
+    def parts(geom):
+        if geom.geom_type == "Polygon":
+            return [geom]
+        if geom.geom_type in {"MultiPolygon", "GeometryCollection"}:
+            return [p for child in geom.geoms for p in parts(child)]
+        return []
+
+    with zipfile.ZipFile(path) as z:
+        root = ET.fromstring(z.read("English BRMA(LHA) Layer.gml"))
+    records = []
+    for feature in root.findall("gml:featureMember/ogr:English_BRMA_LHA__Layer", ns):
+        node = feature.find("ogr:geometryProperty", ns)[0]
+        assert {n.attrib["srsName"] for n in node.iter() if "srsName" in n.attrib} == {
+            "EPSG:27700"
+        }
+        if node.tag.endswith("}Polygon"):
+            geom = polygon(node)
+        else:
+            assert node.tag.endswith("}MultiPolygon")
+            geom = MultiPolygon(
+                [polygon(n) for n in node.findall("gml:polygonMember/gml:Polygon", ns)]
+            )
+        repaired = parts(make_valid(geom))
+        assert repaired
+        geom = repaired[0] if len(repaired) == 1 else MultiPolygon(repaired)
+        assert geom.is_valid and not geom.is_empty
+        records.append(
+            {
+                "brma": brma_key(feature.findtext("ogr:Name", namespaces=ns)),
+                "geometry": geom,
+            }
+        )
+    assert len(records) == len({r["brma"] for r in records}) == 152
+    return gpd.GeoDataFrame(records, crs=27700)
+
+
+def lsoa_geography(cache):
+    regions = dict(
+        zip(
+            [f"E1200000{i}" for i in range(1, 10)],
+            [
+                "NORTH_EAST",
+                "NORTH_WEST",
+                "YORKSHIRE",
+                "EAST_MIDLANDS",
+                "WEST_MIDLANDS",
+                "EAST_OF_ENGLAND",
+                "LONDON",
+                "SOUTH_EAST",
+                "SOUTH_WEST",
+            ],
+        )
+    )
+    hierarchy = pd.read_csv(
+        cache / "ons_geo_oa21_lsoa21.csv", usecols=["OA21CD", "LSOA21CD"], dtype=str
+    )
+    hierarchy = hierarchy[hierarchy.LSOA21CD.str.startswith("E")]
+    region = pd.read_csv(
+        cache / "ons_geo_oa21_region.csv", usecols=["oa21cd", "rgn22cd"], dtype=str
+    )
+    hierarchy = hierarchy.merge(
+        region, left_on="OA21CD", right_on="oa21cd", how="left", validate="one_to_one"
+    )
+    assert (
+        hierarchy.rgn22cd.notna().all()
+        and hierarchy.groupby("LSOA21CD").rgn22cd.nunique().eq(1).all()
+    )
+    points = gpd.read_file(f"zip://{cache / 'ons_geo_lsoa21_pwc.zip'}")
+    points = points[points.LSOA21CD.str.startswith("E")].to_crs(27700)
+    assert points.LSOA21CD.is_unique and len(points) == 33755
+    eng = gpd.sjoin(
+        points,
+        voa_polygons(cache / "brma_england_voa_may2020.zip"),
+        how="left",
+        predicate="intersects",
+    )
+    assert eng.LSOA21CD.is_unique
+    outside = eng[eng.brma.isna()].copy()
+    assert set(outside.LSOA21CD) == {c for c in LSOA_OVERRIDES if c.startswith("E")}, (
+        "English override set changed"
+    )
+    outside["brma"] = outside.LSOA21CD.map(LSOA_OVERRIDES)
+    wal_points = gpd.read_file(cache / "geo_lsoa21_wales_pwc.geojson").to_crs(27700)
+    assert wal_points.LSOA21CD.is_unique and len(wal_points) == 1917
+    wal_polygons = gpd.read_file(
+        f"zip://{cache / 'geo_brma_wales_2014.zip'}!Broad Rental Market Areas/wales_brma.shp"
+    ).to_crs(27700)
+    wal_polygons.geometry = wal_polygons.geometry.make_valid()
+    wal = gpd.sjoin(
+        wal_points,
+        wal_polygons[["brma_name", "geometry"]],
+        how="left",
+        predicate="intersects",
+    )
+    assert wal.LSOA21CD.is_unique
+    assert set(wal.loc[wal.brma_name.isna(), "LSOA21CD"]) == {
+        c for c in LSOA_OVERRIDES if c.startswith("W")
+    }, "Welsh override set changed"
+    wal["brma"] = wal.brma_name.map(lambda n: brma_key(n) if pd.notna(n) else None)
+    wal["brma"] = wal.brma.fillna(wal.LSOA21CD.map(LSOA_OVERRIDES))
+    xw = pd.concat(
+        [
+            eng[eng.brma.notna()].sort_values("LSOA21CD"),
+            wal.sort_values("LSOA21CD"),
+            outside.sort_values("LSOA21CD"),
+        ],
+        ignore_index=True,
+    )[["LSOA21CD", "brma"]]
+    regional = (
+        hierarchy[["LSOA21CD", "rgn22cd"]]
+        .drop_duplicates()
+        .set_index("LSOA21CD")
+        .rgn22cd.map(regions)
+    )
+    xw["region"] = xw.LSOA21CD.map(regional)
+    xw.loc[xw.LSOA21CD.str.startswith("W"), "region"] = "WALES"
+    assert xw.region.notna().all() and xw.LSOA21CD.is_unique
+    return xw.rename(columns={"LSOA21CD": "area_code"})
+
+
+def england_and_wales(cache, entries):
+    with zipfile.ZipFile(cache / "census_nomis_ts054.zip") as z:
+        ts = pd.read_csv(io.BytesIO(z.read("census2021-ts054-lsoa.csv")))
+    col = "Tenure of household: Private rented"
+    assert (
+        ts[col]
+        .eq(
+            ts[col + ": Private landlord or letting agency"]
+            + ts[col + ": Other private rented"]
+        )
+        .all()
+    )
+    prs = ts.set_index("geography code")[col].astype(float)
+    rows = []
+    paths = sorted(
+        cache / e["filename"]
+        for e in entries
+        if e["filename"].startswith(
+            ("census_lsoa_tenure5a_bedrooms_", "census_wales_lsoa_tenure5a_bedrooms_")
+        )
+    )
+    for path in paths:
+        data = read_json(path)
+        assert not data.get("blocked_areas"), path
+        for observation in data["observations"]:
+            area, tenure, beds = (d["option_id"] for d in observation["dimensions"])
+            if tenure == "3" and beds in {"1", "2", "3", "4"}:
+                rows.append(
+                    (area, "4+" if beds == "4" else beds, observation["observation"])
+                )
+    mix = pd.DataFrame(rows, columns=["lsoa", "bedrooms", "n"]).pivot_table(
+        index="lsoa", columns="bedrooms", values="n", aggfunc="sum"
+    )
+    mix = mix.div(mix.sum(axis=1), axis=0)
+    xw = lsoa_geography(cache)
+    assert set(xw.area_code) == set(prs.index) == set(mix.index)
+    assert mix.notna().all().all() or prs[mix.isna().any(axis=1)].eq(0).all()
+    cells = mix.fillna(0).mul(prs, axis=0).stack().rename("households").reset_index()
+    cells.columns = ["area_code", "bedrooms", "households"]
+    out = xw.merge(cells, on="area_code")
+    return out.groupby(["region", "brma", "bedrooms"], as_index=False).households.sum()
+
+
+def scotland(cache):
+    """Allocate ward tenure × bedrooms using OA household-weighted BRMA shares."""
+
+    def name(value):
+        value = re.sub(r"\s*/\s*", " AND ", value.strip()).upper().replace(" ", "_")
+        return "AYRSHIRES" if value == "AYRSHIRE" else value
+
+    def postcode(value):
+        key = re.sub(r"\s+", "", str(value).upper())
+        match = re.fullmatch(r"([A-Z]{1,2}[0-9][0-9A-Z]?[0-9][A-Z]{2})[A-Z]?", key)
+        if not match:
+            raise ValueError(f"Invalid Scottish postcode: {value!r}")
+        return match.group(1)
+
+    brmas = gpd.read_file(f"zip://{cache / 'scot_brma_boundaries_2009.zip'}")
+    assert brmas.geometry.is_valid.all() and len(brmas) == 18
+    brmas["brma"] = brmas.BRMAName.map(name)
+    oas = gpd.read_file(
+        f"zip://{cache / 'scot_oa2022_population_weighted_centroids.zip'}!OutputArea2022_PWC/OutputArea2022_PWC.shp"
+    )
+    oas = oas.rename(columns={"code": "area_code", "HHcount": "households"}).to_crs(
+        brmas.crs
+    )
+    assert len(oas) == 46363 and not oas.area_code.duplicated().any()
+    matches = gpd.sjoin(
+        oas, brmas[["brma", "geometry"]], how="left", predicate="within"
+    )
+    missing = matches.loc[matches.brma.isna(), "area_code"]
+    boundary = gpd.sjoin(
+        oas[oas.area_code.isin(missing)],
+        brmas[["brma", "geometry"]],
+        how="left",
+        predicate="intersects",
+    )
+    matches = pd.concat(
+        [matches[~matches.area_code.isin(missing)], boundary], ignore_index=True
+    )
+    single = matches[
+        matches.area_code.map(matches.groupby("area_code").brma.count()).eq(1)
+    ]
+    oas = oas.drop(columns="geometry").merge(
+        single[["area_code", "brma"]], on="area_code", how="left", validate="one_to_one"
+    )
+    original_brma = oas.brma.copy()
+    official = pd.read_excel(
+        cache / "scot_foi_postcodes_brma_2023.xlsx", usecols=[0, 1], dtype=str
+    )
+    official.columns = ["brma", "postcode"]
+    official["postcode"] = official.postcode.map(postcode)
+    official["brma"] = official.brma.map(name)
+    candidates = official.groupby("postcode").brma.agg(lambda x: set(x))
+    oas["candidates"] = oas.masterpc.map(postcode).map(candidates)
+    unique = oas.candidates.map(
+        lambda x: next(iter(x)) if isinstance(x, set) and len(x) == 1 else None
+    )
+    oas["brma"] = oas.brma.fillna(unique)
+    assert oas.brma.notna().all(), "OA outside polygons without unique postcode BRMA"
+    with zipfile.ZipFile(cache / "scot_census2022_geography_index.zip") as z:
+        higher = pd.read_csv(
+            z.open("Census_2022_Index/OA_TO_HIGHER_AREAS.csv"),
+            encoding="utf-8-sig",
+            usecols=["OA2022", "EW2022", "CA2019"],
+        )
+    oas = oas.merge(
+        higher, left_on="area_code", right_on="OA2022", validate="one_to_one"
+    )
+    conflict = pd.Series(
+        [
+            isinstance(cs, set) and pd.notna(b) and b not in cs
+            for b, cs in zip(original_brma, oas.candidates)
+        ],
+        index=oas.index,
+    )
+    oas.loc[conflict & unique.notna(), "brma"] = unique
+    unresolved = conflict & unique.isna()
+    assert set(oas.loc[unresolved, "area_code"]) == set(SCOTTISH_OVERRIDES), (
+        "Scottish override set changed"
+    )
+    assert oas.loc[unresolved, "CA2019"].eq("S12000039").all()
+    # Clear unresolved polygon assignments before applying the documented exceptions.
+    oas.loc[unresolved, "brma"] = None
+    assert set(oas.loc[oas.brma.isna(), "area_code"]) == set(SCOTTISH_OVERRIDES)
+    oas["brma"] = oas.brma.fillna(oas.area_code.map(SCOTTISH_OVERRIDES))
+    assert all(
+        not isinstance(cs, set) or b in cs for b, cs in zip(oas.brma, oas.candidates)
+    )
+    shares = oas.groupby(["EW2022", "brma"], as_index=False).households.sum()
+    shares["share"] = shares.households / shares.EW2022.map(
+        oas.groupby("EW2022").households.sum()
+    )
+    shares = shares.rename(columns={"EW2022": "area_code"})
+    assert shares.area_code.nunique() == 355
+    assert shares.groupby("area_code").share.sum().sub(1).abs().max() < 1e-12
+
+    with (cache / "census_2022_ward2022_tenure_bedrooms.csv").open(
+        encoding="utf-8-sig", newline=""
+    ) as f:
+        rows = list(csv.reader(f))
+    assert ["Counting: Households"] in rows and not any(
+        r and r[0].strip() == "ERROR" for r in rows
+    )
+    tenures = {
+        "Private rented: Private landlord or letting agency": "private_rented_landlord_or_agent",
+        "Private rented: Other": "private_rented_other",
+    }
+    bands = {
+        "One bedroom": "1",
+        "Two bedrooms": "2",
+        "Three bedrooms": "3",
+        "Four bedrooms": "4+",
+        "Five or more bedrooms": "4+",
+    }
+    records, tenure, headers = [], None, None
+    for row in rows:
+        if not row:
+            continue
+        label = row[0].strip()
+        if len(row) == 1:
+            tenure = tenures.get(label)
+        elif label == "Number of bedrooms":
+            headers = row[1:]
+        elif tenure and re.fullmatch(r"S13\d{6}", label):
+            for bedroom, value in zip(headers, row[1:], strict=True):
+                if bedroom in bands:
+                    records.append((label, bands[bedroom], tenure, int(value)))
+    census = pd.DataFrame(
+        records, columns=["area_code", "bedrooms", "tenure", "households"]
+    )
+    census = census.groupby(
+        ["area_code", "bedrooms", "tenure"], as_index=False
+    ).households.sum()
+    assert len(census) == 355 * 4 * 2 and set(census.area_code) == set(shares.area_code)
+    joined = census.merge(shares[["area_code", "brma", "share"]], on="area_code")
+    joined["households"] *= joined.share
+    output = joined.groupby(
+        ["brma", "bedrooms", "tenure"], as_index=False
+    ).households.sum()
+    output = output.groupby(["brma", "bedrooms"], as_index=False).households.sum()
+    output["region"] = "SCOTLAND"
+    return output[["region", "brma", "bedrooms", "households"]]
+
+
+def northern_ireland(cache):
+    """Allocate Data Zone private tenure totals by NIHE postcode-district BRMA."""
+    names = {
+        "Belfast": "BELFAST",
+        "Lough Neagh Lower": "LOUGH_NEAGH_LOWER",
+        "Lough Neagh Upper": "LOUGH_NEAGH_UPPER",
+        "North": "NORTH_NI",
+        "North West": "NORTH_WEST_NI",
+        "South East": "SOUTH_EAST_NI",
+        "South": "SOUTH_NI",
+        "South West": "SOUTH_WEST_NI",
+    }
+    text = html.unescape(
+        re.sub(
+            r"<[^>]+>", " ", (cache / "nihe_current_lha_archive_2021.html").read_text()
+        )
+    )
+    choices = "|".join(sorted(names, key=len, reverse=True))
+    records = re.findall(rf"({choices}) BRMA:\s*((?:BT|[\d,\s\-])+)", text)
+    assert len(records) == 8
+    memberships = {}
+    for name, districts in records:
+        for first, last in re.findall(r"BT(\d+)(?:-BT(\d+))?", districts):
+            for number in range(int(first), int(last or first) + 1):
+                district = f"BT{number}"
+                assert district not in memberships
+                memberships[district] = names[name]
+    assert len(memberships) == 80
+    pages = [
+        json.loads((cache / f"ons_onspd_feb2026_ni_p{i}.json").read_text())
+        for i in range(2)
+    ]
+    onspd = pd.DataFrame([f["attributes"] for page in pages for f in page["features"]])
+    assert len(onspd) == 63179 and onspd.pcds.is_unique
+    onspd["district"] = onspd.pcds.str.split().str[0]
+    onspd["brma"] = onspd.district.map(memberships)
+    obsolete = onspd[onspd.brma.isna()]
+    assert obsolete.doterm.notna().all() and obsolete.doterm.le("202103").all()
+    onspd = onspd[onspd.brma.notna()].copy()
+    census = pd.read_excel(
+        cache / "nisra_census2021_postcode_households.xlsx",
+        sheet_name="Postcode",
+        header=5,
+    ).rename(columns={"Postcode": "pcds"})
+    census = census.merge(onspd, on="pcds", how="left", validate="one_to_one")
+    assert census.brma.notna().all()
+    census["households"] = pd.to_numeric(census.Households, errors="coerce")
+    suppressed = census.Households.eq("*")
+    assert (census.households.notna() | suppressed).all()
+    averages = pd.read_excel(
+        cache / "nisra_census2021_postcode_households.xlsx",
+        sheet_name="Suppressed averages",
+        header=5,
+    )
+    averages = averages.set_index(averages["Postcode district"].str.strip())[
+        "Average households per supressed Postcode"
+    ]
+    census.loc[suppressed, "households"] = census.loc[suppressed, "district"].map(
+        averages
+    )
+    assert census.households.notna().all(), (
+        "Suppressed postcode missing district average"
+    )
+    # Whole-zone assignment follows every in-scope ONSPD postcode, not just census ones.
+    singleton = onspd.groupby("oa21cd").brma.agg(
+        lambda s: s.iloc[0] if s.nunique() == 1 else None
+    )
+    singleton = singleton.dropna().rename("brma").reset_index()
+    singleton["share"] = 1.0
+    mixed = census[census.oa21cd.notna() & ~census.oa21cd.isin(singleton.oa21cd)]
+    weights = mixed.groupby(["oa21cd", "brma"]).households.sum()
+    shares = (
+        (weights / weights.groupby(level=0).transform("sum"))
+        .rename("share")
+        .reset_index()
+    )
+    # The lane excludes zero-weight shares on mixed zones without suppressed counts.
+    shares = shares[shares.share > 0]
+    crosswalk = pd.concat([singleton, shares], ignore_index=True).rename(
+        columns={"oa21cd": "area_code"}
+    )
+    assert crosswalk.area_code.nunique() == 3780
+    assert crosswalk.groupby("area_code").share.sum().sub(1).abs().max() < 1e-9
+    tenure = pd.read_csv(cache / "tenure_7_data_zones.csv")
+    tenure.columns = [
+        "area_code",
+        "area_name",
+        "tenure_code",
+        "tenure_label",
+        "households",
+    ]
+    assert len(tenure) == 3780 * 7 and tenure.area_code.nunique() == 3780
+    private = (
+        tenure[tenure.tenure_code.isin([5, 6])].groupby("area_code").households.sum()
+    )
+    assert set(private.index) == set(crosswalk.area_code)
+    out = crosswalk.merge(private.rename("households"), on="area_code")
+    out["households"] *= out.share
+    out = out.groupby("brma", as_index=False).households.sum()
+    out["region"], out["bedrooms"] = "NORTHERN_IRELAND", "all"
+    return out[["region", "brma", "bedrooms", "households"]]
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("cache", type=Path)
+    parser.add_argument(
+        "--output",
+        type=Path,
+        default=Path("policyengine_uk_data/storage/brma_private_rented_households.csv"),
+    )
+    args = parser.parse_args()
+    entries = sources()
+    verify_cache(args.cache, entries)
+    table = pd.concat(
+        [
+            england_and_wales(args.cache, entries),
+            scotland(args.cache),
+            northern_ireland(args.cache),
+        ],
+        ignore_index=True,
+    )
+    table = table[table.households > 0].copy()
+    table["households"] = table.households.round().astype(int)
+    table = table[table.households > 0].sort_values(["region", "brma", "bedrooms"])
+    data = (
+        table[["region", "brma", "bedrooms", "households"]]
+        .to_csv(index=False, lineterminator="\n")
+        .encode("utf-8")
+    )
+    digest = hashlib.sha256(data).hexdigest()
+    expected = "3ba714d4f5267656f19cd60785da379804420f633aa4c5745bdd0fba20473e72"
+    if digest != expected:
+        raise ValueError(f"Rebuilt table differs: expected {expected}, got {digest}")
+    args.output.write_bytes(data)
+    print(f"Wrote {len(table)} rows to {args.output}; SHA256 {digest}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/brma_households/fetch.py b/tools/brma_households/fetch.py
new file mode 100644
index 000000000..be7fc7bfa
--- /dev/null
+++ b/tools/brma_households/fetch.py
@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""Fetch pinned originals without a login, browser session or accepting terms."""
+
+import argparse
+import gzip
+import hashlib
+from pathlib import Path
+import shutil
+from urllib.request import Request, urlopen
+
+import yaml
+
+
+def sources():
+    entries = yaml.safe_load(Path(__file__).with_name("sources.yaml").read_text())
+    required = {
+        "id",
+        "publisher",
+        "title",
+        "url",
+        "landing_page",
+        "licence",
+        "notes",
+        "filename",
+    }
+    ids, filenames = set(), set()
+    for entry in entries:
+        if not required <= entry.keys() or not (
+            {"sha256", "content_sha256"} & entry.keys()
+        ):
+            raise ValueError(f"Incomplete source: {entry.get('id')}")
+        name = entry["filename"]
+        if entry["id"] in ids or name in filenames or Path(name).name != name:
+            raise ValueError(f"Duplicate source or unsafe cache filename: {name}")
+        ids.add(entry["id"])
+        filenames.add(name)
+    return entries
+
+
+def decoded_content(path):
+    data = path.read_bytes()
+    return gzip.decompress(data) if data.startswith(b"\x1f\x8b") else data
+
+
+def verify(path, entry):
+    if not path.is_file():
+        instructions = f"\n{entry['notes']}" if entry.get("manual") else ""
+        raise FileNotFoundError(f"Missing source {entry['id']}: {path}{instructions}")
+    if "content_sha256" in entry:
+        actual = hashlib.sha256(decoded_content(path)).hexdigest()
+        expected = entry["content_sha256"]
+    else:
+        with path.open("rb") as stream:
+            actual = hashlib.file_digest(stream, "sha256").hexdigest()
+        expected = entry["sha256"]
+    if actual != expected:
+        raise ValueError(
+            f"SHA256 mismatch for {entry['id']}: expected {expected}, got {actual}"
+        )
+
+
+def verify_cache(cache, entries):
+    """Check every original before the builder interprets any source."""
+    for entry in entries:
+        verify(cache / entry["filename"], entry)
+
+
+def fetch(cache, entries):
+    cache.mkdir(parents=True, exist_ok=True)
+    missing = []
+    for entry in entries:
+        path = cache / entry["filename"]
+        if not path.exists() and entry.get("manual"):
+            print(f"Manual download required: {path}\n{entry['notes']}", flush=True)
+            missing.append(entry["id"])
+            continue
+        if not path.exists():
+            print(f"Downloading {entry['id']}", flush=True)
+            request = Request(
+                entry["url"],
+                headers={
+                    "User-Agent": "BRMA-household-pipeline/1.0",
+                    "Accept-Encoding": "gzip" if path.suffix == ".gz" else "identity",
+                },
+            )
+            temporary = path.with_name(path.name + ".part")
+            try:
+                # Keep entity bytes; custom-API pins verify decoded content instead.
+                with (
+                    urlopen(request, timeout=180) as response,
+                    temporary.open("wb") as stream,
+                ):
+                    shutil.copyfileobj(response, stream)
+                verify(temporary, entry)
+                temporary.replace(path)
+            finally:
+                temporary.unlink(missing_ok=True)
+        verify(path, entry)
+        print(f"Verified {entry['id']}", flush=True)
+    if missing:
+        raise FileNotFoundError(
+            "Supply the manual downloads above: " + ", ".join(missing)
+        )
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("cache", type=Path)
+    parser.add_argument(
+        "--only",
+        nargs="+",
+        metavar="SOURCE_ID",
+        help="Fetch/verify selected sources (default: all)",
+    )
+    args = parser.parse_args()
+    entries = sources()
+    if args.only:
+        unknown = set(args.only) - {entry["id"] for entry in entries}
+        if unknown:
+            parser.error("Unknown source IDs: " + ", ".join(sorted(unknown)))
+        entries = [entry for entry in entries if entry["id"] in args.only]
+    fetch(args.cache, entries)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/brma_households/sources.yaml b/tools/brma_households/sources.yaml
new file mode 100644
index 000000000..9135569da
--- /dev/null
+++ b/tools/brma_households/sources.yaml
@@ -0,0 +1,1063 @@
+- id: census_lsoa_tenure5a_bedrooms_001
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 001
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01000001,E01000002,E01000003,E01000005,E01000006,E01000007,E01000008,E01000009,E01000011,E01000012,E01000013,E01000014,E01000015,E01000016,E01000017,E01000018,E01000019,E01000020,E01000021,E01000022,E01000024,E01000025,E01000027,E01000028,E01000029,E01000030,E01000031,E01000032,E01000033,E01000034,E01000035,E01000036,E01000037,E01000038,E01000039,E01000040,E01000041,E01000042,E01000043,E01000044,E01000045,E01000046,E01000049,E01000050,E01000051,E01000052,E01000053,E01000054,E01000055,E01000056,E01000057,E01000058,E01000059,E01000060,E01000061,E01000062,E01000063,E01000064,E01000065,E01000066,E01000067,E01000068,E01000069,E01000070,E01000071,E01000072,E01000073,E01000074,E01000075,E01000076,E01000077,E01000078,E01000079,E01000080,E01000081,E01000082,E01000083,E01000084,E01000085,E01000086,E01000087,E01000088,E01000089,E01000090,E01000091,E01000093,E01000094,E01000095,E01000096,E01000097,E01000098,E01000099,E01000100,E01000101,E01000102,E01000103,E01000104,E01000105,E01000106,E01000107,E01000108,E01000110,E01000111,E01000112,E01000113,E01000114,E01000115,E01000116,E01000117,E01000118,E01000119,E01000120,E01000121,E01000122,E01000123,E01000124,E01000126,E01000127,E01000128,E01000129,E01000130,E01000131,E01000132,E01000133,E01000134,E01000135,E01000136,E01000137,E01000138,E01000139,E01000140,E01000141,E01000142,E01000143,E01000144,E01000145,E01000146,E01000147,E01000150,E01000151,E01000152,E01000153,E01000154,E01000156,E01000157,E01000158,E01000159,E01000160,E01000161,E01000162,E01000163,E01000164,E01000165,E01000166,E01000167,E01000168,E01000169,E01000170,E01000171,E01000172,E01000173,E01000174,E01000175,E01000176,E01000177,E01000178,E01000179,E01000180,E01000181,E01000182,E01000183,E01000184,E01000185,E01000186,E01000187,E01000188,E01000189,E01000190,E01000191,E01000192,E01000193,E01000194,E01000195,E01000196,E01000197,E01000198,E01000199,E01000200,E01000201,E01000202,E01000203,E01000204,E01000205,E01000206,E01000207,E01000208,E01000209,E01000210,E01000211,E01000212,E01000213,E01000214,E01000215,E01000216,E01000217,E01000218,E01000219,E01000220,E01000221,E01000222,E01000223,E01000224,E01000225,E01000226,E01000227,E01000228,E01000229,E01000230,E01000231,E01000232,E01000233,E01000234,E01000235,E01000236,E01000237,E01000238,E01000239,E01000240,E01000241,E01000242,E01000243,E01000244,E01000245,E01000246,E01000247,E01000248,E01000249,E01000250,E01000251,E01000252,E01000253,E01000254,E01000255,E01000256,E01000257,E01000258,E01000259,E01000260,E01000261,E01000263,E01000264,E01000265,E01000266,E01000267,E01000268,E01000269,E01000270,E01000271,E01000272,E01000273,E01000274,E01000275,E01000276,E01000277,E01000278,E01000279,E01000280,E01000281,E01000282,E01000283,E01000284,E01000285,E01000286,E01000287,E01000288,E01000289,E01000290,E01000291,E01000292,E01000293,E01000294,E01000295,E01000296,E01000297,E01000298,E01000299,E01000300,E01000301,E01000302,E01000303,E01000304,E01000305,E01000306,E01000307,E01000308,E01000309,E01000310,E01000311,E01000312,E01000313,E01000314,E01000315,E01000316,E01000317,E01000318,E01000319,E01000320,E01000321,E01000322,E01000323,E01000324,E01000325,E01000326,E01000327,E01000328,E01000329,E01000330,E01000331,E01000332,E01000333,E01000334,E01000335,E01000336,E01000337,E01000338,E01000339,E01000340,E01000341,E01000342,E01000343,E01000344,E01000345,E01000346,E01000347,E01000348,E01000349,E01000350,E01000351,E01000352,E01000353,E01000354,E01000355,E01000356,E01000357,E01000358,E01000359,E01000360,E01000361,E01000362,E01000363,E01000364,E01000365,E01000366,E01000367,E01000368,E01000369,E01000370,E01000371,E01000372,E01000373,E01000374,E01000375,E01000376,E01000377,E01000379,E01000380,E01000381,E01000382,E01000383,E01000384,E01000385,E01000386,E01000387,E01000388,E01000389,E01000390,E01000391,E01000392,E01000393,E01000394,E01000395,E01000396,E01000398,E01000399,E01000400,E01000401,E01000402,E01000403,E01000404,E01000405,E01000406,E01000407,E01000408,E01000409,E01000410,E01000411,E01000412,E01000413,E01000414,E01000415,E01000416,E01000417,E01000418,E01000419,E01000420,E01000421,E01000422,E01000423,E01000424,E01000425,E01000426,E01000427,E01000428,E01000429,E01000430,E01000431,E01000433,E01000434,E01000435,E01000436,E01000437,E01000438,E01000439,E01000440,E01000441,E01000442,E01000443,E01000444,E01000445,E01000446,E01000447,E01000448,E01000449,E01000450,E01000451,E01000452,E01000453,E01000454,E01000455,E01000456,E01000457,E01000458,E01000459,E01000460,E01000461,E01000462,E01000463,E01000464,E01000465,E01000466,E01000467,E01000468,E01000469,E01000470,E01000471,E01000472,E01000473,E01000474,E01000475,E01000476,E01000477,E01000478,E01000479,E01000480,E01000481,E01000483,E01000484,E01000485,E01000486,E01000487,E01000488,E01000489,E01000490,E01000491,E01000492,E01000493,E01000494,E01000495,E01000497,E01000498,E01000499,E01000501,E01000502,E01000503,E01000504,E01000505,E01000506,E01000507,E01000508,E01000509,E01000510,E01000511,E01000512,E01000513,E01000514,E01000515,E01000516,E01000517,E01000518,E01000519&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_001.json.gz
+  content_sha256: 2c0395055bd054fda769f99aaa1acb0f23317afa5408fc9a98112a9c6bb2f255
+- id: census_lsoa_tenure5a_bedrooms_002
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 002
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01000520,E01000521,E01000522,E01000523,E01000524,E01000525,E01000526,E01000527,E01000528,E01000530,E01000531,E01000532,E01000533,E01000534,E01000535,E01000536,E01000538,E01000539,E01000540,E01000541,E01000543,E01000544,E01000545,E01000546,E01000547,E01000548,E01000549,E01000550,E01000551,E01000552,E01000553,E01000554,E01000555,E01000556,E01000557,E01000558,E01000559,E01000560,E01000561,E01000562,E01000563,E01000564,E01000565,E01000566,E01000567,E01000568,E01000569,E01000570,E01000571,E01000572,E01000573,E01000574,E01000575,E01000576,E01000577,E01000578,E01000579,E01000580,E01000581,E01000582,E01000583,E01000584,E01000585,E01000586,E01000587,E01000589,E01000590,E01000591,E01000592,E01000593,E01000594,E01000595,E01000596,E01000599,E01000601,E01000602,E01000603,E01000604,E01000605,E01000606,E01000607,E01000608,E01000609,E01000610,E01000611,E01000612,E01000613,E01000615,E01000616,E01000617,E01000618,E01000619,E01000620,E01000621,E01000622,E01000623,E01000624,E01000625,E01000626,E01000627,E01000628,E01000629,E01000630,E01000631,E01000632,E01000633,E01000634,E01000636,E01000637,E01000638,E01000639,E01000640,E01000641,E01000642,E01000643,E01000644,E01000645,E01000646,E01000647,E01000648,E01000649,E01000650,E01000651,E01000652,E01000653,E01000654,E01000655,E01000656,E01000657,E01000658,E01000659,E01000661,E01000662,E01000663,E01000664,E01000665,E01000666,E01000667,E01000668,E01000669,E01000670,E01000671,E01000672,E01000673,E01000674,E01000676,E01000677,E01000678,E01000679,E01000680,E01000681,E01000682,E01000683,E01000684,E01000685,E01000686,E01000687,E01000688,E01000689,E01000690,E01000691,E01000692,E01000693,E01000694,E01000695,E01000696,E01000697,E01000698,E01000699,E01000700,E01000701,E01000702,E01000703,E01000704,E01000705,E01000706,E01000707,E01000708,E01000709,E01000710,E01000711,E01000712,E01000713,E01000714,E01000715,E01000716,E01000717,E01000718,E01000719,E01000720,E01000721,E01000722,E01000723,E01000724,E01000725,E01000726,E01000727,E01000728,E01000729,E01000730,E01000731,E01000732,E01000733,E01000734,E01000735,E01000736,E01000737,E01000738,E01000739,E01000741,E01000742,E01000743,E01000744,E01000745,E01000746,E01000747,E01000748,E01000749,E01000750,E01000751,E01000752,E01000753,E01000754,E01000755,E01000756,E01000757,E01000758,E01000759,E01000760,E01000761,E01000762,E01000763,E01000764,E01000765,E01000766,E01000767,E01000768,E01000769,E01000770,E01000771,E01000772,E01000773,E01000774,E01000775,E01000777,E01000778,E01000779,E01000780,E01000781,E01000782,E01000783,E01000784,E01000785,E01000786,E01000787,E01000788,E01000789,E01000790,E01000791,E01000792,E01000793,E01000794,E01000795,E01000796,E01000797,E01000798,E01000799,E01000800,E01000801,E01000802,E01000803,E01000804,E01000805,E01000806,E01000807,E01000808,E01000809,E01000810,E01000811,E01000812,E01000813,E01000814,E01000815,E01000816,E01000818,E01000819,E01000820,E01000822,E01000823,E01000824,E01000825,E01000826,E01000827,E01000828,E01000829,E01000830,E01000831,E01000832,E01000833,E01000834,E01000835,E01000836,E01000837,E01000838,E01000839,E01000840,E01000841,E01000842,E01000843,E01000844,E01000845,E01000846,E01000847,E01000848,E01000849,E01000850,E01000851,E01000853,E01000855,E01000856,E01000857,E01000858,E01000859,E01000860,E01000861,E01000862,E01000863,E01000866,E01000867,E01000868,E01000869,E01000870,E01000871,E01000872,E01000873,E01000874,E01000875,E01000876,E01000877,E01000878,E01000879,E01000880,E01000881,E01000882,E01000883,E01000884,E01000885,E01000886,E01000887,E01000888,E01000889,E01000890,E01000891,E01000892,E01000893,E01000894,E01000895,E01000896,E01000897,E01000898,E01000899,E01000900,E01000901,E01000902,E01000903,E01000904,E01000905,E01000906,E01000907,E01000908,E01000909,E01000910,E01000911,E01000912,E01000913,E01000914,E01000915,E01000916,E01000917,E01000918,E01000919,E01000920,E01000921,E01000922,E01000923,E01000924,E01000925,E01000926,E01000927,E01000928,E01000929,E01000930,E01000931,E01000932,E01000933,E01000934,E01000935,E01000937,E01000938,E01000939,E01000941,E01000942,E01000943,E01000944,E01000946,E01000947,E01000948,E01000949,E01000951,E01000952,E01000954,E01000955,E01000956,E01000957,E01000958,E01000959,E01000960,E01000961,E01000962,E01000963,E01000964,E01000965,E01000966,E01000967,E01000968,E01000969,E01000970,E01000971,E01000972,E01000973,E01000974,E01000975,E01000976,E01000977,E01000978,E01000979,E01000980,E01000981,E01000982,E01000983,E01000984,E01000985,E01000986,E01000987,E01000989,E01000990,E01000991,E01000992,E01000993,E01000994,E01000995,E01000996,E01000997,E01000998,E01000999,E01001000,E01001001,E01001002,E01001003,E01001004,E01001005,E01001006,E01001007,E01001008,E01001009,E01001010,E01001011,E01001012,E01001013,E01001015,E01001016,E01001017,E01001018,E01001019,E01001020,E01001021,E01001022,E01001023,E01001024,E01001025,E01001026,E01001027,E01001028,E01001029,E01001030,E01001031,E01001032,E01001033,E01001034,E01001035,E01001036,E01001037,E01001038,E01001039,E01001040,E01001041,E01001042,E01001044,E01001046,E01001047&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_002.json.gz
+  content_sha256: beb8c647f39d8e239002c273d03b1dcfb33cffe927892747c7f2cb4a8171d90a
+- id: census_lsoa_tenure5a_bedrooms_003
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 003
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01001049,E01001050,E01001051,E01001052,E01001053,E01001054,E01001055,E01001056,E01001057,E01001058,E01001059,E01001060,E01001061,E01001062,E01001063,E01001064,E01001065,E01001066,E01001067,E01001068,E01001069,E01001070,E01001071,E01001072,E01001073,E01001074,E01001075,E01001076,E01001077,E01001078,E01001079,E01001080,E01001081,E01001082,E01001083,E01001084,E01001085,E01001086,E01001087,E01001088,E01001089,E01001090,E01001091,E01001092,E01001093,E01001094,E01001095,E01001096,E01001097,E01001098,E01001099,E01001100,E01001101,E01001102,E01001103,E01001104,E01001105,E01001106,E01001107,E01001108,E01001109,E01001110,E01001111,E01001112,E01001113,E01001114,E01001115,E01001116,E01001118,E01001119,E01001120,E01001121,E01001122,E01001123,E01001124,E01001125,E01001126,E01001127,E01001128,E01001129,E01001130,E01001131,E01001132,E01001133,E01001134,E01001135,E01001136,E01001137,E01001138,E01001139,E01001140,E01001141,E01001142,E01001143,E01001144,E01001145,E01001146,E01001147,E01001148,E01001149,E01001150,E01001151,E01001152,E01001153,E01001154,E01001155,E01001156,E01001157,E01001158,E01001159,E01001160,E01001161,E01001162,E01001163,E01001164,E01001165,E01001166,E01001167,E01001168,E01001169,E01001170,E01001171,E01001172,E01001173,E01001174,E01001175,E01001176,E01001177,E01001178,E01001179,E01001180,E01001181,E01001182,E01001183,E01001184,E01001185,E01001186,E01001187,E01001188,E01001189,E01001190,E01001191,E01001192,E01001193,E01001194,E01001195,E01001196,E01001197,E01001198,E01001199,E01001200,E01001201,E01001202,E01001203,E01001204,E01001205,E01001206,E01001207,E01001208,E01001209,E01001210,E01001211,E01001212,E01001213,E01001214,E01001215,E01001216,E01001217,E01001218,E01001219,E01001220,E01001223,E01001224,E01001225,E01001226,E01001227,E01001228,E01001229,E01001230,E01001231,E01001232,E01001233,E01001234,E01001235,E01001236,E01001237,E01001239,E01001240,E01001241,E01001242,E01001243,E01001244,E01001245,E01001246,E01001247,E01001248,E01001249,E01001250,E01001251,E01001252,E01001253,E01001254,E01001255,E01001257,E01001258,E01001259,E01001260,E01001261,E01001262,E01001263,E01001264,E01001265,E01001266,E01001267,E01001268,E01001269,E01001270,E01001271,E01001272,E01001273,E01001274,E01001275,E01001276,E01001277,E01001278,E01001279,E01001280,E01001281,E01001282,E01001283,E01001284,E01001285,E01001286,E01001287,E01001288,E01001289,E01001290,E01001291,E01001292,E01001293,E01001294,E01001295,E01001296,E01001297,E01001298,E01001299,E01001300,E01001301,E01001302,E01001303,E01001304,E01001305,E01001306,E01001307,E01001308,E01001309,E01001310,E01001311,E01001312,E01001313,E01001314,E01001315,E01001316,E01001317,E01001318,E01001319,E01001320,E01001321,E01001322,E01001323,E01001324,E01001325,E01001326,E01001327,E01001328,E01001329,E01001330,E01001331,E01001332,E01001333,E01001334,E01001335,E01001336,E01001337,E01001338,E01001339,E01001340,E01001341,E01001342,E01001343,E01001344,E01001345,E01001346,E01001347,E01001348,E01001349,E01001350,E01001351,E01001352,E01001353,E01001354,E01001355,E01001356,E01001357,E01001358,E01001359,E01001360,E01001361,E01001362,E01001363,E01001364,E01001365,E01001366,E01001367,E01001368,E01001369,E01001370,E01001371,E01001372,E01001373,E01001374,E01001375,E01001376,E01001377,E01001379,E01001380,E01001381,E01001382,E01001383,E01001384,E01001385,E01001386,E01001387,E01001388,E01001389,E01001390,E01001391,E01001392,E01001393,E01001394,E01001395,E01001396,E01001397,E01001398,E01001399,E01001400,E01001401,E01001402,E01001403,E01001404,E01001405,E01001406,E01001407,E01001408,E01001409,E01001410,E01001411,E01001412,E01001413,E01001414,E01001415,E01001416,E01001417,E01001418,E01001419,E01001420,E01001421,E01001422,E01001423,E01001424,E01001425,E01001426,E01001427,E01001428,E01001429,E01001430,E01001431,E01001432,E01001433,E01001434,E01001435,E01001436,E01001437,E01001438,E01001439,E01001440,E01001441,E01001442,E01001444,E01001445,E01001446,E01001447,E01001448,E01001449,E01001450,E01001451,E01001452,E01001453,E01001454,E01001455,E01001456,E01001457,E01001458,E01001459,E01001460,E01001461,E01001462,E01001463,E01001464,E01001465,E01001466,E01001467,E01001468,E01001469,E01001470,E01001471,E01001472,E01001473,E01001474,E01001475,E01001476,E01001477,E01001478,E01001479,E01001480,E01001481,E01001482,E01001483,E01001484,E01001485,E01001486,E01001487,E01001488,E01001489,E01001491,E01001492,E01001493,E01001494,E01001495,E01001496,E01001497,E01001498,E01001499,E01001500,E01001501,E01001502,E01001503,E01001504,E01001505,E01001507,E01001508,E01001509,E01001511,E01001512,E01001513,E01001514,E01001515,E01001516,E01001517,E01001518,E01001520,E01001521,E01001522,E01001523,E01001524,E01001525,E01001526,E01001527,E01001528,E01001529,E01001530,E01001531,E01001532,E01001533,E01001534,E01001535,E01001536,E01001537,E01001538,E01001539,E01001540,E01001541,E01001542,E01001543,E01001544,E01001545,E01001546,E01001547,E01001548,E01001549,E01001550,E01001551,E01001552,E01001553,E01001554,E01001555,E01001556,E01001557,E01001558,E01001559&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_003.json.gz
+  content_sha256: b79c9c84f0e058c95fd6ef1639579eba119195328859ed83d699fc292c2d78ad
+- id: census_lsoa_tenure5a_bedrooms_004
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 004
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01001560,E01001562,E01001563,E01001564,E01001565,E01001566,E01001567,E01001568,E01001569,E01001570,E01001571,E01001572,E01001573,E01001574,E01001575,E01001576,E01001577,E01001578,E01001579,E01001580,E01001581,E01001582,E01001583,E01001584,E01001585,E01001586,E01001587,E01001588,E01001589,E01001590,E01001591,E01001592,E01001593,E01001594,E01001595,E01001596,E01001597,E01001598,E01001599,E01001600,E01001601,E01001602,E01001603,E01001604,E01001605,E01001606,E01001607,E01001608,E01001609,E01001610,E01001611,E01001613,E01001614,E01001615,E01001617,E01001618,E01001619,E01001620,E01001621,E01001623,E01001624,E01001625,E01001629,E01001631,E01001632,E01001633,E01001634,E01001635,E01001636,E01001637,E01001638,E01001641,E01001642,E01001644,E01001645,E01001646,E01001647,E01001648,E01001649,E01001650,E01001651,E01001652,E01001653,E01001654,E01001655,E01001656,E01001657,E01001658,E01001659,E01001660,E01001661,E01001664,E01001665,E01001666,E01001669,E01001671,E01001672,E01001673,E01001674,E01001675,E01001676,E01001677,E01001678,E01001679,E01001680,E01001681,E01001682,E01001683,E01001684,E01001685,E01001686,E01001687,E01001688,E01001690,E01001692,E01001693,E01001694,E01001695,E01001696,E01001697,E01001698,E01001699,E01001700,E01001701,E01001702,E01001703,E01001704,E01001705,E01001708,E01001710,E01001712,E01001714,E01001715,E01001716,E01001717,E01001718,E01001719,E01001720,E01001722,E01001723,E01001724,E01001725,E01001726,E01001727,E01001728,E01001729,E01001730,E01001731,E01001732,E01001733,E01001734,E01001735,E01001736,E01001737,E01001738,E01001739,E01001740,E01001741,E01001742,E01001744,E01001745,E01001746,E01001747,E01001749,E01001750,E01001751,E01001753,E01001755,E01001756,E01001757,E01001758,E01001759,E01001760,E01001761,E01001762,E01001763,E01001764,E01001765,E01001767,E01001768,E01001769,E01001770,E01001772,E01001773,E01001774,E01001775,E01001776,E01001777,E01001780,E01001781,E01001783,E01001784,E01001785,E01001786,E01001787,E01001788,E01001789,E01001790,E01001791,E01001792,E01001793,E01001794,E01001795,E01001796,E01001798,E01001799,E01001800,E01001801,E01001802,E01001803,E01001804,E01001805,E01001806,E01001807,E01001808,E01001809,E01001811,E01001812,E01001813,E01001814,E01001815,E01001816,E01001819,E01001820,E01001821,E01001822,E01001823,E01001824,E01001825,E01001826,E01001827,E01001828,E01001829,E01001830,E01001831,E01001832,E01001833,E01001834,E01001835,E01001836,E01001837,E01001838,E01001839,E01001840,E01001841,E01001842,E01001843,E01001844,E01001845,E01001846,E01001847,E01001848,E01001849,E01001850,E01001851,E01001852,E01001853,E01001854,E01001855,E01001856,E01001857,E01001858,E01001859,E01001860,E01001861,E01001862,E01001863,E01001864,E01001865,E01001866,E01001867,E01001868,E01001869,E01001870,E01001871,E01001872,E01001873,E01001875,E01001876,E01001877,E01001878,E01001879,E01001880,E01001881,E01001882,E01001883,E01001884,E01001885,E01001886,E01001887,E01001888,E01001889,E01001890,E01001891,E01001892,E01001893,E01001894,E01001895,E01001896,E01001897,E01001898,E01001899,E01001900,E01001901,E01001902,E01001903,E01001904,E01001905,E01001906,E01001907,E01001908,E01001909,E01001910,E01001911,E01001912,E01001913,E01001914,E01001915,E01001916,E01001917,E01001918,E01001919,E01001920,E01001921,E01001922,E01001923,E01001924,E01001925,E01001926,E01001927,E01001928,E01001929,E01001930,E01001931,E01001932,E01001933,E01001934,E01001935,E01001937,E01001938,E01001940,E01001941,E01001942,E01001943,E01001944,E01001945,E01001946,E01001947,E01001948,E01001949,E01001950,E01001951,E01001952,E01001953,E01001954,E01001955,E01001956,E01001957,E01001958,E01001959,E01001960,E01001961,E01001962,E01001963,E01001964,E01001965,E01001966,E01001967,E01001968,E01001969,E01001970,E01001971,E01001972,E01001973,E01001974,E01001975,E01001976,E01001977,E01001978,E01001979,E01001980,E01001981,E01001982,E01001983,E01001984,E01001985,E01001986,E01001987,E01001988,E01001989,E01001990,E01001991,E01001992,E01001993,E01001994,E01001995,E01001996,E01001997,E01001998,E01001999,E01002000,E01002001,E01002002,E01002003,E01002004,E01002005,E01002006,E01002007,E01002008,E01002009,E01002010,E01002011,E01002013,E01002014,E01002015,E01002016,E01002017,E01002018,E01002019,E01002020,E01002021,E01002022,E01002023,E01002024,E01002025,E01002026,E01002027,E01002028,E01002029,E01002030,E01002031,E01002032,E01002033,E01002034,E01002036,E01002037,E01002038,E01002039,E01002040,E01002041,E01002042,E01002043,E01002044,E01002045,E01002046,E01002047,E01002048,E01002049,E01002050,E01002051,E01002052,E01002053,E01002054,E01002055,E01002056,E01002057,E01002058,E01002059,E01002060,E01002061,E01002062,E01002063,E01002064,E01002065,E01002066,E01002067,E01002068,E01002069,E01002070,E01002071,E01002072,E01002073,E01002074,E01002075,E01002076,E01002077,E01002078,E01002079,E01002081,E01002082,E01002083,E01002084,E01002085,E01002086,E01002087,E01002088,E01002089,E01002090,E01002091,E01002092,E01002093,E01002094,E01002095,E01002096,E01002097,E01002098,E01002099,E01002100,E01002101,E01002102&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_004.json.gz
+  content_sha256: 0e425fc25ba556b447c0d91ea280d55ae563ab5ed21b8710e7466a01d2885b14
+- id: census_lsoa_tenure5a_bedrooms_005
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 005
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01002103,E01002104,E01002105,E01002106,E01002107,E01002108,E01002109,E01002110,E01002111,E01002112,E01002113,E01002114,E01002115,E01002116,E01002118,E01002119,E01002120,E01002121,E01002122,E01002123,E01002124,E01002125,E01002127,E01002128,E01002129,E01002130,E01002131,E01002132,E01002133,E01002134,E01002135,E01002136,E01002137,E01002138,E01002139,E01002140,E01002141,E01002142,E01002143,E01002144,E01002145,E01002146,E01002147,E01002148,E01002149,E01002150,E01002151,E01002152,E01002153,E01002155,E01002156,E01002157,E01002158,E01002159,E01002160,E01002161,E01002162,E01002163,E01002164,E01002165,E01002166,E01002167,E01002168,E01002169,E01002170,E01002171,E01002172,E01002173,E01002174,E01002175,E01002176,E01002177,E01002178,E01002179,E01002181,E01002182,E01002183,E01002184,E01002185,E01002186,E01002187,E01002188,E01002189,E01002190,E01002191,E01002192,E01002193,E01002194,E01002195,E01002196,E01002197,E01002198,E01002199,E01002200,E01002201,E01002202,E01002203,E01002204,E01002206,E01002207,E01002208,E01002209,E01002210,E01002211,E01002212,E01002214,E01002215,E01002216,E01002217,E01002218,E01002219,E01002220,E01002221,E01002222,E01002223,E01002224,E01002225,E01002226,E01002227,E01002228,E01002229,E01002230,E01002231,E01002232,E01002233,E01002234,E01002235,E01002236,E01002237,E01002238,E01002239,E01002240,E01002241,E01002242,E01002243,E01002244,E01002245,E01002246,E01002247,E01002248,E01002250,E01002251,E01002252,E01002253,E01002254,E01002255,E01002256,E01002257,E01002258,E01002259,E01002260,E01002261,E01002262,E01002263,E01002264,E01002265,E01002266,E01002267,E01002268,E01002269,E01002270,E01002271,E01002272,E01002273,E01002274,E01002275,E01002276,E01002277,E01002278,E01002279,E01002280,E01002281,E01002282,E01002283,E01002284,E01002285,E01002286,E01002287,E01002288,E01002289,E01002290,E01002291,E01002292,E01002294,E01002295,E01002296,E01002297,E01002298,E01002299,E01002300,E01002301,E01002302,E01002303,E01002304,E01002305,E01002306,E01002307,E01002308,E01002309,E01002310,E01002311,E01002312,E01002313,E01002314,E01002315,E01002316,E01002317,E01002318,E01002319,E01002320,E01002321,E01002322,E01002323,E01002324,E01002325,E01002326,E01002327,E01002328,E01002329,E01002330,E01002331,E01002332,E01002333,E01002334,E01002335,E01002336,E01002337,E01002338,E01002339,E01002340,E01002341,E01002342,E01002343,E01002344,E01002345,E01002346,E01002347,E01002348,E01002349,E01002350,E01002351,E01002352,E01002353,E01002354,E01002355,E01002356,E01002358,E01002359,E01002360,E01002361,E01002362,E01002363,E01002364,E01002365,E01002366,E01002367,E01002368,E01002369,E01002370,E01002371,E01002372,E01002373,E01002374,E01002375,E01002376,E01002377,E01002378,E01002379,E01002380,E01002381,E01002382,E01002383,E01002384,E01002385,E01002386,E01002387,E01002388,E01002389,E01002390,E01002391,E01002392,E01002393,E01002394,E01002395,E01002396,E01002397,E01002398,E01002399,E01002400,E01002401,E01002402,E01002403,E01002404,E01002405,E01002408,E01002409,E01002410,E01002411,E01002412,E01002413,E01002414,E01002415,E01002416,E01002417,E01002418,E01002419,E01002420,E01002421,E01002422,E01002423,E01002424,E01002425,E01002426,E01002427,E01002428,E01002429,E01002430,E01002431,E01002432,E01002433,E01002434,E01002435,E01002436,E01002437,E01002438,E01002439,E01002440,E01002441,E01002442,E01002443,E01002444,E01002445,E01002446,E01002447,E01002448,E01002449,E01002450,E01002451,E01002452,E01002453,E01002454,E01002455,E01002456,E01002457,E01002458,E01002459,E01002460,E01002461,E01002462,E01002463,E01002464,E01002465,E01002466,E01002467,E01002468,E01002469,E01002470,E01002471,E01002472,E01002473,E01002474,E01002475,E01002476,E01002477,E01002478,E01002479,E01002480,E01002481,E01002482,E01002483,E01002484,E01002485,E01002486,E01002487,E01002488,E01002489,E01002490,E01002491,E01002492,E01002493,E01002494,E01002495,E01002496,E01002497,E01002498,E01002499,E01002501,E01002502,E01002503,E01002505,E01002506,E01002507,E01002508,E01002509,E01002511,E01002512,E01002513,E01002514,E01002516,E01002518,E01002519,E01002520,E01002521,E01002523,E01002525,E01002526,E01002527,E01002528,E01002529,E01002530,E01002531,E01002533,E01002534,E01002535,E01002536,E01002537,E01002538,E01002539,E01002540,E01002541,E01002542,E01002543,E01002544,E01002545,E01002546,E01002547,E01002548,E01002549,E01002550,E01002551,E01002552,E01002553,E01002555,E01002556,E01002557,E01002558,E01002559,E01002560,E01002561,E01002562,E01002563,E01002564,E01002565,E01002567,E01002569,E01002570,E01002571,E01002572,E01002573,E01002574,E01002575,E01002576,E01002577,E01002578,E01002579,E01002580,E01002581,E01002582,E01002583,E01002584,E01002585,E01002586,E01002587,E01002588,E01002589,E01002590,E01002591,E01002592,E01002593,E01002594,E01002595,E01002596,E01002598,E01002599,E01002600,E01002601,E01002602,E01002603,E01002604,E01002605,E01002606,E01002607,E01002608,E01002609,E01002610,E01002611,E01002612,E01002613,E01002614,E01002615,E01002616,E01002617,E01002618,E01002619,E01002620,E01002621,E01002622,E01002623,E01002624,E01002625&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_005.json.gz
+  content_sha256: 58f73b7f289066ee70cc2b92d1f84df3195210d45d98e5661fed265683405862
+- id: census_lsoa_tenure5a_bedrooms_006
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 006
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01002626,E01002627,E01002628,E01002629,E01002630,E01002631,E01002632,E01002633,E01002634,E01002635,E01002636,E01002637,E01002638,E01002639,E01002640,E01002641,E01002643,E01002645,E01002646,E01002647,E01002649,E01002650,E01002651,E01002652,E01002653,E01002654,E01002655,E01002656,E01002657,E01002658,E01002659,E01002660,E01002661,E01002662,E01002663,E01002665,E01002666,E01002667,E01002668,E01002669,E01002670,E01002671,E01002672,E01002673,E01002674,E01002675,E01002676,E01002677,E01002678,E01002679,E01002680,E01002681,E01002682,E01002686,E01002687,E01002689,E01002690,E01002691,E01002692,E01002693,E01002694,E01002695,E01002696,E01002697,E01002698,E01002699,E01002700,E01002701,E01002702,E01002703,E01002704,E01002706,E01002707,E01002708,E01002709,E01002710,E01002711,E01002712,E01002713,E01002714,E01002715,E01002716,E01002717,E01002718,E01002719,E01002720,E01002721,E01002722,E01002723,E01002724,E01002725,E01002726,E01002727,E01002728,E01002729,E01002730,E01002732,E01002733,E01002734,E01002735,E01002736,E01002737,E01002738,E01002739,E01002740,E01002741,E01002742,E01002743,E01002744,E01002745,E01002747,E01002748,E01002749,E01002751,E01002752,E01002753,E01002754,E01002755,E01002756,E01002757,E01002758,E01002759,E01002760,E01002762,E01002763,E01002764,E01002765,E01002766,E01002767,E01002768,E01002769,E01002770,E01002771,E01002772,E01002773,E01002774,E01002775,E01002776,E01002777,E01002778,E01002779,E01002780,E01002781,E01002782,E01002783,E01002784,E01002785,E01002786,E01002787,E01002788,E01002789,E01002790,E01002791,E01002792,E01002793,E01002794,E01002795,E01002796,E01002797,E01002798,E01002799,E01002800,E01002801,E01002802,E01002804,E01002805,E01002806,E01002807,E01002808,E01002809,E01002810,E01002811,E01002812,E01002813,E01002814,E01002815,E01002816,E01002817,E01002818,E01002819,E01002821,E01002823,E01002824,E01002825,E01002826,E01002827,E01002828,E01002829,E01002830,E01002831,E01002832,E01002833,E01002834,E01002835,E01002836,E01002837,E01002839,E01002840,E01002841,E01002842,E01002843,E01002844,E01002845,E01002846,E01002847,E01002848,E01002849,E01002850,E01002851,E01002852,E01002853,E01002854,E01002855,E01002856,E01002857,E01002858,E01002859,E01002860,E01002861,E01002862,E01002863,E01002864,E01002865,E01002866,E01002867,E01002868,E01002869,E01002870,E01002871,E01002872,E01002873,E01002874,E01002875,E01002876,E01002877,E01002878,E01002879,E01002880,E01002881,E01002882,E01002883,E01002884,E01002885,E01002886,E01002887,E01002888,E01002889,E01002890,E01002891,E01002892,E01002893,E01002894,E01002896,E01002897,E01002898,E01002899,E01002900,E01002901,E01002902,E01002903,E01002904,E01002905,E01002906,E01002907,E01002908,E01002909,E01002910,E01002911,E01002912,E01002913,E01002914,E01002915,E01002916,E01002917,E01002918,E01002919,E01002920,E01002921,E01002922,E01002923,E01002924,E01002925,E01002926,E01002927,E01002928,E01002929,E01002930,E01002931,E01002932,E01002933,E01002934,E01002935,E01002937,E01002938,E01002939,E01002940,E01002941,E01002942,E01002943,E01002944,E01002945,E01002946,E01002947,E01002948,E01002949,E01002950,E01002951,E01002952,E01002953,E01002954,E01002955,E01002956,E01002957,E01002958,E01002959,E01002960,E01002961,E01002962,E01002963,E01002964,E01002965,E01002966,E01002967,E01002968,E01002969,E01002970,E01002971,E01002972,E01002973,E01002974,E01002975,E01002976,E01002977,E01002978,E01002979,E01002980,E01002981,E01002982,E01002983,E01002984,E01002985,E01002986,E01002987,E01002988,E01002989,E01002990,E01002991,E01002992,E01002993,E01002994,E01002995,E01002996,E01002997,E01002998,E01002999,E01003000,E01003001,E01003002,E01003003,E01003004,E01003005,E01003006,E01003007,E01003008,E01003009,E01003010,E01003011,E01003013,E01003014,E01003015,E01003016,E01003017,E01003018,E01003019,E01003020,E01003021,E01003022,E01003023,E01003024,E01003025,E01003026,E01003027,E01003028,E01003029,E01003030,E01003031,E01003032,E01003033,E01003034,E01003035,E01003036,E01003037,E01003038,E01003039,E01003040,E01003041,E01003042,E01003043,E01003044,E01003045,E01003046,E01003047,E01003048,E01003049,E01003050,E01003051,E01003052,E01003053,E01003054,E01003055,E01003056,E01003057,E01003058,E01003059,E01003060,E01003061,E01003062,E01003063,E01003064,E01003065,E01003066,E01003067,E01003068,E01003069,E01003070,E01003071,E01003072,E01003073,E01003074,E01003075,E01003076,E01003077,E01003078,E01003079,E01003080,E01003081,E01003082,E01003083,E01003084,E01003085,E01003086,E01003087,E01003088,E01003089,E01003090,E01003091,E01003092,E01003093,E01003094,E01003095,E01003096,E01003097,E01003098,E01003099,E01003100,E01003101,E01003102,E01003104,E01003105,E01003106,E01003107,E01003108,E01003110,E01003111,E01003112,E01003113,E01003114,E01003115,E01003116,E01003117,E01003118,E01003119,E01003120,E01003121,E01003122,E01003123,E01003124,E01003125,E01003126,E01003127,E01003128,E01003129,E01003130,E01003131,E01003132,E01003133,E01003134,E01003135,E01003136,E01003137,E01003138,E01003139,E01003140,E01003141,E01003142,E01003143,E01003144,E01003145,E01003146,E01003147&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_006.json.gz
+  content_sha256: d359a4cc9fd12dc44ffb14c375438e6e950315c64950b40bb9ff2671f4c2f609
+- id: census_lsoa_tenure5a_bedrooms_007
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 007
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01003148,E01003149,E01003150,E01003151,E01003152,E01003153,E01003154,E01003155,E01003156,E01003157,E01003158,E01003159,E01003160,E01003161,E01003162,E01003163,E01003164,E01003165,E01003166,E01003167,E01003168,E01003169,E01003170,E01003171,E01003172,E01003173,E01003174,E01003175,E01003176,E01003177,E01003178,E01003179,E01003180,E01003181,E01003182,E01003183,E01003184,E01003185,E01003186,E01003187,E01003188,E01003189,E01003190,E01003191,E01003192,E01003193,E01003194,E01003195,E01003196,E01003197,E01003198,E01003199,E01003200,E01003201,E01003202,E01003203,E01003204,E01003205,E01003206,E01003207,E01003209,E01003210,E01003211,E01003212,E01003213,E01003214,E01003215,E01003216,E01003217,E01003218,E01003219,E01003220,E01003221,E01003222,E01003223,E01003224,E01003225,E01003226,E01003227,E01003228,E01003229,E01003230,E01003231,E01003232,E01003233,E01003235,E01003236,E01003237,E01003238,E01003239,E01003240,E01003241,E01003243,E01003244,E01003245,E01003246,E01003247,E01003248,E01003249,E01003251,E01003252,E01003253,E01003254,E01003255,E01003256,E01003257,E01003258,E01003259,E01003260,E01003261,E01003262,E01003263,E01003264,E01003265,E01003266,E01003267,E01003268,E01003269,E01003270,E01003271,E01003272,E01003273,E01003274,E01003275,E01003276,E01003277,E01003278,E01003279,E01003280,E01003281,E01003282,E01003283,E01003284,E01003285,E01003286,E01003287,E01003288,E01003290,E01003291,E01003292,E01003293,E01003295,E01003297,E01003298,E01003299,E01003300,E01003301,E01003302,E01003303,E01003304,E01003305,E01003307,E01003308,E01003309,E01003310,E01003311,E01003312,E01003313,E01003314,E01003315,E01003316,E01003318,E01003319,E01003320,E01003321,E01003322,E01003323,E01003324,E01003325,E01003326,E01003327,E01003328,E01003329,E01003330,E01003331,E01003332,E01003333,E01003334,E01003335,E01003336,E01003337,E01003338,E01003339,E01003340,E01003341,E01003342,E01003343,E01003344,E01003345,E01003346,E01003347,E01003348,E01003349,E01003350,E01003351,E01003352,E01003353,E01003354,E01003355,E01003356,E01003357,E01003358,E01003359,E01003360,E01003361,E01003362,E01003363,E01003364,E01003365,E01003366,E01003368,E01003369,E01003370,E01003371,E01003372,E01003373,E01003374,E01003375,E01003376,E01003377,E01003378,E01003379,E01003380,E01003381,E01003382,E01003383,E01003384,E01003385,E01003386,E01003387,E01003388,E01003389,E01003390,E01003391,E01003392,E01003393,E01003394,E01003395,E01003396,E01003397,E01003398,E01003399,E01003400,E01003401,E01003402,E01003403,E01003404,E01003405,E01003406,E01003407,E01003408,E01003409,E01003410,E01003411,E01003412,E01003413,E01003414,E01003415,E01003416,E01003417,E01003418,E01003419,E01003420,E01003421,E01003422,E01003423,E01003424,E01003425,E01003426,E01003427,E01003428,E01003429,E01003430,E01003431,E01003432,E01003433,E01003434,E01003435,E01003436,E01003437,E01003438,E01003439,E01003440,E01003441,E01003442,E01003443,E01003444,E01003445,E01003446,E01003447,E01003448,E01003449,E01003450,E01003451,E01003452,E01003453,E01003454,E01003455,E01003456,E01003457,E01003458,E01003459,E01003460,E01003461,E01003462,E01003463,E01003464,E01003465,E01003466,E01003467,E01003468,E01003469,E01003470,E01003471,E01003472,E01003473,E01003474,E01003476,E01003477,E01003478,E01003479,E01003480,E01003481,E01003483,E01003484,E01003485,E01003486,E01003487,E01003488,E01003489,E01003490,E01003491,E01003492,E01003494,E01003495,E01003496,E01003497,E01003498,E01003499,E01003500,E01003501,E01003502,E01003503,E01003506,E01003507,E01003508,E01003509,E01003511,E01003512,E01003513,E01003514,E01003515,E01003516,E01003517,E01003518,E01003519,E01003520,E01003521,E01003522,E01003523,E01003524,E01003525,E01003526,E01003527,E01003528,E01003529,E01003530,E01003531,E01003532,E01003533,E01003534,E01003535,E01003536,E01003537,E01003538,E01003539,E01003540,E01003541,E01003542,E01003543,E01003544,E01003545,E01003546,E01003547,E01003548,E01003549,E01003550,E01003551,E01003552,E01003553,E01003554,E01003555,E01003556,E01003557,E01003558,E01003559,E01003560,E01003561,E01003562,E01003563,E01003564,E01003565,E01003566,E01003567,E01003568,E01003569,E01003570,E01003571,E01003572,E01003573,E01003574,E01003575,E01003576,E01003577,E01003578,E01003579,E01003580,E01003581,E01003582,E01003583,E01003584,E01003585,E01003586,E01003587,E01003588,E01003589,E01003590,E01003591,E01003592,E01003593,E01003594,E01003595,E01003596,E01003597,E01003598,E01003599,E01003600,E01003601,E01003602,E01003603,E01003604,E01003605,E01003607,E01003608,E01003611,E01003615,E01003616,E01003618,E01003619,E01003621,E01003622,E01003623,E01003624,E01003625,E01003626,E01003627,E01003628,E01003629,E01003630,E01003631,E01003632,E01003633,E01003634,E01003635,E01003636,E01003637,E01003638,E01003639,E01003640,E01003641,E01003642,E01003643,E01003644,E01003645,E01003646,E01003647,E01003648,E01003649,E01003650,E01003651,E01003652,E01003653,E01003654,E01003655,E01003656,E01003657,E01003658,E01003659,E01003660,E01003661,E01003662,E01003663,E01003664,E01003665,E01003666,E01003668,E01003669,E01003670,E01003671,E01003672&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_007.json.gz
+  content_sha256: 225ceac0a244949186db90b49a1f0706c2190bef9497b1527b67f35db2440f9b
+- id: census_lsoa_tenure5a_bedrooms_008
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 008
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01003673,E01003674,E01003675,E01003676,E01003677,E01003678,E01003679,E01003680,E01003681,E01003682,E01003683,E01003684,E01003685,E01003686,E01003688,E01003689,E01003690,E01003691,E01003692,E01003693,E01003694,E01003695,E01003696,E01003697,E01003698,E01003699,E01003700,E01003701,E01003702,E01003703,E01003704,E01003705,E01003706,E01003707,E01003708,E01003709,E01003710,E01003711,E01003712,E01003713,E01003714,E01003715,E01003716,E01003718,E01003719,E01003720,E01003721,E01003722,E01003723,E01003724,E01003725,E01003726,E01003727,E01003729,E01003730,E01003731,E01003732,E01003733,E01003734,E01003735,E01003736,E01003737,E01003738,E01003739,E01003740,E01003741,E01003742,E01003743,E01003744,E01003745,E01003746,E01003747,E01003748,E01003749,E01003750,E01003751,E01003752,E01003753,E01003754,E01003755,E01003756,E01003757,E01003759,E01003760,E01003761,E01003762,E01003763,E01003764,E01003765,E01003766,E01003767,E01003768,E01003769,E01003770,E01003771,E01003772,E01003773,E01003774,E01003775,E01003776,E01003777,E01003778,E01003779,E01003780,E01003781,E01003782,E01003783,E01003784,E01003785,E01003786,E01003787,E01003788,E01003789,E01003790,E01003791,E01003792,E01003793,E01003794,E01003795,E01003796,E01003797,E01003798,E01003799,E01003800,E01003801,E01003802,E01003803,E01003804,E01003805,E01003806,E01003807,E01003808,E01003809,E01003810,E01003811,E01003812,E01003813,E01003814,E01003815,E01003816,E01003817,E01003818,E01003819,E01003820,E01003821,E01003822,E01003823,E01003824,E01003825,E01003826,E01003827,E01003828,E01003829,E01003830,E01003831,E01003832,E01003833,E01003834,E01003835,E01003836,E01003837,E01003838,E01003839,E01003840,E01003841,E01003842,E01003843,E01003844,E01003845,E01003846,E01003847,E01003848,E01003849,E01003851,E01003852,E01003853,E01003854,E01003855,E01003856,E01003857,E01003858,E01003859,E01003860,E01003861,E01003862,E01003863,E01003864,E01003865,E01003866,E01003867,E01003868,E01003869,E01003870,E01003871,E01003872,E01003873,E01003874,E01003875,E01003876,E01003877,E01003878,E01003879,E01003880,E01003881,E01003882,E01003883,E01003884,E01003885,E01003886,E01003887,E01003888,E01003889,E01003890,E01003891,E01003892,E01003893,E01003894,E01003895,E01003896,E01003897,E01003898,E01003899,E01003900,E01003901,E01003902,E01003903,E01003904,E01003905,E01003906,E01003907,E01003908,E01003909,E01003910,E01003911,E01003912,E01003913,E01003914,E01003915,E01003916,E01003917,E01003918,E01003919,E01003920,E01003921,E01003922,E01003923,E01003924,E01003925,E01003926,E01003929,E01003930,E01003932,E01003933,E01003934,E01003935,E01003936,E01003937,E01003938,E01003939,E01003940,E01003941,E01003942,E01003943,E01003945,E01003946,E01003947,E01003948,E01003949,E01003950,E01003951,E01003952,E01003953,E01003954,E01003955,E01003956,E01003957,E01003958,E01003960,E01003961,E01003963,E01003966,E01003969,E01003971,E01003972,E01003973,E01003975,E01003976,E01003977,E01003979,E01003980,E01003981,E01003983,E01003984,E01003986,E01003987,E01003988,E01003989,E01003990,E01003991,E01003992,E01003993,E01003994,E01003995,E01003997,E01003998,E01003999,E01004000,E01004001,E01004002,E01004003,E01004004,E01004005,E01004006,E01004007,E01004008,E01004010,E01004011,E01004012,E01004013,E01004014,E01004015,E01004016,E01004017,E01004018,E01004019,E01004020,E01004021,E01004023,E01004024,E01004028,E01004029,E01004030,E01004031,E01004032,E01004033,E01004034,E01004035,E01004036,E01004037,E01004038,E01004039,E01004040,E01004041,E01004042,E01004043,E01004044,E01004045,E01004046,E01004047,E01004048,E01004049,E01004050,E01004051,E01004052,E01004053,E01004054,E01004055,E01004056,E01004057,E01004058,E01004059,E01004060,E01004061,E01004062,E01004063,E01004064,E01004065,E01004066,E01004067,E01004068,E01004069,E01004070,E01004071,E01004072,E01004073,E01004074,E01004075,E01004076,E01004077,E01004078,E01004079,E01004080,E01004081,E01004082,E01004083,E01004084,E01004085,E01004086,E01004087,E01004088,E01004089,E01004090,E01004091,E01004092,E01004093,E01004094,E01004095,E01004096,E01004097,E01004098,E01004099,E01004100,E01004101,E01004102,E01004103,E01004104,E01004105,E01004106,E01004107,E01004108,E01004109,E01004110,E01004111,E01004112,E01004114,E01004115,E01004116,E01004117,E01004118,E01004119,E01004120,E01004121,E01004122,E01004123,E01004124,E01004125,E01004126,E01004127,E01004128,E01004129,E01004130,E01004131,E01004132,E01004133,E01004134,E01004135,E01004136,E01004137,E01004138,E01004139,E01004140,E01004141,E01004143,E01004144,E01004145,E01004146,E01004147,E01004148,E01004149,E01004150,E01004151,E01004152,E01004153,E01004154,E01004155,E01004156,E01004157,E01004158,E01004159,E01004160,E01004161,E01004162,E01004163,E01004164,E01004165,E01004166,E01004167,E01004168,E01004169,E01004170,E01004171,E01004172,E01004173,E01004174,E01004175,E01004176,E01004177,E01004178,E01004179,E01004180,E01004181,E01004182,E01004183,E01004184,E01004185,E01004186,E01004187,E01004189,E01004190,E01004191,E01004192,E01004193,E01004194,E01004195,E01004196,E01004198,E01004199,E01004200,E01004201,E01004202&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_008.json.gz
+  content_sha256: a88693ef4a27708857fad2ed9812ef3495d2d4f10c46baa569aee1cb753743e9
+- id: census_lsoa_tenure5a_bedrooms_009
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 009
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01004203,E01004204,E01004205,E01004206,E01004207,E01004208,E01004209,E01004211,E01004212,E01004214,E01004216,E01004217,E01004218,E01004219,E01004221,E01004222,E01004223,E01004226,E01004228,E01004229,E01004230,E01004231,E01004232,E01004233,E01004234,E01004235,E01004236,E01004237,E01004239,E01004240,E01004241,E01004243,E01004244,E01004245,E01004247,E01004248,E01004249,E01004251,E01004255,E01004256,E01004257,E01004258,E01004259,E01004260,E01004263,E01004264,E01004266,E01004267,E01004268,E01004270,E01004271,E01004272,E01004274,E01004275,E01004276,E01004277,E01004280,E01004281,E01004283,E01004286,E01004287,E01004288,E01004289,E01004290,E01004291,E01004292,E01004293,E01004295,E01004296,E01004297,E01004298,E01004300,E01004301,E01004302,E01004303,E01004304,E01004305,E01004306,E01004307,E01004308,E01004309,E01004310,E01004311,E01004312,E01004313,E01004315,E01004316,E01004319,E01004321,E01004322,E01004323,E01004324,E01004326,E01004327,E01004328,E01004329,E01004330,E01004331,E01004332,E01004333,E01004334,E01004335,E01004336,E01004337,E01004338,E01004339,E01004340,E01004341,E01004342,E01004343,E01004344,E01004345,E01004346,E01004347,E01004348,E01004349,E01004350,E01004351,E01004352,E01004353,E01004354,E01004355,E01004356,E01004357,E01004358,E01004359,E01004360,E01004361,E01004362,E01004363,E01004364,E01004365,E01004366,E01004367,E01004368,E01004369,E01004370,E01004371,E01004372,E01004373,E01004374,E01004375,E01004376,E01004377,E01004378,E01004379,E01004380,E01004381,E01004382,E01004383,E01004384,E01004385,E01004386,E01004387,E01004388,E01004389,E01004390,E01004391,E01004392,E01004393,E01004394,E01004395,E01004396,E01004397,E01004398,E01004399,E01004400,E01004401,E01004402,E01004403,E01004404,E01004405,E01004406,E01004407,E01004408,E01004409,E01004410,E01004411,E01004412,E01004413,E01004414,E01004415,E01004416,E01004417,E01004418,E01004419,E01004420,E01004421,E01004422,E01004423,E01004424,E01004425,E01004426,E01004427,E01004428,E01004429,E01004430,E01004432,E01004434,E01004435,E01004436,E01004437,E01004438,E01004439,E01004440,E01004441,E01004442,E01004443,E01004444,E01004445,E01004446,E01004447,E01004448,E01004449,E01004450,E01004451,E01004452,E01004453,E01004454,E01004455,E01004456,E01004457,E01004458,E01004459,E01004460,E01004462,E01004463,E01004464,E01004465,E01004466,E01004467,E01004468,E01004469,E01004470,E01004471,E01004472,E01004473,E01004474,E01004475,E01004476,E01004477,E01004478,E01004479,E01004480,E01004481,E01004482,E01004483,E01004484,E01004485,E01004486,E01004487,E01004488,E01004489,E01004490,E01004491,E01004492,E01004493,E01004494,E01004495,E01004496,E01004497,E01004498,E01004499,E01004500,E01004501,E01004502,E01004503,E01004504,E01004505,E01004506,E01004507,E01004508,E01004510,E01004511,E01004512,E01004513,E01004514,E01004515,E01004516,E01004517,E01004518,E01004519,E01004520,E01004521,E01004522,E01004523,E01004524,E01004525,E01004526,E01004527,E01004528,E01004529,E01004530,E01004531,E01004532,E01004533,E01004534,E01004535,E01004536,E01004537,E01004538,E01004539,E01004540,E01004541,E01004542,E01004543,E01004544,E01004545,E01004546,E01004547,E01004548,E01004549,E01004550,E01004551,E01004552,E01004553,E01004554,E01004555,E01004556,E01004557,E01004558,E01004559,E01004560,E01004563,E01004564,E01004565,E01004566,E01004567,E01004568,E01004569,E01004570,E01004571,E01004572,E01004573,E01004574,E01004575,E01004576,E01004577,E01004579,E01004580,E01004581,E01004582,E01004583,E01004584,E01004585,E01004586,E01004587,E01004588,E01004589,E01004590,E01004591,E01004592,E01004593,E01004594,E01004595,E01004596,E01004597,E01004598,E01004599,E01004601,E01004602,E01004604,E01004605,E01004606,E01004607,E01004608,E01004609,E01004610,E01004611,E01004612,E01004613,E01004614,E01004615,E01004616,E01004617,E01004618,E01004619,E01004620,E01004621,E01004622,E01004623,E01004624,E01004625,E01004626,E01004627,E01004628,E01004629,E01004630,E01004631,E01004632,E01004633,E01004634,E01004635,E01004636,E01004637,E01004638,E01004639,E01004640,E01004641,E01004642,E01004643,E01004644,E01004645,E01004646,E01004647,E01004648,E01004649,E01004650,E01004651,E01004652,E01004653,E01004654,E01004656,E01004657,E01004658,E01004659,E01004660,E01004661,E01004662,E01004663,E01004665,E01004668,E01004669,E01004670,E01004674,E01004675,E01004676,E01004677,E01004678,E01004679,E01004680,E01004681,E01004682,E01004683,E01004684,E01004686,E01004687,E01004690,E01004691,E01004692,E01004693,E01004694,E01004695,E01004696,E01004697,E01004698,E01004699,E01004700,E01004701,E01004702,E01004703,E01004705,E01004706,E01004707,E01004708,E01004709,E01004710,E01004711,E01004712,E01004713,E01004714,E01004715,E01004716,E01004717,E01004718,E01004719,E01004720,E01004721,E01004722,E01004723,E01004724,E01004725,E01004726,E01004727,E01004728,E01004729,E01004730,E01004731,E01004732,E01004733,E01004734,E01004735,E01004736,E01004737,E01004740,E01004741,E01004742,E01004743,E01004744,E01004746,E01004747,E01004748,E01004749,E01004750,E01004751,E01004752,E01004753,E01004754,E01004755,E01004756&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_009.json.gz
+  content_sha256: 1c39708ece58bc09201f490d8bf2c4a8e697ea7a675026cc168e38da5cb8c484
+- id: census_lsoa_tenure5a_bedrooms_010
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 010
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01004757,E01004760,E01004761,E01004762,E01004763,E01004765,E01004766,E01004767,E01004768,E01004769,E01004770,E01004771,E01004772,E01004773,E01004774,E01004775,E01004776,E01004777,E01004778,E01004779,E01004780,E01004781,E01004782,E01004783,E01004784,E01004785,E01004786,E01004787,E01004788,E01004789,E01004790,E01004791,E01004792,E01004793,E01004794,E01004795,E01004796,E01004797,E01004798,E01004799,E01004800,E01004801,E01004802,E01004803,E01004804,E01004805,E01004806,E01004807,E01004808,E01004809,E01004810,E01004811,E01004812,E01004813,E01004814,E01004815,E01004816,E01004817,E01004818,E01004819,E01004820,E01004822,E01004823,E01004824,E01004825,E01004826,E01004827,E01004828,E01004829,E01004830,E01004831,E01004832,E01004833,E01004834,E01004835,E01004836,E01004837,E01004838,E01004839,E01004840,E01004841,E01004842,E01004843,E01004844,E01004845,E01004846,E01004847,E01004848,E01004849,E01004850,E01004851,E01004852,E01004853,E01004854,E01004855,E01004856,E01004857,E01004858,E01004859,E01004860,E01004861,E01004862,E01004863,E01004864,E01004865,E01004866,E01004867,E01004868,E01004869,E01004870,E01004871,E01004872,E01004873,E01004874,E01004875,E01004876,E01004877,E01004878,E01004879,E01004880,E01004881,E01004882,E01004883,E01004884,E01004885,E01004886,E01004887,E01004888,E01004889,E01004890,E01004891,E01004892,E01004893,E01004894,E01004895,E01004896,E01004897,E01004899,E01004900,E01004901,E01004902,E01004903,E01004904,E01004905,E01004906,E01004907,E01004908,E01004909,E01004910,E01004911,E01004912,E01004913,E01004914,E01004915,E01004916,E01004917,E01004918,E01004919,E01004920,E01004921,E01004922,E01004923,E01004924,E01004925,E01004926,E01004927,E01004928,E01004929,E01004930,E01004931,E01004932,E01004933,E01004934,E01004935,E01004936,E01004937,E01004938,E01004939,E01004940,E01004941,E01004942,E01004943,E01004944,E01004945,E01004946,E01004947,E01004948,E01004949,E01004950,E01004951,E01004952,E01004953,E01004954,E01004955,E01004956,E01004957,E01004958,E01004959,E01004960,E01004961,E01004962,E01004963,E01004964,E01004965,E01004966,E01004967,E01004968,E01004969,E01004970,E01004971,E01004972,E01004973,E01004974,E01004975,E01004976,E01004977,E01004978,E01004979,E01004980,E01004981,E01004982,E01004983,E01004984,E01004985,E01004986,E01004987,E01004988,E01004989,E01004990,E01004991,E01004992,E01004993,E01004994,E01004995,E01004996,E01004997,E01004998,E01004999,E01005000,E01005001,E01005002,E01005003,E01005004,E01005005,E01005006,E01005007,E01005008,E01005009,E01005010,E01005011,E01005012,E01005013,E01005014,E01005015,E01005016,E01005017,E01005018,E01005019,E01005020,E01005021,E01005022,E01005023,E01005024,E01005025,E01005026,E01005027,E01005028,E01005029,E01005030,E01005031,E01005032,E01005033,E01005034,E01005035,E01005036,E01005037,E01005038,E01005039,E01005040,E01005041,E01005042,E01005043,E01005044,E01005045,E01005046,E01005047,E01005048,E01005049,E01005050,E01005051,E01005052,E01005053,E01005054,E01005055,E01005056,E01005057,E01005058,E01005059,E01005060,E01005061,E01005063,E01005065,E01005066,E01005067,E01005068,E01005069,E01005070,E01005071,E01005072,E01005073,E01005074,E01005075,E01005076,E01005077,E01005078,E01005079,E01005081,E01005082,E01005083,E01005084,E01005085,E01005086,E01005087,E01005088,E01005089,E01005090,E01005091,E01005092,E01005093,E01005094,E01005097,E01005098,E01005099,E01005100,E01005101,E01005102,E01005103,E01005104,E01005105,E01005106,E01005107,E01005111,E01005112,E01005113,E01005114,E01005115,E01005116,E01005117,E01005118,E01005119,E01005120,E01005121,E01005122,E01005123,E01005124,E01005125,E01005126,E01005128,E01005129,E01005130,E01005132,E01005135,E01005136,E01005138,E01005139,E01005141,E01005142,E01005143,E01005145,E01005147,E01005149,E01005150,E01005151,E01005152,E01005153,E01005154,E01005155,E01005156,E01005157,E01005158,E01005159,E01005160,E01005161,E01005162,E01005163,E01005164,E01005165,E01005166,E01005167,E01005168,E01005169,E01005170,E01005171,E01005172,E01005173,E01005174,E01005175,E01005176,E01005177,E01005178,E01005179,E01005180,E01005181,E01005182,E01005183,E01005184,E01005185,E01005186,E01005187,E01005188,E01005189,E01005190,E01005191,E01005192,E01005193,E01005195,E01005197,E01005198,E01005199,E01005200,E01005201,E01005202,E01005203,E01005205,E01005206,E01005207,E01005208,E01005210,E01005212,E01005213,E01005214,E01005215,E01005216,E01005217,E01005218,E01005219,E01005220,E01005221,E01005222,E01005223,E01005224,E01005225,E01005226,E01005227,E01005228,E01005229,E01005230,E01005231,E01005232,E01005233,E01005234,E01005235,E01005236,E01005237,E01005238,E01005239,E01005243,E01005244,E01005245,E01005246,E01005247,E01005248,E01005249,E01005250,E01005251,E01005252,E01005253,E01005254,E01005255,E01005256,E01005257,E01005258,E01005259,E01005260,E01005261,E01005262,E01005263,E01005264,E01005265,E01005266,E01005267,E01005268,E01005269,E01005270,E01005271,E01005272,E01005273,E01005274,E01005275,E01005276,E01005277,E01005278,E01005279,E01005280,E01005281,E01005284,E01005285,E01005286,E01005287,E01005288&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_010.json.gz
+  content_sha256: ba48282eb8ac7649cb0c8d20a7c1c9d4c0238defa6d62c2f55b584fb5b95c590
+- id: census_lsoa_tenure5a_bedrooms_011
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 011
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01005289,E01005290,E01005291,E01005292,E01005293,E01005294,E01005295,E01005296,E01005297,E01005298,E01005299,E01005300,E01005301,E01005302,E01005304,E01005305,E01005306,E01005307,E01005308,E01005309,E01005310,E01005311,E01005312,E01005313,E01005314,E01005315,E01005316,E01005317,E01005318,E01005319,E01005320,E01005321,E01005322,E01005323,E01005324,E01005325,E01005326,E01005327,E01005328,E01005329,E01005330,E01005331,E01005332,E01005333,E01005334,E01005335,E01005336,E01005337,E01005338,E01005339,E01005340,E01005341,E01005342,E01005343,E01005344,E01005345,E01005346,E01005347,E01005348,E01005349,E01005350,E01005351,E01005352,E01005353,E01005354,E01005355,E01005356,E01005357,E01005358,E01005359,E01005360,E01005361,E01005362,E01005363,E01005364,E01005365,E01005366,E01005367,E01005368,E01005369,E01005370,E01005371,E01005372,E01005373,E01005374,E01005375,E01005376,E01005377,E01005378,E01005379,E01005380,E01005381,E01005382,E01005383,E01005386,E01005387,E01005388,E01005389,E01005390,E01005391,E01005392,E01005393,E01005394,E01005395,E01005396,E01005397,E01005398,E01005399,E01005400,E01005401,E01005402,E01005403,E01005406,E01005407,E01005408,E01005409,E01005410,E01005411,E01005412,E01005413,E01005414,E01005415,E01005416,E01005417,E01005418,E01005419,E01005420,E01005421,E01005422,E01005427,E01005428,E01005429,E01005430,E01005431,E01005432,E01005433,E01005434,E01005435,E01005436,E01005437,E01005438,E01005439,E01005440,E01005441,E01005442,E01005443,E01005444,E01005445,E01005446,E01005447,E01005448,E01005449,E01005450,E01005451,E01005452,E01005453,E01005454,E01005455,E01005456,E01005459,E01005460,E01005461,E01005462,E01005463,E01005464,E01005465,E01005466,E01005467,E01005468,E01005469,E01005470,E01005471,E01005472,E01005473,E01005474,E01005475,E01005476,E01005477,E01005478,E01005479,E01005480,E01005481,E01005482,E01005483,E01005484,E01005485,E01005486,E01005487,E01005488,E01005489,E01005490,E01005491,E01005492,E01005493,E01005494,E01005495,E01005496,E01005497,E01005498,E01005499,E01005500,E01005501,E01005502,E01005503,E01005504,E01005505,E01005506,E01005507,E01005508,E01005509,E01005510,E01005511,E01005512,E01005513,E01005514,E01005515,E01005516,E01005517,E01005518,E01005519,E01005520,E01005521,E01005522,E01005523,E01005524,E01005525,E01005526,E01005527,E01005528,E01005529,E01005530,E01005531,E01005532,E01005533,E01005534,E01005535,E01005536,E01005537,E01005538,E01005539,E01005540,E01005541,E01005542,E01005543,E01005544,E01005545,E01005546,E01005547,E01005548,E01005549,E01005550,E01005551,E01005552,E01005553,E01005554,E01005555,E01005556,E01005557,E01005558,E01005559,E01005560,E01005561,E01005562,E01005563,E01005564,E01005565,E01005566,E01005567,E01005568,E01005569,E01005570,E01005571,E01005572,E01005573,E01005574,E01005575,E01005576,E01005577,E01005578,E01005579,E01005580,E01005581,E01005583,E01005585,E01005586,E01005587,E01005588,E01005589,E01005590,E01005591,E01005592,E01005593,E01005594,E01005595,E01005596,E01005597,E01005598,E01005599,E01005600,E01005601,E01005602,E01005603,E01005604,E01005605,E01005610,E01005611,E01005612,E01005613,E01005614,E01005615,E01005616,E01005617,E01005618,E01005619,E01005620,E01005621,E01005622,E01005623,E01005624,E01005625,E01005626,E01005627,E01005628,E01005629,E01005630,E01005631,E01005632,E01005633,E01005634,E01005635,E01005636,E01005637,E01005638,E01005639,E01005640,E01005641,E01005642,E01005643,E01005644,E01005645,E01005646,E01005647,E01005648,E01005649,E01005650,E01005651,E01005652,E01005653,E01005654,E01005656,E01005657,E01005659,E01005660,E01005661,E01005662,E01005663,E01005664,E01005665,E01005667,E01005670,E01005671,E01005672,E01005673,E01005674,E01005676,E01005677,E01005678,E01005679,E01005680,E01005681,E01005682,E01005683,E01005686,E01005687,E01005688,E01005689,E01005690,E01005691,E01005692,E01005693,E01005694,E01005695,E01005696,E01005697,E01005698,E01005699,E01005700,E01005701,E01005702,E01005703,E01005704,E01005706,E01005707,E01005708,E01005709,E01005710,E01005711,E01005712,E01005713,E01005714,E01005715,E01005716,E01005717,E01005718,E01005719,E01005721,E01005722,E01005723,E01005724,E01005725,E01005726,E01005727,E01005728,E01005729,E01005730,E01005731,E01005732,E01005733,E01005734,E01005735,E01005736,E01005737,E01005738,E01005739,E01005740,E01005741,E01005742,E01005743,E01005744,E01005745,E01005746,E01005747,E01005748,E01005749,E01005750,E01005751,E01005752,E01005753,E01005754,E01005755,E01005756,E01005757,E01005759,E01005760,E01005761,E01005762,E01005763,E01005764,E01005765,E01005766,E01005767,E01005768,E01005769,E01005770,E01005771,E01005772,E01005773,E01005774,E01005775,E01005776,E01005777,E01005778,E01005779,E01005780,E01005781,E01005782,E01005783,E01005784,E01005785,E01005786,E01005787,E01005788,E01005789,E01005790,E01005791,E01005792,E01005793,E01005794,E01005795,E01005796,E01005797,E01005798,E01005799,E01005800,E01005801,E01005802,E01005803,E01005804,E01005805,E01005806,E01005807,E01005808,E01005809,E01005810,E01005811,E01005812,E01005813,E01005814,E01005815,E01005816&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_011.json.gz
+  content_sha256: 9543fc7aabcaff820d0c6eeb943204daa12e28d8594d8016cb20e64baa971d8a
+- id: census_lsoa_tenure5a_bedrooms_012
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 012
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01005817,E01005818,E01005819,E01005820,E01005821,E01005822,E01005823,E01005824,E01005825,E01005826,E01005827,E01005828,E01005829,E01005830,E01005831,E01005832,E01005833,E01005834,E01005835,E01005836,E01005837,E01005838,E01005839,E01005840,E01005841,E01005842,E01005843,E01005844,E01005845,E01005846,E01005847,E01005848,E01005849,E01005850,E01005851,E01005852,E01005853,E01005854,E01005855,E01005856,E01005857,E01005858,E01005859,E01005860,E01005861,E01005862,E01005863,E01005864,E01005865,E01005866,E01005867,E01005868,E01005869,E01005870,E01005871,E01005872,E01005873,E01005874,E01005875,E01005876,E01005877,E01005878,E01005879,E01005880,E01005881,E01005882,E01005883,E01005884,E01005885,E01005886,E01005887,E01005888,E01005889,E01005890,E01005891,E01005892,E01005893,E01005894,E01005895,E01005896,E01005897,E01005898,E01005899,E01005900,E01005901,E01005902,E01005903,E01005904,E01005905,E01005906,E01005907,E01005908,E01005909,E01005910,E01005911,E01005912,E01005913,E01005914,E01005915,E01005916,E01005917,E01005918,E01005919,E01005920,E01005921,E01005922,E01005923,E01005924,E01005925,E01005926,E01005927,E01005928,E01005929,E01005930,E01005931,E01005932,E01005933,E01005934,E01005935,E01005936,E01005937,E01005938,E01005939,E01005940,E01005941,E01005942,E01005943,E01005944,E01005945,E01005946,E01005947,E01005948,E01005949,E01005950,E01005951,E01005953,E01005954,E01005955,E01005956,E01005957,E01005958,E01005959,E01005960,E01005961,E01005962,E01005963,E01005964,E01005965,E01005966,E01005967,E01005968,E01005969,E01005970,E01005971,E01005972,E01005973,E01005974,E01005975,E01005976,E01005977,E01005978,E01005979,E01005980,E01005981,E01005982,E01005983,E01005984,E01005985,E01005986,E01005987,E01005988,E01005989,E01005990,E01005991,E01005992,E01005993,E01005994,E01005995,E01005996,E01005997,E01005998,E01005999,E01006000,E01006001,E01006002,E01006003,E01006004,E01006005,E01006006,E01006007,E01006008,E01006009,E01006010,E01006011,E01006012,E01006013,E01006014,E01006015,E01006016,E01006017,E01006018,E01006019,E01006020,E01006021,E01006022,E01006023,E01006024,E01006025,E01006026,E01006027,E01006028,E01006029,E01006030,E01006031,E01006032,E01006033,E01006034,E01006035,E01006036,E01006037,E01006038,E01006039,E01006040,E01006041,E01006042,E01006043,E01006044,E01006045,E01006046,E01006047,E01006048,E01006049,E01006050,E01006051,E01006052,E01006053,E01006054,E01006055,E01006056,E01006057,E01006058,E01006059,E01006060,E01006061,E01006062,E01006063,E01006064,E01006065,E01006066,E01006067,E01006068,E01006069,E01006070,E01006071,E01006072,E01006073,E01006074,E01006075,E01006076,E01006077,E01006078,E01006079,E01006080,E01006081,E01006082,E01006083,E01006084,E01006085,E01006086,E01006087,E01006088,E01006089,E01006090,E01006091,E01006092,E01006093,E01006094,E01006095,E01006096,E01006097,E01006098,E01006099,E01006100,E01006101,E01006102,E01006103,E01006104,E01006105,E01006106,E01006107,E01006108,E01006109,E01006110,E01006111,E01006112,E01006113,E01006114,E01006115,E01006116,E01006117,E01006118,E01006119,E01006120,E01006121,E01006122,E01006123,E01006124,E01006125,E01006126,E01006127,E01006128,E01006129,E01006130,E01006131,E01006132,E01006133,E01006134,E01006135,E01006136,E01006137,E01006138,E01006139,E01006140,E01006141,E01006142,E01006143,E01006144,E01006145,E01006146,E01006147,E01006148,E01006149,E01006150,E01006151,E01006152,E01006153,E01006154,E01006155,E01006156,E01006157,E01006158,E01006159,E01006160,E01006161,E01006162,E01006163,E01006164,E01006165,E01006166,E01006167,E01006168,E01006169,E01006170,E01006171,E01006172,E01006173,E01006174,E01006175,E01006176,E01006177,E01006178,E01006179,E01006180,E01006181,E01006182,E01006183,E01006184,E01006186,E01006187,E01006188,E01006189,E01006190,E01006191,E01006192,E01006193,E01006194,E01006195,E01006196,E01006197,E01006198,E01006199,E01006200,E01006201,E01006202,E01006203,E01006204,E01006205,E01006206,E01006207,E01006208,E01006209,E01006210,E01006211,E01006212,E01006213,E01006214,E01006215,E01006216,E01006217,E01006218,E01006219,E01006220,E01006221,E01006222,E01006223,E01006224,E01006225,E01006226,E01006227,E01006228,E01006229,E01006230,E01006231,E01006232,E01006233,E01006234,E01006235,E01006236,E01006237,E01006238,E01006239,E01006240,E01006241,E01006242,E01006243,E01006244,E01006245,E01006246,E01006247,E01006248,E01006249,E01006250,E01006251,E01006252,E01006253,E01006254,E01006255,E01006256,E01006257,E01006258,E01006259,E01006260,E01006261,E01006262,E01006263,E01006264,E01006265,E01006266,E01006267,E01006268,E01006269,E01006270,E01006271,E01006272,E01006273,E01006274,E01006275,E01006276,E01006277,E01006278,E01006279,E01006280,E01006281,E01006282,E01006283,E01006284,E01006285,E01006286,E01006287,E01006288,E01006289,E01006290,E01006291,E01006292,E01006293,E01006294,E01006295,E01006296,E01006297,E01006298,E01006299,E01006300,E01006301,E01006302,E01006303,E01006304,E01006305,E01006306,E01006307,E01006308,E01006309,E01006310,E01006311,E01006312,E01006313,E01006314,E01006315,E01006316,E01006317,E01006318&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_012.json.gz
+  content_sha256: d3821236bbdb8bfd3a97c066f949ff0f9b7e3a82bd0d462228f51518f57609f3
+- id: census_lsoa_tenure5a_bedrooms_013
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 013
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01006319,E01006320,E01006321,E01006322,E01006323,E01006324,E01006325,E01006326,E01006327,E01006328,E01006329,E01006330,E01006331,E01006332,E01006333,E01006334,E01006335,E01006336,E01006337,E01006338,E01006339,E01006340,E01006341,E01006342,E01006343,E01006344,E01006345,E01006346,E01006347,E01006348,E01006349,E01006350,E01006351,E01006352,E01006353,E01006354,E01006355,E01006356,E01006357,E01006358,E01006359,E01006360,E01006361,E01006362,E01006363,E01006364,E01006365,E01006366,E01006367,E01006368,E01006369,E01006370,E01006371,E01006372,E01006373,E01006374,E01006375,E01006376,E01006377,E01006378,E01006379,E01006380,E01006381,E01006382,E01006383,E01006384,E01006385,E01006386,E01006387,E01006388,E01006389,E01006390,E01006391,E01006392,E01006393,E01006394,E01006395,E01006396,E01006397,E01006398,E01006399,E01006400,E01006401,E01006402,E01006403,E01006404,E01006405,E01006406,E01006407,E01006408,E01006409,E01006410,E01006411,E01006412,E01006413,E01006414,E01006415,E01006416,E01006417,E01006418,E01006419,E01006420,E01006421,E01006422,E01006423,E01006424,E01006425,E01006426,E01006427,E01006428,E01006429,E01006430,E01006431,E01006432,E01006433,E01006434,E01006435,E01006436,E01006437,E01006438,E01006439,E01006440,E01006442,E01006443,E01006444,E01006445,E01006446,E01006447,E01006448,E01006449,E01006450,E01006451,E01006452,E01006453,E01006454,E01006455,E01006456,E01006457,E01006458,E01006459,E01006460,E01006461,E01006462,E01006464,E01006465,E01006466,E01006467,E01006470,E01006471,E01006472,E01006473,E01006474,E01006475,E01006476,E01006477,E01006478,E01006479,E01006480,E01006481,E01006482,E01006483,E01006484,E01006485,E01006486,E01006487,E01006488,E01006489,E01006490,E01006491,E01006492,E01006493,E01006494,E01006495,E01006496,E01006497,E01006498,E01006499,E01006500,E01006501,E01006502,E01006503,E01006504,E01006505,E01006506,E01006507,E01006508,E01006509,E01006510,E01006512,E01006514,E01006518,E01006519,E01006520,E01006521,E01006522,E01006523,E01006524,E01006525,E01006526,E01006527,E01006528,E01006529,E01006530,E01006531,E01006532,E01006533,E01006534,E01006535,E01006536,E01006537,E01006538,E01006539,E01006540,E01006541,E01006542,E01006543,E01006544,E01006545,E01006546,E01006547,E01006548,E01006549,E01006550,E01006551,E01006552,E01006553,E01006554,E01006556,E01006557,E01006558,E01006560,E01006562,E01006563,E01006564,E01006565,E01006566,E01006567,E01006568,E01006569,E01006570,E01006571,E01006572,E01006573,E01006574,E01006575,E01006576,E01006577,E01006578,E01006579,E01006580,E01006581,E01006582,E01006583,E01006584,E01006585,E01006586,E01006587,E01006588,E01006589,E01006590,E01006591,E01006592,E01006593,E01006594,E01006595,E01006596,E01006597,E01006598,E01006599,E01006600,E01006603,E01006604,E01006605,E01006606,E01006607,E01006608,E01006609,E01006610,E01006611,E01006612,E01006613,E01006614,E01006615,E01006616,E01006617,E01006618,E01006619,E01006620,E01006621,E01006622,E01006623,E01006624,E01006625,E01006626,E01006627,E01006628,E01006629,E01006630,E01006632,E01006633,E01006637,E01006638,E01006639,E01006640,E01006641,E01006642,E01006643,E01006644,E01006645,E01006646,E01006647,E01006648,E01006651,E01006652,E01006653,E01006654,E01006655,E01006656,E01006657,E01006658,E01006659,E01006660,E01006661,E01006662,E01006663,E01006664,E01006665,E01006666,E01006667,E01006668,E01006669,E01006670,E01006671,E01006672,E01006673,E01006674,E01006675,E01006676,E01006677,E01006678,E01006679,E01006680,E01006681,E01006682,E01006683,E01006684,E01006685,E01006686,E01006687,E01006688,E01006689,E01006690,E01006691,E01006692,E01006693,E01006694,E01006695,E01006696,E01006697,E01006698,E01006699,E01006700,E01006701,E01006702,E01006703,E01006705,E01006706,E01006707,E01006708,E01006709,E01006710,E01006711,E01006712,E01006713,E01006716,E01006717,E01006718,E01006719,E01006721,E01006722,E01006723,E01006724,E01006725,E01006726,E01006727,E01006728,E01006729,E01006730,E01006731,E01006732,E01006734,E01006735,E01006736,E01006737,E01006738,E01006739,E01006740,E01006741,E01006742,E01006743,E01006744,E01006745,E01006746,E01006747,E01006748,E01006751,E01006753,E01006754,E01006755,E01006756,E01006757,E01006758,E01006759,E01006760,E01006761,E01006762,E01006763,E01006764,E01006765,E01006766,E01006767,E01006768,E01006769,E01006770,E01006771,E01006772,E01006773,E01006774,E01006775,E01006776,E01006778,E01006779,E01006780,E01006781,E01006782,E01006783,E01006784,E01006785,E01006786,E01006787,E01006788,E01006790,E01006791,E01006792,E01006793,E01006794,E01006795,E01006796,E01006797,E01006798,E01006799,E01006800,E01006801,E01006802,E01006803,E01006804,E01006805,E01006806,E01006807,E01006808,E01006809,E01006810,E01006811,E01006812,E01006813,E01006814,E01006815,E01006816,E01006817,E01006818,E01006819,E01006820,E01006821,E01006822,E01006823,E01006824,E01006825,E01006826,E01006827,E01006828,E01006829,E01006830,E01006831,E01006832,E01006833,E01006834,E01006835,E01006836,E01006837,E01006838,E01006839,E01006840,E01006841,E01006842,E01006843,E01006844,E01006845,E01006846,E01006847,E01006848&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_013.json.gz
+  content_sha256: 9d09995d95ebde885e26ff7771c14d466e8b50785ea764cdd4b4fe4ccdb33d69
+- id: census_lsoa_tenure5a_bedrooms_014
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 014
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01006849,E01006850,E01006851,E01006852,E01006853,E01006854,E01006855,E01006856,E01006857,E01006858,E01006859,E01006860,E01006861,E01006862,E01006863,E01006864,E01006866,E01006867,E01006868,E01006869,E01006870,E01006871,E01006872,E01006873,E01006874,E01006875,E01006876,E01006877,E01006878,E01006879,E01006880,E01006881,E01006882,E01006883,E01006884,E01006885,E01006886,E01006887,E01006888,E01006889,E01006890,E01006891,E01006892,E01006893,E01006894,E01006895,E01006896,E01006897,E01006898,E01006899,E01006901,E01006903,E01006904,E01006905,E01006906,E01006907,E01006908,E01006909,E01006910,E01006911,E01006912,E01006913,E01006914,E01006915,E01006916,E01006917,E01006918,E01006919,E01006920,E01006921,E01006922,E01006923,E01006924,E01006925,E01006926,E01006927,E01006928,E01006929,E01006930,E01006931,E01006932,E01006933,E01006934,E01006935,E01006936,E01006937,E01006938,E01006939,E01006940,E01006941,E01006942,E01006943,E01006944,E01006945,E01006946,E01006947,E01006948,E01006949,E01006950,E01006951,E01006952,E01006953,E01006954,E01006955,E01006956,E01006957,E01006958,E01006959,E01006960,E01006961,E01006962,E01006963,E01006964,E01006965,E01006966,E01006967,E01006969,E01006970,E01006971,E01006972,E01006973,E01006974,E01006976,E01006977,E01006978,E01006979,E01006980,E01006981,E01006982,E01006983,E01006984,E01006985,E01006986,E01006987,E01006988,E01006989,E01006990,E01006991,E01006992,E01006993,E01006994,E01006995,E01006996,E01006997,E01006998,E01006999,E01007000,E01007001,E01007002,E01007003,E01007004,E01007005,E01007006,E01007007,E01007008,E01007009,E01007010,E01007011,E01007012,E01007013,E01007014,E01007016,E01007017,E01007019,E01007020,E01007021,E01007022,E01007023,E01007024,E01007025,E01007026,E01007027,E01007028,E01007029,E01007030,E01007031,E01007032,E01007033,E01007034,E01007035,E01007036,E01007037,E01007038,E01007039,E01007040,E01007041,E01007042,E01007043,E01007044,E01007045,E01007046,E01007047,E01007048,E01007049,E01007050,E01007051,E01007052,E01007053,E01007054,E01007055,E01007056,E01007057,E01007058,E01007059,E01007060,E01007061,E01007062,E01007063,E01007064,E01007065,E01007066,E01007067,E01007068,E01007069,E01007070,E01007071,E01007072,E01007073,E01007074,E01007075,E01007076,E01007077,E01007078,E01007079,E01007080,E01007081,E01007082,E01007083,E01007084,E01007085,E01007086,E01007087,E01007088,E01007089,E01007090,E01007091,E01007092,E01007095,E01007096,E01007097,E01007098,E01007099,E01007100,E01007101,E01007102,E01007103,E01007104,E01007105,E01007106,E01007107,E01007108,E01007109,E01007110,E01007111,E01007112,E01007113,E01007114,E01007115,E01007116,E01007117,E01007118,E01007119,E01007120,E01007121,E01007122,E01007123,E01007124,E01007125,E01007127,E01007128,E01007129,E01007130,E01007131,E01007133,E01007135,E01007136,E01007137,E01007138,E01007139,E01007141,E01007142,E01007143,E01007144,E01007145,E01007146,E01007147,E01007148,E01007149,E01007150,E01007151,E01007152,E01007153,E01007154,E01007155,E01007156,E01007157,E01007158,E01007159,E01007160,E01007161,E01007162,E01007163,E01007164,E01007165,E01007166,E01007167,E01007168,E01007169,E01007170,E01007171,E01007172,E01007173,E01007174,E01007175,E01007176,E01007177,E01007178,E01007179,E01007180,E01007181,E01007182,E01007183,E01007184,E01007185,E01007186,E01007187,E01007188,E01007189,E01007190,E01007191,E01007192,E01007193,E01007194,E01007195,E01007196,E01007197,E01007198,E01007199,E01007200,E01007201,E01007202,E01007203,E01007204,E01007205,E01007206,E01007207,E01007208,E01007209,E01007210,E01007211,E01007212,E01007213,E01007214,E01007215,E01007216,E01007217,E01007218,E01007219,E01007220,E01007221,E01007222,E01007223,E01007224,E01007225,E01007226,E01007227,E01007228,E01007229,E01007230,E01007231,E01007232,E01007233,E01007234,E01007235,E01007236,E01007237,E01007238,E01007239,E01007240,E01007241,E01007242,E01007243,E01007244,E01007245,E01007246,E01007247,E01007248,E01007249,E01007250,E01007251,E01007252,E01007253,E01007254,E01007255,E01007256,E01007257,E01007258,E01007259,E01007260,E01007261,E01007262,E01007263,E01007264,E01007265,E01007266,E01007267,E01007268,E01007269,E01007270,E01007271,E01007272,E01007273,E01007274,E01007275,E01007276,E01007277,E01007278,E01007279,E01007280,E01007281,E01007282,E01007283,E01007284,E01007285,E01007286,E01007287,E01007288,E01007289,E01007290,E01007291,E01007292,E01007293,E01007294,E01007295,E01007296,E01007297,E01007298,E01007299,E01007300,E01007301,E01007302,E01007303,E01007304,E01007305,E01007306,E01007307,E01007308,E01007309,E01007310,E01007311,E01007312,E01007313,E01007314,E01007315,E01007316,E01007317,E01007318,E01007319,E01007320,E01007321,E01007322,E01007323,E01007324,E01007325,E01007326,E01007327,E01007328,E01007329,E01007330,E01007331,E01007332,E01007333,E01007334,E01007335,E01007336,E01007337,E01007338,E01007339,E01007340,E01007341,E01007342,E01007343,E01007344,E01007345,E01007346,E01007347,E01007348,E01007349,E01007350,E01007351,E01007352,E01007353,E01007354,E01007355,E01007356,E01007357,E01007358,E01007359,E01007360,E01007361&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_014.json.gz
+  content_sha256: a2c2b270bb056313d216efe12af93fde3ba5c7fd547ebce7d31ced4cd45cca18
+- id: census_lsoa_tenure5a_bedrooms_015
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 015
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01007362,E01007363,E01007364,E01007365,E01007366,E01007367,E01007368,E01007369,E01007370,E01007371,E01007372,E01007373,E01007374,E01007375,E01007376,E01007377,E01007378,E01007379,E01007380,E01007381,E01007382,E01007383,E01007384,E01007385,E01007386,E01007387,E01007388,E01007389,E01007390,E01007391,E01007392,E01007393,E01007394,E01007395,E01007396,E01007397,E01007398,E01007399,E01007400,E01007402,E01007403,E01007404,E01007405,E01007406,E01007408,E01007409,E01007410,E01007411,E01007413,E01007414,E01007415,E01007416,E01007417,E01007418,E01007419,E01007420,E01007421,E01007422,E01007423,E01007424,E01007425,E01007426,E01007427,E01007428,E01007429,E01007430,E01007431,E01007432,E01007433,E01007434,E01007435,E01007436,E01007437,E01007438,E01007439,E01007440,E01007441,E01007442,E01007443,E01007444,E01007445,E01007446,E01007447,E01007448,E01007449,E01007450,E01007451,E01007452,E01007453,E01007455,E01007456,E01007457,E01007458,E01007459,E01007460,E01007461,E01007462,E01007463,E01007464,E01007465,E01007466,E01007467,E01007468,E01007469,E01007470,E01007471,E01007472,E01007473,E01007474,E01007475,E01007476,E01007477,E01007478,E01007479,E01007480,E01007482,E01007483,E01007484,E01007485,E01007486,E01007487,E01007488,E01007489,E01007490,E01007491,E01007492,E01007493,E01007494,E01007495,E01007496,E01007497,E01007498,E01007499,E01007500,E01007501,E01007503,E01007504,E01007505,E01007506,E01007507,E01007508,E01007509,E01007510,E01007511,E01007512,E01007513,E01007514,E01007515,E01007516,E01007517,E01007518,E01007519,E01007520,E01007521,E01007522,E01007523,E01007524,E01007525,E01007526,E01007528,E01007529,E01007530,E01007531,E01007532,E01007533,E01007534,E01007535,E01007536,E01007537,E01007538,E01007539,E01007540,E01007541,E01007542,E01007543,E01007544,E01007545,E01007546,E01007547,E01007548,E01007549,E01007550,E01007551,E01007552,E01007553,E01007554,E01007555,E01007556,E01007557,E01007558,E01007559,E01007560,E01007561,E01007562,E01007563,E01007564,E01007565,E01007566,E01007567,E01007568,E01007569,E01007570,E01007571,E01007572,E01007573,E01007574,E01007575,E01007576,E01007577,E01007578,E01007579,E01007580,E01007581,E01007582,E01007583,E01007584,E01007585,E01007586,E01007587,E01007588,E01007589,E01007590,E01007591,E01007592,E01007593,E01007594,E01007595,E01007596,E01007597,E01007598,E01007599,E01007600,E01007601,E01007602,E01007603,E01007604,E01007605,E01007606,E01007607,E01007609,E01007610,E01007611,E01007612,E01007613,E01007614,E01007615,E01007616,E01007617,E01007618,E01007619,E01007620,E01007621,E01007622,E01007623,E01007624,E01007625,E01007626,E01007627,E01007628,E01007629,E01007630,E01007631,E01007632,E01007633,E01007634,E01007635,E01007636,E01007637,E01007638,E01007639,E01007640,E01007641,E01007642,E01007643,E01007644,E01007646,E01007647,E01007649,E01007650,E01007651,E01007652,E01007653,E01007654,E01007655,E01007656,E01007657,E01007658,E01007659,E01007660,E01007661,E01007662,E01007663,E01007664,E01007666,E01007667,E01007668,E01007669,E01007670,E01007671,E01007672,E01007673,E01007674,E01007675,E01007676,E01007677,E01007678,E01007679,E01007680,E01007681,E01007682,E01007683,E01007684,E01007685,E01007686,E01007687,E01007688,E01007689,E01007690,E01007691,E01007692,E01007694,E01007695,E01007696,E01007697,E01007698,E01007699,E01007700,E01007701,E01007702,E01007703,E01007704,E01007705,E01007706,E01007707,E01007708,E01007709,E01007710,E01007711,E01007712,E01007713,E01007714,E01007715,E01007716,E01007717,E01007718,E01007719,E01007720,E01007721,E01007723,E01007724,E01007725,E01007726,E01007727,E01007728,E01007729,E01007730,E01007731,E01007732,E01007733,E01007734,E01007735,E01007736,E01007737,E01007738,E01007739,E01007740,E01007741,E01007742,E01007743,E01007744,E01007745,E01007746,E01007747,E01007748,E01007749,E01007750,E01007751,E01007752,E01007753,E01007754,E01007755,E01007756,E01007757,E01007758,E01007759,E01007760,E01007761,E01007762,E01007763,E01007764,E01007765,E01007766,E01007767,E01007768,E01007769,E01007770,E01007771,E01007772,E01007773,E01007774,E01007775,E01007776,E01007777,E01007778,E01007779,E01007780,E01007781,E01007782,E01007783,E01007784,E01007785,E01007786,E01007788,E01007789,E01007790,E01007791,E01007792,E01007793,E01007794,E01007795,E01007796,E01007797,E01007798,E01007799,E01007800,E01007801,E01007802,E01007803,E01007804,E01007805,E01007806,E01007807,E01007808,E01007809,E01007810,E01007811,E01007812,E01007815,E01007816,E01007817,E01007818,E01007819,E01007820,E01007821,E01007822,E01007823,E01007824,E01007825,E01007826,E01007827,E01007828,E01007829,E01007830,E01007831,E01007832,E01007833,E01007834,E01007835,E01007836,E01007838,E01007839,E01007841,E01007842,E01007843,E01007846,E01007847,E01007848,E01007849,E01007850,E01007851,E01007852,E01007853,E01007854,E01007855,E01007856,E01007857,E01007858,E01007861,E01007862,E01007863,E01007864,E01007865,E01007866,E01007867,E01007868,E01007869,E01007870,E01007871,E01007872,E01007873,E01007874,E01007875,E01007878,E01007879,E01007881,E01007882,E01007883,E01007885,E01007886,E01007887&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_015.json.gz
+  content_sha256: ff8597c95828e7376ef99d0726a528ccec1463ad5640314841c72f8f08a5ea0f
+- id: census_lsoa_tenure5a_bedrooms_016
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 016
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01007888,E01007889,E01007890,E01007891,E01007892,E01007893,E01007894,E01007895,E01007896,E01007897,E01007898,E01007899,E01007900,E01007901,E01007902,E01007903,E01007904,E01007905,E01007906,E01007907,E01007908,E01007909,E01007910,E01007911,E01007912,E01007913,E01007914,E01007915,E01007916,E01007917,E01007918,E01007919,E01007920,E01007921,E01007922,E01007923,E01007924,E01007925,E01007926,E01007927,E01007928,E01007929,E01007930,E01007931,E01007932,E01007933,E01007934,E01007935,E01007936,E01007937,E01007938,E01007939,E01007940,E01007941,E01007942,E01007943,E01007945,E01007946,E01007947,E01007948,E01007949,E01007950,E01007951,E01007952,E01007953,E01007954,E01007955,E01007956,E01007957,E01007958,E01007959,E01007960,E01007961,E01007962,E01007963,E01007964,E01007965,E01007966,E01007967,E01007968,E01007969,E01007970,E01007971,E01007972,E01007973,E01007974,E01007975,E01007976,E01007977,E01007978,E01007979,E01007980,E01007981,E01007982,E01007983,E01007984,E01007985,E01007986,E01007987,E01007988,E01007989,E01007990,E01007991,E01007992,E01007993,E01007994,E01007995,E01007996,E01007997,E01007998,E01007999,E01008000,E01008001,E01008002,E01008003,E01008004,E01008005,E01008006,E01008007,E01008008,E01008009,E01008010,E01008011,E01008012,E01008013,E01008014,E01008015,E01008016,E01008017,E01008018,E01008019,E01008020,E01008021,E01008022,E01008023,E01008024,E01008025,E01008027,E01008028,E01008029,E01008030,E01008031,E01008032,E01008033,E01008034,E01008035,E01008036,E01008037,E01008038,E01008039,E01008040,E01008041,E01008042,E01008043,E01008044,E01008045,E01008046,E01008047,E01008048,E01008049,E01008050,E01008051,E01008052,E01008053,E01008054,E01008055,E01008056,E01008057,E01008058,E01008059,E01008060,E01008061,E01008062,E01008063,E01008064,E01008065,E01008066,E01008067,E01008068,E01008069,E01008072,E01008073,E01008074,E01008075,E01008076,E01008077,E01008078,E01008079,E01008080,E01008081,E01008082,E01008083,E01008084,E01008085,E01008086,E01008087,E01008088,E01008089,E01008090,E01008091,E01008092,E01008093,E01008094,E01008095,E01008096,E01008097,E01008098,E01008099,E01008100,E01008101,E01008102,E01008103,E01008104,E01008105,E01008107,E01008108,E01008109,E01008111,E01008112,E01008113,E01008114,E01008115,E01008116,E01008117,E01008118,E01008119,E01008122,E01008123,E01008124,E01008125,E01008126,E01008128,E01008129,E01008130,E01008131,E01008132,E01008133,E01008134,E01008135,E01008136,E01008137,E01008138,E01008139,E01008140,E01008141,E01008142,E01008143,E01008144,E01008145,E01008146,E01008147,E01008148,E01008149,E01008150,E01008151,E01008152,E01008153,E01008154,E01008155,E01008156,E01008157,E01008158,E01008159,E01008160,E01008161,E01008162,E01008163,E01008164,E01008165,E01008166,E01008167,E01008170,E01008172,E01008173,E01008174,E01008175,E01008176,E01008177,E01008178,E01008179,E01008180,E01008181,E01008182,E01008183,E01008184,E01008185,E01008186,E01008188,E01008189,E01008190,E01008191,E01008192,E01008193,E01008194,E01008195,E01008196,E01008197,E01008198,E01008199,E01008200,E01008201,E01008202,E01008203,E01008204,E01008205,E01008206,E01008207,E01008208,E01008209,E01008210,E01008211,E01008212,E01008213,E01008214,E01008215,E01008216,E01008217,E01008218,E01008219,E01008220,E01008221,E01008222,E01008223,E01008224,E01008225,E01008226,E01008227,E01008228,E01008229,E01008230,E01008231,E01008232,E01008233,E01008234,E01008235,E01008236,E01008237,E01008238,E01008239,E01008240,E01008241,E01008242,E01008243,E01008244,E01008245,E01008246,E01008247,E01008248,E01008249,E01008250,E01008251,E01008252,E01008253,E01008254,E01008255,E01008256,E01008257,E01008258,E01008259,E01008260,E01008261,E01008262,E01008263,E01008264,E01008265,E01008266,E01008267,E01008268,E01008269,E01008270,E01008271,E01008272,E01008273,E01008274,E01008275,E01008276,E01008277,E01008278,E01008279,E01008280,E01008281,E01008282,E01008283,E01008284,E01008285,E01008286,E01008287,E01008288,E01008289,E01008290,E01008291,E01008292,E01008293,E01008294,E01008295,E01008296,E01008297,E01008298,E01008299,E01008300,E01008301,E01008302,E01008303,E01008305,E01008306,E01008307,E01008309,E01008311,E01008312,E01008313,E01008314,E01008315,E01008316,E01008317,E01008318,E01008319,E01008320,E01008321,E01008322,E01008323,E01008324,E01008325,E01008326,E01008327,E01008328,E01008329,E01008330,E01008331,E01008333,E01008334,E01008335,E01008336,E01008337,E01008338,E01008339,E01008340,E01008341,E01008342,E01008343,E01008344,E01008345,E01008346,E01008347,E01008348,E01008349,E01008350,E01008351,E01008352,E01008353,E01008354,E01008355,E01008356,E01008357,E01008358,E01008359,E01008360,E01008361,E01008362,E01008363,E01008364,E01008365,E01008366,E01008367,E01008368,E01008369,E01008370,E01008371,E01008372,E01008373,E01008374,E01008375,E01008376,E01008377,E01008378,E01008379,E01008380,E01008381,E01008382,E01008383,E01008384,E01008385,E01008386,E01008387,E01008388,E01008389,E01008390,E01008391,E01008392,E01008396,E01008397,E01008398,E01008399,E01008400,E01008401,E01008402,E01008403,E01008404,E01008405,E01008406,E01008407&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_016.json.gz
+  content_sha256: 3593580efd7daa4b0443eaf636a9561a7cf930836b9649a086bc167ae3dd8359
+- id: census_lsoa_tenure5a_bedrooms_017
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 017
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01008409,E01008410,E01008411,E01008412,E01008413,E01008415,E01008416,E01008418,E01008419,E01008420,E01008421,E01008422,E01008423,E01008424,E01008425,E01008426,E01008427,E01008428,E01008429,E01008430,E01008431,E01008432,E01008433,E01008434,E01008435,E01008436,E01008437,E01008438,E01008439,E01008441,E01008442,E01008443,E01008444,E01008445,E01008446,E01008447,E01008448,E01008449,E01008450,E01008451,E01008452,E01008453,E01008455,E01008456,E01008457,E01008458,E01008459,E01008460,E01008461,E01008462,E01008463,E01008464,E01008465,E01008466,E01008467,E01008468,E01008469,E01008470,E01008471,E01008472,E01008473,E01008474,E01008475,E01008476,E01008477,E01008478,E01008479,E01008480,E01008481,E01008482,E01008483,E01008484,E01008485,E01008486,E01008487,E01008488,E01008489,E01008490,E01008491,E01008492,E01008493,E01008494,E01008495,E01008496,E01008497,E01008498,E01008499,E01008500,E01008502,E01008503,E01008504,E01008505,E01008506,E01008507,E01008508,E01008509,E01008510,E01008511,E01008512,E01008513,E01008514,E01008516,E01008517,E01008518,E01008519,E01008520,E01008521,E01008522,E01008523,E01008524,E01008525,E01008526,E01008527,E01008528,E01008529,E01008530,E01008531,E01008532,E01008533,E01008534,E01008535,E01008536,E01008537,E01008538,E01008539,E01008540,E01008541,E01008542,E01008543,E01008544,E01008545,E01008546,E01008547,E01008548,E01008549,E01008550,E01008551,E01008552,E01008553,E01008554,E01008555,E01008556,E01008557,E01008558,E01008559,E01008560,E01008561,E01008562,E01008563,E01008564,E01008567,E01008568,E01008569,E01008570,E01008571,E01008572,E01008573,E01008574,E01008575,E01008576,E01008577,E01008578,E01008579,E01008580,E01008581,E01008582,E01008583,E01008584,E01008585,E01008586,E01008587,E01008588,E01008589,E01008590,E01008591,E01008592,E01008593,E01008594,E01008595,E01008596,E01008597,E01008598,E01008599,E01008600,E01008601,E01008602,E01008603,E01008604,E01008605,E01008606,E01008607,E01008608,E01008609,E01008610,E01008611,E01008612,E01008613,E01008614,E01008615,E01008616,E01008617,E01008618,E01008619,E01008620,E01008621,E01008622,E01008624,E01008625,E01008627,E01008628,E01008629,E01008630,E01008631,E01008632,E01008633,E01008634,E01008635,E01008636,E01008637,E01008638,E01008639,E01008640,E01008641,E01008642,E01008643,E01008644,E01008645,E01008646,E01008647,E01008648,E01008649,E01008650,E01008651,E01008652,E01008653,E01008654,E01008655,E01008656,E01008657,E01008658,E01008659,E01008660,E01008661,E01008662,E01008663,E01008664,E01008665,E01008666,E01008667,E01008668,E01008669,E01008670,E01008671,E01008672,E01008673,E01008674,E01008675,E01008676,E01008677,E01008678,E01008679,E01008680,E01008681,E01008682,E01008683,E01008684,E01008685,E01008686,E01008687,E01008688,E01008689,E01008690,E01008691,E01008692,E01008693,E01008694,E01008695,E01008696,E01008697,E01008698,E01008699,E01008700,E01008701,E01008702,E01008703,E01008704,E01008705,E01008706,E01008707,E01008708,E01008709,E01008711,E01008712,E01008713,E01008714,E01008715,E01008716,E01008717,E01008718,E01008719,E01008720,E01008721,E01008722,E01008723,E01008724,E01008725,E01008726,E01008727,E01008728,E01008730,E01008732,E01008733,E01008734,E01008735,E01008736,E01008737,E01008738,E01008739,E01008740,E01008741,E01008742,E01008743,E01008744,E01008745,E01008746,E01008747,E01008748,E01008749,E01008750,E01008751,E01008752,E01008753,E01008754,E01008755,E01008756,E01008757,E01008758,E01008759,E01008760,E01008761,E01008762,E01008763,E01008764,E01008765,E01008766,E01008767,E01008768,E01008769,E01008770,E01008771,E01008772,E01008773,E01008774,E01008775,E01008776,E01008777,E01008778,E01008779,E01008780,E01008781,E01008782,E01008783,E01008784,E01008785,E01008786,E01008787,E01008788,E01008789,E01008790,E01008791,E01008792,E01008793,E01008795,E01008796,E01008799,E01008800,E01008801,E01008802,E01008803,E01008804,E01008805,E01008808,E01008809,E01008810,E01008811,E01008812,E01008813,E01008814,E01008817,E01008819,E01008821,E01008822,E01008823,E01008824,E01008825,E01008826,E01008827,E01008828,E01008829,E01008830,E01008831,E01008832,E01008833,E01008834,E01008835,E01008836,E01008837,E01008838,E01008839,E01008840,E01008841,E01008842,E01008843,E01008844,E01008845,E01008846,E01008847,E01008848,E01008849,E01008851,E01008852,E01008853,E01008854,E01008855,E01008856,E01008857,E01008858,E01008859,E01008860,E01008861,E01008862,E01008863,E01008864,E01008865,E01008866,E01008867,E01008868,E01008869,E01008870,E01008871,E01008872,E01008873,E01008874,E01008875,E01008876,E01008877,E01008878,E01008879,E01008880,E01008881,E01008882,E01008883,E01008884,E01008885,E01008886,E01008887,E01008888,E01008889,E01008890,E01008891,E01008892,E01008893,E01008894,E01008895,E01008896,E01008897,E01008898,E01008899,E01008901,E01008905,E01008906,E01008907,E01008909,E01008910,E01008911,E01008913,E01008915,E01008916,E01008917,E01008918,E01008919,E01008920,E01008921,E01008923,E01008924,E01008925,E01008927,E01008928,E01008929,E01008930,E01008931,E01008932,E01008933,E01008934,E01008935,E01008936,E01008937,E01008938,E01008939,E01008940&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_017.json.gz
+  content_sha256: 2735279c62a9cbaacd0fa27b5de7dbd010881e5e985f00689aadd05842c9e897
+- id: census_lsoa_tenure5a_bedrooms_018
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 018
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01008941,E01008942,E01008943,E01008944,E01008945,E01008946,E01008947,E01008948,E01008949,E01008950,E01008951,E01008952,E01008953,E01008954,E01008955,E01008956,E01008957,E01008958,E01008959,E01008960,E01008961,E01008962,E01008963,E01008964,E01008965,E01008966,E01008967,E01008968,E01008969,E01008970,E01008971,E01008972,E01008973,E01008974,E01008975,E01008976,E01008977,E01008978,E01008979,E01008980,E01008981,E01008982,E01008984,E01008986,E01008987,E01008988,E01008989,E01008990,E01008991,E01008992,E01008994,E01008995,E01008996,E01008997,E01008998,E01008999,E01009000,E01009001,E01009002,E01009003,E01009005,E01009006,E01009007,E01009008,E01009009,E01009011,E01009012,E01009013,E01009014,E01009015,E01009016,E01009017,E01009018,E01009019,E01009020,E01009021,E01009022,E01009023,E01009024,E01009025,E01009026,E01009028,E01009029,E01009030,E01009031,E01009032,E01009033,E01009034,E01009035,E01009036,E01009037,E01009039,E01009040,E01009041,E01009042,E01009043,E01009044,E01009045,E01009046,E01009047,E01009048,E01009049,E01009050,E01009051,E01009053,E01009054,E01009056,E01009057,E01009058,E01009059,E01009060,E01009061,E01009062,E01009064,E01009065,E01009066,E01009067,E01009068,E01009069,E01009070,E01009071,E01009072,E01009073,E01009074,E01009075,E01009077,E01009078,E01009079,E01009080,E01009081,E01009082,E01009083,E01009084,E01009085,E01009086,E01009087,E01009088,E01009089,E01009090,E01009091,E01009092,E01009093,E01009094,E01009095,E01009096,E01009097,E01009098,E01009099,E01009100,E01009101,E01009102,E01009103,E01009104,E01009105,E01009106,E01009107,E01009108,E01009109,E01009110,E01009111,E01009112,E01009113,E01009114,E01009115,E01009116,E01009117,E01009118,E01009119,E01009120,E01009122,E01009123,E01009124,E01009125,E01009126,E01009127,E01009128,E01009129,E01009130,E01009131,E01009132,E01009133,E01009134,E01009135,E01009136,E01009137,E01009138,E01009139,E01009140,E01009141,E01009143,E01009145,E01009146,E01009147,E01009151,E01009152,E01009153,E01009155,E01009157,E01009158,E01009159,E01009160,E01009161,E01009162,E01009164,E01009165,E01009166,E01009167,E01009168,E01009169,E01009170,E01009171,E01009172,E01009173,E01009174,E01009175,E01009176,E01009177,E01009178,E01009179,E01009182,E01009183,E01009184,E01009185,E01009186,E01009187,E01009188,E01009189,E01009192,E01009195,E01009197,E01009199,E01009200,E01009201,E01009202,E01009204,E01009205,E01009206,E01009207,E01009208,E01009209,E01009210,E01009211,E01009212,E01009213,E01009214,E01009215,E01009216,E01009217,E01009218,E01009219,E01009220,E01009221,E01009222,E01009223,E01009224,E01009225,E01009226,E01009227,E01009228,E01009229,E01009230,E01009231,E01009232,E01009233,E01009234,E01009235,E01009236,E01009237,E01009238,E01009239,E01009240,E01009241,E01009242,E01009243,E01009244,E01009245,E01009246,E01009247,E01009248,E01009249,E01009250,E01009251,E01009252,E01009253,E01009254,E01009255,E01009256,E01009257,E01009258,E01009259,E01009260,E01009261,E01009262,E01009263,E01009264,E01009265,E01009266,E01009267,E01009268,E01009269,E01009270,E01009271,E01009272,E01009273,E01009274,E01009275,E01009276,E01009278,E01009279,E01009280,E01009281,E01009282,E01009283,E01009284,E01009286,E01009288,E01009289,E01009290,E01009291,E01009292,E01009293,E01009294,E01009295,E01009296,E01009297,E01009298,E01009299,E01009300,E01009301,E01009302,E01009303,E01009304,E01009305,E01009306,E01009307,E01009308,E01009309,E01009310,E01009311,E01009312,E01009313,E01009314,E01009315,E01009316,E01009317,E01009318,E01009319,E01009320,E01009321,E01009322,E01009323,E01009324,E01009325,E01009326,E01009327,E01009328,E01009329,E01009331,E01009332,E01009333,E01009334,E01009335,E01009337,E01009338,E01009339,E01009341,E01009342,E01009343,E01009344,E01009345,E01009346,E01009347,E01009348,E01009349,E01009350,E01009351,E01009352,E01009353,E01009354,E01009355,E01009358,E01009359,E01009360,E01009361,E01009362,E01009363,E01009364,E01009365,E01009366,E01009367,E01009368,E01009371,E01009372,E01009373,E01009374,E01009375,E01009376,E01009377,E01009379,E01009380,E01009382,E01009383,E01009384,E01009385,E01009389,E01009390,E01009391,E01009392,E01009393,E01009394,E01009395,E01009396,E01009397,E01009399,E01009400,E01009401,E01009403,E01009404,E01009405,E01009406,E01009407,E01009408,E01009409,E01009410,E01009411,E01009412,E01009413,E01009414,E01009415,E01009416,E01009417,E01009418,E01009419,E01009420,E01009421,E01009422,E01009423,E01009424,E01009425,E01009426,E01009427,E01009428,E01009429,E01009430,E01009431,E01009432,E01009433,E01009434,E01009435,E01009436,E01009437,E01009438,E01009439,E01009440,E01009441,E01009442,E01009443,E01009444,E01009445,E01009446,E01009447,E01009448,E01009449,E01009450,E01009451,E01009452,E01009453,E01009454,E01009455,E01009456,E01009457,E01009458,E01009459,E01009460,E01009461,E01009462,E01009463,E01009464,E01009465,E01009466,E01009467,E01009468,E01009469,E01009470,E01009471,E01009472,E01009473,E01009474,E01009475,E01009476,E01009477,E01009478,E01009479,E01009480,E01009481,E01009482,E01009483,E01009484,E01009485,E01009486&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_018.json.gz
+  content_sha256: 6ab662cda6344f8b67014580148f51e5eb3e3c1b7638b9a2a8a0314940d3526d
+- id: census_lsoa_tenure5a_bedrooms_019
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 019
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01009487,E01009488,E01009489,E01009490,E01009491,E01009492,E01009493,E01009494,E01009495,E01009496,E01009497,E01009498,E01009499,E01009500,E01009501,E01009502,E01009503,E01009504,E01009505,E01009506,E01009507,E01009508,E01009509,E01009510,E01009511,E01009512,E01009513,E01009514,E01009515,E01009516,E01009517,E01009518,E01009519,E01009520,E01009521,E01009522,E01009523,E01009524,E01009525,E01009526,E01009527,E01009528,E01009529,E01009530,E01009531,E01009532,E01009535,E01009536,E01009537,E01009538,E01009539,E01009540,E01009541,E01009542,E01009543,E01009544,E01009548,E01009549,E01009550,E01009552,E01009553,E01009554,E01009555,E01009556,E01009557,E01009558,E01009559,E01009560,E01009561,E01009562,E01009563,E01009564,E01009565,E01009566,E01009567,E01009568,E01009569,E01009570,E01009571,E01009573,E01009574,E01009575,E01009576,E01009577,E01009578,E01009579,E01009580,E01009581,E01009582,E01009583,E01009584,E01009585,E01009586,E01009587,E01009588,E01009589,E01009590,E01009591,E01009592,E01009593,E01009594,E01009596,E01009597,E01009598,E01009599,E01009604,E01009605,E01009606,E01009607,E01009608,E01009609,E01009610,E01009612,E01009613,E01009614,E01009615,E01009616,E01009617,E01009618,E01009620,E01009621,E01009622,E01009623,E01009624,E01009625,E01009626,E01009627,E01009628,E01009630,E01009631,E01009632,E01009633,E01009634,E01009636,E01009637,E01009638,E01009639,E01009640,E01009641,E01009643,E01009644,E01009645,E01009646,E01009647,E01009648,E01009649,E01009650,E01009651,E01009652,E01009653,E01009654,E01009655,E01009656,E01009657,E01009658,E01009659,E01009660,E01009661,E01009662,E01009663,E01009664,E01009665,E01009666,E01009667,E01009668,E01009669,E01009670,E01009671,E01009672,E01009674,E01009676,E01009679,E01009681,E01009682,E01009683,E01009684,E01009685,E01009686,E01009687,E01009688,E01009689,E01009690,E01009691,E01009692,E01009693,E01009694,E01009695,E01009696,E01009697,E01009698,E01009699,E01009700,E01009701,E01009702,E01009704,E01009705,E01009706,E01009707,E01009708,E01009709,E01009710,E01009711,E01009712,E01009713,E01009714,E01009715,E01009716,E01009717,E01009718,E01009719,E01009720,E01009721,E01009722,E01009723,E01009724,E01009725,E01009726,E01009727,E01009728,E01009729,E01009730,E01009731,E01009732,E01009733,E01009734,E01009735,E01009736,E01009737,E01009738,E01009739,E01009740,E01009741,E01009742,E01009743,E01009744,E01009745,E01009746,E01009747,E01009748,E01009749,E01009750,E01009751,E01009752,E01009753,E01009754,E01009756,E01009757,E01009758,E01009759,E01009760,E01009762,E01009763,E01009764,E01009765,E01009766,E01009767,E01009768,E01009769,E01009770,E01009771,E01009772,E01009773,E01009774,E01009775,E01009776,E01009777,E01009778,E01009779,E01009780,E01009781,E01009782,E01009783,E01009784,E01009785,E01009786,E01009787,E01009788,E01009789,E01009790,E01009791,E01009792,E01009793,E01009794,E01009795,E01009796,E01009797,E01009798,E01009799,E01009800,E01009801,E01009802,E01009803,E01009804,E01009805,E01009806,E01009807,E01009808,E01009809,E01009810,E01009811,E01009812,E01009813,E01009814,E01009815,E01009816,E01009817,E01009818,E01009819,E01009820,E01009821,E01009822,E01009823,E01009824,E01009825,E01009826,E01009827,E01009828,E01009829,E01009830,E01009831,E01009832,E01009833,E01009834,E01009835,E01009836,E01009837,E01009838,E01009839,E01009840,E01009841,E01009842,E01009843,E01009844,E01009845,E01009846,E01009847,E01009848,E01009849,E01009850,E01009851,E01009852,E01009853,E01009854,E01009855,E01009856,E01009857,E01009858,E01009859,E01009860,E01009861,E01009862,E01009863,E01009864,E01009865,E01009866,E01009867,E01009868,E01009869,E01009870,E01009871,E01009872,E01009873,E01009874,E01009875,E01009876,E01009877,E01009878,E01009879,E01009880,E01009882,E01009883,E01009884,E01009885,E01009886,E01009887,E01009888,E01009889,E01009890,E01009891,E01009892,E01009893,E01009894,E01009895,E01009896,E01009897,E01009898,E01009899,E01009900,E01009901,E01009902,E01009903,E01009904,E01009905,E01009906,E01009907,E01009908,E01009909,E01009910,E01009911,E01009912,E01009913,E01009914,E01009915,E01009916,E01009917,E01009918,E01009919,E01009920,E01009921,E01009922,E01009923,E01009924,E01009925,E01009926,E01009927,E01009928,E01009929,E01009930,E01009931,E01009932,E01009933,E01009934,E01009935,E01009936,E01009937,E01009938,E01009939,E01009940,E01009941,E01009942,E01009943,E01009944,E01009946,E01009947,E01009948,E01009949,E01009950,E01009951,E01009952,E01009953,E01009954,E01009955,E01009956,E01009957,E01009958,E01009959,E01009960,E01009961,E01009962,E01009963,E01009964,E01009965,E01009966,E01009967,E01009968,E01009969,E01009970,E01009971,E01009972,E01009973,E01009974,E01009975,E01009976,E01009977,E01009978,E01009979,E01009980,E01009981,E01009982,E01009983,E01009985,E01009986,E01009987,E01009988,E01009989,E01009991,E01009992,E01009993,E01009994,E01009995,E01009996,E01009997,E01009998,E01009999,E01010000,E01010001,E01010002,E01010003,E01010004,E01010005,E01010006,E01010007,E01010008,E01010009,E01010010,E01010011,E01010012,E01010013,E01010014,E01010015&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_019.json.gz
+  content_sha256: 991bfb82cfec76af908a8d96b87346722b5f82856a9bd9118a2fd9c03eacfe2d
+- id: census_lsoa_tenure5a_bedrooms_020
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 020
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01010016,E01010017,E01010018,E01010019,E01010020,E01010021,E01010022,E01010023,E01010024,E01010025,E01010026,E01010027,E01010028,E01010029,E01010030,E01010031,E01010032,E01010033,E01010034,E01010035,E01010036,E01010037,E01010038,E01010039,E01010040,E01010041,E01010042,E01010043,E01010044,E01010045,E01010046,E01010047,E01010048,E01010049,E01010050,E01010051,E01010052,E01010053,E01010054,E01010055,E01010056,E01010057,E01010058,E01010059,E01010060,E01010061,E01010062,E01010064,E01010066,E01010067,E01010068,E01010069,E01010070,E01010071,E01010072,E01010073,E01010075,E01010076,E01010077,E01010078,E01010079,E01010080,E01010081,E01010082,E01010083,E01010084,E01010085,E01010086,E01010087,E01010088,E01010089,E01010090,E01010091,E01010092,E01010093,E01010094,E01010095,E01010096,E01010097,E01010098,E01010099,E01010100,E01010101,E01010102,E01010103,E01010104,E01010105,E01010106,E01010107,E01010108,E01010109,E01010110,E01010111,E01010112,E01010113,E01010114,E01010115,E01010116,E01010117,E01010118,E01010119,E01010120,E01010121,E01010122,E01010123,E01010124,E01010125,E01010126,E01010127,E01010128,E01010129,E01010130,E01010131,E01010132,E01010133,E01010134,E01010135,E01010136,E01010137,E01010138,E01010139,E01010140,E01010141,E01010142,E01010143,E01010144,E01010145,E01010146,E01010147,E01010148,E01010149,E01010150,E01010151,E01010152,E01010153,E01010154,E01010155,E01010156,E01010157,E01010158,E01010159,E01010160,E01010161,E01010162,E01010163,E01010164,E01010165,E01010166,E01010167,E01010168,E01010169,E01010170,E01010171,E01010172,E01010173,E01010174,E01010175,E01010176,E01010177,E01010178,E01010179,E01010180,E01010181,E01010183,E01010184,E01010185,E01010186,E01010187,E01010188,E01010189,E01010190,E01010191,E01010192,E01010193,E01010194,E01010195,E01010196,E01010197,E01010198,E01010199,E01010200,E01010201,E01010202,E01010203,E01010204,E01010205,E01010206,E01010207,E01010208,E01010209,E01010210,E01010211,E01010212,E01010213,E01010214,E01010215,E01010216,E01010217,E01010218,E01010219,E01010220,E01010221,E01010222,E01010223,E01010225,E01010226,E01010227,E01010228,E01010229,E01010230,E01010231,E01010232,E01010233,E01010234,E01010235,E01010236,E01010237,E01010238,E01010239,E01010240,E01010241,E01010242,E01010243,E01010244,E01010245,E01010246,E01010247,E01010248,E01010249,E01010250,E01010251,E01010252,E01010253,E01010254,E01010255,E01010256,E01010257,E01010258,E01010259,E01010260,E01010261,E01010262,E01010263,E01010264,E01010265,E01010266,E01010267,E01010268,E01010269,E01010270,E01010271,E01010272,E01010273,E01010274,E01010275,E01010277,E01010279,E01010282,E01010283,E01010284,E01010285,E01010286,E01010287,E01010288,E01010289,E01010290,E01010291,E01010292,E01010293,E01010294,E01010295,E01010296,E01010297,E01010298,E01010299,E01010300,E01010301,E01010302,E01010303,E01010304,E01010305,E01010306,E01010307,E01010308,E01010309,E01010310,E01010311,E01010312,E01010313,E01010314,E01010315,E01010316,E01010317,E01010318,E01010319,E01010320,E01010321,E01010322,E01010323,E01010324,E01010325,E01010326,E01010327,E01010328,E01010329,E01010330,E01010331,E01010332,E01010333,E01010334,E01010335,E01010336,E01010337,E01010338,E01010339,E01010340,E01010341,E01010342,E01010343,E01010344,E01010345,E01010346,E01010347,E01010348,E01010349,E01010350,E01010351,E01010352,E01010353,E01010354,E01010355,E01010356,E01010357,E01010358,E01010359,E01010360,E01010361,E01010362,E01010363,E01010364,E01010365,E01010366,E01010367,E01010368,E01010369,E01010370,E01010372,E01010373,E01010374,E01010375,E01010376,E01010377,E01010378,E01010379,E01010380,E01010381,E01010382,E01010383,E01010384,E01010385,E01010386,E01010387,E01010388,E01010389,E01010390,E01010391,E01010392,E01010393,E01010394,E01010395,E01010396,E01010397,E01010398,E01010399,E01010400,E01010401,E01010402,E01010403,E01010404,E01010405,E01010406,E01010407,E01010408,E01010409,E01010410,E01010411,E01010412,E01010413,E01010414,E01010415,E01010416,E01010417,E01010418,E01010419,E01010420,E01010421,E01010422,E01010423,E01010424,E01010425,E01010426,E01010427,E01010428,E01010429,E01010430,E01010431,E01010432,E01010433,E01010434,E01010436,E01010437,E01010438,E01010439,E01010440,E01010441,E01010442,E01010443,E01010444,E01010445,E01010446,E01010447,E01010448,E01010449,E01010450,E01010451,E01010452,E01010454,E01010455,E01010456,E01010457,E01010458,E01010459,E01010460,E01010461,E01010462,E01010463,E01010464,E01010465,E01010466,E01010467,E01010468,E01010469,E01010470,E01010471,E01010472,E01010473,E01010474,E01010475,E01010476,E01010477,E01010478,E01010479,E01010480,E01010481,E01010482,E01010483,E01010484,E01010485,E01010486,E01010487,E01010488,E01010489,E01010490,E01010491,E01010492,E01010493,E01010494,E01010495,E01010496,E01010497,E01010498,E01010499,E01010500,E01010501,E01010502,E01010503,E01010504,E01010505,E01010506,E01010507,E01010508,E01010509,E01010510,E01010511,E01010512,E01010513,E01010514,E01010515,E01010516,E01010517,E01010518,E01010519,E01010520,E01010522,E01010523,E01010524,E01010525,E01010526,E01010527,E01010528&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_020.json.gz
+  content_sha256: b387b523cd9cc997de28791e403126500c110014b46efc3b1fc2a75aaedff642
+- id: census_lsoa_tenure5a_bedrooms_021
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 021
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01010529,E01010530,E01010531,E01010532,E01010533,E01010534,E01010535,E01010536,E01010537,E01010538,E01010539,E01010540,E01010541,E01010542,E01010543,E01010544,E01010545,E01010546,E01010547,E01010548,E01010549,E01010550,E01010551,E01010552,E01010553,E01010554,E01010555,E01010556,E01010557,E01010558,E01010559,E01010560,E01010561,E01010562,E01010563,E01010564,E01010565,E01010566,E01010567,E01010568,E01010569,E01010570,E01010571,E01010572,E01010573,E01010574,E01010575,E01010576,E01010577,E01010578,E01010579,E01010580,E01010581,E01010582,E01010583,E01010584,E01010585,E01010586,E01010587,E01010588,E01010589,E01010590,E01010591,E01010592,E01010593,E01010594,E01010595,E01010596,E01010597,E01010598,E01010599,E01010600,E01010601,E01010602,E01010603,E01010604,E01010605,E01010606,E01010607,E01010608,E01010609,E01010610,E01010611,E01010612,E01010613,E01010614,E01010615,E01010616,E01010617,E01010618,E01010619,E01010620,E01010621,E01010622,E01010623,E01010624,E01010625,E01010626,E01010627,E01010628,E01010629,E01010630,E01010631,E01010632,E01010633,E01010634,E01010635,E01010636,E01010637,E01010638,E01010639,E01010640,E01010641,E01010642,E01010643,E01010644,E01010645,E01010646,E01010647,E01010648,E01010649,E01010650,E01010651,E01010652,E01010653,E01010654,E01010655,E01010656,E01010657,E01010658,E01010659,E01010662,E01010663,E01010664,E01010665,E01010666,E01010667,E01010668,E01010669,E01010670,E01010671,E01010672,E01010673,E01010674,E01010675,E01010676,E01010678,E01010679,E01010680,E01010681,E01010682,E01010683,E01010684,E01010685,E01010686,E01010687,E01010688,E01010689,E01010690,E01010691,E01010692,E01010693,E01010694,E01010695,E01010696,E01010697,E01010698,E01010699,E01010700,E01010701,E01010702,E01010703,E01010704,E01010705,E01010706,E01010707,E01010708,E01010709,E01010710,E01010711,E01010712,E01010713,E01010714,E01010715,E01010716,E01010717,E01010718,E01010719,E01010720,E01010721,E01010722,E01010723,E01010724,E01010725,E01010726,E01010727,E01010728,E01010729,E01010730,E01010731,E01010732,E01010733,E01010734,E01010735,E01010736,E01010737,E01010738,E01010739,E01010740,E01010741,E01010742,E01010743,E01010744,E01010745,E01010746,E01010747,E01010748,E01010749,E01010750,E01010751,E01010752,E01010753,E01010754,E01010755,E01010756,E01010758,E01010759,E01010760,E01010761,E01010762,E01010763,E01010764,E01010765,E01010766,E01010767,E01010768,E01010769,E01010770,E01010771,E01010772,E01010773,E01010774,E01010775,E01010776,E01010777,E01010778,E01010779,E01010781,E01010782,E01010783,E01010784,E01010785,E01010786,E01010787,E01010788,E01010789,E01010790,E01010791,E01010792,E01010793,E01010794,E01010795,E01010796,E01010797,E01010798,E01010799,E01010800,E01010801,E01010802,E01010803,E01010804,E01010805,E01010806,E01010807,E01010808,E01010809,E01010810,E01010811,E01010812,E01010813,E01010814,E01010815,E01010816,E01010817,E01010818,E01010819,E01010820,E01010821,E01010822,E01010824,E01010825,E01010826,E01010827,E01010828,E01010829,E01010830,E01010831,E01010832,E01010833,E01010834,E01010836,E01010837,E01010838,E01010839,E01010840,E01010841,E01010842,E01010843,E01010845,E01010846,E01010847,E01010848,E01010849,E01010850,E01010851,E01010852,E01010853,E01010854,E01010855,E01010856,E01010857,E01010858,E01010859,E01010860,E01010861,E01010862,E01010863,E01010864,E01010865,E01010866,E01010867,E01010868,E01010869,E01010870,E01010871,E01010872,E01010873,E01010874,E01010875,E01010876,E01010877,E01010878,E01010879,E01010880,E01010881,E01010882,E01010883,E01010884,E01010885,E01010886,E01010887,E01010888,E01010889,E01010890,E01010891,E01010892,E01010893,E01010894,E01010895,E01010896,E01010897,E01010898,E01010899,E01010900,E01010901,E01010902,E01010903,E01010904,E01010905,E01010906,E01010907,E01010908,E01010909,E01010910,E01010911,E01010912,E01010913,E01010914,E01010915,E01010916,E01010917,E01010918,E01010919,E01010920,E01010921,E01010922,E01010923,E01010924,E01010925,E01010926,E01010927,E01010928,E01010929,E01010930,E01010931,E01010932,E01010933,E01010934,E01010935,E01010936,E01010937,E01010938,E01010939,E01010940,E01010941,E01010942,E01010943,E01010944,E01010945,E01010946,E01010947,E01010948,E01010949,E01010950,E01010951,E01010952,E01010953,E01010954,E01010955,E01010956,E01010957,E01010958,E01010959,E01010960,E01010961,E01010964,E01010965,E01010966,E01010967,E01010968,E01010969,E01010970,E01010971,E01010972,E01010973,E01010974,E01010975,E01010976,E01010977,E01010978,E01010979,E01010980,E01010981,E01010982,E01010983,E01010984,E01010985,E01010986,E01010987,E01010988,E01010989,E01010990,E01010991,E01010992,E01010993,E01010995,E01010996,E01010997,E01010998,E01010999,E01011000,E01011001,E01011002,E01011003,E01011004,E01011005,E01011006,E01011007,E01011008,E01011009,E01011010,E01011011,E01011012,E01011013,E01011014,E01011015,E01011016,E01011017,E01011018,E01011019,E01011020,E01011021,E01011022,E01011023,E01011024,E01011025,E01011026,E01011027,E01011028,E01011029,E01011030,E01011031,E01011032,E01011033,E01011034,E01011035,E01011036,E01011037,E01011038,E01011039&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_021.json.gz
+  content_sha256: 3637e46ec114e299a839f91b0862f412e352fcf6e80e3f7fb2e57462be3e0310
+- id: census_lsoa_tenure5a_bedrooms_022
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 022
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01011040,E01011041,E01011042,E01011043,E01011044,E01011045,E01011046,E01011047,E01011048,E01011049,E01011050,E01011051,E01011052,E01011053,E01011054,E01011055,E01011056,E01011057,E01011058,E01011059,E01011060,E01011061,E01011062,E01011063,E01011064,E01011065,E01011066,E01011067,E01011068,E01011069,E01011070,E01011071,E01011072,E01011073,E01011074,E01011075,E01011076,E01011077,E01011078,E01011079,E01011080,E01011081,E01011082,E01011083,E01011084,E01011085,E01011086,E01011087,E01011088,E01011089,E01011090,E01011091,E01011092,E01011093,E01011094,E01011095,E01011096,E01011097,E01011098,E01011099,E01011100,E01011101,E01011102,E01011103,E01011104,E01011105,E01011106,E01011107,E01011108,E01011109,E01011110,E01011111,E01011112,E01011113,E01011114,E01011115,E01011116,E01011117,E01011118,E01011119,E01011120,E01011121,E01011122,E01011123,E01011124,E01011125,E01011126,E01011127,E01011128,E01011129,E01011130,E01011132,E01011133,E01011134,E01011136,E01011137,E01011138,E01011139,E01011140,E01011141,E01011142,E01011143,E01011144,E01011145,E01011146,E01011147,E01011148,E01011149,E01011150,E01011151,E01011152,E01011153,E01011154,E01011155,E01011156,E01011157,E01011158,E01011159,E01011160,E01011161,E01011162,E01011163,E01011164,E01011165,E01011166,E01011167,E01011168,E01011169,E01011170,E01011173,E01011174,E01011175,E01011176,E01011177,E01011178,E01011179,E01011180,E01011181,E01011182,E01011183,E01011184,E01011185,E01011186,E01011187,E01011188,E01011189,E01011190,E01011191,E01011192,E01011193,E01011194,E01011195,E01011196,E01011197,E01011198,E01011199,E01011200,E01011201,E01011202,E01011203,E01011204,E01011205,E01011206,E01011207,E01011208,E01011210,E01011211,E01011212,E01011213,E01011214,E01011215,E01011216,E01011217,E01011218,E01011219,E01011220,E01011221,E01011222,E01011223,E01011224,E01011225,E01011226,E01011227,E01011228,E01011229,E01011230,E01011231,E01011232,E01011233,E01011234,E01011235,E01011236,E01011237,E01011238,E01011239,E01011240,E01011241,E01011242,E01011243,E01011244,E01011245,E01011246,E01011247,E01011248,E01011249,E01011250,E01011251,E01011252,E01011253,E01011254,E01011255,E01011256,E01011257,E01011258,E01011259,E01011260,E01011261,E01011262,E01011263,E01011264,E01011265,E01011266,E01011267,E01011268,E01011269,E01011270,E01011271,E01011272,E01011273,E01011274,E01011275,E01011276,E01011277,E01011278,E01011279,E01011280,E01011281,E01011282,E01011283,E01011284,E01011286,E01011287,E01011292,E01011293,E01011294,E01011295,E01011296,E01011297,E01011298,E01011299,E01011300,E01011301,E01011302,E01011303,E01011304,E01011305,E01011306,E01011307,E01011308,E01011309,E01011310,E01011311,E01011312,E01011313,E01011314,E01011315,E01011316,E01011317,E01011318,E01011319,E01011320,E01011321,E01011322,E01011323,E01011324,E01011325,E01011326,E01011327,E01011328,E01011329,E01011330,E01011331,E01011332,E01011333,E01011334,E01011335,E01011336,E01011337,E01011338,E01011339,E01011340,E01011341,E01011342,E01011343,E01011344,E01011345,E01011346,E01011347,E01011348,E01011349,E01011350,E01011351,E01011352,E01011353,E01011354,E01011355,E01011356,E01011357,E01011358,E01011359,E01011360,E01011361,E01011362,E01011363,E01011364,E01011366,E01011368,E01011369,E01011370,E01011371,E01011372,E01011373,E01011374,E01011375,E01011376,E01011377,E01011378,E01011379,E01011380,E01011381,E01011382,E01011383,E01011384,E01011385,E01011386,E01011388,E01011390,E01011391,E01011392,E01011393,E01011394,E01011395,E01011396,E01011397,E01011398,E01011399,E01011400,E01011401,E01011402,E01011403,E01011404,E01011405,E01011406,E01011407,E01011408,E01011409,E01011410,E01011411,E01011412,E01011413,E01011414,E01011415,E01011416,E01011417,E01011418,E01011419,E01011420,E01011421,E01011422,E01011423,E01011424,E01011425,E01011426,E01011427,E01011428,E01011429,E01011430,E01011431,E01011432,E01011433,E01011434,E01011435,E01011440,E01011441,E01011442,E01011443,E01011444,E01011445,E01011446,E01011447,E01011448,E01011449,E01011450,E01011451,E01011452,E01011453,E01011454,E01011455,E01011456,E01011457,E01011458,E01011459,E01011460,E01011461,E01011462,E01011463,E01011464,E01011465,E01011466,E01011467,E01011468,E01011469,E01011470,E01011471,E01011472,E01011473,E01011474,E01011475,E01011476,E01011477,E01011478,E01011479,E01011480,E01011481,E01011482,E01011483,E01011484,E01011485,E01011488,E01011489,E01011491,E01011492,E01011493,E01011494,E01011496,E01011497,E01011500,E01011502,E01011504,E01011505,E01011506,E01011507,E01011508,E01011509,E01011510,E01011511,E01011512,E01011513,E01011514,E01011515,E01011516,E01011517,E01011518,E01011519,E01011520,E01011521,E01011522,E01011523,E01011524,E01011525,E01011526,E01011527,E01011528,E01011529,E01011530,E01011531,E01011532,E01011533,E01011534,E01011535,E01011536,E01011537,E01011538,E01011540,E01011541,E01011545,E01011546,E01011547,E01011548,E01011549,E01011550,E01011551,E01011552,E01011553,E01011554,E01011555,E01011556,E01011557,E01011558,E01011559,E01011560,E01011561,E01011562,E01011563,E01011564,E01011565,E01011566,E01011567,E01011568,E01011569&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_022.json.gz
+  content_sha256: 8fc48dd1a975c7afdc6c0136da9be4ce29438d6fcabe9e845d120841126630b8
+- id: census_lsoa_tenure5a_bedrooms_023
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 023
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01011570,E01011571,E01011572,E01011573,E01011574,E01011576,E01011578,E01011579,E01011580,E01011581,E01011582,E01011583,E01011584,E01011585,E01011586,E01011587,E01011588,E01011589,E01011590,E01011591,E01011592,E01011593,E01011594,E01011595,E01011596,E01011597,E01011598,E01011599,E01011600,E01011601,E01011602,E01011603,E01011604,E01011605,E01011606,E01011607,E01011608,E01011609,E01011610,E01011611,E01011612,E01011613,E01011614,E01011615,E01011616,E01011617,E01011618,E01011620,E01011621,E01011622,E01011623,E01011624,E01011625,E01011626,E01011627,E01011628,E01011629,E01011630,E01011632,E01011633,E01011634,E01011635,E01011636,E01011637,E01011638,E01011639,E01011641,E01011642,E01011643,E01011644,E01011645,E01011646,E01011647,E01011648,E01011649,E01011650,E01011651,E01011652,E01011653,E01011654,E01011655,E01011656,E01011657,E01011658,E01011659,E01011660,E01011661,E01011662,E01011663,E01011664,E01011665,E01011666,E01011667,E01011668,E01011669,E01011670,E01011671,E01011673,E01011677,E01011678,E01011681,E01011684,E01011685,E01011686,E01011687,E01011688,E01011689,E01011690,E01011691,E01011692,E01011693,E01011694,E01011695,E01011696,E01011697,E01011698,E01011699,E01011700,E01011701,E01011702,E01011703,E01011704,E01011705,E01011706,E01011707,E01011708,E01011709,E01011710,E01011711,E01011712,E01011713,E01011714,E01011715,E01011716,E01011717,E01011718,E01011719,E01011720,E01011721,E01011722,E01011723,E01011724,E01011725,E01011726,E01011727,E01011728,E01011729,E01011730,E01011731,E01011732,E01011733,E01011734,E01011735,E01011736,E01011737,E01011738,E01011739,E01011740,E01011741,E01011742,E01011743,E01011744,E01011745,E01011746,E01011747,E01011748,E01011750,E01011751,E01011752,E01011753,E01011754,E01011755,E01011756,E01011757,E01011758,E01011759,E01011761,E01011762,E01011763,E01011764,E01011765,E01011766,E01011767,E01011768,E01011770,E01011771,E01011772,E01011773,E01011774,E01011776,E01011777,E01011778,E01011779,E01011780,E01011781,E01011782,E01011783,E01011784,E01011785,E01011786,E01011787,E01011788,E01011789,E01011790,E01011791,E01011792,E01011793,E01011794,E01011795,E01011796,E01011797,E01011798,E01011799,E01011800,E01011801,E01011802,E01011803,E01011804,E01011805,E01011806,E01011807,E01011808,E01011809,E01011810,E01011811,E01011812,E01011813,E01011814,E01011815,E01011816,E01011817,E01011818,E01011819,E01011820,E01011821,E01011822,E01011823,E01011824,E01011825,E01011826,E01011827,E01011828,E01011829,E01011830,E01011831,E01011832,E01011833,E01011834,E01011835,E01011836,E01011837,E01011838,E01011839,E01011840,E01011841,E01011842,E01011843,E01011844,E01011845,E01011846,E01011847,E01011848,E01011849,E01011850,E01011851,E01011852,E01011853,E01011854,E01011855,E01011856,E01011857,E01011858,E01011859,E01011860,E01011861,E01011862,E01011863,E01011864,E01011865,E01011866,E01011867,E01011868,E01011869,E01011870,E01011871,E01011872,E01011873,E01011874,E01011875,E01011876,E01011877,E01011878,E01011879,E01011880,E01011881,E01011882,E01011883,E01011884,E01011885,E01011886,E01011887,E01011888,E01011889,E01011890,E01011891,E01011892,E01011893,E01011894,E01011895,E01011896,E01011897,E01011898,E01011899,E01011900,E01011901,E01011902,E01011903,E01011904,E01011905,E01011906,E01011907,E01011908,E01011909,E01011910,E01011911,E01011912,E01011913,E01011914,E01011915,E01011917,E01011918,E01011919,E01011920,E01011921,E01011922,E01011923,E01011924,E01011925,E01011926,E01011927,E01011928,E01011929,E01011930,E01011931,E01011932,E01011933,E01011934,E01011935,E01011936,E01011937,E01011938,E01011939,E01011940,E01011941,E01011942,E01011943,E01011944,E01011945,E01011946,E01011947,E01011948,E01011949,E01011950,E01011951,E01011952,E01011953,E01011954,E01011955,E01011957,E01011959,E01011960,E01011961,E01011962,E01011963,E01011964,E01011965,E01011966,E01011967,E01011968,E01011969,E01011970,E01011971,E01011974,E01011975,E01011977,E01011978,E01011979,E01011980,E01011981,E01011982,E01011983,E01011984,E01011985,E01011986,E01011987,E01011988,E01011989,E01011990,E01011991,E01011992,E01011993,E01011994,E01011995,E01011996,E01011997,E01011998,E01011999,E01012000,E01012001,E01012002,E01012003,E01012005,E01012007,E01012008,E01012009,E01012010,E01012011,E01012012,E01012013,E01012014,E01012015,E01012016,E01012017,E01012018,E01012019,E01012020,E01012021,E01012022,E01012023,E01012024,E01012025,E01012026,E01012027,E01012030,E01012031,E01012032,E01012033,E01012034,E01012035,E01012036,E01012037,E01012038,E01012039,E01012040,E01012041,E01012042,E01012043,E01012044,E01012045,E01012046,E01012047,E01012048,E01012049,E01012050,E01012051,E01012052,E01012053,E01012054,E01012055,E01012056,E01012057,E01012058,E01012059,E01012060,E01012061,E01012062,E01012063,E01012065,E01012066,E01012067,E01012068,E01012071,E01012073,E01012074,E01012075,E01012076,E01012077,E01012078,E01012079,E01012080,E01012081,E01012082,E01012083,E01012084,E01012085,E01012088,E01012089,E01012090,E01012091,E01012092,E01012093,E01012095,E01012096,E01012097,E01012098,E01012099,E01012100,E01012101,E01012102,E01012105&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_023.json.gz
+  content_sha256: 7e28d330b422adf7961aee0fc36b52cb1d9fcc12f6f033c071c901a0a8e2dcf3
+- id: census_lsoa_tenure5a_bedrooms_024
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 024
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01012106,E01012107,E01012108,E01012109,E01012110,E01012111,E01012112,E01012113,E01012114,E01012117,E01012118,E01012119,E01012120,E01012121,E01012124,E01012127,E01012128,E01012129,E01012130,E01012131,E01012132,E01012134,E01012136,E01012137,E01012138,E01012139,E01012140,E01012141,E01012142,E01012143,E01012144,E01012145,E01012146,E01012147,E01012148,E01012149,E01012150,E01012151,E01012152,E01012154,E01012155,E01012156,E01012157,E01012158,E01012159,E01012160,E01012161,E01012162,E01012163,E01012164,E01012165,E01012167,E01012168,E01012170,E01012171,E01012172,E01012173,E01012174,E01012175,E01012176,E01012177,E01012178,E01012179,E01012180,E01012181,E01012182,E01012183,E01012184,E01012185,E01012186,E01012187,E01012188,E01012189,E01012190,E01012191,E01012192,E01012193,E01012194,E01012195,E01012196,E01012197,E01012198,E01012199,E01012200,E01012201,E01012202,E01012203,E01012204,E01012205,E01012206,E01012207,E01012208,E01012209,E01012210,E01012211,E01012212,E01012213,E01012214,E01012215,E01012216,E01012217,E01012218,E01012219,E01012223,E01012224,E01012225,E01012226,E01012227,E01012228,E01012230,E01012231,E01012232,E01012233,E01012234,E01012235,E01012236,E01012237,E01012238,E01012239,E01012240,E01012241,E01012242,E01012243,E01012244,E01012245,E01012246,E01012247,E01012248,E01012249,E01012250,E01012251,E01012252,E01012253,E01012254,E01012255,E01012256,E01012257,E01012258,E01012259,E01012260,E01012261,E01012262,E01012267,E01012268,E01012269,E01012270,E01012271,E01012272,E01012273,E01012274,E01012275,E01012276,E01012277,E01012278,E01012279,E01012280,E01012281,E01012282,E01012283,E01012284,E01012285,E01012286,E01012287,E01012288,E01012289,E01012290,E01012291,E01012292,E01012294,E01012295,E01012296,E01012297,E01012298,E01012299,E01012300,E01012301,E01012302,E01012303,E01012304,E01012305,E01012306,E01012307,E01012309,E01012310,E01012311,E01012312,E01012313,E01012314,E01012315,E01012317,E01012318,E01012319,E01012323,E01012324,E01012325,E01012326,E01012327,E01012328,E01012329,E01012330,E01012331,E01012332,E01012333,E01012334,E01012335,E01012336,E01012337,E01012338,E01012339,E01012340,E01012341,E01012342,E01012344,E01012345,E01012346,E01012347,E01012348,E01012349,E01012350,E01012351,E01012352,E01012353,E01012354,E01012355,E01012356,E01012357,E01012358,E01012359,E01012360,E01012361,E01012362,E01012363,E01012364,E01012365,E01012366,E01012367,E01012368,E01012369,E01012370,E01012371,E01012372,E01012373,E01012374,E01012375,E01012376,E01012377,E01012378,E01012379,E01012380,E01012381,E01012382,E01012383,E01012384,E01012385,E01012386,E01012387,E01012388,E01012389,E01012390,E01012391,E01012392,E01012393,E01012394,E01012395,E01012396,E01012397,E01012398,E01012399,E01012400,E01012401,E01012402,E01012403,E01012404,E01012405,E01012406,E01012407,E01012408,E01012409,E01012410,E01012411,E01012412,E01012413,E01012414,E01012415,E01012416,E01012417,E01012418,E01012419,E01012420,E01012421,E01012422,E01012423,E01012424,E01012425,E01012426,E01012427,E01012429,E01012430,E01012431,E01012432,E01012433,E01012434,E01012435,E01012436,E01012437,E01012438,E01012439,E01012440,E01012441,E01012442,E01012443,E01012444,E01012445,E01012446,E01012447,E01012448,E01012449,E01012450,E01012451,E01012452,E01012453,E01012454,E01012455,E01012457,E01012458,E01012459,E01012460,E01012461,E01012462,E01012463,E01012464,E01012465,E01012466,E01012467,E01012468,E01012469,E01012470,E01012471,E01012472,E01012473,E01012474,E01012475,E01012476,E01012477,E01012478,E01012479,E01012480,E01012481,E01012482,E01012483,E01012485,E01012486,E01012487,E01012488,E01012489,E01012490,E01012491,E01012492,E01012493,E01012494,E01012495,E01012496,E01012497,E01012498,E01012499,E01012500,E01012501,E01012502,E01012503,E01012505,E01012506,E01012507,E01012508,E01012509,E01012510,E01012511,E01012512,E01012513,E01012514,E01012515,E01012516,E01012517,E01012518,E01012519,E01012520,E01012521,E01012522,E01012523,E01012524,E01012525,E01012526,E01012527,E01012528,E01012529,E01012530,E01012531,E01012532,E01012533,E01012534,E01012535,E01012536,E01012537,E01012538,E01012539,E01012540,E01012541,E01012542,E01012543,E01012544,E01012545,E01012546,E01012547,E01012548,E01012549,E01012550,E01012551,E01012552,E01012553,E01012554,E01012555,E01012556,E01012557,E01012558,E01012559,E01012560,E01012561,E01012562,E01012563,E01012564,E01012566,E01012569,E01012570,E01012571,E01012572,E01012573,E01012574,E01012575,E01012576,E01012577,E01012578,E01012579,E01012580,E01012581,E01012582,E01012583,E01012584,E01012585,E01012586,E01012587,E01012588,E01012589,E01012590,E01012591,E01012592,E01012593,E01012594,E01012595,E01012596,E01012597,E01012598,E01012599,E01012600,E01012601,E01012602,E01012603,E01012604,E01012605,E01012606,E01012607,E01012608,E01012609,E01012610,E01012611,E01012612,E01012613,E01012614,E01012615,E01012616,E01012617,E01012618,E01012619,E01012620,E01012621,E01012622,E01012623,E01012624,E01012625,E01012628,E01012629,E01012630,E01012631,E01012632,E01012633,E01012634,E01012635,E01012636,E01012637,E01012638,E01012639,E01012640&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_024.json.gz
+  content_sha256: da3732c1ecbaa0c915735de41d5fbeb2986a7ec2d6a1f2ca3afc4266d8e66058
+- id: census_lsoa_tenure5a_bedrooms_025
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 025
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01012641,E01012642,E01012643,E01012644,E01012645,E01012646,E01012647,E01012648,E01012649,E01012650,E01012651,E01012652,E01012653,E01012654,E01012655,E01012656,E01012657,E01012658,E01012659,E01012660,E01012661,E01012662,E01012663,E01012664,E01012665,E01012666,E01012667,E01012668,E01012669,E01012670,E01012671,E01012672,E01012673,E01012674,E01012675,E01012676,E01012677,E01012678,E01012679,E01012680,E01012681,E01012682,E01012683,E01012684,E01012685,E01012686,E01012687,E01012688,E01012689,E01012690,E01012691,E01012692,E01012693,E01012694,E01012695,E01012696,E01012697,E01012698,E01012699,E01012700,E01012701,E01012702,E01012703,E01012704,E01012705,E01012706,E01012707,E01012708,E01012709,E01012710,E01012711,E01012712,E01012713,E01012714,E01012715,E01012716,E01012717,E01012718,E01012719,E01012720,E01012721,E01012722,E01012723,E01012724,E01012725,E01012726,E01012727,E01012728,E01012729,E01012730,E01012731,E01012732,E01012733,E01012734,E01012735,E01012736,E01012737,E01012738,E01012739,E01012740,E01012741,E01012742,E01012743,E01012744,E01012745,E01012746,E01012747,E01012748,E01012749,E01012750,E01012751,E01012752,E01012753,E01012754,E01012755,E01012756,E01012757,E01012758,E01012759,E01012760,E01012761,E01012762,E01012763,E01012764,E01012765,E01012766,E01012767,E01012768,E01012769,E01012770,E01012771,E01012772,E01012773,E01012774,E01012775,E01012776,E01012777,E01012778,E01012779,E01012780,E01012781,E01012782,E01012783,E01012784,E01012785,E01012786,E01012787,E01012788,E01012789,E01012790,E01012791,E01012792,E01012793,E01012794,E01012795,E01012796,E01012797,E01012798,E01012799,E01012800,E01012801,E01012802,E01012803,E01012804,E01012805,E01012806,E01012807,E01012808,E01012809,E01012810,E01012811,E01012812,E01012813,E01012814,E01012815,E01012816,E01012817,E01012818,E01012819,E01012820,E01012821,E01012822,E01012824,E01012825,E01012827,E01012828,E01012829,E01012830,E01012832,E01012833,E01012834,E01012835,E01012836,E01012837,E01012838,E01012839,E01012840,E01012841,E01012842,E01012843,E01012844,E01012845,E01012846,E01012847,E01012848,E01012849,E01012850,E01012852,E01012854,E01012855,E01012856,E01012857,E01012858,E01012859,E01012860,E01012862,E01012863,E01012864,E01012865,E01012866,E01012867,E01012868,E01012869,E01012870,E01012871,E01012872,E01012873,E01012874,E01012875,E01012876,E01012877,E01012878,E01012879,E01012880,E01012881,E01012882,E01012883,E01012884,E01012886,E01012887,E01012888,E01012889,E01012891,E01012892,E01012893,E01012894,E01012896,E01012898,E01012899,E01012900,E01012901,E01012902,E01012903,E01012904,E01012905,E01012906,E01012907,E01012908,E01012909,E01012910,E01012911,E01012912,E01012913,E01012914,E01012915,E01012916,E01012917,E01012918,E01012919,E01012921,E01012922,E01012923,E01012924,E01012925,E01012926,E01012927,E01012928,E01012929,E01012930,E01012931,E01012932,E01012933,E01012934,E01012935,E01012936,E01012937,E01012939,E01012940,E01012941,E01012942,E01012943,E01012944,E01012945,E01012946,E01012947,E01012948,E01012949,E01012950,E01012951,E01012952,E01012953,E01012954,E01012955,E01012956,E01012957,E01012958,E01012959,E01012960,E01012961,E01012962,E01012963,E01012964,E01012966,E01012967,E01012968,E01012969,E01012970,E01012972,E01012974,E01012975,E01012976,E01012978,E01012979,E01012980,E01012981,E01012982,E01012983,E01012984,E01012985,E01012986,E01012987,E01012988,E01012989,E01012990,E01012991,E01012992,E01012993,E01012994,E01012995,E01012996,E01012997,E01012998,E01012999,E01013000,E01013001,E01013002,E01013003,E01013004,E01013005,E01013006,E01013007,E01013008,E01013009,E01013010,E01013011,E01013012,E01013013,E01013014,E01013015,E01013016,E01013017,E01013018,E01013019,E01013020,E01013021,E01013022,E01013023,E01013024,E01013025,E01013026,E01013027,E01013028,E01013029,E01013030,E01013031,E01013032,E01013033,E01013034,E01013035,E01013036,E01013037,E01013038,E01013039,E01013040,E01013041,E01013042,E01013043,E01013044,E01013045,E01013046,E01013047,E01013048,E01013049,E01013050,E01013051,E01013053,E01013054,E01013055,E01013056,E01013057,E01013058,E01013059,E01013060,E01013061,E01013062,E01013063,E01013064,E01013065,E01013066,E01013067,E01013068,E01013069,E01013070,E01013071,E01013072,E01013073,E01013074,E01013075,E01013076,E01013077,E01013078,E01013079,E01013080,E01013081,E01013082,E01013083,E01013084,E01013085,E01013086,E01013087,E01013088,E01013089,E01013090,E01013091,E01013092,E01013093,E01013094,E01013095,E01013096,E01013097,E01013098,E01013099,E01013100,E01013101,E01013102,E01013103,E01013104,E01013105,E01013106,E01013107,E01013108,E01013110,E01013111,E01013112,E01013113,E01013114,E01013115,E01013116,E01013117,E01013118,E01013119,E01013120,E01013121,E01013122,E01013123,E01013124,E01013125,E01013126,E01013127,E01013128,E01013129,E01013130,E01013131,E01013132,E01013133,E01013134,E01013135,E01013136,E01013138,E01013140,E01013141,E01013142,E01013143,E01013144,E01013145,E01013146,E01013147,E01013148,E01013149,E01013150,E01013151,E01013152,E01013153,E01013154,E01013155,E01013156,E01013157,E01013158,E01013159,E01013160&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_025.json.gz
+  content_sha256: f6958c55f0bad4b336e3ee602e95a6f813a22e166131886e60f4c4532dbf3bce
+- id: census_lsoa_tenure5a_bedrooms_026
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 026
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01013161,E01013162,E01013163,E01013164,E01013165,E01013167,E01013170,E01013171,E01013172,E01013173,E01013174,E01013175,E01013176,E01013177,E01013178,E01013179,E01013180,E01013181,E01013182,E01013183,E01013184,E01013185,E01013186,E01013187,E01013188,E01013189,E01013191,E01013192,E01013193,E01013194,E01013195,E01013196,E01013197,E01013198,E01013199,E01013200,E01013201,E01013202,E01013203,E01013204,E01013205,E01013206,E01013207,E01013208,E01013209,E01013210,E01013211,E01013212,E01013213,E01013214,E01013215,E01013216,E01013217,E01013218,E01013219,E01013220,E01013221,E01013222,E01013224,E01013225,E01013226,E01013227,E01013228,E01013229,E01013231,E01013233,E01013234,E01013236,E01013237,E01013238,E01013239,E01013241,E01013242,E01013243,E01013244,E01013245,E01013246,E01013247,E01013248,E01013249,E01013250,E01013251,E01013252,E01013253,E01013254,E01013255,E01013256,E01013257,E01013258,E01013259,E01013260,E01013261,E01013262,E01013263,E01013264,E01013265,E01013266,E01013267,E01013268,E01013269,E01013270,E01013271,E01013272,E01013273,E01013274,E01013275,E01013276,E01013277,E01013278,E01013279,E01013280,E01013281,E01013282,E01013283,E01013284,E01013285,E01013286,E01013287,E01013288,E01013289,E01013290,E01013291,E01013292,E01013293,E01013294,E01013295,E01013296,E01013297,E01013298,E01013299,E01013300,E01013301,E01013302,E01013303,E01013304,E01013305,E01013306,E01013307,E01013308,E01013309,E01013310,E01013311,E01013312,E01013313,E01013314,E01013315,E01013316,E01013317,E01013318,E01013319,E01013320,E01013321,E01013322,E01013323,E01013324,E01013325,E01013326,E01013327,E01013328,E01013329,E01013330,E01013331,E01013332,E01013333,E01013334,E01013335,E01013336,E01013337,E01013338,E01013339,E01013340,E01013341,E01013342,E01013343,E01013344,E01013345,E01013346,E01013347,E01013348,E01013349,E01013350,E01013351,E01013352,E01013353,E01013354,E01013355,E01013356,E01013357,E01013358,E01013359,E01013360,E01013361,E01013362,E01013363,E01013364,E01013366,E01013368,E01013369,E01013370,E01013371,E01013372,E01013373,E01013374,E01013375,E01013376,E01013377,E01013378,E01013379,E01013380,E01013381,E01013382,E01013383,E01013384,E01013385,E01013386,E01013387,E01013388,E01013389,E01013390,E01013391,E01013392,E01013393,E01013394,E01013395,E01013396,E01013397,E01013398,E01013399,E01013400,E01013401,E01013402,E01013403,E01013404,E01013405,E01013406,E01013407,E01013408,E01013409,E01013410,E01013411,E01013412,E01013413,E01013414,E01013416,E01013417,E01013418,E01013419,E01013420,E01013421,E01013422,E01013423,E01013424,E01013425,E01013426,E01013427,E01013428,E01013429,E01013430,E01013431,E01013432,E01013433,E01013434,E01013435,E01013436,E01013437,E01013438,E01013439,E01013440,E01013441,E01013442,E01013443,E01013444,E01013445,E01013446,E01013447,E01013448,E01013449,E01013450,E01013451,E01013452,E01013453,E01013454,E01013455,E01013456,E01013457,E01013458,E01013459,E01013460,E01013461,E01013462,E01013463,E01013464,E01013465,E01013466,E01013467,E01013468,E01013469,E01013470,E01013471,E01013472,E01013473,E01013474,E01013475,E01013476,E01013477,E01013480,E01013481,E01013483,E01013484,E01013485,E01013486,E01013487,E01013488,E01013489,E01013490,E01013491,E01013492,E01013493,E01013494,E01013495,E01013496,E01013497,E01013498,E01013499,E01013500,E01013501,E01013502,E01013503,E01013504,E01013505,E01013506,E01013507,E01013508,E01013509,E01013510,E01013511,E01013512,E01013513,E01013514,E01013515,E01013517,E01013518,E01013519,E01013520,E01013521,E01013522,E01013523,E01013524,E01013525,E01013526,E01013527,E01013528,E01013529,E01013530,E01013531,E01013532,E01013533,E01013534,E01013535,E01013536,E01013537,E01013538,E01013539,E01013540,E01013541,E01013542,E01013543,E01013544,E01013545,E01013546,E01013547,E01013548,E01013549,E01013550,E01013551,E01013552,E01013553,E01013554,E01013555,E01013556,E01013557,E01013558,E01013559,E01013560,E01013561,E01013562,E01013563,E01013564,E01013565,E01013566,E01013567,E01013568,E01013569,E01013570,E01013571,E01013572,E01013573,E01013574,E01013575,E01013576,E01013577,E01013578,E01013579,E01013580,E01013581,E01013582,E01013583,E01013584,E01013585,E01013586,E01013587,E01013588,E01013589,E01013590,E01013591,E01013592,E01013593,E01013594,E01013595,E01013596,E01013597,E01013598,E01013599,E01013600,E01013601,E01013602,E01013603,E01013604,E01013605,E01013606,E01013608,E01013609,E01013610,E01013611,E01013612,E01013613,E01013614,E01013615,E01013616,E01013617,E01013618,E01013619,E01013620,E01013621,E01013622,E01013623,E01013624,E01013625,E01013626,E01013627,E01013628,E01013629,E01013630,E01013631,E01013632,E01013633,E01013634,E01013635,E01013636,E01013637,E01013638,E01013639,E01013640,E01013641,E01013642,E01013643,E01013648,E01013649,E01013650,E01013651,E01013652,E01013653,E01013654,E01013655,E01013656,E01013657,E01013658,E01013659,E01013660,E01013661,E01013662,E01013663,E01013664,E01013665,E01013666,E01013667,E01013668,E01013669,E01013670,E01013671,E01013672,E01013673,E01013674,E01013675,E01013676,E01013677,E01013678,E01013679,E01013680,E01013681&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_026.json.gz
+  content_sha256: 45c57944d2375289f8bdc5cede4578974725dc05589dba58eba2042ff9fd98d8
+- id: census_lsoa_tenure5a_bedrooms_027
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 027
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01013682,E01013683,E01013684,E01013685,E01013686,E01013687,E01013688,E01013689,E01013690,E01013691,E01013692,E01013693,E01013694,E01013696,E01013698,E01013699,E01013700,E01013702,E01013703,E01013704,E01013705,E01013706,E01013707,E01013709,E01013710,E01013711,E01013712,E01013713,E01013714,E01013715,E01013717,E01013718,E01013719,E01013720,E01013721,E01013722,E01013723,E01013725,E01013726,E01013727,E01013728,E01013729,E01013730,E01013731,E01013732,E01013733,E01013734,E01013735,E01013736,E01013737,E01013738,E01013739,E01013740,E01013741,E01013742,E01013743,E01013744,E01013745,E01013746,E01013747,E01013748,E01013749,E01013750,E01013751,E01013752,E01013753,E01013754,E01013755,E01013756,E01013757,E01013758,E01013759,E01013760,E01013761,E01013762,E01013763,E01013764,E01013765,E01013766,E01013767,E01013768,E01013770,E01013771,E01013772,E01013773,E01013774,E01013776,E01013777,E01013779,E01013780,E01013781,E01013782,E01013783,E01013784,E01013785,E01013786,E01013787,E01013788,E01013789,E01013790,E01013791,E01013792,E01013793,E01013794,E01013795,E01013796,E01013797,E01013798,E01013799,E01013800,E01013803,E01013804,E01013805,E01013806,E01013807,E01013808,E01013809,E01013810,E01013811,E01013812,E01013813,E01013814,E01013815,E01013816,E01013817,E01013818,E01013819,E01013820,E01013821,E01013822,E01013823,E01013824,E01013825,E01013826,E01013827,E01013828,E01013829,E01013830,E01013831,E01013832,E01013833,E01013834,E01013835,E01013836,E01013837,E01013838,E01013839,E01013840,E01013841,E01013842,E01013843,E01013844,E01013845,E01013846,E01013847,E01013849,E01013850,E01013851,E01013852,E01013853,E01013854,E01013855,E01013856,E01013858,E01013859,E01013860,E01013861,E01013862,E01013863,E01013864,E01013865,E01013866,E01013867,E01013868,E01013870,E01013871,E01013872,E01013873,E01013874,E01013875,E01013876,E01013877,E01013878,E01013879,E01013880,E01013881,E01013882,E01013883,E01013884,E01013885,E01013886,E01013887,E01013888,E01013889,E01013890,E01013891,E01013892,E01013893,E01013894,E01013895,E01013896,E01013897,E01013898,E01013899,E01013900,E01013901,E01013902,E01013903,E01013904,E01013905,E01013906,E01013907,E01013908,E01013909,E01013910,E01013911,E01013912,E01013913,E01013914,E01013915,E01013916,E01013917,E01013918,E01013919,E01013920,E01013921,E01013928,E01013929,E01013930,E01013931,E01013932,E01013933,E01013934,E01013935,E01013936,E01013937,E01013938,E01013939,E01013940,E01013941,E01013942,E01013943,E01013944,E01013945,E01013947,E01013948,E01013953,E01013954,E01013957,E01013958,E01013959,E01013960,E01013961,E01013962,E01013963,E01013964,E01013965,E01013966,E01013967,E01013968,E01013969,E01013970,E01013971,E01013972,E01013977,E01013978,E01013979,E01013980,E01013981,E01013982,E01013983,E01013984,E01013985,E01013986,E01013987,E01013988,E01013989,E01013990,E01013991,E01013992,E01013993,E01013994,E01013995,E01013996,E01013997,E01013998,E01013999,E01014000,E01014001,E01014002,E01014003,E01014004,E01014005,E01014006,E01014008,E01014009,E01014010,E01014011,E01014012,E01014013,E01014014,E01014015,E01014016,E01014017,E01014018,E01014019,E01014020,E01014021,E01014022,E01014023,E01014024,E01014025,E01014026,E01014027,E01014028,E01014029,E01014030,E01014031,E01014032,E01014033,E01014034,E01014035,E01014036,E01014037,E01014038,E01014039,E01014040,E01014041,E01014042,E01014043,E01014044,E01014045,E01014046,E01014047,E01014048,E01014049,E01014050,E01014051,E01014052,E01014053,E01014054,E01014055,E01014056,E01014057,E01014058,E01014059,E01014060,E01014061,E01014062,E01014063,E01014064,E01014065,E01014066,E01014067,E01014068,E01014069,E01014070,E01014071,E01014072,E01014073,E01014074,E01014075,E01014076,E01014077,E01014078,E01014079,E01014080,E01014081,E01014082,E01014083,E01014084,E01014085,E01014086,E01014087,E01014088,E01014089,E01014090,E01014091,E01014092,E01014093,E01014094,E01014095,E01014096,E01014097,E01014098,E01014099,E01014100,E01014101,E01014102,E01014103,E01014104,E01014105,E01014106,E01014107,E01014108,E01014109,E01014110,E01014111,E01014112,E01014113,E01014114,E01014115,E01014116,E01014117,E01014118,E01014119,E01014120,E01014121,E01014122,E01014123,E01014124,E01014125,E01014126,E01014127,E01014128,E01014129,E01014130,E01014131,E01014132,E01014133,E01014134,E01014135,E01014136,E01014137,E01014138,E01014139,E01014140,E01014142,E01014143,E01014144,E01014145,E01014148,E01014149,E01014150,E01014151,E01014152,E01014153,E01014154,E01014155,E01014157,E01014158,E01014159,E01014160,E01014161,E01014162,E01014163,E01014164,E01014165,E01014166,E01014167,E01014168,E01014169,E01014170,E01014171,E01014172,E01014173,E01014174,E01014175,E01014176,E01014177,E01014178,E01014179,E01014180,E01014181,E01014182,E01014183,E01014184,E01014185,E01014186,E01014187,E01014188,E01014189,E01014190,E01014191,E01014192,E01014193,E01014194,E01014195,E01014196,E01014197,E01014198,E01014199,E01014200,E01014201,E01014202,E01014203,E01014204,E01014205,E01014206,E01014207,E01014208,E01014209,E01014210,E01014211,E01014212,E01014213,E01014214,E01014215,E01014216,E01014217&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_027.json.gz
+  content_sha256: 424bd7326a6d94ecd9f66de11e20fa771a1286e48c234c0268377fc58071e59f
+- id: census_lsoa_tenure5a_bedrooms_028
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 028
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01014218,E01014219,E01014220,E01014221,E01014222,E01014223,E01014224,E01014225,E01014226,E01014228,E01014229,E01014230,E01014233,E01014234,E01014235,E01014237,E01014238,E01014239,E01014240,E01014241,E01014242,E01014243,E01014244,E01014245,E01014246,E01014247,E01014248,E01014249,E01014250,E01014252,E01014253,E01014254,E01014255,E01014256,E01014257,E01014258,E01014259,E01014260,E01014261,E01014262,E01014263,E01014264,E01014265,E01014266,E01014267,E01014268,E01014269,E01014270,E01014271,E01014272,E01014273,E01014274,E01014275,E01014276,E01014277,E01014278,E01014279,E01014280,E01014281,E01014282,E01014283,E01014284,E01014285,E01014286,E01014287,E01014288,E01014289,E01014290,E01014291,E01014292,E01014293,E01014294,E01014295,E01014296,E01014297,E01014298,E01014299,E01014300,E01014301,E01014302,E01014303,E01014304,E01014305,E01014306,E01014307,E01014308,E01014309,E01014310,E01014311,E01014312,E01014313,E01014314,E01014315,E01014316,E01014317,E01014318,E01014319,E01014320,E01014321,E01014322,E01014323,E01014324,E01014325,E01014326,E01014327,E01014328,E01014329,E01014330,E01014331,E01014332,E01014333,E01014334,E01014335,E01014336,E01014337,E01014338,E01014339,E01014340,E01014341,E01014342,E01014343,E01014344,E01014345,E01014346,E01014347,E01014348,E01014349,E01014350,E01014351,E01014352,E01014353,E01014354,E01014355,E01014356,E01014357,E01014358,E01014359,E01014360,E01014361,E01014362,E01014363,E01014364,E01014365,E01014366,E01014367,E01014368,E01014369,E01014370,E01014371,E01014372,E01014373,E01014374,E01014375,E01014376,E01014377,E01014380,E01014381,E01014382,E01014383,E01014384,E01014385,E01014386,E01014387,E01014388,E01014389,E01014390,E01014391,E01014392,E01014393,E01014394,E01014395,E01014396,E01014397,E01014398,E01014399,E01014400,E01014401,E01014403,E01014404,E01014405,E01014407,E01014409,E01014410,E01014411,E01014412,E01014413,E01014414,E01014415,E01014416,E01014417,E01014418,E01014419,E01014420,E01014421,E01014422,E01014423,E01014424,E01014425,E01014426,E01014427,E01014428,E01014429,E01014430,E01014431,E01014432,E01014433,E01014434,E01014435,E01014436,E01014437,E01014438,E01014439,E01014440,E01014441,E01014442,E01014443,E01014444,E01014445,E01014446,E01014447,E01014448,E01014449,E01014450,E01014451,E01014452,E01014453,E01014454,E01014455,E01014456,E01014457,E01014458,E01014459,E01014460,E01014461,E01014462,E01014463,E01014464,E01014465,E01014466,E01014467,E01014468,E01014469,E01014470,E01014471,E01014472,E01014473,E01014474,E01014476,E01014477,E01014478,E01014479,E01014480,E01014481,E01014482,E01014483,E01014484,E01014485,E01014486,E01014487,E01014488,E01014489,E01014491,E01014492,E01014493,E01014494,E01014495,E01014496,E01014497,E01014498,E01014499,E01014500,E01014501,E01014502,E01014504,E01014505,E01014506,E01014507,E01014508,E01014509,E01014510,E01014511,E01014512,E01014513,E01014514,E01014515,E01014516,E01014517,E01014518,E01014519,E01014520,E01014521,E01014522,E01014523,E01014524,E01014525,E01014526,E01014527,E01014528,E01014529,E01014530,E01014531,E01014532,E01014533,E01014534,E01014535,E01014536,E01014537,E01014538,E01014539,E01014544,E01014545,E01014546,E01014547,E01014548,E01014549,E01014550,E01014551,E01014552,E01014553,E01014554,E01014555,E01014556,E01014557,E01014558,E01014559,E01014560,E01014561,E01014562,E01014563,E01014564,E01014565,E01014566,E01014567,E01014568,E01014569,E01014570,E01014571,E01014572,E01014573,E01014574,E01014575,E01014576,E01014577,E01014578,E01014579,E01014580,E01014581,E01014582,E01014583,E01014584,E01014585,E01014586,E01014587,E01014588,E01014589,E01014590,E01014591,E01014592,E01014593,E01014594,E01014595,E01014596,E01014597,E01014598,E01014599,E01014600,E01014601,E01014602,E01014603,E01014605,E01014607,E01014608,E01014609,E01014610,E01014611,E01014612,E01014613,E01014614,E01014615,E01014616,E01014617,E01014618,E01014619,E01014620,E01014621,E01014622,E01014623,E01014624,E01014626,E01014627,E01014629,E01014630,E01014631,E01014632,E01014633,E01014634,E01014635,E01014636,E01014637,E01014638,E01014639,E01014640,E01014641,E01014642,E01014643,E01014644,E01014645,E01014646,E01014647,E01014648,E01014649,E01014650,E01014651,E01014653,E01014654,E01014655,E01014658,E01014659,E01014660,E01014661,E01014662,E01014663,E01014664,E01014665,E01014666,E01014667,E01014668,E01014669,E01014670,E01014671,E01014672,E01014673,E01014674,E01014675,E01014676,E01014677,E01014678,E01014679,E01014680,E01014681,E01014682,E01014683,E01014684,E01014685,E01014686,E01014687,E01014688,E01014689,E01014690,E01014691,E01014692,E01014693,E01014694,E01014695,E01014696,E01014698,E01014699,E01014700,E01014701,E01014702,E01014703,E01014704,E01014705,E01014706,E01014707,E01014708,E01014709,E01014710,E01014711,E01014712,E01014713,E01014714,E01014715,E01014716,E01014717,E01014718,E01014719,E01014720,E01014721,E01014722,E01014723,E01014725,E01014726,E01014727,E01014729,E01014730,E01014731,E01014732,E01014733,E01014734,E01014735,E01014736,E01014737,E01014738,E01014739,E01014740,E01014741,E01014742,E01014744,E01014745&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_028.json.gz
+  content_sha256: bc064e5a9155549f19b9102719aac41dd3e2a121dff9a89a0c0bd88fb082ebc7
+- id: census_lsoa_tenure5a_bedrooms_029
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 029
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01014746,E01014747,E01014748,E01014749,E01014750,E01014751,E01014752,E01014753,E01014754,E01014755,E01014756,E01014757,E01014758,E01014759,E01014760,E01014761,E01014762,E01014763,E01014764,E01014765,E01014766,E01014767,E01014770,E01014771,E01014772,E01014773,E01014774,E01014775,E01014776,E01014777,E01014778,E01014779,E01014780,E01014781,E01014782,E01014783,E01014784,E01014785,E01014786,E01014787,E01014789,E01014790,E01014792,E01014793,E01014794,E01014795,E01014796,E01014797,E01014798,E01014799,E01014800,E01014801,E01014802,E01014803,E01014804,E01014805,E01014806,E01014807,E01014808,E01014809,E01014811,E01014812,E01014813,E01014815,E01014816,E01014817,E01014818,E01014819,E01014820,E01014821,E01014822,E01014823,E01014824,E01014825,E01014826,E01014827,E01014828,E01014829,E01014830,E01014831,E01014832,E01014833,E01014834,E01014835,E01014836,E01014838,E01014839,E01014840,E01014841,E01014842,E01014843,E01014844,E01014845,E01014846,E01014847,E01014848,E01014849,E01014850,E01014851,E01014852,E01014853,E01014854,E01014855,E01014856,E01014857,E01014858,E01014859,E01014860,E01014861,E01014862,E01014863,E01014864,E01014865,E01014866,E01014867,E01014869,E01014870,E01014871,E01014872,E01014873,E01014874,E01014875,E01014876,E01014877,E01014878,E01014879,E01014880,E01014881,E01014882,E01014883,E01014884,E01014885,E01014886,E01014887,E01014888,E01014889,E01014890,E01014891,E01014892,E01014893,E01014894,E01014895,E01014896,E01014897,E01014898,E01014899,E01014900,E01014901,E01014902,E01014903,E01014904,E01014905,E01014906,E01014907,E01014908,E01014909,E01014910,E01014911,E01014912,E01014913,E01014914,E01014915,E01014916,E01014917,E01014918,E01014919,E01014920,E01014921,E01014922,E01014923,E01014924,E01014925,E01014926,E01014927,E01014928,E01014929,E01014930,E01014931,E01014932,E01014933,E01014934,E01014935,E01014936,E01014937,E01014938,E01014939,E01014940,E01014941,E01014942,E01014943,E01014944,E01014945,E01014946,E01014947,E01014948,E01014950,E01014951,E01014953,E01014954,E01014955,E01014956,E01014957,E01014958,E01014959,E01014960,E01014961,E01014962,E01014963,E01014964,E01014965,E01014967,E01014968,E01014969,E01014972,E01014973,E01014974,E01014975,E01014976,E01014977,E01014978,E01014979,E01014980,E01014982,E01014983,E01014984,E01014985,E01014986,E01014987,E01014988,E01014989,E01014990,E01014991,E01014992,E01014993,E01014994,E01014995,E01014996,E01014998,E01014999,E01015000,E01015001,E01015002,E01015003,E01015004,E01015005,E01015006,E01015007,E01015008,E01015009,E01015010,E01015011,E01015012,E01015013,E01015014,E01015015,E01015016,E01015017,E01015018,E01015019,E01015020,E01015021,E01015022,E01015023,E01015024,E01015025,E01015026,E01015027,E01015028,E01015029,E01015030,E01015031,E01015032,E01015033,E01015034,E01015035,E01015036,E01015037,E01015038,E01015039,E01015040,E01015041,E01015042,E01015043,E01015044,E01015045,E01015046,E01015047,E01015048,E01015049,E01015050,E01015051,E01015052,E01015053,E01015054,E01015055,E01015056,E01015057,E01015058,E01015059,E01015060,E01015061,E01015062,E01015063,E01015064,E01015065,E01015066,E01015067,E01015068,E01015069,E01015070,E01015071,E01015072,E01015073,E01015074,E01015075,E01015076,E01015077,E01015078,E01015079,E01015080,E01015081,E01015082,E01015083,E01015084,E01015085,E01015086,E01015087,E01015088,E01015089,E01015090,E01015091,E01015092,E01015093,E01015094,E01015095,E01015096,E01015097,E01015098,E01015099,E01015100,E01015101,E01015102,E01015103,E01015104,E01015105,E01015106,E01015107,E01015108,E01015109,E01015110,E01015111,E01015112,E01015113,E01015114,E01015115,E01015116,E01015117,E01015118,E01015119,E01015120,E01015121,E01015122,E01015123,E01015124,E01015125,E01015126,E01015127,E01015128,E01015129,E01015130,E01015131,E01015132,E01015133,E01015134,E01015136,E01015137,E01015138,E01015139,E01015140,E01015141,E01015142,E01015143,E01015144,E01015145,E01015146,E01015147,E01015148,E01015149,E01015150,E01015151,E01015152,E01015153,E01015155,E01015156,E01015159,E01015160,E01015161,E01015162,E01015163,E01015164,E01015165,E01015166,E01015167,E01015168,E01015169,E01015170,E01015171,E01015172,E01015173,E01015174,E01015175,E01015176,E01015177,E01015178,E01015179,E01015180,E01015181,E01015182,E01015183,E01015184,E01015185,E01015186,E01015187,E01015188,E01015189,E01015190,E01015191,E01015192,E01015193,E01015194,E01015196,E01015197,E01015198,E01015199,E01015200,E01015201,E01015202,E01015203,E01015204,E01015205,E01015206,E01015207,E01015208,E01015209,E01015210,E01015211,E01015212,E01015213,E01015214,E01015215,E01015216,E01015217,E01015218,E01015219,E01015220,E01015221,E01015222,E01015223,E01015224,E01015225,E01015226,E01015227,E01015228,E01015229,E01015230,E01015231,E01015232,E01015233,E01015234,E01015235,E01015236,E01015237,E01015238,E01015239,E01015240,E01015241,E01015242,E01015243,E01015244,E01015245,E01015246,E01015247,E01015248,E01015249,E01015250,E01015251,E01015253,E01015254,E01015255,E01015256,E01015257,E01015258,E01015259,E01015260,E01015261,E01015262,E01015263,E01015264,E01015265,E01015266&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_029.json.gz
+  content_sha256: 859b8604c440fcf38b5d0afe2f4c9b12d2a74077f8e553df4a30b4d035dd8e0a
+- id: census_lsoa_tenure5a_bedrooms_030
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 030
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01015267,E01015268,E01015269,E01015270,E01015271,E01015272,E01015273,E01015274,E01015275,E01015276,E01015277,E01015279,E01015280,E01015281,E01015286,E01015288,E01015289,E01015290,E01015291,E01015292,E01015293,E01015294,E01015295,E01015296,E01015297,E01015298,E01015299,E01015300,E01015301,E01015302,E01015303,E01015304,E01015305,E01015306,E01015307,E01015308,E01015309,E01015310,E01015311,E01015312,E01015313,E01015314,E01015315,E01015316,E01015317,E01015318,E01015319,E01015320,E01015321,E01015322,E01015323,E01015324,E01015325,E01015326,E01015327,E01015328,E01015329,E01015330,E01015331,E01015332,E01015333,E01015334,E01015335,E01015336,E01015337,E01015338,E01015339,E01015340,E01015341,E01015342,E01015343,E01015344,E01015345,E01015346,E01015348,E01015349,E01015350,E01015351,E01015352,E01015353,E01015354,E01015355,E01015356,E01015357,E01015358,E01015359,E01015360,E01015361,E01015362,E01015363,E01015364,E01015365,E01015366,E01015367,E01015368,E01015369,E01015370,E01015371,E01015372,E01015373,E01015374,E01015375,E01015376,E01015377,E01015378,E01015379,E01015380,E01015381,E01015382,E01015383,E01015384,E01015385,E01015386,E01015387,E01015388,E01015389,E01015390,E01015391,E01015392,E01015393,E01015394,E01015395,E01015396,E01015397,E01015398,E01015399,E01015400,E01015401,E01015402,E01015403,E01015404,E01015405,E01015406,E01015407,E01015408,E01015409,E01015410,E01015411,E01015412,E01015413,E01015414,E01015415,E01015416,E01015417,E01015418,E01015419,E01015420,E01015421,E01015422,E01015424,E01015425,E01015426,E01015427,E01015428,E01015429,E01015430,E01015431,E01015432,E01015433,E01015434,E01015435,E01015436,E01015437,E01015438,E01015439,E01015440,E01015441,E01015442,E01015443,E01015444,E01015445,E01015446,E01015447,E01015448,E01015449,E01015450,E01015451,E01015452,E01015453,E01015454,E01015455,E01015456,E01015457,E01015458,E01015459,E01015460,E01015461,E01015462,E01015463,E01015464,E01015465,E01015466,E01015471,E01015473,E01015475,E01015477,E01015478,E01015479,E01015480,E01015481,E01015482,E01015483,E01015484,E01015485,E01015486,E01015487,E01015488,E01015489,E01015490,E01015491,E01015492,E01015493,E01015494,E01015496,E01015497,E01015498,E01015499,E01015500,E01015501,E01015502,E01015503,E01015504,E01015505,E01015506,E01015507,E01015508,E01015509,E01015510,E01015511,E01015512,E01015513,E01015514,E01015515,E01015516,E01015517,E01015518,E01015519,E01015520,E01015521,E01015522,E01015523,E01015524,E01015525,E01015526,E01015527,E01015528,E01015529,E01015530,E01015532,E01015533,E01015534,E01015535,E01015537,E01015538,E01015539,E01015540,E01015541,E01015542,E01015543,E01015544,E01015545,E01015546,E01015547,E01015548,E01015549,E01015550,E01015551,E01015552,E01015553,E01015554,E01015555,E01015556,E01015557,E01015558,E01015559,E01015560,E01015561,E01015562,E01015563,E01015564,E01015565,E01015566,E01015567,E01015568,E01015569,E01015570,E01015571,E01015572,E01015573,E01015574,E01015575,E01015576,E01015578,E01015579,E01015580,E01015581,E01015582,E01015584,E01015585,E01015586,E01015587,E01015588,E01015589,E01015590,E01015591,E01015592,E01015593,E01015594,E01015595,E01015596,E01015597,E01015598,E01015599,E01015600,E01015601,E01015603,E01015604,E01015605,E01015606,E01015607,E01015608,E01015609,E01015610,E01015612,E01015613,E01015614,E01015615,E01015616,E01015617,E01015618,E01015619,E01015620,E01015622,E01015623,E01015624,E01015626,E01015627,E01015628,E01015629,E01015630,E01015631,E01015632,E01015633,E01015634,E01015635,E01015636,E01015637,E01015638,E01015639,E01015640,E01015641,E01015642,E01015643,E01015644,E01015645,E01015646,E01015647,E01015648,E01015650,E01015651,E01015652,E01015653,E01015654,E01015655,E01015656,E01015658,E01015659,E01015660,E01015661,E01015662,E01015663,E01015664,E01015665,E01015666,E01015667,E01015668,E01015669,E01015670,E01015672,E01015673,E01015674,E01015675,E01015676,E01015677,E01015678,E01015679,E01015680,E01015681,E01015682,E01015683,E01015684,E01015685,E01015686,E01015687,E01015688,E01015689,E01015690,E01015691,E01015692,E01015693,E01015694,E01015695,E01015696,E01015697,E01015698,E01015699,E01015700,E01015701,E01015702,E01015703,E01015704,E01015705,E01015706,E01015707,E01015708,E01015709,E01015710,E01015711,E01015712,E01015713,E01015714,E01015715,E01015716,E01015717,E01015718,E01015719,E01015720,E01015721,E01015722,E01015723,E01015724,E01015725,E01015726,E01015727,E01015728,E01015729,E01015730,E01015731,E01015732,E01015733,E01015734,E01015735,E01015736,E01015737,E01015738,E01015739,E01015740,E01015742,E01015743,E01015744,E01015745,E01015746,E01015747,E01015748,E01015749,E01015750,E01015751,E01015752,E01015753,E01015754,E01015755,E01015756,E01015757,E01015758,E01015759,E01015760,E01015761,E01015762,E01015763,E01015764,E01015765,E01015766,E01015767,E01015768,E01015769,E01015770,E01015771,E01015772,E01015773,E01015774,E01015775,E01015776,E01015777,E01015778,E01015779,E01015780,E01015781,E01015782,E01015783,E01015784,E01015785,E01015786,E01015787,E01015788,E01015789,E01015790,E01015792,E01015793,E01015795,E01015796&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_030.json.gz
+  content_sha256: e9f395b9d484259865c3d93a015f9e2f42e8f60172de2333666f1d650d1380c1
+- id: census_lsoa_tenure5a_bedrooms_031
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 031
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01015797,E01015798,E01015799,E01015800,E01015801,E01015802,E01015803,E01015804,E01015805,E01015806,E01015807,E01015808,E01015809,E01015810,E01015811,E01015812,E01015813,E01015814,E01015815,E01015816,E01015817,E01015818,E01015819,E01015820,E01015821,E01015822,E01015823,E01015824,E01015825,E01015826,E01015827,E01015828,E01015829,E01015830,E01015831,E01015832,E01015833,E01015834,E01015835,E01015836,E01015837,E01015838,E01015839,E01015840,E01015841,E01015842,E01015843,E01015844,E01015845,E01015846,E01015847,E01015848,E01015849,E01015850,E01015851,E01015852,E01015853,E01015854,E01015855,E01015856,E01015857,E01015858,E01015859,E01015860,E01015861,E01015862,E01015863,E01015864,E01015865,E01015866,E01015867,E01015868,E01015869,E01015870,E01015871,E01015872,E01015873,E01015874,E01015875,E01015876,E01015877,E01015878,E01015879,E01015880,E01015881,E01015882,E01015883,E01015884,E01015885,E01015886,E01015887,E01015888,E01015889,E01015890,E01015891,E01015892,E01015893,E01015894,E01015896,E01015897,E01015898,E01015899,E01015900,E01015901,E01015902,E01015903,E01015904,E01015905,E01015906,E01015907,E01015908,E01015909,E01015910,E01015911,E01015912,E01015913,E01015914,E01015915,E01015916,E01015917,E01015918,E01015919,E01015920,E01015921,E01015922,E01015923,E01015924,E01015925,E01015926,E01015927,E01015928,E01015929,E01015930,E01015931,E01015932,E01015933,E01015934,E01015935,E01015936,E01015937,E01015938,E01015939,E01015941,E01015942,E01015943,E01015944,E01015945,E01015946,E01015947,E01015948,E01015949,E01015950,E01015951,E01015952,E01015953,E01015954,E01015955,E01015956,E01015957,E01015958,E01015959,E01015960,E01015961,E01015962,E01015963,E01015964,E01015965,E01015966,E01015967,E01015968,E01015969,E01015970,E01015971,E01015972,E01015974,E01015975,E01015976,E01015977,E01015978,E01015979,E01015980,E01015982,E01015983,E01015984,E01015985,E01015986,E01015987,E01015988,E01015989,E01015990,E01015991,E01015992,E01015993,E01015994,E01015995,E01015996,E01015997,E01015998,E01015999,E01016000,E01016001,E01016002,E01016003,E01016004,E01016005,E01016006,E01016007,E01016008,E01016009,E01016010,E01016011,E01016013,E01016014,E01016015,E01016016,E01016017,E01016018,E01016019,E01016020,E01016021,E01016022,E01016023,E01016024,E01016025,E01016026,E01016027,E01016028,E01016029,E01016030,E01016031,E01016032,E01016033,E01016034,E01016035,E01016037,E01016038,E01016039,E01016040,E01016041,E01016042,E01016043,E01016044,E01016045,E01016046,E01016047,E01016048,E01016049,E01016050,E01016051,E01016052,E01016053,E01016054,E01016055,E01016056,E01016057,E01016058,E01016059,E01016060,E01016061,E01016062,E01016063,E01016064,E01016065,E01016066,E01016067,E01016068,E01016069,E01016070,E01016071,E01016072,E01016073,E01016074,E01016075,E01016077,E01016078,E01016079,E01016080,E01016081,E01016082,E01016083,E01016084,E01016085,E01016086,E01016087,E01016088,E01016089,E01016090,E01016091,E01016092,E01016093,E01016094,E01016095,E01016096,E01016097,E01016098,E01016099,E01016100,E01016101,E01016102,E01016103,E01016104,E01016105,E01016106,E01016107,E01016111,E01016113,E01016114,E01016115,E01016116,E01016117,E01016118,E01016119,E01016120,E01016121,E01016122,E01016123,E01016124,E01016125,E01016126,E01016127,E01016128,E01016129,E01016130,E01016131,E01016132,E01016133,E01016134,E01016135,E01016136,E01016137,E01016138,E01016139,E01016140,E01016141,E01016142,E01016143,E01016144,E01016145,E01016147,E01016148,E01016149,E01016150,E01016151,E01016152,E01016153,E01016154,E01016155,E01016156,E01016157,E01016158,E01016159,E01016160,E01016161,E01016162,E01016163,E01016164,E01016165,E01016166,E01016167,E01016168,E01016169,E01016170,E01016171,E01016172,E01016173,E01016174,E01016175,E01016176,E01016177,E01016178,E01016179,E01016180,E01016181,E01016182,E01016183,E01016185,E01016187,E01016188,E01016190,E01016191,E01016192,E01016193,E01016194,E01016195,E01016196,E01016197,E01016198,E01016199,E01016200,E01016201,E01016202,E01016203,E01016204,E01016205,E01016206,E01016207,E01016208,E01016209,E01016210,E01016212,E01016213,E01016214,E01016215,E01016216,E01016217,E01016218,E01016219,E01016220,E01016221,E01016222,E01016223,E01016224,E01016226,E01016227,E01016228,E01016229,E01016230,E01016231,E01016232,E01016233,E01016234,E01016235,E01016236,E01016237,E01016238,E01016239,E01016240,E01016241,E01016242,E01016243,E01016245,E01016246,E01016247,E01016248,E01016250,E01016252,E01016253,E01016254,E01016255,E01016256,E01016257,E01016258,E01016259,E01016260,E01016261,E01016262,E01016263,E01016264,E01016265,E01016266,E01016267,E01016268,E01016269,E01016270,E01016271,E01016272,E01016273,E01016274,E01016275,E01016276,E01016277,E01016279,E01016280,E01016281,E01016282,E01016284,E01016285,E01016286,E01016287,E01016288,E01016289,E01016290,E01016291,E01016292,E01016294,E01016295,E01016296,E01016297,E01016298,E01016299,E01016300,E01016301,E01016302,E01016303,E01016304,E01016305,E01016306,E01016307,E01016308,E01016309,E01016310,E01016311,E01016312,E01016313,E01016314,E01016315,E01016316,E01016317,E01016318,E01016319&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_031.json.gz
+  content_sha256: 57fca429d8f8b27e39be04352b2cedbc7b95ab3869e3509031b3a4b5708b4334
+- id: census_lsoa_tenure5a_bedrooms_032
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 032
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01016320,E01016321,E01016322,E01016323,E01016324,E01016325,E01016326,E01016327,E01016328,E01016329,E01016330,E01016331,E01016332,E01016333,E01016334,E01016335,E01016336,E01016337,E01016338,E01016339,E01016340,E01016341,E01016342,E01016343,E01016344,E01016345,E01016346,E01016348,E01016349,E01016350,E01016353,E01016355,E01016356,E01016357,E01016358,E01016359,E01016360,E01016361,E01016362,E01016363,E01016364,E01016365,E01016366,E01016367,E01016368,E01016369,E01016370,E01016371,E01016372,E01016373,E01016374,E01016375,E01016376,E01016377,E01016379,E01016380,E01016381,E01016382,E01016383,E01016384,E01016385,E01016386,E01016387,E01016388,E01016389,E01016390,E01016391,E01016392,E01016393,E01016394,E01016395,E01016396,E01016397,E01016398,E01016399,E01016400,E01016401,E01016402,E01016403,E01016404,E01016405,E01016406,E01016407,E01016408,E01016409,E01016410,E01016411,E01016412,E01016413,E01016414,E01016415,E01016416,E01016417,E01016418,E01016419,E01016420,E01016421,E01016422,E01016423,E01016424,E01016425,E01016426,E01016427,E01016428,E01016429,E01016430,E01016431,E01016432,E01016433,E01016434,E01016435,E01016436,E01016437,E01016438,E01016439,E01016440,E01016441,E01016443,E01016444,E01016445,E01016446,E01016447,E01016448,E01016449,E01016450,E01016451,E01016452,E01016453,E01016454,E01016455,E01016456,E01016457,E01016458,E01016460,E01016462,E01016463,E01016464,E01016465,E01016467,E01016468,E01016469,E01016470,E01016471,E01016472,E01016473,E01016474,E01016475,E01016476,E01016477,E01016478,E01016479,E01016480,E01016481,E01016482,E01016483,E01016484,E01016485,E01016486,E01016487,E01016488,E01016490,E01016491,E01016492,E01016493,E01016494,E01016495,E01016496,E01016497,E01016498,E01016499,E01016500,E01016501,E01016502,E01016503,E01016504,E01016505,E01016506,E01016507,E01016508,E01016509,E01016510,E01016511,E01016514,E01016515,E01016516,E01016517,E01016518,E01016519,E01016520,E01016521,E01016522,E01016523,E01016524,E01016525,E01016526,E01016527,E01016528,E01016529,E01016530,E01016531,E01016532,E01016533,E01016534,E01016535,E01016536,E01016537,E01016538,E01016539,E01016540,E01016541,E01016542,E01016543,E01016544,E01016545,E01016546,E01016547,E01016548,E01016549,E01016550,E01016551,E01016552,E01016553,E01016554,E01016555,E01016556,E01016557,E01016558,E01016559,E01016560,E01016561,E01016562,E01016563,E01016564,E01016565,E01016566,E01016567,E01016568,E01016569,E01016570,E01016571,E01016572,E01016573,E01016574,E01016575,E01016576,E01016577,E01016578,E01016579,E01016580,E01016581,E01016582,E01016583,E01016584,E01016585,E01016586,E01016587,E01016588,E01016589,E01016591,E01016592,E01016593,E01016594,E01016595,E01016596,E01016597,E01016598,E01016599,E01016600,E01016601,E01016602,E01016603,E01016604,E01016605,E01016606,E01016607,E01016608,E01016609,E01016611,E01016612,E01016613,E01016614,E01016615,E01016616,E01016617,E01016618,E01016619,E01016620,E01016621,E01016622,E01016623,E01016624,E01016625,E01016626,E01016627,E01016628,E01016629,E01016630,E01016631,E01016632,E01016633,E01016634,E01016635,E01016636,E01016637,E01016638,E01016639,E01016640,E01016643,E01016644,E01016645,E01016646,E01016647,E01016648,E01016649,E01016651,E01016652,E01016653,E01016654,E01016655,E01016656,E01016657,E01016658,E01016659,E01016660,E01016661,E01016662,E01016663,E01016664,E01016665,E01016666,E01016668,E01016669,E01016670,E01016671,E01016672,E01016673,E01016674,E01016676,E01016678,E01016679,E01016680,E01016684,E01016685,E01016686,E01016687,E01016688,E01016689,E01016690,E01016691,E01016692,E01016693,E01016694,E01016695,E01016696,E01016699,E01016700,E01016701,E01016702,E01016703,E01016704,E01016705,E01016706,E01016707,E01016708,E01016709,E01016710,E01016711,E01016713,E01016714,E01016715,E01016716,E01016717,E01016718,E01016719,E01016720,E01016721,E01016722,E01016723,E01016724,E01016725,E01016726,E01016727,E01016728,E01016729,E01016731,E01016732,E01016733,E01016734,E01016736,E01016737,E01016738,E01016739,E01016740,E01016741,E01016742,E01016744,E01016745,E01016746,E01016748,E01016750,E01016751,E01016752,E01016753,E01016754,E01016755,E01016756,E01016757,E01016758,E01016759,E01016761,E01016762,E01016763,E01016764,E01016765,E01016766,E01016767,E01016768,E01016769,E01016770,E01016771,E01016772,E01016773,E01016774,E01016775,E01016776,E01016777,E01016778,E01016780,E01016781,E01016783,E01016784,E01016786,E01016787,E01016788,E01016789,E01016790,E01016791,E01016792,E01016793,E01016794,E01016795,E01016796,E01016797,E01016798,E01016799,E01016800,E01016801,E01016802,E01016803,E01016804,E01016805,E01016806,E01016807,E01016808,E01016809,E01016810,E01016811,E01016812,E01016813,E01016815,E01016816,E01016817,E01016818,E01016819,E01016820,E01016821,E01016822,E01016823,E01016824,E01016825,E01016826,E01016827,E01016828,E01016829,E01016830,E01016831,E01016832,E01016833,E01016834,E01016835,E01016836,E01016837,E01016838,E01016839,E01016841,E01016842,E01016843,E01016844,E01016845,E01016846,E01016847,E01016848,E01016849,E01016850,E01016851,E01016852,E01016853,E01016854,E01016855,E01016856&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_032.json.gz
+  content_sha256: 32df4eb7f57a5eec6f0921bf99fc84a9a6e86b7ef8bfbfbfb1d27fdbf89f2c09
+- id: census_lsoa_tenure5a_bedrooms_033
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 033
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01016857,E01016858,E01016859,E01016860,E01016861,E01016862,E01016863,E01016864,E01016865,E01016866,E01016867,E01016868,E01016869,E01016870,E01016871,E01016872,E01016873,E01016874,E01016875,E01016876,E01016877,E01016878,E01016879,E01016880,E01016881,E01016882,E01016883,E01016884,E01016885,E01016886,E01016887,E01016888,E01016889,E01016890,E01016891,E01016892,E01016893,E01016894,E01016895,E01016896,E01016897,E01016898,E01016899,E01016900,E01016901,E01016902,E01016903,E01016904,E01016905,E01016906,E01016907,E01016908,E01016909,E01016910,E01016911,E01016912,E01016913,E01016914,E01016915,E01016916,E01016917,E01016918,E01016919,E01016920,E01016921,E01016922,E01016923,E01016924,E01016925,E01016926,E01016927,E01016928,E01016929,E01016930,E01016931,E01016932,E01016933,E01016934,E01016935,E01016936,E01016937,E01016938,E01016939,E01016940,E01016941,E01016942,E01016943,E01016944,E01016945,E01016946,E01016947,E01016948,E01016949,E01016950,E01016951,E01016952,E01016953,E01016954,E01016955,E01016956,E01016957,E01016958,E01016959,E01016960,E01016961,E01016962,E01016963,E01016964,E01016966,E01016967,E01016968,E01016969,E01016970,E01016971,E01016972,E01016973,E01016974,E01016975,E01016976,E01016977,E01016978,E01016979,E01016980,E01016981,E01016982,E01016983,E01016984,E01016985,E01016986,E01016987,E01016988,E01016989,E01016990,E01016991,E01016992,E01016993,E01016994,E01016995,E01016996,E01016997,E01016998,E01016999,E01017000,E01017001,E01017002,E01017003,E01017004,E01017005,E01017006,E01017007,E01017008,E01017009,E01017010,E01017011,E01017012,E01017013,E01017014,E01017015,E01017016,E01017017,E01017018,E01017019,E01017020,E01017021,E01017022,E01017023,E01017024,E01017025,E01017026,E01017027,E01017028,E01017029,E01017030,E01017031,E01017032,E01017034,E01017035,E01017036,E01017037,E01017038,E01017039,E01017040,E01017041,E01017042,E01017043,E01017044,E01017045,E01017046,E01017047,E01017048,E01017049,E01017050,E01017051,E01017052,E01017053,E01017054,E01017055,E01017056,E01017057,E01017058,E01017059,E01017060,E01017061,E01017062,E01017063,E01017064,E01017066,E01017067,E01017068,E01017069,E01017070,E01017071,E01017072,E01017073,E01017074,E01017075,E01017076,E01017077,E01017078,E01017079,E01017080,E01017081,E01017082,E01017083,E01017084,E01017085,E01017086,E01017087,E01017088,E01017089,E01017090,E01017091,E01017092,E01017093,E01017094,E01017095,E01017096,E01017097,E01017098,E01017099,E01017100,E01017101,E01017102,E01017103,E01017104,E01017105,E01017106,E01017107,E01017108,E01017109,E01017110,E01017111,E01017112,E01017113,E01017114,E01017115,E01017116,E01017117,E01017118,E01017119,E01017120,E01017121,E01017122,E01017123,E01017124,E01017125,E01017126,E01017127,E01017128,E01017129,E01017130,E01017131,E01017132,E01017133,E01017135,E01017136,E01017137,E01017138,E01017139,E01017144,E01017145,E01017146,E01017147,E01017148,E01017149,E01017150,E01017151,E01017152,E01017153,E01017154,E01017155,E01017156,E01017157,E01017158,E01017159,E01017160,E01017162,E01017163,E01017164,E01017165,E01017166,E01017167,E01017168,E01017169,E01017170,E01017171,E01017172,E01017173,E01017174,E01017175,E01017176,E01017177,E01017178,E01017179,E01017180,E01017181,E01017182,E01017183,E01017184,E01017185,E01017186,E01017187,E01017188,E01017189,E01017190,E01017191,E01017192,E01017193,E01017194,E01017195,E01017196,E01017197,E01017198,E01017199,E01017200,E01017201,E01017202,E01017203,E01017204,E01017205,E01017206,E01017207,E01017208,E01017209,E01017210,E01017211,E01017212,E01017213,E01017214,E01017215,E01017216,E01017217,E01017218,E01017219,E01017220,E01017221,E01017222,E01017223,E01017224,E01017225,E01017226,E01017227,E01017228,E01017231,E01017232,E01017233,E01017234,E01017235,E01017236,E01017237,E01017238,E01017239,E01017240,E01017241,E01017242,E01017243,E01017244,E01017245,E01017246,E01017247,E01017248,E01017249,E01017250,E01017251,E01017252,E01017253,E01017254,E01017255,E01017256,E01017257,E01017258,E01017259,E01017260,E01017261,E01017262,E01017263,E01017264,E01017265,E01017266,E01017267,E01017270,E01017271,E01017272,E01017273,E01017274,E01017275,E01017276,E01017277,E01017279,E01017280,E01017281,E01017282,E01017283,E01017284,E01017285,E01017286,E01017287,E01017288,E01017289,E01017290,E01017291,E01017292,E01017293,E01017294,E01017295,E01017296,E01017297,E01017298,E01017299,E01017300,E01017301,E01017302,E01017303,E01017304,E01017305,E01017306,E01017307,E01017308,E01017309,E01017310,E01017311,E01017312,E01017315,E01017316,E01017317,E01017318,E01017319,E01017320,E01017321,E01017322,E01017323,E01017324,E01017325,E01017326,E01017327,E01017328,E01017329,E01017331,E01017332,E01017333,E01017334,E01017335,E01017336,E01017337,E01017338,E01017339,E01017340,E01017341,E01017342,E01017343,E01017344,E01017345,E01017346,E01017347,E01017348,E01017349,E01017350,E01017351,E01017352,E01017353,E01017354,E01017355,E01017356,E01017357,E01017358,E01017359,E01017360,E01017361,E01017362,E01017363,E01017364,E01017365,E01017366,E01017367,E01017368,E01017369,E01017370,E01017371,E01017372,E01017373&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_033.json.gz
+  content_sha256: 9b842fec612087a0d3d37c52d1c1134627bdc8b7d2628e5a019dfed0586ac19e
+- id: census_lsoa_tenure5a_bedrooms_034
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 034
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01017374,E01017375,E01017376,E01017377,E01017378,E01017379,E01017380,E01017381,E01017382,E01017383,E01017384,E01017385,E01017386,E01017387,E01017388,E01017391,E01017392,E01017393,E01017394,E01017397,E01017398,E01017399,E01017400,E01017401,E01017402,E01017403,E01017404,E01017405,E01017406,E01017407,E01017408,E01017409,E01017410,E01017412,E01017413,E01017414,E01017415,E01017418,E01017419,E01017420,E01017421,E01017422,E01017423,E01017424,E01017425,E01017426,E01017427,E01017428,E01017429,E01017430,E01017431,E01017432,E01017433,E01017434,E01017435,E01017436,E01017437,E01017438,E01017439,E01017440,E01017441,E01017442,E01017443,E01017444,E01017446,E01017447,E01017449,E01017450,E01017451,E01017452,E01017453,E01017454,E01017455,E01017456,E01017457,E01017458,E01017459,E01017460,E01017462,E01017463,E01017464,E01017465,E01017466,E01017467,E01017468,E01017470,E01017471,E01017472,E01017474,E01017475,E01017476,E01017477,E01017478,E01017479,E01017480,E01017481,E01017482,E01017483,E01017484,E01017485,E01017487,E01017488,E01017489,E01017490,E01017491,E01017492,E01017493,E01017495,E01017496,E01017497,E01017498,E01017499,E01017500,E01017501,E01017502,E01017503,E01017504,E01017505,E01017506,E01017507,E01017508,E01017509,E01017510,E01017511,E01017512,E01017513,E01017514,E01017515,E01017517,E01017518,E01017519,E01017520,E01017521,E01017522,E01017523,E01017524,E01017525,E01017526,E01017527,E01017528,E01017529,E01017530,E01017531,E01017532,E01017533,E01017534,E01017535,E01017536,E01017537,E01017538,E01017539,E01017540,E01017541,E01017542,E01017543,E01017544,E01017545,E01017546,E01017547,E01017549,E01017552,E01017553,E01017554,E01017555,E01017556,E01017557,E01017558,E01017559,E01017560,E01017561,E01017562,E01017563,E01017564,E01017565,E01017566,E01017567,E01017568,E01017569,E01017571,E01017572,E01017573,E01017576,E01017577,E01017578,E01017579,E01017580,E01017581,E01017582,E01017583,E01017584,E01017585,E01017586,E01017587,E01017588,E01017589,E01017590,E01017591,E01017592,E01017593,E01017594,E01017595,E01017596,E01017597,E01017598,E01017600,E01017601,E01017602,E01017603,E01017604,E01017605,E01017606,E01017607,E01017608,E01017609,E01017610,E01017611,E01017612,E01017613,E01017614,E01017616,E01017617,E01017618,E01017619,E01017620,E01017621,E01017622,E01017623,E01017624,E01017625,E01017626,E01017627,E01017628,E01017629,E01017630,E01017631,E01017634,E01017635,E01017636,E01017637,E01017638,E01017639,E01017640,E01017642,E01017643,E01017644,E01017645,E01017646,E01017647,E01017648,E01017649,E01017651,E01017652,E01017653,E01017654,E01017655,E01017657,E01017658,E01017659,E01017660,E01017661,E01017662,E01017663,E01017664,E01017665,E01017666,E01017667,E01017668,E01017669,E01017670,E01017671,E01017672,E01017673,E01017674,E01017675,E01017676,E01017677,E01017678,E01017679,E01017680,E01017681,E01017682,E01017683,E01017684,E01017687,E01017688,E01017689,E01017690,E01017691,E01017692,E01017693,E01017694,E01017695,E01017696,E01017697,E01017698,E01017699,E01017700,E01017701,E01017702,E01017703,E01017704,E01017706,E01017707,E01017708,E01017709,E01017710,E01017711,E01017712,E01017713,E01017714,E01017715,E01017716,E01017717,E01017718,E01017719,E01017720,E01017721,E01017722,E01017723,E01017724,E01017726,E01017727,E01017729,E01017730,E01017731,E01017732,E01017733,E01017734,E01017735,E01017736,E01017737,E01017738,E01017739,E01017740,E01017741,E01017742,E01017743,E01017744,E01017745,E01017746,E01017747,E01017748,E01017749,E01017750,E01017751,E01017752,E01017753,E01017754,E01017755,E01017756,E01017757,E01017758,E01017759,E01017760,E01017761,E01017762,E01017763,E01017764,E01017765,E01017766,E01017767,E01017768,E01017769,E01017770,E01017771,E01017773,E01017774,E01017775,E01017776,E01017777,E01017778,E01017779,E01017780,E01017781,E01017782,E01017783,E01017784,E01017785,E01017786,E01017787,E01017788,E01017789,E01017790,E01017791,E01017792,E01017793,E01017794,E01017795,E01017796,E01017797,E01017798,E01017799,E01017800,E01017801,E01017802,E01017803,E01017804,E01017805,E01017806,E01017807,E01017808,E01017809,E01017810,E01017811,E01017812,E01017813,E01017814,E01017815,E01017816,E01017817,E01017818,E01017819,E01017820,E01017821,E01017822,E01017823,E01017824,E01017825,E01017826,E01017827,E01017828,E01017829,E01017830,E01017831,E01017832,E01017833,E01017834,E01017835,E01017836,E01017837,E01017838,E01017839,E01017840,E01017841,E01017843,E01017844,E01017845,E01017846,E01017847,E01017848,E01017849,E01017850,E01017851,E01017852,E01017853,E01017854,E01017855,E01017856,E01017857,E01017858,E01017859,E01017860,E01017861,E01017862,E01017863,E01017864,E01017865,E01017866,E01017867,E01017868,E01017869,E01017870,E01017871,E01017872,E01017873,E01017874,E01017875,E01017876,E01017877,E01017878,E01017880,E01017881,E01017882,E01017883,E01017884,E01017885,E01017886,E01017887,E01017888,E01017889,E01017890,E01017891,E01017892,E01017893,E01017894,E01017895,E01017896,E01017897,E01017898,E01017899,E01017900,E01017901,E01017902,E01017903,E01017904,E01017905,E01017906,E01017907,E01017910,E01017911&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_034.json.gz
+  content_sha256: 33728b44fb8ccaf3cd3c512a4778b037be091b47bd34c6eaf674a9d77b896642
+- id: census_lsoa_tenure5a_bedrooms_035
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 035
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01017912,E01017913,E01017914,E01017915,E01017916,E01017917,E01017918,E01017919,E01017920,E01017921,E01017922,E01017923,E01017924,E01017925,E01017926,E01017927,E01017928,E01017929,E01017930,E01017931,E01017932,E01017933,E01017934,E01017935,E01017936,E01017937,E01017938,E01017939,E01017940,E01017941,E01017942,E01017943,E01017944,E01017945,E01017946,E01017947,E01017948,E01017949,E01017950,E01017951,E01017952,E01017953,E01017954,E01017955,E01017959,E01017960,E01017961,E01017962,E01017963,E01017964,E01017965,E01017967,E01017968,E01017969,E01017971,E01017972,E01017973,E01017974,E01017975,E01017976,E01017977,E01017978,E01017979,E01017980,E01017983,E01017985,E01017987,E01017988,E01017989,E01017990,E01017991,E01017992,E01017994,E01017995,E01017996,E01017997,E01017998,E01017999,E01018000,E01018001,E01018005,E01018006,E01018007,E01018008,E01018009,E01018010,E01018011,E01018012,E01018013,E01018014,E01018015,E01018016,E01018017,E01018019,E01018020,E01018021,E01018022,E01018023,E01018024,E01018025,E01018026,E01018027,E01018032,E01018033,E01018034,E01018035,E01018036,E01018037,E01018038,E01018039,E01018040,E01018041,E01018042,E01018043,E01018044,E01018045,E01018047,E01018049,E01018050,E01018051,E01018052,E01018053,E01018054,E01018055,E01018056,E01018057,E01018058,E01018059,E01018060,E01018061,E01018062,E01018063,E01018064,E01018065,E01018066,E01018067,E01018068,E01018069,E01018070,E01018071,E01018072,E01018073,E01018074,E01018075,E01018076,E01018077,E01018078,E01018079,E01018080,E01018081,E01018082,E01018083,E01018084,E01018085,E01018086,E01018087,E01018088,E01018090,E01018091,E01018092,E01018093,E01018094,E01018095,E01018096,E01018097,E01018098,E01018099,E01018100,E01018101,E01018103,E01018104,E01018105,E01018106,E01018107,E01018108,E01018109,E01018110,E01018111,E01018113,E01018115,E01018116,E01018117,E01018118,E01018119,E01018120,E01018121,E01018122,E01018123,E01018124,E01018125,E01018126,E01018127,E01018128,E01018129,E01018130,E01018131,E01018133,E01018134,E01018136,E01018137,E01018138,E01018139,E01018140,E01018141,E01018142,E01018143,E01018144,E01018145,E01018146,E01018147,E01018148,E01018149,E01018150,E01018152,E01018153,E01018154,E01018155,E01018156,E01018157,E01018158,E01018159,E01018160,E01018161,E01018162,E01018163,E01018164,E01018165,E01018166,E01018167,E01018168,E01018169,E01018170,E01018171,E01018172,E01018173,E01018174,E01018175,E01018176,E01018177,E01018178,E01018179,E01018180,E01018182,E01018183,E01018184,E01018185,E01018186,E01018187,E01018188,E01018189,E01018190,E01018191,E01018192,E01018193,E01018194,E01018195,E01018196,E01018197,E01018198,E01018199,E01018200,E01018201,E01018202,E01018203,E01018204,E01018205,E01018206,E01018207,E01018208,E01018209,E01018210,E01018211,E01018212,E01018213,E01018214,E01018215,E01018216,E01018217,E01018218,E01018219,E01018220,E01018221,E01018222,E01018223,E01018224,E01018225,E01018226,E01018227,E01018228,E01018231,E01018232,E01018233,E01018234,E01018235,E01018236,E01018237,E01018238,E01018239,E01018240,E01018241,E01018242,E01018243,E01018244,E01018245,E01018246,E01018247,E01018248,E01018251,E01018252,E01018253,E01018254,E01018255,E01018256,E01018257,E01018258,E01018259,E01018260,E01018262,E01018263,E01018264,E01018265,E01018267,E01018268,E01018269,E01018270,E01018271,E01018272,E01018273,E01018274,E01018275,E01018276,E01018277,E01018278,E01018279,E01018280,E01018281,E01018282,E01018283,E01018284,E01018285,E01018286,E01018287,E01018288,E01018289,E01018290,E01018291,E01018292,E01018293,E01018294,E01018295,E01018296,E01018297,E01018298,E01018299,E01018300,E01018301,E01018302,E01018303,E01018304,E01018305,E01018306,E01018307,E01018308,E01018309,E01018310,E01018311,E01018312,E01018313,E01018314,E01018315,E01018316,E01018317,E01018319,E01018320,E01018321,E01018322,E01018323,E01018324,E01018325,E01018328,E01018330,E01018331,E01018332,E01018333,E01018334,E01018335,E01018336,E01018337,E01018338,E01018339,E01018340,E01018341,E01018342,E01018343,E01018344,E01018345,E01018346,E01018347,E01018349,E01018350,E01018351,E01018352,E01018353,E01018354,E01018355,E01018356,E01018357,E01018358,E01018359,E01018360,E01018361,E01018362,E01018363,E01018364,E01018365,E01018366,E01018367,E01018368,E01018369,E01018370,E01018371,E01018372,E01018373,E01018374,E01018375,E01018376,E01018377,E01018378,E01018379,E01018380,E01018381,E01018382,E01018383,E01018384,E01018385,E01018386,E01018387,E01018388,E01018389,E01018390,E01018391,E01018392,E01018395,E01018396,E01018397,E01018398,E01018399,E01018400,E01018401,E01018402,E01018403,E01018404,E01018405,E01018406,E01018407,E01018408,E01018409,E01018410,E01018411,E01018412,E01018413,E01018414,E01018415,E01018416,E01018417,E01018418,E01018419,E01018420,E01018421,E01018422,E01018423,E01018424,E01018425,E01018426,E01018427,E01018428,E01018429,E01018430,E01018431,E01018432,E01018433,E01018434,E01018435,E01018436,E01018437,E01018438,E01018439,E01018440,E01018441,E01018443,E01018445,E01018446,E01018447,E01018448,E01018449,E01018450,E01018451,E01018452,E01018454,E01018455&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_035.json.gz
+  content_sha256: 0473b6621f33b1e32877e6d78c25ed9080626371ce5fe2d25a625c128e2b98d7
+- id: census_lsoa_tenure5a_bedrooms_036
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 036
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01018456,E01018457,E01018458,E01018459,E01018460,E01018461,E01018462,E01018463,E01018464,E01018466,E01018468,E01018469,E01018470,E01018471,E01018472,E01018473,E01018474,E01018475,E01018476,E01018477,E01018478,E01018479,E01018481,E01018482,E01018483,E01018484,E01018485,E01018486,E01018487,E01018488,E01018489,E01018490,E01018491,E01018492,E01018493,E01018494,E01018495,E01018496,E01018497,E01018498,E01018499,E01018500,E01018501,E01018502,E01018503,E01018504,E01018505,E01018506,E01018507,E01018508,E01018509,E01018510,E01018511,E01018512,E01018513,E01018514,E01018515,E01018516,E01018518,E01018519,E01018520,E01018521,E01018522,E01018523,E01018524,E01018525,E01018526,E01018527,E01018528,E01018529,E01018530,E01018531,E01018532,E01018533,E01018534,E01018535,E01018536,E01018537,E01018538,E01018539,E01018540,E01018541,E01018542,E01018543,E01018544,E01018545,E01018546,E01018547,E01018548,E01018549,E01018550,E01018551,E01018552,E01018553,E01018554,E01018555,E01018556,E01018557,E01018558,E01018559,E01018560,E01018561,E01018562,E01018565,E01018566,E01018567,E01018568,E01018569,E01018570,E01018571,E01018572,E01018573,E01018574,E01018575,E01018576,E01018577,E01018578,E01018579,E01018580,E01018581,E01018582,E01018583,E01018584,E01018585,E01018586,E01018587,E01018588,E01018589,E01018590,E01018591,E01018592,E01018593,E01018594,E01018595,E01018596,E01018598,E01018599,E01018600,E01018601,E01018602,E01018603,E01018604,E01018605,E01018606,E01018607,E01018608,E01018609,E01018610,E01018611,E01018612,E01018613,E01018614,E01018615,E01018616,E01018617,E01018618,E01018619,E01018620,E01018621,E01018622,E01018623,E01018624,E01018625,E01018627,E01018628,E01018629,E01018630,E01018631,E01018632,E01018633,E01018634,E01018635,E01018636,E01018637,E01018638,E01018639,E01018640,E01018641,E01018642,E01018643,E01018644,E01018645,E01018646,E01018647,E01018648,E01018649,E01018650,E01018651,E01018652,E01018653,E01018654,E01018655,E01018657,E01018658,E01018659,E01018661,E01018662,E01018663,E01018664,E01018665,E01018666,E01018667,E01018668,E01018669,E01018670,E01018671,E01018672,E01018673,E01018674,E01018675,E01018676,E01018677,E01018678,E01018679,E01018680,E01018681,E01018682,E01018683,E01018684,E01018685,E01018686,E01018687,E01018688,E01018689,E01018690,E01018691,E01018692,E01018693,E01018694,E01018695,E01018696,E01018697,E01018698,E01018699,E01018700,E01018701,E01018702,E01018703,E01018704,E01018705,E01018706,E01018707,E01018708,E01018709,E01018710,E01018711,E01018712,E01018714,E01018715,E01018716,E01018717,E01018718,E01018719,E01018720,E01018721,E01018722,E01018723,E01018724,E01018725,E01018726,E01018727,E01018728,E01018729,E01018730,E01018731,E01018732,E01018733,E01018734,E01018735,E01018736,E01018737,E01018738,E01018739,E01018740,E01018741,E01018742,E01018743,E01018744,E01018745,E01018746,E01018747,E01018748,E01018749,E01018750,E01018751,E01018752,E01018753,E01018754,E01018755,E01018756,E01018757,E01018759,E01018760,E01018761,E01018762,E01018763,E01018764,E01018765,E01018766,E01018767,E01018768,E01018770,E01018771,E01018772,E01018773,E01018774,E01018777,E01018778,E01018779,E01018780,E01018781,E01018782,E01018783,E01018784,E01018785,E01018786,E01018787,E01018788,E01018789,E01018790,E01018791,E01018792,E01018793,E01018794,E01018795,E01018796,E01018797,E01018798,E01018799,E01018800,E01018803,E01018804,E01018805,E01018806,E01018807,E01018808,E01018810,E01018811,E01018812,E01018813,E01018814,E01018815,E01018816,E01018817,E01018818,E01018819,E01018820,E01018821,E01018822,E01018823,E01018824,E01018825,E01018826,E01018827,E01018828,E01018829,E01018830,E01018831,E01018832,E01018833,E01018834,E01018835,E01018836,E01018837,E01018838,E01018839,E01018840,E01018841,E01018842,E01018843,E01018844,E01018845,E01018846,E01018847,E01018848,E01018849,E01018850,E01018851,E01018853,E01018854,E01018856,E01018857,E01018858,E01018859,E01018860,E01018861,E01018862,E01018863,E01018864,E01018865,E01018866,E01018868,E01018869,E01018870,E01018871,E01018872,E01018873,E01018874,E01018875,E01018876,E01018877,E01018878,E01018879,E01018880,E01018881,E01018882,E01018883,E01018884,E01018885,E01018886,E01018887,E01018888,E01018889,E01018890,E01018891,E01018892,E01018893,E01018894,E01018895,E01018896,E01018897,E01018898,E01018899,E01018900,E01018901,E01018902,E01018903,E01018904,E01018905,E01018906,E01018907,E01018908,E01018909,E01018910,E01018911,E01018912,E01018913,E01018914,E01018915,E01018916,E01018917,E01018918,E01018919,E01018920,E01018921,E01018922,E01018923,E01018924,E01018925,E01018926,E01018927,E01018928,E01018929,E01018930,E01018931,E01018932,E01018933,E01018934,E01018935,E01018936,E01018937,E01018938,E01018940,E01018941,E01018942,E01018943,E01018944,E01018945,E01018946,E01018948,E01018949,E01018950,E01018951,E01018952,E01018953,E01018954,E01018955,E01018956,E01018957,E01018958,E01018959,E01018960,E01018961,E01018962,E01018965,E01018966,E01018967,E01018968,E01018969,E01018970,E01018971,E01018972,E01018973,E01018974,E01018975,E01018976,E01018977,E01018978,E01018979,E01018980&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_036.json.gz
+  content_sha256: 7dd4f617f9e0b3fda9109c9065fdba482cd3830361f5c985ee268b2f237b30ee
+- id: census_lsoa_tenure5a_bedrooms_037
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 037
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01018981,E01018982,E01018983,E01018985,E01018986,E01018987,E01018988,E01018989,E01018990,E01018991,E01018992,E01018993,E01018994,E01018995,E01018996,E01018997,E01018998,E01018999,E01019000,E01019001,E01019002,E01019003,E01019004,E01019005,E01019006,E01019007,E01019008,E01019009,E01019010,E01019011,E01019012,E01019013,E01019014,E01019015,E01019016,E01019018,E01019019,E01019020,E01019021,E01019022,E01019023,E01019024,E01019025,E01019026,E01019027,E01019028,E01019029,E01019030,E01019031,E01019032,E01019033,E01019034,E01019035,E01019036,E01019037,E01019038,E01019039,E01019040,E01019041,E01019042,E01019043,E01019044,E01019045,E01019046,E01019047,E01019048,E01019050,E01019051,E01019052,E01019053,E01019054,E01019055,E01019056,E01019057,E01019058,E01019059,E01019060,E01019061,E01019062,E01019063,E01019064,E01019065,E01019066,E01019067,E01019068,E01019069,E01019070,E01019071,E01019072,E01019073,E01019074,E01019075,E01019076,E01019077,E01019078,E01019079,E01019080,E01019081,E01019082,E01019083,E01019084,E01019085,E01019086,E01019087,E01019088,E01019089,E01019090,E01019091,E01019092,E01019093,E01019094,E01019095,E01019096,E01019097,E01019098,E01019099,E01019100,E01019101,E01019102,E01019104,E01019106,E01019107,E01019108,E01019109,E01019110,E01019111,E01019112,E01019113,E01019114,E01019115,E01019116,E01019117,E01019118,E01019119,E01019120,E01019121,E01019122,E01019123,E01019124,E01019125,E01019126,E01019127,E01019128,E01019129,E01019130,E01019131,E01019132,E01019133,E01019134,E01019135,E01019136,E01019137,E01019138,E01019139,E01019141,E01019142,E01019143,E01019144,E01019145,E01019146,E01019147,E01019148,E01019149,E01019150,E01019151,E01019152,E01019153,E01019154,E01019155,E01019156,E01019157,E01019158,E01019159,E01019160,E01019161,E01019162,E01019163,E01019164,E01019165,E01019166,E01019167,E01019168,E01019169,E01019170,E01019171,E01019172,E01019173,E01019175,E01019176,E01019177,E01019178,E01019179,E01019180,E01019182,E01019184,E01019185,E01019186,E01019187,E01019188,E01019189,E01019190,E01019191,E01019192,E01019193,E01019194,E01019195,E01019196,E01019197,E01019198,E01019199,E01019200,E01019201,E01019202,E01019203,E01019204,E01019205,E01019206,E01019207,E01019208,E01019209,E01019210,E01019211,E01019212,E01019215,E01019216,E01019217,E01019218,E01019219,E01019220,E01019221,E01019222,E01019223,E01019224,E01019225,E01019226,E01019227,E01019228,E01019229,E01019230,E01019231,E01019232,E01019233,E01019234,E01019235,E01019236,E01019237,E01019238,E01019239,E01019240,E01019241,E01019242,E01019243,E01019244,E01019245,E01019246,E01019247,E01019248,E01019249,E01019250,E01019251,E01019252,E01019253,E01019254,E01019255,E01019256,E01019257,E01019258,E01019259,E01019260,E01019261,E01019262,E01019264,E01019265,E01019266,E01019267,E01019269,E01019270,E01019271,E01019272,E01019273,E01019274,E01019275,E01019277,E01019278,E01019279,E01019280,E01019281,E01019282,E01019283,E01019284,E01019285,E01019286,E01019287,E01019288,E01019289,E01019290,E01019291,E01019292,E01019293,E01019294,E01019295,E01019296,E01019298,E01019299,E01019300,E01019301,E01019302,E01019303,E01019304,E01019305,E01019306,E01019307,E01019308,E01019309,E01019310,E01019311,E01019312,E01019313,E01019314,E01019315,E01019316,E01019317,E01019318,E01019319,E01019320,E01019321,E01019322,E01019323,E01019324,E01019325,E01019326,E01019327,E01019328,E01019329,E01019330,E01019331,E01019332,E01019333,E01019334,E01019335,E01019336,E01019337,E01019338,E01019339,E01019340,E01019343,E01019344,E01019345,E01019346,E01019347,E01019348,E01019349,E01019350,E01019351,E01019352,E01019353,E01019354,E01019355,E01019356,E01019357,E01019358,E01019359,E01019360,E01019361,E01019362,E01019363,E01019364,E01019365,E01019366,E01019368,E01019369,E01019370,E01019371,E01019372,E01019373,E01019374,E01019375,E01019376,E01019377,E01019378,E01019379,E01019380,E01019381,E01019382,E01019383,E01019384,E01019385,E01019386,E01019387,E01019388,E01019389,E01019390,E01019391,E01019392,E01019393,E01019394,E01019395,E01019396,E01019397,E01019400,E01019401,E01019402,E01019403,E01019404,E01019405,E01019406,E01019407,E01019408,E01019409,E01019410,E01019411,E01019412,E01019413,E01019414,E01019415,E01019416,E01019417,E01019418,E01019419,E01019420,E01019421,E01019422,E01019423,E01019424,E01019425,E01019426,E01019427,E01019428,E01019429,E01019430,E01019431,E01019432,E01019433,E01019434,E01019435,E01019436,E01019437,E01019438,E01019439,E01019440,E01019441,E01019442,E01019443,E01019444,E01019445,E01019446,E01019447,E01019448,E01019449,E01019450,E01019451,E01019452,E01019453,E01019454,E01019455,E01019456,E01019457,E01019458,E01019459,E01019460,E01019461,E01019462,E01019463,E01019464,E01019465,E01019467,E01019468,E01019469,E01019470,E01019471,E01019473,E01019474,E01019475,E01019476,E01019477,E01019478,E01019479,E01019480,E01019481,E01019482,E01019483,E01019484,E01019485,E01019486,E01019487,E01019488,E01019489,E01019490,E01019491,E01019492,E01019493,E01019495,E01019496,E01019497,E01019498,E01019499,E01019500,E01019501,E01019502,E01019503&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_037.json.gz
+  content_sha256: f22929bfe86ffe13f66ce567b290b51a1e471466c3271864d42d0312ad6bdc19
+- id: census_lsoa_tenure5a_bedrooms_038
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 038
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01019504,E01019505,E01019506,E01019507,E01019508,E01019509,E01019510,E01019511,E01019513,E01019514,E01019515,E01019516,E01019517,E01019518,E01019519,E01019520,E01019521,E01019522,E01019523,E01019524,E01019525,E01019526,E01019527,E01019528,E01019529,E01019530,E01019531,E01019532,E01019533,E01019534,E01019535,E01019536,E01019537,E01019538,E01019539,E01019540,E01019541,E01019542,E01019543,E01019544,E01019545,E01019546,E01019547,E01019548,E01019549,E01019550,E01019551,E01019552,E01019553,E01019554,E01019555,E01019556,E01019557,E01019558,E01019559,E01019560,E01019561,E01019562,E01019563,E01019564,E01019565,E01019566,E01019567,E01019568,E01019569,E01019570,E01019571,E01019572,E01019573,E01019574,E01019575,E01019576,E01019577,E01019578,E01019579,E01019580,E01019581,E01019582,E01019583,E01019585,E01019586,E01019587,E01019588,E01019589,E01019590,E01019591,E01019592,E01019593,E01019594,E01019595,E01019596,E01019597,E01019598,E01019599,E01019600,E01019601,E01019602,E01019603,E01019604,E01019605,E01019606,E01019607,E01019608,E01019609,E01019610,E01019611,E01019612,E01019613,E01019614,E01019615,E01019616,E01019617,E01019618,E01019619,E01019620,E01019621,E01019622,E01019623,E01019624,E01019625,E01019626,E01019627,E01019628,E01019629,E01019630,E01019631,E01019632,E01019633,E01019634,E01019635,E01019636,E01019638,E01019639,E01019640,E01019641,E01019642,E01019643,E01019644,E01019645,E01019646,E01019647,E01019648,E01019649,E01019650,E01019651,E01019652,E01019653,E01019654,E01019655,E01019656,E01019657,E01019658,E01019659,E01019660,E01019661,E01019662,E01019663,E01019664,E01019665,E01019666,E01019667,E01019668,E01019669,E01019670,E01019671,E01019672,E01019673,E01019674,E01019675,E01019676,E01019677,E01019678,E01019679,E01019680,E01019681,E01019682,E01019683,E01019684,E01019685,E01019686,E01019687,E01019688,E01019689,E01019690,E01019691,E01019692,E01019693,E01019694,E01019695,E01019696,E01019697,E01019698,E01019699,E01019700,E01019701,E01019702,E01019703,E01019704,E01019705,E01019706,E01019707,E01019708,E01019709,E01019710,E01019711,E01019712,E01019713,E01019714,E01019715,E01019716,E01019717,E01019718,E01019719,E01019720,E01019721,E01019722,E01019723,E01019724,E01019725,E01019726,E01019727,E01019728,E01019729,E01019730,E01019731,E01019732,E01019733,E01019734,E01019735,E01019736,E01019737,E01019738,E01019739,E01019740,E01019741,E01019742,E01019743,E01019744,E01019745,E01019746,E01019747,E01019748,E01019749,E01019750,E01019751,E01019752,E01019753,E01019754,E01019755,E01019756,E01019757,E01019758,E01019759,E01019760,E01019761,E01019762,E01019763,E01019764,E01019765,E01019766,E01019767,E01019768,E01019769,E01019770,E01019771,E01019772,E01019774,E01019775,E01019776,E01019777,E01019778,E01019779,E01019780,E01019781,E01019782,E01019783,E01019784,E01019785,E01019786,E01019787,E01019788,E01019790,E01019791,E01019792,E01019793,E01019794,E01019795,E01019796,E01019797,E01019798,E01019799,E01019800,E01019801,E01019802,E01019803,E01019804,E01019805,E01019806,E01019807,E01019808,E01019809,E01019810,E01019811,E01019812,E01019813,E01019814,E01019815,E01019816,E01019817,E01019818,E01019819,E01019820,E01019821,E01019822,E01019823,E01019824,E01019825,E01019826,E01019827,E01019828,E01019829,E01019830,E01019831,E01019832,E01019833,E01019834,E01019837,E01019838,E01019839,E01019840,E01019841,E01019842,E01019843,E01019844,E01019845,E01019846,E01019847,E01019848,E01019850,E01019851,E01019852,E01019853,E01019854,E01019855,E01019856,E01019857,E01019858,E01019859,E01019860,E01019861,E01019862,E01019863,E01019864,E01019865,E01019866,E01019867,E01019868,E01019869,E01019870,E01019871,E01019872,E01019873,E01019874,E01019875,E01019876,E01019877,E01019878,E01019879,E01019880,E01019882,E01019885,E01019887,E01019888,E01019889,E01019890,E01019891,E01019894,E01019895,E01019896,E01019897,E01019898,E01019899,E01019900,E01019901,E01019902,E01019903,E01019904,E01019905,E01019906,E01019907,E01019908,E01019909,E01019910,E01019911,E01019912,E01019913,E01019914,E01019915,E01019916,E01019917,E01019918,E01019919,E01019920,E01019921,E01019922,E01019923,E01019924,E01019925,E01019927,E01019928,E01019929,E01019930,E01019931,E01019932,E01019933,E01019934,E01019935,E01019936,E01019937,E01019938,E01019939,E01019940,E01019942,E01019943,E01019944,E01019945,E01019946,E01019947,E01019948,E01019949,E01019950,E01019951,E01019952,E01019953,E01019954,E01019955,E01019956,E01019957,E01019958,E01019959,E01019960,E01019961,E01019962,E01019963,E01019964,E01019965,E01019966,E01019967,E01019968,E01019969,E01019970,E01019971,E01019972,E01019973,E01019974,E01019975,E01019976,E01019977,E01019980,E01019981,E01019982,E01019983,E01019984,E01019985,E01019986,E01019987,E01019988,E01019989,E01019990,E01019991,E01019992,E01019993,E01019994,E01019995,E01019996,E01019997,E01019998,E01019999,E01020000,E01020001,E01020002,E01020003,E01020004,E01020005,E01020006,E01020007,E01020008,E01020009,E01020010,E01020011,E01020012,E01020013,E01020014,E01020015,E01020017,E01020018,E01020019,E01020020,E01020021,E01020022&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_038.json.gz
+  content_sha256: 03b5c98a1fcfed82f84aad564392c4d0efb72a86c3235a33a5ec3c493c5d4ece
+- id: census_lsoa_tenure5a_bedrooms_039
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 039
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01020023,E01020024,E01020025,E01020026,E01020027,E01020029,E01020030,E01020031,E01020032,E01020036,E01020037,E01020038,E01020039,E01020040,E01020041,E01020042,E01020043,E01020044,E01020045,E01020046,E01020047,E01020048,E01020049,E01020050,E01020051,E01020052,E01020053,E01020054,E01020055,E01020056,E01020057,E01020058,E01020059,E01020060,E01020061,E01020062,E01020063,E01020064,E01020065,E01020067,E01020068,E01020069,E01020070,E01020071,E01020072,E01020073,E01020074,E01020075,E01020076,E01020077,E01020078,E01020079,E01020080,E01020081,E01020082,E01020083,E01020084,E01020085,E01020087,E01020088,E01020089,E01020090,E01020091,E01020092,E01020093,E01020094,E01020095,E01020096,E01020097,E01020098,E01020099,E01020100,E01020101,E01020102,E01020103,E01020104,E01020105,E01020106,E01020107,E01020108,E01020109,E01020110,E01020111,E01020112,E01020113,E01020114,E01020115,E01020116,E01020117,E01020118,E01020119,E01020120,E01020121,E01020122,E01020123,E01020124,E01020125,E01020126,E01020127,E01020128,E01020129,E01020130,E01020131,E01020132,E01020133,E01020134,E01020135,E01020136,E01020137,E01020138,E01020139,E01020140,E01020141,E01020142,E01020143,E01020144,E01020145,E01020146,E01020147,E01020148,E01020149,E01020150,E01020151,E01020152,E01020153,E01020154,E01020155,E01020156,E01020157,E01020158,E01020159,E01020160,E01020161,E01020162,E01020163,E01020164,E01020165,E01020166,E01020167,E01020168,E01020169,E01020170,E01020171,E01020172,E01020173,E01020174,E01020175,E01020176,E01020177,E01020178,E01020179,E01020180,E01020181,E01020182,E01020183,E01020184,E01020185,E01020186,E01020187,E01020188,E01020189,E01020190,E01020191,E01020192,E01020193,E01020194,E01020195,E01020196,E01020197,E01020198,E01020199,E01020200,E01020201,E01020202,E01020203,E01020204,E01020205,E01020206,E01020208,E01020209,E01020210,E01020211,E01020212,E01020213,E01020214,E01020215,E01020216,E01020217,E01020218,E01020219,E01020220,E01020221,E01020222,E01020223,E01020224,E01020225,E01020226,E01020227,E01020228,E01020230,E01020231,E01020232,E01020233,E01020234,E01020235,E01020236,E01020237,E01020238,E01020239,E01020240,E01020241,E01020242,E01020243,E01020244,E01020245,E01020246,E01020247,E01020248,E01020249,E01020250,E01020251,E01020252,E01020253,E01020254,E01020255,E01020256,E01020257,E01020258,E01020259,E01020260,E01020261,E01020262,E01020263,E01020264,E01020265,E01020266,E01020267,E01020268,E01020269,E01020270,E01020271,E01020272,E01020273,E01020274,E01020275,E01020276,E01020277,E01020278,E01020279,E01020280,E01020281,E01020282,E01020283,E01020284,E01020285,E01020286,E01020287,E01020288,E01020289,E01020290,E01020291,E01020292,E01020293,E01020294,E01020295,E01020296,E01020297,E01020298,E01020299,E01020300,E01020301,E01020302,E01020303,E01020304,E01020305,E01020306,E01020307,E01020308,E01020309,E01020311,E01020312,E01020313,E01020314,E01020315,E01020316,E01020317,E01020318,E01020319,E01020320,E01020321,E01020323,E01020324,E01020325,E01020326,E01020327,E01020328,E01020329,E01020330,E01020331,E01020332,E01020333,E01020334,E01020335,E01020336,E01020337,E01020338,E01020339,E01020340,E01020341,E01020342,E01020343,E01020344,E01020345,E01020346,E01020347,E01020348,E01020349,E01020350,E01020351,E01020352,E01020353,E01020354,E01020355,E01020356,E01020357,E01020358,E01020359,E01020360,E01020361,E01020362,E01020363,E01020364,E01020365,E01020366,E01020367,E01020369,E01020370,E01020371,E01020372,E01020373,E01020374,E01020375,E01020376,E01020377,E01020378,E01020379,E01020380,E01020381,E01020382,E01020383,E01020384,E01020385,E01020386,E01020387,E01020388,E01020389,E01020390,E01020391,E01020392,E01020393,E01020394,E01020395,E01020396,E01020397,E01020398,E01020400,E01020401,E01020402,E01020403,E01020404,E01020405,E01020406,E01020407,E01020408,E01020409,E01020410,E01020411,E01020412,E01020413,E01020414,E01020415,E01020416,E01020417,E01020418,E01020419,E01020420,E01020421,E01020422,E01020423,E01020424,E01020425,E01020426,E01020427,E01020428,E01020429,E01020430,E01020431,E01020432,E01020433,E01020434,E01020435,E01020436,E01020437,E01020438,E01020439,E01020440,E01020441,E01020442,E01020443,E01020444,E01020445,E01020446,E01020447,E01020448,E01020449,E01020450,E01020452,E01020453,E01020454,E01020456,E01020457,E01020458,E01020460,E01020462,E01020463,E01020464,E01020465,E01020466,E01020467,E01020468,E01020469,E01020470,E01020471,E01020472,E01020473,E01020474,E01020475,E01020476,E01020477,E01020478,E01020479,E01020480,E01020481,E01020482,E01020483,E01020484,E01020485,E01020486,E01020487,E01020488,E01020489,E01020490,E01020491,E01020492,E01020493,E01020494,E01020495,E01020496,E01020497,E01020498,E01020499,E01020500,E01020501,E01020502,E01020503,E01020504,E01020505,E01020506,E01020507,E01020508,E01020509,E01020510,E01020511,E01020512,E01020513,E01020514,E01020515,E01020516,E01020517,E01020518,E01020519,E01020520,E01020523,E01020524,E01020525,E01020526,E01020527,E01020528,E01020529,E01020530,E01020531,E01020532,E01020533,E01020534,E01020535,E01020536,E01020537,E01020538,E01020539,E01020540&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_039.json.gz
+  content_sha256: 531f6983a6f07c6a9f956d457a99a8c0c8171469887d4180e5d2b37a863e249c
+- id: census_lsoa_tenure5a_bedrooms_040
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 040
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01020541,E01020542,E01020543,E01020544,E01020546,E01020547,E01020549,E01020550,E01020551,E01020552,E01020553,E01020554,E01020555,E01020556,E01020557,E01020558,E01020559,E01020560,E01020561,E01020562,E01020563,E01020564,E01020565,E01020566,E01020569,E01020570,E01020571,E01020572,E01020573,E01020574,E01020575,E01020576,E01020577,E01020578,E01020579,E01020580,E01020581,E01020582,E01020583,E01020584,E01020585,E01020586,E01020587,E01020588,E01020589,E01020590,E01020591,E01020592,E01020593,E01020594,E01020595,E01020596,E01020597,E01020598,E01020599,E01020600,E01020601,E01020602,E01020603,E01020604,E01020605,E01020606,E01020607,E01020608,E01020609,E01020610,E01020611,E01020612,E01020613,E01020614,E01020615,E01020616,E01020617,E01020618,E01020619,E01020620,E01020621,E01020622,E01020623,E01020624,E01020625,E01020626,E01020627,E01020628,E01020629,E01020630,E01020631,E01020632,E01020633,E01020634,E01020635,E01020636,E01020637,E01020638,E01020639,E01020640,E01020641,E01020642,E01020643,E01020645,E01020646,E01020647,E01020648,E01020649,E01020650,E01020651,E01020652,E01020653,E01020654,E01020655,E01020656,E01020657,E01020658,E01020659,E01020660,E01020661,E01020662,E01020663,E01020664,E01020665,E01020666,E01020667,E01020668,E01020669,E01020670,E01020671,E01020672,E01020673,E01020674,E01020675,E01020676,E01020677,E01020678,E01020679,E01020680,E01020681,E01020682,E01020683,E01020685,E01020686,E01020687,E01020688,E01020689,E01020690,E01020691,E01020692,E01020693,E01020694,E01020695,E01020696,E01020697,E01020698,E01020699,E01020700,E01020701,E01020702,E01020703,E01020704,E01020705,E01020706,E01020708,E01020709,E01020710,E01020711,E01020712,E01020714,E01020716,E01020717,E01020718,E01020719,E01020720,E01020721,E01020722,E01020723,E01020724,E01020725,E01020726,E01020727,E01020729,E01020730,E01020731,E01020732,E01020733,E01020734,E01020735,E01020737,E01020739,E01020740,E01020741,E01020742,E01020743,E01020744,E01020745,E01020746,E01020747,E01020748,E01020749,E01020750,E01020751,E01020752,E01020753,E01020754,E01020756,E01020757,E01020758,E01020759,E01020760,E01020761,E01020762,E01020763,E01020764,E01020765,E01020766,E01020767,E01020768,E01020769,E01020770,E01020771,E01020772,E01020773,E01020775,E01020776,E01020777,E01020778,E01020779,E01020780,E01020782,E01020783,E01020784,E01020786,E01020787,E01020788,E01020789,E01020790,E01020791,E01020792,E01020793,E01020794,E01020795,E01020796,E01020797,E01020798,E01020799,E01020801,E01020802,E01020805,E01020806,E01020807,E01020808,E01020809,E01020810,E01020811,E01020812,E01020813,E01020814,E01020815,E01020816,E01020817,E01020818,E01020819,E01020820,E01020821,E01020822,E01020823,E01020824,E01020825,E01020826,E01020827,E01020828,E01020829,E01020830,E01020831,E01020832,E01020833,E01020834,E01020835,E01020837,E01020838,E01020839,E01020840,E01020841,E01020843,E01020844,E01020845,E01020846,E01020847,E01020848,E01020849,E01020850,E01020851,E01020852,E01020853,E01020854,E01020855,E01020856,E01020857,E01020858,E01020859,E01020860,E01020861,E01020862,E01020863,E01020864,E01020867,E01020868,E01020869,E01020870,E01020871,E01020872,E01020873,E01020874,E01020875,E01020876,E01020877,E01020878,E01020879,E01020880,E01020881,E01020882,E01020883,E01020884,E01020885,E01020886,E01020887,E01020888,E01020889,E01020890,E01020891,E01020892,E01020893,E01020894,E01020895,E01020896,E01020898,E01020899,E01020900,E01020901,E01020902,E01020903,E01020904,E01020905,E01020906,E01020907,E01020908,E01020909,E01020910,E01020911,E01020912,E01020913,E01020914,E01020915,E01020916,E01020917,E01020918,E01020919,E01020920,E01020921,E01020922,E01020923,E01020924,E01020925,E01020926,E01020927,E01020928,E01020929,E01020930,E01020931,E01020932,E01020935,E01020936,E01020937,E01020938,E01020939,E01020940,E01020941,E01020942,E01020943,E01020944,E01020945,E01020946,E01020947,E01020948,E01020949,E01020950,E01020951,E01020952,E01020953,E01020954,E01020955,E01020956,E01020957,E01020959,E01020960,E01020961,E01020962,E01020963,E01020964,E01020965,E01020966,E01020967,E01020968,E01020969,E01020970,E01020971,E01020972,E01020973,E01020974,E01020975,E01020976,E01020977,E01020978,E01020979,E01020980,E01020981,E01020982,E01020983,E01020984,E01020985,E01020986,E01020987,E01020988,E01020989,E01020990,E01020991,E01020992,E01020993,E01020994,E01020995,E01020996,E01020997,E01020998,E01020999,E01021000,E01021001,E01021002,E01021003,E01021004,E01021005,E01021006,E01021007,E01021008,E01021009,E01021010,E01021011,E01021012,E01021013,E01021014,E01021015,E01021016,E01021017,E01021018,E01021019,E01021020,E01021021,E01021022,E01021023,E01021024,E01021025,E01021026,E01021027,E01021028,E01021029,E01021030,E01021031,E01021032,E01021033,E01021034,E01021035,E01021036,E01021037,E01021038,E01021039,E01021040,E01021041,E01021042,E01021043,E01021044,E01021045,E01021046,E01021047,E01021048,E01021049,E01021050,E01021051,E01021052,E01021053,E01021054,E01021055,E01021056,E01021057,E01021058,E01021059,E01021060,E01021061,E01021062,E01021063,E01021064,E01021065,E01021066,E01021067&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_040.json.gz
+  content_sha256: f0faf625f73a08164b4c9204785c5477af3f6c88bea5a35ffb9f182451bcce7f
+- id: census_lsoa_tenure5a_bedrooms_041
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 041
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01021068,E01021069,E01021070,E01021071,E01021072,E01021073,E01021074,E01021075,E01021076,E01021077,E01021078,E01021079,E01021080,E01021081,E01021082,E01021083,E01021084,E01021085,E01021086,E01021087,E01021088,E01021089,E01021090,E01021091,E01021092,E01021093,E01021094,E01021095,E01021096,E01021097,E01021098,E01021099,E01021100,E01021101,E01021102,E01021103,E01021104,E01021105,E01021106,E01021107,E01021108,E01021109,E01021110,E01021111,E01021112,E01021113,E01021114,E01021115,E01021116,E01021117,E01021118,E01021119,E01021120,E01021121,E01021122,E01021123,E01021124,E01021125,E01021126,E01021127,E01021128,E01021129,E01021130,E01021131,E01021132,E01021133,E01021134,E01021135,E01021136,E01021137,E01021138,E01021139,E01021140,E01021141,E01021142,E01021143,E01021144,E01021145,E01021146,E01021147,E01021148,E01021149,E01021150,E01021151,E01021152,E01021153,E01021154,E01021155,E01021156,E01021157,E01021158,E01021159,E01021160,E01021161,E01021162,E01021163,E01021164,E01021165,E01021166,E01021167,E01021168,E01021169,E01021170,E01021171,E01021172,E01021173,E01021174,E01021175,E01021176,E01021177,E01021178,E01021179,E01021180,E01021181,E01021182,E01021183,E01021184,E01021185,E01021186,E01021187,E01021188,E01021189,E01021190,E01021191,E01021192,E01021193,E01021194,E01021196,E01021198,E01021199,E01021200,E01021201,E01021202,E01021203,E01021204,E01021205,E01021206,E01021207,E01021208,E01021209,E01021210,E01021211,E01021213,E01021214,E01021215,E01021216,E01021217,E01021218,E01021219,E01021220,E01021221,E01021222,E01021223,E01021224,E01021225,E01021226,E01021227,E01021228,E01021229,E01021230,E01021231,E01021232,E01021233,E01021234,E01021235,E01021236,E01021237,E01021238,E01021239,E01021240,E01021241,E01021242,E01021243,E01021244,E01021245,E01021246,E01021247,E01021248,E01021249,E01021250,E01021251,E01021252,E01021253,E01021254,E01021255,E01021256,E01021257,E01021258,E01021259,E01021260,E01021261,E01021262,E01021263,E01021264,E01021265,E01021266,E01021267,E01021268,E01021269,E01021270,E01021271,E01021272,E01021273,E01021274,E01021275,E01021276,E01021277,E01021279,E01021280,E01021281,E01021282,E01021283,E01021284,E01021285,E01021286,E01021287,E01021288,E01021289,E01021290,E01021291,E01021292,E01021293,E01021294,E01021295,E01021297,E01021298,E01021299,E01021300,E01021301,E01021302,E01021303,E01021304,E01021305,E01021306,E01021307,E01021308,E01021309,E01021310,E01021311,E01021312,E01021313,E01021314,E01021315,E01021316,E01021317,E01021318,E01021319,E01021320,E01021321,E01021322,E01021323,E01021324,E01021325,E01021326,E01021327,E01021328,E01021329,E01021330,E01021331,E01021332,E01021333,E01021334,E01021335,E01021336,E01021337,E01021338,E01021339,E01021340,E01021341,E01021342,E01021343,E01021344,E01021345,E01021346,E01021347,E01021348,E01021349,E01021350,E01021351,E01021352,E01021353,E01021354,E01021355,E01021356,E01021357,E01021358,E01021359,E01021360,E01021361,E01021362,E01021364,E01021365,E01021366,E01021367,E01021368,E01021369,E01021370,E01021371,E01021372,E01021374,E01021375,E01021376,E01021377,E01021378,E01021379,E01021380,E01021381,E01021382,E01021383,E01021384,E01021385,E01021386,E01021387,E01021388,E01021389,E01021390,E01021391,E01021392,E01021393,E01021394,E01021395,E01021396,E01021397,E01021398,E01021399,E01021400,E01021401,E01021402,E01021403,E01021404,E01021405,E01021406,E01021407,E01021408,E01021409,E01021410,E01021411,E01021412,E01021413,E01021414,E01021415,E01021416,E01021417,E01021418,E01021419,E01021420,E01021422,E01021423,E01021424,E01021425,E01021426,E01021427,E01021428,E01021429,E01021430,E01021431,E01021432,E01021433,E01021434,E01021435,E01021437,E01021438,E01021441,E01021442,E01021443,E01021444,E01021445,E01021446,E01021447,E01021448,E01021449,E01021450,E01021451,E01021452,E01021453,E01021454,E01021455,E01021456,E01021457,E01021458,E01021459,E01021460,E01021461,E01021462,E01021463,E01021464,E01021465,E01021466,E01021467,E01021468,E01021469,E01021470,E01021471,E01021472,E01021473,E01021474,E01021475,E01021476,E01021477,E01021478,E01021479,E01021480,E01021481,E01021482,E01021483,E01021484,E01021485,E01021486,E01021487,E01021488,E01021489,E01021490,E01021491,E01021492,E01021493,E01021494,E01021495,E01021496,E01021497,E01021498,E01021499,E01021500,E01021501,E01021502,E01021503,E01021504,E01021505,E01021506,E01021507,E01021508,E01021509,E01021510,E01021511,E01021512,E01021513,E01021514,E01021515,E01021516,E01021517,E01021518,E01021519,E01021520,E01021521,E01021522,E01021523,E01021524,E01021525,E01021526,E01021527,E01021528,E01021529,E01021530,E01021531,E01021532,E01021533,E01021535,E01021536,E01021537,E01021538,E01021539,E01021540,E01021542,E01021543,E01021544,E01021545,E01021546,E01021547,E01021548,E01021550,E01021551,E01021552,E01021553,E01021554,E01021555,E01021556,E01021557,E01021558,E01021559,E01021560,E01021561,E01021562,E01021563,E01021564,E01021565,E01021566,E01021568,E01021569,E01021570,E01021571,E01021572,E01021573,E01021575,E01021576,E01021577,E01021578,E01021579,E01021580,E01021582,E01021583,E01021584&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_041.json.gz
+  content_sha256: 22a5cb7d451e2a391d832b5783d1135e7570d5afe2c4229c938d0a197de5f139
+- id: census_lsoa_tenure5a_bedrooms_042
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 042
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01021585,E01021586,E01021587,E01021588,E01021589,E01021590,E01021591,E01021592,E01021593,E01021595,E01021596,E01021597,E01021598,E01021599,E01021600,E01021601,E01021602,E01021604,E01021605,E01021606,E01021607,E01021608,E01021609,E01021610,E01021611,E01021612,E01021613,E01021614,E01021615,E01021617,E01021618,E01021619,E01021620,E01021621,E01021622,E01021623,E01021624,E01021625,E01021626,E01021627,E01021628,E01021629,E01021631,E01021632,E01021633,E01021634,E01021635,E01021636,E01021637,E01021638,E01021639,E01021640,E01021641,E01021642,E01021643,E01021644,E01021645,E01021646,E01021647,E01021648,E01021650,E01021651,E01021652,E01021653,E01021655,E01021656,E01021657,E01021658,E01021659,E01021660,E01021661,E01021662,E01021663,E01021664,E01021665,E01021666,E01021667,E01021668,E01021669,E01021670,E01021671,E01021673,E01021674,E01021675,E01021676,E01021677,E01021678,E01021683,E01021684,E01021687,E01021688,E01021689,E01021690,E01021691,E01021692,E01021693,E01021694,E01021695,E01021696,E01021697,E01021698,E01021699,E01021700,E01021702,E01021703,E01021704,E01021705,E01021706,E01021707,E01021708,E01021709,E01021710,E01021711,E01021713,E01021714,E01021715,E01021717,E01021718,E01021719,E01021720,E01021721,E01021722,E01021723,E01021724,E01021725,E01021726,E01021727,E01021728,E01021729,E01021730,E01021731,E01021732,E01021733,E01021734,E01021735,E01021736,E01021737,E01021738,E01021739,E01021740,E01021741,E01021742,E01021743,E01021744,E01021745,E01021746,E01021747,E01021748,E01021749,E01021750,E01021751,E01021752,E01021753,E01021754,E01021755,E01021756,E01021757,E01021758,E01021759,E01021760,E01021761,E01021762,E01021763,E01021764,E01021765,E01021766,E01021767,E01021768,E01021769,E01021770,E01021771,E01021772,E01021773,E01021774,E01021775,E01021776,E01021777,E01021778,E01021779,E01021780,E01021781,E01021782,E01021783,E01021784,E01021785,E01021786,E01021787,E01021788,E01021789,E01021790,E01021791,E01021792,E01021793,E01021794,E01021795,E01021796,E01021797,E01021798,E01021799,E01021800,E01021801,E01021802,E01021803,E01021804,E01021805,E01021806,E01021807,E01021808,E01021809,E01021810,E01021811,E01021812,E01021813,E01021814,E01021815,E01021816,E01021817,E01021818,E01021819,E01021820,E01021821,E01021822,E01021823,E01021824,E01021825,E01021826,E01021827,E01021828,E01021829,E01021830,E01021831,E01021832,E01021833,E01021834,E01021835,E01021836,E01021837,E01021838,E01021839,E01021840,E01021841,E01021842,E01021843,E01021844,E01021845,E01021846,E01021847,E01021848,E01021849,E01021850,E01021851,E01021852,E01021853,E01021854,E01021855,E01021856,E01021858,E01021859,E01021860,E01021861,E01021862,E01021863,E01021864,E01021865,E01021866,E01021867,E01021868,E01021869,E01021870,E01021871,E01021873,E01021874,E01021875,E01021878,E01021879,E01021880,E01021881,E01021882,E01021883,E01021884,E01021885,E01021886,E01021887,E01021888,E01021889,E01021890,E01021891,E01021892,E01021893,E01021894,E01021895,E01021896,E01021897,E01021898,E01021899,E01021900,E01021901,E01021902,E01021903,E01021904,E01021905,E01021906,E01021907,E01021908,E01021909,E01021910,E01021911,E01021912,E01021913,E01021914,E01021915,E01021916,E01021917,E01021918,E01021919,E01021920,E01021921,E01021922,E01021923,E01021924,E01021925,E01021926,E01021927,E01021928,E01021929,E01021930,E01021931,E01021932,E01021933,E01021934,E01021935,E01021936,E01021937,E01021938,E01021939,E01021940,E01021941,E01021942,E01021943,E01021944,E01021945,E01021946,E01021947,E01021948,E01021949,E01021950,E01021951,E01021952,E01021953,E01021954,E01021955,E01021956,E01021957,E01021958,E01021959,E01021960,E01021961,E01021962,E01021963,E01021964,E01021965,E01021966,E01021967,E01021968,E01021969,E01021970,E01021971,E01021972,E01021973,E01021974,E01021975,E01021976,E01021977,E01021978,E01021979,E01021980,E01021981,E01021982,E01021983,E01021984,E01021985,E01021986,E01021987,E01021988,E01021989,E01021990,E01021991,E01021992,E01021993,E01021994,E01021995,E01021996,E01021997,E01021998,E01021999,E01022000,E01022001,E01022002,E01022004,E01022005,E01022006,E01022007,E01022008,E01022009,E01022010,E01022011,E01022012,E01022013,E01022014,E01022015,E01022016,E01022017,E01022018,E01022019,E01022020,E01022021,E01022022,E01022023,E01022024,E01022025,E01022026,E01022027,E01022028,E01022030,E01022031,E01022032,E01022033,E01022034,E01022035,E01022036,E01022037,E01022038,E01022039,E01022040,E01022041,E01022042,E01022043,E01022044,E01022045,E01022046,E01022047,E01022048,E01022049,E01022050,E01022051,E01022052,E01022053,E01022054,E01022055,E01022056,E01022057,E01022058,E01022059,E01022060,E01022061,E01022062,E01022063,E01022064,E01022067,E01022068,E01022069,E01022070,E01022071,E01022072,E01022073,E01022074,E01022075,E01022076,E01022077,E01022078,E01022079,E01022080,E01022081,E01022082,E01022083,E01022084,E01022085,E01022086,E01022087,E01022088,E01022089,E01022092,E01022093,E01022094,E01022095,E01022096,E01022097,E01022098,E01022099,E01022100,E01022101,E01022102,E01022104,E01022105,E01022106,E01022107,E01022108,E01022109,E01022110,E01022111&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_042.json.gz
+  content_sha256: 0fd5cbe06f11236f70e81e4ba0aee77e275b9af226dc0da175b9fb99196e383c
+- id: census_lsoa_tenure5a_bedrooms_043
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 043
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01022112,E01022113,E01022114,E01022115,E01022116,E01022117,E01022118,E01022119,E01022120,E01022121,E01022122,E01022123,E01022124,E01022125,E01022126,E01022127,E01022128,E01022129,E01022130,E01022131,E01022132,E01022133,E01022134,E01022135,E01022136,E01022137,E01022138,E01022139,E01022140,E01022141,E01022142,E01022143,E01022144,E01022145,E01022146,E01022147,E01022148,E01022149,E01022150,E01022151,E01022152,E01022153,E01022154,E01022155,E01022156,E01022157,E01022159,E01022160,E01022161,E01022162,E01022163,E01022164,E01022165,E01022166,E01022167,E01022168,E01022169,E01022170,E01022171,E01022172,E01022173,E01022174,E01022175,E01022176,E01022177,E01022178,E01022179,E01022181,E01022182,E01022183,E01022184,E01022185,E01022186,E01022187,E01022188,E01022189,E01022190,E01022191,E01022192,E01022193,E01022194,E01022195,E01022196,E01022197,E01022198,E01022199,E01022200,E01022201,E01022202,E01022203,E01022204,E01022205,E01022207,E01022208,E01022209,E01022210,E01022211,E01022213,E01022215,E01022216,E01022217,E01022218,E01022219,E01022220,E01022221,E01022222,E01022224,E01022225,E01022226,E01022227,E01022228,E01022229,E01022230,E01022231,E01022232,E01022233,E01022234,E01022235,E01022236,E01022237,E01022238,E01022239,E01022240,E01022241,E01022242,E01022243,E01022244,E01022245,E01022246,E01022247,E01022248,E01022249,E01022250,E01022251,E01022252,E01022253,E01022254,E01022255,E01022256,E01022257,E01022258,E01022259,E01022260,E01022261,E01022262,E01022263,E01022264,E01022265,E01022266,E01022267,E01022268,E01022269,E01022270,E01022271,E01022272,E01022273,E01022274,E01022275,E01022276,E01022277,E01022278,E01022279,E01022280,E01022281,E01022282,E01022283,E01022284,E01022285,E01022286,E01022287,E01022288,E01022289,E01022290,E01022291,E01022292,E01022293,E01022294,E01022295,E01022296,E01022297,E01022298,E01022299,E01022300,E01022301,E01022302,E01022303,E01022304,E01022305,E01022306,E01022307,E01022308,E01022309,E01022310,E01022311,E01022312,E01022313,E01022314,E01022315,E01022316,E01022317,E01022318,E01022319,E01022320,E01022321,E01022322,E01022323,E01022324,E01022325,E01022326,E01022327,E01022328,E01022329,E01022330,E01022331,E01022332,E01022333,E01022334,E01022335,E01022336,E01022338,E01022339,E01022340,E01022341,E01022342,E01022343,E01022344,E01022345,E01022346,E01022350,E01022351,E01022352,E01022353,E01022354,E01022355,E01022356,E01022357,E01022358,E01022359,E01022360,E01022361,E01022362,E01022363,E01022364,E01022366,E01022367,E01022368,E01022369,E01022370,E01022371,E01022372,E01022373,E01022374,E01022375,E01022376,E01022377,E01022378,E01022379,E01022381,E01022382,E01022383,E01022384,E01022385,E01022386,E01022387,E01022388,E01022389,E01022390,E01022391,E01022392,E01022393,E01022394,E01022395,E01022396,E01022397,E01022398,E01022399,E01022400,E01022401,E01022402,E01022403,E01022404,E01022405,E01022406,E01022407,E01022408,E01022409,E01022410,E01022411,E01022413,E01022414,E01022415,E01022416,E01022417,E01022418,E01022421,E01022423,E01022424,E01022425,E01022426,E01022427,E01022428,E01022429,E01022430,E01022431,E01022432,E01022433,E01022434,E01022437,E01022438,E01022439,E01022440,E01022441,E01022442,E01022443,E01022444,E01022445,E01022447,E01022448,E01022449,E01022450,E01022451,E01022452,E01022453,E01022454,E01022455,E01022456,E01022457,E01022458,E01022459,E01022460,E01022461,E01022462,E01022463,E01022464,E01022465,E01022466,E01022467,E01022468,E01022469,E01022470,E01022471,E01022472,E01022473,E01022474,E01022475,E01022476,E01022477,E01022478,E01022479,E01022480,E01022481,E01022482,E01022484,E01022485,E01022486,E01022487,E01022488,E01022489,E01022490,E01022491,E01022492,E01022493,E01022494,E01022495,E01022497,E01022498,E01022499,E01022500,E01022502,E01022503,E01022504,E01022505,E01022506,E01022507,E01022509,E01022510,E01022511,E01022512,E01022513,E01022514,E01022515,E01022516,E01022517,E01022518,E01022519,E01022520,E01022521,E01022522,E01022523,E01022524,E01022525,E01022527,E01022528,E01022529,E01022530,E01022531,E01022532,E01022533,E01022534,E01022535,E01022536,E01022537,E01022539,E01022540,E01022542,E01022543,E01022547,E01022548,E01022549,E01022550,E01022551,E01022552,E01022553,E01022554,E01022555,E01022556,E01022557,E01022558,E01022559,E01022560,E01022561,E01022562,E01022563,E01022564,E01022565,E01022566,E01022567,E01022568,E01022569,E01022570,E01022571,E01022572,E01022573,E01022574,E01022575,E01022576,E01022577,E01022579,E01022580,E01022581,E01022582,E01022583,E01022584,E01022585,E01022586,E01022587,E01022588,E01022589,E01022590,E01022591,E01022592,E01022593,E01022594,E01022595,E01022596,E01022597,E01022599,E01022600,E01022601,E01022602,E01022603,E01022604,E01022605,E01022606,E01022607,E01022608,E01022610,E01022611,E01022613,E01022614,E01022615,E01022616,E01022617,E01022618,E01022619,E01022620,E01022621,E01022622,E01022623,E01022624,E01022625,E01022627,E01022628,E01022629,E01022630,E01022631,E01022632,E01022633,E01022634,E01022635,E01022636,E01022639,E01022640,E01022641,E01022642,E01022643,E01022644,E01022645,E01022646,E01022647&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_043.json.gz
+  content_sha256: 85496d2afd977e20c5df255fefc7583af1f62228ac8156c6dad975de9c425c01
+- id: census_lsoa_tenure5a_bedrooms_044
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 044
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01022648,E01022649,E01022650,E01022651,E01022652,E01022654,E01022655,E01022656,E01022657,E01022658,E01022659,E01022660,E01022661,E01022662,E01022663,E01022664,E01022665,E01022666,E01022667,E01022669,E01022670,E01022671,E01022672,E01022673,E01022674,E01022675,E01022676,E01022678,E01022679,E01022680,E01022681,E01022682,E01022683,E01022684,E01022685,E01022686,E01022687,E01022688,E01022689,E01022690,E01022691,E01022693,E01022694,E01022695,E01022696,E01022697,E01022698,E01022699,E01022700,E01022701,E01022702,E01022703,E01022704,E01022705,E01022706,E01022707,E01022708,E01022709,E01022710,E01022711,E01022712,E01022713,E01022714,E01022715,E01022716,E01022717,E01022718,E01022719,E01022720,E01022721,E01022722,E01022723,E01022724,E01022725,E01022726,E01022727,E01022728,E01022729,E01022730,E01022731,E01022732,E01022733,E01022734,E01022735,E01022736,E01022737,E01022738,E01022739,E01022740,E01022741,E01022742,E01022743,E01022744,E01022745,E01022746,E01022747,E01022748,E01022749,E01022750,E01022751,E01022752,E01022753,E01022754,E01022755,E01022756,E01022757,E01022758,E01022759,E01022760,E01022761,E01022762,E01022763,E01022764,E01022765,E01022766,E01022767,E01022768,E01022769,E01022770,E01022771,E01022772,E01022773,E01022774,E01022776,E01022777,E01022778,E01022780,E01022781,E01022782,E01022783,E01022784,E01022785,E01022786,E01022787,E01022788,E01022789,E01022790,E01022791,E01022792,E01022793,E01022794,E01022795,E01022796,E01022797,E01022798,E01022799,E01022800,E01022801,E01022802,E01022803,E01022804,E01022805,E01022806,E01022807,E01022810,E01022812,E01022813,E01022814,E01022815,E01022816,E01022817,E01022818,E01022819,E01022820,E01022823,E01022825,E01022826,E01022828,E01022829,E01022830,E01022831,E01022832,E01022833,E01022834,E01022835,E01022836,E01022837,E01022838,E01022839,E01022840,E01022841,E01022842,E01022843,E01022845,E01022846,E01022847,E01022848,E01022849,E01022850,E01022851,E01022852,E01022853,E01022854,E01022855,E01022856,E01022858,E01022859,E01022860,E01022861,E01022862,E01022863,E01022864,E01022865,E01022867,E01022868,E01022869,E01022870,E01022871,E01022872,E01022874,E01022875,E01022876,E01022877,E01022878,E01022879,E01022880,E01022881,E01022882,E01022883,E01022884,E01022885,E01022887,E01022888,E01022889,E01022890,E01022891,E01022892,E01022893,E01022894,E01022895,E01022896,E01022897,E01022898,E01022899,E01022900,E01022901,E01022902,E01022903,E01022904,E01022905,E01022906,E01022907,E01022908,E01022909,E01022910,E01022911,E01022913,E01022914,E01022915,E01022916,E01022917,E01022918,E01022919,E01022920,E01022921,E01022922,E01022924,E01022925,E01022926,E01022927,E01022928,E01022929,E01022930,E01022931,E01022933,E01022934,E01022935,E01022936,E01022937,E01022938,E01022939,E01022940,E01022941,E01022942,E01022943,E01022944,E01022945,E01022946,E01022947,E01022948,E01022949,E01022950,E01022951,E01022952,E01022954,E01022955,E01022956,E01022957,E01022958,E01022959,E01022960,E01022961,E01022962,E01022963,E01022964,E01022965,E01022966,E01022967,E01022968,E01022969,E01022970,E01022971,E01022972,E01022973,E01022974,E01022975,E01022976,E01022977,E01022978,E01022979,E01022980,E01022981,E01022982,E01022983,E01022984,E01022985,E01022986,E01022987,E01022988,E01022989,E01022990,E01022991,E01022992,E01022993,E01022994,E01022995,E01022996,E01022997,E01022998,E01022999,E01023000,E01023001,E01023002,E01023003,E01023004,E01023005,E01023006,E01023007,E01023008,E01023009,E01023010,E01023011,E01023012,E01023013,E01023014,E01023015,E01023016,E01023017,E01023018,E01023019,E01023020,E01023021,E01023022,E01023023,E01023024,E01023025,E01023026,E01023027,E01023028,E01023029,E01023030,E01023031,E01023032,E01023033,E01023034,E01023035,E01023036,E01023037,E01023038,E01023039,E01023040,E01023041,E01023042,E01023043,E01023044,E01023045,E01023046,E01023047,E01023048,E01023049,E01023050,E01023051,E01023052,E01023053,E01023054,E01023055,E01023056,E01023057,E01023058,E01023059,E01023060,E01023061,E01023062,E01023063,E01023064,E01023065,E01023066,E01023067,E01023068,E01023069,E01023070,E01023071,E01023072,E01023073,E01023074,E01023075,E01023076,E01023077,E01023078,E01023079,E01023080,E01023081,E01023082,E01023083,E01023084,E01023085,E01023086,E01023087,E01023088,E01023089,E01023090,E01023091,E01023092,E01023093,E01023094,E01023095,E01023096,E01023097,E01023098,E01023099,E01023100,E01023101,E01023102,E01023103,E01023104,E01023105,E01023106,E01023107,E01023108,E01023109,E01023110,E01023111,E01023112,E01023113,E01023114,E01023115,E01023116,E01023117,E01023118,E01023119,E01023120,E01023121,E01023122,E01023123,E01023124,E01023125,E01023128,E01023129,E01023130,E01023131,E01023132,E01023133,E01023134,E01023135,E01023136,E01023137,E01023138,E01023139,E01023140,E01023141,E01023144,E01023145,E01023146,E01023147,E01023148,E01023149,E01023150,E01023151,E01023152,E01023153,E01023154,E01023156,E01023157,E01023158,E01023159,E01023160,E01023162,E01023163,E01023164,E01023165,E01023166,E01023167,E01023168,E01023169,E01023170,E01023171,E01023172,E01023173,E01023174,E01023175&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_044.json.gz
+  content_sha256: 6a1620b87e87a985f55b14d2b4a15aaf0c09e2502c486279209a757dfa95a2af
+- id: census_lsoa_tenure5a_bedrooms_045
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 045
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01023176,E01023177,E01023178,E01023179,E01023180,E01023181,E01023182,E01023183,E01023184,E01023185,E01023186,E01023187,E01023188,E01023189,E01023190,E01023191,E01023192,E01023193,E01023194,E01023195,E01023196,E01023197,E01023198,E01023200,E01023202,E01023203,E01023204,E01023205,E01023206,E01023207,E01023208,E01023209,E01023210,E01023211,E01023212,E01023213,E01023214,E01023215,E01023216,E01023217,E01023218,E01023219,E01023220,E01023221,E01023222,E01023223,E01023225,E01023226,E01023227,E01023228,E01023229,E01023230,E01023231,E01023232,E01023233,E01023234,E01023235,E01023236,E01023237,E01023238,E01023239,E01023240,E01023241,E01023242,E01023243,E01023244,E01023245,E01023246,E01023247,E01023248,E01023249,E01023250,E01023251,E01023252,E01023253,E01023254,E01023255,E01023256,E01023257,E01023258,E01023259,E01023260,E01023261,E01023262,E01023263,E01023264,E01023265,E01023266,E01023267,E01023268,E01023269,E01023270,E01023271,E01023272,E01023273,E01023274,E01023275,E01023276,E01023277,E01023278,E01023279,E01023280,E01023281,E01023282,E01023285,E01023286,E01023287,E01023288,E01023289,E01023290,E01023291,E01023292,E01023293,E01023294,E01023295,E01023296,E01023297,E01023298,E01023299,E01023300,E01023301,E01023302,E01023303,E01023304,E01023305,E01023306,E01023307,E01023308,E01023309,E01023310,E01023311,E01023312,E01023313,E01023314,E01023315,E01023316,E01023317,E01023318,E01023319,E01023320,E01023321,E01023322,E01023323,E01023324,E01023325,E01023326,E01023327,E01023328,E01023329,E01023330,E01023331,E01023332,E01023333,E01023334,E01023335,E01023336,E01023337,E01023339,E01023340,E01023341,E01023343,E01023344,E01023345,E01023346,E01023347,E01023348,E01023349,E01023350,E01023351,E01023352,E01023354,E01023355,E01023356,E01023357,E01023358,E01023359,E01023360,E01023361,E01023362,E01023363,E01023364,E01023365,E01023366,E01023367,E01023368,E01023369,E01023370,E01023371,E01023372,E01023373,E01023374,E01023375,E01023376,E01023377,E01023378,E01023379,E01023380,E01023381,E01023382,E01023383,E01023384,E01023385,E01023386,E01023387,E01023388,E01023389,E01023390,E01023391,E01023392,E01023393,E01023394,E01023395,E01023396,E01023397,E01023399,E01023400,E01023401,E01023402,E01023403,E01023404,E01023405,E01023406,E01023407,E01023408,E01023409,E01023410,E01023411,E01023412,E01023413,E01023414,E01023415,E01023416,E01023417,E01023418,E01023419,E01023420,E01023421,E01023422,E01023423,E01023424,E01023425,E01023426,E01023427,E01023428,E01023429,E01023430,E01023431,E01023432,E01023433,E01023434,E01023435,E01023436,E01023437,E01023438,E01023439,E01023440,E01023441,E01023442,E01023443,E01023444,E01023445,E01023447,E01023448,E01023449,E01023451,E01023452,E01023453,E01023454,E01023455,E01023456,E01023457,E01023458,E01023459,E01023460,E01023461,E01023462,E01023463,E01023465,E01023466,E01023467,E01023468,E01023469,E01023470,E01023471,E01023472,E01023473,E01023474,E01023475,E01023476,E01023477,E01023479,E01023480,E01023481,E01023482,E01023483,E01023484,E01023485,E01023486,E01023487,E01023488,E01023489,E01023490,E01023491,E01023492,E01023493,E01023494,E01023495,E01023496,E01023497,E01023499,E01023500,E01023501,E01023502,E01023503,E01023504,E01023505,E01023506,E01023507,E01023509,E01023510,E01023511,E01023512,E01023513,E01023514,E01023515,E01023516,E01023517,E01023518,E01023519,E01023520,E01023523,E01023524,E01023525,E01023526,E01023527,E01023528,E01023529,E01023530,E01023531,E01023532,E01023533,E01023534,E01023535,E01023536,E01023537,E01023538,E01023540,E01023541,E01023542,E01023543,E01023544,E01023545,E01023546,E01023547,E01023548,E01023549,E01023550,E01023551,E01023552,E01023553,E01023554,E01023555,E01023556,E01023557,E01023558,E01023559,E01023560,E01023561,E01023562,E01023563,E01023564,E01023565,E01023566,E01023567,E01023568,E01023569,E01023570,E01023571,E01023572,E01023573,E01023574,E01023575,E01023576,E01023577,E01023578,E01023579,E01023580,E01023581,E01023582,E01023583,E01023584,E01023585,E01023586,E01023587,E01023588,E01023589,E01023590,E01023591,E01023592,E01023593,E01023594,E01023595,E01023596,E01023597,E01023598,E01023600,E01023601,E01023602,E01023603,E01023604,E01023605,E01023606,E01023607,E01023608,E01023609,E01023610,E01023611,E01023612,E01023613,E01023614,E01023615,E01023616,E01023617,E01023618,E01023619,E01023620,E01023621,E01023622,E01023623,E01023624,E01023625,E01023626,E01023627,E01023628,E01023629,E01023630,E01023631,E01023632,E01023633,E01023634,E01023635,E01023636,E01023637,E01023638,E01023639,E01023640,E01023641,E01023642,E01023643,E01023644,E01023645,E01023646,E01023647,E01023648,E01023649,E01023650,E01023651,E01023652,E01023653,E01023654,E01023655,E01023656,E01023657,E01023658,E01023659,E01023660,E01023661,E01023662,E01023663,E01023664,E01023665,E01023666,E01023667,E01023668,E01023669,E01023670,E01023671,E01023672,E01023673,E01023674,E01023675,E01023676,E01023677,E01023678,E01023680,E01023681,E01023682,E01023683,E01023684,E01023685,E01023686,E01023687,E01023688,E01023689,E01023690,E01023691,E01023692,E01023693,E01023694,E01023695&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_045.json.gz
+  content_sha256: ae2697c10b9bef138d1169a822dce47cdc34fa55cd679147379cfbc622364226
+- id: census_lsoa_tenure5a_bedrooms_046
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 046
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01023696,E01023697,E01023698,E01023699,E01023700,E01023701,E01023702,E01023703,E01023704,E01023705,E01023706,E01023708,E01023709,E01023710,E01023711,E01023712,E01023713,E01023714,E01023715,E01023716,E01023717,E01023718,E01023719,E01023720,E01023721,E01023722,E01023723,E01023724,E01023725,E01023726,E01023727,E01023728,E01023730,E01023731,E01023732,E01023733,E01023734,E01023735,E01023736,E01023737,E01023738,E01023739,E01023740,E01023741,E01023742,E01023743,E01023744,E01023745,E01023746,E01023747,E01023748,E01023749,E01023750,E01023751,E01023752,E01023753,E01023754,E01023755,E01023756,E01023757,E01023759,E01023760,E01023761,E01023762,E01023763,E01023764,E01023765,E01023766,E01023767,E01023769,E01023770,E01023771,E01023772,E01023773,E01023774,E01023775,E01023776,E01023777,E01023778,E01023779,E01023780,E01023781,E01023782,E01023783,E01023784,E01023785,E01023786,E01023787,E01023788,E01023790,E01023791,E01023792,E01023793,E01023794,E01023795,E01023796,E01023797,E01023798,E01023799,E01023800,E01023801,E01023802,E01023803,E01023804,E01023805,E01023806,E01023807,E01023809,E01023810,E01023811,E01023812,E01023813,E01023814,E01023815,E01023816,E01023817,E01023818,E01023819,E01023820,E01023821,E01023822,E01023823,E01023824,E01023825,E01023826,E01023827,E01023828,E01023829,E01023830,E01023831,E01023832,E01023834,E01023835,E01023836,E01023837,E01023838,E01023839,E01023840,E01023841,E01023842,E01023843,E01023844,E01023845,E01023846,E01023847,E01023848,E01023849,E01023850,E01023851,E01023852,E01023853,E01023854,E01023855,E01023856,E01023857,E01023858,E01023860,E01023862,E01023863,E01023864,E01023865,E01023866,E01023868,E01023869,E01023870,E01023871,E01023872,E01023873,E01023874,E01023875,E01023876,E01023877,E01023878,E01023880,E01023881,E01023882,E01023883,E01023884,E01023885,E01023886,E01023887,E01023888,E01023889,E01023890,E01023891,E01023892,E01023893,E01023894,E01023895,E01023896,E01023897,E01023898,E01023899,E01023900,E01023901,E01023902,E01023903,E01023904,E01023905,E01023906,E01023907,E01023908,E01023909,E01023910,E01023911,E01023912,E01023913,E01023914,E01023915,E01023916,E01023917,E01023918,E01023919,E01023920,E01023921,E01023922,E01023923,E01023924,E01023925,E01023926,E01023927,E01023929,E01023930,E01023932,E01023933,E01023934,E01023935,E01023936,E01023937,E01023938,E01023939,E01023940,E01023942,E01023943,E01023944,E01023945,E01023946,E01023947,E01023948,E01023949,E01023950,E01023951,E01023952,E01023953,E01023955,E01023956,E01023957,E01023958,E01023959,E01023960,E01023961,E01023962,E01023963,E01023965,E01023966,E01023967,E01023968,E01023969,E01023970,E01023971,E01023972,E01023973,E01023974,E01023975,E01023976,E01023977,E01023978,E01023979,E01023980,E01023981,E01023983,E01023984,E01023985,E01023986,E01023987,E01023988,E01023989,E01023990,E01023992,E01023993,E01023995,E01023996,E01023997,E01023998,E01023999,E01024001,E01024002,E01024003,E01024004,E01024005,E01024006,E01024008,E01024009,E01024010,E01024011,E01024012,E01024013,E01024014,E01024015,E01024016,E01024017,E01024019,E01024020,E01024021,E01024022,E01024023,E01024024,E01024025,E01024026,E01024028,E01024029,E01024031,E01024032,E01024033,E01024035,E01024036,E01024037,E01024038,E01024039,E01024040,E01024041,E01024042,E01024043,E01024044,E01024045,E01024046,E01024047,E01024048,E01024049,E01024052,E01024053,E01024054,E01024055,E01024056,E01024057,E01024058,E01024059,E01024060,E01024061,E01024062,E01024063,E01024064,E01024065,E01024066,E01024067,E01024068,E01024069,E01024070,E01024071,E01024072,E01024073,E01024074,E01024075,E01024076,E01024077,E01024078,E01024079,E01024080,E01024081,E01024082,E01024083,E01024084,E01024085,E01024086,E01024087,E01024088,E01024089,E01024090,E01024091,E01024092,E01024093,E01024094,E01024095,E01024096,E01024097,E01024098,E01024099,E01024100,E01024101,E01024102,E01024103,E01024104,E01024105,E01024106,E01024107,E01024108,E01024109,E01024110,E01024111,E01024112,E01024113,E01024114,E01024115,E01024116,E01024117,E01024118,E01024119,E01024120,E01024121,E01024122,E01024123,E01024124,E01024126,E01024127,E01024128,E01024129,E01024131,E01024132,E01024133,E01024134,E01024135,E01024136,E01024137,E01024138,E01024139,E01024143,E01024144,E01024145,E01024146,E01024147,E01024150,E01024151,E01024152,E01024153,E01024154,E01024155,E01024156,E01024157,E01024158,E01024159,E01024160,E01024161,E01024162,E01024163,E01024164,E01024165,E01024166,E01024167,E01024168,E01024169,E01024170,E01024171,E01024172,E01024173,E01024174,E01024175,E01024176,E01024177,E01024178,E01024179,E01024180,E01024181,E01024182,E01024183,E01024184,E01024185,E01024186,E01024187,E01024188,E01024189,E01024190,E01024192,E01024193,E01024194,E01024195,E01024196,E01024197,E01024198,E01024200,E01024201,E01024202,E01024203,E01024204,E01024205,E01024206,E01024207,E01024208,E01024209,E01024212,E01024213,E01024214,E01024215,E01024216,E01024218,E01024219,E01024220,E01024221,E01024222,E01024223,E01024224,E01024225,E01024226,E01024227,E01024228,E01024229,E01024230,E01024231,E01024232,E01024233,E01024234&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_046.json.gz
+  content_sha256: aeddd24572cd5b140484bc355f3b36113914e9c8fb67248e54a31373159b35d9
+- id: census_lsoa_tenure5a_bedrooms_047
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 047
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01024235,E01024236,E01024237,E01024238,E01024239,E01024240,E01024241,E01024242,E01024243,E01024244,E01024245,E01024246,E01024247,E01024248,E01024249,E01024250,E01024251,E01024252,E01024253,E01024254,E01024255,E01024256,E01024257,E01024258,E01024259,E01024260,E01024261,E01024262,E01024263,E01024264,E01024265,E01024266,E01024267,E01024268,E01024269,E01024270,E01024271,E01024272,E01024273,E01024274,E01024275,E01024276,E01024277,E01024278,E01024279,E01024281,E01024282,E01024283,E01024284,E01024285,E01024286,E01024287,E01024288,E01024290,E01024291,E01024292,E01024293,E01024294,E01024295,E01024296,E01024297,E01024298,E01024299,E01024300,E01024301,E01024302,E01024303,E01024304,E01024305,E01024306,E01024307,E01024308,E01024309,E01024310,E01024311,E01024312,E01024313,E01024314,E01024315,E01024316,E01024317,E01024318,E01024319,E01024320,E01024321,E01024322,E01024323,E01024324,E01024325,E01024326,E01024327,E01024328,E01024329,E01024330,E01024331,E01024332,E01024333,E01024335,E01024336,E01024337,E01024339,E01024340,E01024341,E01024342,E01024343,E01024344,E01024345,E01024346,E01024347,E01024348,E01024349,E01024350,E01024351,E01024352,E01024353,E01024354,E01024355,E01024356,E01024357,E01024358,E01024360,E01024361,E01024362,E01024364,E01024365,E01024366,E01024367,E01024369,E01024371,E01024372,E01024373,E01024374,E01024375,E01024376,E01024377,E01024378,E01024379,E01024380,E01024381,E01024382,E01024383,E01024384,E01024385,E01024386,E01024387,E01024390,E01024391,E01024392,E01024393,E01024394,E01024395,E01024396,E01024397,E01024398,E01024399,E01024400,E01024401,E01024402,E01024403,E01024404,E01024405,E01024406,E01024407,E01024408,E01024409,E01024410,E01024411,E01024413,E01024414,E01024415,E01024416,E01024417,E01024418,E01024419,E01024420,E01024421,E01024423,E01024424,E01024425,E01024426,E01024427,E01024428,E01024429,E01024430,E01024431,E01024432,E01024433,E01024434,E01024435,E01024436,E01024437,E01024438,E01024439,E01024440,E01024441,E01024442,E01024443,E01024444,E01024445,E01024446,E01024447,E01024448,E01024449,E01024450,E01024451,E01024452,E01024453,E01024454,E01024455,E01024456,E01024457,E01024458,E01024459,E01024460,E01024461,E01024462,E01024463,E01024464,E01024465,E01024466,E01024467,E01024468,E01024469,E01024470,E01024471,E01024472,E01024473,E01024474,E01024475,E01024476,E01024477,E01024478,E01024479,E01024480,E01024481,E01024482,E01024483,E01024484,E01024485,E01024486,E01024487,E01024488,E01024489,E01024490,E01024491,E01024492,E01024493,E01024494,E01024495,E01024496,E01024497,E01024498,E01024499,E01024500,E01024501,E01024502,E01024503,E01024504,E01024505,E01024507,E01024508,E01024509,E01024510,E01024511,E01024512,E01024513,E01024514,E01024515,E01024516,E01024517,E01024518,E01024519,E01024520,E01024521,E01024522,E01024523,E01024524,E01024525,E01024526,E01024527,E01024528,E01024529,E01024530,E01024531,E01024532,E01024533,E01024534,E01024535,E01024536,E01024537,E01024538,E01024539,E01024540,E01024541,E01024542,E01024544,E01024545,E01024546,E01024547,E01024548,E01024549,E01024550,E01024551,E01024552,E01024553,E01024554,E01024555,E01024556,E01024557,E01024558,E01024559,E01024560,E01024561,E01024562,E01024563,E01024564,E01024565,E01024566,E01024569,E01024570,E01024571,E01024572,E01024573,E01024575,E01024576,E01024577,E01024578,E01024580,E01024581,E01024582,E01024583,E01024584,E01024585,E01024586,E01024587,E01024588,E01024589,E01024590,E01024591,E01024592,E01024593,E01024594,E01024595,E01024596,E01024597,E01024598,E01024599,E01024600,E01024601,E01024602,E01024603,E01024604,E01024605,E01024606,E01024608,E01024609,E01024610,E01024611,E01024612,E01024613,E01024614,E01024615,E01024616,E01024619,E01024620,E01024621,E01024622,E01024623,E01024624,E01024625,E01024626,E01024627,E01024628,E01024629,E01024630,E01024631,E01024632,E01024633,E01024634,E01024635,E01024636,E01024637,E01024638,E01024639,E01024640,E01024641,E01024642,E01024643,E01024644,E01024645,E01024646,E01024647,E01024648,E01024649,E01024650,E01024651,E01024652,E01024653,E01024654,E01024655,E01024656,E01024657,E01024658,E01024659,E01024660,E01024661,E01024662,E01024663,E01024664,E01024665,E01024666,E01024667,E01024668,E01024669,E01024670,E01024671,E01024672,E01024673,E01024674,E01024675,E01024676,E01024677,E01024679,E01024680,E01024681,E01024682,E01024683,E01024684,E01024685,E01024686,E01024687,E01024688,E01024689,E01024690,E01024691,E01024692,E01024693,E01024694,E01024695,E01024696,E01024697,E01024698,E01024699,E01024700,E01024702,E01024703,E01024704,E01024705,E01024706,E01024707,E01024708,E01024709,E01024710,E01024711,E01024712,E01024713,E01024714,E01024715,E01024716,E01024717,E01024718,E01024719,E01024720,E01024721,E01024722,E01024723,E01024724,E01024725,E01024726,E01024728,E01024729,E01024730,E01024731,E01024732,E01024733,E01024734,E01024735,E01024736,E01024737,E01024740,E01024741,E01024742,E01024743,E01024744,E01024745,E01024746,E01024747,E01024748,E01024749,E01024750,E01024751,E01024752,E01024753,E01024754,E01024755,E01024756,E01024757,E01024758,E01024760,E01024761&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_047.json.gz
+  content_sha256: 84e44e35a054632b76da278ce914de7a547d7582090a22fb1ab803e36a35d22c
+- id: census_lsoa_tenure5a_bedrooms_048
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 048
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01024762,E01024763,E01024764,E01024765,E01024767,E01024768,E01024771,E01024772,E01024773,E01024775,E01024776,E01024777,E01024778,E01024779,E01024780,E01024781,E01024782,E01024783,E01024785,E01024786,E01024787,E01024788,E01024789,E01024790,E01024791,E01024792,E01024793,E01024794,E01024795,E01024796,E01024797,E01024798,E01024799,E01024800,E01024802,E01024803,E01024804,E01024805,E01024806,E01024807,E01024808,E01024809,E01024810,E01024811,E01024812,E01024813,E01024814,E01024815,E01024816,E01024817,E01024818,E01024819,E01024820,E01024821,E01024822,E01024823,E01024824,E01024825,E01024826,E01024827,E01024828,E01024829,E01024830,E01024831,E01024832,E01024833,E01024834,E01024835,E01024836,E01024837,E01024838,E01024839,E01024840,E01024841,E01024842,E01024843,E01024844,E01024845,E01024846,E01024847,E01024848,E01024849,E01024850,E01024851,E01024852,E01024853,E01024854,E01024855,E01024856,E01024857,E01024858,E01024859,E01024860,E01024861,E01024862,E01024863,E01024864,E01024865,E01024866,E01024867,E01024868,E01024869,E01024870,E01024871,E01024872,E01024873,E01024874,E01024875,E01024876,E01024877,E01024878,E01024879,E01024880,E01024881,E01024882,E01024883,E01024884,E01024885,E01024886,E01024887,E01024888,E01024889,E01024890,E01024891,E01024892,E01024893,E01024894,E01024895,E01024896,E01024897,E01024898,E01024899,E01024900,E01024901,E01024902,E01024903,E01024904,E01024905,E01024906,E01024907,E01024908,E01024909,E01024910,E01024911,E01024912,E01024913,E01024914,E01024915,E01024916,E01024917,E01024918,E01024920,E01024921,E01024922,E01024923,E01024924,E01024925,E01024926,E01024927,E01024928,E01024930,E01024931,E01024933,E01024934,E01024935,E01024936,E01024937,E01024938,E01024939,E01024940,E01024941,E01024942,E01024943,E01024944,E01024946,E01024947,E01024948,E01024950,E01024951,E01024952,E01024953,E01024954,E01024955,E01024956,E01024957,E01024958,E01024959,E01024960,E01024961,E01024962,E01024963,E01024964,E01024965,E01024966,E01024967,E01024968,E01024969,E01024970,E01024971,E01024972,E01024973,E01024974,E01024975,E01024976,E01024977,E01024978,E01024979,E01024980,E01024981,E01024982,E01024983,E01024984,E01024985,E01024986,E01024987,E01024988,E01024989,E01024990,E01024991,E01024992,E01024993,E01024994,E01024995,E01024996,E01024997,E01024998,E01024999,E01025000,E01025001,E01025002,E01025003,E01025004,E01025005,E01025006,E01025007,E01025008,E01025009,E01025010,E01025011,E01025012,E01025013,E01025014,E01025015,E01025016,E01025017,E01025018,E01025019,E01025020,E01025021,E01025022,E01025023,E01025024,E01025025,E01025026,E01025027,E01025028,E01025029,E01025030,E01025031,E01025032,E01025033,E01025034,E01025035,E01025036,E01025037,E01025038,E01025039,E01025040,E01025041,E01025042,E01025044,E01025045,E01025046,E01025048,E01025049,E01025050,E01025051,E01025052,E01025053,E01025054,E01025055,E01025056,E01025057,E01025058,E01025059,E01025060,E01025061,E01025062,E01025063,E01025064,E01025065,E01025066,E01025067,E01025068,E01025069,E01025070,E01025071,E01025072,E01025073,E01025074,E01025075,E01025076,E01025077,E01025078,E01025079,E01025080,E01025081,E01025082,E01025083,E01025084,E01025085,E01025086,E01025087,E01025088,E01025089,E01025090,E01025091,E01025092,E01025093,E01025094,E01025095,E01025096,E01025097,E01025098,E01025099,E01025100,E01025101,E01025102,E01025103,E01025105,E01025106,E01025107,E01025108,E01025109,E01025110,E01025111,E01025112,E01025113,E01025114,E01025115,E01025116,E01025117,E01025118,E01025119,E01025120,E01025121,E01025122,E01025123,E01025124,E01025126,E01025127,E01025128,E01025130,E01025131,E01025132,E01025133,E01025136,E01025137,E01025138,E01025139,E01025140,E01025141,E01025142,E01025143,E01025144,E01025145,E01025146,E01025147,E01025148,E01025149,E01025150,E01025151,E01025152,E01025153,E01025154,E01025155,E01025156,E01025157,E01025158,E01025159,E01025160,E01025161,E01025162,E01025163,E01025164,E01025165,E01025166,E01025167,E01025168,E01025169,E01025170,E01025171,E01025172,E01025173,E01025174,E01025175,E01025176,E01025177,E01025178,E01025179,E01025180,E01025181,E01025182,E01025183,E01025184,E01025185,E01025186,E01025187,E01025188,E01025189,E01025190,E01025191,E01025192,E01025193,E01025194,E01025195,E01025196,E01025197,E01025198,E01025199,E01025200,E01025201,E01025202,E01025203,E01025204,E01025205,E01025206,E01025207,E01025208,E01025209,E01025210,E01025211,E01025212,E01025213,E01025214,E01025215,E01025216,E01025217,E01025218,E01025219,E01025220,E01025221,E01025222,E01025223,E01025224,E01025225,E01025226,E01025227,E01025228,E01025229,E01025230,E01025231,E01025232,E01025233,E01025234,E01025235,E01025236,E01025237,E01025238,E01025239,E01025240,E01025241,E01025242,E01025243,E01025244,E01025245,E01025246,E01025247,E01025248,E01025249,E01025250,E01025251,E01025252,E01025253,E01025254,E01025255,E01025256,E01025257,E01025258,E01025259,E01025260,E01025261,E01025262,E01025263,E01025264,E01025265,E01025266,E01025267,E01025268,E01025269,E01025270,E01025271,E01025272,E01025273,E01025274,E01025275,E01025276,E01025277,E01025278,E01025279&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_048.json.gz
+  content_sha256: 2105cbf28b18162f48d8c578c2446e3973a7b693294e6c4b45cdc231432162df
+- id: census_lsoa_tenure5a_bedrooms_049
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 049
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01025281,E01025282,E01025283,E01025284,E01025285,E01025286,E01025287,E01025288,E01025289,E01025290,E01025291,E01025292,E01025293,E01025294,E01025295,E01025296,E01025297,E01025298,E01025299,E01025300,E01025301,E01025302,E01025303,E01025304,E01025305,E01025306,E01025308,E01025309,E01025310,E01025311,E01025312,E01025313,E01025314,E01025315,E01025316,E01025317,E01025318,E01025319,E01025320,E01025321,E01025322,E01025323,E01025324,E01025325,E01025326,E01025327,E01025328,E01025329,E01025330,E01025331,E01025333,E01025334,E01025335,E01025336,E01025337,E01025338,E01025339,E01025340,E01025341,E01025342,E01025343,E01025344,E01025345,E01025346,E01025347,E01025348,E01025349,E01025350,E01025351,E01025352,E01025354,E01025355,E01025356,E01025357,E01025358,E01025359,E01025360,E01025361,E01025362,E01025363,E01025364,E01025365,E01025366,E01025367,E01025368,E01025369,E01025370,E01025371,E01025373,E01025374,E01025375,E01025376,E01025377,E01025378,E01025379,E01025380,E01025381,E01025382,E01025383,E01025384,E01025385,E01025387,E01025388,E01025389,E01025390,E01025391,E01025392,E01025393,E01025394,E01025395,E01025396,E01025397,E01025398,E01025399,E01025400,E01025401,E01025402,E01025403,E01025404,E01025405,E01025406,E01025407,E01025408,E01025409,E01025410,E01025411,E01025412,E01025413,E01025414,E01025415,E01025416,E01025417,E01025418,E01025419,E01025421,E01025422,E01025423,E01025424,E01025425,E01025426,E01025427,E01025428,E01025429,E01025431,E01025433,E01025434,E01025435,E01025436,E01025437,E01025438,E01025439,E01025440,E01025441,E01025442,E01025443,E01025444,E01025445,E01025446,E01025447,E01025448,E01025451,E01025452,E01025453,E01025454,E01025455,E01025456,E01025457,E01025458,E01025459,E01025460,E01025461,E01025462,E01025463,E01025464,E01025465,E01025466,E01025467,E01025468,E01025469,E01025470,E01025471,E01025472,E01025473,E01025474,E01025475,E01025476,E01025477,E01025478,E01025479,E01025480,E01025481,E01025482,E01025483,E01025484,E01025485,E01025486,E01025487,E01025488,E01025489,E01025490,E01025491,E01025492,E01025493,E01025494,E01025495,E01025496,E01025497,E01025498,E01025499,E01025500,E01025501,E01025502,E01025503,E01025504,E01025505,E01025506,E01025507,E01025508,E01025509,E01025510,E01025511,E01025512,E01025513,E01025514,E01025515,E01025516,E01025517,E01025518,E01025519,E01025520,E01025521,E01025522,E01025523,E01025524,E01025525,E01025526,E01025527,E01025528,E01025529,E01025530,E01025531,E01025532,E01025533,E01025534,E01025535,E01025536,E01025537,E01025538,E01025539,E01025540,E01025541,E01025542,E01025543,E01025544,E01025545,E01025546,E01025547,E01025548,E01025549,E01025550,E01025551,E01025552,E01025553,E01025554,E01025555,E01025556,E01025557,E01025558,E01025559,E01025560,E01025561,E01025562,E01025563,E01025564,E01025565,E01025566,E01025567,E01025568,E01025569,E01025570,E01025571,E01025572,E01025573,E01025574,E01025575,E01025576,E01025577,E01025578,E01025579,E01025580,E01025581,E01025582,E01025583,E01025584,E01025585,E01025586,E01025587,E01025588,E01025589,E01025590,E01025591,E01025592,E01025593,E01025594,E01025595,E01025596,E01025597,E01025598,E01025599,E01025600,E01025601,E01025602,E01025603,E01025604,E01025605,E01025606,E01025607,E01025608,E01025609,E01025610,E01025611,E01025612,E01025613,E01025614,E01025615,E01025616,E01025617,E01025618,E01025619,E01025620,E01025621,E01025622,E01025623,E01025624,E01025625,E01025626,E01025627,E01025629,E01025630,E01025631,E01025632,E01025633,E01025634,E01025635,E01025636,E01025637,E01025638,E01025639,E01025640,E01025641,E01025642,E01025643,E01025644,E01025645,E01025646,E01025647,E01025648,E01025649,E01025650,E01025651,E01025652,E01025653,E01025654,E01025655,E01025656,E01025657,E01025658,E01025659,E01025660,E01025661,E01025662,E01025663,E01025664,E01025665,E01025666,E01025667,E01025668,E01025669,E01025670,E01025671,E01025672,E01025673,E01025674,E01025675,E01025676,E01025677,E01025678,E01025680,E01025681,E01025682,E01025683,E01025684,E01025685,E01025686,E01025687,E01025688,E01025689,E01025690,E01025692,E01025693,E01025695,E01025696,E01025697,E01025698,E01025699,E01025700,E01025701,E01025702,E01025703,E01025704,E01025705,E01025706,E01025707,E01025708,E01025709,E01025710,E01025711,E01025712,E01025713,E01025715,E01025716,E01025718,E01025719,E01025720,E01025721,E01025722,E01025723,E01025724,E01025725,E01025726,E01025727,E01025728,E01025729,E01025730,E01025731,E01025732,E01025733,E01025734,E01025735,E01025736,E01025737,E01025738,E01025739,E01025740,E01025741,E01025742,E01025743,E01025744,E01025745,E01025746,E01025747,E01025748,E01025749,E01025750,E01025751,E01025752,E01025753,E01025754,E01025755,E01025756,E01025757,E01025758,E01025759,E01025760,E01025761,E01025762,E01025763,E01025764,E01025765,E01025766,E01025767,E01025768,E01025769,E01025770,E01025772,E01025773,E01025774,E01025775,E01025776,E01025777,E01025778,E01025779,E01025780,E01025781,E01025782,E01025783,E01025784,E01025786,E01025787,E01025788,E01025789,E01025790,E01025791,E01025792,E01025794,E01025795,E01025796,E01025797,E01025798,E01025799&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_049.json.gz
+  content_sha256: 8046130b8fbca58764e2693e7109bebf89fa821a94b88d2a77afbb250a47dae7
+- id: census_lsoa_tenure5a_bedrooms_050
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 050
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01025800,E01025801,E01025802,E01025803,E01025804,E01025806,E01025807,E01025808,E01025809,E01025810,E01025812,E01025813,E01025814,E01025815,E01025816,E01025817,E01025818,E01025819,E01025820,E01025821,E01025822,E01025823,E01025824,E01025825,E01025826,E01025827,E01025828,E01025829,E01025830,E01025831,E01025832,E01025833,E01025834,E01025835,E01025836,E01025837,E01025838,E01025839,E01025840,E01025841,E01025842,E01025843,E01025844,E01025845,E01025846,E01025847,E01025848,E01025849,E01025850,E01025851,E01025852,E01025854,E01025855,E01025856,E01025857,E01025858,E01025859,E01025860,E01025861,E01025862,E01025863,E01025864,E01025865,E01025866,E01025867,E01025868,E01025869,E01025870,E01025871,E01025872,E01025873,E01025874,E01025875,E01025876,E01025877,E01025879,E01025880,E01025881,E01025882,E01025883,E01025884,E01025885,E01025886,E01025887,E01025888,E01025889,E01025890,E01025891,E01025892,E01025893,E01025894,E01025895,E01025896,E01025897,E01025898,E01025899,E01025900,E01025901,E01025902,E01025903,E01025904,E01025905,E01025906,E01025907,E01025908,E01025909,E01025910,E01025911,E01025912,E01025913,E01025914,E01025915,E01025916,E01025918,E01025919,E01025920,E01025921,E01025923,E01025924,E01025926,E01025927,E01025928,E01025929,E01025930,E01025931,E01025932,E01025933,E01025934,E01025935,E01025936,E01025937,E01025938,E01025939,E01025940,E01025941,E01025942,E01025943,E01025944,E01025945,E01025946,E01025947,E01025948,E01025949,E01025950,E01025951,E01025952,E01025953,E01025954,E01025955,E01025956,E01025957,E01025958,E01025959,E01025960,E01025961,E01025962,E01025963,E01025964,E01025965,E01025966,E01025967,E01025968,E01025969,E01025970,E01025971,E01025972,E01025973,E01025974,E01025975,E01025976,E01025977,E01025978,E01025979,E01025980,E01025981,E01025982,E01025983,E01025984,E01025985,E01025986,E01025987,E01025988,E01025989,E01025990,E01025991,E01025992,E01025993,E01025994,E01025995,E01025997,E01025998,E01025999,E01026000,E01026001,E01026003,E01026004,E01026005,E01026007,E01026008,E01026009,E01026010,E01026012,E01026013,E01026014,E01026015,E01026016,E01026017,E01026018,E01026019,E01026020,E01026021,E01026022,E01026023,E01026024,E01026025,E01026026,E01026027,E01026028,E01026030,E01026031,E01026032,E01026033,E01026034,E01026035,E01026036,E01026037,E01026038,E01026039,E01026040,E01026041,E01026042,E01026043,E01026044,E01026045,E01026046,E01026047,E01026048,E01026049,E01026050,E01026051,E01026052,E01026053,E01026054,E01026055,E01026056,E01026057,E01026058,E01026059,E01026060,E01026061,E01026062,E01026063,E01026064,E01026065,E01026066,E01026067,E01026068,E01026069,E01026070,E01026071,E01026072,E01026073,E01026074,E01026075,E01026076,E01026077,E01026078,E01026079,E01026080,E01026081,E01026082,E01026083,E01026084,E01026087,E01026088,E01026089,E01026090,E01026091,E01026092,E01026093,E01026094,E01026095,E01026096,E01026097,E01026098,E01026099,E01026100,E01026101,E01026102,E01026103,E01026104,E01026105,E01026106,E01026107,E01026108,E01026109,E01026110,E01026111,E01026112,E01026113,E01026114,E01026115,E01026116,E01026117,E01026118,E01026119,E01026120,E01026121,E01026122,E01026123,E01026124,E01026125,E01026126,E01026127,E01026129,E01026130,E01026131,E01026132,E01026134,E01026135,E01026136,E01026137,E01026138,E01026139,E01026140,E01026141,E01026142,E01026144,E01026145,E01026146,E01026147,E01026148,E01026149,E01026150,E01026151,E01026152,E01026153,E01026154,E01026155,E01026156,E01026157,E01026158,E01026159,E01026160,E01026161,E01026162,E01026163,E01026164,E01026165,E01026166,E01026167,E01026168,E01026169,E01026170,E01026171,E01026172,E01026173,E01026174,E01026175,E01026176,E01026177,E01026178,E01026179,E01026180,E01026182,E01026183,E01026184,E01026185,E01026186,E01026187,E01026188,E01026189,E01026190,E01026191,E01026192,E01026193,E01026194,E01026195,E01026196,E01026197,E01026198,E01026199,E01026200,E01026201,E01026202,E01026203,E01026204,E01026205,E01026206,E01026207,E01026208,E01026209,E01026210,E01026211,E01026212,E01026213,E01026214,E01026215,E01026218,E01026219,E01026220,E01026221,E01026222,E01026223,E01026224,E01026225,E01026226,E01026227,E01026228,E01026229,E01026230,E01026231,E01026232,E01026233,E01026235,E01026236,E01026237,E01026238,E01026239,E01026240,E01026241,E01026242,E01026243,E01026244,E01026245,E01026246,E01026247,E01026248,E01026249,E01026250,E01026251,E01026252,E01026253,E01026254,E01026255,E01026256,E01026257,E01026258,E01026259,E01026260,E01026262,E01026263,E01026264,E01026265,E01026266,E01026268,E01026269,E01026270,E01026271,E01026272,E01026273,E01026275,E01026276,E01026277,E01026278,E01026279,E01026280,E01026281,E01026282,E01026283,E01026284,E01026285,E01026286,E01026287,E01026288,E01026289,E01026290,E01026291,E01026292,E01026293,E01026294,E01026296,E01026297,E01026298,E01026299,E01026300,E01026302,E01026303,E01026304,E01026305,E01026306,E01026307,E01026308,E01026309,E01026310,E01026311,E01026312,E01026313,E01026314,E01026315,E01026316,E01026317,E01026318,E01026319,E01026321,E01026322,E01026323,E01026324,E01026325,E01026326&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_050.json.gz
+  content_sha256: 0bdafc5faa2a2e07bad09b4933fb479969611399206e352942760d1a4382a5f7
+- id: census_lsoa_tenure5a_bedrooms_051
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 051
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01026327,E01026328,E01026329,E01026330,E01026331,E01026332,E01026333,E01026334,E01026335,E01026336,E01026337,E01026338,E01026339,E01026340,E01026341,E01026342,E01026343,E01026344,E01026345,E01026346,E01026347,E01026348,E01026349,E01026350,E01026351,E01026352,E01026353,E01026354,E01026355,E01026356,E01026357,E01026358,E01026359,E01026360,E01026361,E01026362,E01026363,E01026364,E01026365,E01026366,E01026367,E01026368,E01026369,E01026370,E01026371,E01026372,E01026373,E01026374,E01026375,E01026376,E01026377,E01026378,E01026379,E01026380,E01026381,E01026382,E01026383,E01026384,E01026385,E01026386,E01026387,E01026388,E01026389,E01026390,E01026391,E01026392,E01026393,E01026394,E01026395,E01026396,E01026398,E01026399,E01026400,E01026401,E01026402,E01026403,E01026404,E01026405,E01026406,E01026407,E01026408,E01026409,E01026410,E01026411,E01026412,E01026413,E01026414,E01026415,E01026416,E01026419,E01026420,E01026421,E01026423,E01026424,E01026425,E01026426,E01026427,E01026428,E01026429,E01026430,E01026431,E01026432,E01026433,E01026434,E01026435,E01026436,E01026437,E01026438,E01026439,E01026440,E01026441,E01026442,E01026443,E01026444,E01026445,E01026446,E01026447,E01026448,E01026449,E01026450,E01026451,E01026452,E01026453,E01026454,E01026456,E01026457,E01026458,E01026459,E01026461,E01026462,E01026464,E01026465,E01026466,E01026467,E01026468,E01026469,E01026470,E01026471,E01026472,E01026473,E01026474,E01026475,E01026476,E01026477,E01026478,E01026479,E01026480,E01026481,E01026483,E01026484,E01026485,E01026486,E01026487,E01026488,E01026489,E01026490,E01026491,E01026492,E01026493,E01026494,E01026495,E01026496,E01026497,E01026498,E01026499,E01026500,E01026501,E01026502,E01026503,E01026504,E01026505,E01026506,E01026507,E01026508,E01026509,E01026510,E01026511,E01026512,E01026513,E01026514,E01026515,E01026516,E01026517,E01026518,E01026519,E01026520,E01026521,E01026522,E01026523,E01026524,E01026525,E01026526,E01026527,E01026528,E01026529,E01026530,E01026531,E01026532,E01026533,E01026534,E01026535,E01026536,E01026537,E01026538,E01026539,E01026540,E01026541,E01026542,E01026543,E01026544,E01026545,E01026546,E01026547,E01026548,E01026549,E01026550,E01026551,E01026552,E01026553,E01026554,E01026555,E01026556,E01026557,E01026558,E01026559,E01026560,E01026562,E01026563,E01026564,E01026565,E01026566,E01026567,E01026568,E01026569,E01026570,E01026571,E01026572,E01026573,E01026574,E01026575,E01026576,E01026577,E01026578,E01026579,E01026580,E01026581,E01026582,E01026583,E01026584,E01026587,E01026588,E01026589,E01026590,E01026591,E01026592,E01026593,E01026594,E01026595,E01026596,E01026597,E01026598,E01026599,E01026600,E01026601,E01026602,E01026603,E01026604,E01026605,E01026606,E01026607,E01026608,E01026609,E01026610,E01026611,E01026612,E01026613,E01026614,E01026615,E01026616,E01026617,E01026618,E01026619,E01026620,E01026621,E01026622,E01026623,E01026624,E01026625,E01026626,E01026627,E01026628,E01026629,E01026630,E01026631,E01026632,E01026633,E01026634,E01026635,E01026636,E01026637,E01026638,E01026639,E01026640,E01026641,E01026642,E01026643,E01026644,E01026645,E01026646,E01026647,E01026648,E01026649,E01026650,E01026651,E01026652,E01026653,E01026654,E01026655,E01026656,E01026659,E01026660,E01026661,E01026662,E01026663,E01026664,E01026665,E01026666,E01026667,E01026668,E01026669,E01026670,E01026671,E01026672,E01026673,E01026674,E01026675,E01026676,E01026677,E01026678,E01026679,E01026680,E01026681,E01026682,E01026683,E01026684,E01026685,E01026687,E01026688,E01026689,E01026690,E01026691,E01026692,E01026693,E01026694,E01026695,E01026696,E01026697,E01026699,E01026700,E01026701,E01026702,E01026703,E01026704,E01026705,E01026706,E01026708,E01026709,E01026710,E01026711,E01026712,E01026713,E01026714,E01026715,E01026716,E01026717,E01026718,E01026719,E01026720,E01026721,E01026722,E01026723,E01026724,E01026725,E01026726,E01026727,E01026728,E01026729,E01026730,E01026733,E01026734,E01026735,E01026736,E01026737,E01026738,E01026739,E01026740,E01026741,E01026742,E01026743,E01026744,E01026745,E01026746,E01026747,E01026748,E01026749,E01026750,E01026751,E01026752,E01026753,E01026754,E01026755,E01026756,E01026757,E01026758,E01026759,E01026760,E01026761,E01026763,E01026764,E01026765,E01026766,E01026767,E01026768,E01026769,E01026770,E01026771,E01026772,E01026773,E01026774,E01026775,E01026776,E01026777,E01026778,E01026779,E01026780,E01026781,E01026782,E01026783,E01026784,E01026785,E01026786,E01026787,E01026788,E01026789,E01026790,E01026791,E01026792,E01026793,E01026794,E01026796,E01026797,E01026798,E01026799,E01026800,E01026801,E01026802,E01026803,E01026804,E01026805,E01026806,E01026807,E01026808,E01026809,E01026810,E01026811,E01026812,E01026813,E01026814,E01026815,E01026816,E01026817,E01026818,E01026819,E01026820,E01026821,E01026823,E01026824,E01026825,E01026826,E01026827,E01026828,E01026829,E01026830,E01026831,E01026832,E01026833,E01026834,E01026835,E01026836,E01026837,E01026838,E01026839,E01026840,E01026841,E01026842,E01026843,E01026844,E01026845,E01026846,E01026848&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_051.json.gz
+  content_sha256: c30d69d31a1679d417c5f3ce6c8d29e2d90ea0bf2a54e95888bca22514785a8b
+- id: census_lsoa_tenure5a_bedrooms_052
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 052
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01026849,E01026850,E01026852,E01026853,E01026855,E01026856,E01026857,E01026858,E01026859,E01026860,E01026861,E01026862,E01026863,E01026864,E01026865,E01026866,E01026867,E01026868,E01026869,E01026870,E01026871,E01026872,E01026873,E01026874,E01026875,E01026876,E01026877,E01026878,E01026879,E01026880,E01026881,E01026883,E01026884,E01026885,E01026886,E01026887,E01026888,E01026889,E01026891,E01026892,E01026893,E01026894,E01026895,E01026896,E01026897,E01026898,E01026899,E01026900,E01026901,E01026902,E01026903,E01026904,E01026905,E01026906,E01026907,E01026908,E01026909,E01026910,E01026911,E01026912,E01026913,E01026914,E01026915,E01026916,E01026917,E01026918,E01026919,E01026920,E01026921,E01026922,E01026924,E01026925,E01026927,E01026928,E01026929,E01026930,E01026931,E01026932,E01026934,E01026935,E01026936,E01026937,E01026938,E01026939,E01026940,E01026941,E01026942,E01026943,E01026944,E01026947,E01026948,E01026949,E01026950,E01026951,E01026952,E01026953,E01026954,E01026955,E01026956,E01026957,E01026958,E01026959,E01026960,E01026961,E01026962,E01026963,E01026965,E01026966,E01026967,E01026968,E01026969,E01026970,E01026971,E01026972,E01026973,E01026974,E01026975,E01026976,E01026979,E01026980,E01026981,E01026982,E01026983,E01026984,E01026985,E01026986,E01026987,E01026988,E01026989,E01026991,E01026992,E01026993,E01026994,E01026998,E01026999,E01027000,E01027001,E01027002,E01027003,E01027004,E01027005,E01027006,E01027007,E01027008,E01027009,E01027010,E01027011,E01027012,E01027013,E01027015,E01027016,E01027017,E01027018,E01027019,E01027020,E01027021,E01027022,E01027023,E01027024,E01027025,E01027026,E01027027,E01027028,E01027029,E01027030,E01027031,E01027032,E01027033,E01027034,E01027035,E01027037,E01027038,E01027039,E01027040,E01027041,E01027042,E01027043,E01027044,E01027045,E01027046,E01027047,E01027048,E01027049,E01027050,E01027051,E01027052,E01027053,E01027054,E01027055,E01027056,E01027057,E01027058,E01027059,E01027060,E01027061,E01027062,E01027063,E01027064,E01027065,E01027066,E01027067,E01027069,E01027070,E01027071,E01027072,E01027073,E01027074,E01027075,E01027076,E01027078,E01027079,E01027080,E01027081,E01027082,E01027083,E01027084,E01027085,E01027086,E01027087,E01027088,E01027089,E01027090,E01027091,E01027092,E01027094,E01027095,E01027096,E01027097,E01027098,E01027099,E01027100,E01027102,E01027103,E01027104,E01027105,E01027106,E01027107,E01027108,E01027109,E01027111,E01027112,E01027113,E01027114,E01027115,E01027116,E01027117,E01027119,E01027120,E01027121,E01027122,E01027123,E01027125,E01027126,E01027127,E01027128,E01027129,E01027130,E01027131,E01027132,E01027133,E01027134,E01027135,E01027136,E01027137,E01027138,E01027139,E01027140,E01027141,E01027142,E01027143,E01027144,E01027145,E01027146,E01027147,E01027148,E01027149,E01027151,E01027152,E01027153,E01027154,E01027155,E01027156,E01027158,E01027159,E01027160,E01027161,E01027162,E01027163,E01027164,E01027165,E01027166,E01027167,E01027168,E01027169,E01027170,E01027171,E01027172,E01027173,E01027174,E01027175,E01027176,E01027177,E01027178,E01027179,E01027180,E01027181,E01027182,E01027183,E01027184,E01027185,E01027186,E01027187,E01027188,E01027189,E01027190,E01027191,E01027192,E01027193,E01027194,E01027195,E01027196,E01027197,E01027198,E01027199,E01027200,E01027201,E01027202,E01027203,E01027204,E01027205,E01027207,E01027208,E01027209,E01027210,E01027211,E01027212,E01027213,E01027214,E01027215,E01027216,E01027217,E01027218,E01027219,E01027220,E01027221,E01027222,E01027224,E01027225,E01027226,E01027227,E01027228,E01027229,E01027230,E01027231,E01027232,E01027233,E01027234,E01027235,E01027236,E01027237,E01027238,E01027239,E01027240,E01027241,E01027242,E01027243,E01027244,E01027245,E01027246,E01027247,E01027248,E01027249,E01027250,E01027252,E01027253,E01027254,E01027255,E01027256,E01027257,E01027258,E01027259,E01027260,E01027261,E01027262,E01027263,E01027264,E01027265,E01027267,E01027268,E01027269,E01027270,E01027271,E01027272,E01027273,E01027274,E01027275,E01027277,E01027279,E01027280,E01027281,E01027282,E01027283,E01027284,E01027285,E01027286,E01027287,E01027288,E01027289,E01027290,E01027291,E01027292,E01027293,E01027294,E01027295,E01027296,E01027297,E01027298,E01027299,E01027300,E01027301,E01027302,E01027303,E01027304,E01027305,E01027306,E01027307,E01027308,E01027309,E01027310,E01027311,E01027312,E01027313,E01027314,E01027316,E01027317,E01027318,E01027319,E01027320,E01027321,E01027322,E01027323,E01027324,E01027325,E01027326,E01027327,E01027328,E01027329,E01027330,E01027331,E01027332,E01027333,E01027334,E01027335,E01027336,E01027337,E01027338,E01027339,E01027340,E01027341,E01027342,E01027343,E01027344,E01027345,E01027346,E01027347,E01027348,E01027350,E01027351,E01027352,E01027353,E01027354,E01027355,E01027356,E01027357,E01027358,E01027359,E01027361,E01027362,E01027363,E01027364,E01027365,E01027366,E01027367,E01027368,E01027369,E01027370,E01027371,E01027372,E01027373,E01027374,E01027375,E01027376,E01027377,E01027378,E01027379,E01027380,E01027381,E01027382,E01027383,E01027384&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_052.json.gz
+  content_sha256: 3e3b6dffbb6028dbf780880e432917fb80b9c6d7ce91e22828512e696573541a
+- id: census_lsoa_tenure5a_bedrooms_053
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 053
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01027385,E01027386,E01027387,E01027388,E01027389,E01027390,E01027391,E01027392,E01027393,E01027394,E01027395,E01027396,E01027397,E01027398,E01027399,E01027400,E01027401,E01027402,E01027403,E01027404,E01027405,E01027406,E01027407,E01027408,E01027409,E01027410,E01027411,E01027412,E01027413,E01027414,E01027415,E01027416,E01027417,E01027418,E01027419,E01027420,E01027421,E01027422,E01027423,E01027424,E01027426,E01027427,E01027428,E01027429,E01027430,E01027431,E01027432,E01027433,E01027434,E01027435,E01027436,E01027437,E01027438,E01027439,E01027440,E01027442,E01027443,E01027444,E01027445,E01027446,E01027447,E01027449,E01027450,E01027451,E01027452,E01027453,E01027455,E01027456,E01027457,E01027458,E01027460,E01027461,E01027462,E01027463,E01027464,E01027465,E01027466,E01027468,E01027469,E01027471,E01027472,E01027473,E01027474,E01027475,E01027476,E01027477,E01027478,E01027479,E01027480,E01027481,E01027482,E01027483,E01027484,E01027485,E01027486,E01027487,E01027488,E01027489,E01027490,E01027491,E01027492,E01027493,E01027494,E01027495,E01027496,E01027497,E01027498,E01027499,E01027500,E01027503,E01027504,E01027505,E01027507,E01027508,E01027509,E01027510,E01027511,E01027512,E01027513,E01027514,E01027515,E01027516,E01027517,E01027518,E01027519,E01027520,E01027521,E01027522,E01027523,E01027524,E01027525,E01027526,E01027527,E01027528,E01027529,E01027530,E01027531,E01027532,E01027533,E01027534,E01027535,E01027536,E01027537,E01027538,E01027539,E01027540,E01027541,E01027542,E01027543,E01027544,E01027545,E01027546,E01027549,E01027550,E01027551,E01027552,E01027553,E01027554,E01027555,E01027556,E01027557,E01027558,E01027559,E01027560,E01027561,E01027562,E01027563,E01027564,E01027565,E01027566,E01027567,E01027568,E01027569,E01027570,E01027571,E01027572,E01027573,E01027574,E01027575,E01027576,E01027577,E01027578,E01027579,E01027580,E01027582,E01027583,E01027584,E01027585,E01027586,E01027587,E01027588,E01027589,E01027590,E01027591,E01027592,E01027593,E01027594,E01027595,E01027596,E01027597,E01027598,E01027599,E01027600,E01027601,E01027603,E01027604,E01027605,E01027606,E01027607,E01027608,E01027609,E01027610,E01027612,E01027613,E01027614,E01027615,E01027616,E01027617,E01027618,E01027619,E01027620,E01027622,E01027623,E01027624,E01027625,E01027626,E01027627,E01027628,E01027629,E01027630,E01027631,E01027632,E01027633,E01027634,E01027635,E01027636,E01027637,E01027638,E01027639,E01027640,E01027641,E01027642,E01027643,E01027644,E01027645,E01027646,E01027647,E01027648,E01027649,E01027650,E01027651,E01027652,E01027653,E01027654,E01027655,E01027656,E01027657,E01027658,E01027659,E01027660,E01027661,E01027662,E01027663,E01027665,E01027666,E01027667,E01027668,E01027669,E01027670,E01027671,E01027672,E01027673,E01027674,E01027675,E01027676,E01027677,E01027678,E01027679,E01027680,E01027681,E01027682,E01027683,E01027684,E01027685,E01027686,E01027687,E01027688,E01027689,E01027690,E01027691,E01027692,E01027693,E01027694,E01027695,E01027696,E01027697,E01027698,E01027699,E01027700,E01027701,E01027702,E01027703,E01027704,E01027705,E01027706,E01027707,E01027708,E01027709,E01027710,E01027711,E01027712,E01027713,E01027714,E01027715,E01027716,E01027717,E01027718,E01027719,E01027720,E01027721,E01027722,E01027723,E01027724,E01027725,E01027726,E01027727,E01027728,E01027729,E01027730,E01027731,E01027732,E01027733,E01027734,E01027735,E01027736,E01027737,E01027738,E01027739,E01027740,E01027741,E01027742,E01027743,E01027744,E01027745,E01027746,E01027747,E01027748,E01027749,E01027751,E01027752,E01027753,E01027756,E01027758,E01027759,E01027760,E01027761,E01027762,E01027763,E01027764,E01027765,E01027766,E01027767,E01027768,E01027769,E01027770,E01027771,E01027772,E01027773,E01027774,E01027775,E01027776,E01027777,E01027778,E01027779,E01027780,E01027781,E01027782,E01027783,E01027784,E01027786,E01027787,E01027788,E01027790,E01027791,E01027792,E01027793,E01027794,E01027795,E01027796,E01027797,E01027798,E01027799,E01027800,E01027801,E01027802,E01027803,E01027804,E01027805,E01027806,E01027807,E01027808,E01027809,E01027810,E01027811,E01027812,E01027813,E01027814,E01027815,E01027816,E01027817,E01027818,E01027819,E01027820,E01027821,E01027822,E01027823,E01027824,E01027825,E01027826,E01027827,E01027828,E01027829,E01027830,E01027833,E01027834,E01027835,E01027836,E01027837,E01027838,E01027839,E01027840,E01027841,E01027842,E01027843,E01027844,E01027845,E01027847,E01027849,E01027850,E01027851,E01027852,E01027853,E01027854,E01027855,E01027856,E01027857,E01027858,E01027859,E01027860,E01027861,E01027862,E01027863,E01027864,E01027865,E01027866,E01027867,E01027868,E01027871,E01027872,E01027873,E01027874,E01027875,E01027876,E01027877,E01027878,E01027879,E01027880,E01027881,E01027882,E01027883,E01027884,E01027885,E01027886,E01027887,E01027888,E01027889,E01027890,E01027891,E01027892,E01027893,E01027894,E01027895,E01027896,E01027897,E01027899,E01027900,E01027901,E01027902,E01027903,E01027904,E01027906,E01027907,E01027908,E01027909,E01027913,E01027914,E01027915,E01027916,E01027918,E01027919&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_053.json.gz
+  content_sha256: 8a0b4ab76408fdd610d421ba253a08aa53b6c3743ef9d49f9161aabf3a296815
+- id: census_lsoa_tenure5a_bedrooms_054
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 054
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01027920,E01027921,E01027922,E01027923,E01027924,E01027925,E01027926,E01027927,E01027928,E01027930,E01027932,E01027933,E01027934,E01027935,E01027936,E01027937,E01027938,E01027939,E01027940,E01027941,E01027942,E01027943,E01027944,E01027945,E01027946,E01027947,E01027948,E01027949,E01027950,E01027951,E01027952,E01027953,E01027954,E01027955,E01027956,E01027957,E01027958,E01027959,E01027960,E01027961,E01027962,E01027963,E01027964,E01027965,E01027966,E01027967,E01027968,E01027969,E01027970,E01027971,E01027972,E01027973,E01027974,E01027975,E01027976,E01027977,E01027978,E01027979,E01027980,E01027981,E01027982,E01027983,E01027984,E01027985,E01027986,E01027987,E01027988,E01027989,E01027990,E01027991,E01027992,E01027993,E01027994,E01027995,E01027996,E01027997,E01027998,E01027999,E01028000,E01028001,E01028002,E01028003,E01028004,E01028005,E01028006,E01028007,E01028008,E01028009,E01028010,E01028011,E01028012,E01028013,E01028014,E01028015,E01028016,E01028017,E01028018,E01028019,E01028020,E01028021,E01028022,E01028023,E01028024,E01028025,E01028026,E01028027,E01028028,E01028029,E01028030,E01028031,E01028032,E01028033,E01028034,E01028035,E01028036,E01028037,E01028038,E01028039,E01028042,E01028043,E01028044,E01028045,E01028046,E01028047,E01028048,E01028049,E01028050,E01028051,E01028052,E01028053,E01028054,E01028055,E01028056,E01028057,E01028058,E01028059,E01028060,E01028061,E01028062,E01028063,E01028064,E01028065,E01028066,E01028067,E01028068,E01028069,E01028070,E01028073,E01028074,E01028075,E01028076,E01028077,E01028078,E01028079,E01028080,E01028081,E01028082,E01028083,E01028084,E01028085,E01028086,E01028087,E01028088,E01028089,E01028090,E01028091,E01028092,E01028093,E01028094,E01028095,E01028096,E01028097,E01028098,E01028099,E01028100,E01028101,E01028102,E01028103,E01028104,E01028105,E01028106,E01028107,E01028108,E01028109,E01028110,E01028111,E01028112,E01028113,E01028114,E01028115,E01028116,E01028117,E01028118,E01028119,E01028120,E01028121,E01028122,E01028123,E01028124,E01028125,E01028126,E01028127,E01028128,E01028129,E01028130,E01028131,E01028132,E01028133,E01028134,E01028135,E01028136,E01028137,E01028138,E01028139,E01028142,E01028143,E01028144,E01028146,E01028147,E01028148,E01028149,E01028150,E01028151,E01028152,E01028153,E01028154,E01028155,E01028156,E01028157,E01028158,E01028159,E01028160,E01028161,E01028162,E01028163,E01028164,E01028165,E01028166,E01028167,E01028168,E01028169,E01028170,E01028171,E01028172,E01028173,E01028174,E01028175,E01028176,E01028177,E01028178,E01028179,E01028180,E01028181,E01028182,E01028183,E01028184,E01028185,E01028186,E01028187,E01028188,E01028189,E01028190,E01028191,E01028192,E01028193,E01028194,E01028195,E01028196,E01028197,E01028198,E01028199,E01028200,E01028201,E01028202,E01028203,E01028204,E01028205,E01028206,E01028207,E01028208,E01028209,E01028210,E01028211,E01028212,E01028213,E01028214,E01028215,E01028216,E01028217,E01028218,E01028219,E01028220,E01028222,E01028223,E01028224,E01028225,E01028226,E01028227,E01028228,E01028229,E01028230,E01028231,E01028232,E01028233,E01028234,E01028235,E01028236,E01028237,E01028238,E01028239,E01028240,E01028242,E01028243,E01028244,E01028245,E01028246,E01028247,E01028248,E01028249,E01028250,E01028251,E01028252,E01028253,E01028254,E01028255,E01028256,E01028257,E01028258,E01028259,E01028260,E01028261,E01028262,E01028263,E01028264,E01028267,E01028268,E01028269,E01028270,E01028271,E01028272,E01028273,E01028274,E01028275,E01028276,E01028277,E01028278,E01028279,E01028280,E01028281,E01028282,E01028283,E01028284,E01028285,E01028286,E01028287,E01028288,E01028289,E01028290,E01028291,E01028292,E01028293,E01028294,E01028295,E01028296,E01028297,E01028298,E01028299,E01028300,E01028301,E01028302,E01028303,E01028304,E01028305,E01028306,E01028307,E01028308,E01028309,E01028310,E01028311,E01028312,E01028313,E01028315,E01028316,E01028317,E01028318,E01028319,E01028320,E01028321,E01028322,E01028323,E01028325,E01028326,E01028327,E01028328,E01028329,E01028330,E01028331,E01028332,E01028333,E01028334,E01028335,E01028336,E01028337,E01028338,E01028339,E01028340,E01028342,E01028343,E01028344,E01028345,E01028346,E01028347,E01028348,E01028349,E01028350,E01028351,E01028352,E01028353,E01028354,E01028355,E01028356,E01028357,E01028358,E01028359,E01028360,E01028361,E01028362,E01028363,E01028364,E01028365,E01028366,E01028367,E01028368,E01028369,E01028370,E01028371,E01028372,E01028374,E01028375,E01028376,E01028377,E01028378,E01028379,E01028380,E01028381,E01028382,E01028383,E01028384,E01028385,E01028386,E01028387,E01028388,E01028389,E01028390,E01028391,E01028392,E01028393,E01028394,E01028395,E01028396,E01028397,E01028398,E01028399,E01028400,E01028401,E01028402,E01028403,E01028404,E01028405,E01028406,E01028407,E01028408,E01028409,E01028410,E01028411,E01028412,E01028413,E01028414,E01028415,E01028416,E01028417,E01028418,E01028419,E01028420,E01028421,E01028422,E01028423,E01028426,E01028427,E01028428,E01028429,E01028430,E01028431,E01028432,E01028433,E01028434,E01028435,E01028437,E01028439,E01028440&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_054.json.gz
+  content_sha256: 55cb7ef91e0b9e410ad79019018f032db44bef6a5a27055ed8f902c0ef4d8710
+- id: census_lsoa_tenure5a_bedrooms_055
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 055
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01028441,E01028443,E01028444,E01028445,E01028446,E01028447,E01028448,E01028449,E01028450,E01028451,E01028452,E01028453,E01028454,E01028455,E01028456,E01028457,E01028458,E01028459,E01028460,E01028461,E01028462,E01028463,E01028464,E01028465,E01028466,E01028467,E01028469,E01028470,E01028471,E01028472,E01028473,E01028474,E01028476,E01028477,E01028478,E01028479,E01028480,E01028481,E01028482,E01028483,E01028484,E01028485,E01028486,E01028487,E01028488,E01028489,E01028490,E01028491,E01028492,E01028493,E01028494,E01028495,E01028496,E01028497,E01028498,E01028499,E01028500,E01028501,E01028502,E01028503,E01028504,E01028506,E01028507,E01028508,E01028510,E01028511,E01028512,E01028513,E01028514,E01028515,E01028516,E01028517,E01028518,E01028519,E01028520,E01028521,E01028522,E01028523,E01028524,E01028525,E01028526,E01028527,E01028528,E01028529,E01028530,E01028531,E01028532,E01028534,E01028535,E01028536,E01028537,E01028538,E01028539,E01028540,E01028541,E01028542,E01028543,E01028544,E01028545,E01028546,E01028547,E01028548,E01028549,E01028550,E01028551,E01028552,E01028553,E01028554,E01028555,E01028556,E01028557,E01028558,E01028559,E01028560,E01028561,E01028562,E01028563,E01028565,E01028567,E01028568,E01028569,E01028570,E01028571,E01028572,E01028573,E01028574,E01028575,E01028576,E01028577,E01028578,E01028579,E01028580,E01028581,E01028582,E01028584,E01028586,E01028587,E01028588,E01028591,E01028592,E01028594,E01028595,E01028596,E01028597,E01028598,E01028599,E01028600,E01028601,E01028602,E01028603,E01028604,E01028605,E01028606,E01028607,E01028608,E01028609,E01028610,E01028611,E01028612,E01028613,E01028614,E01028615,E01028616,E01028617,E01028619,E01028622,E01028623,E01028624,E01028625,E01028626,E01028627,E01028628,E01028629,E01028630,E01028631,E01028632,E01028633,E01028634,E01028635,E01028636,E01028637,E01028638,E01028639,E01028640,E01028641,E01028642,E01028643,E01028644,E01028645,E01028646,E01028647,E01028649,E01028650,E01028651,E01028652,E01028653,E01028654,E01028655,E01028656,E01028657,E01028658,E01028659,E01028660,E01028661,E01028662,E01028663,E01028664,E01028665,E01028666,E01028667,E01028668,E01028669,E01028670,E01028671,E01028672,E01028673,E01028674,E01028675,E01028676,E01028677,E01028678,E01028679,E01028680,E01028681,E01028682,E01028683,E01028684,E01028685,E01028686,E01028688,E01028689,E01028690,E01028691,E01028692,E01028693,E01028694,E01028695,E01028696,E01028697,E01028698,E01028699,E01028700,E01028701,E01028702,E01028703,E01028704,E01028705,E01028706,E01028707,E01028708,E01028709,E01028710,E01028711,E01028712,E01028713,E01028715,E01028716,E01028717,E01028718,E01028719,E01028720,E01028721,E01028722,E01028723,E01028724,E01028725,E01028726,E01028728,E01028729,E01028730,E01028731,E01028732,E01028733,E01028734,E01028735,E01028737,E01028738,E01028739,E01028740,E01028741,E01028742,E01028743,E01028744,E01028745,E01028746,E01028747,E01028748,E01028749,E01028750,E01028751,E01028752,E01028753,E01028754,E01028755,E01028757,E01028758,E01028759,E01028760,E01028761,E01028762,E01028763,E01028764,E01028765,E01028767,E01028771,E01028772,E01028773,E01028774,E01028775,E01028776,E01028777,E01028778,E01028779,E01028780,E01028781,E01028782,E01028783,E01028784,E01028785,E01028786,E01028787,E01028788,E01028789,E01028790,E01028791,E01028792,E01028793,E01028794,E01028795,E01028796,E01028797,E01028798,E01028799,E01028800,E01028801,E01028802,E01028804,E01028806,E01028807,E01028808,E01028809,E01028810,E01028811,E01028813,E01028814,E01028815,E01028816,E01028817,E01028818,E01028819,E01028820,E01028821,E01028822,E01028823,E01028824,E01028825,E01028826,E01028827,E01028828,E01028829,E01028830,E01028831,E01028832,E01028833,E01028834,E01028835,E01028836,E01028837,E01028838,E01028839,E01028840,E01028841,E01028842,E01028843,E01028844,E01028845,E01028846,E01028847,E01028848,E01028849,E01028850,E01028851,E01028852,E01028853,E01028854,E01028855,E01028856,E01028858,E01028859,E01028860,E01028861,E01028862,E01028863,E01028864,E01028865,E01028866,E01028867,E01028868,E01028869,E01028870,E01028871,E01028872,E01028873,E01028874,E01028875,E01028876,E01028877,E01028878,E01028879,E01028880,E01028881,E01028882,E01028883,E01028884,E01028886,E01028887,E01028888,E01028889,E01028890,E01028891,E01028892,E01028893,E01028894,E01028895,E01028896,E01028897,E01028898,E01028899,E01028902,E01028903,E01028904,E01028905,E01028906,E01028907,E01028908,E01028909,E01028910,E01028911,E01028912,E01028913,E01028914,E01028915,E01028916,E01028917,E01028918,E01028919,E01028920,E01028921,E01028922,E01028923,E01028924,E01028925,E01028926,E01028927,E01028928,E01028929,E01028930,E01028931,E01028933,E01028934,E01028935,E01028936,E01028937,E01028938,E01028939,E01028940,E01028942,E01028943,E01028944,E01028945,E01028947,E01028948,E01028949,E01028950,E01028951,E01028952,E01028953,E01028954,E01028955,E01028956,E01028957,E01028958,E01028960,E01028961,E01028962,E01028963,E01028964,E01028965,E01028966,E01028967,E01028968,E01028969,E01028970,E01028971,E01028972,E01028973,E01028974,E01028975,E01028976,E01028977&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_055.json.gz
+  content_sha256: e06e7c217d366a8217e216a1a4babcb925bcdb3704112c25ab9d16c0be223b7f
+- id: census_lsoa_tenure5a_bedrooms_056
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 056
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01028978,E01028979,E01028980,E01028981,E01028982,E01028983,E01028984,E01028985,E01028986,E01028988,E01028989,E01028990,E01028992,E01028993,E01028994,E01028995,E01028996,E01028997,E01028998,E01028999,E01029000,E01029001,E01029002,E01029003,E01029005,E01029006,E01029007,E01029009,E01029010,E01029011,E01029012,E01029013,E01029014,E01029016,E01029018,E01029019,E01029020,E01029021,E01029022,E01029023,E01029024,E01029025,E01029027,E01029028,E01029029,E01029030,E01029031,E01029032,E01029033,E01029034,E01029036,E01029037,E01029038,E01029039,E01029040,E01029041,E01029042,E01029043,E01029044,E01029045,E01029046,E01029047,E01029050,E01029051,E01029052,E01029053,E01029054,E01029055,E01029056,E01029057,E01029058,E01029059,E01029060,E01029062,E01029063,E01029064,E01029065,E01029066,E01029067,E01029068,E01029070,E01029071,E01029072,E01029073,E01029074,E01029075,E01029076,E01029077,E01029078,E01029079,E01029080,E01029081,E01029082,E01029083,E01029084,E01029085,E01029086,E01029089,E01029091,E01029092,E01029093,E01029094,E01029095,E01029096,E01029097,E01029098,E01029099,E01029100,E01029101,E01029102,E01029103,E01029104,E01029105,E01029106,E01029107,E01029108,E01029109,E01029110,E01029111,E01029112,E01029113,E01029114,E01029115,E01029116,E01029117,E01029118,E01029119,E01029120,E01029121,E01029122,E01029123,E01029124,E01029125,E01029126,E01029127,E01029128,E01029129,E01029130,E01029131,E01029132,E01029133,E01029134,E01029135,E01029136,E01029137,E01029138,E01029139,E01029142,E01029143,E01029144,E01029145,E01029147,E01029148,E01029149,E01029150,E01029151,E01029152,E01029153,E01029154,E01029155,E01029156,E01029157,E01029158,E01029159,E01029161,E01029162,E01029163,E01029164,E01029165,E01029166,E01029167,E01029168,E01029169,E01029170,E01029171,E01029172,E01029173,E01029174,E01029175,E01029176,E01029177,E01029178,E01029179,E01029180,E01029181,E01029182,E01029183,E01029184,E01029185,E01029186,E01029187,E01029188,E01029189,E01029190,E01029191,E01029192,E01029193,E01029194,E01029195,E01029196,E01029197,E01029198,E01029199,E01029200,E01029201,E01029202,E01029203,E01029204,E01029205,E01029206,E01029207,E01029208,E01029209,E01029210,E01029211,E01029212,E01029213,E01029214,E01029215,E01029216,E01029217,E01029218,E01029219,E01029220,E01029221,E01029222,E01029223,E01029224,E01029225,E01029226,E01029227,E01029228,E01029229,E01029230,E01029231,E01029232,E01029233,E01029235,E01029236,E01029237,E01029238,E01029239,E01029240,E01029241,E01029242,E01029243,E01029244,E01029245,E01029246,E01029247,E01029248,E01029249,E01029250,E01029251,E01029252,E01029254,E01029255,E01029256,E01029257,E01029258,E01029259,E01029260,E01029261,E01029262,E01029263,E01029264,E01029265,E01029266,E01029267,E01029268,E01029269,E01029270,E01029271,E01029273,E01029274,E01029275,E01029276,E01029277,E01029278,E01029279,E01029280,E01029281,E01029282,E01029283,E01029284,E01029285,E01029286,E01029287,E01029288,E01029289,E01029290,E01029291,E01029292,E01029293,E01029294,E01029295,E01029296,E01029297,E01029298,E01029299,E01029300,E01029301,E01029302,E01029303,E01029304,E01029305,E01029306,E01029307,E01029308,E01029310,E01029311,E01029312,E01029313,E01029315,E01029316,E01029317,E01029319,E01029320,E01029321,E01029322,E01029324,E01029325,E01029326,E01029327,E01029328,E01029329,E01029330,E01029331,E01029333,E01029334,E01029335,E01029336,E01029337,E01029338,E01029339,E01029341,E01029342,E01029343,E01029345,E01029346,E01029347,E01029348,E01029349,E01029350,E01029351,E01029352,E01029353,E01029354,E01029355,E01029356,E01029357,E01029358,E01029359,E01029360,E01029361,E01029362,E01029363,E01029364,E01029365,E01029366,E01029367,E01029368,E01029369,E01029370,E01029371,E01029372,E01029373,E01029374,E01029375,E01029376,E01029377,E01029378,E01029379,E01029380,E01029381,E01029382,E01029383,E01029385,E01029386,E01029387,E01029388,E01029389,E01029390,E01029391,E01029392,E01029393,E01029394,E01029395,E01029396,E01029397,E01029398,E01029399,E01029400,E01029401,E01029402,E01029403,E01029404,E01029405,E01029406,E01029407,E01029408,E01029409,E01029410,E01029411,E01029412,E01029413,E01029414,E01029415,E01029416,E01029417,E01029418,E01029419,E01029420,E01029422,E01029423,E01029424,E01029425,E01029426,E01029427,E01029428,E01029429,E01029430,E01029431,E01029432,E01029433,E01029434,E01029435,E01029436,E01029437,E01029438,E01029439,E01029440,E01029441,E01029442,E01029443,E01029444,E01029445,E01029446,E01029447,E01029448,E01029449,E01029450,E01029451,E01029452,E01029453,E01029454,E01029455,E01029456,E01029457,E01029458,E01029459,E01029460,E01029461,E01029462,E01029463,E01029464,E01029465,E01029466,E01029467,E01029468,E01029469,E01029470,E01029471,E01029472,E01029473,E01029474,E01029475,E01029476,E01029477,E01029478,E01029480,E01029482,E01029483,E01029484,E01029485,E01029486,E01029487,E01029488,E01029489,E01029490,E01029491,E01029492,E01029493,E01029494,E01029495,E01029496,E01029498,E01029499,E01029500,E01029501,E01029502,E01029503,E01029504,E01029505,E01029506,E01029507,E01029508,E01029509,E01029511,E01029512&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_056.json.gz
+  content_sha256: 5676d1faffca42028dab24e147ab1cbb356de1e239b814dc692e11fdbe78ce01
+- id: census_lsoa_tenure5a_bedrooms_057
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 057
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01029513,E01029514,E01029515,E01029516,E01029517,E01029518,E01029519,E01029520,E01029521,E01029522,E01029523,E01029524,E01029525,E01029526,E01029527,E01029528,E01029529,E01029531,E01029532,E01029533,E01029534,E01029535,E01029536,E01029537,E01029538,E01029539,E01029540,E01029541,E01029542,E01029543,E01029544,E01029545,E01029546,E01029547,E01029548,E01029549,E01029551,E01029552,E01029553,E01029554,E01029555,E01029556,E01029557,E01029558,E01029559,E01029560,E01029561,E01029562,E01029563,E01029564,E01029565,E01029566,E01029567,E01029568,E01029569,E01029570,E01029571,E01029572,E01029573,E01029574,E01029575,E01029576,E01029577,E01029578,E01029579,E01029580,E01029581,E01029582,E01029583,E01029584,E01029585,E01029586,E01029587,E01029588,E01029589,E01029590,E01029591,E01029593,E01029594,E01029595,E01029596,E01029597,E01029598,E01029599,E01029600,E01029601,E01029602,E01029603,E01029604,E01029605,E01029606,E01029607,E01029608,E01029609,E01029610,E01029611,E01029612,E01029613,E01029614,E01029615,E01029616,E01029617,E01029618,E01029619,E01029620,E01029621,E01029622,E01029623,E01029624,E01029625,E01029626,E01029627,E01029628,E01029629,E01029630,E01029631,E01029632,E01029633,E01029634,E01029635,E01029636,E01029637,E01029638,E01029639,E01029640,E01029641,E01029642,E01029643,E01029644,E01029645,E01029646,E01029647,E01029648,E01029649,E01029650,E01029651,E01029652,E01029653,E01029654,E01029655,E01029656,E01029657,E01029658,E01029659,E01029660,E01029661,E01029662,E01029663,E01029664,E01029665,E01029666,E01029667,E01029668,E01029669,E01029670,E01029671,E01029672,E01029673,E01029674,E01029675,E01029676,E01029677,E01029678,E01029679,E01029680,E01029681,E01029682,E01029683,E01029684,E01029685,E01029686,E01029687,E01029688,E01029689,E01029690,E01029691,E01029692,E01029693,E01029694,E01029695,E01029696,E01029697,E01029698,E01029699,E01029700,E01029702,E01029703,E01029704,E01029705,E01029706,E01029707,E01029708,E01029709,E01029710,E01029711,E01029712,E01029713,E01029714,E01029715,E01029716,E01029717,E01029718,E01029719,E01029720,E01029721,E01029722,E01029723,E01029724,E01029725,E01029726,E01029727,E01029728,E01029729,E01029733,E01029734,E01029735,E01029736,E01029737,E01029738,E01029739,E01029740,E01029741,E01029742,E01029743,E01029744,E01029745,E01029746,E01029747,E01029748,E01029749,E01029750,E01029751,E01029752,E01029753,E01029754,E01029755,E01029756,E01029757,E01029758,E01029759,E01029760,E01029761,E01029762,E01029763,E01029764,E01029765,E01029766,E01029767,E01029768,E01029769,E01029770,E01029771,E01029772,E01029773,E01029774,E01029775,E01029776,E01029777,E01029778,E01029779,E01029780,E01029781,E01029782,E01029783,E01029784,E01029785,E01029786,E01029787,E01029788,E01029789,E01029790,E01029791,E01029792,E01029793,E01029794,E01029795,E01029796,E01029797,E01029798,E01029799,E01029800,E01029801,E01029802,E01029803,E01029804,E01029805,E01029806,E01029807,E01029808,E01029809,E01029810,E01029811,E01029812,E01029813,E01029814,E01029815,E01029816,E01029817,E01029818,E01029819,E01029820,E01029821,E01029822,E01029823,E01029824,E01029825,E01029826,E01029827,E01029828,E01029829,E01029830,E01029831,E01029832,E01029833,E01029834,E01029835,E01029836,E01029837,E01029838,E01029839,E01029840,E01029841,E01029842,E01029843,E01029844,E01029845,E01029846,E01029847,E01029848,E01029849,E01029850,E01029851,E01029852,E01029853,E01029854,E01029855,E01029856,E01029857,E01029858,E01029859,E01029860,E01029861,E01029862,E01029863,E01029864,E01029865,E01029867,E01029868,E01029869,E01029870,E01029871,E01029872,E01029873,E01029874,E01029875,E01029876,E01029877,E01029878,E01029879,E01029880,E01029881,E01029882,E01029883,E01029884,E01029885,E01029886,E01029887,E01029888,E01029889,E01029890,E01029891,E01029892,E01029894,E01029895,E01029896,E01029897,E01029898,E01029899,E01029900,E01029901,E01029902,E01029903,E01029904,E01029905,E01029906,E01029907,E01029908,E01029909,E01029910,E01029911,E01029912,E01029913,E01029914,E01029915,E01029916,E01029917,E01029918,E01029919,E01029920,E01029921,E01029922,E01029923,E01029925,E01029926,E01029927,E01029928,E01029929,E01029930,E01029937,E01029938,E01029939,E01029940,E01029941,E01029942,E01029944,E01029945,E01029946,E01029947,E01029949,E01029950,E01029951,E01029952,E01029953,E01029955,E01029956,E01029957,E01029958,E01029960,E01029961,E01029962,E01029963,E01029964,E01029965,E01029966,E01029967,E01029968,E01029969,E01029970,E01029971,E01029973,E01029974,E01029975,E01029976,E01029977,E01029978,E01029979,E01029980,E01029981,E01029982,E01029983,E01029984,E01029985,E01029986,E01029987,E01029988,E01029990,E01029991,E01029992,E01029994,E01029995,E01029996,E01029997,E01029998,E01029999,E01030000,E01030001,E01030002,E01030003,E01030004,E01030005,E01030006,E01030007,E01030008,E01030009,E01030010,E01030011,E01030012,E01030013,E01030014,E01030015,E01030016,E01030017,E01030018,E01030019,E01030020,E01030021,E01030022,E01030023,E01030024,E01030025,E01030026,E01030027,E01030028,E01030029,E01030030,E01030031,E01030032,E01030033,E01030034,E01030035&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_057.json.gz
+  content_sha256: a56380236b3ba115265d14756944f7e58b8d086c7b80300ffb55bceafca3dcef
+- id: census_lsoa_tenure5a_bedrooms_058
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 058
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01030036,E01030037,E01030038,E01030039,E01030040,E01030041,E01030042,E01030043,E01030044,E01030045,E01030046,E01030047,E01030048,E01030049,E01030050,E01030051,E01030052,E01030053,E01030054,E01030055,E01030056,E01030057,E01030058,E01030059,E01030060,E01030061,E01030062,E01030063,E01030064,E01030065,E01030066,E01030067,E01030068,E01030069,E01030070,E01030071,E01030072,E01030073,E01030075,E01030076,E01030077,E01030078,E01030079,E01030080,E01030081,E01030083,E01030084,E01030085,E01030086,E01030087,E01030088,E01030089,E01030090,E01030091,E01030092,E01030093,E01030094,E01030095,E01030096,E01030097,E01030098,E01030099,E01030100,E01030101,E01030104,E01030105,E01030106,E01030107,E01030108,E01030109,E01030110,E01030111,E01030112,E01030113,E01030114,E01030115,E01030116,E01030117,E01030118,E01030119,E01030121,E01030122,E01030123,E01030124,E01030125,E01030127,E01030128,E01030129,E01030130,E01030131,E01030132,E01030133,E01030134,E01030135,E01030137,E01030138,E01030139,E01030140,E01030141,E01030142,E01030143,E01030144,E01030145,E01030146,E01030147,E01030148,E01030149,E01030150,E01030151,E01030152,E01030153,E01030154,E01030155,E01030158,E01030159,E01030160,E01030161,E01030162,E01030163,E01030164,E01030165,E01030166,E01030167,E01030168,E01030169,E01030170,E01030171,E01030172,E01030173,E01030174,E01030175,E01030176,E01030179,E01030180,E01030181,E01030183,E01030184,E01030185,E01030186,E01030187,E01030188,E01030189,E01030190,E01030191,E01030192,E01030193,E01030194,E01030195,E01030196,E01030197,E01030198,E01030199,E01030200,E01030201,E01030203,E01030204,E01030205,E01030206,E01030207,E01030208,E01030209,E01030210,E01030211,E01030212,E01030214,E01030215,E01030216,E01030217,E01030218,E01030219,E01030220,E01030221,E01030222,E01030223,E01030224,E01030225,E01030226,E01030227,E01030228,E01030229,E01030230,E01030231,E01030232,E01030233,E01030234,E01030235,E01030236,E01030237,E01030238,E01030239,E01030240,E01030241,E01030242,E01030243,E01030244,E01030245,E01030246,E01030247,E01030248,E01030249,E01030250,E01030251,E01030252,E01030253,E01030254,E01030255,E01030256,E01030257,E01030258,E01030259,E01030260,E01030261,E01030262,E01030263,E01030264,E01030265,E01030266,E01030267,E01030268,E01030269,E01030270,E01030271,E01030272,E01030273,E01030274,E01030275,E01030276,E01030277,E01030278,E01030279,E01030280,E01030281,E01030283,E01030285,E01030286,E01030287,E01030288,E01030289,E01030290,E01030291,E01030292,E01030293,E01030294,E01030295,E01030296,E01030297,E01030298,E01030299,E01030300,E01030301,E01030302,E01030303,E01030304,E01030305,E01030306,E01030307,E01030308,E01030309,E01030310,E01030311,E01030312,E01030313,E01030314,E01030315,E01030316,E01030317,E01030318,E01030319,E01030320,E01030321,E01030322,E01030323,E01030324,E01030325,E01030326,E01030327,E01030328,E01030329,E01030330,E01030331,E01030332,E01030333,E01030334,E01030335,E01030336,E01030337,E01030338,E01030339,E01030340,E01030341,E01030342,E01030343,E01030344,E01030345,E01030346,E01030347,E01030348,E01030349,E01030350,E01030351,E01030352,E01030353,E01030354,E01030355,E01030356,E01030357,E01030358,E01030360,E01030361,E01030362,E01030363,E01030364,E01030365,E01030366,E01030367,E01030368,E01030369,E01030370,E01030371,E01030372,E01030373,E01030374,E01030375,E01030376,E01030377,E01030378,E01030379,E01030380,E01030381,E01030382,E01030384,E01030385,E01030386,E01030387,E01030388,E01030389,E01030390,E01030391,E01030392,E01030393,E01030394,E01030395,E01030396,E01030398,E01030399,E01030400,E01030401,E01030402,E01030403,E01030404,E01030405,E01030406,E01030407,E01030408,E01030409,E01030410,E01030412,E01030413,E01030414,E01030415,E01030416,E01030417,E01030418,E01030419,E01030420,E01030421,E01030422,E01030423,E01030424,E01030425,E01030426,E01030427,E01030428,E01030429,E01030430,E01030431,E01030432,E01030433,E01030434,E01030435,E01030436,E01030437,E01030438,E01030439,E01030440,E01030441,E01030442,E01030443,E01030444,E01030445,E01030446,E01030447,E01030448,E01030449,E01030450,E01030451,E01030452,E01030453,E01030454,E01030455,E01030456,E01030457,E01030458,E01030459,E01030460,E01030461,E01030462,E01030463,E01030464,E01030465,E01030466,E01030467,E01030468,E01030469,E01030470,E01030471,E01030472,E01030473,E01030474,E01030475,E01030476,E01030477,E01030478,E01030479,E01030480,E01030481,E01030482,E01030483,E01030484,E01030485,E01030486,E01030487,E01030488,E01030489,E01030490,E01030491,E01030492,E01030493,E01030494,E01030495,E01030496,E01030497,E01030498,E01030499,E01030500,E01030501,E01030502,E01030503,E01030504,E01030505,E01030506,E01030507,E01030508,E01030509,E01030510,E01030511,E01030512,E01030513,E01030514,E01030515,E01030516,E01030517,E01030518,E01030519,E01030520,E01030521,E01030522,E01030523,E01030524,E01030525,E01030526,E01030527,E01030528,E01030529,E01030530,E01030531,E01030532,E01030533,E01030534,E01030535,E01030536,E01030537,E01030538,E01030539,E01030540,E01030541,E01030542,E01030543,E01030544,E01030545,E01030546,E01030547,E01030548,E01030549,E01030550,E01030551,E01030552,E01030553,E01030554,E01030555&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_058.json.gz
+  content_sha256: 8112e503327781f9fbabb6acde99a6bb7729b380ca9e384d356da3cb33924f70
+- id: census_lsoa_tenure5a_bedrooms_059
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 059
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01030556,E01030557,E01030558,E01030559,E01030560,E01030561,E01030562,E01030563,E01030564,E01030565,E01030566,E01030567,E01030568,E01030569,E01030570,E01030571,E01030572,E01030573,E01030574,E01030576,E01030577,E01030578,E01030579,E01030580,E01030581,E01030582,E01030583,E01030585,E01030586,E01030587,E01030588,E01030589,E01030590,E01030591,E01030592,E01030593,E01030594,E01030595,E01030596,E01030597,E01030598,E01030599,E01030600,E01030601,E01030602,E01030603,E01030604,E01030605,E01030606,E01030607,E01030608,E01030609,E01030610,E01030613,E01030614,E01030615,E01030616,E01030617,E01030618,E01030619,E01030620,E01030621,E01030622,E01030623,E01030624,E01030625,E01030626,E01030627,E01030628,E01030629,E01030630,E01030631,E01030632,E01030633,E01030634,E01030635,E01030636,E01030637,E01030638,E01030639,E01030640,E01030641,E01030642,E01030643,E01030644,E01030645,E01030646,E01030647,E01030648,E01030649,E01030650,E01030651,E01030652,E01030653,E01030654,E01030655,E01030656,E01030657,E01030658,E01030659,E01030660,E01030661,E01030662,E01030663,E01030664,E01030665,E01030666,E01030667,E01030668,E01030669,E01030670,E01030671,E01030672,E01030673,E01030674,E01030675,E01030676,E01030677,E01030678,E01030679,E01030680,E01030681,E01030682,E01030683,E01030684,E01030685,E01030686,E01030687,E01030688,E01030689,E01030690,E01030691,E01030692,E01030693,E01030694,E01030695,E01030696,E01030697,E01030698,E01030699,E01030700,E01030701,E01030702,E01030703,E01030704,E01030705,E01030706,E01030707,E01030708,E01030709,E01030710,E01030711,E01030712,E01030713,E01030714,E01030715,E01030716,E01030717,E01030718,E01030719,E01030720,E01030721,E01030722,E01030723,E01030724,E01030725,E01030726,E01030727,E01030728,E01030729,E01030730,E01030731,E01030732,E01030733,E01030734,E01030735,E01030737,E01030738,E01030739,E01030740,E01030741,E01030742,E01030743,E01030744,E01030745,E01030746,E01030747,E01030748,E01030749,E01030750,E01030751,E01030752,E01030753,E01030754,E01030755,E01030756,E01030757,E01030758,E01030759,E01030760,E01030761,E01030762,E01030763,E01030764,E01030765,E01030766,E01030767,E01030768,E01030769,E01030770,E01030771,E01030772,E01030773,E01030774,E01030775,E01030776,E01030777,E01030778,E01030779,E01030780,E01030781,E01030782,E01030783,E01030784,E01030785,E01030786,E01030787,E01030788,E01030789,E01030790,E01030791,E01030792,E01030793,E01030794,E01030795,E01030796,E01030797,E01030798,E01030799,E01030800,E01030801,E01030802,E01030803,E01030804,E01030805,E01030806,E01030807,E01030808,E01030809,E01030810,E01030811,E01030812,E01030813,E01030814,E01030815,E01030816,E01030817,E01030818,E01030819,E01030820,E01030821,E01030822,E01030823,E01030824,E01030825,E01030826,E01030827,E01030828,E01030829,E01030830,E01030831,E01030832,E01030833,E01030834,E01030835,E01030836,E01030837,E01030838,E01030839,E01030840,E01030841,E01030842,E01030843,E01030844,E01030845,E01030846,E01030847,E01030848,E01030850,E01030851,E01030852,E01030853,E01030854,E01030855,E01030856,E01030857,E01030858,E01030859,E01030861,E01030862,E01030863,E01030864,E01030865,E01030866,E01030867,E01030868,E01030869,E01030870,E01030871,E01030872,E01030873,E01030874,E01030875,E01030876,E01030877,E01030878,E01030879,E01030880,E01030881,E01030882,E01030883,E01030884,E01030885,E01030886,E01030887,E01030888,E01030889,E01030890,E01030891,E01030892,E01030893,E01030894,E01030895,E01030896,E01030897,E01030898,E01030899,E01030900,E01030901,E01030902,E01030903,E01030904,E01030905,E01030906,E01030907,E01030908,E01030909,E01030910,E01030911,E01030912,E01030913,E01030914,E01030915,E01030916,E01030917,E01030918,E01030919,E01030920,E01030921,E01030922,E01030923,E01030924,E01030925,E01030926,E01030927,E01030928,E01030929,E01030930,E01030931,E01030932,E01030933,E01030934,E01030935,E01030937,E01030938,E01030939,E01030940,E01030941,E01030942,E01030943,E01030944,E01030945,E01030946,E01030947,E01030948,E01030949,E01030950,E01030951,E01030952,E01030953,E01030954,E01030955,E01030956,E01030957,E01030958,E01030959,E01030960,E01030961,E01030962,E01030963,E01030964,E01030965,E01030966,E01030967,E01030968,E01030969,E01030970,E01030971,E01030972,E01030974,E01030975,E01030976,E01030977,E01030978,E01030979,E01030980,E01030981,E01030983,E01030984,E01030986,E01030987,E01030988,E01030989,E01030990,E01030991,E01030992,E01030994,E01030995,E01030996,E01030997,E01030998,E01030999,E01031000,E01031001,E01031002,E01031003,E01031004,E01031005,E01031006,E01031007,E01031008,E01031009,E01031010,E01031011,E01031012,E01031013,E01031014,E01031015,E01031016,E01031017,E01031018,E01031019,E01031020,E01031021,E01031022,E01031023,E01031024,E01031025,E01031026,E01031027,E01031028,E01031029,E01031030,E01031031,E01031032,E01031033,E01031034,E01031035,E01031036,E01031037,E01031038,E01031039,E01031040,E01031041,E01031042,E01031043,E01031044,E01031045,E01031046,E01031047,E01031048,E01031049,E01031050,E01031051,E01031052,E01031053,E01031054,E01031055,E01031056,E01031057,E01031058,E01031059,E01031060,E01031061,E01031062,E01031063,E01031064,E01031065,E01031066,E01031067&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_059.json.gz
+  content_sha256: 9261bad8eaaf0c057542f5d1ba0ccd54045fb9f7c53dc91c7adc87381710d64e
+- id: census_lsoa_tenure5a_bedrooms_060
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 060
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01031068,E01031069,E01031070,E01031073,E01031074,E01031075,E01031077,E01031078,E01031079,E01031080,E01031081,E01031082,E01031083,E01031084,E01031085,E01031086,E01031087,E01031088,E01031089,E01031090,E01031091,E01031092,E01031093,E01031094,E01031095,E01031096,E01031097,E01031098,E01031099,E01031100,E01031101,E01031102,E01031104,E01031105,E01031106,E01031107,E01031108,E01031109,E01031110,E01031111,E01031112,E01031114,E01031115,E01031116,E01031117,E01031118,E01031119,E01031120,E01031121,E01031122,E01031123,E01031124,E01031125,E01031127,E01031129,E01031130,E01031131,E01031132,E01031133,E01031134,E01031135,E01031136,E01031137,E01031138,E01031139,E01031140,E01031142,E01031143,E01031144,E01031145,E01031146,E01031148,E01031149,E01031150,E01031151,E01031152,E01031153,E01031154,E01031155,E01031156,E01031157,E01031158,E01031159,E01031160,E01031161,E01031162,E01031163,E01031165,E01031166,E01031167,E01031168,E01031169,E01031170,E01031171,E01031172,E01031173,E01031174,E01031175,E01031176,E01031177,E01031178,E01031179,E01031180,E01031181,E01031182,E01031183,E01031184,E01031185,E01031186,E01031187,E01031188,E01031189,E01031190,E01031191,E01031192,E01031193,E01031194,E01031195,E01031196,E01031197,E01031198,E01031199,E01031200,E01031201,E01031202,E01031203,E01031204,E01031205,E01031206,E01031207,E01031208,E01031209,E01031210,E01031211,E01031213,E01031214,E01031215,E01031216,E01031217,E01031218,E01031219,E01031220,E01031221,E01031222,E01031223,E01031224,E01031225,E01031227,E01031228,E01031229,E01031231,E01031232,E01031233,E01031234,E01031235,E01031236,E01031237,E01031238,E01031239,E01031240,E01031241,E01031242,E01031243,E01031244,E01031245,E01031246,E01031247,E01031248,E01031249,E01031251,E01031252,E01031253,E01031254,E01031255,E01031256,E01031257,E01031258,E01031259,E01031260,E01031261,E01031262,E01031263,E01031264,E01031265,E01031266,E01031267,E01031268,E01031269,E01031271,E01031272,E01031273,E01031274,E01031275,E01031276,E01031277,E01031278,E01031279,E01031280,E01031281,E01031282,E01031283,E01031284,E01031285,E01031286,E01031287,E01031288,E01031289,E01031290,E01031291,E01031292,E01031293,E01031294,E01031295,E01031296,E01031297,E01031298,E01031299,E01031300,E01031301,E01031302,E01031303,E01031304,E01031305,E01031306,E01031307,E01031308,E01031309,E01031310,E01031311,E01031312,E01031313,E01031314,E01031315,E01031316,E01031317,E01031319,E01031320,E01031321,E01031322,E01031323,E01031324,E01031325,E01031327,E01031328,E01031329,E01031330,E01031331,E01031332,E01031333,E01031334,E01031335,E01031336,E01031337,E01031338,E01031339,E01031340,E01031341,E01031342,E01031343,E01031344,E01031345,E01031346,E01031347,E01031348,E01031349,E01031350,E01031351,E01031352,E01031353,E01031354,E01031355,E01031356,E01031357,E01031358,E01031359,E01031360,E01031361,E01031362,E01031363,E01031364,E01031365,E01031366,E01031367,E01031368,E01031369,E01031370,E01031371,E01031372,E01031373,E01031374,E01031375,E01031376,E01031377,E01031378,E01031379,E01031380,E01031381,E01031382,E01031383,E01031384,E01031385,E01031386,E01031387,E01031389,E01031390,E01031391,E01031392,E01031393,E01031394,E01031395,E01031396,E01031397,E01031398,E01031399,E01031400,E01031402,E01031403,E01031404,E01031405,E01031406,E01031407,E01031408,E01031409,E01031410,E01031411,E01031412,E01031413,E01031414,E01031415,E01031416,E01031417,E01031418,E01031419,E01031420,E01031422,E01031423,E01031424,E01031425,E01031426,E01031427,E01031428,E01031429,E01031430,E01031431,E01031432,E01031433,E01031434,E01031435,E01031436,E01031437,E01031438,E01031439,E01031440,E01031441,E01031442,E01031443,E01031444,E01031445,E01031446,E01031447,E01031448,E01031449,E01031450,E01031451,E01031452,E01031453,E01031454,E01031455,E01031456,E01031457,E01031458,E01031459,E01031460,E01031461,E01031462,E01031463,E01031464,E01031465,E01031466,E01031469,E01031470,E01031471,E01031472,E01031473,E01031474,E01031475,E01031476,E01031477,E01031478,E01031479,E01031480,E01031481,E01031482,E01031483,E01031484,E01031485,E01031486,E01031487,E01031489,E01031490,E01031491,E01031492,E01031493,E01031494,E01031495,E01031496,E01031497,E01031498,E01031499,E01031500,E01031501,E01031502,E01031503,E01031504,E01031505,E01031506,E01031507,E01031508,E01031511,E01031512,E01031514,E01031515,E01031516,E01031517,E01031518,E01031519,E01031520,E01031521,E01031522,E01031523,E01031524,E01031525,E01031526,E01031527,E01031528,E01031529,E01031530,E01031531,E01031532,E01031533,E01031534,E01031535,E01031536,E01031538,E01031539,E01031540,E01031541,E01031542,E01031543,E01031544,E01031545,E01031546,E01031547,E01031548,E01031549,E01031550,E01031551,E01031552,E01031553,E01031554,E01031555,E01031556,E01031557,E01031558,E01031559,E01031560,E01031561,E01031562,E01031563,E01031564,E01031565,E01031566,E01031567,E01031568,E01031569,E01031570,E01031571,E01031572,E01031573,E01031574,E01031575,E01031576,E01031577,E01031578,E01031579,E01031580,E01031581,E01031582,E01031583,E01031584,E01031585,E01031586,E01031588,E01031589,E01031590,E01031591,E01031592,E01031593,E01031594,E01031595&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_060.json.gz
+  content_sha256: a929fdf15d96a4626b78cf5b877ef64b67e5674df6e82f1f0a6dcb848549499b
+- id: census_lsoa_tenure5a_bedrooms_061
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 061
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01031596,E01031597,E01031598,E01031599,E01031600,E01031601,E01031603,E01031604,E01031605,E01031606,E01031607,E01031608,E01031609,E01031610,E01031611,E01031612,E01031614,E01031615,E01031616,E01031617,E01031618,E01031620,E01031621,E01031622,E01031623,E01031624,E01031625,E01031626,E01031627,E01031628,E01031629,E01031630,E01031631,E01031632,E01031633,E01031634,E01031635,E01031638,E01031639,E01031640,E01031641,E01031642,E01031643,E01031644,E01031645,E01031646,E01031647,E01031648,E01031649,E01031650,E01031651,E01031652,E01031653,E01031654,E01031655,E01031656,E01031657,E01031658,E01031659,E01031660,E01031661,E01031662,E01031663,E01031664,E01031665,E01031666,E01031667,E01031668,E01031669,E01031670,E01031671,E01031672,E01031673,E01031674,E01031675,E01031677,E01031678,E01031679,E01031680,E01031681,E01031682,E01031683,E01031684,E01031685,E01031686,E01031687,E01031688,E01031689,E01031690,E01031691,E01031693,E01031694,E01031695,E01031696,E01031697,E01031698,E01031699,E01031700,E01031701,E01031702,E01031703,E01031704,E01031705,E01031706,E01031707,E01031708,E01031709,E01031710,E01031712,E01031713,E01031714,E01031715,E01031716,E01031717,E01031718,E01031719,E01031720,E01031721,E01031722,E01031723,E01031724,E01031725,E01031726,E01031727,E01031729,E01031730,E01031731,E01031732,E01031733,E01031734,E01031735,E01031736,E01031737,E01031738,E01031739,E01031740,E01031741,E01031743,E01031744,E01031745,E01031746,E01031747,E01031748,E01031749,E01031750,E01031751,E01031752,E01031753,E01031754,E01031755,E01031757,E01031758,E01031759,E01031760,E01031763,E01031764,E01031765,E01031766,E01031767,E01031768,E01031769,E01031770,E01031771,E01031772,E01031773,E01031774,E01031775,E01031776,E01031777,E01031778,E01031779,E01031780,E01031781,E01031782,E01031783,E01031785,E01031786,E01031787,E01031788,E01031790,E01031791,E01031792,E01031793,E01031794,E01031795,E01031796,E01031797,E01031798,E01031799,E01031800,E01031801,E01031802,E01031803,E01031804,E01031805,E01031806,E01031807,E01031808,E01031809,E01031810,E01031811,E01031812,E01031813,E01031814,E01031815,E01031816,E01031817,E01031818,E01031820,E01031821,E01031822,E01031823,E01031824,E01031825,E01031826,E01031827,E01031828,E01031829,E01031830,E01031831,E01031832,E01031833,E01031834,E01031835,E01031836,E01031837,E01031838,E01031839,E01031840,E01031841,E01031842,E01031843,E01031845,E01031846,E01031847,E01031848,E01031849,E01031850,E01031851,E01031852,E01031853,E01031854,E01031855,E01031856,E01031857,E01031858,E01031859,E01031860,E01031861,E01031862,E01031863,E01031864,E01031865,E01031866,E01031867,E01031868,E01031869,E01031870,E01031871,E01031872,E01031873,E01031874,E01031876,E01031877,E01031879,E01031882,E01031883,E01031884,E01031885,E01031886,E01031887,E01031888,E01031889,E01031890,E01031892,E01031893,E01031894,E01031895,E01031896,E01031897,E01031898,E01031901,E01031902,E01031903,E01031904,E01031905,E01031906,E01031907,E01031908,E01031909,E01031910,E01031911,E01031912,E01031913,E01031914,E01031915,E01031916,E01031917,E01031918,E01031919,E01031920,E01031921,E01031922,E01031923,E01031924,E01031925,E01031926,E01031927,E01031928,E01031929,E01031930,E01031931,E01031932,E01031933,E01031934,E01031935,E01031936,E01031937,E01031938,E01031939,E01031940,E01031941,E01031942,E01031944,E01031946,E01031947,E01031948,E01031949,E01031950,E01031951,E01031952,E01031955,E01031956,E01031957,E01031958,E01031959,E01031960,E01031961,E01031962,E01031963,E01031964,E01031965,E01031966,E01031967,E01031968,E01031969,E01031970,E01031971,E01031972,E01031973,E01031974,E01031975,E01031976,E01031978,E01031979,E01031980,E01031981,E01031982,E01031983,E01031984,E01031985,E01031986,E01031988,E01031989,E01031990,E01031991,E01031992,E01031993,E01031994,E01031995,E01031996,E01031997,E01031999,E01032000,E01032001,E01032002,E01032003,E01032004,E01032005,E01032006,E01032007,E01032008,E01032009,E01032010,E01032012,E01032013,E01032014,E01032015,E01032016,E01032017,E01032018,E01032019,E01032020,E01032021,E01032022,E01032023,E01032024,E01032025,E01032026,E01032027,E01032029,E01032030,E01032031,E01032032,E01032033,E01032034,E01032035,E01032036,E01032037,E01032038,E01032039,E01032040,E01032041,E01032042,E01032043,E01032044,E01032045,E01032046,E01032047,E01032048,E01032049,E01032051,E01032052,E01032053,E01032054,E01032055,E01032056,E01032057,E01032058,E01032059,E01032060,E01032061,E01032062,E01032063,E01032064,E01032065,E01032066,E01032067,E01032068,E01032069,E01032070,E01032072,E01032073,E01032074,E01032075,E01032076,E01032077,E01032078,E01032079,E01032080,E01032081,E01032082,E01032083,E01032085,E01032087,E01032088,E01032089,E01032090,E01032091,E01032092,E01032093,E01032094,E01032095,E01032096,E01032097,E01032098,E01032100,E01032101,E01032102,E01032103,E01032104,E01032105,E01032106,E01032107,E01032108,E01032109,E01032110,E01032111,E01032112,E01032113,E01032114,E01032115,E01032117,E01032118,E01032119,E01032121,E01032122,E01032123,E01032124,E01032125,E01032126,E01032127,E01032128,E01032129,E01032131,E01032132,E01032133,E01032134,E01032135,E01032136&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_061.json.gz
+  content_sha256: 224c3d13d6aedfdbbdc03f3d27d6c2f0c499387a5e2fc3e501ac4d29e6951613
+- id: census_lsoa_tenure5a_bedrooms_062
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 062
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01032137,E01032138,E01032139,E01032140,E01032141,E01032142,E01032143,E01032144,E01032145,E01032146,E01032147,E01032148,E01032149,E01032150,E01032151,E01032152,E01032153,E01032154,E01032155,E01032156,E01032157,E01032158,E01032159,E01032160,E01032161,E01032162,E01032168,E01032169,E01032170,E01032171,E01032172,E01032173,E01032174,E01032175,E01032176,E01032177,E01032178,E01032179,E01032180,E01032181,E01032182,E01032183,E01032184,E01032185,E01032186,E01032187,E01032188,E01032189,E01032190,E01032191,E01032192,E01032193,E01032194,E01032195,E01032196,E01032197,E01032198,E01032199,E01032200,E01032201,E01032202,E01032203,E01032204,E01032205,E01032206,E01032207,E01032208,E01032209,E01032210,E01032211,E01032212,E01032213,E01032214,E01032215,E01032216,E01032217,E01032218,E01032219,E01032220,E01032221,E01032222,E01032223,E01032224,E01032225,E01032226,E01032227,E01032228,E01032229,E01032230,E01032231,E01032232,E01032234,E01032235,E01032236,E01032237,E01032238,E01032239,E01032240,E01032241,E01032242,E01032243,E01032244,E01032245,E01032246,E01032247,E01032248,E01032249,E01032250,E01032251,E01032252,E01032253,E01032254,E01032255,E01032256,E01032257,E01032258,E01032259,E01032260,E01032261,E01032262,E01032263,E01032264,E01032265,E01032266,E01032267,E01032268,E01032269,E01032270,E01032271,E01032272,E01032273,E01032274,E01032275,E01032276,E01032277,E01032278,E01032279,E01032280,E01032281,E01032282,E01032283,E01032284,E01032285,E01032286,E01032287,E01032288,E01032289,E01032290,E01032292,E01032294,E01032296,E01032297,E01032298,E01032299,E01032300,E01032301,E01032302,E01032303,E01032304,E01032305,E01032306,E01032307,E01032308,E01032309,E01032310,E01032311,E01032312,E01032313,E01032314,E01032315,E01032316,E01032317,E01032318,E01032319,E01032320,E01032321,E01032322,E01032323,E01032324,E01032325,E01032326,E01032327,E01032328,E01032329,E01032330,E01032331,E01032332,E01032333,E01032334,E01032335,E01032336,E01032337,E01032338,E01032339,E01032340,E01032341,E01032342,E01032344,E01032345,E01032346,E01032347,E01032348,E01032349,E01032350,E01032351,E01032352,E01032353,E01032355,E01032356,E01032357,E01032358,E01032359,E01032360,E01032361,E01032363,E01032364,E01032365,E01032366,E01032367,E01032368,E01032369,E01032370,E01032371,E01032372,E01032373,E01032374,E01032375,E01032376,E01032377,E01032378,E01032379,E01032380,E01032381,E01032382,E01032383,E01032384,E01032385,E01032386,E01032387,E01032388,E01032389,E01032390,E01032391,E01032392,E01032393,E01032394,E01032395,E01032396,E01032397,E01032398,E01032399,E01032400,E01032401,E01032402,E01032403,E01032404,E01032405,E01032406,E01032407,E01032408,E01032409,E01032410,E01032411,E01032412,E01032413,E01032414,E01032415,E01032416,E01032417,E01032418,E01032419,E01032420,E01032421,E01032422,E01032423,E01032424,E01032425,E01032426,E01032427,E01032428,E01032429,E01032430,E01032431,E01032432,E01032433,E01032434,E01032435,E01032436,E01032437,E01032438,E01032439,E01032440,E01032441,E01032442,E01032443,E01032444,E01032445,E01032446,E01032447,E01032448,E01032449,E01032451,E01032452,E01032453,E01032454,E01032455,E01032456,E01032457,E01032458,E01032459,E01032460,E01032461,E01032462,E01032463,E01032464,E01032465,E01032466,E01032467,E01032468,E01032469,E01032471,E01032472,E01032473,E01032474,E01032475,E01032476,E01032477,E01032478,E01032479,E01032480,E01032481,E01032482,E01032483,E01032484,E01032485,E01032486,E01032487,E01032488,E01032489,E01032490,E01032491,E01032492,E01032493,E01032494,E01032496,E01032497,E01032498,E01032499,E01032500,E01032501,E01032502,E01032503,E01032504,E01032505,E01032506,E01032507,E01032508,E01032509,E01032510,E01032511,E01032512,E01032513,E01032514,E01032515,E01032516,E01032517,E01032518,E01032519,E01032520,E01032522,E01032523,E01032524,E01032526,E01032527,E01032528,E01032529,E01032530,E01032532,E01032533,E01032534,E01032535,E01032536,E01032537,E01032538,E01032540,E01032541,E01032542,E01032543,E01032544,E01032545,E01032546,E01032548,E01032549,E01032551,E01032552,E01032554,E01032555,E01032556,E01032557,E01032558,E01032559,E01032560,E01032561,E01032562,E01032563,E01032564,E01032565,E01032566,E01032567,E01032568,E01032569,E01032570,E01032571,E01032572,E01032573,E01032574,E01032575,E01032576,E01032577,E01032579,E01032580,E01032581,E01032582,E01032583,E01032584,E01032585,E01032586,E01032587,E01032588,E01032589,E01032590,E01032591,E01032592,E01032593,E01032594,E01032595,E01032596,E01032597,E01032599,E01032600,E01032601,E01032602,E01032603,E01032604,E01032605,E01032606,E01032607,E01032608,E01032609,E01032610,E01032611,E01032612,E01032613,E01032614,E01032615,E01032616,E01032617,E01032618,E01032619,E01032620,E01032621,E01032622,E01032623,E01032624,E01032625,E01032626,E01032627,E01032628,E01032631,E01032632,E01032633,E01032634,E01032635,E01032636,E01032637,E01032638,E01032639,E01032640,E01032643,E01032644,E01032645,E01032646,E01032647,E01032648,E01032649,E01032650,E01032651,E01032652,E01032653,E01032655,E01032656,E01032657,E01032658,E01032659,E01032660,E01032661,E01032662,E01032664,E01032665,E01032666&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_062.json.gz
+  content_sha256: 43fd1e175a047cab541de95c0bc759a0b2da9f654aa497688f2a66c41036bbaf
+- id: census_lsoa_tenure5a_bedrooms_063
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 063
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01032667,E01032668,E01032669,E01032670,E01032671,E01032672,E01032673,E01032675,E01032676,E01032677,E01032678,E01032679,E01032680,E01032681,E01032682,E01032683,E01032684,E01032688,E01032689,E01032690,E01032691,E01032692,E01032693,E01032694,E01032695,E01032696,E01032697,E01032699,E01032700,E01032701,E01032702,E01032703,E01032704,E01032705,E01032706,E01032707,E01032708,E01032709,E01032710,E01032711,E01032712,E01032713,E01032714,E01032715,E01032716,E01032719,E01032724,E01032726,E01032728,E01032729,E01032730,E01032731,E01032732,E01032733,E01032734,E01032735,E01032737,E01032738,E01032739,E01032740,E01032741,E01032742,E01032743,E01032744,E01032745,E01032746,E01032748,E01032750,E01032751,E01032753,E01032755,E01032759,E01032764,E01032765,E01032767,E01032768,E01032769,E01032770,E01032771,E01032772,E01032774,E01032775,E01032776,E01032777,E01032779,E01032780,E01032781,E01032782,E01032783,E01032784,E01032785,E01032786,E01032787,E01032788,E01032789,E01032790,E01032792,E01032794,E01032799,E01032800,E01032802,E01032803,E01032807,E01032808,E01032810,E01032811,E01032812,E01032813,E01032814,E01032815,E01032816,E01032817,E01032818,E01032819,E01032820,E01032821,E01032822,E01032823,E01032824,E01032825,E01032826,E01032827,E01032828,E01032829,E01032830,E01032831,E01032832,E01032833,E01032834,E01032835,E01032836,E01032837,E01032838,E01032839,E01032841,E01032842,E01032843,E01032844,E01032845,E01032846,E01032847,E01032848,E01032849,E01032850,E01032852,E01032853,E01032854,E01032855,E01032856,E01032857,E01032858,E01032859,E01032860,E01032862,E01032863,E01032865,E01032868,E01032870,E01032871,E01032872,E01032873,E01032876,E01032877,E01032878,E01032879,E01032880,E01032881,E01032883,E01032884,E01032885,E01032886,E01032888,E01032891,E01032893,E01032894,E01032895,E01032896,E01032897,E01032898,E01032899,E01032900,E01032901,E01032902,E01032904,E01032905,E01032906,E01032909,E01032912,E01032914,E01032915,E01032920,E01032922,E01032923,E01032924,E01032925,E01032926,E01032927,E01032930,E01032931,E01032932,E01032933,E01032935,E01032937,E01032938,E01032939,E01032940,E01032941,E01032942,E01032943,E01032944,E01032946,E01032947,E01032948,E01032949,E01032950,E01032951,E01032952,E01032953,E01032955,E01032956,E01032957,E01032958,E01032961,E01032962,E01032963,E01032964,E01032965,E01032966,E01032967,E01032969,E01032970,E01032971,E01032972,E01032973,E01032974,E01032976,E01032978,E01032979,E01032980,E01032981,E01032982,E01032983,E01032984,E01032985,E01032986,E01032987,E01032988,E01032989,E01032991,E01032992,E01032993,E01032994,E01032995,E01032996,E01032997,E01032998,E01032999,E01033000,E01033002,E01033003,E01033008,E01033010,E01033011,E01033013,E01033015,E01033016,E01033021,E01033022,E01033023,E01033024,E01033025,E01033026,E01033027,E01033028,E01033029,E01033030,E01033031,E01033033,E01033034,E01033035,E01033036,E01033037,E01033038,E01033039,E01033040,E01033041,E01033042,E01033043,E01033044,E01033045,E01033046,E01033047,E01033048,E01033049,E01033050,E01033051,E01033052,E01033053,E01033054,E01033055,E01033056,E01033058,E01033059,E01033060,E01033061,E01033062,E01033063,E01033064,E01033067,E01033068,E01033069,E01033070,E01033071,E01033072,E01033073,E01033076,E01033077,E01033078,E01033079,E01033080,E01033081,E01033082,E01033083,E01033084,E01033086,E01033087,E01033088,E01033089,E01033091,E01033092,E01033094,E01033095,E01033097,E01033098,E01033099,E01033100,E01033102,E01033103,E01033104,E01033105,E01033107,E01033109,E01033111,E01033112,E01033113,E01033114,E01033115,E01033116,E01033117,E01033118,E01033120,E01033121,E01033122,E01033123,E01033124,E01033125,E01033126,E01033127,E01033128,E01033129,E01033130,E01033131,E01033132,E01033133,E01033135,E01033137,E01033138,E01033139,E01033140,E01033142,E01033144,E01033145,E01033146,E01033148,E01033150,E01033151,E01033156,E01033158,E01033160,E01033161,E01033163,E01033166,E01033167,E01033168,E01033169,E01033170,E01033171,E01033172,E01033173,E01033174,E01033175,E01033177,E01033179,E01033180,E01033181,E01033182,E01033184,E01033185,E01033186,E01033188,E01033189,E01033190,E01033191,E01033192,E01033195,E01033197,E01033198,E01033200,E01033205,E01033206,E01033208,E01033209,E01033210,E01033211,E01033212,E01033213,E01033214,E01033215,E01033217,E01033218,E01033219,E01033220,E01033223,E01033224,E01033226,E01033228,E01033229,E01033232,E01033234,E01033235,E01033236,E01033237,E01033239,E01033241,E01033242,E01033243,E01033244,E01033245,E01033246,E01033247,E01033248,E01033249,E01033250,E01033251,E01033252,E01033253,E01033254,E01033255,E01033256,E01033257,E01033259,E01033260,E01033261,E01033262,E01033263,E01033264,E01033265,E01033268,E01033269,E01033270,E01033271,E01033272,E01033273,E01033274,E01033275,E01033276,E01033277,E01033279,E01033280,E01033281,E01033283,E01033285,E01033286,E01033288,E01033289,E01033291,E01033292,E01033294,E01033296,E01033297,E01033299,E01033300,E01033302,E01033303,E01033305,E01033306,E01033308,E01033310,E01033311,E01033313,E01033314,E01033316,E01033317,E01033319,E01033320,E01033322,E01033324,E01033325,E01033327,E01033328&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_063.json.gz
+  content_sha256: c6f2e01d108b961bbf608e60e0a774d2d5194e913f13ddd5806f9c89118c4a34
+- id: census_lsoa_tenure5a_bedrooms_064
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 064
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01033329,E01033330,E01033334,E01033336,E01033338,E01033339,E01033341,E01033342,E01033344,E01033347,E01033348,E01033350,E01033352,E01033353,E01033356,E01033358,E01033359,E01033361,E01033362,E01033364,E01033366,E01033367,E01033369,E01033370,E01033372,E01033373,E01033375,E01033376,E01033378,E01033380,E01033381,E01033383,E01033384,E01033386,E01033387,E01033388,E01033389,E01033390,E01033391,E01033392,E01033394,E01033395,E01033396,E01033397,E01033398,E01033399,E01033400,E01033402,E01033407,E01033409,E01033410,E01033411,E01033412,E01033413,E01033414,E01033415,E01033417,E01033418,E01033420,E01033421,E01033422,E01033423,E01033425,E01033426,E01033427,E01033429,E01033430,E01033431,E01033434,E01033435,E01033436,E01033437,E01033438,E01033440,E01033441,E01033442,E01033443,E01033444,E01033445,E01033446,E01033447,E01033448,E01033449,E01033450,E01033452,E01033454,E01033455,E01033456,E01033457,E01033458,E01033459,E01033460,E01033461,E01033462,E01033463,E01033465,E01033466,E01033467,E01033469,E01033470,E01033471,E01033472,E01033473,E01033474,E01033475,E01033476,E01033479,E01033480,E01033481,E01033482,E01033485,E01033486,E01033487,E01033488,E01033489,E01033490,E01033491,E01033493,E01033494,E01033495,E01033496,E01033497,E01033498,E01033499,E01033500,E01033501,E01033503,E01033506,E01033508,E01033511,E01033514,E01033515,E01033517,E01033519,E01033520,E01033522,E01033523,E01033526,E01033527,E01033528,E01033530,E01033532,E01033533,E01033534,E01033535,E01033536,E01033537,E01033539,E01033540,E01033541,E01033543,E01033545,E01033547,E01033548,E01033550,E01033551,E01033553,E01033556,E01033557,E01033559,E01033561,E01033562,E01033564,E01033565,E01033567,E01033569,E01033570,E01033571,E01033579,E01033580,E01033581,E01033582,E01033584,E01033586,E01033587,E01033589,E01033590,E01033591,E01033592,E01033593,E01033594,E01033597,E01033598,E01033599,E01033601,E01033602,E01033603,E01033604,E01033605,E01033609,E01033610,E01033611,E01033612,E01033613,E01033614,E01033615,E01033616,E01033617,E01033618,E01033620,E01033622,E01033623,E01033624,E01033625,E01033626,E01033627,E01033629,E01033630,E01033638,E01033640,E01033642,E01033643,E01033646,E01033648,E01033649,E01033650,E01033651,E01033653,E01033654,E01033655,E01033656,E01033658,E01033660,E01033661,E01033662,E01033664,E01033665,E01033667,E01033668,E01033669,E01033670,E01033671,E01033672,E01033673,E01033674,E01033675,E01033676,E01033677,E01033678,E01033679,E01033680,E01033681,E01033682,E01033684,E01033685,E01033686,E01033687,E01033688,E01033689,E01033690,E01033691,E01033692,E01033693,E01033694,E01033695,E01033696,E01033697,E01033699,E01033700,E01033702,E01033703,E01033704,E01033705,E01033706,E01033708,E01033709,E01033710,E01033711,E01033712,E01033713,E01033714,E01033715,E01033716,E01033717,E01033718,E01033719,E01033720,E01033721,E01033722,E01033727,E01033728,E01033729,E01033732,E01033734,E01033735,E01033736,E01033737,E01033738,E01033739,E01033740,E01033741,E01033742,E01033743,E01033744,E01033745,E01033746,E01033747,E01033748,E01033750,E01033751,E01033756,E01033757,E01033758,E01033760,E01033761,E01033762,E01033763,E01033765,E01033767,E01033768,E01033769,E01033770,E01033771,E01033772,E01033773,E01033774,E01033775,E01033776,E01033777,E01033778,E01033779,E01033780,E01033781,E01033782,E01033783,E01033784,E01033785,E01033786,E01033787,E01033788,E01033789,E01033790,E01033791,E01033792,E01033793,E01033794,E01033795,E01033796,E01033797,E01033798,E01033799,E01033800,E01033801,E01033802,E01033803,E01033804,E01033805,E01033806,E01033807,E01033808,E01033809,E01033810,E01033811,E01033812,E01033813,E01033814,E01033815,E01033816,E01033817,E01033818,E01033819,E01033820,E01033821,E01033822,E01033823,E01033824,E01033825,E01033826,E01033827,E01033828,E01033829,E01033830,E01033831,E01033832,E01033833,E01033834,E01033835,E01033836,E01033837,E01033838,E01033839,E01033840,E01033841,E01033842,E01033843,E01033844,E01033845,E01033846,E01033847,E01033848,E01033849,E01033850,E01033851,E01033852,E01033853,E01033854,E01033855,E01033856,E01033857,E01033858,E01033859,E01033860,E01033861,E01033862,E01033863,E01033864,E01033865,E01033866,E01033867,E01033868,E01033869,E01033870,E01033871,E01033872,E01033873,E01033874,E01033875,E01033876,E01033877,E01033878,E01033879,E01033880,E01033881,E01033882,E01033883,E01033884,E01033885,E01033886,E01033887,E01033888,E01033889,E01033890,E01033891,E01033892,E01033893,E01033894,E01033895,E01033896,E01033897,E01033898,E01033899,E01033900,E01033901,E01033902,E01033903,E01033904,E01033905,E01033906,E01033907,E01033908,E01033909,E01033910,E01033911,E01033912,E01033913,E01033914,E01033915,E01033916,E01033917,E01033918,E01033919,E01033920,E01033921,E01033922,E01033923,E01033924,E01033925,E01033926,E01033927,E01033928,E01033929,E01033930,E01033931,E01033932,E01033933,E01033934,E01033935,E01033936,E01033937,E01033938,E01033939,E01033940,E01033941,E01033942,E01033943,E01033944,E01033945,E01033946,E01033947,E01033948,E01033949,E01033950,E01033951,E01033952,E01033953,E01033954,E01033955,E01033956,E01033957,E01033958&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_064.json.gz
+  content_sha256: 85d74d6f7105f986cede580cdd5104f002ec7e2d74775bba5b55284a3b82ce63
+- id: census_lsoa_tenure5a_bedrooms_065
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 065
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01033959,E01033960,E01033961,E01033962,E01033963,E01033964,E01033965,E01033966,E01033967,E01033968,E01033969,E01033970,E01033971,E01033972,E01033973,E01033974,E01033975,E01033976,E01033977,E01033978,E01033979,E01033980,E01033981,E01033982,E01033983,E01033984,E01033985,E01033986,E01033987,E01033988,E01033989,E01033990,E01033991,E01033992,E01033993,E01033994,E01033995,E01033996,E01033997,E01033998,E01033999,E01034000,E01034001,E01034002,E01034003,E01034004,E01034005,E01034006,E01034007,E01034008,E01034009,E01034010,E01034011,E01034012,E01034013,E01034014,E01034015,E01034016,E01034017,E01034018,E01034019,E01034020,E01034021,E01034022,E01034023,E01034024,E01034025,E01034026,E01034027,E01034028,E01034029,E01034030,E01034031,E01034032,E01034033,E01034034,E01034035,E01034036,E01034037,E01034038,E01034039,E01034040,E01034041,E01034042,E01034043,E01034044,E01034045,E01034046,E01034047,E01034048,E01034049,E01034050,E01034051,E01034052,E01034053,E01034054,E01034055,E01034056,E01034057,E01034058,E01034059,E01034060,E01034061,E01034062,E01034063,E01034064,E01034065,E01034066,E01034067,E01034068,E01034069,E01034070,E01034071,E01034072,E01034073,E01034074,E01034075,E01034076,E01034077,E01034078,E01034079,E01034080,E01034081,E01034082,E01034083,E01034084,E01034085,E01034086,E01034087,E01034088,E01034089,E01034090,E01034091,E01034092,E01034093,E01034094,E01034095,E01034096,E01034097,E01034098,E01034099,E01034100,E01034101,E01034102,E01034103,E01034104,E01034105,E01034106,E01034107,E01034108,E01034109,E01034110,E01034111,E01034112,E01034113,E01034114,E01034115,E01034116,E01034117,E01034118,E01034119,E01034120,E01034121,E01034122,E01034123,E01034124,E01034125,E01034126,E01034127,E01034128,E01034129,E01034130,E01034131,E01034132,E01034133,E01034134,E01034135,E01034136,E01034137,E01034138,E01034139,E01034140,E01034141,E01034142,E01034143,E01034144,E01034145,E01034146,E01034147,E01034148,E01034149,E01034150,E01034151,E01034152,E01034153,E01034154,E01034155,E01034156,E01034157,E01034158,E01034159,E01034160,E01034161,E01034162,E01034165,E01034166,E01034167,E01034168,E01034169,E01034170,E01034171,E01034172,E01034173,E01034174,E01034175,E01034176,E01034177,E01034178,E01034179,E01034180,E01034181,E01034182,E01034183,E01034184,E01034185,E01034186,E01034187,E01034188,E01034189,E01034190,E01034191,E01034192,E01034193,E01034194,E01034195,E01034196,E01034198,E01034199,E01034200,E01034201,E01034202,E01034203,E01034204,E01034205,E01034206,E01034207,E01034208,E01034209,E01034210,E01034211,E01034212,E01034213,E01034214,E01034215,E01034216,E01034217,E01034218,E01034219,E01034220,E01034221,E01034222,E01034223,E01034224,E01034225,E01034226,E01034227,E01034228,E01034229,E01034230,E01034231,E01034232,E01034233,E01034234,E01034235,E01034236,E01034237,E01034238,E01034239,E01034240,E01034241,E01034242,E01034243,E01034244,E01034245,E01034246,E01034247,E01034248,E01034249,E01034250,E01034251,E01034252,E01034253,E01034254,E01034255,E01034256,E01034257,E01034258,E01034259,E01034260,E01034261,E01034262,E01034263,E01034264,E01034265,E01034266,E01034267,E01034268,E01034269,E01034270,E01034271,E01034272,E01034273,E01034274,E01034275,E01034276,E01034277,E01034278,E01034279,E01034280,E01034281,E01034282,E01034283,E01034284,E01034285,E01034286,E01034287,E01034288,E01034289,E01034290,E01034291,E01034292,E01034293,E01034294,E01034295,E01034296,E01034297,E01034298,E01034299,E01034300,E01034301,E01034302,E01034303,E01034304,E01034305,E01034306,E01034307,E01034308,E01034309,E01034310,E01034311,E01034312,E01034313,E01034314,E01034315,E01034316,E01034317,E01034318,E01034319,E01034320,E01034321,E01034322,E01034323,E01034324,E01034325,E01034326,E01034327,E01034328,E01034329,E01034330,E01034331,E01034332,E01034333,E01034334,E01034335,E01034336,E01034337,E01034338,E01034339,E01034340,E01034341,E01034342,E01034343,E01034344,E01034345,E01034346,E01034347,E01034348,E01034349,E01034350,E01034351,E01034352,E01034353,E01034354,E01034355,E01034356,E01034357,E01034358,E01034359,E01034360,E01034361,E01034362,E01034363,E01034364,E01034365,E01034366,E01034367,E01034368,E01034369,E01034370,E01034371,E01034372,E01034373,E01034374,E01034375,E01034376,E01034377,E01034378,E01034379,E01034380,E01034381,E01034382,E01034383,E01034384,E01034385,E01034386,E01034387,E01034388,E01034389,E01034390,E01034391,E01034392,E01034393,E01034394,E01034395,E01034396,E01034397,E01034398,E01034399,E01034400,E01034401,E01034402,E01034403,E01034404,E01034405,E01034406,E01034407,E01034408,E01034409,E01034410,E01034411,E01034412,E01034413,E01034414,E01034415,E01034416,E01034417,E01034418,E01034419,E01034420,E01034421,E01034422,E01034423,E01034424,E01034425,E01034426,E01034427,E01034428,E01034429,E01034430,E01034431,E01034432,E01034433,E01034434,E01034435,E01034436,E01034437,E01034438,E01034439,E01034440,E01034441,E01034442,E01034443,E01034444,E01034445,E01034446,E01034447,E01034448,E01034449,E01034450,E01034451,E01034452,E01034453,E01034454,E01034455,E01034456,E01034457,E01034458,E01034459,E01034460,E01034461&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_065.json.gz
+  content_sha256: 135531545463a7a2ed838c00370ff00ac7dcd18f9ec77be39c6e3770f6ac8569
+- id: census_lsoa_tenure5a_bedrooms_066
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 066
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01034462,E01034463,E01034464,E01034465,E01034466,E01034467,E01034468,E01034469,E01034470,E01034471,E01034472,E01034473,E01034474,E01034475,E01034476,E01034477,E01034478,E01034479,E01034480,E01034481,E01034482,E01034483,E01034484,E01034485,E01034486,E01034487,E01034488,E01034489,E01034490,E01034491,E01034492,E01034493,E01034494,E01034495,E01034496,E01034497,E01034498,E01034499,E01034500,E01034501,E01034502,E01034503,E01034504,E01034505,E01034506,E01034507,E01034508,E01034509,E01034510,E01034511,E01034512,E01034513,E01034514,E01034515,E01034516,E01034517,E01034518,E01034519,E01034520,E01034521,E01034522,E01034523,E01034524,E01034525,E01034526,E01034527,E01034528,E01034529,E01034530,E01034531,E01034532,E01034533,E01034534,E01034535,E01034536,E01034537,E01034538,E01034539,E01034540,E01034541,E01034542,E01034543,E01034544,E01034545,E01034546,E01034547,E01034548,E01034549,E01034550,E01034551,E01034552,E01034553,E01034554,E01034555,E01034556,E01034557,E01034558,E01034559,E01034560,E01034561,E01034562,E01034563,E01034564,E01034565,E01034566,E01034567,E01034568,E01034569,E01034570,E01034571,E01034572,E01034573,E01034574,E01034575,E01034576,E01034577,E01034578,E01034579,E01034580,E01034581,E01034582,E01034583,E01034584,E01034585,E01034586,E01034587,E01034588,E01034589,E01034590,E01034591,E01034592,E01034593,E01034594,E01034595,E01034596,E01034597,E01034598,E01034599,E01034600,E01034601,E01034602,E01034603,E01034604,E01034605,E01034606,E01034607,E01034608,E01034609,E01034610,E01034611,E01034612,E01034613,E01034614,E01034615,E01034616,E01034617,E01034618,E01034619,E01034620,E01034621,E01034622,E01034623,E01034624,E01034625,E01034626,E01034627,E01034628,E01034629,E01034630,E01034631,E01034632,E01034633,E01034634,E01034635,E01034636,E01034637,E01034638,E01034639,E01034640,E01034641,E01034642,E01034643,E01034644,E01034645,E01034646,E01034647,E01034648,E01034649,E01034650,E01034651,E01034652,E01034653,E01034654,E01034655,E01034656,E01034657,E01034658,E01034659,E01034660,E01034661,E01034662,E01034663,E01034664,E01034665,E01034666,E01034667,E01034668,E01034669,E01034670,E01034671,E01034672,E01034673,E01034674,E01034675,E01034676,E01034677,E01034678,E01034679,E01034680,E01034681,E01034682,E01034683,E01034684,E01034685,E01034686,E01034687,E01034688,E01034689,E01034690,E01034691,E01034692,E01034693,E01034694,E01034695,E01034696,E01034697,E01034698,E01034699,E01034700,E01034701,E01034702,E01034703,E01034704,E01034705,E01034706,E01034707,E01034708,E01034709,E01034710,E01034711,E01034712,E01034713,E01034714,E01034715,E01034716,E01034717,E01034718,E01034719,E01034720,E01034721,E01034722,E01034723,E01034724,E01034725,E01034726,E01034727,E01034728,E01034729,E01034730,E01034731,E01034732,E01034733,E01034734,E01034735,E01034736,E01034737,E01034738,E01034739,E01034740,E01034741,E01034742,E01034743,E01034744,E01034745,E01034746,E01034747,E01034748,E01034749,E01034750,E01034751,E01034752,E01034753,E01034754,E01034755,E01034756,E01034757,E01034758,E01034759,E01034760,E01034761,E01034762,E01034763,E01034764,E01034765,E01034766,E01034767,E01034768,E01034769,E01034770,E01034771,E01034772,E01034773,E01034774,E01034775,E01034776,E01034777,E01034778,E01034779,E01034780,E01034781,E01034782,E01034783,E01034784,E01034785,E01034786,E01034787,E01034788,E01034789,E01034790,E01034791,E01034792,E01034793,E01034794,E01034795,E01034796,E01034797,E01034798,E01034799,E01034800,E01034801,E01034802,E01034803,E01034804,E01034805,E01034806,E01034807,E01034808,E01034809,E01034810,E01034811,E01034812,E01034813,E01034814,E01034815,E01034816,E01034817,E01034818,E01034819,E01034820,E01034821,E01034822,E01034823,E01034824,E01034825,E01034826,E01034827,E01034828,E01034829,E01034830,E01034831,E01034832,E01034833,E01034834,E01034835,E01034836,E01034837,E01034838,E01034839,E01034840,E01034841,E01034842,E01034843,E01034844,E01034845,E01034846,E01034847,E01034848,E01034849,E01034850,E01034851,E01034852,E01034853,E01034854,E01034855,E01034856,E01034857,E01034858,E01034859,E01034860,E01034862,E01034863,E01034864,E01034865,E01034866,E01034867,E01034868,E01034869,E01034913,E01034914,E01034915,E01034916,E01034917,E01034918,E01034919,E01034920,E01034921,E01034922,E01034923,E01034924,E01034925,E01034926,E01034927,E01034928,E01034929,E01034930,E01034931,E01034932,E01034933,E01034934,E01034935,E01034936,E01034937,E01034938,E01034939,E01034940,E01034941,E01034942,E01034943,E01034944,E01034945,E01034946,E01034947,E01034948,E01034949,E01034950,E01034951,E01034952,E01034953,E01034954,E01034955,E01034956,E01034957,E01034958,E01034959,E01034960,E01034961,E01034962,E01034963,E01034964,E01034965,E01034966,E01034967,E01034968,E01034969,E01034970,E01034971,E01034972,E01034973,E01034974,E01034975,E01034976,E01034977,E01034978,E01034979,E01034980,E01034981,E01034982,E01034983,E01034984,E01034985,E01034986,E01034987,E01034988,E01034989,E01034990,E01034991,E01034992,E01034993,E01034994,E01034995,E01034996,E01034997,E01034998,E01034999,E01035000,E01035001,E01035002,E01035003,E01035004,E01035005&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_066.json.gz
+  content_sha256: 6bdd4223863583e57e40ff82659baf39ab5a7d0319a89c2bc90b1880c5d86c18
+- id: census_lsoa_tenure5a_bedrooms_067
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 067
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01035006,E01035007,E01035008,E01035009,E01035010,E01035011,E01035012,E01035013,E01035014,E01035015,E01035016,E01035017,E01035018,E01035019,E01035020,E01035021,E01035022,E01035023,E01035024,E01035025,E01035026,E01035027,E01035028,E01035029,E01035030,E01035031,E01035032,E01035033,E01035034,E01035035,E01035036,E01035037,E01035038,E01035039,E01035040,E01035041,E01035042,E01035043,E01035044,E01035045,E01035046,E01035047,E01035048,E01035049,E01035050,E01035051,E01035052,E01035053,E01035054,E01035055,E01035056,E01035057,E01035058,E01035059,E01035060,E01035061,E01035062,E01035063,E01035064,E01035065,E01035066,E01035067,E01035068,E01035069,E01035070,E01035071,E01035072,E01035073,E01035074,E01035075,E01035076,E01035077,E01035078,E01035079,E01035080,E01035081,E01035082,E01035083,E01035084,E01035085,E01035086,E01035087,E01035088,E01035089,E01035090,E01035091,E01035092,E01035093,E01035094,E01035095,E01035096,E01035097,E01035098,E01035099,E01035100,E01035101,E01035102,E01035103,E01035104,E01035105,E01035106,E01035107,E01035108,E01035109,E01035110,E01035111,E01035112,E01035113,E01035114,E01035115,E01035116,E01035117,E01035118,E01035119,E01035120,E01035121,E01035122,E01035123,E01035124,E01035125,E01035126,E01035127,E01035128,E01035129,E01035130,E01035131,E01035132,E01035133,E01035134,E01035135,E01035136,E01035137,E01035138,E01035139,E01035140,E01035141,E01035142,E01035143,E01035144,E01035145,E01035146,E01035147,E01035148,E01035149,E01035150,E01035151,E01035152,E01035153,E01035154,E01035155,E01035156,E01035157,E01035158,E01035159,E01035160,E01035161,E01035162,E01035163,E01035164,E01035165,E01035166,E01035167,E01035168,E01035169,E01035170,E01035171,E01035172,E01035173,E01035174,E01035175,E01035176,E01035177,E01035178,E01035179,E01035180,E01035181,E01035182,E01035183,E01035184,E01035185,E01035186,E01035187,E01035188,E01035189,E01035190,E01035191,E01035192,E01035193,E01035194,E01035195,E01035196,E01035197,E01035198,E01035199,E01035200,E01035201,E01035202,E01035203,E01035204,E01035205,E01035206,E01035207,E01035208,E01035209,E01035210,E01035211,E01035212,E01035213,E01035214,E01035215,E01035216,E01035217,E01035218,E01035219,E01035220,E01035221,E01035222,E01035223,E01035224,E01035225,E01035226,E01035227,E01035228,E01035229,E01035230,E01035231,E01035232,E01035233,E01035234,E01035235,E01035236,E01035237,E01035238,E01035239,E01035240,E01035241,E01035242,E01035243,E01035244,E01035245,E01035246,E01035247,E01035248,E01035249,E01035250,E01035251,E01035252,E01035253,E01035254,E01035255,E01035256,E01035257,E01035258,E01035259,E01035260,E01035261,E01035262,E01035263,E01035264,E01035265,E01035266,E01035267,E01035268,E01035269,E01035270,E01035271,E01035272,E01035273,E01035274,E01035275,E01035276,E01035277,E01035278,E01035279,E01035280,E01035281,E01035282,E01035283,E01035284,E01035285,E01035286,E01035287,E01035288,E01035289,E01035290,E01035291,E01035292,E01035293,E01035294,E01035295,E01035296,E01035297,E01035298,E01035299,E01035300,E01035301,E01035302,E01035303,E01035304,E01035305,E01035306,E01035307,E01035308,E01035309,E01035310,E01035311,E01035312,E01035313,E01035314,E01035315,E01035316,E01035317,E01035318,E01035319,E01035320,E01035321,E01035322,E01035323,E01035324,E01035325,E01035326,E01035327,E01035328,E01035329,E01035330,E01035331,E01035332,E01035333,E01035334,E01035335,E01035336,E01035337,E01035338,E01035339,E01035340,E01035341,E01035342,E01035343,E01035344,E01035345,E01035346,E01035347,E01035348,E01035349,E01035350,E01035351,E01035352,E01035353,E01035354,E01035355,E01035356,E01035357,E01035358,E01035359,E01035360,E01035361,E01035362,E01035363,E01035364,E01035365,E01035366,E01035367,E01035368,E01035369,E01035370,E01035371,E01035372,E01035373,E01035374,E01035375,E01035376,E01035377,E01035378,E01035379,E01035380,E01035381,E01035382,E01035383,E01035384,E01035385,E01035386,E01035387,E01035388,E01035389,E01035390,E01035391,E01035392,E01035393,E01035394,E01035395,E01035396,E01035397,E01035398,E01035399,E01035400,E01035401,E01035402,E01035403,E01035404,E01035405,E01035406,E01035407,E01035408,E01035409,E01035410,E01035411,E01035412,E01035413,E01035414,E01035415,E01035416,E01035417,E01035418,E01035419,E01035420,E01035421,E01035422,E01035423,E01035424,E01035425,E01035426,E01035427,E01035428,E01035429,E01035430,E01035431,E01035432,E01035433,E01035434,E01035435,E01035436,E01035437,E01035438,E01035439,E01035440,E01035441,E01035442,E01035443,E01035444,E01035445,E01035446,E01035447,E01035448,E01035449,E01035450,E01035451,E01035452,E01035453,E01035454,E01035455,E01035456,E01035457,E01035458,E01035459,E01035460,E01035461,E01035462,E01035463,E01035464,E01035465,E01035466,E01035467,E01035468,E01035469,E01035470,E01035471,E01035472,E01035473,E01035474,E01035475,E01035476,E01035477,E01035478,E01035479,E01035480,E01035481,E01035482,E01035483,E01035484,E01035485,E01035486,E01035487,E01035488,E01035489,E01035490,E01035491,E01035492,E01035493,E01035494,E01035495,E01035496,E01035497,E01035498,E01035499,E01035500,E01035501,E01035502,E01035503,E01035504,E01035505&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_067.json.gz
+  content_sha256: 5e778d7c2fe058e32b70bc5ed241f7e50ffdba4593dc28a8085389a43907cb96
+- id: census_lsoa_tenure5a_bedrooms_068
+  publisher: Office for National Statistics
+  title: Census 2021 English LSOA21 households by 5a tenure and bedrooms, batch 068
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,E01035506,E01035507,E01035508,E01035509,E01035510,E01035511,E01035512,E01035513,E01035514,E01035515,E01035516,E01035517,E01035518,E01035519,E01035520,E01035521,E01035522,E01035523,E01035524,E01035525,E01035526,E01035527,E01035528,E01035529,E01035530,E01035531,E01035532,E01035533,E01035534,E01035535,E01035536,E01035537,E01035538,E01035539,E01035540,E01035541,E01035542,E01035543,E01035544,E01035545,E01035546,E01035547,E01035548,E01035549,E01035551,E01035552,E01035553,E01035554,E01035555,E01035556,E01035557,E01035558,E01035559,E01035560,E01035561,E01035562,E01035563,E01035564,E01035565,E01035566,E01035567,E01035568,E01035569,E01035570,E01035571,E01035572,E01035573,E01035574,E01035575,E01035576,E01035577,E01035578,E01035579,E01035580,E01035581,E01035582,E01035583,E01035584,E01035585,E01035586,E01035587,E01035588,E01035589,E01035590,E01035591,E01035592,E01035593,E01035594,E01035595,E01035596,E01035597,E01035598,E01035599,E01035600,E01035601,E01035602,E01035603,E01035604,E01035605,E01035606,E01035607,E01035608,E01035609,E01035610,E01035611,E01035612,E01035613,E01035614,E01035615,E01035616,E01035617,E01035618,E01035619,E01035620,E01035621,E01035622,E01035623,E01035624,E01035625,E01035626,E01035627,E01035628,E01035629,E01035630,E01035631,E01035632,E01035633,E01035634,E01035635,E01035636,E01035637,E01035638,E01035639,E01035640,E01035641,E01035642,E01035643,E01035644,E01035645,E01035646,E01035647,E01035648,E01035649,E01035650,E01035651,E01035652,E01035653,E01035654,E01035655,E01035656,E01035657,E01035658,E01035659,E01035660,E01035661,E01035662,E01035663,E01035664,E01035665,E01035666,E01035667,E01035668,E01035669,E01035670,E01035671,E01035672,E01035673,E01035674,E01035675,E01035676,E01035677,E01035678,E01035679,E01035680,E01035681,E01035682,E01035683,E01035684,E01035685,E01035686,E01035687,E01035688,E01035689,E01035690,E01035691,E01035692,E01035693,E01035694,E01035695,E01035696,E01035697,E01035698,E01035699,E01035700,E01035701,E01035702,E01035703,E01035704,E01035705,E01035706,E01035707,E01035708,E01035709,E01035710,E01035711,E01035712,E01035713,E01035714,E01035715,E01035716,E01035717,E01035718,E01035719,E01035720,E01035721,E01035722,E01035723,E01035724,E01035725,E01035726,E01035727,E01035728,E01035729,E01035730,E01035731,E01035733,E01035734,E01035735,E01035736,E01035737,E01035738,E01035739,E01035740,E01035741,E01035742,E01035743,E01035744,E01035745,E01035746,E01035747,E01035748,E01035749,E01035750,E01035751,E01035752,E01035753,E01035754,E01035755,E01035756,E01035757,E01035758,E01035759,E01035760,E01035761,E01035762&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_lsoa_tenure5a_bedrooms_068.json.gz
+  content_sha256: 3c2d87d64a00c752c1ea0de6b14693fdae032b592992953c3a6d68ae0003cef3
+- id: ons_geo_lsoa21_pwc
+  publisher: Office for National Statistics
+  title: Lower layer Super Output Areas (December 2021) EW Population Weighted Centroids
+  url: https://open-geography-portalx-ons.hub.arcgis.com/api/download/v1/items/32729e42d05e4e23bc7e43a36aa4ae8b/shapefile?layers=0
+  landing_page: https://geoportal.statistics.gov.uk/datasets/32729e42d05e4e23bc7e43a36aa4ae8b/about
+  sha256: 52ad61266629ca2a312c9840a93a12c4f76efa1162ac53d71cd942beaebf59c2
+  licence: 'Open Government Licence v3.0; ONS Geography licence terms: https://www.ons.gov.uk/methodology/geography/licences'
+  notes: Official ONS V4 population-weighted centroids; source contains both ONS and Ordnance Survey IPR.
+    PWC for E01027305 was revised to reflect Boundary-Line topological improvements.
+  filename: ons_geo_lsoa21_pwc.zip
+- id: ons_geo_oa21_lsoa21
+  publisher: Office for National Statistics
+  title: Output Area (2021) to LSOA to MSOA to LAD (December 2021) Exact Fit Lookup in EW (V3)
+  url: https://open-geography-portalx-ons.hub.arcgis.com/api/download/v1/items/b9ca90c10aaa4b8d9791e9859a38ca67/csv?layers=0
+  landing_page: https://geoportal.statistics.gov.uk/datasets/b9ca90c10aaa4b8d9791e9859a38ca67/about
+  sha256: 1b7e715936374c586efbe669d82e3f3d6d093dc5295c2d99ce6ea2349f85153a
+  licence: 'Open Government Licence v3.0; ONS Geography licence terms: https://www.ons.gov.uk/methodology/geography/licences'
+  notes: Official exact-fit OA21-to-LSOA21 hierarchy; join with ons_geo_oa21_region on OA21CD, require
+    all constituent OAs of each LSOA have one government office region.
+  filename: ons_geo_oa21_lsoa21.csv
+- id: ons_geo_oa21_region
+  publisher: Office for National Statistics
+  title: Output Area (2021) to Region (December 2021) Exact Fit Lookup in EW
+  url: https://www.arcgis.com/sharing/rest/content/items/efda0d0e14da4badbd8bdf8ae31d2f00/data
+  landing_page: https://geoportal.statistics.gov.uk/datasets/efda0d0e14da4badbd8bdf8ae31d2f00/about
+  sha256: 87bc05c0f667e0578f052ab9f3a7f21628aa658c37a3bf3fba8e5482dd6b2f60
+  licence: 'Open Government Licence v3.0; ONS Geography licence terms: https://www.ons.gov.uk/methodology/geography/licences'
+  notes: Official exact-fit OA21-to-government office region hierarchy; fields use RGN22 names despite
+    December 2021 title.
+  filename: ons_geo_oa21_region.csv
+- id: voa_english_brma_may2020
+  publisher: Valuation Office Agency
+  title: 'English BRMA GIS: Broad Rental Market Area boundary layer applicable May 2020'
+  url: https://assets.publishing.service.gov.uk/media/5ee0ef64d3bf7f1ec05388df/GML.zip
+  landing_page: https://www.gov.uk/government/publications/broad-rental-market-area-boundary-layer-for-geographical-information-system-gis-applicable-may-2020
+  sha256: 66515b0a1403e4557e6501d2d5ada5e7e17b29deef2da9165ba515a66907f6c6
+  licence: Open Government Licence v3.0 (GOV.UK publication footer; no separately stated file licence)
+  notes: Official VOA download published 4 June 2020 for May 2020. The ZIP contains GML and XSD, despite
+    the landing page calling it a shapefile. Direct XML parsing preserves all polygon members in nine
+    MultiPolygons incorrectly declared Polygon by the XSD, including 63 Kernow West components. Shapely
+    make_valid repairs self-intersections in Basingstoke, Exeter, Mid & West Dorset, Newbury, Oxford,
+    Reading and West Cheshire. All 152 BRMAs are valid and nonempty, EPSG:27700.
+  filename: brma_england_voa_may2020.zip
+- id: ons_nomis_ts054
+  publisher: Office for National Statistics
+  title: 'Census 2021 TS054 tenure of household: England and Wales bulk CSV release'
+  url: https://www.nomisweb.co.uk/output/census/2021/census2021-ts054.zip
+  landing_page: https://www.nomisweb.co.uk/output/census/2021/census2021-ts054.zip
+  sha256: aa07311021776a93fdd7e8f6b3ad261b919305ef821ae68174fb1a66b85304c2
+  licence: Open Government Licence v3.0
+  notes: 'Nomis Census 2021 TS054 bulk, metadata version 2. Exact private-rented totals per English and
+    Welsh LSOA21: private landlord/letting agency plus other private rented. Aggregate equals both components;
+    rent-free is excluded.'
+  filename: census_nomis_ts054.zip
+- id: geo_lsoa21_wales_pwc
+  publisher: Office for National Statistics
+  title: 'Lower layer Super Output Areas (December 2021) EW Population Weighted Centroids: Welsh LSOAs'
+  url: https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LSOA_PopCentroids_EW_2021_V4/FeatureServer/0/query?where=LSOA21CD+LIKE+%27W%25%27&outFields=%2A&outSR=27700&returnGeometry=true&orderByFields=LSOA21CD&f=geojson
+  landing_page: https://www.data.gov.uk/dataset/e3e903a6-1864-4083-8837-017b6bdf8cc5/lower-layer-super-output-areas-december-2021-ew-population-weighted-centroids1
+  sha256: 4a072579da4bc4913949d19e742009280ed8dbe97712e75dd9fc1a5c977291a3
+  licence: Open Government Licence v3.0; contains Ordnance Survey and ONS intellectual property rights
+  notes: Unmodified ArcGIS GeoJSON response; 1,917 Welsh LSOAs selected by LSOA21CD LIKE W%; all fit below
+    2,000 feature query limit. Spatial reference EPSG:27700. The V4 revision adjusts only an English centroid.
+  filename: geo_lsoa21_wales_pwc.geojson
+- id: geo_brma_wales_2014
+  publisher: Welsh Government / Rent Officers Wales
+  title: 'Broad Rental Market Areas (Wales): Rent Officers Wales release of 23 September 2014'
+  url: https://datashare.ed.ac.uk/bitstreams/c2fe4c38-fd3e-4498-a4f9-62ac32163311/download
+  landing_page: https://datashare.ed.ac.uk/handle/10283/2615?show=full
+  sha256: 49de861258ba945eb659648d2a040aef1e9089ac80374f0bf4e85fcb062edd6c
+  licence: Open Government Licence
+  notes: Third-party archived copy of an official Rent Officers Wales release, deposited at Edinburgh
+    DataShare by Owen Boswarva. ZIP encloses the official 23 September 2014 correspondence explicitly
+    granting OGL and stating that West Cheshire covers north-east Wales. The May 2012 ESRI shapefile is
+    used; depositor-created MapInfo files are excluded. Contains 22 Welsh BRMAs plus West Cheshire; make_valid
+    repairs three invalid geometries.
+  filename: geo_brma_wales_2014.zip
+- id: census_wales_lsoa_tenure5a_bedrooms_001
+  publisher: Office for National Statistics
+  title: 'Census 2021 Welsh LSOA households by tenure5a and bedrooms: batch 001'
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,W01000003,W01000004,W01000005,W01000006,W01000007,W01000008,W01000009,W01000010,W01000011,W01000012,W01000013,W01000014,W01000015,W01000016,W01000017,W01000018,W01000019,W01000021,W01000022,W01000023,W01000024,W01000025,W01000026,W01000027,W01000028,W01000029,W01000030,W01000031,W01000033,W01000034,W01000035,W01000036,W01000037,W01000038,W01000040,W01000041,W01000042,W01000044,W01000047,W01000050,W01000051,W01000052,W01000053,W01000054,W01000056,W01000057,W01000059,W01000060,W01000061,W01000062,W01000063,W01000064,W01000065,W01000066,W01000067,W01000068,W01000070,W01000071,W01000072,W01000073,W01000074,W01000075,W01000076,W01000077,W01000078,W01000080,W01000081,W01000082,W01000083,W01000084,W01000085,W01000086,W01000087,W01000088,W01000089,W01000090,W01000091,W01000092,W01000093,W01000094,W01000095,W01000096,W01000097,W01000098,W01000099,W01000100,W01000101,W01000102,W01000103,W01000104,W01000105,W01000106,W01000107,W01000108,W01000109,W01000110,W01000111,W01000112,W01000113,W01000114,W01000115,W01000116,W01000117,W01000118,W01000119,W01000121,W01000122,W01000123,W01000124,W01000125,W01000126,W01000127,W01000128,W01000129,W01000130,W01000131,W01000132,W01000133,W01000134,W01000135,W01000136,W01000137,W01000138,W01000139,W01000140,W01000141,W01000142,W01000143,W01000144,W01000145,W01000146,W01000147,W01000148,W01000149,W01000150,W01000151,W01000152,W01000153,W01000154,W01000155,W01000156,W01000157,W01000158,W01000159,W01000162,W01000163,W01000164,W01000165,W01000166,W01000167,W01000168,W01000169,W01000170,W01000171,W01000172,W01000173,W01000174,W01000175,W01000176,W01000177,W01000178,W01000179,W01000180,W01000181,W01000182,W01000183,W01000184,W01000185,W01000186,W01000187,W01000188,W01000189,W01000190,W01000191,W01000192,W01000193,W01000194,W01000195,W01000196,W01000197,W01000198,W01000199,W01000200,W01000201,W01000202,W01000203,W01000204,W01000205,W01000206,W01000207,W01000208,W01000209,W01000210,W01000211,W01000212,W01000213,W01000214,W01000215,W01000216,W01000217,W01000218,W01000219,W01000220,W01000221,W01000222,W01000223,W01000224,W01000225,W01000226,W01000227,W01000228,W01000229,W01000230,W01000231,W01000232,W01000233,W01000234,W01000235,W01000236,W01000237,W01000238,W01000239,W01000240,W01000241,W01000242,W01000243,W01000244,W01000245,W01000246,W01000247,W01000248,W01000249,W01000250,W01000251,W01000252,W01000253,W01000254,W01000255,W01000256,W01000257,W01000258,W01000259,W01000260,W01000261,W01000262,W01000263,W01000264,W01000265,W01000266,W01000267,W01000268,W01000269,W01000270,W01000271,W01000272,W01000273,W01000274,W01000275,W01000276,W01000277,W01000278,W01000279,W01000280,W01000281,W01000282,W01000283,W01000284,W01000285,W01000286,W01000287,W01000288,W01000289,W01000290,W01000291,W01000292,W01000293,W01000294,W01000295,W01000296,W01000297,W01000298,W01000299,W01000300,W01000301,W01000302,W01000303,W01000304,W01000305,W01000306,W01000307,W01000308,W01000309,W01000310,W01000311,W01000312,W01000313,W01000314,W01000315,W01000316,W01000317,W01000318,W01000319,W01000320,W01000321,W01000322,W01000323,W01000324,W01000325,W01000326,W01000327,W01000328,W01000329,W01000330,W01000331,W01000332,W01000333,W01000334,W01000335,W01000336,W01000337,W01000338,W01000339,W01000340,W01000341,W01000342,W01000343,W01000344,W01000345,W01000346,W01000347,W01000348,W01000349,W01000350,W01000351,W01000354,W01000355,W01000356,W01000359,W01000360,W01000361,W01000362,W01000363,W01000364,W01000365,W01000366,W01000367,W01000368,W01000369,W01000370,W01000371,W01000372,W01000373,W01000374,W01000375,W01000376,W01000377,W01000378,W01000379,W01000380,W01000381,W01000382,W01000384,W01000385,W01000386,W01000387,W01000388,W01000389,W01000390,W01000391,W01000392,W01000393,W01000394,W01000395,W01000398,W01000399,W01000400,W01000401,W01000402,W01000403,W01000404,W01000405,W01000406,W01000407,W01000408,W01000409,W01000410,W01000411,W01000412,W01000413,W01000414,W01000415,W01000416,W01000417,W01000418,W01000419,W01000420,W01000422,W01000423,W01000424,W01000425,W01000426,W01000427,W01000428,W01000429,W01000430,W01000431,W01000432,W01000433,W01000434,W01000438,W01000439,W01000440,W01000441,W01000442,W01000443,W01000444,W01000445,W01000446,W01000447,W01000448,W01000449,W01000450,W01000451,W01000452,W01000453,W01000454,W01000455,W01000456,W01000457,W01000458,W01000459,W01000460,W01000461,W01000464,W01000465,W01000466,W01000467,W01000470,W01000471,W01000472,W01000473,W01000474,W01000475,W01000476,W01000477,W01000478,W01000479,W01000480,W01000481,W01000482,W01000483,W01000484,W01000485,W01000486,W01000487,W01000488,W01000489,W01000492,W01000493,W01000494,W01000496,W01000497,W01000498,W01000499,W01000500,W01000501,W01000502,W01000503,W01000504,W01000505,W01000506,W01000507,W01000508,W01000509,W01000510,W01000511,W01000512,W01000513,W01000515,W01000516,W01000517,W01000518,W01000519,W01000522,W01000523,W01000524,W01000525,W01000527,W01000528,W01000531,W01000533,W01000534,W01000535,W01000536,W01000537,W01000538,W01000539,W01000540,W01000541,W01000542&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_wales_lsoa_tenure5a_bedrooms_001.json.gz
+  content_sha256: 349cc31457a195d673c7edf6ce46a3dfa5f02d344e783e3bcc099da800f7d861
+- id: census_wales_lsoa_tenure5a_bedrooms_002
+  publisher: Office for National Statistics
+  title: 'Census 2021 Welsh LSOA households by tenure5a and bedrooms: batch 002'
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,W01000543,W01000545,W01000546,W01000547,W01000548,W01000549,W01000550,W01000551,W01000552,W01000553,W01000554,W01000555,W01000556,W01000557,W01000558,W01000559,W01000560,W01000561,W01000562,W01000563,W01000564,W01000565,W01000566,W01000567,W01000569,W01000570,W01000571,W01000572,W01000573,W01000574,W01000575,W01000576,W01000577,W01000578,W01000579,W01000580,W01000581,W01000582,W01000583,W01000584,W01000585,W01000586,W01000587,W01000588,W01000589,W01000590,W01000591,W01000592,W01000594,W01000595,W01000596,W01000597,W01000598,W01000599,W01000600,W01000601,W01000602,W01000603,W01000604,W01000605,W01000606,W01000607,W01000608,W01000609,W01000610,W01000611,W01000612,W01000613,W01000614,W01000617,W01000618,W01000619,W01000622,W01000623,W01000624,W01000625,W01000626,W01000627,W01000628,W01000629,W01000630,W01000631,W01000632,W01000633,W01000634,W01000635,W01000636,W01000637,W01000638,W01000639,W01000640,W01000641,W01000642,W01000643,W01000644,W01000645,W01000646,W01000647,W01000648,W01000649,W01000650,W01000651,W01000652,W01000653,W01000654,W01000655,W01000656,W01000657,W01000658,W01000659,W01000660,W01000662,W01000663,W01000664,W01000665,W01000666,W01000667,W01000670,W01000671,W01000672,W01000673,W01000674,W01000675,W01000676,W01000677,W01000678,W01000679,W01000680,W01000681,W01000682,W01000683,W01000684,W01000685,W01000686,W01000687,W01000688,W01000689,W01000690,W01000691,W01000692,W01000693,W01000694,W01000695,W01000696,W01000697,W01000698,W01000699,W01000700,W01000701,W01000702,W01000703,W01000704,W01000705,W01000706,W01000707,W01000708,W01000709,W01000710,W01000711,W01000712,W01000713,W01000714,W01000715,W01000716,W01000717,W01000718,W01000719,W01000720,W01000721,W01000722,W01000723,W01000724,W01000725,W01000728,W01000729,W01000730,W01000731,W01000732,W01000733,W01000734,W01000735,W01000736,W01000737,W01000738,W01000739,W01000740,W01000741,W01000742,W01000744,W01000745,W01000746,W01000747,W01000749,W01000750,W01000751,W01000752,W01000753,W01000754,W01000755,W01000756,W01000757,W01000758,W01000759,W01000760,W01000761,W01000762,W01000763,W01000764,W01000765,W01000766,W01000767,W01000768,W01000769,W01000770,W01000771,W01000772,W01000774,W01000775,W01000776,W01000777,W01000778,W01000779,W01000781,W01000782,W01000784,W01000785,W01000786,W01000787,W01000788,W01000789,W01000790,W01000791,W01000792,W01000793,W01000794,W01000795,W01000797,W01000798,W01000799,W01000800,W01000801,W01000802,W01000803,W01000804,W01000805,W01000806,W01000807,W01000808,W01000809,W01000810,W01000811,W01000812,W01000813,W01000814,W01000815,W01000816,W01000817,W01000818,W01000819,W01000820,W01000821,W01000822,W01000823,W01000824,W01000825,W01000826,W01000827,W01000828,W01000829,W01000830,W01000831,W01000832,W01000833,W01000834,W01000835,W01000836,W01000837,W01000838,W01000839,W01000840,W01000841,W01000842,W01000843,W01000844,W01000845,W01000846,W01000847,W01000848,W01000849,W01000851,W01000852,W01000853,W01000854,W01000855,W01000856,W01000857,W01000858,W01000859,W01000860,W01000861,W01000862,W01000863,W01000864,W01000865,W01000866,W01000867,W01000868,W01000869,W01000870,W01000871,W01000872,W01000873,W01000874,W01000875,W01000876,W01000877,W01000878,W01000879,W01000880,W01000881,W01000882,W01000883,W01000884,W01000885,W01000886,W01000887,W01000888,W01000889,W01000890,W01000891,W01000892,W01000893,W01000894,W01000895,W01000896,W01000897,W01000898,W01000899,W01000900,W01000901,W01000904,W01000905,W01000907,W01000908,W01000909,W01000910,W01000911,W01000912,W01000913,W01000914,W01000915,W01000916,W01000918,W01000919,W01000921,W01000922,W01000925,W01000926,W01000927,W01000928,W01000929,W01000930,W01000931,W01000932,W01000933,W01000934,W01000935,W01000936,W01000937,W01000938,W01000939,W01000940,W01000941,W01000942,W01000943,W01000944,W01000945,W01000946,W01000947,W01000948,W01000949,W01000950,W01000951,W01000952,W01000953,W01000954,W01000955,W01000956,W01000957,W01000958,W01000959,W01000960,W01000961,W01000962,W01000963,W01000964,W01000965,W01000966,W01000967,W01000968,W01000969,W01000970,W01000971,W01000973,W01000974,W01000975,W01000976,W01000977,W01000978,W01000979,W01000980,W01000981,W01000982,W01000983,W01000984,W01000985,W01000986,W01000987,W01000989,W01000990,W01000991,W01000992,W01000993,W01000994,W01000995,W01000996,W01000997,W01000999,W01001000,W01001001,W01001002,W01001003,W01001004,W01001005,W01001006,W01001007,W01001008,W01001009,W01001010,W01001011,W01001012,W01001013,W01001014,W01001015,W01001016,W01001017,W01001018,W01001019,W01001020,W01001021,W01001022,W01001023,W01001024,W01001025,W01001026,W01001027,W01001028,W01001029,W01001030,W01001031,W01001032,W01001033,W01001034,W01001035,W01001036,W01001037,W01001038,W01001039,W01001040,W01001041,W01001042,W01001043,W01001044,W01001045,W01001046,W01001047,W01001048,W01001049,W01001050,W01001051,W01001052,W01001053,W01001054,W01001055,W01001056,W01001057,W01001058,W01001059,W01001060,W01001062,W01001063,W01001064,W01001065,W01001066,W01001067,W01001068,W01001069,W01001070,W01001071,W01001072&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_wales_lsoa_tenure5a_bedrooms_002.json.gz
+  content_sha256: dab6a9b66947188d8e37bea9f4cbddfe7fc1ec1cdafdd643fe7d8b6afeabae92
+- id: census_wales_lsoa_tenure5a_bedrooms_003
+  publisher: Office for National Statistics
+  title: 'Census 2021 Welsh LSOA households by tenure5a and bedrooms: batch 003'
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,W01001073,W01001075,W01001076,W01001077,W01001078,W01001079,W01001080,W01001081,W01001082,W01001083,W01001084,W01001085,W01001086,W01001087,W01001088,W01001089,W01001090,W01001091,W01001092,W01001093,W01001094,W01001095,W01001096,W01001097,W01001098,W01001099,W01001100,W01001101,W01001102,W01001103,W01001104,W01001105,W01001106,W01001107,W01001108,W01001109,W01001110,W01001111,W01001112,W01001113,W01001114,W01001115,W01001116,W01001117,W01001119,W01001120,W01001121,W01001122,W01001123,W01001124,W01001125,W01001127,W01001128,W01001129,W01001130,W01001131,W01001132,W01001133,W01001134,W01001135,W01001136,W01001137,W01001138,W01001139,W01001140,W01001141,W01001142,W01001143,W01001144,W01001145,W01001146,W01001147,W01001148,W01001149,W01001150,W01001151,W01001152,W01001153,W01001154,W01001155,W01001156,W01001157,W01001158,W01001159,W01001160,W01001162,W01001164,W01001165,W01001166,W01001167,W01001168,W01001169,W01001170,W01001171,W01001172,W01001173,W01001174,W01001175,W01001176,W01001177,W01001178,W01001179,W01001180,W01001181,W01001184,W01001185,W01001186,W01001187,W01001188,W01001189,W01001190,W01001192,W01001193,W01001194,W01001195,W01001196,W01001197,W01001198,W01001199,W01001202,W01001203,W01001204,W01001205,W01001206,W01001207,W01001208,W01001209,W01001210,W01001211,W01001212,W01001214,W01001216,W01001217,W01001218,W01001219,W01001220,W01001221,W01001222,W01001223,W01001224,W01001225,W01001226,W01001227,W01001228,W01001229,W01001230,W01001231,W01001232,W01001233,W01001234,W01001235,W01001236,W01001237,W01001238,W01001239,W01001240,W01001241,W01001242,W01001243,W01001244,W01001245,W01001246,W01001247,W01001248,W01001249,W01001250,W01001251,W01001252,W01001253,W01001255,W01001256,W01001257,W01001258,W01001259,W01001260,W01001261,W01001262,W01001263,W01001264,W01001265,W01001266,W01001267,W01001268,W01001269,W01001270,W01001271,W01001272,W01001273,W01001274,W01001275,W01001276,W01001277,W01001278,W01001279,W01001280,W01001281,W01001282,W01001283,W01001284,W01001285,W01001286,W01001287,W01001288,W01001289,W01001290,W01001291,W01001292,W01001293,W01001294,W01001295,W01001296,W01001297,W01001298,W01001299,W01001300,W01001301,W01001302,W01001305,W01001306,W01001307,W01001308,W01001309,W01001310,W01001311,W01001312,W01001313,W01001314,W01001315,W01001317,W01001318,W01001319,W01001320,W01001321,W01001322,W01001324,W01001325,W01001326,W01001327,W01001328,W01001329,W01001330,W01001331,W01001332,W01001333,W01001334,W01001335,W01001336,W01001337,W01001338,W01001339,W01001340,W01001341,W01001342,W01001343,W01001344,W01001345,W01001346,W01001347,W01001348,W01001349,W01001350,W01001351,W01001352,W01001353,W01001354,W01001355,W01001356,W01001357,W01001358,W01001359,W01001360,W01001361,W01001362,W01001363,W01001364,W01001365,W01001366,W01001367,W01001368,W01001369,W01001370,W01001371,W01001372,W01001373,W01001374,W01001375,W01001376,W01001377,W01001378,W01001379,W01001380,W01001381,W01001382,W01001383,W01001384,W01001385,W01001386,W01001387,W01001388,W01001389,W01001390,W01001391,W01001392,W01001393,W01001394,W01001395,W01001396,W01001397,W01001398,W01001399,W01001400,W01001401,W01001402,W01001403,W01001404,W01001405,W01001406,W01001407,W01001408,W01001409,W01001410,W01001411,W01001412,W01001413,W01001414,W01001415,W01001416,W01001417,W01001418,W01001419,W01001420,W01001421,W01001422,W01001423,W01001424,W01001425,W01001426,W01001427,W01001428,W01001429,W01001430,W01001431,W01001432,W01001433,W01001434,W01001435,W01001436,W01001437,W01001438,W01001439,W01001440,W01001441,W01001442,W01001443,W01001444,W01001445,W01001446,W01001447,W01001448,W01001449,W01001450,W01001451,W01001452,W01001453,W01001454,W01001455,W01001456,W01001457,W01001458,W01001459,W01001460,W01001461,W01001462,W01001463,W01001464,W01001465,W01001466,W01001467,W01001468,W01001469,W01001470,W01001471,W01001472,W01001473,W01001474,W01001475,W01001478,W01001479,W01001480,W01001481,W01001482,W01001483,W01001484,W01001485,W01001486,W01001487,W01001488,W01001489,W01001490,W01001491,W01001492,W01001493,W01001494,W01001495,W01001496,W01001497,W01001498,W01001499,W01001500,W01001501,W01001502,W01001503,W01001504,W01001505,W01001506,W01001507,W01001508,W01001509,W01001510,W01001511,W01001512,W01001513,W01001514,W01001515,W01001516,W01001517,W01001518,W01001519,W01001520,W01001521,W01001522,W01001523,W01001524,W01001525,W01001526,W01001527,W01001528,W01001529,W01001530,W01001531,W01001532,W01001533,W01001534,W01001535,W01001536,W01001537,W01001538,W01001539,W01001540,W01001541,W01001542,W01001543,W01001545,W01001546,W01001548,W01001549,W01001551,W01001552,W01001554,W01001555,W01001556,W01001557,W01001558,W01001559,W01001560,W01001561,W01001562,W01001563,W01001565,W01001568,W01001569,W01001571,W01001572,W01001573,W01001574,W01001575,W01001576,W01001577,W01001578,W01001579,W01001580,W01001581,W01001582,W01001583,W01001584,W01001585,W01001586,W01001587,W01001588,W01001589,W01001590,W01001591,W01001592,W01001593,W01001594,W01001595,W01001596,W01001597,W01001598,W01001599&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_wales_lsoa_tenure5a_bedrooms_003.json.gz
+  content_sha256: e5c35c196eca90ca87b32f79d91458c9be40655a56044b9bc1bea9a3877691c6
+- id: census_wales_lsoa_tenure5a_bedrooms_004
+  publisher: Office for National Statistics
+  title: 'Census 2021 Welsh LSOA households by tenure5a and bedrooms: batch 004'
+  url: https://api.beta.ons.gov.uk/v1/population-types/HH/census-observations?area-type=lsoa,W01001600,W01001601,W01001602,W01001603,W01001604,W01001605,W01001606,W01001607,W01001608,W01001609,W01001610,W01001611,W01001612,W01001613,W01001614,W01001615,W01001616,W01001617,W01001618,W01001619,W01001620,W01001621,W01001622,W01001623,W01001624,W01001625,W01001626,W01001627,W01001628,W01001629,W01001630,W01001631,W01001632,W01001633,W01001634,W01001635,W01001636,W01001637,W01001638,W01001639,W01001640,W01001641,W01001642,W01001643,W01001645,W01001646,W01001648,W01001650,W01001651,W01001652,W01001653,W01001654,W01001655,W01001657,W01001658,W01001659,W01001660,W01001661,W01001662,W01001663,W01001664,W01001665,W01001666,W01001667,W01001668,W01001669,W01001670,W01001671,W01001673,W01001674,W01001675,W01001676,W01001677,W01001678,W01001679,W01001680,W01001681,W01001682,W01001683,W01001684,W01001685,W01001686,W01001687,W01001689,W01001690,W01001691,W01001692,W01001693,W01001694,W01001695,W01001696,W01001697,W01001698,W01001699,W01001702,W01001703,W01001704,W01001705,W01001706,W01001707,W01001708,W01001710,W01001711,W01001712,W01001713,W01001714,W01001715,W01001716,W01001717,W01001718,W01001719,W01001720,W01001721,W01001722,W01001724,W01001725,W01001726,W01001727,W01001728,W01001729,W01001730,W01001731,W01001732,W01001733,W01001734,W01001735,W01001736,W01001737,W01001738,W01001739,W01001740,W01001741,W01001742,W01001743,W01001744,W01001745,W01001746,W01001747,W01001748,W01001749,W01001750,W01001751,W01001752,W01001753,W01001754,W01001755,W01001756,W01001757,W01001758,W01001759,W01001760,W01001761,W01001762,W01001764,W01001765,W01001766,W01001767,W01001768,W01001769,W01001770,W01001771,W01001772,W01001773,W01001774,W01001775,W01001776,W01001777,W01001778,W01001779,W01001780,W01001781,W01001782,W01001783,W01001784,W01001785,W01001786,W01001787,W01001788,W01001789,W01001790,W01001791,W01001792,W01001793,W01001794,W01001795,W01001796,W01001797,W01001798,W01001799,W01001800,W01001802,W01001803,W01001804,W01001805,W01001806,W01001807,W01001808,W01001809,W01001810,W01001811,W01001812,W01001813,W01001814,W01001815,W01001816,W01001817,W01001818,W01001819,W01001820,W01001821,W01001822,W01001823,W01001824,W01001825,W01001826,W01001827,W01001828,W01001829,W01001830,W01001831,W01001832,W01001833,W01001834,W01001835,W01001836,W01001837,W01001838,W01001839,W01001840,W01001841,W01001842,W01001844,W01001846,W01001847,W01001848,W01001849,W01001850,W01001851,W01001852,W01001853,W01001854,W01001855,W01001856,W01001857,W01001858,W01001859,W01001860,W01001861,W01001862,W01001863,W01001864,W01001865,W01001866,W01001867,W01001868,W01001869,W01001870,W01001871,W01001872,W01001873,W01001874,W01001876,W01001877,W01001878,W01001879,W01001880,W01001881,W01001882,W01001883,W01001884,W01001885,W01001886,W01001887,W01001888,W01001889,W01001890,W01001891,W01001892,W01001893,W01001894,W01001895,W01001896,W01001897,W01001898,W01001899,W01001900,W01001901,W01001902,W01001903,W01001904,W01001905,W01001906,W01001907,W01001908,W01001910,W01001911,W01001912,W01001913,W01001914,W01001915,W01001916,W01001917,W01001918,W01001919,W01001920,W01001921,W01001922,W01001923,W01001924,W01001925,W01001926,W01001927,W01001928,W01001929,W01001930,W01001931,W01001932,W01001933,W01001935,W01001936,W01001937,W01001940,W01001942,W01001943,W01001944,W01001946,W01001947,W01001948,W01001949,W01001950,W01001951,W01001952,W01001953,W01001954,W01001955,W01001956,W01001958,W01001959,W01001960,W01001961,W01001962,W01001963,W01001964,W01001965,W01001966,W01001967,W01001968,W01001969,W01001970,W01001971,W01001972,W01001973,W01001974,W01001975,W01001976,W01001977,W01001978,W01001979,W01001980,W01001981,W01001982,W01001983,W01001984,W01001985,W01001986,W01001987,W01001988,W01001989,W01001990,W01001991,W01001992,W01001993,W01001994,W01001995,W01001996,W01001997,W01001998,W01001999,W01002000,W01002001,W01002002,W01002003,W01002004,W01002005,W01002007,W01002008,W01002009,W01002010,W01002011,W01002012,W01002013,W01002014,W01002016,W01002017,W01002018,W01002019,W01002020,W01002021,W01002022,W01002023,W01002024,W01002025,W01002026,W01002027,W01002028,W01002029,W01002030,W01002031,W01002032,W01002033,W01002034,W01002035,W01002036,W01002037,W01002038,W01002039,W01002040&dimensions=hh_tenure_5a,number_bedrooms_5a
+  landing_page: https://www.ons.gov.uk/datasets/create
+  licence: Open Government Licence v3.0
+  notes: 'Census 2021 HH custom query: hh_tenure_5a × number_bedrooms_5a, explicit LSOA21 batch. Category
+    3 (private rented or lives rent free) supplies bedroom proportions multiplied by exact TS054 private-rented
+    totals; no blocked areas. content_sha256 pins the decoded JSON bytes, without parsing or reserialising.
+    Cache may contain gzip entity bytes or the decoded body; transport compression is not pinned.'
+  filename: census_wales_lsoa_tenure5a_bedrooms_004.json.gz
+  content_sha256: 18f40e236f6d052fc8dc8095af2337abf0edead62fe8abfcb141e67ff63bf546
+- id: scot_brma_boundaries_2009
+  publisher: Scottish Government
+  title: Broad Rental Market Areas (2009 boundaries, revised 28 July 2015)
+  url: https://maps.gov.scot/ATOM/shapefiles/SG_BroadRentalMarketAreas_2009.zip
+  landing_page: https://www.data.gov.uk/dataset/39d08296-874a-4604-9ca4-6fe807aee4a8/broad-rental-market-areas
+  sha256: 4d121236a35ce896edeb663a7c2c390053c0deebe0cc149c84696bbfd4bddec0
+  licence: 'Open Government Licence v3.0; attribution: Copyright Scottish Government, contains Ordnance
+    Survey data © Crown copyright and database right 2026'
+  notes: Official 18 BRMA polygons. ZIP original unchanged; shapefile components dated 29 July 2015. Metadata
+    records revision 2015-07-28.
+  filename: scot_brma_boundaries_2009.zip
+- id: scot_foi_postcodes_brma_2023
+  publisher: Scottish Government
+  title: FOI 202300368850 Information Released - Postcodes
+  url: https://www.gov.scot/binaries/content/documents/govscot/publications/foi-eir-release/2023/11/foi-202300368850/documents/foi-202300368850---information-released---postcodes/foi-202300368850---information-released---postcodes/govscot%3Adocument/FOI%2B202300368850%2B-%2BInformation%2BReleased%2B-%2BPostcodes.xlsx
+  landing_page: https://www.gov.scot/publications/foi-202300368850/
+  sha256: 50366fea2b87bd6b697e7aa8736fbc870838b5e327458213294f31c3de7ede5e
+  licence: Open Government Licence v3.0
+  notes: Official exact postcode-to-BRMA candidate sets supply nine missing polygon assignments and correct
+    59 unique Balloch OA assignments. Two further conflicting ambiguous candidate sets are resolved by
+    the documented West Dunbartonshire council overrides in build.py, established on 2 October 2026.
+  filename: scot_foi_postcodes_brma_2023.xlsx
+- id: scot_oa2022_population_weighted_centroids
+  publisher: National Records of Scotland
+  title: 2022 Census Output Area Population Weighted Centroids
+  url: https://www.nrscotland.gov.uk/media/gm4dvdsv/output-area-2022-pwc.zip
+  landing_page: https://www.nrscotland.gov.uk/publications/2022-census-geography-products/
+  sha256: 509d7d937c8cc1477259dcd8e4e0e61240f5cc782721a79e9c279bd589e0ec0c
+  licence: 'Open Government Licence v3.0; attribution: Copyright National Records of Scotland, contains
+    Ordnance Survey data © Crown copyright and database right 2026'
+  notes: 46,363 output area population-weighted centroids, selected from OA master postcodes; EPSG:27700.
+    Original ZIP unchanged.
+  filename: scot_oa2022_population_weighted_centroids.zip
+- id: scot_census2022_geography_index
+  publisher: National Records of Scotland
+  title: '2022 Census Index: Postcode to OA and OA to higher areas'
+  url: https://www.nrscotland.gov.uk/media/utrbt5ze/census_2022_index.zip
+  landing_page: https://www.nrscotland.gov.uk/publications/2022-census-geography-products/
+  sha256: 0e4096a321cba2318dc5d2e35c197bf815ac333aebdf4cb0ddce82112b57a261
+  licence: Open Government Licence v3.0
+  notes: OA_TO_HIGHER_AREAS.csv supplies OA-to-2022 electoral ward and 2019 council membership. Household
+    counts come from the centroid file; geography census counts are independently perturbed.
+  filename: scot_census2022_geography_index.zip
+- id: nrs_census_2022_ward2022_tenure_bedrooms
+  publisher: National Records of Scotland
+  title: Scotland Census 2022 electoral-ward households by detailed tenure and bedrooms
+  url: https://www.scotlandscensus.gov.uk/webapi/downloadTable?type=csv
+  landing_page: https://www.scotlandscensus.gov.uk/webapi/home
+  sha256: 03d6157d34bd3df44e79ded1c03510954dee55e9e05d3c276b26ad463a565677
+  licence: Crown copyright; Open Government Licence v3.0
+  notes: 'Manual browser download: 1. Open https://www.scotlandscensus.gov.uk/webapi/home; continue as
+    a guest and read and accept the site terms yourself. No account is needed. 2. Select the Census 2022
+    Household dataset for larger geographies (database household2022ftb). 3. Add all 355 Electoral Ward
+    2022 values as rows, displaying ward codes rather than names (field SXV4__Census20223CHHv1__F_GEOGRAPHY__EW2022_FLD).
+    4. Add Number of bedrooms as columns (field SXV4__Census20223CHHv1__F_HOUSEHOLD__BEDROOMS_CAT_H_FLD;
+    all five values, One to Five or more, and Total). 5. Add Household tenure as wafers/layers (field
+    SXV4__Census20223CHHv1__F_HOUSEHOLD__TENURE_LANDLORD_CAT_H_FLD; all eight values and Total); keep
+    Counting: Households. 6. Retrieve the complete table, select Download CSV, and save the unmodified
+    export in the cache as census_2022_ward2022_tenure_bedrooms.csv. The downloadTable URL is session-dependent;
+    this script never accepts terms. The pinned export includes independently perturbed cells and margins;
+    future exports must match its SHA256 exactly.'
+  filename: census_2022_ward2022_tenure_bedrooms.csv
+  manual: true
+- id: nisra_tenure_7_data_zones
+  publisher: Northern Ireland Statistics and Research Agency (NISRA)
+  title: 'Census 2021 households by tenure: Data Zone 2021, seven categories'
+  url: https://build.nisra.gov.uk/en/custom/table.csv?d=HOUSEHOLD&v=DZ21&v=HH_TENURE_AGG7
+  landing_page: https://build.nisra.gov.uk/
+  sha256: 933303a4430b7b9075d55b7cace165a2b8114c9f1bde57e2895ed8a79a920c76
+  licence: Open Government Licence v3.0; Crown copyright (NISRA crown-copyright page)
+  notes: Seven tenure categories for all 3,780 Data Zones. Codes 5 and 6 are private rented; no bedroom
+    question was asked in Northern Ireland.
+  filename: tenure_7_data_zones.csv
+- id: nihe_lha_brma_membership_archive_2021
+  publisher: Northern Ireland Housing Executive (NIHE)
+  title: 'NIHE Current LHA rent levels: official 80 postcode-district BRMA definition, 2021 archive'
+  url: https://web.archive.org/web/20210802181148id_/https://www.nihe.gov.uk/Housing-Help/Local-Housing-Allowance/Current-LHA-rent-levels
+  landing_page: https://www.nihe.gov.uk/Housing-Help/Local-Housing-Allowance/Current-LHA-rent-levels
+  sha256: ba71c9e5d63f11614b6c80284a63b8e0e0d2ae77ca07e107c1190c8331624a81
+  licence: Copyright Northern Ireland Housing Executive; no express open reuse licence found in archived
+    page.
+  notes: Official-page archive on 2021-08-02. Geography membership only is used; no LHA rates extracted.
+    Live NIHE requests returned HTTP 403. Matches the 2024 membership list exactly.
+  filename: nihe_current_lha_archive_2021.html
+- id: nisra_census2021_postcode_households
+  publisher: Northern Ireland Statistics and Research Agency (NISRA)
+  title: Census 2021 person and household estimates for postcodes in Northern Ireland
+  url: https://www.nisra.gov.uk/system/files/statistics/census-2021-person-and-household-estimates-for-postcodes-in-northern-ireland.xlsx
+  landing_page: https://www.nisra.gov.uk/publications/census-2021-person-and-household-estimates-postcodes-northern-ireland
+  sha256: 61a77375ccf91b6504c6b267fec13d4cac61d15c9a4db5cc3f946530ee796ee0
+  licence: 'Open Government Licence v3.0; Source: NISRA, www.nisra.gov.uk. Excludes third-party LPS mapping
+    data.'
+  notes: Households as at 21 March 2021, published 29 February 2024; 5,591 postcode household counts suppressed.
+    Mixed-BRMA Data Zones use the published postcode-district averages. Worksheet district keys are stripped
+    before mapping, so BT1–BT9 suppressed postcodes receive their averages.
+  filename: nisra_census2021_postcode_households.xlsx
+- id: ons_onspd_feb2026_ni_page_0
+  publisher: Office for National Statistics
+  title: 'ONS Postcode Directory February 2026: Northern Ireland filtered API page 1'
+  url: https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/ONS_Postcode_Directory_(February_2026)_for_the_UK_(Hosted_Table)/FeatureServer/0/query?f=json&where=ctry25cd%3D%27N92000002%27&outFields=ObjectId%2Cpcds%2Coa21cd%2Clsoa21cd%2Cdoterm&returnGeometry=false&resultRecordCount=32000&orderByFields=ObjectId&resultType=standard
+  landing_page: https://geoportal.statistics.gov.uk/datasets/7efd49be24fb4ed8b21eedeb2540ea8c/about
+  sha256: 1d247eb01fdc973ee4cc00bed3b4d35fc33e6dd28daff4de7ee27af39dfdfdc5
+  licence: LPS Northern Ireland End User Licence (LPS Crown copyright), internal business use only; BT
+    records excluded from general OGL. https://www.ons.gov.uk/methodology/geography/licences
+  notes: Original API response; page 1 of 2. Filter country N92000002, ordered ObjectId, 32000-row standard
+    pages. Together 63179 current/terminated postcode records. Census weights joined exactly by postcode
+    identifier, never by coordinates. Cache privately; do not redistribute the Northern Ireland postcode
+    extract.
+  filename: ons_onspd_feb2026_ni_p0.json
+- id: ons_onspd_feb2026_ni_page_1
+  publisher: Office for National Statistics
+  title: 'ONS Postcode Directory February 2026: Northern Ireland filtered API page 2'
+  url: https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/ONS_Postcode_Directory_(February_2026)_for_the_UK_(Hosted_Table)/FeatureServer/0/query?f=json&where=ctry25cd%3D%27N92000002%27&outFields=ObjectId%2Cpcds%2Coa21cd%2Clsoa21cd%2Cdoterm&returnGeometry=false&resultRecordCount=32000&orderByFields=ObjectId&resultType=standard&resultOffset=32000
+  landing_page: https://geoportal.statistics.gov.uk/datasets/7efd49be24fb4ed8b21eedeb2540ea8c/about
+  sha256: 665eb8bd8b4abae1962b2bca3e8b7b36fcefb9a4d5c7fed2f459e4ae918bcf3d
+  licence: LPS Northern Ireland End User Licence (LPS Crown copyright), internal business use only; BT
+    records excluded from general OGL. https://www.ons.gov.uk/methodology/geography/licences
+  notes: Original API response; page 2 of 2. Filter country N92000002, ordered ObjectId, 32000-row standard
+    pages. Together 63179 current/terminated postcode records. Census weights joined exactly by postcode
+    identifier, never by coordinates. Cache privately; do not redistribute the Northern Ireland postcode
+    extract.
+  filename: ons_onspd_feb2026_ni_p1.json

From ae9b9b1028ca7b76b090276a4994137c2969a599 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 03:34:23 -0400
Subject: [PATCH 16/70] Address review: OGL-only Northern Ireland weights and
 stronger tests

- Northern Ireland: build each BRMA's private-rented households from
  NISRA's postcode-district household counts and NI's private-rented
  share, dropping the ONS Postcode Directory Northern Ireland records,
  whose LPS end user licence does not clearly allow publishing derived
  figures. BRMA shares move by 1.5 points on average.
- Move the household pick into brma.pick_household_brmas and test it.
- Test that every category uses its bedroom band, with spot values; give
  each region's BRMAs distinct names in the property tests; make the
  fail-closed test remove a cell rather than skip.
- Decode enum regions; assert Scotland's bedroom headers exactly.
- Docs: Cardiff Bay and Balloch overrides, centroid wording, what the
  validation correlates.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/brma.py         |  16 +++
 policyengine_uk_data/datasets/frs.py          |  23 +---
 .../storage/BRMA_DATA_SOURCES.md              |  18 +--
 .../brma_private_rented_households.csv        |  16 +--
 .../tests/test_brma_assignment.py             |  60 +++++++++-
 tools/brma_households/README.md               | 105 +++++++-----------
 tools/brma_households/build.py                |  88 +++++----------
 tools/brma_households/sources.yaml            |  26 -----
 8 files changed, 166 insertions(+), 186 deletions(-)

diff --git a/policyengine_uk_data/datasets/brma.py b/policyengine_uk_data/datasets/brma.py
index 18566c43d..9aec9bc62 100644
--- a/policyengine_uk_data/datasets/brma.py
+++ b/policyengine_uk_data/datasets/brma.py
@@ -71,6 +71,8 @@ def assign_brmas(
     """
     if weights is None:
         weights = load_brma_weights()
+    if hasattr(region, "decode_to_str"):  # EnumArray of region codes
+        region = region.decode_to_str()
     region = np.asarray(region).astype(str)
     lha_category = np.asarray(lha_category).astype(str)
     brma = np.empty(len(region), dtype=object)
@@ -88,3 +90,17 @@ def assign_brmas(
         cells = sorted(set(zip(region[missing], lha_category[missing])))
         raise ValueError(f"No BRMA weights for region × LHA category cells {cells}.")
     return brma
+
+
+def pick_household_brmas(
+    brma: np.ndarray, household_id: np.ndarray, rng: np.random.Generator
+) -> pd.Series:
+    """Give each household the BRMA of one of its benefit units, chosen at random.
+
+    ``brma`` and ``household_id`` are per benefit unit; returns a Series
+    indexed by household ID.
+    """
+    units = pd.DataFrame({"brma": brma, "household_id": household_id})
+    return units.groupby("household_id").brma.aggregate(
+        lambda x: x.sample(n=1, random_state=rng).iloc[0]
+    )
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index bbe037a95..96b4f4532 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -19,7 +19,7 @@
 from policyengine_uk.variables.household.income.employment_status import (
     EmploymentStatus,
 )
-from policyengine_uk_data.datasets.brma import assign_brmas
+from policyengine_uk_data.datasets.brma import assign_brmas, pick_household_brmas
 from policyengine_uk_data.datasets.disability_benefits import (
     add_disability_benefit_categories_from_reported_amounts,
     add_disability_benefit_flags_from_reported_amounts,
@@ -1418,23 +1418,12 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     brma_rng = np.random.default_rng(0)
     brma = assign_brmas(region, lha_category, brma_rng)
 
-    # Convert benunit-level BRMAs to household-level BRMAs (pick a random one)
-
-    df = pd.DataFrame(
-        {
-            "brma": brma,
-            "household_id": sim.populations["benunit"].household(
-                "household_id", sim.dataset.time_period
-            ),
-        }
+    household_brma = pick_household_brmas(
+        brma,
+        sim.populations["benunit"].household("household_id", dataset.time_period),
+        brma_rng,
     )
-
-    df = df.groupby("household_id").brma.aggregate(
-        lambda x: x.sample(n=1, random_state=brma_rng).iloc[0]
-    )
-    brmas = df[sim.calculate("household_id")].values
-
-    pe_household["brma"] = brmas
+    pe_household["brma"] = household_brma[sim.calculate("household_id")].values
 
     pe_person = add_disability_benefit_flags_from_reported_amounts(
         pe_person,
diff --git a/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md b/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
index 1a2547d45..2c01e058b 100644
--- a/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
+++ b/policyengine_uk_data/storage/BRMA_DATA_SOURCES.md
@@ -8,10 +8,12 @@ File: `brma_private_rented_households.csv`, with columns `region, brma, bedrooms
 
 | Nation | Households | BRMA geography |
 |---|---|---|
-| England | Census 2021 (ONS). Private-rented households per LSOA from TS054, split by bedrooms using the LSOA's mix for "private rented or lives rent free" (custom table `hh_tenure_5a` × `number_bedrooms_5a`). Rent-free households are 0.3-1.2% of that category. | VOA BRMA boundary layer, May 2020. Each LSOA is assigned by its population-weighted centroid. Five border LSOAs fall outside the English layer and take the Welsh BRMA that VOA's LHA lookup returns at their centroids. |
-| Wales | As for England. | Rent Officers Wales BRMA layer. 11 LSOAs fall in West Cheshire. |
-| Scotland | Scotland's Census 2022 (NRS), tenure by bedrooms by 2022 electoral ward. This is the finest geography at which the table is not suppressed. | Scottish Government BRMA polygons. Each ward is split by its output areas' household-weighted centroids, corrected by Rent Service Scotland's postcode-to-BRMA lookup (FOI 202300368850). |
-| Northern Ireland | NISRA Census 2021, tenure by Data Zone 2021. | NIHE postcode-district definition of BRMAs. Data Zones that span BRMAs are split by Census 2021 postcode household counts, with suppressed postcodes given NISRA's published district averages. Postcode-to-Data Zone links come from the ONS Postcode Directory, whose Northern Ireland records are under the LPS end user licence; only these BRMA aggregates are published. |
+| England | Census 2021 (ONS). Private-rented households per LSOA from TS054, split by bedrooms using the LSOA's mix for "private rented or lives rent free" (custom table `hh_tenure_5a` × `number_bedrooms_5a`). This is the only tenure × bedrooms split ONS releases for every LSOA; rent-free households are 0.3-1.2% of the category. | VOA BRMA boundary layer, May 2020. Each LSOA is assigned by its population-weighted centroid. Five border LSOAs fall outside the English layer and take the Welsh BRMA that VOA's LHA lookup returns near their centroids. |
+| Wales | As for England. | Rent Officers Wales BRMA layer (2012 boundaries, 2014 release). 11 LSOAs fall in West Cheshire. Cardiff Bay (W01002024) lies outside the layer and is assigned to Cardiff, as VOA's lookup returns for its postcodes. |
+| Scotland | Scotland's Census 2022 (NRS), tenure by bedrooms by 2022 electoral ward. This is the finest geography at which the table is not suppressed. | Scottish Government BRMA polygons. Each ward is split across BRMAs by its output areas' household counts, placing each output area by its population-weighted centroid. Rent Service Scotland's postcode-to-BRMA lookup (FOI 202300368850) moves 61 Balloch output areas to West Dunbartonshire: 59 on unique postcode matches and 2 via the council's own lookup. |
+| Northern Ireland | NISRA Census 2021 households by postcode district, times Northern Ireland's private-rented share (NISRA tenure by Data Zone, 17.2%). | NIHE's definition of each BRMA as a set of postcode districts. |
+
+Northern Ireland's private-rented households are not split by area within the nation. Linking NISRA's tenure areas to postcode districts would need the ONS Postcode Directory's Northern Ireland records, whose licence (LPS end user licence) does not clearly allow publishing derived figures. Using every BRMA's all-tenure households instead moves BRMA shares by 1.5 percentage points on average; for example, Belfast gets 20.1% of NI's private renters where the private-rented split would give 23.5%.
 
 Totals reconcile with the published national private-rented counts to within 0.03%:
 
@@ -20,7 +22,7 @@ Totals reconcile with the published national private-rented counts to within 0.0
 | England | 4,795,158 | 4,794,889 |
 | Wales | 228,601 | 228,642 |
 | Scotland | 323,001 | 323,042 |
-| Northern Ireland | 132,466 | 132,436 |
+| Northern Ireland | 132,449 | 132,436 |
 
 Small-area census counts are perturbed for disclosure control, so their sums differ slightly from national tables.
 
@@ -28,7 +30,9 @@ Small-area census counts are perturbed for disclosure control, so their sums dif
 
 The check below correlates, within each region, the BRMA shares of each candidate weight with DWP's count of Universal Credit households whose housing costs are assessed under LHA ("LHA covers rent" plus "does not cover rent"). The DWP figures are the mean of April 2019 to November 2020 (UC statistics supplementary table 3.2, February 2021).
 
-| Nation | Census (this file) | `lha_list_of_rents.csv.gz` row counts (previous weights) |
+To make the comparison, each BRMA's households from this file are summed across bedroom bands and region parts, then placed in policyengine-uk's single region for that BRMA.
+
+| Nation | This file | `lha_list_of_rents.csv.gz` row counts (previous weights) |
 |---|---|---|
 | England | 0.94 | 0.82 |
 | Wales | 0.95 | 0.23 |
@@ -42,4 +46,4 @@ Against the genuine list-of-rents category counts, the census bedroom bands corr
 
 ## Rebuilding
 
-`tools/brma_households/` (from the repository root) downloads every source, checks each one against its pinned sha256 and rebuilds this file byte for byte; see its `README.md`.
+`tools/brma_households/` (run from the repository root) downloads every source, checks each one against a pinned sha256 and rebuilds this file byte for byte; see its `README.md`.
diff --git a/policyengine_uk_data/storage/brma_private_rented_households.csv b/policyengine_uk_data/storage/brma_private_rented_households.csv
index d07a7fbc9..fd84570e3 100644
--- a/policyengine_uk_data/storage/brma_private_rented_households.csv
+++ b/policyengine_uk_data/storage/brma_private_rented_households.csv
@@ -251,14 +251,14 @@ LONDON,OUTER_WEST_LONDON,1,13522
 LONDON,OUTER_WEST_LONDON,2,19527
 LONDON,OUTER_WEST_LONDON,3,16016
 LONDON,OUTER_WEST_LONDON,4+,7743
-NORTHERN_IRELAND,BELFAST,all,31175
-NORTHERN_IRELAND,LOUGH_NEAGH_LOWER,all,15652
-NORTHERN_IRELAND,LOUGH_NEAGH_UPPER,all,23933
-NORTHERN_IRELAND,NORTH_NI,all,7169
-NORTHERN_IRELAND,NORTH_WEST_NI,all,12592
-NORTHERN_IRELAND,SOUTH_EAST_NI,all,19530
-NORTHERN_IRELAND,SOUTH_NI,all,12697
-NORTHERN_IRELAND,SOUTH_WEST_NI,all,9718
+NORTHERN_IRELAND,BELFAST,all,26599
+NORTHERN_IRELAND,LOUGH_NEAGH_LOWER,all,14176
+NORTHERN_IRELAND,LOUGH_NEAGH_UPPER,all,26520
+NORTHERN_IRELAND,NORTH_NI,all,6892
+NORTHERN_IRELAND,NORTH_WEST_NI,all,11924
+NORTHERN_IRELAND,SOUTH_EAST_NI,all,25164
+NORTHERN_IRELAND,SOUTH_NI,all,12101
+NORTHERN_IRELAND,SOUTH_WEST_NI,all,9073
 NORTH_EAST,DARLINGTON,1,1231
 NORTH_EAST,DARLINGTON,2,6387
 NORTH_EAST,DARLINGTON,3,4590
diff --git a/policyengine_uk_data/tests/test_brma_assignment.py b/policyengine_uk_data/tests/test_brma_assignment.py
index e6fa57aa1..8de49f999 100644
--- a/policyengine_uk_data/tests/test_brma_assignment.py
+++ b/policyengine_uk_data/tests/test_brma_assignment.py
@@ -7,12 +7,15 @@
 2. Determinism: the same generator state gives the same BRMAs.
 3. Fail closed: a benefit unit in a cell with no positive weight raises.
 4. Proportionality: draws converge on the cell's weights.
+
+``pick_household_brmas`` gives each household one of its benefit units' BRMAs,
+deterministically for a given generator state.
 """
 
 import numpy as np
 import pandas as pd
 import pytest
-from hypothesis import given, settings
+from hypothesis import assume, given, settings
 from hypothesis import strategies as st
 from policyengine_uk.variables.household.demographic.locations import BRMAName
 
@@ -21,6 +24,7 @@
     LHA_CATEGORY_BEDROOMS,
     assign_brmas,
     load_brma_weights,
+    pick_household_brmas,
 )
 
 REGIONS = [
@@ -105,6 +109,28 @@ def test_shared_and_one_bedroom_categories_use_the_same_weights(weights):
     pd.testing.assert_frame_equal(a.reset_index(drop=True), b.reset_index(drop=True))
 
 
+def test_each_category_uses_its_bedroom_band(households, weights):
+    banded = households[households.bedrooms != "all"]
+    for category, band in LHA_CATEGORY_BEDROOMS.items():
+        got = weights[weights.lha_category == category].set_index(["region", "brma"])
+        want = banded[banded.bedrooms == band].set_index(["region", "brma"])
+        assert (
+            got.weight.drop("NORTHERN_IRELAND")
+            .sort_index()
+            .equals(want.households.sort_index().rename("weight"))
+        ), category
+    # Spot values: Lothian's two-, three- and four-plus-bedroom private renters
+    # (Census 2022) and Belfast, the same for every category.
+    lothian = weights[(weights.region == "SCOTLAND") & (weights.brma == "LOTHIAN")]
+    assert lothian.set_index("lha_category").weight[["C", "D", "E"]].tolist() == [
+        30012,
+        10044,
+        4327,
+    ]
+    belfast = weights[weights.brma == "BELFAST"]
+    assert len(belfast) == 5 and belfast.weight.nunique() == 1
+
+
 def test_large_draw_matches_scottish_two_bedroom_weights(weights):
     cell = weights[(weights.region == "SCOTLAND") & (weights.lha_category == "C")]
     n = 400_000
@@ -124,12 +150,13 @@ def weights_and_units(draw):
     regions = draw(
         st.lists(st.sampled_from(REGIONS), min_size=1, max_size=3, unique=True)
     )
-    brmas = [f"B{i}" for i in range(draw(st.integers(1, 5)))]
+    count = draw(st.integers(1, 5))
+    # BRMA names are unique to a region, so a draw from the wrong region is caught.
     rows = [
-        (region, category, brma, draw(st.sampled_from([0, 0.5, 1, 7, 1000])))
+        (region, category, f"{region}_{i}", draw(st.sampled_from([0, 0.5, 1, 7, 1000])))
         for region in regions
         for category in CATEGORIES
-        for brma in brmas
+        for i in range(count)
     ]
     table = pd.DataFrame(rows, columns=["region", "lha_category", "brma", "weight"])
     table = table[table.weight > 0]
@@ -155,14 +182,35 @@ def test_draws_stay_on_positive_weights_and_are_deterministic(case):
 @given(weights_and_units(), st.sampled_from(REGIONS), st.sampled_from(CATEGORIES))
 def test_cells_without_positive_weight_fail_closed(case, region, category):
     table, *_ = case
-    if ((table.region == region) & (table.lha_category == category)).any():
-        return
+    # Remove the cell, so every example exercises a missing one.
+    table = table[~((table.region == region) & (table.lha_category == category))]
+    assume(len(table))
     with pytest.raises(ValueError, match="No BRMA weights"):
         assign_brmas(
             np.array([region]), np.array([category]), np.random.default_rng(0), table
         )
 
 
+@settings(max_examples=200, deadline=None, derandomize=True)
+@given(
+    st.lists(
+        st.tuples(st.integers(0, 6), st.sampled_from(["X", "Y", "Z"])),
+        min_size=1,
+        max_size=30,
+    ),
+    st.integers(0, 2**32 - 1),
+)
+def test_households_take_one_of_their_benefit_units_brmas(units, seed):
+    household_id = np.array([h for h, _ in units])
+    brma = np.array([b for _, b in units], dtype=object)
+    first = pick_household_brmas(brma, household_id, np.random.default_rng(seed))
+    second = pick_household_brmas(brma, household_id, np.random.default_rng(seed))
+    assert first.equals(second)
+    assert set(first.index) == set(household_id)
+    for household, chosen in first.items():
+        assert chosen in set(brma[household_id == household])
+
+
 def test_built_dataset_brmas_lie_in_their_regions_weights(enhanced_frs, weights):
     household = enhanced_frs.household
     supported = set(zip(weights.region, weights.brma))
diff --git a/tools/brma_households/README.md b/tools/brma_households/README.md
index 0a2462144..08206015a 100644
--- a/tools/brma_households/README.md
+++ b/tools/brma_households/README.md
@@ -1,79 +1,60 @@
 # BRMA private-rented households
 
-`policyengine_uk_data/storage/brma_private_rented_households.csv` (the default `--output`) contains 936 rows: `region,brma,bedrooms,households`.
-It counts private landlord/letting agency and other private-rented households by
-Broad Rental Market Area (BRMA), retaining each area's census region. Bedrooms are
-`1`, `2`, `3`, `4+`; Northern Ireland uses `all` because its census did not ask bedrooms.
-
-## Sources and method
-
-- **England, Census 2021:** ONS/Nomis TS054 LSOA private-rented totals multiplied
-  by each LSOA's ONS custom-API tenure5a code 3 bedroom proportions. ONS V4
-  population-weighted centroids intersect VOA May 2020 BRMA polygons; regions
-  come from ONS OA-to-LSOA and OA-to-region lookups. GML is parsed directly to
-  preserve nine incorrectly declared MultiPolygons; `make_valid` repairs seven
-  self-intersections. Five border LSOAs use documented official VOA lookup overrides:
-  E01014018 → Brecon and Radnor; E01022272–E01022275 → Monmouthshire.
-- **Wales, Census 2021:** the same TS054 × tenure5a method and Welsh ONS V4
-  centroids, with the archived Rent Officers Wales polygons (May 2012 geometry,
-  September 2014 release). Invalid geometry is repaired. Cardiff Bay W01002024
-  uses a fixed override established by 13 official postcode replies on 2 October 2026.
-  Eleven Welsh LSOAs in West Cheshire retain region `WALES`.
-- **Scotland, Census 2022:** NRS ward tenure × bedrooms, allocated using OA
-  household-weighted centroid shares in Scottish Government BRMA polygons.
-  Official postcode candidates supply nine missing assignments and correct 61
-  Balloch OAs; two ambiguous candidates use West Dunbartonshire council membership.
-  Four and five-or-more bedrooms combine into `4+`.
-- **Northern Ireland, Census 2021:** NISRA Data Zone tenure codes 5 and 6,
-  allocated using NIHE postcode-district BRMAs and ONSPD postcode-to-zone records.
-  Single-BRMA zones receive share 1; mixed zones use census postcode household
-  counts, substituting NISRA district averages after stripping district-key whitespace.
-
-BRMA cells are rounded to whole households with pandas; zero rows are dropped.
-No national balancing adjustment is made. Each of 89 inputs has its exact URL and cache filename in `sources.yaml`.
-The 72 ONS custom-API batches pin decoded JSON bytes with `content_sha256`;
-other inputs pin original bytes with `sha256`. Gzip and plain JSON are accepted
-without parsing or reserialising for verification. Live reply pages and postcode
-extracts used only to establish overrides are excluded, as are research checks.
-All eight overrides have dated evidence comments in `build.py`; exact unresolved
-area sets are asserted before assignment to guard against boundary changes.
+`build.py` writes `policyengine_uk_data/storage/brma_private_rented_households.csv` (936 rows: `region,brma,bedrooms,households`). The table counts private-rented households (private landlord or letting agency, plus other private rented) by Broad Rental Market Area (BRMA), keeping each area's census region. Bedrooms are `1`, `2`, `3` or `4+`; Northern Ireland uses `all` because its census did not ask about bedrooms. Sources, method and validation are summarised in `policyengine_uk_data/storage/BRMA_DATA_SOURCES.md`.
+
+## Method
+
+- **England, Census 2021.**
+  - Each LSOA's TS054 private-rented total is split by that LSOA's bedroom mix for ONS custom-API tenure category 3, "private rented or lives rent free".
+  - The LSOA's ONS population-weighted centroid places it in a VOA May 2020 BRMA polygon.
+  - Regions come from the ONS OA-to-LSOA and OA-to-region lookups.
+  - The GML is parsed directly so that nine MultiPolygons declared as Polygons keep all their parts. `make_valid` repairs seven self-intersections.
+- **Wales, Census 2021.**
+  - Same method as England, using the archived Rent Officers Wales polygons (May 2012 geometry, September 2014 release).
+  - Eleven Welsh LSOAs fall in West Cheshire and keep region `WALES`.
+- **Scotland, Census 2022.**
+  - NRS ward tenure × bedrooms tables are split across BRMAs by output-area household counts. Each output area is placed by its population-weighted centroid in the Scottish Government BRMA polygons.
+  - Official postcode candidates assign nine output areas whose centroids fall outside every polygon.
+  - Rent Service Scotland's postcode lookup moves 61 Balloch output areas to West Dunbartonshire: 59 on unique postcode matches and 2 via the council's own lookup.
+  - Four and five-or-more bedrooms are combined into `4+`.
+- **Northern Ireland, Census 2021.**
+  - NISRA households by postcode district are summed into NIHE's postcode-district BRMAs.
+  - The totals are scaled by Northern Ireland's private-rented share from NISRA tenure by Data Zone.
+  - A private-rented split by area would need ONS Postcode Directory Northern Ireland records, which are under the LPS end user licence, so it is not attempted.
+- **Overrides.** Eight area assignments come from official lookups rather than polygons: five English border LSOAs, Cardiff Bay and two Scottish output areas. Each carries a dated evidence comment in `build.py`. The build asserts the exact set of areas needing an override, so a boundary change fails loudly.
+- **Output.** Cells are rounded to whole households, and empty cells are dropped. There is no national balancing adjustment.
+
+## Inputs
+
+`sources.yaml` lists the 87 inputs with their URLs, landing pages, licences and cache file names.
+- The 72 ONS custom-API batches are pinned on the sha256 of the decoded JSON (`content_sha256`), because their gzip bytes vary between downloads.
+- Every other input is pinned on the sha256 of its original bytes.
 
 ## Rebuild
 
-Run from the repository root. The sole browser/session download is Scotland's
-ward export; follow its `manual: true` manifest instructions and place it in the
-cache. Scripts never log in or accept terms. Both commands verify all input pins;
-the builder uses only cached originals and checks the expected output digest.
+Run from the repository root. Scotland's ward table is the one manual download: follow its `manual: true` instructions in `sources.yaml` and place the file in the cache. The scripts never log in or accept terms. Both scripts check every input's hash, and `build.py` reads only cached originals and checks the output's digest.
 
 ```sh
 nice -n 10 uvx --with pandas --with geopandas --with shapely --with pyogrio --with openpyxl --with pyyaml python tools/brma_households/fetch.py .brma-cache
 nice -n 10 uvx --with pandas --with geopandas --with shapely --with pyogrio --with openpyxl --with pyyaml python tools/brma_households/build.py .brma-cache
 ```
 
-`fetch.py --only SOURCE_ID ...` supports selected download checks; `build.py
---output PATH` chooses the output destination. Missing files or changed hashes fail.
+- `fetch.py --only SOURCE_ID ...` fetches or checks only the listed sources.
+- `build.py --output PATH` writes the table elsewhere.
 
 ## Licences and limits
 
-Census and most geography inputs use the Open Government Licence; retain ONS,
-NRS, Scottish/Welsh Government and Ordnance Survey attributions in the manifest.
-NIHE's archived page has no stated open reuse licence; live VOA replies are not inputs.
-**NI postcode geography comes from ONSPD records under the LPS Northern Ireland
-End User Licence; only aggregated BRMA counts are published, never raw BT records.**
-Centroids and ward/postcode shares approximate household locations. England/Wales
-bedroom proportions include rent-free households; census cells are perturbed.
-Geography vintages differ and bedrooms describe accommodation, not LHA entitlement.
-The 34 postcode records without Data Zone codes do not contribute to mixed-zone shares.
+- **Licences.** Census and geography inputs are under the Open Government Licence. NIHE's archived BRMA page has no stated reuse licence; the build uses only its postcode-district lists.
+- **Locations are approximate.** Centroids and ward shares approximate where households live.
+- **Rent-free households.** England and Wales bedroom mixes include them.
+- **Disclosure control.** Census cells are perturbed.
+- **Geography vintages differ** between nations.
+- **Bedrooms describe the home,** not the household's LHA entitlement.
+- **Northern Ireland has one private-rented share for every BRMA.**
 
 ## Verification
 
-On 2 October 2026, all 89 inputs copied from retained originals passed `fetch.py`
-verification with no downloads, including all 72 decoded-content hashes.
-Fresh `fetch.py` downloads of eight originals covered all seven publishers;
-both an English and a Welsh custom-API batch passed decoded-content verification.
-The corrected full rebuild was byte-identical to the updated reference, 936 rows:
-`3ba714d4f5267656f19cd60785da379804420f633aa4c5745bdd0fba20473e72`.
-The NI key fix changes Belfast by +1 and South East NI by −1 household.
-Changed-content, missing-manual and override-set mismatch checks were rejected.
-Scotland's manual export was verified from its retained original, not re-exported.
-`nice -n 10` was invoked; the sandbox refused priority changes and Git index writes.
+On 2 October 2026:
+- All inputs passed `fetch.py` against a cache filled from retained originals.
+- Fresh downloads of eight originals, covering all seven publishers and including an English and a Welsh ONS batch, matched their pins.
+- The rebuild has 936 rows, sha256 `fd40dae019e5eefb8c976873f69c66f9b74a0747505326e8098b0ad99cc2ae1f`.
diff --git a/tools/brma_households/build.py b/tools/brma_households/build.py
index b5172a61e..115c8b317 100644
--- a/tools/brma_households/build.py
+++ b/tools/brma_households/build.py
@@ -346,6 +346,7 @@ def postcode(value):
             tenure = tenures.get(label)
         elif label == "Number of bedrooms":
             headers = row[1:]
+            assert {h for h in headers if h} == set(bands) | {"Total"}, headers
         elif tenure and re.fullmatch(r"S13\d{6}", label):
             for bedroom, value in zip(headers, row[1:], strict=True):
                 if bedroom in bands:
@@ -368,7 +369,17 @@ def postcode(value):
 
 
 def northern_ireland(cache):
-    """Allocate Data Zone private tenure totals by NIHE postcode-district BRMA."""
+    """Private-rented households by NIHE BRMA, from Open Government Licence data.
+
+    NIHE defines each BRMA as a set of postcode districts, and NISRA publishes
+    Census 2021 households for every postcode district (no suppression at that
+    level). Tenure is published only for census areas, and linking those to
+    postcode districts needs ONS Postcode Directory records whose Northern
+    Ireland licence (LPS end user licence) does not clearly allow publishing
+    derived figures. So each BRMA's private-rented households are its census
+    households times Northern Ireland's private-rented share (NISRA tenure by
+    Data Zone, summed).
+    """
     names = {
         "Belfast": "BELFAST",
         "Lough Neagh Lower": "LOUGH_NEAGH_LOWER",
@@ -395,61 +406,15 @@ def northern_ireland(cache):
                 assert district not in memberships
                 memberships[district] = names[name]
     assert len(memberships) == 80
-    pages = [
-        json.loads((cache / f"ons_onspd_feb2026_ni_p{i}.json").read_text())
-        for i in range(2)
-    ]
-    onspd = pd.DataFrame([f["attributes"] for page in pages for f in page["features"]])
-    assert len(onspd) == 63179 and onspd.pcds.is_unique
-    onspd["district"] = onspd.pcds.str.split().str[0]
-    onspd["brma"] = onspd.district.map(memberships)
-    obsolete = onspd[onspd.brma.isna()]
-    assert obsolete.doterm.notna().all() and obsolete.doterm.le("202103").all()
-    onspd = onspd[onspd.brma.notna()].copy()
-    census = pd.read_excel(
+    districts = pd.read_excel(
         cache / "nisra_census2021_postcode_households.xlsx",
-        sheet_name="Postcode",
-        header=5,
-    ).rename(columns={"Postcode": "pcds"})
-    census = census.merge(onspd, on="pcds", how="left", validate="one_to_one")
-    assert census.brma.notna().all()
-    census["households"] = pd.to_numeric(census.Households, errors="coerce")
-    suppressed = census.Households.eq("*")
-    assert (census.households.notna() | suppressed).all()
-    averages = pd.read_excel(
-        cache / "nisra_census2021_postcode_households.xlsx",
-        sheet_name="Suppressed averages",
+        sheet_name="Postcode district",
         header=5,
     )
-    averages = averages.set_index(averages["Postcode district"].str.strip())[
-        "Average households per supressed Postcode"
-    ]
-    census.loc[suppressed, "households"] = census.loc[suppressed, "district"].map(
-        averages
-    )
-    assert census.households.notna().all(), (
-        "Suppressed postcode missing district average"
-    )
-    # Whole-zone assignment follows every in-scope ONSPD postcode, not just census ones.
-    singleton = onspd.groupby("oa21cd").brma.agg(
-        lambda s: s.iloc[0] if s.nunique() == 1 else None
-    )
-    singleton = singleton.dropna().rename("brma").reset_index()
-    singleton["share"] = 1.0
-    mixed = census[census.oa21cd.notna() & ~census.oa21cd.isin(singleton.oa21cd)]
-    weights = mixed.groupby(["oa21cd", "brma"]).households.sum()
-    shares = (
-        (weights / weights.groupby(level=0).transform("sum"))
-        .rename("share")
-        .reset_index()
-    )
-    # The lane excludes zero-weight shares on mixed zones without suppressed counts.
-    shares = shares[shares.share > 0]
-    crosswalk = pd.concat([singleton, shares], ignore_index=True).rename(
-        columns={"oa21cd": "area_code"}
-    )
-    assert crosswalk.area_code.nunique() == 3780
-    assert crosswalk.groupby("area_code").share.sum().sub(1).abs().max() < 1e-9
+    districts["district"] = districts["Postcode district"].astype(str).str.strip()
+    districts = districts[districts.district.str.fullmatch(r"BT\d+")]
+    assert set(districts.district) == set(memberships)
+    households = districts.groupby(districts.district.map(memberships)).Households.sum()
     tenure = pd.read_csv(cache / "tenure_7_data_zones.csv")
     tenure.columns = [
         "area_code",
@@ -459,13 +424,16 @@ def northern_ireland(cache):
         "households",
     ]
     assert len(tenure) == 3780 * 7 and tenure.area_code.nunique() == 3780
-    private = (
-        tenure[tenure.tenure_code.isin([5, 6])].groupby("area_code").households.sum()
+    private_share = (
+        tenure.loc[tenure.tenure_code.isin([5, 6]), "households"].sum()
+        / tenure.households.sum()
+    )
+    out = (
+        (households * private_share)
+        .rename("households")
+        .rename_axis("brma")
+        .reset_index()
     )
-    assert set(private.index) == set(crosswalk.area_code)
-    out = crosswalk.merge(private.rename("households"), on="area_code")
-    out["households"] *= out.share
-    out = out.groupby("brma", as_index=False).households.sum()
     out["region"], out["bedrooms"] = "NORTHERN_IRELAND", "all"
     return out[["region", "brma", "bedrooms", "households"]]
 
@@ -498,7 +466,7 @@ def main():
         .encode("utf-8")
     )
     digest = hashlib.sha256(data).hexdigest()
-    expected = "3ba714d4f5267656f19cd60785da379804420f633aa4c5745bdd0fba20473e72"
+    expected = "fd40dae019e5eefb8c976873f69c66f9b74a0747505326e8098b0ad99cc2ae1f"
     if digest != expected:
         raise ValueError(f"Rebuilt table differs: expected {expected}, got {digest}")
     args.output.write_bytes(data)
diff --git a/tools/brma_households/sources.yaml b/tools/brma_households/sources.yaml
index 9135569da..39929e889 100644
--- a/tools/brma_households/sources.yaml
+++ b/tools/brma_households/sources.yaml
@@ -1035,29 +1035,3 @@
     Mixed-BRMA Data Zones use the published postcode-district averages. Worksheet district keys are stripped
     before mapping, so BT1–BT9 suppressed postcodes receive their averages.
   filename: nisra_census2021_postcode_households.xlsx
-- id: ons_onspd_feb2026_ni_page_0
-  publisher: Office for National Statistics
-  title: 'ONS Postcode Directory February 2026: Northern Ireland filtered API page 1'
-  url: https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/ONS_Postcode_Directory_(February_2026)_for_the_UK_(Hosted_Table)/FeatureServer/0/query?f=json&where=ctry25cd%3D%27N92000002%27&outFields=ObjectId%2Cpcds%2Coa21cd%2Clsoa21cd%2Cdoterm&returnGeometry=false&resultRecordCount=32000&orderByFields=ObjectId&resultType=standard
-  landing_page: https://geoportal.statistics.gov.uk/datasets/7efd49be24fb4ed8b21eedeb2540ea8c/about
-  sha256: 1d247eb01fdc973ee4cc00bed3b4d35fc33e6dd28daff4de7ee27af39dfdfdc5
-  licence: LPS Northern Ireland End User Licence (LPS Crown copyright), internal business use only; BT
-    records excluded from general OGL. https://www.ons.gov.uk/methodology/geography/licences
-  notes: Original API response; page 1 of 2. Filter country N92000002, ordered ObjectId, 32000-row standard
-    pages. Together 63179 current/terminated postcode records. Census weights joined exactly by postcode
-    identifier, never by coordinates. Cache privately; do not redistribute the Northern Ireland postcode
-    extract.
-  filename: ons_onspd_feb2026_ni_p0.json
-- id: ons_onspd_feb2026_ni_page_1
-  publisher: Office for National Statistics
-  title: 'ONS Postcode Directory February 2026: Northern Ireland filtered API page 2'
-  url: https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/ONS_Postcode_Directory_(February_2026)_for_the_UK_(Hosted_Table)/FeatureServer/0/query?f=json&where=ctry25cd%3D%27N92000002%27&outFields=ObjectId%2Cpcds%2Coa21cd%2Clsoa21cd%2Cdoterm&returnGeometry=false&resultRecordCount=32000&orderByFields=ObjectId&resultType=standard&resultOffset=32000
-  landing_page: https://geoportal.statistics.gov.uk/datasets/7efd49be24fb4ed8b21eedeb2540ea8c/about
-  sha256: 665eb8bd8b4abae1962b2bca3e8b7b36fcefb9a4d5c7fed2f459e4ae918bcf3d
-  licence: LPS Northern Ireland End User Licence (LPS Crown copyright), internal business use only; BT
-    records excluded from general OGL. https://www.ons.gov.uk/methodology/geography/licences
-  notes: Original API response; page 2 of 2. Filter country N92000002, ordered ObjectId, 32000-row standard
-    pages. Together 63179 current/terminated postcode records. Census weights joined exactly by postcode
-    identifier, never by coordinates. Cache privately; do not redistribute the Northern Ireland postcode
-    extract.
-  filename: ons_onspd_feb2026_ni_p1.json

From 29e1bd7a18a80942cc9014946d2dc8bea7e40b44 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 07:57:21 -0400
Subject: [PATCH 17/70] Describe the Northern Ireland postcode workbook as the
 build now uses it

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 tools/brma_households/sources.yaml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tools/brma_households/sources.yaml b/tools/brma_households/sources.yaml
index 39929e889..262d8a919 100644
--- a/tools/brma_households/sources.yaml
+++ b/tools/brma_households/sources.yaml
@@ -1031,7 +1031,7 @@
   sha256: 61a77375ccf91b6504c6b267fec13d4cac61d15c9a4db5cc3f946530ee796ee0
   licence: 'Open Government Licence v3.0; Source: NISRA, www.nisra.gov.uk. Excludes third-party LPS mapping
     data.'
-  notes: Households as at 21 March 2021, published 29 February 2024; 5,591 postcode household counts suppressed.
-    Mixed-BRMA Data Zones use the published postcode-district averages. Worksheet district keys are stripped
-    before mapping, so BT1–BT9 suppressed postcodes receive their averages.
+  notes: Households as at 21 March 2021, published 29 February 2024. Only the "Postcode district" sheet
+    is used; district totals are not suppressed. Each NIHE BRMA's households are the sum of its districts,
+    scaled by Northern Ireland's private-rented share from the Data Zone tenure table.
   filename: nisra_census2021_postcode_households.xlsx

From f8b21d31c3b165598b08ec9d3055919604784000 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 08:16:28 -0400
Subject: [PATCH 18/70] Draw each cloned household's Output Area from its own
 FRS region

clone_and_assign sampled a household's OA by population from anywhere in
its country, then wrote the OA's region, LA and constituency onto the
household without touching `region`. In release 1.57.4, 88.5% of English
households (86% of English weight) carry an OA in a different region from
the one the FRS recorded, and the median English LA draws 93% of its
weight from households surveyed in another region.

policyengine.py groups households by `constituency_code_oa` / `la_code_oa`
for its constituency and local-authority impacts and for area-scoped
simulations, so every English area was reporting an England-wide mix.

assign_random_geography now takes the households' regions and samples
within the region (Wales and Scotland are one region each; a household
with no region below the country falls back to its country). Collision
avoidance draws from the same region. A region with no OAs in the
crosswalk, a region that contradicts the country, or an unrecognised
region value raises. Called without regions it draws exactly as before:
seed 42 reproduces the released `oa_code` column.

Adds Hypothesis to the dev extras for the property tests.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/oa-region-constrained.fixed.md    |   1 +
 docs/oa_calibration_pipeline.md               |   5 +-
 .../calibration/clone_and_assign.py           |  15 +-
 .../calibration/oa_assignment.py              | 223 ++++++--
 .../tests/test_oa_region_assignment.py        | 491 ++++++++++++++++++
 pyproject.toml                                |   1 +
 uv.lock                                       |  73 ++-
 7 files changed, 755 insertions(+), 54 deletions(-)
 create mode 100644 changelog.d/oa-region-constrained.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_oa_region_assignment.py

diff --git a/changelog.d/oa-region-constrained.fixed.md b/changelog.d/oa-region-constrained.fixed.md
new file mode 100644
index 000000000..740395723
--- /dev/null
+++ b/changelog.d/oa-region-constrained.fixed.md
@@ -0,0 +1 @@
+Draw each cloned household's Output Area from its own FRS region rather than from anywhere in its country, so `region_code_oa`, `la_code_oa` and `constituency_code_oa` no longer contradict `region` (88.5% of English households in release 1.57.4 carried an OA from another region).
diff --git a/docs/oa_calibration_pipeline.md b/docs/oa_calibration_pipeline.md
index 8339931f0..be147e61c 100644
--- a/docs/oa_calibration_pipeline.md
+++ b/docs/oa_calibration_pipeline.md
@@ -23,7 +23,7 @@ Build the OA crosswalk and population-weighted assignment function.
 **Deliverables:**
 - `policyengine_uk_data/calibration/oa_crosswalk.py` — downloads/builds the OA → LSOA → MSOA → LA → constituency → region → country crosswalk
 - `policyengine_uk_data/storage/oa_crosswalk.csv.gz` — compressed crosswalk file
-- `policyengine_uk_data/calibration/oa_assignment.py` — assigns cloned records to OAs (population-weighted, country-constrained)
+- `policyengine_uk_data/calibration/oa_assignment.py` — assigns cloned records to OAs (population-weighted within the household's FRS region)
 - Tests validating crosswalk completeness and assignment correctness
 
 **Data sources:**
@@ -48,7 +48,8 @@ Clone each FRS household N times and assign each clone a different OA.
 **Key design:**
 - N=10 clones in production, N=2 in testing mode
 - Constituency collision avoidance: each clone gets a different constituency where possible
-- Country constraint preserved: English households → English OAs only
+- Region constraint: each clone's OA is drawn from the household's own FRS region (Wales and Scotland are one region each), so `region_code_oa`, `la_code_oa` and `constituency_code_oa` never contradict `region`. A household with no region below the country falls back to its country. No LA or constituency straddles a region, so every OA stays reachable
+- Collision avoidance draws from the same region; the smallest (North East) has 27 constituencies, more than the 10 production clones
 - Weights divided by N so population totals are preserved
 - Pure pandas/numpy operations — no simulation required, fast execution
 
diff --git a/policyengine_uk_data/calibration/clone_and_assign.py b/policyengine_uk_data/calibration/clone_and_assign.py
index 397567c1f..bc710ab61 100644
--- a/policyengine_uk_data/calibration/clone_and_assign.py
+++ b/policyengine_uk_data/calibration/clone_and_assign.py
@@ -1,9 +1,9 @@
 """Clone-and-assign: duplicate FRS households and assign OA geography.
 
 Each FRS household is cloned N times. Each clone gets a different
-Output Area (population-weighted, country-constrained, with
-constituency collision avoidance). Weights are divided by N so
-population totals are preserved.
+Output Area (population-weighted, drawn from the household's FRS
+region, with constituency collision avoidance). Weights are divided
+by N so population totals are preserved.
 
 This is the UK equivalent of policyengine-us-data's clone-and-assign
 approach (PRs #457, #531).
@@ -76,9 +76,11 @@ def clone_and_assign(
 ) -> UKSingleYearDataset:
     """Clone each FRS household N times and assign OA geography.
 
-    Each clone gets a population-weighted random Output Area,
-    constrained to its country, with constituency collision
-    avoidance across clones.
+    Each clone gets a population-weighted random Output Area in
+    its household's FRS region (Wales and Scotland are one region
+    each), with constituency collision avoidance across clones.
+    The OA's region, LA and constituency therefore never
+    contradict the household's ``region``.
 
     Household weights are divided by n_clones so aggregate
     population totals are preserved.
@@ -145,6 +147,7 @@ def clone_and_assign(
             n_clones=n_clones,
             seed=seed,
             crosswalk_path=crosswalk_path,
+            household_regions=hh["region"].values[has_oa],
         )
     else:
         geography = None
diff --git a/policyengine_uk_data/calibration/oa_assignment.py b/policyengine_uk_data/calibration/oa_assignment.py
index d57c10a49..15f3e59ee 100644
--- a/policyengine_uk_data/calibration/oa_assignment.py
+++ b/policyengine_uk_data/calibration/oa_assignment.py
@@ -3,8 +3,10 @@
 Assigns population-weighted random Output Areas to household
 clones, with constituency collision avoidance (each clone of
 the same household gets a different constituency where
-possible) and country constraints (English households get
-English OAs only, etc.).
+possible) and region constraints (a London household gets a
+London OA, a Welsh household a Welsh OA, and so on). The FRS
+region is the finest geography the survey records, so it bounds
+where a household's OA can be.
 
 Analogous to policyengine-us-data's clone_and_assign.py.
 """
@@ -39,6 +41,32 @@
     "NORTHERN_IRELAND": "Northern Ireland",
 }
 
+# FRS regions (the policyengine-uk ``region`` enum) to the region codes
+# the OA crosswalk carries: ONS RGN codes in England, and the
+# crosswalk's country-level pseudo-codes elsewhere.
+FRS_REGION_TO_CODE = {
+    "NORTH_EAST": "E12000001",
+    "NORTH_WEST": "E12000002",
+    "YORKSHIRE": "E12000003",
+    "EAST_MIDLANDS": "E12000004",
+    "WEST_MIDLANDS": "E12000005",
+    "EAST_OF_ENGLAND": "E12000006",
+    "LONDON": "E12000007",
+    "SOUTH_EAST": "E12000008",
+    "SOUTH_WEST": "E12000009",
+    "WALES": "W99999999",
+    "SCOTLAND": "S99999999",
+    "NORTHERN_IRELAND": "N99999999",
+}
+_REGION_CODE_PREFIX_TO_COUNTRY = {
+    "E": "England",
+    "W": "Wales",
+    "S": "Scotland",
+    "N": "Northern Ireland",
+}
+# Region values that carry no location below the country.
+_UNKNOWN_REGIONS = {"", "UNKNOWN"}
+
 
 @dataclass
 class GeographyAssignment:
@@ -60,6 +88,54 @@ class GeographyAssignment:
     n_clones: int
 
 
+def _distribution(subset: pd.DataFrame) -> Dict[str, np.ndarray]:
+    """Population-weighted sampling frame for a set of OAs."""
+    pop = subset["population"].values.astype(np.float64)
+    total = pop.sum()
+    if total == 0:
+        # Uniform if no population data
+        probs = np.ones(len(subset)) / len(subset)
+    else:
+        probs = pop / total
+
+    return {
+        "oa_codes": subset["oa_code"].values,
+        "constituencies": subset["constituency_code"].values,
+        "lsoa_codes": subset["lsoa_code"].values,
+        "msoa_codes": subset["msoa_code"].values,
+        "la_codes": subset["la_code"].values,
+        "region_codes": subset["region_code"].values,
+        "probs": probs,
+    }
+
+
+def _load_numeric_crosswalk(crosswalk_path: Optional[str]) -> pd.DataFrame:
+    path = Path(crosswalk_path) if crosswalk_path else None
+    xw = load_oa_crosswalk(path)
+    # Ensure population is numeric
+    xw["population"] = pd.to_numeric(xw["population"], errors="coerce").fillna(0)
+    return xw
+
+
+@lru_cache(maxsize=1)
+def _load_region_distributions(
+    crosswalk_path: Optional[str] = None,
+) -> Dict[str, Dict]:
+    """Load OA distributions grouped by crosswalk region code.
+
+    Returns:
+        Dict mapping region code (e.g. ``E12000007``, ``W99999999``)
+        to the same sampling frame as ``_load_country_distributions``,
+        with probabilities summing to 1 within the region.
+    """
+    xw = _load_numeric_crosswalk(crosswalk_path)
+    region_codes = xw["region_code"].fillna("").astype(str).str.strip()
+    return {
+        code: _distribution(xw[region_codes == code])
+        for code in sorted(set(region_codes) - {""})
+    }
+
+
 @lru_cache(maxsize=1)
 def _load_country_distributions(
     crosswalk_path: Optional[str] = None,
@@ -75,11 +151,7 @@ def _load_country_distributions(
         - crosswalk_idx: np.ndarray of indices into the
           full crosswalk DataFrame
     """
-    path = Path(crosswalk_path) if crosswalk_path else None
-    xw = load_oa_crosswalk(path)
-
-    # Ensure population is numeric
-    xw["population"] = pd.to_numeric(xw["population"], errors="coerce").fillna(0)
+    xw = _load_numeric_crosswalk(crosswalk_path)
 
     distributions = {}
     for country_name in [
@@ -95,23 +167,7 @@ def _load_country_distributions(
             logger.warning(f"No OAs found for {country_name}")
             continue
 
-        pop = subset["population"].values.astype(np.float64)
-        total = pop.sum()
-        if total == 0:
-            # Uniform if no population data
-            probs = np.ones(len(subset)) / len(subset)
-        else:
-            probs = pop / total
-
-        distributions[country_name] = {
-            "oa_codes": subset["oa_code"].values,
-            "constituencies": subset["constituency_code"].values,
-            "lsoa_codes": subset["lsoa_code"].values,
-            "msoa_codes": subset["msoa_code"].values,
-            "la_codes": subset["la_code"].values,
-            "region_codes": subset["region_code"].values,
-            "probs": probs,
-        }
+        distributions[country_name] = _distribution(subset)
 
     return distributions
 
@@ -133,22 +189,46 @@ def _normalise_country(value) -> Optional[str]:
     return COUNTRY_NAME_MAP.get(text.upper().replace("-", "_").replace(" ", "_"))
 
 
+def _normalise_region(value) -> Optional[str]:
+    """Map an FRS region name or crosswalk region code to a region code.
+
+    Returns None when the region says nothing below the country
+    (missing or ``UNKNOWN``). Raises on anything unrecognised.
+    """
+    if isinstance(value, bytes):
+        value = value.decode()
+    if value is None or (not isinstance(value, str) and pd.isna(value)):
+        return None
+    text = str(value).strip().upper()
+    if text in _UNKNOWN_REGIONS:
+        return None
+    if text in FRS_REGION_TO_CODE:
+        return FRS_REGION_TO_CODE[text]
+    if text in FRS_REGION_TO_CODE.values():
+        return text
+    raise ValueError(f"Unrecognised household region value: {value!r}")
+
+
 def assign_random_geography(
     household_countries: np.ndarray,
     n_clones: int = 10,
     seed: int = 42,
     crosswalk_path: Optional[str] = None,
+    household_regions: Optional[np.ndarray] = None,
 ) -> GeographyAssignment:
     """Assign random OA geography to cloned FRS records.
 
     Each of n_records * n_clones total records gets a random
-    Output Area sampled from the country-specific
-    population-weighted distribution. LA, constituency,
-    region are derived from the OA.
+    Output Area sampled from the population-weighted
+    distribution of its household's region (or of its country,
+    when no region is given). LA, constituency and region are
+    derived from the OA, so they always agree with the
+    household's region.
 
     Constituency collision avoidance: each clone of the same
     household gets a different constituency where possible
-    (up to 50 retry iterations).
+    (up to 50 retry iterations), drawing only from the
+    household's own region.
 
     Args:
         household_countries: Array of length n_records with
@@ -156,6 +236,11 @@ def assign_random_geography(
         n_clones: Number of clones per household.
         seed: Random seed for reproducibility.
         crosswalk_path: Override crosswalk file path.
+        household_regions: Optional array of length n_records
+            with FRS region names (e.g. ``LONDON``) or crosswalk
+            region codes (e.g. ``E12000007``). Households with a
+            region draw OAs from that region only; missing or
+            ``UNKNOWN`` regions fall back to the country.
 
     Returns:
         GeographyAssignment with arrays of length
@@ -178,9 +263,69 @@ def assign_random_geography(
             "Unrecognised household country values: " + ", ".join(bad_values)
         )
 
-    distributions = _load_country_distributions(
-        str(crosswalk_path) if crosswalk_path else None
+    if household_regions is None:
+        regions = np.full(n_records, None, dtype=object)
+    else:
+        if len(household_regions) != n_records:
+            raise ValueError(
+                f"household_regions has {len(household_regions)} values, "
+                f"expected {n_records}."
+            )
+        regions = np.array(
+            [_normalise_region(value) for value in household_regions],
+            dtype=object,
+        )
+        mismatched = sorted(
+            {
+                f"{region} in {country}"
+                for region, country in zip(regions, countries)
+                if region is not None
+                and _REGION_CODE_PREFIX_TO_COUNTRY[region[0]] != country
+            }
+        )
+        if mismatched:
+            raise ValueError(
+                "Household regions disagree with household countries: "
+                + ", ".join(mismatched)
+            )
+        n_unknown = sum(region is None for region in regions)
+        if n_unknown:
+            logger.warning(
+                "%d households have no region below the country; "
+                "sampling their OAs country-wide",
+                n_unknown,
+            )
+
+    # Sampling stratum per household: its region code where known,
+    # otherwise its country name.
+    strata = np.array(
+        [
+            region if region is not None else country
+            for region, country in zip(regions, countries)
+        ],
+        dtype=object,
+    )
+    path_key = str(crosswalk_path) if crosswalk_path else None
+    country_distributions = _load_country_distributions(path_key)
+    region_distributions = (
+        _load_region_distributions(path_key) if household_regions is not None else {}
     )
+    distributions = {}
+    missing_distributions = []
+    for stratum in sorted(set(strata)):
+        is_country = stratum in COUNTRY_NAME_MAP.values()
+        source = country_distributions if is_country else region_distributions
+        if stratum in source:
+            distributions[stratum] = source[stratum]
+        elif is_country:
+            missing_distributions.append(stratum)
+        else:
+            country = _REGION_CODE_PREFIX_TO_COUNTRY[stratum[0]]
+            missing_distributions.append(f"{stratum} ({country})")
+    if missing_distributions:
+        raise ValueError(
+            "No OA distribution available for: " + ", ".join(missing_distributions)
+        )
 
     rng = np.random.default_rng(seed)
 
@@ -193,29 +338,17 @@ def assign_random_geography(
     region_codes = np.empty(n_total, dtype=object)
     country_names = np.empty(n_total, dtype=object)
 
-    # Group households by country for efficient sampling
-    unique_countries = np.unique(countries)
-
     # Track assigned constituencies per record for
     # collision avoidance
     assigned_const = np.full((n_clones, n_records), "", dtype=object)
-    missing_distributions = sorted(set(unique_countries) - set(distributions))
-    if missing_distributions:
-        raise ValueError(
-            "No OA distribution available for: " + ", ".join(missing_distributions)
-        )
 
     for clone_idx in range(n_clones):
         start = clone_idx * n_records
 
-        for country_name in unique_countries:
-            dist = distributions[country_name]
-            hh_mask = countries == country_name
+        for stratum, dist in distributions.items():
+            hh_mask = strata == stratum
             n_hh = hh_mask.sum()
 
-            if n_hh == 0:
-                continue
-
             # Sample OAs
             indices = rng.choice(
                 len(dist["oa_codes"]),
@@ -260,7 +393,7 @@ def assign_random_geography(
             msoa_codes[store_idx] = dist["msoa_codes"][indices]
             la_codes[store_idx] = dist["la_codes"][indices]
             region_codes[store_idx] = dist["region_codes"][indices]
-            country_names[store_idx] = country_name
+            country_names[store_idx] = countries[positions]
 
             assigned_const[clone_idx, positions] = sampled_const
 
diff --git a/policyengine_uk_data/tests/test_oa_region_assignment.py b/policyengine_uk_data/tests/test_oa_region_assignment.py
new file mode 100644
index 000000000..7cf39ff05
--- /dev/null
+++ b/policyengine_uk_data/tests/test_oa_region_assignment.py
@@ -0,0 +1,491 @@
+"""Invariants for region-constrained Output Area assignment.
+
+A household's OA is drawn from its own FRS region, so the OA's region,
+LA and constituency can never contradict ``household.region``. The
+property tests state that for every input; the example tests pin the
+edge cases and the real crosswalk's shape.
+"""
+
+import itertools
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+from policyengine_uk.data import UKSingleYearDataset
+
+from policyengine_uk_data.calibration.clone_and_assign import clone_and_assign
+from policyengine_uk_data.calibration.oa_assignment import (
+    FRS_REGION_TO_CODE,
+    assign_random_geography,
+)
+from policyengine_uk_data.calibration.oa_crosswalk import (
+    CROSSWALK_PATH,
+    load_oa_crosswalk,
+)
+from policyengine_uk_data.storage import STORAGE_FOLDER
+
+hypothesis = pytest.importorskip("hypothesis")
+from hypothesis import given, settings  # noqa: E402
+from hypothesis import strategies as st  # noqa: E402
+
+REGION_TO_COUNTRY = {
+    region: {"E": "England", "W": "Wales", "S": "Scotland", "N": "Northern Ireland"}[
+        code[0]
+    ]
+    for region, code in FRS_REGION_TO_CODE.items()
+}
+COUNTRY_TO_FRS_CODE = {"England": 1, "Wales": 2, "Scotland": 3, "Northern Ireland": 4}
+GEOGRAPHY_FIELDS = {
+    "lsoa_code": "lsoa_code",
+    "msoa_code": "msoa_code",
+    "la_code": "la_code",
+    "constituency_code": "constituency_code",
+    "region_code": "region_code",
+}
+_file_counter = itertools.count()
+
+
+def _crosswalk_rows(region: str, constituency_populations: list[list[int]]) -> list:
+    """OAs for one region: one LA per constituency, one OA per population."""
+    code = FRS_REGION_TO_CODE[region]
+    rows = []
+    for c, populations in enumerate(constituency_populations):
+        for o, population in enumerate(populations):
+            rows.append(
+                {
+                    "oa_code": f"{code[0]}00{code[-2:]}{c:02d}{o:02d}",
+                    "lsoa_code": f"{code[0]}01{code[-2:]}{c:02d}{o:02d}",
+                    "msoa_code": f"{code[0]}02{code[-2:]}{c:02d}",
+                    "la_code": f"{code[0]}06{code[-2:]}{c:02d}",
+                    "constituency_code": f"{code[0]}14{code[-2:]}{c:02d}",
+                    "region_code": code,
+                    "country": REGION_TO_COUNTRY[region],
+                    "population": population,
+                }
+            )
+    return rows
+
+
+def _write_crosswalk(directory: Path, rows: list) -> tuple[pd.DataFrame, str]:
+    """Write to a fresh path: the loaders cache on the path string."""
+    frame = pd.DataFrame(rows)
+    path = Path(directory) / f"crosswalk_{next(_file_counter)}.csv.gz"
+    frame.to_csv(path, index=False, compression="gzip")
+    return frame, str(path)
+
+
+def _countries(regions: list[str]) -> np.ndarray:
+    return np.array(
+        [COUNTRY_TO_FRS_CODE[REGION_TO_COUNTRY.get(r, "England")] for r in regions]
+    )
+
+
+def _toy_dataset(regions: list[str], weights: list[float]) -> UKSingleYearDataset:
+    ids = np.arange(1, len(regions) + 1)
+    return UKSingleYearDataset(
+        person=pd.DataFrame(
+            {
+                "person_id": ids * 1000 + 1,
+                "person_household_id": ids,
+                "person_benunit_id": ids * 100 + 1,
+                "age": 40,
+            }
+        ),
+        benunit=pd.DataFrame({"benunit_id": ids * 100 + 1}),
+        household=pd.DataFrame(
+            {"household_id": ids, "household_weight": weights, "region": regions}
+        ),
+        fiscal_year=2024,
+    )
+
+
+@st.composite
+def crosswalk_and_regions(draw, allow_unassignable: bool = False):
+    """A synthetic crosswalk plus household regions it can serve."""
+    crosswalk_regions = draw(
+        st.lists(
+            st.sampled_from(sorted(FRS_REGION_TO_CODE)),
+            min_size=1,
+            max_size=6,
+            unique=True,
+        )
+    )
+    rows = []
+    for region in crosswalk_regions:
+        rows += _crosswalk_rows(
+            region,
+            draw(
+                st.lists(
+                    st.lists(st.integers(0, 500), min_size=1, max_size=4),
+                    min_size=1,
+                    max_size=4,
+                )
+            ),
+        )
+    choices = list(crosswalk_regions)
+    if any(REGION_TO_COUNTRY[r] == "England" for r in crosswalk_regions):
+        choices.append("UNKNOWN")
+    if allow_unassignable and "NORTHERN_IRELAND" not in crosswalk_regions:
+        choices.append("NORTHERN_IRELAND")
+    regions = draw(st.lists(st.sampled_from(choices), min_size=1, max_size=12))
+    return rows, regions
+
+
+def _as_tuple(geography) -> tuple:
+    return tuple(
+        tuple(getattr(geography, field))
+        for field in ["oa_code", "country", *GEOGRAPHY_FIELDS]
+    )
+
+
+class TestRegionConstraintProperties:
+    @given(
+        case=crosswalk_and_regions(),
+        n_clones=st.integers(1, 4),
+        seed=st.integers(0, 2**32 - 1),
+    )
+    @settings(max_examples=60, deadline=None)
+    def test_assignment_invariants(self, case, n_clones, seed):
+        rows, regions = case
+        with tempfile.TemporaryDirectory() as directory:
+            crosswalk, path = _write_crosswalk(directory, rows)
+            kwargs = dict(
+                household_countries=_countries(regions),
+                household_regions=np.array(regions, dtype=object),
+                n_clones=n_clones,
+                seed=seed,
+                crosswalk_path=path,
+            )
+            geography = assign_random_geography(**kwargs)
+            repeat = assign_random_geography(**kwargs)
+
+        # Determinism: same inputs and seed, same assignment.
+        assert _as_tuple(geography) == _as_tuple(repeat)
+
+        n = len(regions)
+        assert len(geography.oa_code) == n * n_clones
+        by_oa = crosswalk.set_index("oa_code")
+        region_population = crosswalk.groupby("region_code")["population"].sum()
+        england_population = crosswalk.loc[
+            crosswalk["country"] == "England", "population"
+        ].sum()
+        for i, oa in enumerate(geography.oa_code):
+            region = regions[i % n]
+            row = by_oa.loc[oa]
+            # The OA's region is the household's region; an unknown region
+            # still keeps the household in its country.
+            if region == "UNKNOWN":
+                assert row["country"] == "England"
+                stratum_population = england_population
+            else:
+                assert geography.region_code[i] == FRS_REGION_TO_CODE[region]
+                stratum_population = region_population[FRS_REGION_TO_CODE[region]]
+            assert geography.country[i] == REGION_TO_COUNTRY.get(region, "England")
+            assert row["country"] == geography.country[i]
+            # Every other code is the crosswalk's own row for that OA.
+            for field, column in GEOGRAPHY_FIELDS.items():
+                assert getattr(geography, field)[i] == row[column]
+            # Unpopulated OAs are never drawn while the stratum has people.
+            if stratum_population > 0:
+                assert row["population"] > 0
+
+    @given(
+        case=crosswalk_and_regions(allow_unassignable=True),
+        weights=st.lists(
+            st.floats(0, 5_000, allow_nan=False), min_size=12, max_size=12
+        ),
+        n_clones=st.integers(1, 4),
+        seed=st.integers(0, 2**32 - 1),
+    )
+    @settings(max_examples=40, deadline=None)
+    def test_clone_and_assign_invariants(self, case, weights, n_clones, seed):
+        rows, regions = case
+        dataset = _toy_dataset(regions, weights[: len(regions)])
+        with tempfile.TemporaryDirectory() as directory:
+            _, path = _write_crosswalk(directory, rows)
+            kwargs = dict(n_clones=n_clones, seed=seed, crosswalk_path=path)
+            household = clone_and_assign(dataset, **kwargs).household
+            repeat = clone_and_assign(dataset, **kwargs).household
+
+        pd.testing.assert_frame_equal(household, repeat)
+
+        original = dataset.household
+        # Weights are preserved in total and for every source household.
+        np.testing.assert_allclose(
+            household["household_weight"].sum(),
+            original["household_weight"].sum(),
+            rtol=1e-12,
+            atol=1e-9,
+        )
+        np.testing.assert_allclose(
+            household.groupby("source_household_id")["household_weight"].sum().values,
+            original.set_index("household_id")["household_weight"].values,
+            rtol=1e-12,
+            atol=1e-9,
+        )
+        # Cloning never rewrites the FRS region.
+        assert household["region"].tolist() == original["region"].tolist() * n_clones
+
+        expected = household["region"].map(FRS_REGION_TO_CODE)
+        known = household["region"].isin(FRS_REGION_TO_CODE) & (
+            household["oa_code"] != ""
+        )
+        assert (household.loc[known, "region_code_oa"] == expected[known]).all()
+        unknown = household["region"] == "UNKNOWN"
+        assert household.loc[unknown, "region_code_oa"].str.startswith("E12").all()
+        # A region the crosswalk does not cover gets no geography at all.
+        covered = {row["region_code"] for row in rows}
+        uncovered = ~unknown & ~expected.isin(covered)
+        for column in ["oa_code", "la_code_oa", "constituency_code_oa"]:
+            assert (household.loc[uncovered, column] == "").all()
+            assert (household.loc[~uncovered, column] != "").all()
+
+    @given(
+        case=crosswalk_and_regions(),
+        n_clones=st.integers(1, 4),
+        seed=st.integers(0, 2**32 - 1),
+    )
+    @settings(max_examples=40, deadline=None)
+    def test_matches_country_sampling_when_region_is_the_country(
+        self, case, n_clones, seed
+    ):
+        """Differential: with one region per country the two paths agree."""
+        _, regions = case
+        one_region_each = ["LONDON", "WALES", "SCOTLAND", "NORTHERN_IRELAND"]
+        regions = [r for r in regions if r in one_region_each] or ["LONDON"]
+        rows = []
+        for region in one_region_each:
+            rows += _crosswalk_rows(region, [[30, 10, 0], [5, 55], [20]])
+        with tempfile.TemporaryDirectory() as directory:
+            _, path = _write_crosswalk(directory, rows)
+            kwargs = dict(
+                household_countries=_countries(regions),
+                n_clones=n_clones,
+                seed=seed,
+                crosswalk_path=path,
+            )
+            by_country = assign_random_geography(**kwargs)
+            by_region = assign_random_geography(
+                household_regions=np.array(regions, dtype=object), **kwargs
+            )
+        assert _as_tuple(by_country) == _as_tuple(by_region)
+
+
+class TestRegionConstraintExamples:
+    @pytest.fixture(scope="class")
+    def two_region_crosswalk(self, tmp_path_factory):
+        rows = _crosswalk_rows("LONDON", [[100, 300]]) + _crosswalk_rows(
+            "NORTH_EAST", [[50_000], [50_000]]
+        )
+        return _write_crosswalk(tmp_path_factory.mktemp("two_region"), rows)
+
+    def test_sampling_is_population_weighted_within_the_region(
+        self, two_region_crosswalk
+    ):
+        """London's two OAs split 1:3 however large the North East is."""
+        crosswalk, path = two_region_crosswalk
+        n = 20_000
+        geography = assign_random_geography(
+            household_countries=np.ones(n, dtype=int),
+            household_regions=np.array(["LONDON"] * n, dtype=object),
+            n_clones=1,
+            seed=0,
+            crosswalk_path=path,
+        )
+        assert set(geography.region_code) == {"E12000007"}
+        larger = crosswalk.loc[crosswalk["population"] == 300, "oa_code"].iloc[0]
+        share = (geography.oa_code == larger).mean()
+        # Binomial(20,000, 0.75): four standard errors is 0.012.
+        assert abs(share - 0.75) < 0.012
+
+    def test_country_sampling_without_regions_is_unchanged(self, two_region_crosswalk):
+        """Callers that know only the country still sample country-wide."""
+        _, path = two_region_crosswalk
+        geography = assign_random_geography(
+            household_countries=np.ones(2_000, dtype=int),
+            n_clones=1,
+            seed=0,
+            crosswalk_path=path,
+        )
+        assert (geography.region_code == "E12000001").mean() > 0.9
+
+    def test_unknown_region_falls_back_to_the_country(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        for unknown in ["UNKNOWN", "", None, np.nan]:
+            geography = assign_random_geography(
+                household_countries=np.ones(500, dtype=int),
+                household_regions=np.array([unknown] * 500, dtype=object),
+                n_clones=1,
+                seed=0,
+                crosswalk_path=path,
+            )
+            assert set(geography.region_code) == {"E12000001", "E12000007"}
+
+    def test_region_names_codes_and_bytes_are_equivalent(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        results = [
+            assign_random_geography(
+                household_countries=np.ones(50, dtype=int),
+                household_regions=np.array([value] * 50, dtype=object),
+                n_clones=2,
+                seed=3,
+                crosswalk_path=path,
+            )
+            for value in ["LONDON", "E12000007", b"LONDON", " london "]
+        ]
+        assert len({_as_tuple(result) for result in results}) == 1
+
+    def test_region_without_oas_raises(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        with pytest.raises(ValueError, match=r"E12000009 \(England\)"):
+            assign_random_geography(
+                household_countries=np.array([1]),
+                household_regions=np.array(["SOUTH_WEST"], dtype=object),
+                n_clones=1,
+                crosswalk_path=path,
+            )
+
+    def test_region_in_the_wrong_country_raises(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        with pytest.raises(ValueError, match="disagree"):
+            assign_random_geography(
+                household_countries=np.array([2]),
+                household_regions=np.array(["LONDON"], dtype=object),
+                n_clones=1,
+                crosswalk_path=path,
+            )
+
+    def test_unrecognised_region_raises(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        with pytest.raises(ValueError, match="Unrecognised household region"):
+            assign_random_geography(
+                household_countries=np.array([1]),
+                household_regions=np.array(["MERCIA"], dtype=object),
+                n_clones=1,
+                crosswalk_path=path,
+            )
+
+    def test_region_length_mismatch_raises(self, two_region_crosswalk):
+        _, path = two_region_crosswalk
+        with pytest.raises(ValueError, match="expected 2"):
+            assign_random_geography(
+                household_countries=np.array([1, 1]),
+                household_regions=np.array(["LONDON"], dtype=object),
+                n_clones=1,
+                crosswalk_path=path,
+            )
+
+    def test_clones_take_different_constituencies_within_the_region(self, tmp_path):
+        """Collision avoidance still works when the pool is one region."""
+        rows = _crosswalk_rows("NORTH_EAST", [[100]] * 12) + _crosswalk_rows(
+            "LONDON", [[100]] * 40
+        )
+        _, path = _write_crosswalk(tmp_path, rows)
+        n, n_clones = 200, 10
+        geography = assign_random_geography(
+            household_countries=np.ones(n, dtype=int),
+            household_regions=np.array(["NORTH_EAST"] * n, dtype=object),
+            n_clones=n_clones,
+            seed=42,
+            crosswalk_path=path,
+        )
+        constituencies = geography.constituency_code.reshape(n_clones, n)
+        assert set(geography.region_code) == {"E12000001"}
+        assert all(len(set(constituencies[:, i])) == n_clones for i in range(n))
+
+    def test_single_constituency_region_terminates(self, tmp_path):
+        """Unavoidable collisions exhaust the retries and still assign."""
+        _, path = _write_crosswalk(tmp_path, _crosswalk_rows("WALES", [[10, 20]]))
+        geography = assign_random_geography(
+            household_countries=np.full(5, 2),
+            household_regions=np.array(["WALES"] * 5, dtype=object),
+            n_clones=3,
+            seed=1,
+            crosswalk_path=path,
+        )
+        assert len(set(geography.constituency_code)) == 1
+
+
+@pytest.fixture(scope="module")
+def real_crosswalk() -> pd.DataFrame:
+    if not CROSSWALK_PATH.exists():
+        pytest.skip("OA crosswalk not built")
+    return load_oa_crosswalk()
+
+
+class TestRealCrosswalk:
+    def test_region_codes_name_the_right_places(self, real_crosswalk):
+        """FRS_REGION_TO_CODE agrees with where well-known LAs are."""
+        names = pd.read_csv(STORAGE_FOLDER / "local_authorities_2021.csv")
+        la_region = (
+            real_crosswalk[["la_code", "region_code"]]
+            .drop_duplicates()
+            .merge(names, left_on="la_code", right_on="code")
+            .set_index("name")["region_code"]
+        )
+        expected = {
+            "Hartlepool": "NORTH_EAST",
+            "Manchester": "NORTH_WEST",
+            "Leeds": "YORKSHIRE",
+            "Nottingham": "EAST_MIDLANDS",
+            "Birmingham": "WEST_MIDLANDS",
+            "Cambridge": "EAST_OF_ENGLAND",
+            "Westminster": "LONDON",
+            "Brighton and Hove": "SOUTH_EAST",
+            "Bristol, City of": "SOUTH_WEST",
+            "Cardiff": "WALES",
+            "Glasgow City": "SCOTLAND",
+        }
+        for name, region in expected.items():
+            assert la_region[name] == FRS_REGION_TO_CODE[region], name
+
+    def test_every_crosswalk_region_is_an_frs_region(self, real_crosswalk):
+        assert set(real_crosswalk["region_code"]) <= set(FRS_REGION_TO_CODE.values())
+
+    def test_local_areas_nest_in_regions(self, real_crosswalk):
+        """No LA or constituency straddles a region, so sampling within the
+        region can still reach every OA of every local area."""
+        for column in ["la_code", "constituency_code"]:
+            regions_per_area = real_crosswalk.groupby(column)["region_code"].nunique()
+            assert (regions_per_area == 1).all(), column
+
+    def test_every_region_has_a_constituency_per_production_clone(self, real_crosswalk):
+        constituencies = real_crosswalk.groupby("region_code")[
+            "constituency_code"
+        ].nunique()
+        assert constituencies.min() >= 10
+
+    def test_assignment_on_the_real_crosswalk(self, real_crosswalk):
+        covered = sorted(
+            region
+            for region, code in FRS_REGION_TO_CODE.items()
+            if code in set(real_crosswalk["region_code"])
+        )
+        regions = np.array(covered * 40, dtype=object)
+        n_clones = 10
+        geography = assign_random_geography(
+            household_countries=_countries(list(regions)),
+            household_regions=regions,
+            n_clones=n_clones,
+            seed=42,
+        )
+        expected = np.tile([FRS_REGION_TO_CODE[r] for r in regions], n_clones)
+        assert (geography.region_code == expected).all()
+        constituencies = geography.constituency_code.reshape(n_clones, len(regions))
+        distinct = np.array(
+            [len(set(constituencies[:, i])) for i in range(len(regions))]
+        )
+        assert (distinct == n_clones).all()
+
+
+def test_built_dataset_oa_region_matches_frs_region(enhanced_frs):
+    """Every household of the built enhanced FRS sits in its own region."""
+    household = enhanced_frs.household
+    decode = lambda value: value.decode() if isinstance(value, bytes) else str(value)  # noqa: E731
+    region_code = household["region_code_oa"].map(decode)
+    expected = household["region"].map(decode).map(FRS_REGION_TO_CODE)
+    has_oa = region_code != ""
+    assert has_oa.any()
+    assert int((region_code[has_oa] != expected[has_oa]).sum()) == 0
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..58de5b1d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
+    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..c301c6c9f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
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+    { url = "https://files.pythonhosted.org/packages/c0/79/be3370fd02734d6b1d950183ae9224580d19fa8d356b95348d60f1e3eeb7/hypothesis-6.168.3-cp315-abi3.abi3t-win_arm64.whl", hash = "sha256:65d78e4357ec48ed2c67825f06740ee3599be4cfe770a6092bed07108d679ac5", size = 679849, upload-time = "2026-09-28T05:19:06.884Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From cb194e4f7ca7c8f0824fd59bce49c73b33b2545c Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 10:12:28 -0400
Subject: [PATCH 19/70] Clear FRS capital on SPI copies, uprate it with
 savings, require PE-UK 2.107.0

- SPI-synthetic copies get pension_credit_reported_capital = -1: the FRS
  donor's capital does not belong with SPI-imputed incomes (24 of 152 SPI
  PC reporters were entitled in build E only because of it).
- uprating_factors.csv and uprating_growth_factors.csv get the column with
  the savings row (policyengine-uk uprates it with the same per-capita GDP
  index), so uprate_dataset keeps the two in step.
- policyengine-uk >= 2.107.0, the first release defining the variable
  (#2018); core 3.32.12 comes with it.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/imputations/income.py            | 14 ++++++++
 .../storage/uprating_factors.csv              |  1 +
 .../storage/uprating_growth_factors.csv       |  1 +
 .../test_pension_credit_reported_capital.py   | 35 +++++++++++++++++++
 pyproject.toml                                |  2 +-
 uv.lock                                       | 16 ++++-----
 6 files changed, 60 insertions(+), 9 deletions(-)

diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
index feafcb27c..bd5c1d113 100644
--- a/policyengine_uk_data/datasets/imputations/income.py
+++ b/policyengine_uk_data/datasets/imputations/income.py
@@ -234,6 +234,19 @@ def impute_over_incomes(
     return dataset
 
 
+def clear_frs_reported_capital(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
+    """Set ``pension_credit_reported_capital`` to -1 (none recorded).
+
+    Used on the SPI-synthetic copy. The FRS benefit-unit capital belongs to the
+    FRS donor, whose incomes the SPI imputation replaces; keeping it would
+    assess an SPI-income unit on the donor's capital. With -1, policyengine-uk
+    uses the household capital proxy for these rows.
+    """
+    if "pension_credit_reported_capital" in dataset.benunit.columns:
+        dataset.benunit["pension_credit_reported_capital"] = -1.0
+    return dataset
+
+
 def impute_income(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
     """
     Impute detailed income components using trained model.
@@ -262,6 +275,7 @@ def impute_income(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
     zero_weight_copy = dataset.copy()
     zero_weight_copy.household.household_weight = 0
     zero_weight_copy.household["household_is_spi_synthetic"] = True
+    zero_weight_copy = clear_frs_reported_capital(zero_weight_copy)
     zero_weight_copy = subsample_dataset(zero_weight_copy, 10_000)
 
     model = create_income_model()
diff --git a/policyengine_uk_data/storage/uprating_factors.csv b/policyengine_uk_data/storage/uprating_factors.csv
index dacd591bd..8453aaa97 100644
--- a/policyengine_uk_data/storage/uprating_factors.csv
+++ b/policyengine_uk_data/storage/uprating_factors.csv
@@ -54,6 +54,7 @@ other_investment_income,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.
 other_residential_property_value,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 owned_land,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 pension_credit_reported,1.0,1.04,1.144,1.209,1.237,1.277,1.301,1.327,1.353,1.38,1.38,1.38,1.38,1.38,1.38
+pension_credit_reported_capital,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 pension_income,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 personal_pension_contributions,1.0,1.059,1.127,1.205,1.261,1.308,1.337,1.365,1.396,1.431,1.431,1.431,1.431,1.431,1.431
 petrol_spending,1.0,1.104,1.635,1.796,1.531,1.483,1.452,1.406,1.363,1.305,1.237,1.237,1.237,1.237,1.237
diff --git a/policyengine_uk_data/storage/uprating_growth_factors.csv b/policyengine_uk_data/storage/uprating_growth_factors.csv
index 122b4ed69..6722cb582 100644
--- a/policyengine_uk_data/storage/uprating_growth_factors.csv
+++ b/policyengine_uk_data/storage/uprating_growth_factors.csv
@@ -54,6 +54,7 @@ other_investment_income,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0
 other_residential_property_value,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 owned_land,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 pension_credit_reported,0,0.04,0.1,0.057,0.023,0.032,0.019,0.02,0.02,0.02,0.0,0.0,0.0,0.0,0.0
+pension_credit_reported_capital,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 pension_income,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 personal_pension_contributions,0,0.059,0.064,0.069,0.046,0.037,0.022,0.021,0.023,0.025,0.0,0.0,0.0,0.0,0.0
 petrol_spending,0,0.104,0.481,0.099,-0.147,-0.031,-0.021,-0.032,-0.031,-0.043,-0.052,0.0,0.0,0.0,0.0
diff --git a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
index ca42cf438..0081769bb 100644
--- a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
+++ b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
@@ -42,3 +42,38 @@ def test_output_is_sentinel_or_non_negative_for_random_inputs():
         keep = np.isfinite(values) & (values >= 0)
         np.testing.assert_array_equal(result[keep], values[keep])
         assert np.all(result[~keep] == -1)
+
+
+def test_uprating_rows_match_savings():
+    """The column grows with ``savings`` (policyengine-uk uprates it with the
+    same per-capita GDP index), so the build's uprating keeps them in step."""
+    from policyengine_uk_data.storage import STORAGE_FOLDER
+
+    for name in ["uprating_factors.csv", "uprating_growth_factors.csv"]:
+        table = pd.read_csv(STORAGE_FOLDER / name).set_index("Variable")
+        pd.testing.assert_series_equal(
+            table.loc["pension_credit_reported_capital"],
+            table.loc["savings"],
+            check_names=False,
+        )
+
+
+def test_spi_copy_records_no_capital():
+    """SPI-synthetic copies carry SPI-imputed incomes, so the FRS donor's
+    capital is cleared to -1 (household proxy) on them."""
+    from types import SimpleNamespace
+
+    from policyengine_uk_data.datasets.imputations.income import (
+        clear_frs_reported_capital,
+    )
+
+    copy = SimpleNamespace(
+        benunit=pd.DataFrame({"pension_credit_reported_capital": [0.0, 300.0, -1.0]})
+    )
+    assert clear_frs_reported_capital(copy).benunit[
+        "pension_credit_reported_capital"
+    ].tolist() == [-1.0, -1.0, -1.0]
+    without = SimpleNamespace(benunit=pd.DataFrame({"benunit_id": [1, 2]}))
+    assert "pension_credit_reported_capital" not in (
+        clear_frs_reported_capital(without).benunit.columns
+    )
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..8b87d71d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,7 +21,7 @@ dependencies = [
     "policyengine",
     "google-cloud-storage",
     "google-auth",
-    "policyengine-uk>=2.93.0",
+    "policyengine-uk>=2.107.0",
     "microcalibrate>=0.18.0",
     "microimpute>=1.0.1",
     "ruff>=0.9.0",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..ab6e353ec 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1322,7 +1322,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-core"
-version = "3.31.1"
+version = "3.32.12"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dpath" },
@@ -1342,14 +1342,14 @@ dependencies = [
     { name = "standard-imghdr" },
     { name = "wheel" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9f/1e/cd58cb6947720a5b7850c06a988abbc0eef53e2be192ed49a17ed62d78d4/policyengine_core-3.31.1.tar.gz", hash = "sha256:3ca8f666c3e67b0c0ce992fa3dc9d1ff2c095a3860f3d719883588b2f9e44ec3", size = 500450, upload-time = "2026-08-28T05:06:43.16Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/cd/2e9c7e4a0c4395a0d556cebde1fa92b94c4c2c594815938e5bfd052b3b47/policyengine_core-3.32.12.tar.gz", hash = "sha256:1d163936b3e4b2704cf0fe33eee3b93832a84aa4bf027b552a745d6f6d35bad8", size = 451537, upload-time = "2026-10-02T04:58:58.42Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/74/ce/995992049664b6b4cecd1e4db76b143e07aaee8239043057436f48ccbe75/policyengine_core-3.31.1-py3-none-any.whl", hash = "sha256:308200b28aa26bb79d7f0badebac391c0a81704d45ba4ef48ac79e88f0b21e6b", size = 245719, upload-time = "2026-08-28T05:06:41.769Z" },
+    { url = "https://files.pythonhosted.org/packages/36/9e/f8f41e2f33454be51e9116c1b96db77ec9982d5d97d8691a0d8bd7b35b99/policyengine_core-3.32.12-py3-none-any.whl", hash = "sha256:44f0cdc100ff171f107671965bdbb41aa0c9965f632e400734e9eb2bf6ed80ee", size = 250582, upload-time = "2026-10-02T04:58:56.986Z" },
 ]
 
 [[package]]
 name = "policyengine-uk"
-version = "2.93.0"
+version = "2.107.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "microdf-python" },
@@ -1357,14 +1357,14 @@ dependencies = [
     { name = "pydantic" },
     { name = "tables" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/04/ab8fbb526c22d5c422eed374244d977b123241baa6f96a604c600876647a/policyengine_uk-2.93.0.tar.gz", hash = "sha256:e3b8eab824e7dad3a1fd61fa8503442a8b1b2285e05fff7ab60f93b6a9b22578", size = 1246786, upload-time = "2026-08-28T14:55:16.266Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/13/d6/cb79e5e93b9209e9af1b3962db951b7655af3d1a3d53f20a15c7208330e4/policyengine_uk-2.107.0.tar.gz", hash = "sha256:e170c7539a891a91405dba8f1bbc534d54c70283b6ff685661f891c8df0b821c", size = 1448364, upload-time = "2026-10-02T13:38:49.609Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6f/74/5e626f9f80b9485b10bcccbef8d93a635abe309bfdfad437b5de38a80fae/policyengine_uk-2.93.0-py3-none-any.whl", hash = "sha256:4f95feffa3b27fab927f9b903f8a9f1f0d63b2fba209d6b8ac010f0f07a5ec7c", size = 2046525, upload-time = "2026-08-28T14:55:14.114Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/1e/195c05d73e563f08ad14dff322f35efa09ef7b168c1732b3ff30d66c8b35/policyengine_uk-2.107.0-py3-none-any.whl", hash = "sha256:1ce84c3edefd4e07ac5b9d9254448ad3f0aada0694212c543b4ba3a6106b1973", size = 2399214, upload-time = "2026-10-02T13:38:47.79Z" },
 ]
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1419,7 +1419,7 @@ requires-dist = [
     { name = "pandas" },
     { name = "policyengine" },
     { name = "policyengine-core", specifier = ">=3.19.4" },
-    { name = "policyengine-uk", specifier = ">=2.93.0" },
+    { name = "policyengine-uk", specifier = ">=2.107.0" },
     { name = "pydantic", specifier = ">=2.0" },
     { name = "pytest", marker = "extra == 'dev'" },
     { name = "pyyaml" },

From e05539e0f6bef87130cc0f4884453b5985313e94 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 11:49:52 -0400
Subject: [PATCH 20/70] Set UC gainful self-employment from the FRS main-job
 status

policyengine-uk#2081 adds the person input uc_is_in_gainful_self_employment
(UC Regs 2013 reg 64). Without it the model reads any self-employment income
other than zero as gainful self-employment and none as none, so a trader who
breaks even, or whose loss this build floors at zero, never gets the minimum
income floor, while an employee's small side trade does.

Set it from the FRS: true when the main job (EMPSTATI) is self-employment,
whatever its profit (ADM H4013, H4054, H4503), and when a side trade's profit
is above the person's employment income (ADM H4034). The SPI copy in
impute_income re-derives it from its own imputed incomes. Releases of
policyengine-uk without the variable skip the column.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-uc-gainful-self-employment.fixed.md   |   1 +
 policyengine_uk_data/datasets/frs.py          |  41 ++++
 .../datasets/imputations/income.py            |  17 ++
 .../tests/test_uc_gainful_self_employment.py  | 201 ++++++++++++++++++
 pyproject.toml                                |   1 +
 uv.lock                                       |  73 ++++++-
 6 files changed, 333 insertions(+), 1 deletion(-)
 create mode 100644 changelog.d/frs-uc-gainful-self-employment.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_uc_gainful_self_employment.py

diff --git a/changelog.d/frs-uc-gainful-self-employment.fixed.md b/changelog.d/frs-uc-gainful-self-employment.fixed.md
new file mode 100644
index 000000000..f29f6caee
--- /dev/null
+++ b/changelog.d/frs-uc-gainful-self-employment.fixed.md
@@ -0,0 +1 @@
+Set policyengine-uk's Universal Credit input uc_is_in_gainful_self_employment from the FRS main-job status (EMPSTATI): true for every adult whose main job is self-employment, including traders who break even or make a loss, and false for a side trade beside an employee's main job (UC Regs 2013 reg 64(a)).
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..bac740ca5 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -53,6 +53,10 @@
     EmploymentStatus.LONG_TERM_DISABLED.name,
     EmploymentStatus.SHORT_TERM_DISABLED.name,
 )
+SELF_EMPLOYED_STATUSES = (
+    EmploymentStatus.FT_SELF_EMPLOYED.name,
+    EmploymentStatus.PT_SELF_EMPLOYED.name,
+)
 FORMULA_MODELED_EDUCATION_GRANT_VARIABLES = (
     "childcare_grant",
     "parents_learning_allowance",
@@ -392,6 +396,34 @@ def derive_is_parent_from_frs_microdata(
     return is_adult_record & has_dependent_children
 
 
+def derive_uc_is_in_gainful_self_employment(
+    employment_status, self_employment_income, employment_income
+) -> np.ndarray:
+    """Whether each person is in gainful self-employment for Universal Credit.
+
+    UC Regs 2013 reg 64(a) asks whether the person carries on a trade as their
+    main employment. DWP's Advice for Decision Making starts from hours
+    (H4031) but lets earnings outweigh them: someone who works more hours as
+    an employee but earns more from self-employment is likely to be gainfully
+    self-employed (H4034). So the flag is true for:
+
+    - a self-employed main job (FRS EMPSTATI, the job the respondent names as
+      their dominant activity, else the one with more hours), whatever its
+      profit: a trade can make a loss or break even and still be carried on
+      in expectation of profit (ADM H4013, H4054, H4503);
+    - a side trade whose profit is above the person's employment income.
+
+    policyengine-uk's default reads any self-employment income other than
+    zero as gainful self-employment, and none as none.
+    """
+    self_employed_main_job = np.isin(
+        np.asarray(employment_status, dtype=object), SELF_EMPLOYED_STATUSES
+    )
+    profit = np.asarray(self_employment_income, dtype=float)
+    pay = np.asarray(employment_income, dtype=float)
+    return self_employed_main_job | ((profit > 0) & (profit > pay))
+
+
 def _as_non_negative_array(values) -> np.ndarray:
     values = np.asarray(values, dtype=float)
     return np.maximum(np.nan_to_num(values, nan=0.0), 0.0)
@@ -1045,6 +1077,15 @@ def determine_education_level(fted_val, typeed2_val, age_val):
     ) * WEEKS_IN_YEAR
 
     pe_person["self_employment_income"] = np.maximum(0, person.seincam2) * WEEKS_IN_YEAR
+    # policyengine-uk releases without this input skip the column and keep
+    # their formula.
+    pe_person["uc_is_in_gainful_self_employment"] = (
+        derive_uc_is_in_gainful_self_employment(
+            pe_person.employment_status,
+            pe_person.self_employment_income,
+            pe_person.employment_income,
+        )
+    )
 
     INVERTED_BASIC_RATE = 1.25
 
diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
index feafcb27c..98fe14a5c 100644
--- a/policyengine_uk_data/datasets/imputations/income.py
+++ b/policyengine_uk_data/datasets/imputations/income.py
@@ -292,6 +292,23 @@ def impute_income(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
         target_dataset=zero_weight_copy,
     )
 
+    # The copy keeps its FRS donor's employment status but now has SPI
+    # incomes, so derive its gainful self-employment flag again from the
+    # copy's own values.
+    if "uc_is_in_gainful_self_employment" in zero_weight_copy.person.columns:
+        from policyengine_uk_data.datasets.frs import (
+            derive_uc_is_in_gainful_self_employment,
+        )
+
+        person = zero_weight_copy.person
+        person["uc_is_in_gainful_self_employment"] = (
+            derive_uc_is_in_gainful_self_employment(
+                person.employment_status,
+                person.self_employment_income,
+                person.employment_income,
+            )
+        )
+
     dataset = impute_over_incomes(
         dataset,
         model,
diff --git a/policyengine_uk_data/tests/test_uc_gainful_self_employment.py b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py
new file mode 100644
index 000000000..589521600
--- /dev/null
+++ b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py
@@ -0,0 +1,201 @@
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+from policyengine_uk.variables.household.income.employment_status import (
+    EmploymentStatus,
+)
+
+from policyengine_uk_data.datasets.frs import (
+    SELF_EMPLOYED_STATUSES,
+    derive_uc_is_in_gainful_self_employment,
+)
+from policyengine_uk_data.tests.test_imputation_source_flags import (
+    _FakeDataset,
+    _stack_without_remapping,
+)
+
+STATUSES = [status.name for status in EmploymentStatus]
+GAINFUL = "uc_is_in_gainful_self_employment"
+incomes = st.floats(0, 1e6, allow_nan=False)
+
+
+def derive(status, profit, pay):
+    return derive_uc_is_in_gainful_self_employment([status], [profit], [pay])[0]
+
+
+def test_self_employed_statuses_are_model_enum_members():
+    assert set(SELF_EMPLOYED_STATUSES) <= set(STATUSES)
+
+
+@pytest.mark.parametrize("status", STATUSES)
+@pytest.mark.parametrize("profit", [0.0, 5_000.0])
+def test_every_status_without_a_side_trade_that_out_earns_pay(status, profit):
+    # Pay at or above profit: only the main-job status decides.
+    expected = status in ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED")
+    assert derive(status, profit, 10_000.0) == expected
+
+
+@pytest.mark.parametrize("profit", [0.0, 1.0, 20_000.0])
+def test_self_employed_main_job_is_gainful_whatever_the_profit(profit):
+    # A loss reaches this build floored at zero, so zero covers losses too.
+    for status in SELF_EMPLOYED_STATUSES:
+        assert derive(status, profit, 0.0)
+        assert derive(status, profit, 50_000.0)
+
+
+def test_side_trade_counts_only_when_it_out_earns_pay():
+    # ADM H4034 example 1 (Jos): more employed hours, more self-employed pay.
+    assert derive("PT_EMPLOYED", 140 * 52, 80 * 52)
+    # ADM H4035 example 2 (Ann): the job earns more.
+    assert not derive("PT_EMPLOYED", 40 * 52, 49.6 * 52)
+    assert not derive("FT_EMPLOYED", 10_000.0, 10_000.0)
+    assert not derive("FT_EMPLOYED", 0.0, 0.0)
+    assert derive("UNEMPLOYED", 1.0, 0.0)
+
+
+@settings(max_examples=500, deadline=None)
+@given(st.sampled_from(STATUSES), incomes, incomes)
+def test_invariants(status, profit, pay):
+    gainful = derive(status, profit, pay)
+    # Every self-employed main job is gainful.
+    if status in SELF_EMPLOYED_STATUSES:
+        assert gainful
+    # Nobody else is gainful without a profit above their pay.
+    if gainful:
+        assert status in SELF_EMPLOYED_STATUSES or profit > max(pay, 0)
+    # More profit never removes the flag; more pay never adds it.
+    assert derive(status, profit * 2 + 1, pay) >= gainful
+    assert derive(status, profit, pay * 2 + 1) <= gainful
+
+
+@settings(max_examples=100, deadline=None)
+@given(st.lists(st.tuples(st.sampled_from(STATUSES), incomes, incomes), max_size=40))
+def test_vectorised_matches_elementwise(rows):
+    statuses = [r[0] for r in rows]
+    profits = [r[1] for r in rows]
+    pays = [r[2] for r in rows]
+    result = derive_uc_is_in_gainful_self_employment(statuses, profits, pays)
+    assert result.dtype == bool
+    assert result.tolist() == [derive(*r) for r in rows]
+    np.testing.assert_array_equal(
+        derive_uc_is_in_gainful_self_employment(
+            pd.Series(statuses, dtype="category"),
+            pd.Series(profits, dtype=float),
+            pd.Series(pays, dtype=float),
+        ),
+        result,
+    )
+
+
+def _frs_like_dataset(statuses, profits, pays):
+    n = len(statuses)
+    person = pd.DataFrame(
+        {
+            "person_id": np.arange(1, n + 1),
+            "person_household_id": np.arange(1, n + 1),
+            "person_benunit_id": np.arange(1, n + 1),
+            "employment_status": statuses,
+            "employment_income": pays,
+            "self_employment_income": profits,
+            "savings_interest_income": 0.0,
+            "dividend_income": 0.0,
+            "private_pension_income": 0.0,
+            "property_income": 0.0,
+        }
+    )
+    person[GAINFUL] = derive_uc_is_in_gainful_self_employment(
+        person.employment_status,
+        person.self_employment_income,
+        person.employment_income,
+    )
+    household = pd.DataFrame(
+        {
+            "household_id": np.arange(1, n + 1),
+            "household_weight": 1.0,
+            "region": "LONDON",
+        }
+    )
+    return _FakeDataset(person=person, household=household)
+
+
+@settings(max_examples=50, deadline=None)
+@given(
+    st.lists(
+        st.tuples(st.sampled_from(STATUSES), incomes, incomes, incomes, incomes),
+        min_size=1,
+        max_size=10,
+    )
+)
+def test_spi_copy_flag_follows_its_own_imputed_incomes(rows):
+    from policyengine_uk_data.datasets import disability_benefits
+    from policyengine_uk_data.datasets.imputations import frs_only
+    from policyengine_uk_data.datasets.imputations import income as income_module
+
+    imputed_profit = [r[3] for r in rows]
+    imputed_pay = [r[4] for r in rows]
+
+    def impute_over_incomes(dataset, _model, output_variables):
+        dataset = dataset.copy()
+        if "self_employment_income" in output_variables:
+            dataset.person["self_employment_income"] = imputed_profit
+            dataset.person["employment_income"] = imputed_pay
+        return dataset
+
+    with pytest.MonkeyPatch.context() as m:
+        m.setattr(income_module, "create_income_model", lambda: object())
+        m.setattr(income_module, "subsample_dataset", lambda d, _n: d.copy())
+        m.setattr(income_module, "impute_over_incomes", impute_over_incomes)
+        m.setattr(
+            frs_only,
+            "impute_frs_only_variables",
+            lambda train_dataset, target_dataset: target_dataset,
+        )
+        m.setattr(
+            disability_benefits,
+            "strip_internal_disability_reported_amounts",
+            lambda dataset: dataset,
+        )
+        m.setattr(income_module, "stack_datasets", _stack_without_remapping)
+        result = income_module.impute_income(
+            _frs_like_dataset(
+                [r[0] for r in rows], [r[1] for r in rows], [r[2] for r in rows]
+            )
+        )
+
+    person = result.person
+    n = len(rows)
+    assert len(person) == 2 * n
+    # The SPI copy kept the donors' statuses and took the imputed incomes.
+    assert person.self_employment_income.iloc[n:].tolist() == imputed_profit
+    np.testing.assert_array_equal(
+        person[GAINFUL].to_numpy(dtype=bool),
+        derive_uc_is_in_gainful_self_employment(
+            person.employment_status,
+            person.self_employment_income,
+            person.employment_income,
+        ),
+    )
+
+
+@pytest.mark.parametrize("fixture", ["frs", "enhanced_frs"])
+def test_built_dataset_flag(fixture, request):
+    dataset = request.getfixturevalue(fixture)
+    person = dataset.person
+    if GAINFUL not in person.columns:
+        pytest.skip(f"{fixture} was built before this input existed")
+    gainful = person[GAINFUL].to_numpy(dtype=bool)
+    self_employed = np.isin(person.employment_status, SELF_EMPLOYED_STATUSES)
+    assert gainful[self_employed].all()
+    profit = person.self_employment_income.to_numpy()
+    assert (profit[gainful & ~self_employed] > 0).all()
+    if fixture == "frs":
+        # Later enhanced-FRS stages reprice incomes, so the exact rule holds on
+        # the base build only.
+        np.testing.assert_array_equal(
+            gainful,
+            derive_uc_is_in_gainful_self_employment(
+                person.employment_status, profit, person.employment_income
+            ),
+        )
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..58de5b1d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
+    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..c301c6c9f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/09/b7/13118bbc45d6d8b9d04e2de779e2a4ff23145ea39efa692b994fb874ca72/hypothesis-6.168.3.tar.gz", hash = "sha256:a43388f9067678fef6e13bdff325b6cfa6961a590498bb37f7ff31589c83bc75", size = 511022, upload-time = "2026-09-28T05:20:58.499Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/cd/746a2fc1e5e5ba34f07ea6ee1ad07525a3dde5ff4836db3a7980af49a891/hypothesis-6.168.3-cp310-abi3-macosx_10_12_x86_64.whl", hash = "sha256:d20972ca134e652e9928ecd200966a8a2857adc2812c1d74b12a872e5cd503eb", size = 791536, upload-time = "2026-09-28T05:18:57.162Z" },
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+    { url = "https://files.pythonhosted.org/packages/e4/3f/2b16e95cc3b9a069afa23830a012aefe377ce4a457a227e5a5026eb827ff/hypothesis-6.168.3-cp310-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:753bb501f8560d2e321ed3b62596b4496c56f15668b0a0669231ccc3e6c4e80d", size = 1149489, upload-time = "2026-09-28T05:19:18.886Z" },
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+    { url = "https://files.pythonhosted.org/packages/a2/28/5bb82b60b836bd2329e1fe01ad94efc2b14dfa806f8e50ed14b795d78770/hypothesis-6.168.3-cp315-abi3.abi3t-musllinux_1_2_x86_64.whl", hash = "sha256:2b52ac363096232bebc2add117e9178d91f4248f4cdb919fd1026b5f86a4bb16", size = 1333977, upload-time = "2026-09-28T05:20:02.483Z" },
+    { url = "https://files.pythonhosted.org/packages/8e/7d/d419841b8f65481ea1a50c4ba36f2670d48b8c7a51e4e8586089564cf7f6/hypothesis-6.168.3-cp315-abi3.abi3t-win32.whl", hash = "sha256:d28e3a6b511a74ce37df5274b51f36c2b274fea7365e00b16f0c81e22acd5957", size = 675394, upload-time = "2026-09-28T05:19:55.237Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/d2/1de6a2ad100e44817f2e9a8e8ba3eeaf4769621f4f87f2d4966c4971fcc4/hypothesis-6.168.3-cp315-abi3.abi3t-win_amd64.whl", hash = "sha256:7b9638789548361a57d984f56619ac694a914c328181d911271618409261ff4a", size = 681699, upload-time = "2026-09-28T05:19:09.957Z" },
+    { url = "https://files.pythonhosted.org/packages/c0/79/be3370fd02734d6b1d950183ae9224580d19fa8d356b95348d60f1e3eeb7/hypothesis-6.168.3-cp315-abi3.abi3t-win_arm64.whl", hash = "sha256:65d78e4357ec48ed2c67825f06740ee3599be4cfe770a6092bed07108d679ac5", size = 679849, upload-time = "2026-09-28T05:19:06.884Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From d3984002c17d2bee0cca5606071e9a5aa2dc4c18 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 12:33:40 -0400
Subject: [PATCH 21/70] Map FRS EMPSTATI 11 to OTHER_INACTIVE and reject
 unknown adult codes

The FRS employment_status mapping zipped range(12) with 11 statuses, so
EMPSTATI 11 ("Other inactive" in the UKDS FRS 2024-25 data dictionary) was
unmapped and the fillna fallback made it LONG_TERM_DISABLED. That also put
other-inactive working-age adults into the ESA health-condition and
support-group proxies.

Map the adult codes through an explicit table keyed to the data dictionary,
make child-table rows CHILD, and fail the build on any adult code the table
does not know rather than guessing a status. Every adult in the 2020-21,
2022-23, 2023-24 and 2024-25 releases carries a code from 1 to 11.

Tests cover codes 0-11 exhaustively, Hypothesis properties of the mapping and
the ESA proxies, create_frs end to end for every code and a child row, and
the built datasets. Adds hypothesis as a dev dependency (same pyproject and uv.lock
change as #522 and #525).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-empstati-other-inactive.fixed.md      |   1 +
 policyengine_uk_data/datasets/frs.py          |  59 ++++--
 .../tests/test_frs_employment_status.py       | 169 ++++++++++++++++++
 .../tests/test_legacy_benefit_proxies.py      |  52 +++++-
 pyproject.toml                                |   1 +
 uv.lock                                       |  73 +++++++-
 6 files changed, 332 insertions(+), 23 deletions(-)
 create mode 100644 changelog.d/frs-empstati-other-inactive.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_frs_employment_status.py

diff --git a/changelog.d/frs-empstati-other-inactive.fixed.md b/changelog.d/frs-empstati-other-inactive.fixed.md
new file mode 100644
index 000000000..f46130465
--- /dev/null
+++ b/changelog.d/frs-empstati-other-inactive.fixed.md
@@ -0,0 +1 @@
+Map FRS EMPSTATI code 11 ("Other inactive") to `employment_status` OTHER_INACTIVE; it had fallen through to LONG_TERM_DISABLED, which also put other-inactive adults into the ESA health-condition and support-group proxies. An adult EMPSTATI code the mapping does not know now fails the build instead of defaulting.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e440d4869..05c7c8dc5 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -49,6 +49,22 @@
 LEGACY_JOBSEEKER_MIN_AGE = 18
 HOURS_WORKED_WEEKS_PER_YEAR = 52
 ESA_MIN_AGE = 16
+# Adult-table EMPSTATI ("Adult - Employment Status - ILO definition") value
+# labels from the UKDS FRS 2024-25 data dictionary, mapped to PolicyEngine
+# statuses. Children have no EMPSTATI and are CHILD.
+FRS_EMPSTATI_EMPLOYMENT_STATUS = {
+    1: EmploymentStatus.FT_EMPLOYED.name,  # Full-time employee
+    2: EmploymentStatus.PT_EMPLOYED.name,  # Part-time employee
+    3: EmploymentStatus.FT_SELF_EMPLOYED.name,  # Full-time self-employed
+    4: EmploymentStatus.PT_SELF_EMPLOYED.name,  # Part-time self-employed
+    5: EmploymentStatus.UNEMPLOYED.name,  # Unemployed
+    6: EmploymentStatus.RETIRED.name,  # Retired
+    7: EmploymentStatus.STUDENT.name,  # Student
+    8: EmploymentStatus.CARER.name,  # Looking after family/home
+    9: EmploymentStatus.LONG_TERM_DISABLED.name,  # Permanently sick/disabled
+    10: EmploymentStatus.SHORT_TERM_DISABLED.name,  # Temporarily sick/injured
+    11: EmploymentStatus.OTHER_INACTIVE.name,  # Other inactive
+}
 ESA_HEALTH_EMPLOYMENT_STATUSES = (
     EmploymentStatus.LONG_TERM_DISABLED.name,
     EmploymentStatus.SHORT_TERM_DISABLED.name,
@@ -112,6 +128,29 @@ def require_variable(name: str, description: str) -> None:
         )
 
 
+def derive_employment_status_from_frs(empstati, is_adult_record) -> np.ndarray:
+    """Map FRS EMPSTATI codes to ``employment_status``.
+
+    Rows from the child table are CHILD. Every adult must carry a code in
+    ``FRS_EMPSTATI_EMPLOYMENT_STATUS``: a missing or unknown adult code fails
+    the build rather than falling back to a guessed status, as the old
+    fallback silently made code 11 (Other inactive) LONG_TERM_DISABLED.
+    """
+
+    codes = pd.Series(np.asarray(empstati, dtype=float))
+    is_adult_record = np.asarray(is_adult_record, dtype=bool)
+    adult_status = codes.map(FRS_EMPSTATI_EMPLOYMENT_STATUS).to_numpy()
+    unknown = is_adult_record & pd.isna(adult_status)
+    if unknown.any():
+        counts = codes[unknown].value_counts(dropna=False).sort_index().to_dict()
+        raise ValueError(
+            f"{int(unknown.sum())} FRS adults have EMPSTATI codes missing from "
+            f"FRS_EMPSTATI_EMPLOYMENT_STATUS (code: adults): {counts}. Map "
+            "them from the release's data dictionary."
+        )
+    return np.where(is_adult_record, adult_status, EmploymentStatus.CHILD.name)
+
+
 def derive_legacy_jobseeker_proxy(
     age,
     employment_status,
@@ -854,23 +893,9 @@ def determine_education_level(fted_val, typeed2_val, age_val):
         "UPPER_SECONDARY"
     )
 
-    # Add employment status
-    EMPLOYMENTS = [
-        "CHILD",
-        "FT_EMPLOYED",
-        "PT_EMPLOYED",
-        "FT_SELF_EMPLOYED",
-        "PT_SELF_EMPLOYED",
-        "UNEMPLOYED",
-        "RETIRED",
-        "STUDENT",
-        "CARER",
-        "LONG_TERM_DISABLED",
-        "SHORT_TERM_DISABLED",
-    ]
-    pe_person["employment_status"] = categorical(
-        person.empstati, 1, range(12), EMPLOYMENTS
-    ).fillna("LONG_TERM_DISABLED")
+    pe_person["employment_status"] = derive_employment_status_from_frs(
+        person.empstati, person.person_id.isin(frs["adult"].person_id)
+    )
 
     # Add employer sector of the main job from FRS `mjobsect`
     # (1 = private, 2 = public; missing/blank = not in paid work).
diff --git a/policyengine_uk_data/tests/test_frs_employment_status.py b/policyengine_uk_data/tests/test_frs_employment_status.py
new file mode 100644
index 000000000..b035358fe
--- /dev/null
+++ b/policyengine_uk_data/tests/test_frs_employment_status.py
@@ -0,0 +1,169 @@
+"""FRS EMPSTATI codes to ``employment_status``, and what the ESA proxies read."""
+
+import numpy as np
+import pytest
+from hypothesis import given, strategies as st
+from policyengine_uk.variables.household.income.employment_status import (
+    EmploymentStatus,
+)
+
+from policyengine_uk_data.datasets.frs import (
+    ESA_HEALTH_EMPLOYMENT_STATUSES,
+    ESA_MIN_AGE,
+    FRS_EMPSTATI_EMPLOYMENT_STATUS,
+    derive_employment_status_from_frs,
+    derive_esa_health_condition_proxy,
+    derive_esa_support_group_proxy,
+)
+
+# EMPSTATI ("Adult - Employment Status - ILO definition") value labels in the
+# UKDS FRS 2024-25 data dictionary (SN 9563, adult table), with the status each
+# label means. The 2020-21, 2022-23 and 2023-24 releases use the same codes.
+DATA_DICTIONARY = {
+    1: ("Full-time Employee", "FT_EMPLOYED"),
+    2: ("Part-time Employee", "PT_EMPLOYED"),
+    3: ("Full-time Self-Employed", "FT_SELF_EMPLOYED"),
+    4: ("Part-time Self-Employed", "PT_SELF_EMPLOYED"),
+    5: ("Unemployed", "UNEMPLOYED"),
+    6: ("Retired", "RETIRED"),
+    7: ("Student", "STUDENT"),
+    8: ("Looking after family/home", "CARER"),
+    9: ("Permanently sick/disabled", "LONG_TERM_DISABLED"),
+    10: ("Temporarily sick/injured", "SHORT_TERM_DISABLED"),
+    11: ("Other Inactive", "OTHER_INACTIVE"),
+}
+ADULT_CODES = sorted(DATA_DICTIONARY)
+HEALTH_CODES = (9, 10)
+
+adult_rows = st.tuples(st.just(True), st.sampled_from(ADULT_CODES))
+# Child-table rows have no EMPSTATI (0 once the person table fills blanks); the
+# code must not matter for them.
+child_rows = st.tuples(
+    st.just(False),
+    st.one_of(st.just(0), st.just(np.nan), st.integers(-9, 99)),
+)
+people = st.lists(st.one_of(adult_rows, child_rows), max_size=60)
+unknown_adult_codes = st.one_of(
+    st.just(np.nan),
+    st.integers(-99, 0),
+    st.integers(12, 999),
+    st.floats(0.5, 11.5).filter(lambda x: not float(x).is_integer()),
+)
+
+
+def derive(rows):
+    is_adult = [adult for adult, _ in rows]
+    codes = [code for _, code in rows]
+    return derive_employment_status_from_frs(codes, is_adult)
+
+
+def expected(adult, code):
+    return DATA_DICTIONARY[code][1] if adult else "CHILD"
+
+
+@pytest.mark.parametrize("code", range(12))
+def test_every_code_from_0_to_11(code):
+    assert derive_employment_status_from_frs([code], [False]).tolist() == ["CHILD"]
+    if code == 0:
+        with pytest.raises(ValueError, match="EMPSTATI"):
+            derive_employment_status_from_frs([code], [True])
+    else:
+        label, status = DATA_DICTIONARY[code]
+        assert derive_employment_status_from_frs([code], [True]).tolist() == [status], (
+            label
+        )
+
+
+def test_other_inactive_is_not_long_term_disabled():
+    result = derive_employment_status_from_frs([9, 11], [True, True])
+    assert result.tolist() == ["LONG_TERM_DISABLED", "OTHER_INACTIVE"]
+
+
+def test_code_table_is_the_data_dictionary():
+    assert FRS_EMPSTATI_EMPLOYMENT_STATUS == {
+        code: status for code, (_, status) in DATA_DICTIONARY.items()
+    }
+
+
+def test_adult_codes_and_child_rows_cover_each_status_once():
+    statuses = [*FRS_EMPSTATI_EMPLOYMENT_STATUS.values(), "CHILD"]
+    assert len(set(statuses)) == len(statuses)
+    assert set(statuses) == set(EmploymentStatus.__members__)
+
+
+@pytest.mark.parametrize("code", [0, -1, 12, 11.5, np.nan])
+def test_unknown_adult_code_fails_the_build(code):
+    with pytest.raises(ValueError, match="EMPSTATI"):
+        derive_employment_status_from_frs([1, code], [True, True])
+
+
+@given(people)
+def test_each_row_maps_on_its_own(rows):
+    result = derive(rows)
+    assert result.tolist() == [expected(adult, code) for adult, code in rows]
+
+
+@given(people, st.randoms(use_true_random=False))
+def test_mapping_commutes_with_row_order(rows, rng):
+    order = list(range(len(rows)))
+    rng.shuffle(order)
+    assert derive([rows[i] for i in order]).tolist() == [derive(rows)[i] for i in order]
+
+
+@given(people, unknown_adult_codes, st.integers(0, 60))
+def test_any_unknown_adult_code_fails_the_build(rows, bad_code, position):
+    rows = list(rows)
+    rows.insert(min(position, len(rows)), (True, bad_code))
+    with pytest.raises(ValueError, match="EMPSTATI"):
+        derive(rows)
+
+
+@given(
+    st.lists(
+        st.tuples(
+            st.integers(0, 100),  # age
+            st.sampled_from(ADULT_CODES),
+            st.integers(60, 68),  # State Pension age
+            st.integers(0, 3_000),  # annual hours worked
+            st.booleans(),  # EMPSTATI reported
+        ),
+        min_size=1,
+        max_size=60,
+    )
+)
+def test_esa_proxies_read_only_the_sick_or_disabled_codes(rows):
+    age, codes, spa, hours, reported = map(np.array, zip(*rows))
+    status = derive_employment_status_from_frs(codes, np.ones(len(rows), bool))
+    health = derive_esa_health_condition_proxy(
+        age=age,
+        employment_status=status,
+        employment_status_reported=reported,
+        state_pension_age=spa,
+    )
+    support = derive_esa_support_group_proxy(
+        age=age,
+        employment_status=status,
+        hours_worked=hours,
+        esa_health_condition_proxy=health,
+        employment_status_reported=reported,
+        state_pension_age=spa,
+    )
+    working_age = (age >= ESA_MIN_AGE) & (age < spa)
+    assert (
+        health.tolist()
+        == (reported & working_age & np.isin(codes, HEALTH_CODES)).tolist()
+    )
+    assert not (support & ~health).any()
+    assert not support[codes != 9].any()
+    assert not health[codes == 11].any()
+
+
+@pytest.mark.parametrize("dataset_name", ["frs", "enhanced_frs"])
+def test_built_dataset_statuses(dataset_name, request):
+    person = request.getfixturevalue(dataset_name).person
+    status = person["employment_status"].astype(str)
+    assert set(status) <= set(EmploymentStatus.__members__)
+    assert (status == "OTHER_INACTIVE").any()
+    not_sick = ~status.isin(ESA_HEALTH_EMPLOYMENT_STATUSES)
+    assert not person.loc[not_sick, "esa_health_condition_proxy"].any()
+    assert not person.loc[not_sick, "esa_support_group_proxy"].any()
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index 5f1acd85f..ebc8ab596 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -1,9 +1,11 @@
 import numpy as np
 import pandas as pd
+import pytest
 import policyengine_uk
 import policyengine_uk_data.datasets.frs as frs_module
 
 from policyengine_uk_data.datasets.frs import (
+    FRS_EMPSTATI_EMPLOYMENT_STATUS,
     add_legacy_benefit_proxies,
     attach_legacy_benefit_proxies_from_frs_person,
     apply_legacy_benefit_proxies,
@@ -359,7 +361,7 @@ def calculate(self, variable, year=None):
         if variable == "household_id":
             return np.array([100])
         if variable == "state_pension_age":
-            return pd.Series([66])
+            return pd.Series([66] * len(self.dataset.person))
         if variable in (
             "childcare_grant",
             "parents_learning_allowance",
@@ -373,7 +375,7 @@ def calculate(self, variable, year=None):
         raise KeyError(variable)
 
 
-def test_create_frs_smoke_includes_legacy_proxy_columns(tmp_path, monkeypatch):
+def create_single_adult_frs(tmp_path, monkeypatch, empstati=8, with_child=False):
     original_read_csv = frs_module.pd.read_csv
 
     def fake_read_csv(path, *args, **kwargs):
@@ -426,7 +428,7 @@ def fake_read_csv(path, *args, **kwargs):
                 "educqual": 0,
                 "eduma": 0,
                 "edumaamt": 0,
-                "empstati": 8,
+                "empstati": empstati,
                 "mjobsect": 0,
                 "sic": 0,
                 "fsbval": 0,
@@ -468,7 +470,13 @@ def fake_read_csv(path, *args, **kwargs):
             }
         ]
     )
-    child = pd.DataFrame(columns=adult.columns)
+    # The FRS child table has no EMPSTATI column.
+    child_columns = adult.columns.drop("empstati")
+    child = pd.DataFrame(columns=child_columns)
+    if with_child:
+        child = pd.DataFrame(
+            [{**dict.fromkeys(child_columns, 0), "sernum": 100, "benunit": 1}]
+        ).assign(person=2, age=5, uperson=2)
     benunit = pd.DataFrame([{"sernum": 100, "benunit": 1, "famtypb2": 1}])
     househol = pd.DataFrame(
         [
@@ -536,7 +544,11 @@ def fake_read_csv(path, *args, **kwargs):
     for name, table in raw_tables.items():
         table.to_csv(tmp_path / f"{name}.tab", sep="\t", index=False)
 
-    dataset = create_frs(tmp_path, 2025)
+    return create_frs(tmp_path, 2025)
+
+
+def test_create_frs_smoke_includes_legacy_proxy_columns(tmp_path, monkeypatch):
+    dataset = create_single_adult_frs(tmp_path, monkeypatch)
 
     assert {
         "legacy_jobseeker_proxy",
@@ -561,3 +573,33 @@ def fake_read_csv(path, *args, **kwargs):
     ].iloc[0]
     assert dataset.person["education_grants"].iloc[0] == 100
     assert dataset.person["disabled_students_allowance_eligible_expenses"].iloc[0] == 0
+
+
+@pytest.mark.parametrize("empstati", sorted(FRS_EMPSTATI_EMPLOYMENT_STATUS))
+def test_create_frs_maps_every_empstati_code(tmp_path, monkeypatch, empstati):
+    person = create_single_adult_frs(tmp_path, monkeypatch, empstati).person
+
+    assert (
+        person["employment_status"].iloc[0]
+        == (FRS_EMPSTATI_EMPLOYMENT_STATUS[empstati])
+    )
+    # A working-age adult reporting no hours: only the sick/disabled codes
+    # (9 permanently, 10 temporarily) are ESA health states.
+    assert person["esa_health_condition_proxy"].iloc[0] == (empstati in (9, 10))
+    assert person["esa_support_group_proxy"].iloc[0] == (empstati == 9)
+
+
+def test_create_frs_child_rows_are_child(tmp_path, monkeypatch):
+    person = create_single_adult_frs(
+        tmp_path, monkeypatch, empstati=11, with_child=True
+    ).person.set_index("person_id")
+
+    assert person["employment_status"].to_dict() == {
+        100_001: "OTHER_INACTIVE",
+        100_002: "CHILD",
+    }
+
+
+def test_create_frs_rejects_unknown_adult_empstati(tmp_path, monkeypatch):
+    with pytest.raises(ValueError, match="EMPSTATI"):
+        create_single_adult_frs(tmp_path, monkeypatch, empstati=12)
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..58de5b1d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
+    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..c301c6c9f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
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+    { url = "https://files.pythonhosted.org/packages/c0/79/be3370fd02734d6b1d950183ae9224580d19fa8d356b95348d60f1e3eeb7/hypothesis-6.168.3-cp315-abi3.abi3t-win_arm64.whl", hash = "sha256:65d78e4357ec48ed2c67825f06740ee3599be4cfe770a6092bed07108d679ac5", size = 679849, upload-time = "2026-09-28T05:19:06.884Z" },
+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From 56e0e89d15607dd44a781530dccdad118b711e03 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 12:37:24 -0400
Subject: [PATCH 22/70] Draw SPI incomes within earnings groups set by FRS
 employment status

The SPI income model drew every SPI-synthetic row's six incomes from age,
gender and region alone, while the row kept its FRS donor's employment
status. Children drew pay, the unemployed and retired drew earnings, and
self-employed rows rarely drew a profit.

Fit one QRF per earnings group (employee, self-employed, both, neither) on
the SPI records in that group, using PAY and the SPI self-employment
indicator SEINC_NUM, and draw each FRS row from the group its employment
status and recorded earnings put it in. Children keep their own incomes.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../spi-income-earnings-groups.fixed.md       |   1 +
 .../datasets/imputations/income.py            | 282 ++++++++++++++++--
 2 files changed, 253 insertions(+), 30 deletions(-)
 create mode 100644 changelog.d/spi-income-earnings-groups.fixed.md

diff --git a/changelog.d/spi-income-earnings-groups.fixed.md b/changelog.d/spi-income-earnings-groups.fixed.md
new file mode 100644
index 000000000..724e2fca4
--- /dev/null
+++ b/changelog.d/spi-income-earnings-groups.fixed.md
@@ -0,0 +1 @@
+Draw SPI incomes for the enhanced FRS's SPI-synthetic rows within earnings groups (employee, self-employed, both, neither) set by each row's FRS employment status, so employees draw pay, the self-employed draw a trade, people out of work draw no earnings, and children keep their own (zero) incomes.
diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
index feafcb27c..7e1d6c71b 100644
--- a/policyengine_uk_data/datasets/imputations/income.py
+++ b/policyengine_uk_data/datasets/imputations/income.py
@@ -4,11 +4,16 @@
 This module imputes detailed income components (employment, self-employment,
 pensions, property, savings interest, dividends) using machine learning
 models trained on HMRC Survey of Personal Incomes (SPI) data.
+
+The draw is conditioned on each person's earnings group (see
+``EARNINGS_GROUPS``) as well as their age, gender and region, so the incomes
+agree with the employment status the FRS donor row keeps.
 """
 
 import pandas as pd
 import numpy as np
 import os
+import pickle
 from policyengine_uk_data.storage import STORAGE_FOLDER
 from policyengine_uk.data import UKSingleYearDataset
 from policyengine_uk import Microsimulation
@@ -51,6 +56,103 @@ def _spi_age_bounds(age_code) -> tuple[int, int]:
         return AGE_RANGES[-1]
 
 
+# Earnings groups that both surveys identify. The SPI has no ILO employment
+# status and no hours, but it records each taxpayer's income sources: pay, and
+# whether they file the self-employment pages of a tax return for a trade or
+# partnership (SEINC_NUM, "Indicator for self-employed cases"). PROFITS is
+# floored at zero, so SEINC_NUM is what finds break-even and loss-making
+# traders. The FRS gives the same split through the main job's ILO status
+# (EMPSTATI) and any second-job earnings.
+NO_EARNINGS = "NO_EARNINGS"
+EMPLOYEE = "EMPLOYEE"
+SELF_EMPLOYED = "SELF_EMPLOYED"
+EMPLOYEE_AND_SELF_EMPLOYED = "EMPLOYEE_AND_SELF_EMPLOYED"
+EARNINGS_GROUPS = (
+    NO_EARNINGS,
+    EMPLOYEE,
+    SELF_EMPLOYED,
+    EMPLOYEE_AND_SELF_EMPLOYED,
+)
+# FRS rows that take no SPI draw and keep their own values: the FRS child
+# table (dependent children, including 16-19-year-olds in education), whose
+# earnings the FRS build does not record, and anyone under 16. The SPI has no
+# record of them as earners.
+NOT_IMPUTED = "NOT_IMPUTED"
+
+# policyengine-uk EmploymentStatus names.
+EMPLOYEE_STATUSES = ("FT_EMPLOYED", "PT_EMPLOYED")
+SELF_EMPLOYED_STATUSES = ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED")
+CHILD_STATUS = "CHILD"
+
+# Every earnings group gets at least this share of the training sample, so the
+# small self-employed groups are not fitted on a few thousand records.
+MIN_GROUP_SAMPLE_SHARE = 0.1
+
+
+def earnings_group(has_pay, has_trade) -> np.ndarray:
+    """Earnings group from whether a person has pay and a trade."""
+    has_pay = np.asarray(has_pay, dtype=bool)
+    has_trade = np.asarray(has_trade, dtype=bool)
+    return np.select(
+        [has_pay & has_trade, has_trade, has_pay],
+        [EMPLOYEE_AND_SELF_EMPLOYED, SELF_EMPLOYED, EMPLOYEE],
+        NO_EARNINGS,
+    ).astype(object)
+
+
+def spi_earnings_group(
+    employment_income, self_employment_income, self_employed_indicator
+) -> np.ndarray:
+    """Earnings group of SPI records.
+
+    Pay is PAY + EPB + TAXTERM. A trade is SEINC_NUM = 1 (self-employment
+    pages filed, whatever the profit) or any assessable profit.
+    """
+    has_trade = (np.asarray(self_employed_indicator) == 1) | (
+        np.asarray(self_employment_income, dtype=float) > 0
+    )
+    return earnings_group(np.asarray(employment_income, dtype=float) > 0, has_trade)
+
+
+def frs_earnings_group(
+    employment_status, age, employment_income, self_employment_income
+) -> np.ndarray:
+    """Earnings group of FRS rows, or ``NOT_IMPUTED`` for children.
+
+    An employee main job always draws pay and a self-employed main job always
+    draws a trade, whatever the FRS recorded for the donor. Earnings the FRS
+    records outside the main job (a second job or a side trade) add the other
+    source. Everyone else, retired, unemployed or inactive, draws from SPI
+    records with neither.
+    """
+    status = np.asarray(employment_status, dtype=object)
+    has_pay = np.isin(status, EMPLOYEE_STATUSES) | (
+        np.asarray(employment_income, dtype=float) > 0
+    )
+    has_trade = np.isin(status, SELF_EMPLOYED_STATUSES) | (
+        np.asarray(self_employment_income, dtype=float) > 0
+    )
+    is_child = (status == CHILD_STATUS) | (np.asarray(age, dtype=float) < 16)
+    return np.where(is_child, NOT_IMPUTED, earnings_group(has_pay, has_trade)).astype(
+        object
+    )
+
+
+def earnings_group_sample_sizes(
+    group_weights: dict[str, float], sample_size: int
+) -> dict[str, int]:
+    """Training records per earnings group: the group's weighted share of
+    ``sample_size``, but never under ``MIN_GROUP_SAMPLE_SHARE`` of it. Groups
+    with no weight get no records."""
+    weights = {group: float(w) for group, w in group_weights.items() if w > 0}
+    total = sum(weights.values())
+    floor = int(np.ceil(MIN_GROUP_SAMPLE_SHARE * sample_size))
+    return {
+        group: max(int(round(sample_size * w / total)), floor)
+        for group, w in weights.items()
+    }
+
+
 def generate_spi_table(
     spi: pd.DataFrame,
     seed: int = 0,
@@ -61,9 +163,13 @@ def generate_spi_table(
 
     Args:
         spi: Raw SPI survey data DataFrame.
+        seed: Seed for the age draw and the resample.
+        sample_size: If set, resample records with replacement, in proportion
+            to their weight within each earnings group, with each group's
+            record count from ``earnings_group_sample_sizes``.
 
     Returns:
-        Cleaned DataFrame with age and region mappings applied.
+        Cleaned DataFrame with age, region and earnings group.
     """
     rng = np.random.default_rng(seed)
     age_range = spi.AGERANGE
@@ -78,16 +184,25 @@ def generate_spi_table(
         spi[rename] = spi[SPI_RENAMES[rename]]
 
     spi["employment_income"] = spi[["PAY", "EPB", "TAXTERM"]].sum(axis=1)
+    spi["earnings_group"] = spi_earnings_group(
+        spi.employment_income, spi.self_employment_income, spi.SEINC_NUM
+    )
 
     if sample_size is not None:
+        sizes = earnings_group_sample_sizes(
+            spi.groupby("earnings_group").person_weight.sum().to_dict(),
+            sample_size,
+        )
         spi = pd.concat(
             [
-                spi.sample(
-                    sample_size,
-                    weights=spi.person_weight,
+                spi[spi.earnings_group == group].sample(
+                    sizes[group],
+                    weights="person_weight",
                     replace=True,
-                    random_state=seed,
-                ),
+                    random_state=seed + i,
+                )
+                for i, group in enumerate(EARNINGS_GROUPS)
+                if group in sizes
             ]
         )
 
@@ -130,6 +245,8 @@ def generate_spi_table(
     "spi_release_name": SPI_RELEASE_NAME,
     "spi_tab_filename": SPI_TAB_FILENAME,
     "imputations": tuple(IMPUTATIONS),
+    "earnings_groups": EARNINGS_GROUPS,
+    "min_group_sample_share": MIN_GROUP_SAMPLE_SHARE,
 }
 INCOME_MODEL_PATH = STORAGE_FOLDER / f"income_{SPI_RELEASE_NAME}.pkl"
 INCOME_MODEL_SAMPLE_SIZE = 100_000
@@ -149,29 +266,98 @@ def get_income_model_metadata() -> dict:
     }
 
 
+class EarningsGroupIncomeModel:
+    """One QRF per earnings group, each fitted on that group's SPI records.
+
+    A person is drawn only from SPI records in their own earnings group, so an
+    employee always draws pay, a self-employed person always draws a trade
+    (whose profit can be zero), and someone with neither draws neither.
+    ``predict`` returns NaN for rows in no group (``NOT_IMPUTED``).
+    """
+
+    def __init__(self, models: dict, metadata: dict | None = None):
+        self.models = models
+        self.metadata = metadata or {}
+
+    @property
+    def imputed_variables(self) -> list[str]:
+        return list(IMPUTATIONS)
+
+    def predict(self, X: pd.DataFrame) -> pd.DataFrame:
+        groups = np.asarray(X["earnings_group"], dtype=object)
+        output = pd.DataFrame(np.nan, index=X.index, columns=IMPUTATIONS)
+        for group, model in self.models.items():
+            in_group = groups == group
+            if in_group.any():
+                inputs = pd.DataFrame(
+                    {column: np.asarray(X[column])[in_group] for column in PREDICTORS}
+                )
+                draws = model.predict(inputs)
+                output.loc[in_group, IMPUTATIONS] = draws[IMPUTATIONS].to_numpy()
+        return output
+
+    def save(self, file_path):
+        with open(file_path, "wb") as f:
+            pickle.dump(
+                {
+                    "models": {
+                        group: model.model for group, model in self.models.items()
+                    },
+                    "input_columns": PREDICTORS,
+                    "metadata": self.metadata,
+                },
+                f,
+            )
+
+    @classmethod
+    def load(cls, file_path):
+        """The cached model, or None if the file holds another format."""
+        from policyengine_uk_data.utils.qrf import QRF
+
+        with open(file_path, "rb") as f:
+            data = pickle.load(f)
+        if not isinstance(data, dict) or "models" not in data:
+            return None
+        models = {}
+        for group, fitted in data["models"].items():
+            model = QRF()
+            model.model = fitted
+            model.input_columns = data.get("input_columns", PREDICTORS)
+            models[group] = model
+        return cls(models, data.get("metadata", {}))
+
+
 def _income_model_matches_current_release(model) -> bool:
-    if getattr(model, "metadata", {}) != get_income_model_metadata():
+    if model is None or getattr(model, "metadata", {}) != get_income_model_metadata():
         return False
 
-    cached_outputs = set(getattr(model.model, "imputed_variables", []))
-    return cached_outputs == set(IMPUTATIONS)
+    models = getattr(model, "models", {})
+    if set(models) != set(EARNINGS_GROUPS):
+        return False
+    return all(
+        set(getattr(group_model.model, "imputed_variables", [])) == set(IMPUTATIONS)
+        for group_model in models.values()
+    )
 
 
 def save_imputation_models():
     """
-    Train and save income imputation model.
+    Train and save the income imputation model: one QRF per earnings group.
 
     Returns:
-        Trained QRF model for income imputation.
+        Trained ``EarningsGroupIncomeModel``.
     """
     from policyengine_uk_data.utils import QRF
 
-    income = QRF()
-    income.metadata = get_income_model_metadata()
     spi = pd.read_csv(SPI_TAB_FOLDER / SPI_TAB_FILENAME, delimiter="\t")
     spi = generate_spi_table(spi, sample_size=get_income_model_sample_size())
-    spi = spi[PREDICTORS + IMPUTATIONS]
-    income.fit(spi[PREDICTORS], spi[IMPUTATIONS])
+    models = {}
+    for group in EARNINGS_GROUPS:
+        training = spi[spi.earnings_group == group]
+        model = QRF()
+        model.fit(training[PREDICTORS], training[IMPUTATIONS])
+        models[group] = model
+    income = EarningsGroupIncomeModel(models, get_income_model_metadata())
     income.save(INCOME_MODEL_PATH)
     return income
 
@@ -180,46 +366,82 @@ def create_income_model(overwrite_existing: bool = False):
     """
     Create or load income imputation model.
 
-    If a cached model exists and its training metadata or output columns don't
-    match the current SPI release and ``IMPUTATIONS`` list, the cache is
-    discarded and the model is retrained.
+    If a cached model exists and its training metadata, earnings groups or
+    output columns don't match the current SPI release and ``IMPUTATIONS``
+    list, the cache is discarded and the model is retrained.
 
     Args:
         overwrite_existing: Whether to retrain model if it exists.
 
     Returns:
-        QRF model for income imputation.
+        ``EarningsGroupIncomeModel`` for income imputation.
     """
-    from policyengine_uk_data.utils.qrf import QRF
-
     if INCOME_MODEL_PATH.exists() and not overwrite_existing:
-        cached = QRF(file_path=INCOME_MODEL_PATH)
+        cached = EarningsGroupIncomeModel.load(INCOME_MODEL_PATH)
         if _income_model_matches_current_release(cached):
             return cached
-        # Cached model was trained against a different SPI release or output set.
+        # Cached model was trained against a different SPI release, output
+        # set or grouping.
     return save_imputation_models()
 
 
+def income_model_inputs(dataset: UKSingleYearDataset) -> pd.DataFrame:
+    """Predictors (age, gender, region) and earnings group of each person."""
+    sim = Microsimulation(dataset=dataset)
+    frame = sim.calculate_dataframe(["age", "gender", "region"])
+    inputs = pd.DataFrame({column: np.asarray(frame[column]) for column in PREDICTORS})
+    person = dataset.person
+    inputs["earnings_group"] = frs_earnings_group(
+        person.employment_status,
+        inputs.age,
+        person.employment_income,
+        person.self_employment_income,
+    )
+    return inputs
+
+
+def apply_income_draws(
+    person: pd.DataFrame, draws: pd.DataFrame, groups, output_variables
+) -> pd.DataFrame:
+    """Write ``draws`` over ``output_variables`` for every person in an
+    earnings group (missing draws become zero). ``NOT_IMPUTED`` rows keep
+    their own values."""
+    person = person.copy()
+    drawn = np.asarray(groups, dtype=object) != NOT_IMPUTED
+    for column in output_variables:
+        draw = np.nan_to_num(np.asarray(draws[column], dtype=float), nan=0.0)
+        own = (
+            np.asarray(person[column], dtype=float)
+            if column in person.columns
+            else np.zeros(len(person))
+        )
+        person[column] = np.where(drawn, draw, own)
+    return person
+
+
 def impute_over_incomes(
     dataset: UKSingleYearDataset, model, output_variables: list[str]
 ) -> pd.DataFrame:
     """
     Impute specified income components using trained model.
 
+    Each person draws from SPI records in their earnings group
+    (``frs_earnings_group``); children keep their own values.
+
     Args:
         dataset: PolicyEngine UK dataset to augment with income data.
+        model: Fitted ``EarningsGroupIncomeModel``.
         output_variables: List of income components to impute.
 
     Returns:
         DataFrame with imputed income components.
     """
     dataset = dataset.copy()
-    sim = Microsimulation(dataset=dataset)
-    input_df = sim.calculate_dataframe(["age", "gender", "region"])
+    input_df = income_model_inputs(dataset)
     output_df = model.predict(input_df)
-
-    for column in output_variables:
-        dataset.person[column] = output_df[column].fillna(0).values
+    dataset.person = apply_income_draws(
+        dataset.person, output_df, input_df.earnings_group, output_variables
+    )
 
     # Housing costs (rent, mortgage interest, mortgage capital) used to be
     # rescaled here by new_income_total / original_income_total across
@@ -239,8 +461,8 @@ def impute_income(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
     Impute detailed income components using trained model.
 
     Uses SPI-trained models to predict various income sources for individuals
-    based on age, gender, and region. Creates a synthetic population with
-    the imputed income data.
+    based on age, gender, region and earnings group. Creates a synthetic
+    population with the imputed income data.
 
     Args:
         dataset: PolicyEngine UK dataset to augment with income data.

From 313468b4282a751d82ec85d054947bd9358ffe67 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 12:39:12 -0400
Subject: [PATCH 23/70] Add hypothesis to the dev dependencies

Same lines as #514 and #524, so whichever lands second merges cleanly.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 pyproject.toml |  5 ++--
 uv.lock        | 73 +++++++++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 75 insertions(+), 3 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..7973343cb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,8 +45,9 @@ dev = [
     "yaml-changelog>=0.1.7",
     "itables",
     "quantile-forest",
-    "build",    "towncrier>=24.8.0",
-
+    "build",
+    "towncrier>=24.8.0",
+    "hypothesis>=6.168.3",
 ]
 
 [tool.setuptools]
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..672737ea1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
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 ]
 
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+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
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+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.168.3" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From 708ab3e06521e3790d73121ffb98ec35a32f08c9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 12:43:30 -0400
Subject: [PATCH 24/70] Test that SPI income draws follow earnings groups

Hypothesis properties for the FRS and SPI group mappings, the training
sample allocation, the per-group model's draws and the donor values kept
for children, plus a check on a built enhanced FRS. The cache tests write
the per-group format.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/tests/test_spi_build.py  |  55 +-
 .../tests/test_spi_income_earnings_groups.py  | 482 ++++++++++++++++++
 2 files changed, 504 insertions(+), 33 deletions(-)
 create mode 100644 policyengine_uk_data/tests/test_spi_income_earnings_groups.py

diff --git a/policyengine_uk_data/tests/test_spi_build.py b/policyengine_uk_data/tests/test_spi_build.py
index efb37f810..dc307442a 100644
--- a/policyengine_uk_data/tests/test_spi_build.py
+++ b/policyengine_uk_data/tests/test_spi_build.py
@@ -66,6 +66,7 @@
     "MCAS",
     "BPADUE",
     "MAIND",
+    "SEINC_NUM",
 ]
 
 
@@ -386,6 +387,24 @@ def __init__(self, path):
     ]
 
 
+def _write_income_model_cache(path, income_module, metadata):
+    """A cache in the per-earnings-group format, with stub fitted models."""
+    with path.open("wb") as f:
+        pickle.dump(
+            {
+                "models": {
+                    group: SimpleNamespace(
+                        imputed_variables=list(income_module.IMPUTATIONS)
+                    )
+                    for group in income_module.EARNINGS_GROUPS
+                },
+                "input_columns": income_module.PREDICTORS,
+                "metadata": metadata,
+            },
+            f,
+        )
+
+
 def test_income_model_cache_rejects_stale_spi_release(tmp_path, monkeypatch):
     from policyengine_uk_data.datasets.imputations import income as income_module
 
@@ -395,17 +414,7 @@ def test_income_model_cache_rejects_stale_spi_release(tmp_path, monkeypatch):
         "spi_release_name": "spi_2020_21",
         "spi_tab_filename": "put2021uk.tab",
     }
-    with cache.open("wb") as f:
-        pickle.dump(
-            {
-                "model": SimpleNamespace(
-                    imputed_variables=list(income_module.IMPUTATIONS)
-                ),
-                "input_columns": income_module.PREDICTORS,
-                "metadata": stale_metadata,
-            },
-            f,
-        )
+    _write_income_model_cache(cache, income_module, stale_metadata)
 
     sentinel = object()
     monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
@@ -423,17 +432,7 @@ def test_income_model_cache_rejects_stale_sample_size(tmp_path, monkeypatch):
         **income_module.get_income_model_metadata(),
         "sample_size": income_module.TESTING_INCOME_MODEL_SAMPLE_SIZE,
     }
-    with cache.open("wb") as f:
-        pickle.dump(
-            {
-                "model": SimpleNamespace(
-                    imputed_variables=list(income_module.IMPUTATIONS)
-                ),
-                "input_columns": income_module.PREDICTORS,
-                "metadata": stale_metadata,
-            },
-            f,
-        )
+    _write_income_model_cache(cache, income_module, stale_metadata)
 
     sentinel = object()
     monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
@@ -447,17 +446,7 @@ def test_income_model_cache_accepts_current_spi_release(tmp_path, monkeypatch):
 
     cache = tmp_path / "income_spi_2022_23.pkl"
     current_metadata = income_module.get_income_model_metadata()
-    with cache.open("wb") as f:
-        pickle.dump(
-            {
-                "model": SimpleNamespace(
-                    imputed_variables=list(income_module.IMPUTATIONS)
-                ),
-                "input_columns": income_module.PREDICTORS,
-                "metadata": current_metadata,
-            },
-            f,
-        )
+    _write_income_model_cache(cache, income_module, current_metadata)
 
     monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
     monkeypatch.setattr(
diff --git a/policyengine_uk_data/tests/test_spi_income_earnings_groups.py b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
new file mode 100644
index 000000000..5225aadca
--- /dev/null
+++ b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
@@ -0,0 +1,482 @@
+"""SPI income draws are conditioned on earnings group.
+
+Invariants (Hypothesis properties unless noted):
+
+1. FRS rows: children (FRS child table or under 16) are ``NOT_IMPUTED``.
+   Everyone else is in exactly one earnings group, which has pay if and only
+   if the main job is as an employee or the FRS records pay, and a trade if
+   and only if the main job is self-employment or the FRS records a profit.
+2. SPI records: the group has pay if and only if PAY + EPB + TAXTERM > 0, and
+   a trade if and only if SEINC_NUM = 1 or PROFITS > 0.
+3. Both mappings are monotone (more income never removes a source) and give
+   the same answer elementwise as row by row.
+4. Training sample: every group with weight gets at least
+   ``MIN_GROUP_SAMPLE_SHARE`` of the sample, groups without weight get none,
+   and groups above the floor get their weighted share.
+5. ``generate_spi_table`` resamples each group only from its own records.
+6. Model draws: pay is positive exactly in the groups with pay, profit is zero
+   in the groups without a trade, and ``NOT_IMPUTED`` rows get no draw.
+7. ``apply_income_draws`` overwrites drawn rows (missing draws become zero),
+   leaves ``NOT_IMPUTED`` rows and other columns alone, and does not mutate
+   its input.
+8. A cached model in another format or for another grouping is retrained.
+9. On a built enhanced FRS, SPI-synthetic rows' incomes agree with their
+   employment status (skipped when no build is present).
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import HealthCheck, given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.datasets.imputations import income as income_module
+from policyengine_uk_data.datasets.imputations.income import (
+    CHILD_STATUS,
+    EARNINGS_GROUPS,
+    EMPLOYEE,
+    EMPLOYEE_AND_SELF_EMPLOYED,
+    EMPLOYEE_STATUSES,
+    IMPUTATIONS,
+    MIN_GROUP_SAMPLE_SHARE,
+    NO_EARNINGS,
+    NOT_IMPUTED,
+    PREDICTORS,
+    SELF_EMPLOYED,
+    SELF_EMPLOYED_STATUSES,
+    EarningsGroupIncomeModel,
+    apply_income_draws,
+    earnings_group_sample_sizes,
+    frs_earnings_group,
+    generate_spi_table,
+    spi_earnings_group,
+)
+
+PAY_GROUPS = {EMPLOYEE, EMPLOYEE_AND_SELF_EMPLOYED}
+TRADE_GROUPS = {SELF_EMPLOYED, EMPLOYEE_AND_SELF_EMPLOYED}
+NON_WORKING_STATUSES = (
+    "UNEMPLOYED",
+    "RETIRED",
+    "STUDENT",
+    "CARER",
+    "LONG_TERM_DISABLED",
+    "SHORT_TERM_DISABLED",
+    "OTHER_INACTIVE",
+)
+STATUSES = (
+    (CHILD_STATUS,) + EMPLOYEE_STATUSES + SELF_EMPLOYED_STATUSES + NON_WORKING_STATUSES
+)
+REGIONS = ("LONDON", "WALES", "SCOTLAND", "NORTH_EAST")
+
+amounts = st.one_of(
+    st.just(0.0), st.floats(0.01, 5e6, allow_nan=False, allow_infinity=False)
+)
+
+
+def test_statuses_are_policyengine_uk_enum_names():
+    from policyengine_uk.variables.household.income.employment_status import (
+        EmploymentStatus,
+    )
+
+    names = {status.name for status in EmploymentStatus}
+    # Every status the FRS build writes is covered: an employee, self-employed,
+    # child or out-of-work group.
+    assert set(STATUSES) == names
+
+
+@st.composite
+def frs_people(draw):
+    n = draw(st.integers(1, 40))
+    return pd.DataFrame(
+        {
+            "employment_status": draw(
+                st.lists(st.sampled_from(STATUSES), min_size=n, max_size=n)
+            ),
+            "age": draw(st.lists(st.integers(0, 95), min_size=n, max_size=n)),
+            "employment_income": draw(st.lists(amounts, min_size=n, max_size=n)),
+            "self_employment_income": draw(st.lists(amounts, min_size=n, max_size=n)),
+        }
+    )
+
+
+def _frs_groups(people: pd.DataFrame) -> np.ndarray:
+    return frs_earnings_group(
+        people.employment_status,
+        people.age,
+        people.employment_income,
+        people.self_employment_income,
+    )
+
+
+@given(frs_people())
+def test_frs_group_follows_status_and_recorded_earnings(people):
+    groups = _frs_groups(people)
+    status = people.employment_status.to_numpy()
+    child = (status == CHILD_STATUS) | (people.age.to_numpy() < 16)
+
+    assert (groups[child] == NOT_IMPUTED).all()
+    adult = groups[~child]
+    assert np.isin(adult, EARNINGS_GROUPS).all()
+
+    has_pay = np.isin(status, EMPLOYEE_STATUSES) | (
+        people.employment_income.to_numpy() > 0
+    )
+    has_trade = np.isin(status, SELF_EMPLOYED_STATUSES) | (
+        people.self_employment_income.to_numpy() > 0
+    )
+    np.testing.assert_array_equal(np.isin(adult, list(PAY_GROUPS)), has_pay[~child])
+    np.testing.assert_array_equal(np.isin(adult, list(TRADE_GROUPS)), has_trade[~child])
+
+
+@given(frs_people(), st.floats(0.01, 1e6), st.sampled_from(["pay", "profit"]))
+def test_frs_group_monotone_in_income(people, extra, source):
+    before = _frs_groups(people)
+    column = "employment_income" if source == "pay" else "self_employment_income"
+    after = _frs_groups(people.assign(**{column: people[column] + extra}))
+    imputed = before != NOT_IMPUTED
+    np.testing.assert_array_equal(after == NOT_IMPUTED, ~imputed)
+    gained, other = (
+        (PAY_GROUPS, TRADE_GROUPS) if source == "pay" else (TRADE_GROUPS, PAY_GROUPS)
+    )
+    # The source that grew is now present; the other source is unchanged.
+    assert np.isin(after[imputed], list(gained)).all()
+    np.testing.assert_array_equal(
+        np.isin(after[imputed], list(other)), np.isin(before[imputed], list(other))
+    )
+
+
+@given(frs_people())
+def test_frs_group_elementwise_equals_rowwise(people):
+    vectorised = _frs_groups(people)
+    rowwise = [
+        frs_earnings_group(
+            [row.employment_status],
+            [row.age],
+            [row.employment_income],
+            [row.self_employment_income],
+        )[0]
+        for row in people.itertuples()
+    ]
+    as_lists = frs_earnings_group(
+        people.employment_status.tolist(),
+        people.age.tolist(),
+        people.employment_income.tolist(),
+        people.self_employment_income.tolist(),
+    )
+    assert list(vectorised) == rowwise == list(as_lists)
+
+
+@given(
+    st.lists(
+        st.tuples(amounts, amounts, st.sampled_from([-1, 0, 1])),
+        min_size=1,
+        max_size=40,
+    )
+)
+def test_spi_group_follows_pay_and_self_employment_pages(records):
+    pay, profit, indicator = (np.array(column) for column in zip(*records))
+    groups = spi_earnings_group(pay, profit, indicator)
+    assert np.isin(groups, EARNINGS_GROUPS).all()
+    np.testing.assert_array_equal(np.isin(groups, list(PAY_GROUPS)), pay > 0)
+    np.testing.assert_array_equal(
+        np.isin(groups, list(TRADE_GROUPS)), (indicator == 1) | (profit > 0)
+    )
+    rowwise = [spi_earnings_group([p], [q], [i])[0] for p, q, i in records]
+    assert list(groups) == rowwise
+
+
+@given(
+    st.dictionaries(
+        st.sampled_from(EARNINGS_GROUPS),
+        st.one_of(st.just(0.0), st.floats(1e-3, 1e8)),
+        min_size=1,
+    ),
+    st.integers(1, 200_000),
+)
+def test_group_sample_sizes(weights, sample_size):
+    sizes = earnings_group_sample_sizes(weights, sample_size)
+    positive = {group for group, w in weights.items() if w > 0}
+    assert set(sizes) == positive
+    if not positive:
+        return
+    floor = int(np.ceil(MIN_GROUP_SAMPLE_SHARE * sample_size))
+    total = sum(weights[group] for group in positive)
+    for group, size in sizes.items():
+        share = sample_size * weights[group] / total
+        assert size >= floor
+        if share > floor + 1:
+            assert abs(size - share) <= 0.5 + 1e-9
+    assert sizes == earnings_group_sample_sizes(weights, sample_size)
+
+
+def _raw_spi(rng: np.random.Generator, n: int) -> pd.DataFrame:
+    """A raw SPI-shaped frame with all four earnings groups."""
+    group = rng.choice(EARNINGS_GROUPS, n)
+    has_pay = np.isin(group, list(PAY_GROUPS))
+    has_trade = np.isin(group, list(TRADE_GROUPS))
+    raw = {
+        column: np.zeros(n)
+        for column in set(income_module.SPI_RENAMES.values())
+        | {"PAY", "EPB", "TAXTERM", "SEINC_NUM", "GIFTINV"}
+    }
+    raw["PAY"] = np.where(has_pay, rng.lognormal(10, 1, n), 0.0)
+    raw["EPB"] = np.where(has_pay & (rng.random(n) < 0.1), 500.0, 0.0)
+    # Three in ten traders make no assessable profit.
+    raw["PROFITS"] = np.where(
+        has_trade & (rng.random(n) < 0.7), rng.lognormal(9, 1, n), 0.0
+    )
+    raw["SEINC_NUM"] = has_trade.astype(int)
+    for column in ("INCBBS", "DIVIDENDS", "PENSION", "INCPROP", "GIFTAID", "GIFTINV"):
+        raw[column] = rng.exponential(1_000, n) * (rng.random(n) < 0.3)
+    raw["FACT"] = rng.uniform(1, 500, n)
+    raw["SEX"] = rng.choice([1, 2], n)
+    raw["GORCODE"] = rng.choice([1, 7, 10, 11], n)
+    raw["AGERANGE"] = rng.choice([1, 2, 3, 4, 5, 6, 7], n)
+    return pd.DataFrame(raw)
+
+
+@settings(deadline=None, max_examples=25)
+@given(st.integers(0, 2**31 - 1), st.integers(50, 2_000))
+def test_generate_spi_table_resamples_within_groups(seed, sample_size):
+    raw = _raw_spi(np.random.default_rng(seed), 600)
+    table = generate_spi_table(raw.copy(), seed=seed % 1000, sample_size=sample_size)
+    np.testing.assert_array_equal(
+        table.earnings_group.to_numpy(),
+        spi_earnings_group(
+            table.employment_income, table.self_employment_income, table.SEINC_NUM
+        ),
+    )
+    raw_groups = spi_earnings_group(
+        raw.PAY + raw.EPB + raw.TAXTERM, raw.PROFITS, raw.SEINC_NUM
+    )
+    weights = raw.FACT.groupby(raw_groups).sum().to_dict()
+    assert table.earnings_group.value_counts().to_dict() == (
+        earnings_group_sample_sizes(weights, sample_size)
+    )
+
+
+@pytest.fixture(scope="module")
+def fitted_model():
+    """A real per-group QRF fitted through ``generate_spi_table``."""
+    from policyengine_uk_data.utils.qrf import QRF
+
+    table = generate_spi_table(
+        _raw_spi(np.random.default_rng(1), 4_000), seed=0, sample_size=2_000
+    )
+    models = {}
+    for group in EARNINGS_GROUPS:
+        training = table[table.earnings_group == group]
+        model = QRF()
+        model.fit(training[PREDICTORS], training[IMPUTATIONS])
+        models[group] = model
+    return EarningsGroupIncomeModel(models, income_module.get_income_model_metadata())
+
+
+@st.composite
+def model_inputs(draw):
+    n = draw(st.integers(1, 30))
+    return pd.DataFrame(
+        {
+            "age": draw(st.lists(st.floats(16, 90), min_size=n, max_size=n)),
+            "gender": draw(
+                st.lists(st.sampled_from(["MALE", "FEMALE"]), min_size=n, max_size=n)
+            ),
+            "region": draw(
+                st.lists(
+                    st.sampled_from(["NORTH_EAST", "LONDON", "WALES", "SCOTLAND"]),
+                    min_size=n,
+                    max_size=n,
+                )
+            ),
+            "earnings_group": draw(
+                st.lists(
+                    st.sampled_from(EARNINGS_GROUPS + (NOT_IMPUTED,)),
+                    min_size=n,
+                    max_size=n,
+                )
+            ),
+        },
+        index=draw(
+            st.lists(st.integers(0, 10**6), min_size=n, max_size=n, unique=True)
+        ),
+    )
+
+
+@settings(
+    deadline=None,
+    max_examples=40,
+    suppress_health_check=[HealthCheck.function_scoped_fixture],
+)
+@given(model_inputs())
+def test_model_draws_agree_with_group(fitted_model, inputs):
+    draws = fitted_model.predict(inputs)
+    assert list(draws.columns) == IMPUTATIONS
+    assert draws.index.equals(inputs.index)
+    groups = inputs.earnings_group.to_numpy()
+    drawn = groups != NOT_IMPUTED
+    assert draws[~drawn].isna().all().all()
+    assert draws[drawn].notna().all().all()
+    pay = draws.employment_income.to_numpy()[drawn]
+    profit = draws.self_employment_income.to_numpy()[drawn]
+    np.testing.assert_array_equal(pay > 0, np.isin(groups[drawn], list(PAY_GROUPS)))
+    assert (profit[~np.isin(groups[drawn], list(TRADE_GROUPS))] == 0).all()
+    assert (draws[drawn].to_numpy() >= 0).all()
+
+
+def test_model_draws_some_profit_for_traders(fitted_model):
+    """The trade groups draw zero and positive profits, as the SPI has both."""
+    n = 400
+    inputs = pd.DataFrame(
+        {
+            "age": np.linspace(20, 80, n),
+            "gender": ["MALE", "FEMALE"] * (n // 2),
+            "region": ["LONDON"] * n,
+            "earnings_group": [SELF_EMPLOYED] * n,
+        }
+    )
+    profit = fitted_model.predict(inputs).self_employment_income
+    assert 0.3 < (profit > 0).mean() < 1
+
+
+@st.composite
+def person_and_draws(draw):
+    n = draw(st.integers(1, 30))
+    columns = ["employment_income", "dividend_income", "rent"]
+    person = pd.DataFrame(
+        {c: draw(st.lists(amounts, min_size=n, max_size=n)) for c in columns}
+    )
+    draws = pd.DataFrame(
+        {
+            c: draw(
+                st.lists(st.one_of(amounts, st.just(np.nan)), min_size=n, max_size=n)
+            )
+            for c in IMPUTATIONS
+        }
+    )
+    groups = draw(
+        st.lists(
+            st.sampled_from(EARNINGS_GROUPS + (NOT_IMPUTED,)), min_size=n, max_size=n
+        )
+    )
+    outputs = draw(
+        st.lists(st.sampled_from(IMPUTATIONS), min_size=1, max_size=4, unique=True)
+    )
+    return person, draws, np.array(groups, dtype=object), outputs
+
+
+@given(person_and_draws())
+def test_apply_income_draws(case):
+    person, draws, groups, outputs = case
+    before = person.copy()
+    result = apply_income_draws(person, draws, groups, outputs)
+    pd.testing.assert_frame_equal(person, before)
+
+    kept = groups == NOT_IMPUTED
+    for column in outputs:
+        own = (
+            before[column].to_numpy()
+            if column in before.columns
+            else np.zeros(len(before))
+        )
+        np.testing.assert_array_equal(result[column].to_numpy()[kept], own[kept])
+        np.testing.assert_array_equal(
+            result[column].to_numpy()[~kept],
+            np.nan_to_num(draws[column].to_numpy(dtype=float)[~kept], nan=0.0),
+        )
+    for column in set(before.columns) - set(outputs):
+        pd.testing.assert_series_equal(result[column], before[column])
+
+
+def test_old_single_model_cache_is_retrained(tmp_path, monkeypatch):
+    import pickle
+    from types import SimpleNamespace
+
+    cache = tmp_path / "income_spi_2022_23.pkl"
+    old_metadata = {
+        key: value
+        for key, value in income_module.get_income_model_metadata().items()
+        if key not in ("earnings_groups", "min_group_sample_share")
+    }
+    with cache.open("wb") as f:
+        pickle.dump(
+            {
+                "model": SimpleNamespace(imputed_variables=list(IMPUTATIONS)),
+                "input_columns": PREDICTORS,
+                "metadata": old_metadata,
+            },
+            f,
+        )
+    sentinel = object()
+    monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
+    monkeypatch.setattr(income_module, "save_imputation_models", lambda: sentinel)
+    assert income_module.create_income_model() is sentinel
+
+
+def test_cache_missing_a_group_is_retrained(tmp_path, monkeypatch, fitted_model):
+    cache = tmp_path / "income_spi_2022_23.pkl"
+    partial = EarningsGroupIncomeModel(
+        {g: m for g, m in fitted_model.models.items() if g != SELF_EMPLOYED},
+        income_module.get_income_model_metadata(),
+    )
+    partial.save(cache)
+    sentinel = object()
+    monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
+    monkeypatch.setattr(income_module, "save_imputation_models", lambda: sentinel)
+    assert income_module.create_income_model() is sentinel
+
+
+def test_current_cache_round_trips(tmp_path, monkeypatch, fitted_model):
+    cache = tmp_path / "income_spi_2022_23.pkl"
+    fitted_model.save(cache)
+    monkeypatch.setattr(income_module, "INCOME_MODEL_PATH", cache)
+    monkeypatch.setattr(
+        income_module,
+        "save_imputation_models",
+        lambda: pytest.fail("a current cache should be reused"),
+    )
+    loaded = income_module.create_income_model()
+    inputs = pd.DataFrame(
+        {
+            "age": [30.0, 50.0, 70.0, 10.0],
+            "gender": ["MALE", "FEMALE", "MALE", "FEMALE"],
+            "region": ["LONDON", "WALES", "SCOTLAND", "LONDON"],
+            "earnings_group": [EMPLOYEE, SELF_EMPLOYED, NO_EARNINGS, NOT_IMPUTED],
+        }
+    )
+    pd.testing.assert_frame_equal(loaded.predict(inputs), fitted_model.predict(inputs))
+
+
+def test_built_enhanced_frs_spi_rows_agree_with_status(enhanced_frs):
+    person = enhanced_frs.person
+    household = enhanced_frs.household.set_index("household_id")
+    spi = person.person_household_id.map(household.household_is_spi_synthetic)
+    if not spi.any():
+        pytest.skip("No SPI-synthetic rows in this build")
+    status = person.employment_status.astype(str)
+    rows = person[spi.to_numpy(dtype=bool)]
+    rows_status = status[spi.to_numpy(dtype=bool)]
+
+    employees = rows_status.isin(EMPLOYEE_STATUSES)
+    assert (rows.employment_income[employees] > 0).all()
+
+    children = rows_status.eq(CHILD_STATUS)
+    assert (rows.employment_income[children] == 0).all()
+    assert (rows.self_employment_income[children] == 0).all()
+
+    # The SPI self-employed group draws a profit for about nine in ten
+    # (zero for traders who break even or make a loss); before the groups
+    # it was under one in ten.
+    self_employed = rows_status.isin(SELF_EMPLOYED_STATUSES)
+    if self_employed.sum() >= 50:
+        assert (rows.self_employment_income[self_employed] > 0).mean() > 0.6
+
+    # Out of work: earnings only where the FRS donor recorded some, which
+    # the FRS does for almost no one.
+    out_of_work = rows_status.isin(NON_WORKING_STATUSES)
+    if out_of_work.sum() >= 50:
+        earning = (rows.employment_income[out_of_work] > 0) | (
+            rows.self_employment_income[out_of_work] > 0
+        )
+        assert earning.mean() < 0.02

From fb0266593cab1dbe4464c4c6db63e4868af43db5 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 13:20:46 -0400
Subject: [PATCH 25/70] Suppress small counts in the unknown-EMPSTATI build
 error

CI build logs are public, so the error now names the unknown codes without
per-code counts and reports a total under 10 adults as "fewer than 10".
Raised by the microcosm port session; no change on current FRS releases,
where every adult has a code from 1 to 11.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py              | 11 +++++++----
 .../tests/test_frs_employment_status.py           | 15 +++++++++++++++
 2 files changed, 22 insertions(+), 4 deletions(-)

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 05c7c8dc5..e53e1ede3 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -142,11 +142,14 @@ def derive_employment_status_from_frs(empstati, is_adult_record) -> np.ndarray:
     adult_status = codes.map(FRS_EMPSTATI_EMPLOYMENT_STATUS).to_numpy()
     unknown = is_adult_record & pd.isna(adult_status)
     if unknown.any():
-        counts = codes[unknown].value_counts(dropna=False).sort_index().to_dict()
+        # Build logs are public: name the codes, not per-code counts, and
+        # suppress a total under 10 adults.
+        n = int(unknown.sum())
+        adults = f"{n} FRS adults" if n >= 10 else "Fewer than 10 FRS adults"
         raise ValueError(
-            f"{int(unknown.sum())} FRS adults have EMPSTATI codes missing from "
-            f"FRS_EMPSTATI_EMPLOYMENT_STATUS (code: adults): {counts}. Map "
-            "them from the release's data dictionary."
+            f"{adults} have EMPSTATI codes missing from "
+            f"FRS_EMPSTATI_EMPLOYMENT_STATUS: {sorted(set(codes[unknown].astype(str)))}. "
+            "Map them from the release's data dictionary."
         )
     return np.where(is_adult_record, adult_status, EmploymentStatus.CHILD.name)
 
diff --git a/policyengine_uk_data/tests/test_frs_employment_status.py b/policyengine_uk_data/tests/test_frs_employment_status.py
index b035358fe..c09582f87 100644
--- a/policyengine_uk_data/tests/test_frs_employment_status.py
+++ b/policyengine_uk_data/tests/test_frs_employment_status.py
@@ -97,6 +97,21 @@ def test_unknown_adult_code_fails_the_build(code):
         derive_employment_status_from_frs([1, code], [True, True])
 
 
+@given(st.integers(1, 30), st.integers(1, 30))
+def test_failure_message_suppresses_small_counts(n_twelve, n_thirteen):
+    codes = [12] * n_twelve + [13] * n_thirteen
+    with pytest.raises(ValueError) as error:
+        derive_employment_status_from_frs(codes, [True] * len(codes))
+    total = n_twelve + n_thirteen
+    prefix = f"{total} FRS adults" if total >= 10 else "Fewer than 10 FRS adults"
+    message = str(error.value)
+    assert message.startswith(prefix)
+    assert "['12.0', '13.0']" in message
+    # No other number (such as a per-code count) is disclosed.
+    rest = message.removeprefix(prefix).replace("['12.0', '13.0']", "")
+    assert not any(character.isdigit() for character in rest)
+
+
 @given(people)
 def test_each_row_maps_on_its_own(rows):
     result = derive(rows)

From 89c48e074f77f196b1a91bbb2a69f78bb29884fc Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 13:24:41 -0400
Subject: [PATCH 26/70] List unknown EMPSTATI codes without Series.astype(str)

Under pandas 3, Series.astype(str) keeps NaN as a float, so sorting a set
holding a blank code and another unknown code raised TypeError and hid the
codes. Format from the float codes instead ("0, 12, blank"), which behaves
the same under pandas 2 and 3. Found by the microcosm port session's
differential under pandas 3.0.3.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py                  |  7 +++++--
 .../tests/test_frs_employment_status.py               | 11 +++++++++--
 2 files changed, 14 insertions(+), 4 deletions(-)

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index e53e1ede3..3195cb18e 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -146,10 +146,13 @@ def derive_employment_status_from_frs(empstati, is_adult_record) -> np.ndarray:
         # suppress a total under 10 adults.
         n = int(unknown.sum())
         adults = f"{n} FRS adults" if n >= 10 else "Fewer than 10 FRS adults"
+        bad = codes[unknown]
+        listed = [f"{code:g}" for code in sorted(bad.dropna().unique())]
+        listed += ["blank"] if bad.isna().any() else []
         raise ValueError(
             f"{adults} have EMPSTATI codes missing from "
-            f"FRS_EMPSTATI_EMPLOYMENT_STATUS: {sorted(set(codes[unknown].astype(str)))}. "
-            "Map them from the release's data dictionary."
+            f"FRS_EMPSTATI_EMPLOYMENT_STATUS: {', '.join(listed)}. Map them "
+            "from the release's data dictionary."
         )
     return np.where(is_adult_record, adult_status, EmploymentStatus.CHILD.name)
 
diff --git a/policyengine_uk_data/tests/test_frs_employment_status.py b/policyengine_uk_data/tests/test_frs_employment_status.py
index c09582f87..283f46b79 100644
--- a/policyengine_uk_data/tests/test_frs_employment_status.py
+++ b/policyengine_uk_data/tests/test_frs_employment_status.py
@@ -97,6 +97,13 @@ def test_unknown_adult_code_fails_the_build(code):
         derive_employment_status_from_frs([1, code], [True, True])
 
 
+def test_failure_message_lists_blank_and_numeric_codes():
+    # Formatted from the float codes, not Series.astype(str), whose NaN
+    # handling differs between pandas 2 and 3.
+    with pytest.raises(ValueError, match=": 0, 12, blank. "):
+        derive_employment_status_from_frs([np.nan, 0, 12, 1], [True] * 4)
+
+
 @given(st.integers(1, 30), st.integers(1, 30))
 def test_failure_message_suppresses_small_counts(n_twelve, n_thirteen):
     codes = [12] * n_twelve + [13] * n_thirteen
@@ -106,9 +113,9 @@ def test_failure_message_suppresses_small_counts(n_twelve, n_thirteen):
     prefix = f"{total} FRS adults" if total >= 10 else "Fewer than 10 FRS adults"
     message = str(error.value)
     assert message.startswith(prefix)
-    assert "['12.0', '13.0']" in message
+    assert ": 12, 13. " in message
     # No other number (such as a per-code count) is disclosed.
-    rest = message.removeprefix(prefix).replace("['12.0', '13.0']", "")
+    rest = message.removeprefix(prefix).replace(": 12, 13. ", "")
     assert not any(character.isdigit() for character in rest)
 
 

From 40fbfea5ba554ca583848237de1a020cf3bb650e Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 14:33:33 -0400
Subject: [PATCH 27/70] Calibrate to LFS counts of employees and the
 self-employed

With SPI incomes drawn by earnings group, the SPI-synthetic rows no longer
give pay to children and people out of work. Calibration then met the HMRC
counts of income-tax payers with employment income, which are annual and
include part-year earners, by moving about 4m people's weight from out of
work to employees (32.2m against the LFS's 29.1m for 2024). Nothing tied
employment status to an official count.

Add national targets for the ONS LFS employee and self-employed levels
(MGRN, MGRQ annual averages, 2022-2025), counted from the FRS ILO main-job
status. Move the status groups to utils/employment_status.py so the income
imputation and the targets share them.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/imputations/income.py            |  9 ++-
 .../targets/sources/ons_labour_market.py      | 81 +++++++++++++++++++
 .../utils/employment_status.py                |  7 ++
 3 files changed, 93 insertions(+), 4 deletions(-)
 create mode 100644 policyengine_uk_data/targets/sources/ons_labour_market.py
 create mode 100644 policyengine_uk_data/utils/employment_status.py

diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
index 7e1d6c71b..f1d9d130e 100644
--- a/policyengine_uk_data/datasets/imputations/income.py
+++ b/policyengine_uk_data/datasets/imputations/income.py
@@ -25,6 +25,11 @@
 )
 from policyengine_uk_data.utils.stack import stack_datasets
 from policyengine_uk_data.utils.subsample import subsample_dataset
+from policyengine_uk_data.utils.employment_status import (
+    CHILD_STATUS,
+    EMPLOYEE_STATUSES,
+    SELF_EMPLOYED_STATUSES,
+)
 
 SPI_TAB_FOLDER = STORAGE_FOLDER / SPI_RELEASE_NAME
 SPI_RENAMES = dict(
@@ -79,10 +84,6 @@ def _spi_age_bounds(age_code) -> tuple[int, int]:
 # record of them as earners.
 NOT_IMPUTED = "NOT_IMPUTED"
 
-# policyengine-uk EmploymentStatus names.
-EMPLOYEE_STATUSES = ("FT_EMPLOYED", "PT_EMPLOYED")
-SELF_EMPLOYED_STATUSES = ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED")
-CHILD_STATUS = "CHILD"
 
 # Every earnings group gets at least this share of the training sample, so the
 # small self-employed groups are not fitted on a few thousand records.
diff --git a/policyengine_uk_data/targets/sources/ons_labour_market.py b/policyengine_uk_data/targets/sources/ons_labour_market.py
new file mode 100644
index 000000000..6a6f50639
--- /dev/null
+++ b/policyengine_uk_data/targets/sources/ons_labour_market.py
@@ -0,0 +1,81 @@
+"""ONS Labour Force Survey employment levels: employees and the self-employed.
+
+The FRS records each adult's ILO employment status for their main job
+(EMPSTATI, `employment_status`), the same concept the LFS uses. Without these
+targets nothing in the calibration ties the number of employees or
+self-employed people to an official count. The HMRC counts of income-tax
+payers with employment income (SPI tables 3.6 and 3.15) are annual: they
+include people with pay for part of the year whose status at interview is
+out of work. So calibration can meet them by moving weight from people out of
+work to employees.
+
+The FRS child table (dependent children, including 16-19-year-olds in
+non-advanced education) carries no employment status, so their jobs are not
+counted here, while the LFS counts them. On the FRS 2024-25 grossing weights,
+the FRS has 28.1m employees against the LFS's 29.1m for 2024.
+
+Source: ONS Labour market overview, series MGRN (LFS: Employees: UK: All,
+aged 16 and over, seasonally adjusted) and MGRQ (LFS: Self-employed: UK:
+All), annual four-quarter averages, release of 14 September 2026.
+"""
+
+import numpy as np
+
+from policyengine_uk_data.targets.schema import (
+    GeographicLevel,
+    Target,
+    Unit,
+)
+from policyengine_uk_data.utils.employment_status import (
+    EMPLOYEE_STATUSES,
+    SELF_EMPLOYED_STATUSES,
+)
+
+_REF = (
+    "https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/"
+    "employmentandemployeetypes/timeseries/{series}/lms"
+)
+
+# Annual four-quarter averages, people.
+EMPLOYEES = {
+    2022: 28_564_000.0,
+    2023: 28_821_000.0,
+    2024: 29_126_000.0,
+    2025: 29_590_000.0,
+}
+SELF_EMPLOYED = {
+    2022: 4_244_000.0,
+    2023: 4_380_000.0,
+    2024: 4_340_000.0,
+    2025: 4_395_000.0,
+}
+
+
+def _status_count(statuses: tuple[str, ...]):
+    def compute(ctx, target, year) -> np.ndarray:
+        status = np.asarray(ctx.pe_person("employment_status")).astype(str)
+        return ctx.household_from_person(np.isin(status, statuses).astype(float))
+
+    return compute
+
+
+def get_targets() -> list[Target]:
+    return [
+        Target(
+            name=f"ons/lfs_{name}",
+            variable="employment_status",
+            source="ons",
+            unit=Unit.COUNT,
+            geographic_level=GeographicLevel.NATIONAL,
+            geo_code="K02000001",
+            geo_name="United Kingdom",
+            values=dict(values),
+            is_count=True,
+            reference_url=_REF.format(series=series),
+            custom_compute=_status_count(statuses),
+        )
+        for name, values, series, statuses in (
+            ("employees", EMPLOYEES, "mgrn", EMPLOYEE_STATUSES),
+            ("self_employed", SELF_EMPLOYED, "mgrq", SELF_EMPLOYED_STATUSES),
+        )
+    ]
diff --git a/policyengine_uk_data/utils/employment_status.py b/policyengine_uk_data/utils/employment_status.py
new file mode 100644
index 000000000..950cbc49f
--- /dev/null
+++ b/policyengine_uk_data/utils/employment_status.py
@@ -0,0 +1,7 @@
+"""Groups of policyengine-uk ``EmploymentStatus`` names (the FRS ILO main-job status)."""
+
+EMPLOYEE_STATUSES = ("FT_EMPLOYED", "PT_EMPLOYED")
+SELF_EMPLOYED_STATUSES = ("FT_SELF_EMPLOYED", "PT_SELF_EMPLOYED")
+# The FRS child table: dependent children, including 16-19-year-olds in
+# non-advanced education.
+CHILD_STATUS = "CHILD"

From beccbad55d3ba71dbebcb392ac8b464f4ab87434 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 15:26:13 -0400
Subject: [PATCH 28/70] Don't fail the SPI earnings-group properties on slow
 input generation

A loaded runner tripped Hypothesis's too_slow health check while the
properties themselves held. Drop the deadline and that health check.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_spi_income_earnings_groups.py        | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/policyengine_uk_data/tests/test_spi_income_earnings_groups.py b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
index 5225aadca..f6233a5df 100644
--- a/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
+++ b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
@@ -70,6 +70,9 @@
 )
 REGIONS = ("LONDON", "WALES", "SCOTLAND", "NORTH_EAST")
 
+# Generation and QRF draws are slow on a loaded runner; that is not a failure.
+RELAXED = settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
+
 amounts = st.one_of(
     st.just(0.0), st.floats(0.01, 5e6, allow_nan=False, allow_infinity=False)
 )
@@ -110,6 +113,7 @@ def _frs_groups(people: pd.DataFrame) -> np.ndarray:
     )
 
 
+@RELAXED
 @given(frs_people())
 def test_frs_group_follows_status_and_recorded_earnings(people):
     groups = _frs_groups(people)
@@ -130,6 +134,7 @@ def test_frs_group_follows_status_and_recorded_earnings(people):
     np.testing.assert_array_equal(np.isin(adult, list(TRADE_GROUPS)), has_trade[~child])
 
 
+@RELAXED
 @given(frs_people(), st.floats(0.01, 1e6), st.sampled_from(["pay", "profit"]))
 def test_frs_group_monotone_in_income(people, extra, source):
     before = _frs_groups(people)
@@ -147,6 +152,7 @@ def test_frs_group_monotone_in_income(people, extra, source):
     )
 
 
+@RELAXED
 @given(frs_people())
 def test_frs_group_elementwise_equals_rowwise(people):
     vectorised = _frs_groups(people)
@@ -168,6 +174,7 @@ def test_frs_group_elementwise_equals_rowwise(people):
     assert list(vectorised) == rowwise == list(as_lists)
 
 
+@RELAXED
 @given(
     st.lists(
         st.tuples(amounts, amounts, st.sampled_from([-1, 0, 1])),
@@ -187,6 +194,7 @@ def test_spi_group_follows_pay_and_self_employment_pages(records):
     assert list(groups) == rowwise
 
 
+@RELAXED
 @given(
     st.dictionaries(
         st.sampled_from(EARNINGS_GROUPS),
@@ -237,7 +245,7 @@ def _raw_spi(rng: np.random.Generator, n: int) -> pd.DataFrame:
     return pd.DataFrame(raw)
 
 
-@settings(deadline=None, max_examples=25)
+@settings(deadline=None, max_examples=25, suppress_health_check=[HealthCheck.too_slow])
 @given(st.integers(0, 2**31 - 1), st.integers(50, 2_000))
 def test_generate_spi_table_resamples_within_groups(seed, sample_size):
     raw = _raw_spi(np.random.default_rng(seed), 600)
@@ -307,7 +315,7 @@ def model_inputs(draw):
 @settings(
     deadline=None,
     max_examples=40,
-    suppress_health_check=[HealthCheck.function_scoped_fixture],
+    suppress_health_check=[HealthCheck.function_scoped_fixture, HealthCheck.too_slow],
 )
 @given(model_inputs())
 def test_model_draws_agree_with_group(fitted_model, inputs):
@@ -366,6 +374,7 @@ def person_and_draws(draw):
     return person, draws, np.array(groups, dtype=object), outputs
 
 
+@RELAXED
 @given(person_and_draws())
 def test_apply_income_draws(case):
     person, draws, groups, outputs = case

From fc46a560f303acbd48a3aaf26c41fc1158b9e0c6 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 15:47:54 -0400
Subject: [PATCH 29/70] Report, not train on, the local HMRC employment-income
 counts

HMRC's counts of income-tax payers with employment income by area are
annual, so they include people with pay for part of the year whose FRS
status at interview is out of work. Trained on, they held employees at
32m against the LFS target of 29.6m. The area amounts and the national
counts by income band still train; the counts stay in the calibration
logs as validation targets.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/create_datasets.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/policyengine_uk_data/datasets/create_datasets.py b/policyengine_uk_data/datasets/create_datasets.py
index 29a73028b..d3c1ef0b6 100644
--- a/policyengine_uk_data/datasets/create_datasets.py
+++ b/policyengine_uk_data/datasets/create_datasets.py
@@ -7,6 +7,15 @@
 
 logging.basicConfig(level=logging.INFO)
 
+# Local targets the calibration logs but does not train on. HMRC's counts of
+# income-tax payers with employment income by area (SPI table 3.15) are annual:
+# they include people with pay for part of the year whose FRS status at
+# interview is out of work, and who therefore have no pay in the FRS. Training
+# on them moves weight from people out of work to employees, away from the LFS
+# employee count (targets/sources/ons_labour_market.py). The area amounts of
+# employment income, and the national counts by income band, still train.
+VALIDATION_ONLY_LOCAL_TARGETS = ["hmrc/employment_income/count"]
+
 
 def _get_positive_int_env(name: str, default: int) -> int:
     raw_value = os.environ.get(name)
@@ -252,7 +261,7 @@ def main():
                 area_count=650,
                 weight_file="parliamentary_constituency_weights.h5",
                 dataset_key=str(frs_release.calibration_year),
-                excluded_training_targets=[],
+                excluded_training_targets=VALIDATION_ONLY_LOCAL_TARGETS,
                 log_csv="constituency_calibration_log.csv",
                 verbose=True,  # Enable nested progress display
                 area_name="Constituency",
@@ -279,7 +288,7 @@ def main():
                 area_count=360,
                 weight_file="local_authority_weights.h5",
                 dataset_key=str(frs_release.calibration_year),
-                excluded_training_targets=[],
+                excluded_training_targets=VALIDATION_ONLY_LOCAL_TARGETS,
                 log_csv="la_calibration_log.csv",
                 verbose=True,  # Enable nested progress display
                 area_name="Local Authority",

From 363d5f251a0236ec70981315562f46f0e800cd08 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 15:48:04 -0400
Subject: [PATCH 30/70] Describe the LFS targets and validation-only counts in
 the changelog

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/spi-income-earnings-groups.fixed.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/changelog.d/spi-income-earnings-groups.fixed.md b/changelog.d/spi-income-earnings-groups.fixed.md
index 724e2fca4..af1530c13 100644
--- a/changelog.d/spi-income-earnings-groups.fixed.md
+++ b/changelog.d/spi-income-earnings-groups.fixed.md
@@ -1 +1 @@
-Draw SPI incomes for the enhanced FRS's SPI-synthetic rows within earnings groups (employee, self-employed, both, neither) set by each row's FRS employment status, so employees draw pay, the self-employed draw a trade, people out of work draw no earnings, and children keep their own (zero) incomes.
+Draw SPI incomes for the enhanced FRS's SPI-synthetic rows within earnings groups (employee, self-employed, both, neither) set by each row's FRS employment status, so employees draw pay, the self-employed draw a trade, people out of work draw no earnings, and children keep their own (zero) incomes (#504). Calibrate to the ONS LFS counts of employees and the self-employed, and report rather than train on HMRC's local counts of taxpayers with employment income, which are annual and include part-year earners the FRS records as out of work.

From 5e320ff2140bf2cb2a7ad0c01bc31d5fa71cb766 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 15:52:18 -0400
Subject: [PATCH 31/70] Harden role tests per review: adults 20+, create_frs
 smoke, changelog

- Built datasets: every person aged 20+ is a claimant or partner (FRS
  dependent children are 19 at most), so an omitted partner in a
  childless couple fails.
- The synthetic create_frs smoke test asserts the column, so dropping the
  assignment now fails a unit test, not only CI's built-dataset test.
- Changelog: stacking refuses to combine a person table that has the role
  with one that does not (two tables both without it still stack).

Suggested by review 20261002-095728-ukdata-cp-review-r1 (APPROVE at 61f93ba).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/frs-claimant-or-partner.added.md              | 2 +-
 policyengine_uk_data/tests/test_claimant_or_partner.py    | 3 +++
 policyengine_uk_data/tests/test_legacy_benefit_proxies.py | 2 ++
 3 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/changelog.d/frs-claimant-or-partner.added.md b/changelog.d/frs-claimant-or-partner.added.md
index f98eb1dac..b20eb8332 100644
--- a/changelog.d/frs-claimant-or-partner.added.md
+++ b/changelog.d/frs-claimant-or-partner.added.md
@@ -1 +1 @@
-Supply `is_claimant_or_partner`, each benefit unit's single adult or couple, from FRS adult-table membership, so policyengine-uk reads the survey's roles instead of inferring them from ages. SPI records and the public transfer dataset (`enhanced_cps_2025.h5`, column added in place) carry it too, and stacking refuses a person table without it.
+Supply `is_claimant_or_partner`, each benefit unit's single adult or couple, from FRS adult-table membership, so policyengine-uk reads the survey's roles instead of inferring them from ages. SPI records and the public transfer dataset (`enhanced_cps_2025.h5`, column added in place) carry it too, and stacking refuses to combine a person table that has it with one that does not.
diff --git a/policyengine_uk_data/tests/test_claimant_or_partner.py b/policyengine_uk_data/tests/test_claimant_or_partner.py
index 31edefbe7..6c19317a7 100644
--- a/policyengine_uk_data/tests/test_claimant_or_partner.py
+++ b/policyengine_uk_data/tests/test_claimant_or_partner.py
@@ -176,3 +176,6 @@ def test_built_dataset_roles(fixture, request):
     assert not (person.is_benunit_head & ~role).any()
     assert not (person.is_parent & ~role).any()
     assert (person.age[role] >= 16).all()
+    # FRS dependent children are 19 at most, so everyone older is in the
+    # adult table: a claimant or partner.
+    assert role[person.age >= 20].all()
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index 5f1acd85f..c00075603 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -548,8 +548,10 @@ def fake_read_csv(path, *args, **kwargs):
         "age_started_or_accepted_current_education_or_training",
         "is_before_universal_credit_qualifying_young_person_terminal_date",
         "is_parent",
+        "is_claimant_or_partner",
     }.issubset(dataset.person.columns)
     assert not dataset.person["is_parent"].iloc[0]
+    assert dataset.person["is_claimant_or_partner"].iloc[0]
     assert not dataset.person["is_in_non_advanced_education"].iloc[0]
     assert not dataset.person["is_in_approved_training"].iloc[0]
     assert (

From 54f09741f499b4ad07ed00ebaf90b74b955fac27 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 15:56:35 -0400
Subject: [PATCH 32/70] Test the LFS employment targets

Values pinned against the ONS release, year resolution, source discovery,
a Hypothesis property for the household column, and a check that a built
enhanced FRS lands near both counts.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_lfs_employment_targets.py      | 125 ++++++++++++++++++
 1 file changed, 125 insertions(+)
 create mode 100644 policyengine_uk_data/tests/test_lfs_employment_targets.py

diff --git a/policyengine_uk_data/tests/test_lfs_employment_targets.py b/policyengine_uk_data/tests/test_lfs_employment_targets.py
new file mode 100644
index 000000000..aaaf5f531
--- /dev/null
+++ b/policyengine_uk_data/tests/test_lfs_employment_targets.py
@@ -0,0 +1,125 @@
+"""ONS LFS employee and self-employed calibration targets.
+
+Invariants:
+
+1. Two national count targets, with the ONS values (held here independently
+   of the source module, so a wrong value is caught) for every year listed.
+2. The calibration and base years resolve to a value.
+3. The household column counts the household's members whose main-job status
+   is in the target's group, for any statuses and household layout
+   (Hypothesis), so the weighted column total is the weighted head count.
+4. On a built enhanced FRS, the weighted counts are near the targets.
+   This also catches the local HMRC employment counts going back into
+   training (``VALIDATION_ONLY_LOCAL_TARGETS``), which moves employees up.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pytest
+from hypothesis import HealthCheck, given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.datasets.frs_release import CURRENT_FRS_RELEASE
+from policyengine_uk_data.targets.build_loss_matrix import _resolve_value
+from policyengine_uk_data.targets.registry import discover_source_modules
+from policyengine_uk_data.targets.sources.ons_labour_market import get_targets
+from policyengine_uk_data.utils.employment_status import (
+    EMPLOYEE_STATUSES,
+    SELF_EMPLOYED_STATUSES,
+)
+
+# ONS Labour market overview, 14 September 2026: MGRN and MGRQ, annual
+# four-quarter averages, thousands.
+ONS_THOUSANDS = {
+    "ons/lfs_employees": {2022: 28_564, 2023: 28_821, 2024: 29_126, 2025: 29_590},
+    "ons/lfs_self_employed": {2022: 4_244, 2023: 4_380, 2024: 4_340, 2025: 4_395},
+}
+STATUS_GROUPS = {
+    "ons/lfs_employees": EMPLOYEE_STATUSES,
+    "ons/lfs_self_employed": SELF_EMPLOYED_STATUSES,
+}
+ALL_STATUSES = (
+    EMPLOYEE_STATUSES
+    + SELF_EMPLOYED_STATUSES
+    + ("CHILD", "UNEMPLOYED", "RETIRED", "STUDENT", "CARER", "OTHER_INACTIVE")
+)
+# Calibration only partly pulls national counts in (see the public sector
+# employment test); the built-data check guards against the 4m drift seen
+# without these targets, not against small misses.
+BUILT_RELATIVE_TOLERANCE = 0.08
+
+
+def _by_name():
+    return {target.name: target for target in get_targets()}
+
+
+def test_targets_and_values():
+    targets = _by_name()
+    assert set(targets) == set(ONS_THOUSANDS)
+    for name, values in ONS_THOUSANDS.items():
+        target = targets[name]
+        assert target.is_count
+        assert target.source == "ons"
+        assert target.variable == "employment_status"
+        assert target.values == {year: v * 1e3 for year, v in values.items()}
+
+
+def test_source_module_is_discovered():
+    # Discovery only imports the source modules; collecting every target
+    # would download the other sources' tables.
+    modules = {module.__name__ for module in discover_source_modules()}
+    assert "policyengine_uk_data.targets.sources.ons_labour_market" in modules
+
+
+@pytest.mark.parametrize(
+    "year",
+    sorted({CURRENT_FRS_RELEASE.base_year, CURRENT_FRS_RELEASE.calibration_year}),
+)
+def test_model_years_resolve(year):
+    for name, target in _by_name().items():
+        assert _resolve_value(target, year) == ONS_THOUSANDS[name][year] * 1e3
+
+
+class _FakeContext:
+    def __init__(self, status, household):
+        self.status = np.array(status, dtype=object)
+        self.household = np.array(household)
+
+    def pe_person(self, variable):
+        assert variable == "employment_status"
+        return self.status
+
+    def household_from_person(self, values):
+        return np.bincount(self.household, weights=values, minlength=10)
+
+
+@settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
+@given(
+    st.lists(
+        st.tuples(st.sampled_from(ALL_STATUSES), st.integers(0, 9)),
+        min_size=1,
+        max_size=60,
+    )
+)
+def test_column_counts_household_members_in_group(people):
+    status, household = zip(*people)
+    ctx = _FakeContext(status, household)
+    for name, target in _by_name().items():
+        column = target.custom_compute(ctx, target, 2025)
+        expected = np.zeros(10)
+        for s, h in people:
+            expected[h] += s in STATUS_GROUPS[name]
+        np.testing.assert_array_equal(column, expected)
+
+
+def test_built_enhanced_frs_near_lfs(enhanced_frs, baseline):
+    year = CURRENT_FRS_RELEASE.calibration_year
+    status = baseline.calculate("employment_status", year).values.astype(str)
+    weight = baseline.calculate("person_weight", year).values
+    for name, statuses in STATUS_GROUPS.items():
+        estimate = weight[np.isin(status, statuses)].sum()
+        target = ONS_THOUSANDS[name][year] * 1e3
+        assert abs(estimate / target - 1) < BUILT_RELATIVE_TOLERANCE, (
+            f"{name}: {estimate / 1e6:.2f}m against {target / 1e6:.2f}m"
+        )

From c3cb23753cd4b03b0de4d3ebdd061bcb98e9630d Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 18:12:09 -0400
Subject: [PATCH 33/70] Keep the calibration validation loss finite when every
 validation target is local

With VALIDATION_ONLY_LOCAL_TARGETS no national target is held out, so the
national validation term was the mean of an empty set and the logged
validation loss was NaN. An empty mask now contributes zero, as the local
term already did. Training and the saved weights are unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../test_calibrate_validation_targets.py      | 95 +++++++++++++++++++
 policyengine_uk_data/utils/calibrate.py       |  7 +-
 2 files changed, 100 insertions(+), 2 deletions(-)
 create mode 100644 policyengine_uk_data/tests/test_calibrate_validation_targets.py

diff --git a/policyengine_uk_data/tests/test_calibrate_validation_targets.py b/policyengine_uk_data/tests/test_calibrate_validation_targets.py
new file mode 100644
index 000000000..4dfba5483
--- /dev/null
+++ b/policyengine_uk_data/tests/test_calibrate_validation_targets.py
@@ -0,0 +1,95 @@
+"""Validation-only targets in calibrate_local_areas.
+
+create_datasets passes VALIDATION_ONLY_LOCAL_TARGETS, which exist only in the
+local matrices. The calibration must then train on the other targets, leave
+the excluded one out of training, and log a finite validation loss (an empty
+national validation set used to make it NaN).
+"""
+
+from __future__ import annotations
+
+import importlib.util
+
+import numpy as np
+import pandas as pd
+import pytest
+
+if (
+    importlib.util.find_spec("torch") is None
+    or importlib.util.find_spec("policyengine_uk") is None
+):
+    pytest.skip(
+        "torch/policyengine_uk not available in test environment",
+        allow_module_level=True,
+    )
+
+from policyengine_uk_data.tests.test_calibrate_save import _StubDataset
+
+
+def _performance(weights, _m_c, _y_c, m_n, y_n, _excluded_targets):
+    estimate = float((weights.sum(axis=0) @ m_n).iloc[0])
+    target = float(y_n.iloc[0])
+    return pd.DataFrame(
+        {
+            "name": ["UK"],
+            "metric": ["national_total"],
+            "estimate": [estimate],
+            "target": [target],
+            "error": [estimate - target],
+            "abs_error": [abs(estimate - target)],
+            "rel_abs_error": [abs(estimate - target) / target],
+            "validation": [False],
+        }
+    )
+
+
+def _calibrate(tmp_path, monkeypatch, excluded, target_b):
+    import h5py
+    import torch
+
+    from policyengine_uk_data.utils import calibrate as calibrate_module
+    from policyengine_uk_data.utils.calibrate import calibrate_local_areas
+
+    monkeypatch.setattr(calibrate_module, "STORAGE_FOLDER", tmp_path)
+    # Household 0 feeds local target "a", household 1 feeds "b".
+    matrix = pd.DataFrame({"a": [1.0, 0.0], "b": [0.0, 1.0]})
+    local = pd.DataFrame({"a": [100.0], "b": [target_b]})
+    national = pd.DataFrame({"national_total": [1.0, 1.0]})
+
+    log_csv = tmp_path / f"log_{len(excluded)}_{target_b:.0f}.csv"
+    # Weight dropout is random; seed it so runs differ only by their inputs.
+    torch.manual_seed(0)
+    calibrate_local_areas(
+        dataset=_StubDataset(np.array([50.0, 50.0])),
+        matrix_fn=lambda _d: (matrix.copy(), local.copy(), np.ones((1, 2))),
+        national_matrix_fn=lambda _d: (national.copy(), pd.Series([200.0])),
+        area_count=1,
+        weight_file=f"weights_{len(excluded)}_{target_b:.0f}.h5",
+        dataset_key="2025",
+        epochs=21,
+        excluded_training_targets=excluded,
+        log_csv=log_csv,
+        get_performance=_performance,
+        verbose=True,
+    )
+    with h5py.File(tmp_path / f"weights_{len(excluded)}_{target_b:.0f}.h5") as f:
+        weights = f["2025"][:]
+    return weights, pd.read_csv(log_csv)
+
+
+def test_local_only_validation_target_logs_finite_validation_loss(
+    tmp_path, monkeypatch
+):
+    _, log = _calibrate(tmp_path, monkeypatch, ["b"], 100.0)
+    assert np.isfinite(log["validation_loss"]).all()
+
+
+def test_validation_only_target_does_not_train(tmp_path, monkeypatch):
+    # Changing an excluded target's value leaves the weights unchanged;
+    # changing it when it trains moves them.
+    excluded_low, _ = _calibrate(tmp_path, monkeypatch, ["b"], 100.0)
+    excluded_high, _ = _calibrate(tmp_path, monkeypatch, ["b"], 10_000.0)
+    np.testing.assert_allclose(excluded_low, excluded_high)
+    trained_low, _ = _calibrate(tmp_path, monkeypatch, [], 100.0)
+    trained_high, _ = _calibrate(tmp_path, monkeypatch, [], 10_000.0)
+    assert not np.allclose(trained_low, trained_high)
diff --git a/policyengine_uk_data/utils/calibrate.py b/policyengine_uk_data/utils/calibrate.py
index a31dcc1fb..f97c4e1ef 100644
--- a/policyengine_uk_data/utils/calibrate.py
+++ b/policyengine_uk_data/utils/calibrate.py
@@ -349,8 +349,11 @@ def loss(w, validation: bool = False):
                 mask = validation_targets_national
             else:
                 mask = ~validation_targets_national
-            pred_national = pred_national[mask]
-            mse_national = torch.mean(sre(pred_national, y_national[mask]))
+            if mask.any():
+                mse_national = torch.mean(sre(pred_national[mask], y_national[mask]))
+            else:
+                # Validation targets can all be local; an empty mean is NaN.
+                mse_national = pred_national.sum() * 0
         else:
             mse_national = torch.mean(sre(pred_national, y_national))
 

From aa604525af6e781b23ceb634c448673086f0691e Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 22:13:19 -0400
Subject: [PATCH 34/70] Target GB universal credit as one OBR total; reach the
 UC payment top band
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

OBR EFO table 4.9 splits UC between spending inside the welfare cap and
outside it. The outside row is DWP's UC equivalent of JSA (the Intensive
Work Search group), not UC for households the benefit cap leaves alone, so
computing it as UC where benefit_cap_reduction is zero asked for £12.9bn of
nearly all UC. policyengine-uk cannot identify the Intensive Work Search
group, so the two rows become one target, obr/universal_credit, restricted
to Great Britain (the rows sit under DWP social security; Northern Ireland
has its own). The GB restriction uses #490's countries field, copied
verbatim.

The DWP UC payment distribution's open top band ("£2500.01 or over")
parsed to NaN bounds, so its four targets had a zero column. Parse it as
(30,000, inf), make bands (lower, upper] so they meet without gaps, and
reject overlapping summary bands.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../obr-uc-welfare-cap-targets.fixed.md       |   1 +
 .../targets/build_loss_matrix.py              |  19 +-
 .../targets/compute/__init__.py               |   2 -
 .../targets/compute/benefits.py               |  20 +--
 policyengine_uk_data/targets/schema.py        |   9 +
 policyengine_uk_data/targets/sources/obr.py   |  88 +++++++---
 .../tests/test_obr_universal_credit_target.py | 166 ++++++++++++++++++
 .../test_uc_payment_distribution_targets.py   | 150 ++++++++++++++++
 policyengine_uk_data/utils/uc_data.py         |  54 ++++--
 pyproject.toml                                |   1 +
 uv.lock                                       |  73 +++++++-
 11 files changed, 522 insertions(+), 61 deletions(-)
 create mode 100644 changelog.d/obr-uc-welfare-cap-targets.fixed.md
 create mode 100644 policyengine_uk_data/tests/test_obr_universal_credit_target.py
 create mode 100644 policyengine_uk_data/tests/test_uc_payment_distribution_targets.py

diff --git a/changelog.d/obr-uc-welfare-cap-targets.fixed.md b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
new file mode 100644
index 000000000..d2e0643ee
--- /dev/null
+++ b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
@@ -0,0 +1 @@
+Calibrate universal credit to one OBR total for Great Britain, the sum of EFO table 4.9's rows inside and outside the welfare cap. The outside-the-cap row is DWP's UC equivalent of JSA (the Intensive Work Search group), not UC for households the benefit cap leaves alone: computed that way, it asked for £12.9bn of nearly all UC (estimate £72bn, +460%), and the inside-the-cap row was compared with all UK UC. Also count the open top band of the DWP UC payment distribution ("£2,500.01 or over" a month): it parsed to missing bounds, so its four targets (1.4k to 83k households) could never be met, and the bands now meet without gaps.
diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 9552aeea2..010501a05 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -52,7 +52,6 @@
     compute_two_child_limit,
     compute_uc_by_children,
     compute_uc_by_family_type,
-    compute_uc_outside_cap,
     compute_uc_payment_dist,
     compute_uk_population,
     compute_vehicles,
@@ -113,7 +112,7 @@ def create_target_matrix(
             col = _compute_column(target, ctx, year)
             if col is None:
                 continue
-            df[target.name] = col
+            df[target.name] = restrict_to_countries(col, ctx.country, target.countries)
             target_names.append(target.name)
             target_values.append(val)
         except Exception as e:
@@ -122,6 +121,18 @@ def create_target_matrix(
     return df, pd.Series(target_values, index=target_names)
 
 
+def restrict_to_countries(column, household_country, countries):
+    """Zero a household column outside the countries a target covers.
+
+    ``countries`` of None means the target covers the whole UK, and the
+    column is returned unchanged.
+    """
+    if countries is None:
+        return column
+    in_scope = np.isin(np.asarray(household_country), countries)
+    return np.asarray(column, dtype=float) * in_scope
+
+
 def _resolve_value(target: Target, year: int) -> float | None:
     """Get the target value for a year, falling back to nearest year.
 
@@ -392,10 +403,6 @@ def _compute_column(target: Target, ctx: _SimContext, year: int) -> np.ndarray |
     ):
         return compute_ss_ni_relief(target, ctx)
 
-    # UC outside benefit cap
-    if name == "obr/universal_credit_outside_cap":
-        return compute_uc_outside_cap(target, ctx)
-
     # Two-child limit
     if "two_child_limit" in name:
         return compute_two_child_limit(target, ctx)
diff --git a/policyengine_uk_data/targets/compute/__init__.py b/policyengine_uk_data/targets/compute/__init__.py
index c7000eb41..877dd8e16 100644
--- a/policyengine_uk_data/targets/compute/__init__.py
+++ b/policyengine_uk_data/targets/compute/__init__.py
@@ -7,7 +7,6 @@
     compute_two_child_limit,
     compute_uc_by_children,
     compute_uc_by_family_type,
-    compute_uc_outside_cap,
     compute_uc_payment_dist,
 )
 from policyengine_uk_data.targets.compute.council_tax import (
@@ -76,7 +75,6 @@
     "compute_two_child_limit",
     "compute_uc_by_children",
     "compute_uc_by_family_type",
-    "compute_uc_outside_cap",
     "compute_uc_payment_dist",
     "compute_uk_population",
     "compute_vehicles",
diff --git a/policyengine_uk_data/targets/compute/benefits.py b/policyengine_uk_data/targets/compute/benefits.py
index 0daca8507..47126b14b 100644
--- a/policyengine_uk_data/targets/compute/benefits.py
+++ b/policyengine_uk_data/targets/compute/benefits.py
@@ -82,7 +82,12 @@ def ft_hh(value):
 
 
 def compute_uc_payment_dist(target, ctx) -> np.ndarray:
-    """Compute UC payment distribution band x family type."""
+    """Compute UC payment distribution band x family type.
+
+    Bands are (lower, upper] annual amounts (see
+    utils.uc_data.parse_monthly_award_band), so consecutive bands meet and
+    the open top band has an infinite upper bound.
+    """
     name = target.name.removeprefix("dwp/uc_payment_dist/")
     idx = name.index("_annual_payment_")
     family_type = name[:idx]
@@ -93,22 +98,11 @@ def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     uc_family_type = ctx.sim.calculate("family_type", map_to="benunit").values
 
     in_band = (
-        (uc_payments >= lower) & (uc_payments < upper) & (uc_family_type == family_type)
+        (uc_payments > lower) & (uc_payments <= upper) & (uc_family_type == family_type)
     )
     return ctx.household_from_family(in_band)
 
 
-def compute_uc_outside_cap(target, ctx) -> np.ndarray:
-    """Compute OBR UC outside benefit cap."""
-    uc = ctx.sim.calculate("universal_credit")
-    uc_hh = ctx.household_from_family(uc)
-    cap_reduction = ctx.sim.calculate(
-        "benefit_cap_reduction", map_to="household"
-    ).values
-    not_capped = cap_reduction == 0
-    return uc_hh * not_capped
-
-
 def compute_two_child_limit(target, ctx) -> np.ndarray | None:
     """Compute two-child limit targets."""
     name = target.name
diff --git a/policyengine_uk_data/targets/schema.py b/policyengine_uk_data/targets/schema.py
index 97b814678..0a678a228 100644
--- a/policyengine_uk_data/targets/schema.py
+++ b/policyengine_uk_data/targets/schema.py
@@ -19,6 +19,11 @@ class Unit(str, Enum):
     RATE = "rate"
 
 
+# DWP statistics cover Great Britain: benefits for Northern Ireland residents
+# are the Northern Ireland Executive's responsibility.
+GREAT_BRITAIN = ("ENGLAND", "SCOTLAND", "WALES")
+
+
 class Target(BaseModel):
     """A single calibration target from an official statistical source.
 
@@ -41,6 +46,10 @@ class Target(BaseModel):
     is_count: bool = False
     reference_url: str | None = None
     forecast_vintage: str | None = None
+    # Countries a national source covers when that is less than the UK, as
+    # values of the model's `country` variable. The loss matrix column only
+    # counts households in these countries. None means the whole UK.
+    countries: tuple[str, ...] | None = None
 
     # For targets needing custom simulation logic (UC splits,
     # counterfactuals). Excluded from serialisation.
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index ad4e3cf17..1207aa435 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -17,7 +17,7 @@
 import openpyxl
 import requests
 
-from policyengine_uk_data.targets.schema import Target, Unit
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Target, Unit
 from policyengine_uk_data.targets.sources._common import (
     HEADERS,
     load_config,
@@ -455,6 +455,31 @@ def _parse_nics(wb: openpyxl.Workbook) -> list[Target]:
     return targets
 
 
+def _universal_credit_rows(ws, max_row: int = 55) -> tuple[int, int]:
+    """Table 4.9's universal credit rows inside and outside the welfare cap.
+
+    Each section has exactly one row starting "Universal credit"; the
+    outside-the-cap section starts at the row headed "Welfare spending
+    outside the welfare cap".
+    """
+    boundary = _find_row(
+        ws, "Welfare spending outside the welfare cap", max_row=max_row
+    )
+    rows = [
+        row
+        for row in range(1, max_row + 1)
+        if str(ws[f"B{row}"].value or "").strip().startswith("Universal credit")
+    ]
+    inside = [row for row in rows if row < boundary]
+    outside = [row for row in rows if row > boundary]
+    if len(inside) != 1 or len(outside) != 1:
+        raise ValueError(
+            f"expected one universal credit row each side of row {boundary}, "
+            f"found rows {rows}"
+        )
+    return inside[0], outside[0]
+
+
 def _parse_welfare(wb: openpyxl.Workbook) -> list[Target]:
     """Parse Table 4.9 (welfare spending) from expenditure xlsx."""
     config = load_config()
@@ -506,10 +531,6 @@ def read_49(row_num: int) -> dict[int, float]:
             "Winter fuel payment",
             "winter_fuel_allowance",
         ),
-        "universal_credit_in_cap": (
-            "Universal credit",
-            "universal_credit",
-        ),
         "child_benefit": ("Child benefit", "child_benefit"),
         "state_pension": ("State pension", "state_pension"),
         "jobseekers_allowance": (
@@ -539,30 +560,41 @@ def read_49(row_num: int) -> dict[int, float]:
         except ValueError:
             logger.warning("OBR welfare: row '%s' not found", label)
 
-    # Universal credit outside cap (row 43) is jobseekers UC
+    # Universal credit has two rows: spending inside the welfare cap (row 18
+    # of the March 2026 table) and outside it (row 43). The welfare cap is the
+    # Charter for Budget Responsibility's limit on welfare spending, not the
+    # household benefit cap. It excludes the State Pension and the payments
+    # most sensitive to the economic cycle: JSA, associated Housing Benefit
+    # and their UC equivalent. DWP's benefit expenditure and caseload tables
+    # (Spring Forecast 2026) give the same £12.876bn for 2025-26 and define
+    # it (Notes) as "the total expenditure ... directed to those in the
+    # Intensive Work Search group"; EFO March 2026 para 4.24 n.20 uses the
+    # same regime. policyengine-uk has no UC conditionality regime. A proxy
+    # built from the legal tests and FRS employment status went from 5% under
+    # to 27% over DWP's 2025-26 figure when one employment-status code was
+    # mapped correctly, too fragile to calibrate to, so the two rows are
+    # targeted as one total. Both sit under "DWP social security",
+    # which covers Great Britain: Northern Ireland's UC is in the "NI social
+    # security" rows.
     try:
-        # UC outside cap = predominantly JSA-conditionality UC
-        uc_outside_row = _find_row(ws, "Universal credit", col="B", max_row=55)
-        # Find the second UC row (outside cap section)
-        for row in range(uc_outside_row + 1, 55):
-            cell_val = ws[f"B{row}"].value
-            if cell_val and str(cell_val).strip().startswith("Universal credit"):
-                values = read_49(row)
-                if values:
-                    targets.append(
-                        Target(
-                            name="obr/universal_credit_outside_cap",
-                            variable="universal_credit",
-                            source="obr",
-                            unit=Unit.GBP,
-                            values=values,
-                            reference_url=ref,
-                            forecast_vintage=vintage,
-                        )
-                    )
-                break
-    except ValueError:
-        logger.warning("OBR welfare: UC outside cap not found")
+        inside_row, outside_row = _universal_credit_rows(ws)
+        inside, outside = read_49(inside_row), read_49(outside_row)
+        if not inside or inside.keys() != outside.keys():
+            raise ValueError("the two universal credit rows cover different years")
+        targets.append(
+            Target(
+                name="obr/universal_credit",
+                variable="universal_credit",
+                source="obr",
+                unit=Unit.GBP,
+                values={year: inside[year] + outside[year] for year in inside},
+                countries=GREAT_BRITAIN,
+                reference_url=ref,
+                forecast_vintage=vintage,
+            )
+        )
+    except ValueError as e:
+        logger.warning("OBR welfare: universal credit not parsed: %s", e)
 
     return targets
 
diff --git a/policyengine_uk_data/tests/test_obr_universal_credit_target.py b/policyengine_uk_data/tests/test_obr_universal_credit_target.py
new file mode 100644
index 000000000..d09f36353
--- /dev/null
+++ b/policyengine_uk_data/tests/test_obr_universal_credit_target.py
@@ -0,0 +1,166 @@
+"""Tests for the OBR universal credit target.
+
+OBR EFO table 4.9 splits universal credit between spending inside the welfare
+cap and outside it (the Intensive Work Search group, DWP's UC equivalent of
+JSA). The split is not the household benefit cap, and policyengine-uk cannot
+identify the Intensive Work Search group, so the two rows are one target:
+GB universal credit, the sum of both rows.
+"""
+
+from types import SimpleNamespace
+
+import numpy as np
+import openpyxl
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.storage import STORAGE_FOLDER
+from policyengine_uk_data.targets.build_loss_matrix import (
+    _compute_column,
+    restrict_to_countries,
+)
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN
+from policyengine_uk_data.targets.sources import obr
+
+COUNTRIES = ("ENGLAND", "SCOTLAND", "WALES", "NORTHERN_IRELAND")
+YEAR_COLUMNS = dict(zip("CDEFGHI", range(2024, 2031)))
+
+
+def _committed_table() -> openpyxl.Workbook:
+    return openpyxl.load_workbook(STORAGE_FOLDER / "obr_efo" / "efo_expenditure.xlsx")
+
+
+def _uc_targets(wb) -> dict:
+    return {t.name: t for t in obr._parse_welfare(wb) if "universal_credit" in t.name}
+
+
+def test_universal_credit_is_one_gb_target():
+    targets = _uc_targets(_committed_table())
+    assert list(targets) == ["obr/universal_credit"]
+    target = targets["obr/universal_credit"]
+    assert target.variable == "universal_credit"
+    assert target.countries == GREAT_BRITAIN
+    # March 2026 EFO table 4.9, 2025-26: £66.411bn inside the welfare cap
+    # (row 18) plus £12.876bn outside it (row 43).
+    assert target.values[2025] == pytest.approx(79.287e9, abs=1e6)
+
+
+def test_target_is_the_sum_of_both_rows_in_every_year():
+    wb = _committed_table()
+    ws = wb["4.9"]
+    rows = [
+        row
+        for row in range(1, 56)
+        if str(ws[f"B{row}"].value or "").startswith("Universal credit")
+    ]
+    assert rows == [18, 43]
+    target = _uc_targets(wb)["obr/universal_credit"]
+    assert sorted(target.values) == list(YEAR_COLUMNS.values())
+    for column, year in YEAR_COLUMNS.items():
+        expected = sum(ws[f"{column}{row}"].value for row in rows) * 1e9
+        assert target.values[year] == pytest.approx(expected)
+
+
+def _table(rows: list[tuple]) -> openpyxl.Workbook:
+    """A minimal sheet 4.9: (label, value per year) in column B onwards."""
+    wb = openpyxl.Workbook()
+    ws = wb.active
+    ws.title = "4.9"
+    for i, (label, value) in enumerate(rows, start=6):
+        ws[f"B{i}"] = label
+        if value is not None:
+            for column in YEAR_COLUMNS:
+                ws[f"{column}{i}"] = value
+    return wb
+
+
+def test_rows_are_found_by_section_not_position():
+    wb = _table(
+        [
+            ("Welfare cap", None),
+            ("Pension credit", 6.0),
+            ("Universal credit", 60.0),
+            ("Child benefit", 13.0),
+            ("Welfare spending outside the welfare cap", None),
+            ("State pension", 140.0),
+            ("Universal credit", 10.0),
+        ]
+    )
+    target = _uc_targets(wb)["obr/universal_credit"]
+    assert set(target.values.values()) == {70e9}
+
+
+@pytest.mark.parametrize(
+    "rows",
+    [
+        # No outside-the-cap row: a partial total would understate UC.
+        [
+            ("Universal credit", 60.0),
+            ("Welfare spending outside the welfare cap", None),
+            ("State pension", 140.0),
+        ],
+        # No section heading: the rows cannot be told apart.
+        [("Universal credit", 60.0), ("Universal credit", 10.0)],
+        # Two rows in one section.
+        [
+            ("Universal credit", 60.0),
+            ("Universal credit", 1.0),
+            ("Welfare spending outside the welfare cap", None),
+            ("Universal credit", 10.0),
+        ],
+    ],
+)
+def test_no_target_unless_both_rows_are_unambiguous(rows):
+    assert _uc_targets(_table(rows)) == {}
+
+
+def _fake_ctx(uc, benunit_household, household_country):
+    n_households = len(household_country)
+    variables = {
+        "universal_credit": SimpleNamespace(entity=SimpleNamespace(key="benunit"))
+    }
+    return SimpleNamespace(
+        sim=SimpleNamespace(
+            tax_benefit_system=SimpleNamespace(variables=variables),
+            calculate=lambda variable, *args, **kwargs: uc,
+        ),
+        household_from_family=lambda values: np.bincount(
+            benunit_household, weights=np.asarray(values, float), minlength=n_households
+        ),
+        country=household_country,
+    )
+
+
+@settings(max_examples=200, deadline=None)
+@given(
+    st.lists(st.sampled_from(COUNTRIES), min_size=1, max_size=20).flatmap(
+        lambda countries: st.tuples(
+            st.just(np.array(countries)),
+            st.lists(
+                st.tuples(
+                    st.integers(0, len(countries) - 1),
+                    st.floats(0, 1e5, allow_nan=False),
+                ),
+                max_size=40,
+            ),
+        )
+    )
+)
+def test_column_counts_gb_universal_credit_once(case):
+    """Property: the column sums UC over benefit units in GB households, each
+    once, and is zero for every Northern Ireland household."""
+    household_country, benunits = case
+    benunit_household = np.array([h for h, _ in benunits], dtype=int)
+    uc = np.array([amount for _, amount in benunits], dtype=float)
+    ctx = _fake_ctx(uc, benunit_household, household_country)
+    target = _uc_targets(_committed_table())["obr/universal_credit"]
+
+    column = restrict_to_countries(
+        _compute_column(target, ctx, 2025), ctx.country, target.countries
+    )
+
+    in_gb = household_country[benunit_household] != "NORTHERN_IRELAND"
+    assert column.sum() == pytest.approx(uc[in_gb].sum())
+    assert (column[household_country == "NORTHERN_IRELAND"] == 0).all()
+    assert (column >= 0).all()
diff --git a/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
new file mode 100644
index 000000000..bccb3290e
--- /dev/null
+++ b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
@@ -0,0 +1,150 @@
+"""Tests for the DWP UC payment distribution targets.
+
+The Stat-Xplore extract (storage/uc_national_payment_dist.xlsx, May 2025)
+counts households on UC by monthly award band and family type. Its top band,
+'£2500.01 or over', is open-ended; it used to parse to NaN bounds, so its four
+targets (1.4k to 83k households) had a column of zeros and could never be met.
+"""
+
+from types import SimpleNamespace
+
+import numpy as np
+import pandas as pd
+import pytest
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.storage import STORAGE_FOLDER
+from policyengine_uk_data.targets.compute.benefits import compute_uc_payment_dist
+from policyengine_uk_data.targets.sources.dwp import _uc_payment_distribution_targets
+from policyengine_uk_data.utils.uc_data import (
+    _check_bands_disjoint,
+    parse_monthly_award_band,
+    uc_national_payment_dist,
+)
+
+FAMILY_TYPES = {
+    "Single, no children": "SINGLE",
+    "Single, with children": "LONE_PARENT",
+    "Couple, no children": "COUPLE_NO_CHILDREN",
+    "Couple, with children": "COUPLE_WITH_CHILDREN",
+}
+
+
+def _raw_extract() -> pd.DataFrame:
+    """Household counts indexed by award band, one column per family type."""
+    raw = pd.read_excel(STORAGE_FOLDER / "uc_national_payment_dist.xlsx", header=None)
+    counts = raw.iloc[9:, 3:7]
+    counts.index = raw.iloc[9:, 1]
+    counts.columns = raw.iloc[7, 3:7]
+    return counts
+
+
+def test_parse_monthly_award_band():
+    assert parse_monthly_award_band("£0.01 to £100.00") == (0, 1_200)
+    assert parse_monthly_award_band("£100.01 to £200.00") == (1_200, 2_400)
+    assert parse_monthly_award_band("£1000.01 to £1100.00") == (12_000, 13_200)
+    assert parse_monthly_award_band("£2,500.01 or over") == (30_000, np.inf)
+    with pytest.raises(ValueError):
+        parse_monthly_award_band("No payment")
+
+
+def test_top_band_targets_are_reachable():
+    targets = {t.name: t for t in _uc_payment_distribution_targets()}
+    top = _raw_extract().loc["£2500.01 or over"]
+    for label, family_type in FAMILY_TYPES.items():
+        target = targets[
+            f"dwp/uc_payment_dist/{family_type}_annual_payment_30_000_to_inf"
+        ]
+        assert target.lower_bound == 30_000
+        assert target.upper_bound == np.inf
+        assert target.values[2025] == top[label]
+    for target in targets.values():
+        assert not np.isnan(target.lower_bound)
+        assert not np.isnan(target.upper_bound)
+        assert "nan" not in target.name
+
+
+def test_band_counts_sum_to_households_with_a_payment():
+    """Conservation: the bands hold every household with a payment, once.
+
+    Stat-Xplore perturbs each cell for disclosure control, so a total can
+    differ from the sum of its cells by a few households.
+    """
+    raw = _raw_extract()
+    for label, family_type in FAMILY_TYPES.items():
+        with_payment = raw.loc["Total", label] - raw.loc["No payment", label]
+        parsed = uc_national_payment_dist[
+            uc_national_payment_dist.family_type == family_type
+        ]
+        assert abs(parsed.household_count.sum() - with_payment) <= 10, label
+
+
+def test_bands_tile_the_positive_awards():
+    for family_type, bands in uc_national_payment_dist.groupby("family_type"):
+        bands = bands.sort_values("uc_annual_payment_min")
+        lower = bands.uc_annual_payment_min.to_numpy()
+        upper = bands.uc_annual_payment_max.to_numpy()
+        assert lower[0] == 0, family_type
+        assert np.array_equal(lower[1:], upper[:-1]), family_type
+        assert upper[-1] == np.inf, family_type
+
+
+def test_overlapping_summary_band_is_rejected():
+    bands = pd.DataFrame(
+        {
+            "family_type": ["SINGLE"] * 3,
+            "uc_annual_payment_min": [18_000.0, 19_200.0, 18_000.0],
+            "uc_annual_payment_max": [19_200.0, 20_400.0, np.inf],
+        }
+    )
+    with pytest.raises(ValueError):
+        _check_bands_disjoint(bands)
+
+
+def _fake_ctx(uc, family_type, household):
+    def calculate(variable, map_to=None):
+        values = {"universal_credit": uc, "family_type": family_type}[variable]
+        return SimpleNamespace(values=values)
+
+    n_households = household.max() + 1
+    return SimpleNamespace(
+        sim=SimpleNamespace(calculate=calculate),
+        household_from_family=lambda values: np.bincount(
+            household, weights=np.asarray(values, float), minlength=n_households
+        ),
+    )
+
+
+_TARGETS = _uc_payment_distribution_targets()
+_EDGES = sorted({t.upper_bound for t in _TARGETS if np.isfinite(t.upper_bound)})
+_AWARDS = st.one_of(
+    st.just(0.0),  # no payment
+    st.floats(0, 1e6, allow_nan=False),  # anywhere, including the open top band
+    st.sampled_from(_EDGES),  # exactly on a band edge
+    st.sampled_from(_EDGES).map(lambda edge: np.nextafter(edge, np.inf)),
+    st.sampled_from(_EDGES).map(lambda edge: np.nextafter(edge, -np.inf)),
+)
+
+
+@settings(max_examples=200, deadline=None)
+@given(
+    st.lists(
+        st.tuples(
+            _AWARDS, st.sampled_from(list(FAMILY_TYPES.values())), st.integers(0, 9)
+        ),
+        min_size=1,
+        max_size=60,
+    )
+)
+def test_every_award_lands_in_exactly_one_band(benefit_units):
+    """Property: summed over bands, each household's column counts its benefit
+    units with a positive award, and never one without."""
+    uc, family_type, household = (np.array(column) for column in zip(*benefit_units))
+    ctx = _fake_ctx(uc.astype(float), family_type, household)
+
+    total = sum(compute_uc_payment_dist(t, ctx) for t in _TARGETS)
+    expected = np.bincount(
+        household, weights=(uc > 0).astype(float), minlength=len(total)
+    )
+    np.testing.assert_array_equal(total, expected)
diff --git a/policyengine_uk_data/utils/uc_data.py b/policyengine_uk_data/utils/uc_data.py
index 7bacebc57..3833d138d 100644
--- a/policyengine_uk_data/utils/uc_data.py
+++ b/policyengine_uk_data/utils/uc_data.py
@@ -1,7 +1,46 @@
+import numpy as np
 import pandas as pd
 from pathlib import Path
 
 
+def parse_monthly_award_band(band: str) -> tuple[float, float]:
+    """Annual (lower, upper] payment bounds of a Stat-Xplore monthly award band.
+
+    Awards are whole pence, so the band '£100.01 to £200.00' holds monthly
+    awards over £100.00 and up to £200.00: annual bounds (1,200, 2,400]. The
+    lower bound is the previous band's top, so consecutive bands meet with no
+    gap. The open top band '£2500.01 or over' is (30,000, inf).
+    """
+    text = band.replace("£", "").replace(",", "").strip()
+    if text.endswith(" or over"):
+        lower = float(text.removesuffix(" or over"))
+        return round((lower - 0.01) * 12, 2), np.inf
+    parts = text.split(" to ")
+    if len(parts) != 2:
+        raise ValueError(f"Unrecognised UC monthly award band: {band!r}")
+    lower, upper = (float(part) for part in parts)
+    return round((lower - 0.01) * 12, 2), upper * 12
+
+
+def _check_bands_disjoint(bands: pd.DataFrame) -> None:
+    """Fail if any family type's payment bands overlap.
+
+    Stat-Xplore also lists summary bands ('£1500.01 or over') that span the
+    finer bands below them. They are suppressed ('..') in the committed
+    extract; if a new extract filled one in, counting it as well would double
+    count those households.
+    """
+    for family_type, group in bands.groupby("family_type"):
+        group = group.sort_values("uc_annual_payment_min")
+        lower = group.uc_annual_payment_min.to_numpy()
+        upper = group.uc_annual_payment_max.to_numpy()
+        if (lower[1:] < upper[:-1]).any():
+            raise ValueError(
+                f"UC payment bands for {family_type} overlap: check for "
+                "summary bands spanning finer ones"
+            )
+
+
 def _parse_uc_national_payment_dist():
     """Parse UC national payment distribution into long format."""
     storage_path = Path(__file__).parent.parent / "storage"
@@ -44,17 +83,10 @@ def _parse_uc_national_payment_dist():
 
     result_df = pd.DataFrame(data_rows)
 
-    # Parse monthly band into min and max, then convert to annual
-    def parse_band(band):
-        """Parse band like '£100.01 to £200.00' into (min, max)."""
-        parts = band.replace("£", "").replace(",", "").split(" to ")
-        if len(parts) == 2:
-            return float(parts[0]) * 12, float(parts[1]) * 12
-        return None, None
-
-    result_df[["uc_annual_payment_min", "uc_annual_payment_max"]] = result_df[
-        "monthly_award_band"
-    ].apply(lambda x: pd.Series(parse_band(x)))
+    result_df[["uc_annual_payment_min", "uc_annual_payment_max"]] = [
+        parse_monthly_award_band(band) for band in result_df["monthly_award_band"]
+    ]
+    _check_bands_disjoint(result_df)
 
     # Map family types to constant names
     family_type_mapping = {
diff --git a/pyproject.toml b/pyproject.toml
index beff7f1a4..58de5b1d3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -38,6 +38,7 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
+    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..c301c6c9f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/35/f4/124858007ddf3c61e9b144107304c9152fa80b5b6c168da07d86fe583cc1/huggingface_hub-1.1.5-py3-none-any.whl", hash = "sha256:e88ecc129011f37b868586bbcfae6c56868cae80cd56a79d61575426a3aa0d7d", size = 516000, upload-time = "2025-11-20T15:49:30.926Z" },
 ]
 
+[[package]]
+name = "hypothesis"
+version = "6.168.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "sortedcontainers" },
+]
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+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1364,7 +1433,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },

From 4a1b1a6cb14ad15fc817896130b7cc83b1dec8f3 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 22:44:26 -0400
Subject: [PATCH 35/70] Keep the UC payment bands' bounds; only open the top
 band
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Making the bands meet without gaps ((lower, upper]) moved penny-a-month
awards into the bottom band: policyengine-uk leaves 1p a month payable
after deductions (SI 2013/380 Sch 6), and 44 records (136k benefit units
in a seeded main build) sit there. The old lower bound of £0.12 a year
leaves them out, and the seeded build of the gapless version then
reweighted against them. Whether they belong in Stat-Xplore's lowest
award band turns on whether its award amount is before or after
deductions, which is outside this change. The bounded bands now parse and
compare exactly as before; only "£2500.01 or over" changes, to
[30,000.12, inf).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../obr-uc-welfare-cap-targets.fixed.md       |  2 +-
 .../targets/compute/benefits.py               |  8 +--
 .../test_uc_payment_distribution_targets.py   | 68 ++++++++++++++-----
 policyengine_uk_data/utils/uc_data.py         | 16 ++---
 4 files changed, 63 insertions(+), 31 deletions(-)

diff --git a/changelog.d/obr-uc-welfare-cap-targets.fixed.md b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
index d2e0643ee..945db66c7 100644
--- a/changelog.d/obr-uc-welfare-cap-targets.fixed.md
+++ b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
@@ -1 +1 @@
-Calibrate universal credit to one OBR total for Great Britain, the sum of EFO table 4.9's rows inside and outside the welfare cap. The outside-the-cap row is DWP's UC equivalent of JSA (the Intensive Work Search group), not UC for households the benefit cap leaves alone: computed that way, it asked for £12.9bn of nearly all UC (estimate £72bn, +460%), and the inside-the-cap row was compared with all UK UC. Also count the open top band of the DWP UC payment distribution ("£2,500.01 or over" a month): it parsed to missing bounds, so its four targets (1.4k to 83k households) could never be met, and the bands now meet without gaps.
+Calibrate universal credit to one OBR total for Great Britain, the sum of EFO table 4.9's rows inside and outside the welfare cap. The outside-the-cap row is DWP's UC equivalent of JSA (the Intensive Work Search group), not UC for households the benefit cap leaves alone: computed that way, it asked for £12.9bn of nearly all UC (estimate £72bn, +460%), and the inside-the-cap row was compared with all UK UC. Also count the open top band of the DWP UC payment distribution ("£2500.01 or over" a month): it parsed to missing bounds, so its four targets (1.4k to 83k households) could never be met.
diff --git a/policyengine_uk_data/targets/compute/benefits.py b/policyengine_uk_data/targets/compute/benefits.py
index 47126b14b..e550a78d2 100644
--- a/policyengine_uk_data/targets/compute/benefits.py
+++ b/policyengine_uk_data/targets/compute/benefits.py
@@ -84,9 +84,9 @@ def ft_hh(value):
 def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     """Compute UC payment distribution band x family type.
 
-    Bands are (lower, upper] annual amounts (see
-    utils.uc_data.parse_monthly_award_band), so consecutive bands meet and
-    the open top band has an infinite upper bound.
+    Bands are [lower, upper) annual amounts (see
+    utils.uc_data.parse_monthly_award_band); the open top band has an
+    infinite upper bound.
     """
     name = target.name.removeprefix("dwp/uc_payment_dist/")
     idx = name.index("_annual_payment_")
@@ -98,7 +98,7 @@ def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     uc_family_type = ctx.sim.calculate("family_type", map_to="benunit").values
 
     in_band = (
-        (uc_payments > lower) & (uc_payments <= upper) & (uc_family_type == family_type)
+        (uc_payments >= lower) & (uc_payments < upper) & (uc_family_type == family_type)
     )
     return ctx.household_from_family(in_band)
 
diff --git a/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
index bccb3290e..7bfc758d1 100644
--- a/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
+++ b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
@@ -4,6 +4,7 @@
 counts households on UC by monthly award band and family type. Its top band,
 '£2500.01 or over', is open-ended; it used to parse to NaN bounds, so its four
 targets (1.4k to 83k households) had a column of zeros and could never be met.
+The other bands keep their bounds: [monthly minimum, monthly maximum) x 12.
 """
 
 from types import SimpleNamespace
@@ -41,10 +42,14 @@ def _raw_extract() -> pd.DataFrame:
 
 
 def test_parse_monthly_award_band():
-    assert parse_monthly_award_band("£0.01 to £100.00") == (0, 1_200)
-    assert parse_monthly_award_band("£100.01 to £200.00") == (1_200, 2_400)
-    assert parse_monthly_award_band("£1000.01 to £1100.00") == (12_000, 13_200)
-    assert parse_monthly_award_band("£2,500.01 or over") == (30_000, np.inf)
+    # The bounded bands are parsed exactly as before this change.
+    for band, (low, high) in {
+        "£0.01 to £100.00": (0.01, 100.00),
+        "£100.01 to £200.00": (100.01, 200.00),
+        "£1000.01 to £1100.00": (1000.01, 1100.00),
+    }.items():
+        assert parse_monthly_award_band(band) == (low * 12, high * 12)
+    assert parse_monthly_award_band("£2,500.01 or over") == (2500.01 * 12, np.inf)
     with pytest.raises(ValueError):
         parse_monthly_award_band("No payment")
 
@@ -56,7 +61,7 @@ def test_top_band_targets_are_reachable():
         target = targets[
             f"dwp/uc_payment_dist/{family_type}_annual_payment_30_000_to_inf"
         ]
-        assert target.lower_bound == 30_000
+        assert target.lower_bound == 2500.01 * 12
         assert target.upper_bound == np.inf
         assert target.values[2025] == top[label]
     for target in targets.values():
@@ -80,13 +85,14 @@ def test_band_counts_sum_to_households_with_a_payment():
         assert abs(parsed.household_count.sum() - with_payment) <= 10, label
 
 
-def test_bands_tile_the_positive_awards():
+def test_bands_run_from_a_penny_to_infinity_without_overlap():
+    """Each band starts a penny a month (12p a year) above the last one ends."""
     for family_type, bands in uc_national_payment_dist.groupby("family_type"):
         bands = bands.sort_values("uc_annual_payment_min")
         lower = bands.uc_annual_payment_min.to_numpy()
         upper = bands.uc_annual_payment_max.to_numpy()
-        assert lower[0] == 0, family_type
-        assert np.array_equal(lower[1:], upper[:-1]), family_type
+        assert lower[0] == pytest.approx(0.12), family_type
+        np.testing.assert_allclose(lower[1:] - upper[:-1], 0.12, atol=1e-6)
         assert upper[-1] == np.inf, family_type
 
 
@@ -117,7 +123,10 @@ def calculate(variable, map_to=None):
 
 
 _TARGETS = _uc_payment_distribution_targets()
-_EDGES = sorted({t.upper_bound for t in _TARGETS if np.isfinite(t.upper_bound)})
+_EDGES = sorted(
+    {t.lower_bound for t in _TARGETS}
+    | {t.upper_bound for t in _TARGETS if np.isfinite(t.upper_bound)}
+)
 _AWARDS = st.one_of(
     st.just(0.0),  # no payment
     st.floats(0, 1e6, allow_nan=False),  # anywhere, including the open top band
@@ -127,6 +136,24 @@ def calculate(variable, map_to=None):
 )
 
 
+def _band_count(uc, family_type):
+    """Oracle: how many of the family type's bands hold each award, by an
+    interval search rather than the compute function's comparisons."""
+    count = np.zeros(len(uc))
+    for ft in np.unique(family_type):
+        bands = sorted(
+            (t.lower_bound, t.upper_bound)
+            for t in _TARGETS
+            if t.name.removeprefix("dwp/uc_payment_dist/").startswith(ft + "_annual")
+        )
+        lower = np.array([b[0] for b in bands])
+        upper = np.array([b[1] for b in bands])
+        m = family_type == ft
+        i = np.searchsorted(lower, uc[m], side="right") - 1
+        count[m] = (i >= 0) & (uc[m] < upper[np.maximum(i, 0)])
+    return count
+
+
 @settings(max_examples=200, deadline=None)
 @given(
     st.lists(
@@ -137,14 +164,21 @@ def calculate(variable, map_to=None):
         max_size=60,
     )
 )
-def test_every_award_lands_in_exactly_one_band(benefit_units):
-    """Property: summed over bands, each household's column counts its benefit
-    units with a positive award, and never one without."""
+def test_each_award_lands_in_at_most_one_band(benefit_units):
+    """Property: summed over the bands, each household's column counts its
+    benefit units whose award lies in one of their family type's bands, once
+    (differential against an interval-search oracle). Awards from the top
+    band's lower bound up are always counted; zero awards never are."""
     uc, family_type, household = (np.array(column) for column in zip(*benefit_units))
-    ctx = _fake_ctx(uc.astype(float), family_type, household)
+    uc = uc.astype(float)
+    ctx = _fake_ctx(uc, family_type, household)
 
-    total = sum(compute_uc_payment_dist(t, ctx) for t in _TARGETS)
-    expected = np.bincount(
-        household, weights=(uc > 0).astype(float), minlength=len(total)
-    )
+    per_target = [compute_uc_payment_dist(t, ctx) for t in _TARGETS]
+    total = sum(per_target)
+    in_band = _band_count(uc, family_type)
+    expected = np.bincount(household, weights=in_band, minlength=len(total))
     np.testing.assert_array_equal(total, expected)
+
+    top = 2500.01 * 12
+    assert (in_band[uc >= top] == 1).all()
+    assert (in_band[uc == 0] == 0).all()
diff --git a/policyengine_uk_data/utils/uc_data.py b/policyengine_uk_data/utils/uc_data.py
index 3833d138d..17d56427c 100644
--- a/policyengine_uk_data/utils/uc_data.py
+++ b/policyengine_uk_data/utils/uc_data.py
@@ -4,22 +4,20 @@
 
 
 def parse_monthly_award_band(band: str) -> tuple[float, float]:
-    """Annual (lower, upper] payment bounds of a Stat-Xplore monthly award band.
+    """Annual [lower, upper) payment bounds of a Stat-Xplore monthly award band.
 
-    Awards are whole pence, so the band '£100.01 to £200.00' holds monthly
-    awards over £100.00 and up to £200.00: annual bounds (1,200, 2,400]. The
-    lower bound is the previous band's top, so consecutive bands meet with no
-    gap. The open top band '£2500.01 or over' is (30,000, inf).
+    '£100.01 to £200.00' gives (1,200.12, 2,400). The open top band
+    '£2500.01 or over' gives (30,000.12, inf); it used to parse to missing
+    bounds, so its targets could never be met.
     """
     text = band.replace("£", "").replace(",", "").strip()
     if text.endswith(" or over"):
-        lower = float(text.removesuffix(" or over"))
-        return round((lower - 0.01) * 12, 2), np.inf
+        return float(text.removesuffix(" or over")) * 12, np.inf
     parts = text.split(" to ")
     if len(parts) != 2:
         raise ValueError(f"Unrecognised UC monthly award band: {band!r}")
-    lower, upper = (float(part) for part in parts)
-    return round((lower - 0.01) * 12, 2), upper * 12
+    lower, upper = (float(part) * 12 for part in parts)
+    return lower, upper
 
 
 def _check_bands_disjoint(bands: pd.DataFrame) -> None:

From dc2abf7b4cd52b9e63c3631020af6e7e0c79c6da Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 22:51:54 -0400
Subject: [PATCH 36/70] Address review 1: independent oracle, fresh-build
 check, changelog

- Test the rule against a per-person oracle written independently of the
  vectorised helper, over every employment status (catches dropping one
  status from the earnings test).
- A built FRS or enhanced FRS without the column now fails instead of
  skipping, so a build that stops writing it is caught.
- Check that the FRS-only imputation stage never rewrites the flag or its
  inputs.
- Changelog states the earnings exception and that the flag is a survey
  proxy overriding policyengine-uk's default; the docstring says the flag
  is fixed at survey or imputation time and does not follow uprating.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-uc-gainful-self-employment.fixed.md   |  2 +-
 policyengine_uk_data/datasets/frs.py          |  5 ++
 .../tests/test_uc_gainful_self_employment.py  | 46 ++++++++++++++++---
 3 files changed, 45 insertions(+), 8 deletions(-)

diff --git a/changelog.d/frs-uc-gainful-self-employment.fixed.md b/changelog.d/frs-uc-gainful-self-employment.fixed.md
index f29f6caee..34363c66f 100644
--- a/changelog.d/frs-uc-gainful-self-employment.fixed.md
+++ b/changelog.d/frs-uc-gainful-self-employment.fixed.md
@@ -1 +1 @@
-Set policyengine-uk's Universal Credit input uc_is_in_gainful_self_employment from the FRS main-job status (EMPSTATI): true for every adult whose main job is self-employment, including traders who break even or make a loss, and false for a side trade beside an employee's main job (UC Regs 2013 reg 64(a)).
+Set policyengine-uk's Universal Credit input uc_is_in_gainful_self_employment from the FRS, as a survey proxy for UC Regs 2013 reg 64 that overrides the model's income-based default: true for every adult whose main job (EMPSTATI) is self-employment, including traders who break even or make a loss, and for anyone whose self-employment profit is above their employment income (ADM H4034); false otherwise. The SPI copy re-derives it from its own imputed incomes.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index bac740ca5..135c3e0cc 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -415,6 +415,11 @@ def derive_uc_is_in_gainful_self_employment(
 
     policyengine-uk's default reads any self-employment income other than
     zero as gainful self-employment, and none as none.
+
+    The flag is fixed from survey-year (or SPI-imputed) incomes. Uprating
+    reprices the two incomes by different indices, so in a projected year a
+    flagged side trade can earn less than the job, and the reverse; the flag
+    does not follow.
     """
     self_employed_main_job = np.isin(
         np.asarray(employment_status, dtype=object), SELF_EMPLOYED_STATUSES
diff --git a/policyengine_uk_data/tests/test_uc_gainful_self_employment.py b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py
index 589521600..48766685e 100644
--- a/policyengine_uk_data/tests/test_uc_gainful_self_employment.py
+++ b/policyengine_uk_data/tests/test_uc_gainful_self_employment.py
@@ -25,6 +25,13 @@ def derive(status, profit, pay):
     return derive_uc_is_in_gainful_self_employment([status], [profit], [pay])[0]
 
 
+def oracle(status, profit, pay):
+    # Written out per person, independently of the vectorised helper.
+    if status == "FT_SELF_EMPLOYED" or status == "PT_SELF_EMPLOYED":
+        return True
+    return profit > 0 and profit > pay
+
+
 def test_self_employed_statuses_are_model_enum_members():
     assert set(SELF_EMPLOYED_STATUSES) <= set(STATUSES)
 
@@ -55,6 +62,15 @@ def test_side_trade_counts_only_when_it_out_earns_pay():
     assert derive("UNEMPLOYED", 1.0, 0.0)
 
 
+@pytest.mark.parametrize("status", STATUSES)
+@pytest.mark.parametrize(
+    "profit, pay",
+    [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0), (2.0, 1.0), (1.0, 2.0)],
+)
+def test_truth_table_against_oracle(status, profit, pay):
+    assert derive(status, profit, pay) == oracle(status, profit, pay)
+
+
 @settings(max_examples=500, deadline=None)
 @given(st.sampled_from(STATUSES), incomes, incomes)
 def test_invariants(status, profit, pay):
@@ -78,7 +94,7 @@ def test_vectorised_matches_elementwise(rows):
     pays = [r[2] for r in rows]
     result = derive_uc_is_in_gainful_self_employment(statuses, profits, pays)
     assert result.dtype == bool
-    assert result.tolist() == [derive(*r) for r in rows]
+    assert result.tolist() == [oracle(*r) for r in rows]
     np.testing.assert_array_equal(
         derive_uc_is_in_gainful_self_employment(
             pd.Series(statuses, dtype="category"),
@@ -169,22 +185,38 @@ def impute_over_incomes(dataset, _model, output_variables):
     assert len(person) == 2 * n
     # The SPI copy kept the donors' statuses and took the imputed incomes.
     assert person.self_employment_income.iloc[n:].tolist() == imputed_profit
-    np.testing.assert_array_equal(
-        person[GAINFUL].to_numpy(dtype=bool),
-        derive_uc_is_in_gainful_self_employment(
+    assert person[GAINFUL].tolist() == [
+        oracle(*values)
+        for values in zip(
             person.employment_status,
             person.self_employment_income,
             person.employment_income,
-        ),
+        )
+    ]
+
+
+def test_frs_only_stage_leaves_the_rule_inputs_alone():
+    # The second-stage QRF rewrites these columns on the SPI copy after the
+    # flag's inputs are set; none of them may be an input to the flag.
+    from policyengine_uk_data.datasets.imputations.frs_only import (
+        FRS_ONLY_PERSON_VARIABLES,
     )
 
+    rule_columns = {
+        GAINFUL,
+        "employment_status",
+        "employment_income",
+        "self_employment_income",
+    }
+    assert rule_columns.isdisjoint(FRS_ONLY_PERSON_VARIABLES)
+
 
 @pytest.mark.parametrize("fixture", ["frs", "enhanced_frs"])
 def test_built_dataset_flag(fixture, request):
+    # Skips only when no built dataset exists; a build without the column fails.
     dataset = request.getfixturevalue(fixture)
     person = dataset.person
-    if GAINFUL not in person.columns:
-        pytest.skip(f"{fixture} was built before this input existed")
+    assert GAINFUL in person.columns, f"{fixture} lacks {GAINFUL}: rebuild it"
     gainful = person[GAINFUL].to_numpy(dtype=bool)
     self_employed = np.isin(person.employment_status, SELF_EMPLOYED_STATUSES)
     assert gainful[self_employed].all()

From 5f9912df1ddd28a8de0fcb41bce1c9159e08bc95 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 22:53:16 -0400
Subject: [PATCH 37/70] Address review r1: derive once per example, tighten a
 test comment

The row-order property recomputed the mapping for every index; compute it
once. The test comment now says what was checked for the older FRS releases
(every adult has a code from 1 to 11), not that their labels match. Drop
redundant parentheses in the create_frs test.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/tests/test_frs_employment_status.py  | 6 ++++--
 policyengine_uk_data/tests/test_legacy_benefit_proxies.py | 6 ++----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/policyengine_uk_data/tests/test_frs_employment_status.py b/policyengine_uk_data/tests/test_frs_employment_status.py
index 283f46b79..735de2a6e 100644
--- a/policyengine_uk_data/tests/test_frs_employment_status.py
+++ b/policyengine_uk_data/tests/test_frs_employment_status.py
@@ -18,7 +18,8 @@
 
 # EMPSTATI ("Adult - Employment Status - ILO definition") value labels in the
 # UKDS FRS 2024-25 data dictionary (SN 9563, adult table), with the status each
-# label means. The 2020-21, 2022-23 and 2023-24 releases use the same codes.
+# label means. Every adult in the 2020-21, 2022-23 and 2023-24 releases also
+# has a code from 1 to 11.
 DATA_DICTIONARY = {
     1: ("Full-time Employee", "FT_EMPLOYED"),
     2: ("Part-time Employee", "PT_EMPLOYED"),
@@ -129,7 +130,8 @@ def test_each_row_maps_on_its_own(rows):
 def test_mapping_commutes_with_row_order(rows, rng):
     order = list(range(len(rows)))
     rng.shuffle(order)
-    assert derive([rows[i] for i in order]).tolist() == [derive(rows)[i] for i in order]
+    statuses = derive(rows)
+    assert derive([rows[i] for i in order]).tolist() == [statuses[i] for i in order]
 
 
 @given(people, unknown_adult_codes, st.integers(0, 60))
diff --git a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
index ebc8ab596..ba20ad600 100644
--- a/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
+++ b/policyengine_uk_data/tests/test_legacy_benefit_proxies.py
@@ -579,10 +579,8 @@ def test_create_frs_smoke_includes_legacy_proxy_columns(tmp_path, monkeypatch):
 def test_create_frs_maps_every_empstati_code(tmp_path, monkeypatch, empstati):
     person = create_single_adult_frs(tmp_path, monkeypatch, empstati).person
 
-    assert (
-        person["employment_status"].iloc[0]
-        == (FRS_EMPSTATI_EMPLOYMENT_STATUS[empstati])
-    )
+    status = person["employment_status"].iloc[0]
+    assert status == FRS_EMPSTATI_EMPLOYMENT_STATUS[empstati]
     # A working-age adult reporting no hours: only the sick/disabled codes
     # (9 permanently, 10 temporarily) are ESA health states.
     assert person["esa_health_condition_proxy"].iloc[0] == (empstati in (9, 10))

From 1832adeb2296b53fe5a7d7194e770118aa34d1f8 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 07:27:34 -0400
Subject: [PATCH 38/70] Address review r2: print no count in the
 unknown-EMPSTATI error

Adults are not survey households, so a total of 10 or more adults could
still come from fewer than 10 households in a public build log. The error
now names the unknown codes only. Tests pin that the message depends only on
which codes occur, and that codes are listed once each in numeric order with
blank last (a string sort or an unsorted list now fails).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py          |  8 ++--
 .../tests/test_frs_employment_status.py       | 42 +++++++++++++------
 2 files changed, 33 insertions(+), 17 deletions(-)

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 3195cb18e..f2412fe8c 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -142,15 +142,13 @@ def derive_employment_status_from_frs(empstati, is_adult_record) -> np.ndarray:
     adult_status = codes.map(FRS_EMPSTATI_EMPLOYMENT_STATUS).to_numpy()
     unknown = is_adult_record & pd.isna(adult_status)
     if unknown.any():
-        # Build logs are public: name the codes, not per-code counts, and
-        # suppress a total under 10 adults.
-        n = int(unknown.sum())
-        adults = f"{n} FRS adults" if n >= 10 else "Fewer than 10 FRS adults"
+        # Build logs are public, and adults are not survey households, so
+        # name the codes and never a count.
         bad = codes[unknown]
         listed = [f"{code:g}" for code in sorted(bad.dropna().unique())]
         listed += ["blank"] if bad.isna().any() else []
         raise ValueError(
-            f"{adults} have EMPSTATI codes missing from "
+            "FRS adults have EMPSTATI codes missing from "
             f"FRS_EMPSTATI_EMPLOYMENT_STATUS: {', '.join(listed)}. Map them "
             "from the release's data dictionary."
         )
diff --git a/policyengine_uk_data/tests/test_frs_employment_status.py b/policyengine_uk_data/tests/test_frs_employment_status.py
index 735de2a6e..c7ccae1e8 100644
--- a/policyengine_uk_data/tests/test_frs_employment_status.py
+++ b/policyengine_uk_data/tests/test_frs_employment_status.py
@@ -98,26 +98,44 @@ def test_unknown_adult_code_fails_the_build(code):
         derive_employment_status_from_frs([1, code], [True, True])
 
 
-def test_failure_message_lists_blank_and_numeric_codes():
+def listed_codes(message):
+    return message.split("FRS_EMPSTATI_EMPLOYMENT_STATUS: ")[1].split(". Map")[0]
+
+
+def test_failure_message_lists_codes_in_numeric_order_with_blank_last():
     # Formatted from the float codes, not Series.astype(str), whose NaN
-    # handling differs between pandas 2 and 3.
-    with pytest.raises(ValueError, match=": 0, 12, blank. "):
-        derive_employment_status_from_frs([np.nan, 0, 12, 1], [True] * 4)
+    # handling differs between pandas 2 and 3. A string sort would put 100
+    # before 11.5.
+    codes = [100, 12, np.nan, -1, 12, 11.5, 1]
+    with pytest.raises(ValueError) as error:
+        derive_employment_status_from_frs(codes, [True] * len(codes))
+    assert listed_codes(str(error.value)) == "-1, 11.5, 12, 100, blank"
+
+
+@given(st.lists(unknown_adult_codes, min_size=1, max_size=20))
+def test_failure_message_lists_each_unknown_code_once_in_order(bad_codes):
+    with pytest.raises(ValueError) as error:
+        derive_employment_status_from_frs(bad_codes, [True] * len(bad_codes))
+    listed = listed_codes(str(error.value)).split(", ")
+    blank = any(np.isnan(code) for code in bad_codes)
+    assert (listed[-1] == "blank") == blank
+    numbers = [float(code) for code in listed[: len(listed) - blank]]
+    assert numbers == sorted(numbers)
+    assert set(listed) - {"blank"} == {
+        f"{code:g}" for code in bad_codes if not np.isnan(code)
+    }
 
 
 @given(st.integers(1, 30), st.integers(1, 30))
-def test_failure_message_suppresses_small_counts(n_twelve, n_thirteen):
+def test_failure_message_discloses_no_count(n_twelve, n_thirteen):
+    # Adults are not survey households, so no count of them is safe to print
+    # in a public build log: the message depends only on which codes occur.
     codes = [12] * n_twelve + [13] * n_thirteen
     with pytest.raises(ValueError) as error:
         derive_employment_status_from_frs(codes, [True] * len(codes))
-    total = n_twelve + n_thirteen
-    prefix = f"{total} FRS adults" if total >= 10 else "Fewer than 10 FRS adults"
     message = str(error.value)
-    assert message.startswith(prefix)
-    assert ": 12, 13. " in message
-    # No other number (such as a per-code count) is disclosed.
-    rest = message.removeprefix(prefix).replace(": 12, 13. ", "")
-    assert not any(character.isdigit() for character in rest)
+    assert listed_codes(message) == "12, 13"
+    assert not any(character.isdigit() for character in message.replace("12, 13", ""))
 
 
 @given(people)

From 88ef576a950d8a961b8e0a253c0ca7ddab9a3a5c Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 07:32:42 -0400
Subject: [PATCH 39/70] Address review: reduced-build bounds, sample-floor
 wording, SPI monotonicity

- Widen the reduced (TESTING) bounds on salary-sacrifice users (40% total,
  45% below cap) and above-AEA gains (relative error 5). The 32-epoch build
  stops short of the OBR salary-sacrifice targets (4.5m at epoch 0, 5.5m at
  epoch 30, 7.9m at epoch 510 in the full build), and main's reduced build
  met the old bounds only through about 1.35m SPI-synthetic children with
  salary sacrifice. Seed-0 reduced builds of main and of this branch both
  exceed the old gains bound. Full builds keep the strict bounds and pass.
- State the training-sample floor as a share of the nominal size, with the
  total's bounds tested.
- Add an SPI monotonicity property and check mixed profits in both trade
  groups.
- ONS release date is 15 September 2026; note how later years resolve.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/imputations/income.py            | 16 ++++---
 .../targets/sources/ons_labour_market.py      |  4 +-
 .../tests/test_cgt_band_donors.py             |  8 +++-
 .../tests/test_lfs_employment_targets.py      |  2 +-
 .../tests/test_salary_sacrifice_headcount.py  | 25 ++++++-----
 .../tests/test_spi_income_earnings_groups.py  | 45 ++++++++++++++++---
 6 files changed, 73 insertions(+), 27 deletions(-)

diff --git a/policyengine_uk_data/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
index f1d9d130e..fdf466b7e 100644
--- a/policyengine_uk_data/datasets/imputations/income.py
+++ b/policyengine_uk_data/datasets/imputations/income.py
@@ -80,13 +80,15 @@ def _spi_age_bounds(age_code) -> tuple[int, int]:
 )
 # FRS rows that take no SPI draw and keep their own values: the FRS child
 # table (dependent children, including 16-19-year-olds in education), whose
-# earnings the FRS build does not record, and anyone under 16. The SPI has no
-# record of them as earners.
+# earnings this FRS build does not record, and anyone under 16. With no
+# earnings or job information, there is nothing to tie a taxpayer's SPI
+# incomes to.
 NOT_IMPUTED = "NOT_IMPUTED"
 
 
-# Every earnings group gets at least this share of the training sample, so the
-# small self-employed groups are not fitted on a few thousand records.
+# Every earnings group gets at least this share of the nominal training sample
+# size, so the small self-employed groups are not fitted on a few thousand
+# records. Floors are added on top, so the sample can exceed the nominal size.
 MIN_GROUP_SAMPLE_SHARE = 0.1
 
 
@@ -143,8 +145,10 @@ def earnings_group_sample_sizes(
     group_weights: dict[str, float], sample_size: int
 ) -> dict[str, int]:
     """Training records per earnings group: the group's weighted share of
-    ``sample_size``, but never under ``MIN_GROUP_SAMPLE_SHARE`` of it. Groups
-    with no weight get no records."""
+    ``sample_size``, but never under ``MIN_GROUP_SAMPLE_SHARE`` of
+    ``sample_size``. Groups with no weight get no records. Floored groups are
+    not offset elsewhere, so the total can exceed ``sample_size`` (by at most
+    one floor per group, plus rounding)."""
     weights = {group: float(w) for group, w in group_weights.items() if w > 0}
     total = sum(weights.values())
     floor = int(np.ceil(MIN_GROUP_SAMPLE_SHARE * sample_size))
diff --git a/policyengine_uk_data/targets/sources/ons_labour_market.py b/policyengine_uk_data/targets/sources/ons_labour_market.py
index 6a6f50639..bae191b88 100644
--- a/policyengine_uk_data/targets/sources/ons_labour_market.py
+++ b/policyengine_uk_data/targets/sources/ons_labour_market.py
@@ -16,7 +16,9 @@
 
 Source: ONS Labour market overview, series MGRN (LFS: Employees: UK: All,
 aged 16 and over, seasonally adjusted) and MGRQ (LFS: Self-employed: UK:
-All), annual four-quarter averages, release of 14 September 2026.
+All), annual four-quarter averages, release of 15 September 2026. The loss
+matrix holds the latest year's value for up to three later years and drops the
+targets after that (``_resolve_value``), so add each new annual average.
 """
 
 import numpy as np
diff --git a/policyengine_uk_data/tests/test_cgt_band_donors.py b/policyengine_uk_data/tests/test_cgt_band_donors.py
index a24e2e1d5..a03b375a5 100644
--- a/policyengine_uk_data/tests/test_cgt_band_donors.py
+++ b/policyengine_uk_data/tests/test_cgt_band_donors.py
@@ -158,6 +158,12 @@ def test_stack_cgt_band_donors(frs):
 # still catches order-of-magnitude pathologies without failing on
 # reduced-fidelity calibration noise. Full builds get the strict bounds.
 _REDUCED_BUILD_SLACK = 5.0 if os.environ.get("TESTING") == "1" else 1.0
+# Reduced builds sit far above HMRC's total gains: the seed-0 reduced builds
+# of main and of #529 carry about £243bn and £267bn (relative errors 2.7 and
+# 3.1, beyond the 0.5 x slack bound), while their full builds carry about
+# £53bn. Under TESTING the total-gains check only guards against
+# order-of-magnitude errors; the full build keeps the 50% bound.
+_TOTAL_GAINS_RELATIVE_BOUND = 5.0 if _REDUCED_BUILD_SLACK > 1 else 0.5
 
 
 def _built_with_band_donors(enhanced_frs):
@@ -215,7 +221,7 @@ def test_built_total_gains(enhanced_frs):
     enhanced_frs = _built_with_band_donors(enhanced_frs)
     gains, weights = _person_gains_and_weights(enhanced_frs)
     total = float((gains * weights)[gains > _AEA].sum())
-    assert abs(total / _HMRC_TOTAL_GAINS - 1) < 0.5 * _REDUCED_BUILD_SLACK, (
+    assert abs(total / _HMRC_TOTAL_GAINS - 1) < _TOTAL_GAINS_RELATIVE_BOUND, (
         f"£{total / 1e9:.1f}bn of above-AEA gains against HMRC's "
         f"£{_HMRC_TOTAL_GAINS / 1e9:.1f}bn "
         f"(relative error {abs(total / _HMRC_TOTAL_GAINS - 1):.0%})."
diff --git a/policyengine_uk_data/tests/test_lfs_employment_targets.py b/policyengine_uk_data/tests/test_lfs_employment_targets.py
index aaaf5f531..ae49b68aa 100644
--- a/policyengine_uk_data/tests/test_lfs_employment_targets.py
+++ b/policyengine_uk_data/tests/test_lfs_employment_targets.py
@@ -29,7 +29,7 @@
     SELF_EMPLOYED_STATUSES,
 )
 
-# ONS Labour market overview, 14 September 2026: MGRN and MGRQ, annual
+# ONS Labour market overview, 15 September 2026: MGRN and MGRQ, annual
 # four-quarter averages, thousands.
 ONS_THOUSANDS = {
     "ons/lfs_employees": {2022: 28_564, 2023: 28_821, 2024: 29_126, 2025: 29_590},
diff --git a/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py b/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
index cbae09725..17a18a42d 100644
--- a/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
+++ b/policyengine_uk_data/tests/test_salary_sacrifice_headcount.py
@@ -9,21 +9,24 @@
 
 from policyengine_uk_data.datasets.frs_release import CURRENT_FRS_RELEASE
 
-# The total combines below-cap and above-cap users and moves slightly with
-# each generated FRS calibration refresh. Widened from 0.16 after the
-# household-weight alignment fix (#436) shifted the calibration starting point
-# under the reduced-epoch CI build (TESTING=1).
-TOTAL_TOLERANCE = 0.20
-# The below-cap count sits right at the 15% boundary under the
-# reduced-epoch CI build (observed 15.09% on one TESTING=1 run and passing
-# the next), so the tolerance widens under TESTING like TOTAL_TOLERANCE
-# did after #436. Full builds keep the strict 15%.
-TOLERANCE = 0.25 if os.environ.get("TESTING") == "1" else 0.15
+REDUCED_BUILD = os.environ.get("TESTING") == "1"
+# The 32-epoch reduced build stops well short of the OBR salary-sacrifice
+# counts that calibration targets: in the seed-0 full build of #529 the users
+# are 4.5m at epoch 0, 5.5m at epoch 30 and 7.9m at epoch 510. Before #529,
+# reduced builds met the old bounds (20% total, 25% below cap) only because
+# SPI-synthetic children were imputed salary sacrifice (about 1.35m users on
+# the seed-0 reduced build of main, which held 5.0m users without them).
+# #529 gives those children no pay, so the reduced bounds widen to 40% and
+# 45%. They still catch a collapse; full builds keep the strict bounds.
+# The total was earlier widened from 0.16 after the household-weight
+# alignment fix (#436).
+TOTAL_TOLERANCE = 0.40 if REDUCED_BUILD else 0.20
+TOLERANCE = 0.45 if REDUCED_BUILD else 0.15
 # Widened under the reduced-epoch CI build after the benunit-table sort fix
 # (#462) shifted the calibration starting point (observed 20.4% on a
 # TESTING=1 run), following the precedent of TOTAL_TOLERANCE (#436) and
 # TOLERANCE above. Full builds keep the strict 20%.
-ABOVE_CAP_TOLERANCE = 0.25 if os.environ.get("TESTING") == "1" else 0.20
+ABOVE_CAP_TOLERANCE = 0.25 if REDUCED_BUILD else 0.20
 PERIOD = CURRENT_FRS_RELEASE.calibration_year
 
 
diff --git a/policyengine_uk_data/tests/test_spi_income_earnings_groups.py b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
index f6233a5df..44cf55de0 100644
--- a/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
+++ b/policyengine_uk_data/tests/test_spi_income_earnings_groups.py
@@ -8,11 +8,13 @@
    and only if the main job is self-employment or the FRS records a profit.
 2. SPI records: the group has pay if and only if PAY + EPB + TAXTERM > 0, and
    a trade if and only if SEINC_NUM = 1 or PROFITS > 0.
-3. Both mappings are monotone (more income never removes a source) and give
-   the same answer elementwise as row by row.
+3. Both mappings are monotone (more of one income adds that source and leaves
+   the other alone) and give the same answer elementwise as row by row.
 4. Training sample: every group with weight gets at least
-   ``MIN_GROUP_SAMPLE_SHARE`` of the sample, groups without weight get none,
-   and groups above the floor get their weighted share.
+   ``MIN_GROUP_SAMPLE_SHARE`` of the nominal sample size, groups without
+   weight get none, and groups above the floor get their weighted share.
+   Floors are not offset elsewhere, so the total lies between the nominal size
+   (less rounding) and the nominal size plus one floor per group.
 5. ``generate_spi_table`` resamples each group only from its own records.
 6. Model draws: pay is positive exactly in the groups with pay, profit is zero
    in the groups without a trade, and ``NOT_IMPUTED`` rows get no draw.
@@ -194,6 +196,31 @@ def test_spi_group_follows_pay_and_self_employment_pages(records):
     assert list(groups) == rowwise
 
 
+@RELAXED
+@given(
+    st.lists(
+        st.tuples(amounts, amounts, st.sampled_from([-1, 0, 1])),
+        min_size=1,
+        max_size=40,
+    ),
+    st.floats(0.01, 1e6),
+    st.sampled_from(["pay", "profit"]),
+)
+def test_spi_group_monotone_in_income(records, extra, source):
+    pay, profit, indicator = (np.array(column, dtype=float) for column in zip(*records))
+    before = spi_earnings_group(pay, profit, indicator)
+    if source == "pay":
+        after = spi_earnings_group(pay + extra, profit, indicator)
+        gained, other = PAY_GROUPS, TRADE_GROUPS
+    else:
+        after = spi_earnings_group(pay, profit + extra, indicator)
+        gained, other = TRADE_GROUPS, PAY_GROUPS
+    assert np.isin(after, list(gained)).all()
+    np.testing.assert_array_equal(
+        np.isin(after, list(other)), np.isin(before, list(other))
+    )
+
+
 @RELAXED
 @given(
     st.dictionaries(
@@ -216,6 +243,9 @@ def test_group_sample_sizes(weights, sample_size):
         assert size >= floor
         if share > floor + 1:
             assert abs(size - share) <= 0.5 + 1e-9
+    groups = len(positive)
+    assert sample_size - 0.5 * groups <= sum(sizes.values())
+    assert sum(sizes.values()) <= sample_size + groups * (floor + 0.5)
     assert sizes == earnings_group_sample_sizes(weights, sample_size)
 
 
@@ -333,15 +363,16 @@ def test_model_draws_agree_with_group(fitted_model, inputs):
     assert (draws[drawn].to_numpy() >= 0).all()
 
 
-def test_model_draws_some_profit_for_traders(fitted_model):
-    """The trade groups draw zero and positive profits, as the SPI has both."""
+@pytest.mark.parametrize("group", [SELF_EMPLOYED, EMPLOYEE_AND_SELF_EMPLOYED])
+def test_model_draws_some_profit_for_traders(fitted_model, group):
+    """Both trade groups draw zero and positive profits, as the SPI has both."""
     n = 400
     inputs = pd.DataFrame(
         {
             "age": np.linspace(20, 80, n),
             "gender": ["MALE", "FEMALE"] * (n // 2),
             "region": ["LONDON"] * n,
-            "earnings_group": [SELF_EMPLOYED] * n,
+            "earnings_group": [group] * n,
         }
     )
     profit = fitted_model.predict(inputs).self_employment_income

From 974fa8cbb90b636fdc06e21195a420a3e0fad7cf Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 07:34:13 -0400
Subject: [PATCH 40/70] Restore gapless UC payment bands; harden the parser and
 tests
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Stat-Xplore's monthly award is the UC due after deductions ("Monthly Award
Amount (payment bands)" metadata), as universal_credit is, so a penny a
month left after deductions belongs in "£0.01 to £100.00". The previous
commit's reason for keeping the old [min x 12, max x 12) bounds was wrong:
their £0.12 lower bound left those awards out only through float32
rounding. Bands are (lower, upper] again, as in the first commit.

From the independent review of the first commit:
- reject NaN, infinite or negative lower bounds and inverted bands;
- scan the whole of table 4.9 for the UC rows, not the first 55 rows;
- test GB scoping through create_target_matrix itself, a year missing
  from one UC row, and each payment band's membership against an
  interval-search oracle (float64 and float32 awards, edges and their
  neighbours); the old sum-over-bands property let an award move to the
  neighbouring band unnoticed;
- parse the committed workbook once in the property test.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../obr-uc-welfare-cap-targets.fixed.md       |   2 +-
 .../targets/compute/benefits.py               |  10 +-
 policyengine_uk_data/targets/sources/obr.py   |   3 +-
 .../tests/test_obr_universal_credit_target.py |  84 +++++++++++++-
 .../test_uc_payment_distribution_targets.py   | 109 +++++++++---------
 policyengine_uk_data/utils/uc_data.py         |  31 ++---
 6 files changed, 166 insertions(+), 73 deletions(-)

diff --git a/changelog.d/obr-uc-welfare-cap-targets.fixed.md b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
index 945db66c7..41872c214 100644
--- a/changelog.d/obr-uc-welfare-cap-targets.fixed.md
+++ b/changelog.d/obr-uc-welfare-cap-targets.fixed.md
@@ -1 +1 @@
-Calibrate universal credit to one OBR total for Great Britain, the sum of EFO table 4.9's rows inside and outside the welfare cap. The outside-the-cap row is DWP's UC equivalent of JSA (the Intensive Work Search group), not UC for households the benefit cap leaves alone: computed that way, it asked for £12.9bn of nearly all UC (estimate £72bn, +460%), and the inside-the-cap row was compared with all UK UC. Also count the open top band of the DWP UC payment distribution ("£2500.01 or over" a month): it parsed to missing bounds, so its four targets (1.4k to 83k households) could never be met.
+Calibrate universal credit to one OBR total for Great Britain, the sum of EFO table 4.9's rows inside and outside the welfare cap. The outside-the-cap row is DWP's UC equivalent of JSA (the Intensive Work Search group), not UC for households the benefit cap leaves alone: computed that way, it asked for £12.9bn of nearly all UC (estimate £72bn, +460%), and the inside-the-cap row was compared with all UK UC. Also count the open top band of the DWP UC payment distribution ("£2500.01 or over" a month), which parsed to missing bounds so its four targets (1.4k to 83k households) could never be met, and make the bands meet without gaps, so awards of exactly a band's top, or a penny a month after deductions, land in their Stat-Xplore band.
diff --git a/policyengine_uk_data/targets/compute/benefits.py b/policyengine_uk_data/targets/compute/benefits.py
index e550a78d2..b1105855c 100644
--- a/policyengine_uk_data/targets/compute/benefits.py
+++ b/policyengine_uk_data/targets/compute/benefits.py
@@ -84,9 +84,11 @@ def ft_hh(value):
 def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     """Compute UC payment distribution band x family type.
 
-    Bands are [lower, upper) annual amounts (see
-    utils.uc_data.parse_monthly_award_band); the open top band has an
-    infinite upper bound.
+    Stat-Xplore's monthly award is the UC due after deductions (its "Monthly
+    Award Amount (payment bands)" metadata), as universal_credit is. Bands
+    are (lower, upper] annual amounts (see
+    utils.uc_data.parse_monthly_award_band), so consecutive bands meet and
+    the open top band has an infinite upper bound.
     """
     name = target.name.removeprefix("dwp/uc_payment_dist/")
     idx = name.index("_annual_payment_")
@@ -98,7 +100,7 @@ def compute_uc_payment_dist(target, ctx) -> np.ndarray:
     uc_family_type = ctx.sim.calculate("family_type", map_to="benunit").values
 
     in_band = (
-        (uc_payments >= lower) & (uc_payments < upper) & (uc_family_type == family_type)
+        (uc_payments > lower) & (uc_payments <= upper) & (uc_family_type == family_type)
     )
     return ctx.household_from_family(in_band)
 
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index 1207aa435..457d6ccb0 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -455,13 +455,14 @@ def _parse_nics(wb: openpyxl.Workbook) -> list[Target]:
     return targets
 
 
-def _universal_credit_rows(ws, max_row: int = 55) -> tuple[int, int]:
+def _universal_credit_rows(ws) -> tuple[int, int]:
     """Table 4.9's universal credit rows inside and outside the welfare cap.
 
     Each section has exactly one row starting "Universal credit"; the
     outside-the-cap section starts at the row headed "Welfare spending
     outside the welfare cap".
     """
+    max_row = ws.max_row
     boundary = _find_row(
         ws, "Welfare spending outside the welfare cap", max_row=max_row
     )
diff --git a/policyengine_uk_data/tests/test_obr_universal_credit_target.py b/policyengine_uk_data/tests/test_obr_universal_credit_target.py
index d09f36353..8fcdb51e0 100644
--- a/policyengine_uk_data/tests/test_obr_universal_credit_target.py
+++ b/policyengine_uk_data/tests/test_obr_universal_credit_target.py
@@ -7,20 +7,23 @@
 GB universal credit, the sum of both rows.
 """
 
+from functools import lru_cache
 from types import SimpleNamespace
 
 import numpy as np
 import openpyxl
+import pandas as pd
 import pytest
 from hypothesis import given, settings
 from hypothesis import strategies as st
 
 from policyengine_uk_data.storage import STORAGE_FOLDER
+from policyengine_uk_data.targets import build_loss_matrix
 from policyengine_uk_data.targets.build_loss_matrix import (
     _compute_column,
     restrict_to_countries,
 )
-from policyengine_uk_data.targets.schema import GREAT_BRITAIN
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, GeographicLevel
 from policyengine_uk_data.targets.sources import obr
 
 COUNTRIES = ("ENGLAND", "SCOTLAND", "WALES", "NORTHERN_IRELAND")
@@ -35,6 +38,11 @@ def _uc_targets(wb) -> dict:
     return {t.name: t for t in obr._parse_welfare(wb) if "universal_credit" in t.name}
 
 
+@lru_cache(maxsize=1)
+def _committed_target():
+    return _uc_targets(_committed_table())["obr/universal_credit"]
+
+
 def test_universal_credit_is_one_gb_target():
     targets = _uc_targets(_committed_table())
     assert list(targets) == ["obr/universal_credit"]
@@ -115,6 +123,78 @@ def test_no_target_unless_both_rows_are_unambiguous(rows):
     assert _uc_targets(_table(rows)) == {}
 
 
+def test_no_target_when_the_rows_cover_different_years():
+    wb = _table(
+        [
+            ("Universal credit", 60.0),
+            ("Welfare spending outside the welfare cap", None),
+            ("Universal credit", 10.0),
+        ]
+    )
+    wb["4.9"]["I8"] = None  # 2030-31 missing from the outside-the-cap row only
+    assert _uc_targets(wb) == {}
+
+
+def test_rows_below_row_55_are_found():
+    padding = [(f"Other benefit {i}", 1.0) for i in range(60)]
+    wb = _table(
+        [("Universal credit", 60.0)]
+        + padding
+        + [("Welfare spending outside the welfare cap", None)]
+        + padding
+        + [("Universal credit", 10.0)]
+    )
+    target = _uc_targets(wb)["obr/universal_credit"]
+    assert set(target.values.values()) == {70e9}
+
+
+def test_target_matrix_counts_gb_households_only(monkeypatch):
+    """Through create_target_matrix itself: England has two benefit units on
+    UC, Northern Ireland and Wales one each, Scotland none."""
+    import policyengine_uk
+
+    target = _committed_target()
+    uc = pd.Series([100.0, 50.0, 300.0, 20.0])
+    benunit_household = np.array([0, 0, 1, 2])
+    country = pd.Series(["ENGLAND", "NORTHERN_IRELAND", "WALES", "SCOTLAND"])
+
+    class FakeMicrosimulation:
+        tax_benefit_system = SimpleNamespace(
+            variables={
+                "universal_credit": SimpleNamespace(
+                    entity=SimpleNamespace(key="benunit")
+                )
+            }
+        )
+
+        def __init__(self, dataset=None, reform=None):
+            pass
+
+        def calculate(self, variable, *args, **kwargs):
+            return {"universal_credit": uc, "country": country}[variable]
+
+        def map_result(self, values, source, target_entity):
+            assert (source, target_entity) == ("benunit", "household")
+            return np.bincount(
+                benunit_household, weights=np.asarray(values), minlength=len(country)
+            )
+
+    monkeypatch.setattr(policyengine_uk, "Microsimulation", FakeMicrosimulation)
+    monkeypatch.setattr(
+        build_loss_matrix,
+        "get_all_targets",
+        lambda geographic_level=None: (
+            [target] if geographic_level == GeographicLevel.NATIONAL else []
+        ),
+    )
+
+    matrix, values = build_loss_matrix.create_target_matrix(
+        SimpleNamespace(time_period="2025"), time_period="2025"
+    )
+    np.testing.assert_array_equal(matrix["obr/universal_credit"], [150, 0, 20, 0])
+    assert values["obr/universal_credit"] == target.values[2025]
+
+
 def _fake_ctx(uc, benunit_household, household_country):
     n_households = len(household_country)
     variables = {
@@ -154,7 +234,7 @@ def test_column_counts_gb_universal_credit_once(case):
     benunit_household = np.array([h for h, _ in benunits], dtype=int)
     uc = np.array([amount for _, amount in benunits], dtype=float)
     ctx = _fake_ctx(uc, benunit_household, household_country)
-    target = _uc_targets(_committed_table())["obr/universal_credit"]
+    target = _committed_target()
 
     column = restrict_to_countries(
         _compute_column(target, ctx, 2025), ctx.country, target.countries
diff --git a/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
index 7bfc758d1..6b0ff8888 100644
--- a/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
+++ b/policyengine_uk_data/tests/test_uc_payment_distribution_targets.py
@@ -4,7 +4,6 @@
 counts households on UC by monthly award band and family type. Its top band,
 '£2500.01 or over', is open-ended; it used to parse to NaN bounds, so its four
 targets (1.4k to 83k households) had a column of zeros and could never be met.
-The other bands keep their bounds: [monthly minimum, monthly maximum) x 12.
 """
 
 from types import SimpleNamespace
@@ -42,18 +41,23 @@ def _raw_extract() -> pd.DataFrame:
 
 
 def test_parse_monthly_award_band():
-    # The bounded bands are parsed exactly as before this change.
-    for band, (low, high) in {
-        "£0.01 to £100.00": (0.01, 100.00),
-        "£100.01 to £200.00": (100.01, 200.00),
-        "£1000.01 to £1100.00": (1000.01, 1100.00),
-    }.items():
-        assert parse_monthly_award_band(band) == (low * 12, high * 12)
-    assert parse_monthly_award_band("£2,500.01 or over") == (2500.01 * 12, np.inf)
+    assert parse_monthly_award_band("£0.01 to £100.00") == (0, 1_200)
+    assert parse_monthly_award_band("£100.01 to £200.00") == (1_200, 2_400)
+    assert parse_monthly_award_band("£1000.01 to £1100.00") == (12_000, 13_200)
+    assert parse_monthly_award_band("£2,500.01 or over") == (30_000, np.inf)
     with pytest.raises(ValueError):
         parse_monthly_award_band("No payment")
 
 
+@pytest.mark.parametrize(
+    "band",
+    ["£nan or over", "£inf or over", "£100.01 to £99.00", "£1.00 to £nan"],
+)
+def test_invalid_bands_are_rejected(band):
+    with pytest.raises(ValueError):
+        parse_monthly_award_band(band)
+
+
 def test_top_band_targets_are_reachable():
     targets = {t.name: t for t in _uc_payment_distribution_targets()}
     top = _raw_extract().loc["£2500.01 or over"]
@@ -61,7 +65,7 @@ def test_top_band_targets_are_reachable():
         target = targets[
             f"dwp/uc_payment_dist/{family_type}_annual_payment_30_000_to_inf"
         ]
-        assert target.lower_bound == 2500.01 * 12
+        assert target.lower_bound == 30_000
         assert target.upper_bound == np.inf
         assert target.values[2025] == top[label]
     for target in targets.values():
@@ -85,14 +89,13 @@ def test_band_counts_sum_to_households_with_a_payment():
         assert abs(parsed.household_count.sum() - with_payment) <= 10, label
 
 
-def test_bands_run_from_a_penny_to_infinity_without_overlap():
-    """Each band starts a penny a month (12p a year) above the last one ends."""
+def test_bands_tile_the_positive_awards():
     for family_type, bands in uc_national_payment_dist.groupby("family_type"):
         bands = bands.sort_values("uc_annual_payment_min")
         lower = bands.uc_annual_payment_min.to_numpy()
         upper = bands.uc_annual_payment_max.to_numpy()
-        assert lower[0] == pytest.approx(0.12), family_type
-        np.testing.assert_allclose(lower[1:] - upper[:-1], 0.12, atol=1e-6)
+        assert lower[0] == 0, family_type
+        assert np.array_equal(lower[1:], upper[:-1]), family_type
         assert upper[-1] == np.inf, family_type
 
 
@@ -123,62 +126,64 @@ def calculate(variable, map_to=None):
 
 
 _TARGETS = _uc_payment_distribution_targets()
-_EDGES = sorted(
-    {t.lower_bound for t in _TARGETS}
-    | {t.upper_bound for t in _TARGETS if np.isfinite(t.upper_bound)}
-)
+_EDGES = sorted({t.upper_bound for t in _TARGETS if np.isfinite(t.upper_bound)})
 _AWARDS = st.one_of(
     st.just(0.0),  # no payment
     st.floats(0, 1e6, allow_nan=False),  # anywhere, including the open top band
     st.sampled_from(_EDGES),  # exactly on a band edge
     st.sampled_from(_EDGES).map(lambda edge: np.nextafter(edge, np.inf)),
     st.sampled_from(_EDGES).map(lambda edge: np.nextafter(edge, -np.inf)),
+    st.sampled_from(_EDGES).map(lambda e: np.nextafter(np.float32(e), np.inf)),
+    st.sampled_from(_EDGES).map(lambda e: np.nextafter(np.float32(e), -np.inf)),
+)
+_BENEFIT_UNITS = st.lists(
+    st.tuples(_AWARDS, st.sampled_from(list(FAMILY_TYPES.values())), st.integers(0, 9)),
+    min_size=1,
+    max_size=60,
 )
 
 
-def _band_count(uc, family_type):
-    """Oracle: how many of the family type's bands hold each award, by an
-    interval search rather than the compute function's comparisons."""
-    count = np.zeros(len(uc))
+def _oracle_band(uc, family_type):
+    """Target name of the band holding each award (None for no payment), found
+    by searching the family type's sorted upper bounds rather than by the
+    compute function's comparisons."""
+    names = np.full(len(uc), None, dtype=object)
     for ft in np.unique(family_type):
         bands = sorted(
-            (t.lower_bound, t.upper_bound)
+            (t.upper_bound, t.lower_bound, t.name)
             for t in _TARGETS
             if t.name.removeprefix("dwp/uc_payment_dist/").startswith(ft + "_annual")
         )
-        lower = np.array([b[0] for b in bands])
-        upper = np.array([b[1] for b in bands])
+        upper = np.array([b[0] for b in bands])
         m = family_type == ft
-        i = np.searchsorted(lower, uc[m], side="right") - 1
-        count[m] = (i >= 0) & (uc[m] < upper[np.maximum(i, 0)])
-    return count
+        i = np.searchsorted(upper, uc[m], side="left")
+        names[m] = [
+            bands[k][2] if k < len(bands) and award > bands[k][1] else None
+            for k, award in zip(i, uc[m])
+        ]
+    return names
 
 
 @settings(max_examples=200, deadline=None)
-@given(
-    st.lists(
-        st.tuples(
-            _AWARDS, st.sampled_from(list(FAMILY_TYPES.values())), st.integers(0, 9)
-        ),
-        min_size=1,
-        max_size=60,
-    )
-)
-def test_each_award_lands_in_at_most_one_band(benefit_units):
-    """Property: summed over the bands, each household's column counts its
-    benefit units whose award lies in one of their family type's bands, once
-    (differential against an interval-search oracle). Awards from the top
-    band's lower bound up are always counted; zero awards never are."""
+@given(_BENEFIT_UNITS, st.booleans())
+def test_each_award_lands_in_its_own_band(benefit_units, as_float32):
+    """Property: every target's column counts exactly the household's benefit
+    units whose award an interval search places in that target's band (so no
+    award is counted twice, moved to a neighbouring band, or lost), for float64
+    and float32 awards, on band edges and one ulp either side."""
     uc, family_type, household = (np.array(column) for column in zip(*benefit_units))
-    uc = uc.astype(float)
+    uc = uc.astype(np.float32 if as_float32 else float)
     ctx = _fake_ctx(uc, family_type, household)
+    band = _oracle_band(uc, family_type)
 
-    per_target = [compute_uc_payment_dist(t, ctx) for t in _TARGETS]
-    total = sum(per_target)
-    in_band = _band_count(uc, family_type)
-    expected = np.bincount(household, weights=in_band, minlength=len(total))
-    np.testing.assert_array_equal(total, expected)
-
-    top = 2500.01 * 12
-    assert (in_band[uc >= top] == 1).all()
-    assert (in_band[uc == 0] == 0).all()
+    for target in _TARGETS:
+        expected = np.bincount(
+            household,
+            weights=(band == target.name).astype(float),
+            minlength=household.max() + 1,
+        )
+        np.testing.assert_array_equal(
+            compute_uc_payment_dist(target, ctx), expected, err_msg=target.name
+        )
+    # Every positive award is in some band; no payment is in none.
+    assert all((b is not None) == (award > 0) for b, award in zip(band, uc))
diff --git a/policyengine_uk_data/utils/uc_data.py b/policyengine_uk_data/utils/uc_data.py
index 17d56427c..ffe20896e 100644
--- a/policyengine_uk_data/utils/uc_data.py
+++ b/policyengine_uk_data/utils/uc_data.py
@@ -4,29 +4,34 @@
 
 
 def parse_monthly_award_band(band: str) -> tuple[float, float]:
-    """Annual [lower, upper) payment bounds of a Stat-Xplore monthly award band.
+    """Annual (lower, upper] payment bounds of a Stat-Xplore monthly award band.
 
-    '£100.01 to £200.00' gives (1,200.12, 2,400). The open top band
-    '£2500.01 or over' gives (30,000.12, inf); it used to parse to missing
-    bounds, so its targets could never be met.
+    Awards are whole pence, so the band '£100.01 to £200.00' holds monthly
+    awards over £100.00 and up to £200.00: annual bounds (1,200, 2,400]. The
+    lower bound is the previous band's top, so consecutive bands meet with no
+    gap. The open top band '£2500.01 or over' is (30,000, inf).
     """
     text = band.replace("£", "").replace(",", "").strip()
     if text.endswith(" or over"):
-        return float(text.removesuffix(" or over")) * 12, np.inf
-    parts = text.split(" to ")
-    if len(parts) != 2:
-        raise ValueError(f"Unrecognised UC monthly award band: {band!r}")
-    lower, upper = (float(part) * 12 for part in parts)
+        lower, upper = float(text.removesuffix(" or over")), np.inf
+    else:
+        parts = text.split(" to ")
+        if len(parts) != 2:
+            raise ValueError(f"Unrecognised UC monthly award band: {band!r}")
+        lower, upper = float(parts[0]), float(parts[1]) * 12
+    lower = round((lower - 0.01) * 12, 2)
+    if not (np.isfinite(lower) and lower >= 0 and upper > lower):
+        raise ValueError(f"Invalid UC monthly award band: {band!r}")
     return lower, upper
 
 
 def _check_bands_disjoint(bands: pd.DataFrame) -> None:
     """Fail if any family type's payment bands overlap.
 
-    Stat-Xplore also lists summary bands ('£1500.01 or over') that span the
-    finer bands below them. They are suppressed ('..') in the committed
-    extract; if a new extract filled one in, counting it as well would double
-    count those households.
+    Stat-Xplore's '£1500.01 or over' band is its top band for months up to
+    August 2022 and spans the finer bands added from September 2022. It is
+    suppressed ('..') in the committed extract; if a new extract filled it
+    in, counting it as well would double count those households.
     """
     for family_type, group in bands.groupby("family_type"):
         group = group.sort_values("uc_annual_payment_min")

From d39371d508c33065e636fd9dcc5f15e750177fa2 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 07:42:18 -0400
Subject: [PATCH 41/70] Bound the reduced-build gains check on both sides

A relative-error bound of 5 could never fail low: a total of zero has a
relative error of 1. Reduced builds now require the total to lie within a
factor of 6 of HMRC's in either direction; full builds keep the 50% bound.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_cgt_band_donors.py                 | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/policyengine_uk_data/tests/test_cgt_band_donors.py b/policyengine_uk_data/tests/test_cgt_band_donors.py
index a03b375a5..ec97d6519 100644
--- a/policyengine_uk_data/tests/test_cgt_band_donors.py
+++ b/policyengine_uk_data/tests/test_cgt_band_donors.py
@@ -162,8 +162,9 @@ def test_stack_cgt_band_donors(frs):
 # of main and of #529 carry about £243bn and £267bn (relative errors 2.7 and
 # 3.1, beyond the 0.5 x slack bound), while their full builds carry about
 # £53bn. Under TESTING the total-gains check only guards against
-# order-of-magnitude errors; the full build keeps the 50% bound.
-_TOTAL_GAINS_RELATIVE_BOUND = 5.0 if _REDUCED_BUILD_SLACK > 1 else 0.5
+# order-of-magnitude errors, in either direction: the total must lie within a
+# factor of 6 of HMRC's. The full build keeps the 50% bound.
+_REDUCED_GAINS_FACTOR = 6.0
 
 
 def _built_with_band_donors(enhanced_frs):
@@ -221,11 +222,15 @@ def test_built_total_gains(enhanced_frs):
     enhanced_frs = _built_with_band_donors(enhanced_frs)
     gains, weights = _person_gains_and_weights(enhanced_frs)
     total = float((gains * weights)[gains > _AEA].sum())
-    assert abs(total / _HMRC_TOTAL_GAINS - 1) < _TOTAL_GAINS_RELATIVE_BOUND, (
+    ratio = total / _HMRC_TOTAL_GAINS
+    message = (
         f"£{total / 1e9:.1f}bn of above-AEA gains against HMRC's "
-        f"£{_HMRC_TOTAL_GAINS / 1e9:.1f}bn "
-        f"(relative error {abs(total / _HMRC_TOTAL_GAINS - 1):.0%})."
+        f"£{_HMRC_TOTAL_GAINS / 1e9:.1f}bn (ratio {ratio:.2f})."
     )
+    if _REDUCED_BUILD_SLACK > 1:
+        assert 1 / _REDUCED_GAINS_FACTOR < ratio < _REDUCED_GAINS_FACTOR, message
+    else:
+        assert abs(ratio - 1) < 0.5, message
 
 
 @pytest.mark.slow

From c84265669d6c920dcca6aeb7e86b13839c5f1c70 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 08:06:51 -0400
Subject: [PATCH 42/70] Address review: shared pools, one region reader,
 unknown-region checks

From the independent review of f8b21d3:

- A region that holds every OA of its country (Wales, Scotland) now shares
  the country's stratum, so mixed known and UNKNOWN households draw exactly
  as a country-only call. Before, the two strata consumed the RNG in a
  different household order. With one clone, Welsh and Scottish households
  keep exactly their released OAs.
- clone_and_assign derives each household's country with the sampler's own
  region normaliser, so bytes, ONS codes and untidy text no longer give a
  contradictory country (an unrecognised region now raises instead of
  silently counting as England).
- The built-dataset test checks known regions exactly and households with
  no region below the country against England, instead of failing them.
- New tests: uniform draws in a zero-population region (the mutant that
  survived), the review's mixed UNKNOWN/London counterexample, and untidy
  regions through clone_and_assign. The property strategies now generate
  UNKNOWN in every country, regions as names/codes/bytes/untidy text, and
  countries the crosswalk does not cover.
- Docs: create_datasets.py exports the long geography weights; it does not
  call publish_local_h5s.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/oa_calibration_pipeline.md               |   2 +-
 .../calibration/clone_and_assign.py           |  45 ++-
 .../calibration/oa_assignment.py              |  27 +-
 .../tests/test_oa_region_assignment.py        | 273 +++++++++++++-----
 4 files changed, 246 insertions(+), 101 deletions(-)

diff --git a/docs/oa_calibration_pipeline.md b/docs/oa_calibration_pipeline.md
index be147e61c..eb2a1f7bd 100644
--- a/docs/oa_calibration_pipeline.md
+++ b/docs/oa_calibration_pipeline.md
@@ -134,7 +134,7 @@ Generate per-area H5 files from sparse L0-calibrated weights.
 **Deliverables:**
 - `policyengine_uk_data/calibration/publish_local_h5s.py` — extracts per-area H5 subsets from the sparse weight vector; each H5 contains only active households (non-zero weight) with their calibrated weights, plus the linked person and benunit rows
 - `policyengine_uk_data/calibration/long_geography.py` — exports matrix-free local geography weights as an OA-first long table, with constituency and LA rows derived from assigned OA geography
-- `datasets/create_datasets.py` — publish step wired in after calibration, before downrating
+- `datasets/create_datasets.py` — exports `local_geography_weights.csv.gz` after calibration; `publish_local_h5s()` is not called by the build
 - `tests/test_publish_local_h5s.py` — 13 tests covering area-household mapping, H5 structure, pruned-household exclusion, weight correctness, person/benunit FK integrity, full publish cycle, summary statistics, and validation
 
 **Key design:**
diff --git a/policyengine_uk_data/calibration/clone_and_assign.py b/policyengine_uk_data/calibration/clone_and_assign.py
index bc710ab61..f1907b9dc 100644
--- a/policyengine_uk_data/calibration/clone_and_assign.py
+++ b/policyengine_uk_data/calibration/clone_and_assign.py
@@ -19,37 +19,35 @@
 from policyengine_uk.data import UKSingleYearDataset
 
 from policyengine_uk_data.calibration.oa_assignment import (
+    FRS_COUNTRY_MAP,
+    _REGION_CODE_PREFIX_TO_COUNTRY,
+    _normalise_region,
     assign_random_geography,
 )
 
 logger = logging.getLogger(__name__)
 
-# FRS region values that map to each country
-_REGION_TO_COUNTRY_CODE = {
-    "NORTH_EAST": 1,
-    "NORTH_WEST": 1,
-    "YORKSHIRE": 1,
-    "EAST_MIDLANDS": 1,
-    "WEST_MIDLANDS": 1,
-    "EAST_OF_ENGLAND": 1,
-    "LONDON": 1,
-    "SOUTH_EAST": 1,
-    "SOUTH_WEST": 1,
-    "WALES": 2,
-    "SCOTLAND": 3,
-    "NORTHERN_IRELAND": 4,
-    "UNKNOWN": 1,  # Default to England
-}
+_COUNTRY_TO_FRS_CODE = {name: code for code, name in FRS_COUNTRY_MAP.items()}
 
 
 def _household_country_codes(dataset: UKSingleYearDataset) -> np.ndarray:
-    """Extract FRS country codes (1-4) from household region."""
-    regions = dataset.household["region"].values
-    codes = np.array(
-        [_REGION_TO_COUNTRY_CODE.get(str(r), 1) for r in regions],
-        dtype=np.int32,
-    )
-    return codes
+    """Extract FRS country codes (1-4) from household region.
+
+    Uses the same region normalisation as the OA sampler, so both read
+    a region the same way. A region with nothing below the country
+    (missing or ``UNKNOWN``) counts as England; an unrecognised value
+    raises.
+    """
+    codes = []
+    for region in dataset.household["region"].values:
+        region_code = _normalise_region(region)
+        country = (
+            "England"
+            if region_code is None
+            else _REGION_CODE_PREFIX_TO_COUNTRY[region_code[0]]
+        )
+        codes.append(_COUNTRY_TO_FRS_CODE[country])
+    return np.array(codes, dtype=np.int32)
 
 
 def _remap_ids(
@@ -114,7 +112,6 @@ def clone_and_assign(
     # NI is excluded until NISRA updates their download URLs.
     from policyengine_uk_data.calibration.oa_assignment import (
         _load_country_distributions,
-        FRS_COUNTRY_MAP,
     )
 
     available_distributions = _load_country_distributions(
diff --git a/policyengine_uk_data/calibration/oa_assignment.py b/policyengine_uk_data/calibration/oa_assignment.py
index 15f3e59ee..c8d23e7a4 100644
--- a/policyengine_uk_data/calibration/oa_assignment.py
+++ b/policyengine_uk_data/calibration/oa_assignment.py
@@ -296,20 +296,35 @@ def assign_random_geography(
                 n_unknown,
             )
 
+    path_key = str(crosswalk_path) if crosswalk_path else None
+    country_distributions = _load_country_distributions(path_key)
+    region_distributions = (
+        _load_region_distributions(path_key) if household_regions is not None else {}
+    )
+    # A region that holds every OA of its country (Wales and Scotland in
+    # the crosswalk) samples from the same pool as the country, so it
+    # shares the country's stratum: its households then draw exactly as a
+    # country-only call would draw them.
+    sole_region_country = {}
+    for country, dist in country_distributions.items():
+        codes = set(pd.Series(dist["region_codes"]).fillna("").astype(str).str.strip())
+        if len(codes) == 1:
+            (code,) = codes
+            region_dist = region_distributions.get(code)
+            if region_dist is not None and len(region_dist["oa_codes"]) == len(
+                dist["oa_codes"]
+            ):
+                sole_region_country[code] = country
+
     # Sampling stratum per household: its region code where known,
     # otherwise its country name.
     strata = np.array(
         [
-            region if region is not None else country
+            country if region is None else sole_region_country.get(region, region)
             for region, country in zip(regions, countries)
         ],
         dtype=object,
     )
-    path_key = str(crosswalk_path) if crosswalk_path else None
-    country_distributions = _load_country_distributions(path_key)
-    region_distributions = (
-        _load_region_distributions(path_key) if household_regions is not None else {}
-    )
     distributions = {}
     missing_distributions = []
     for stratum in sorted(set(strata)):
diff --git a/policyengine_uk_data/tests/test_oa_region_assignment.py b/policyengine_uk_data/tests/test_oa_region_assignment.py
index 7cf39ff05..c350bfc6e 100644
--- a/policyengine_uk_data/tests/test_oa_region_assignment.py
+++ b/policyengine_uk_data/tests/test_oa_region_assignment.py
@@ -15,9 +15,13 @@
 import pytest
 from policyengine_uk.data import UKSingleYearDataset
 
-from policyengine_uk_data.calibration.clone_and_assign import clone_and_assign
+from policyengine_uk_data.calibration.clone_and_assign import (
+    _household_country_codes,
+    clone_and_assign,
+)
 from policyengine_uk_data.calibration.oa_assignment import (
     FRS_REGION_TO_CODE,
+    _normalise_region,
     assign_random_geography,
 )
 from policyengine_uk_data.calibration.oa_crosswalk import (
@@ -76,13 +80,21 @@ def _write_crosswalk(directory: Path, rows: list) -> tuple[pd.DataFrame, str]:
     return frame, str(path)
 
 
-def _countries(regions: list[str]) -> np.ndarray:
-    return np.array(
-        [COUNTRY_TO_FRS_CODE[REGION_TO_COUNTRY.get(r, "England")] for r in regions]
-    )
+def _countries(countries: list[str]) -> np.ndarray:
+    return np.array([COUNTRY_TO_FRS_CODE[c] for c in countries])
+
+
+# Ways a region can arrive: FRS name, crosswalk code, bytes, untidy text.
+REPRESENTATIONS = {
+    "name": lambda region: region,
+    "code": lambda region: FRS_REGION_TO_CODE[region],
+    "bytes": lambda region: region.encode(),
+    "untidy": lambda region: f"  {region.lower()} ",
+}
+UNKNOWN_VALUES = ["UNKNOWN", "", None, np.nan, b"UNKNOWN"]
 
 
-def _toy_dataset(regions: list[str], weights: list[float]) -> UKSingleYearDataset:
+def _toy_dataset(regions: list, weights: list[float]) -> UKSingleYearDataset:
     ids = np.arange(1, len(regions) + 1)
     return UKSingleYearDataset(
         person=pd.DataFrame(
@@ -95,15 +107,35 @@ def _toy_dataset(regions: list[str], weights: list[float]) -> UKSingleYearDatase
         ),
         benunit=pd.DataFrame({"benunit_id": ids * 100 + 1}),
         household=pd.DataFrame(
-            {"household_id": ids, "household_weight": weights, "region": regions}
+            {
+                "household_id": ids,
+                "household_weight": weights,
+                "region": pd.Series(regions, dtype=object),
+            }
         ),
         fiscal_year=2024,
     )
 
 
 @st.composite
-def crosswalk_and_regions(draw, allow_unassignable: bool = False):
-    """A synthetic crosswalk plus household regions it can serve."""
+def households(draw, crosswalk_regions: list[str], unknown_countries: list[str]):
+    """Households as (FRS region or None, country, raw region value)."""
+    known = [(region, REGION_TO_COUNTRY[region]) for region in crosswalk_regions]
+    unknown = [(None, country) for country in unknown_countries]
+    region, country = draw(st.sampled_from(known + unknown))
+    if region is None:
+        raw = draw(st.sampled_from(UNKNOWN_VALUES))
+    else:
+        raw = REPRESENTATIONS[draw(st.sampled_from(sorted(REPRESENTATIONS)))](region)
+    return region, country, raw
+
+
+@st.composite
+def crosswalk_and_households(draw):
+    """A synthetic crosswalk plus households the sampler can serve.
+
+    A household with no region can belong to any country in the crosswalk.
+    """
     crosswalk_regions = draw(
         st.lists(
             st.sampled_from(sorted(FRS_REGION_TO_CODE)),
@@ -124,13 +156,11 @@ def crosswalk_and_regions(draw, allow_unassignable: bool = False):
                 )
             ),
         )
-    choices = list(crosswalk_regions)
-    if any(REGION_TO_COUNTRY[r] == "England" for r in crosswalk_regions):
-        choices.append("UNKNOWN")
-    if allow_unassignable and "NORTHERN_IRELAND" not in crosswalk_regions:
-        choices.append("NORTHERN_IRELAND")
-    regions = draw(st.lists(st.sampled_from(choices), min_size=1, max_size=12))
-    return rows, regions
+    countries = sorted({REGION_TO_COUNTRY[r] for r in crosswalk_regions})
+    sample = draw(
+        st.lists(households(crosswalk_regions, countries), min_size=1, max_size=12)
+    )
+    return rows, sample
 
 
 def _as_tuple(geography) -> tuple:
@@ -142,18 +172,19 @@ def _as_tuple(geography) -> tuple:
 
 class TestRegionConstraintProperties:
     @given(
-        case=crosswalk_and_regions(),
+        case=crosswalk_and_households(),
         n_clones=st.integers(1, 4),
         seed=st.integers(0, 2**32 - 1),
     )
-    @settings(max_examples=60, deadline=None)
+    @settings(max_examples=80, deadline=None)
     def test_assignment_invariants(self, case, n_clones, seed):
-        rows, regions = case
+        rows, sample = case
+        regions, countries, raw = map(list, zip(*sample))
         with tempfile.TemporaryDirectory() as directory:
             crosswalk, path = _write_crosswalk(directory, rows)
             kwargs = dict(
-                household_countries=_countries(regions),
-                household_regions=np.array(regions, dtype=object),
+                household_countries=_countries(countries),
+                household_regions=np.array(raw, dtype=object),
                 n_clones=n_clones,
                 seed=seed,
                 crosswalk_path=path,
@@ -164,26 +195,22 @@ def test_assignment_invariants(self, case, n_clones, seed):
         # Determinism: same inputs and seed, same assignment.
         assert _as_tuple(geography) == _as_tuple(repeat)
 
-        n = len(regions)
+        n = len(sample)
         assert len(geography.oa_code) == n * n_clones
         by_oa = crosswalk.set_index("oa_code")
         region_population = crosswalk.groupby("region_code")["population"].sum()
-        england_population = crosswalk.loc[
-            crosswalk["country"] == "England", "population"
-        ].sum()
+        country_population = crosswalk.groupby("country")["population"].sum()
         for i, oa in enumerate(geography.oa_code):
-            region = regions[i % n]
+            region, country = regions[i % n], countries[i % n]
             row = by_oa.loc[oa]
-            # The OA's region is the household's region; an unknown region
-            # still keeps the household in its country.
-            if region == "UNKNOWN":
-                assert row["country"] == "England"
-                stratum_population = england_population
+            # The OA's region is the household's region; a household with
+            # no region stays in its country.
+            assert row["country"] == country == geography.country[i]
+            if region is None:
+                stratum_population = country_population[country]
             else:
                 assert geography.region_code[i] == FRS_REGION_TO_CODE[region]
                 stratum_population = region_population[FRS_REGION_TO_CODE[region]]
-            assert geography.country[i] == REGION_TO_COUNTRY.get(region, "England")
-            assert row["country"] == geography.country[i]
             # Every other code is the crosswalk's own row for that OA.
             for field, column in GEOGRAPHY_FIELDS.items():
                 assert getattr(geography, field)[i] == row[column]
@@ -192,17 +219,34 @@ def test_assignment_invariants(self, case, n_clones, seed):
                 assert row["population"] > 0
 
     @given(
-        case=crosswalk_and_regions(allow_unassignable=True),
+        case=crosswalk_and_households(),
+        uncovered=st.lists(
+            st.sampled_from(
+                [("NORTHERN_IRELAND", "Northern Ireland"), (None, "England")]
+            ),
+            max_size=3,
+        ),
         weights=st.lists(
-            st.floats(0, 5_000, allow_nan=False), min_size=12, max_size=12
+            st.floats(0, 5_000, allow_nan=False), min_size=15, max_size=15
         ),
         n_clones=st.integers(1, 4),
         seed=st.integers(0, 2**32 - 1),
     )
-    @settings(max_examples=40, deadline=None)
-    def test_clone_and_assign_invariants(self, case, weights, n_clones, seed):
-        rows, regions = case
-        dataset = _toy_dataset(regions, weights[: len(regions)])
+    @settings(max_examples=50, deadline=None)
+    def test_clone_and_assign_invariants(
+        self, case, uncovered, weights, n_clones, seed
+    ):
+        rows, sample = case
+        # clone_and_assign reads a household without a region as English.
+        sample = [(r, "England" if r is None else c, raw) for r, c, raw in sample]
+        covered_countries = {row["country"] for row in rows}
+        sample += [
+            (region, country, region or "UNKNOWN")
+            for region, country in uncovered
+            if country not in covered_countries
+        ]
+        regions, countries, raw = map(list, zip(*sample))
+        dataset = _toy_dataset(raw, weights[: len(sample)])
         with tempfile.TemporaryDirectory() as directory:
             _, path = _write_crosswalk(directory, rows)
             kwargs = dict(n_clones=n_clones, seed=seed, crosswalk_path=path)
@@ -225,50 +269,68 @@ def test_clone_and_assign_invariants(self, case, weights, n_clones, seed):
             rtol=1e-12,
             atol=1e-9,
         )
-        # Cloning never rewrites the FRS region.
-        assert household["region"].tolist() == original["region"].tolist() * n_clones
 
-        expected = household["region"].map(FRS_REGION_TO_CODE)
-        known = household["region"].isin(FRS_REGION_TO_CODE) & (
-            household["oa_code"] != ""
-        )
-        assert (household.loc[known, "region_code_oa"] == expected[known]).all()
-        unknown = household["region"] == "UNKNOWN"
-        assert household.loc[unknown, "region_code_oa"].str.startswith("E12").all()
-        # A region the crosswalk does not cover gets no geography at all.
-        covered = {row["region_code"] for row in rows}
-        uncovered = ~unknown & ~expected.isin(covered)
-        for column in ["oa_code", "la_code_oa", "constituency_code_oa"]:
-            assert (household.loc[uncovered, column] == "").all()
-            assert (household.loc[~uncovered, column] != "").all()
+        # Cloning never rewrites the FRS region (a missing value may come
+        # back as None or NaN; both are missing).
+        def tidy(values):
+            return [None if pd.isna(v) else v for v in values]
+
+        assert tidy(household["region"]) == tidy(original["region"]) * n_clones
+
+        for i, (region, country) in enumerate(
+            zip(regions * n_clones, countries * n_clones)
+        ):
+            assigned = household.iloc[i]
+            if country not in covered_countries:
+                # A country the crosswalk does not cover gets no geography.
+                for column in ["oa_code", "la_code_oa", "constituency_code_oa"]:
+                    assert assigned[column] == ""
+            elif region is None:
+                assert assigned["region_code_oa"].startswith("E12")
+            else:
+                assert assigned["region_code_oa"] == FRS_REGION_TO_CODE[region]
+                assert assigned["oa_code"] != ""
 
     @given(
-        case=crosswalk_and_regions(),
+        unknown_countries=st.lists(
+            st.sampled_from(["England", "Wales", "Scotland", "Northern Ireland"]),
+            max_size=6,
+        ),
+        known=st.lists(
+            st.sampled_from(["LONDON", "WALES", "SCOTLAND", "NORTHERN_IRELAND"]),
+            max_size=6,
+        ),
+        order=st.randoms(use_true_random=False),
         n_clones=st.integers(1, 4),
         seed=st.integers(0, 2**32 - 1),
     )
-    @settings(max_examples=40, deadline=None)
+    @settings(max_examples=60, deadline=None)
     def test_matches_country_sampling_when_region_is_the_country(
-        self, case, n_clones, seed
+        self, unknown_countries, known, order, n_clones, seed
     ):
-        """Differential: with one region per country the two paths agree."""
-        _, regions = case
-        one_region_each = ["LONDON", "WALES", "SCOTLAND", "NORTHERN_IRELAND"]
-        regions = [r for r in regions if r in one_region_each] or ["LONDON"]
+        """Differential: when each country is one region, giving regions
+        changes nothing, even with households of unknown region mixed in."""
+        sample = [(None, c) for c in unknown_countries] + [
+            (r, REGION_TO_COUNTRY[r]) for r in known
+        ] or [("LONDON", "England")]
+        order.shuffle(sample)
         rows = []
-        for region in one_region_each:
+        for region in ["LONDON", "WALES", "SCOTLAND", "NORTHERN_IRELAND"]:
             rows += _crosswalk_rows(region, [[30, 10, 0], [5, 55], [20]])
         with tempfile.TemporaryDirectory() as directory:
             _, path = _write_crosswalk(directory, rows)
             kwargs = dict(
-                household_countries=_countries(regions),
+                household_countries=_countries([c for _, c in sample]),
                 n_clones=n_clones,
                 seed=seed,
                 crosswalk_path=path,
             )
             by_country = assign_random_geography(**kwargs)
             by_region = assign_random_geography(
-                household_regions=np.array(regions, dtype=object), **kwargs
+                household_regions=np.array(
+                    [r or "UNKNOWN" for r, _ in sample], dtype=object
+                ),
+                **kwargs,
             )
         assert _as_tuple(by_country) == _as_tuple(by_region)
 
@@ -407,6 +469,69 @@ def test_single_constituency_region_terminates(self, tmp_path):
         )
         assert len(set(geography.constituency_code)) == 1
 
+    def test_zero_population_region_samples_uniformly(self, tmp_path):
+        """A region with no recorded population draws its OAs uniformly."""
+        rows = _crosswalk_rows("WALES", [[0, 0], [0, 0]]) + _crosswalk_rows(
+            "LONDON", [[100]]
+        )
+        crosswalk, path = _write_crosswalk(tmp_path, rows)
+        n = 8_000
+        geography = assign_random_geography(
+            household_countries=np.full(n, 2),
+            household_regions=np.array(["WALES"] * n, dtype=object),
+            n_clones=1,
+            seed=5,
+            crosswalk_path=path,
+        )
+        shares = pd.Series(geography.oa_code).value_counts(normalize=True)
+        welsh = set(crosswalk.loc[crosswalk["country"] == "Wales", "oa_code"])
+        assert set(shares.index) == welsh
+        # Binomial(8,000, 0.25): four standard errors is 0.019.
+        assert (shares - 0.25).abs().max() < 0.02
+
+    def test_mixed_unknown_and_sole_region_draw_like_the_country(self, tmp_path):
+        """Review counterexample: an UNKNOWN and a London household, London
+        being England's only region, draw exactly as a country-only call."""
+        rows = (
+            _crosswalk_rows("LONDON", [[100], [100]])
+            + _crosswalk_rows("WALES", [[100]])
+            + _crosswalk_rows("SCOTLAND", [[100]])
+            + _crosswalk_rows("NORTHERN_IRELAND", [[100]])
+        )
+        _, path = _write_crosswalk(tmp_path, rows)
+        kwargs = dict(
+            household_countries=np.array([1, 1]),
+            n_clones=1,
+            seed=0,
+            crosswalk_path=path,
+        )
+        by_country = assign_random_geography(**kwargs)
+        by_region = assign_random_geography(
+            household_regions=np.array(["UNKNOWN", "LONDON"], dtype=object),
+            **kwargs,
+        )
+        assert _as_tuple(by_country) == _as_tuple(by_region)
+
+    def test_clone_and_assign_reads_untidy_regions_like_the_sampler(self, tmp_path):
+        """Review counterexample: bytes, codes and untidy text give the
+        same country in cloning as in the sampler."""
+        rows = _crosswalk_rows("WALES", [[100]]) + _crosswalk_rows("LONDON", [[100]])
+        _, path = _write_crosswalk(tmp_path, rows)
+        raw = [b"WALES", " wales ", "W99999999", b"NORTHERN_IRELAND", "UNKNOWN"]
+        assert _household_country_codes(
+            _toy_dataset(raw, [1.0] * len(raw))
+        ).tolist() == [2, 2, 2, 4, 1]
+        household = clone_and_assign(
+            _toy_dataset(raw, [1.0] * len(raw)), n_clones=1, crosswalk_path=path
+        ).household
+        assert household["region_code_oa"].tolist() == [
+            "W99999999",
+            "W99999999",
+            "W99999999",
+            "",
+            "E12000007",
+        ]
+
 
 @pytest.fixture(scope="module")
 def real_crosswalk() -> pd.DataFrame:
@@ -466,7 +591,7 @@ def test_assignment_on_the_real_crosswalk(self, real_crosswalk):
         regions = np.array(covered * 40, dtype=object)
         n_clones = 10
         geography = assign_random_geography(
-            household_countries=_countries(list(regions)),
+            household_countries=_countries([REGION_TO_COUNTRY[r] for r in regions]),
             household_regions=regions,
             n_clones=n_clones,
             seed=42,
@@ -481,11 +606,19 @@ def test_assignment_on_the_real_crosswalk(self, real_crosswalk):
 
 
 def test_built_dataset_oa_region_matches_frs_region(enhanced_frs):
-    """Every household of the built enhanced FRS sits in its own region."""
+    """Every household of the built enhanced FRS sits in its own region.
+
+    A household with no region below the country is English to
+    clone_and_assign, so its OA must be English.
+    """
     household = enhanced_frs.household
-    decode = lambda value: value.decode() if isinstance(value, bytes) else str(value)  # noqa: E731
-    region_code = household["region_code_oa"].map(decode)
-    expected = household["region"].map(decode).map(FRS_REGION_TO_CODE)
+    region_code = household["region_code_oa"].map(
+        lambda value: value.decode() if isinstance(value, bytes) else str(value)
+    )
+    expected = household["region"].map(_normalise_region)
     has_oa = region_code != ""
     assert has_oa.any()
-    assert int((region_code[has_oa] != expected[has_oa]).sum()) == 0
+    known = has_oa & expected.notna()
+    assert int((region_code[known] != expected[known]).sum()) == 0
+    unknown = has_oa & expected.isna()
+    assert region_code[unknown].str.startswith("E12").all()

From 40ea9453385b2197060e70c592e1495c1ac989d3 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 16:26:47 -0400
Subject: [PATCH 43/70] Restore HMRC salary sacrifice relief calibration
 targets

GOV.UK withdrew the July 2025 Tables 6.1/6.2 CSV (410 Gone) when HMRC
published the July 2026 private pension statistics, and get_targets()
logged the error and returned without the relief targets, so every build
since has calibrated without hmrc/salary_sacrifice_it_relief_* and the
HMRC NICs relief targets.

- Point sources.yaml at the July 2026 CSV (tax year 2024-25) and commit
  a copy in storage; a failed download uses it, and a table without the
  expected rows raises instead of returning fewer targets.
- Map the tax year from the table: PolicyEngine year N is tax year
  N to N+1, so 2024-25 is 2024 (the old comment put 2023-24 at 2024).
- Split IT relief across the bands each person's relief straddles,
  as HMRC does, instead of classifying by adjusted net income against
  taxable-income thresholds (which put every basic-rate taxpayer with
  income between 37,700 and 50,270 in the higher-rate target).
- Drop obr/salary_sacrifice_{employee,employer}_ni_relief: static copies
  of HMRC's 2023-24 Table 6.2 figures computed on the same column as the
  restored HMRC NICs targets.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 ...c-salary-sacrifice-relief-targets.fixed.md |   1 +
 ..._pension_relief_tables_6_1_6_2_2024_25.csv |  83 +++++++
 .../targets/build_loss_matrix.py              |   2 -
 .../targets/compute/income.py                 |  84 ++++---
 policyengine_uk_data/targets/sources.yaml     |   4 +-
 .../targets/sources/hmrc_salary_sacrifice.py  | 215 ++++++++++--------
 policyengine_uk_data/targets/sources/obr.py   |  24 +-
 .../test_hmrc_salary_sacrifice_targets.py     | 178 +++++++++++++++
 .../tests/test_target_registry.py             |  19 ++
 9 files changed, 468 insertions(+), 142 deletions(-)
 create mode 100644 changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
 create mode 100644 policyengine_uk_data/storage/hmrc_pension_relief_tables_6_1_6_2_2024_25.csv
 create mode 100644 policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py

diff --git a/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md b/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
new file mode 100644
index 000000000..46b3fb23a
--- /dev/null
+++ b/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
@@ -0,0 +1 @@
+Restore the HMRC salary sacrifice income tax and NICs relief calibration targets, lost to a withdrawn (410 Gone) HMRC CSV since the July 2026 private pension statistics release. They now use the 2024-25 Tables 6.1 and 6.2, mapped to year 2024, with a committed copy used when the download fails; a changed table fails the build. The rate-band targets split each person's relief across the bands it is given in (they had been assigned by adjusted net income against taxable-income thresholds), and the OBR-labelled NICs relief targets, which repeated the 2023-24 HMRC figures, are removed.
diff --git a/policyengine_uk_data/storage/hmrc_pension_relief_tables_6_1_6_2_2024_25.csv b/policyengine_uk_data/storage/hmrc_pension_relief_tables_6_1_6_2_2024_25.csv
new file mode 100644
index 000000000..48b149176
--- /dev/null
+++ b/policyengine_uk_data/storage/hmrc_pension_relief_tables_6_1_6_2_2024_25.csv
@@ -0,0 +1,83 @@
+"tax_year","income_tax_nics","contribution_type","nics_relief_class","sector_scheme","scheme_type","tax_rate","value_of_relief"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Total","Total","7000"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Total","Total","5900"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Total","Total","8800"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Total","Total","24800"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Total","Total","9200"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Public sector occupational scheme","Total","Total","4500"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Public sector occupational scheme","Total","Total","200"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Public sector occupational scheme","Total","Total","600"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Public sector occupational scheme","Total","Total","14000"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Public sector occupational scheme","Total","Total","400"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Personal or private sector occupational scheme","Total","Total","2500"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Personal or private sector occupational scheme","Total","Total","5700"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Personal or private sector occupational scheme","Total","Total","8300"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Personal or private sector occupational scheme","Total","Total","10800"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Personal or private sector occupational scheme","Total","Total","8800"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Defined benefit","Total","5200"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Defined benefit","Total","0"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Defined benefit","Total","1500"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Defined benefit","Total","16600"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Defined benefit","Total","0"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Defined contribution","Total","1800"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Defined contribution","Total","5900"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Defined contribution","Total","7400"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Defined contribution","Total","8200"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Defined contribution","Total","9200"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Total","Basic Rate","2400"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Total","Basic Rate","3200"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Total","Basic Rate","1600"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Total","Basic Rate","6700"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Total","Basic Rate","2200"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Total","Higher Rate","3800"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Total","Higher Rate","1600"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Total","Higher Rate","5500"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Total","Higher Rate","15600"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Total","Higher Rate","5200"
+"2024 to 2025","Income Tax","Individual contributions to net pay arrangements","Not applicable","Total","Total","Additional Rate","900"
+"2024 to 2025","Income Tax","Individual contributions to relief at source schemes","Not applicable","Total","Total","Additional Rate","1100"
+"2024 to 2025","Income Tax","Salary sacrificed contributions","Not applicable","Total","Total","Additional Rate","1800"
+"2024 to 2025","Income Tax","Employer contributions to net pay arrangements plus deficit reduction contributions","Not applicable","Total","Total","Additional Rate","2500"
+"2024 to 2025","Income Tax","Employer contributions to relief at source schemes","Not applicable","Total","Total","Additional Rate","1700"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Total","Total","Total","3600"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Total","Total","Total","1200"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Total","Total","Total","1000"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Total","Total","Total","10600"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Total","Total","Total","3800"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Total","Total","Total","3400"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Public sector occupational scheme","Total","Total","2300"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Public sector occupational scheme","Total","Total","900"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Public sector occupational scheme","Total","Total","100"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Public sector occupational scheme","Total","Total","6300"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Public sector occupational scheme","Total","Total","200"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Public sector occupational scheme","Total","Total","200"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Personal or private sector occupational scheme","Total","Total","1300"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Personal or private sector occupational scheme","Total","Total","300"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Personal or private sector occupational scheme","Total","Total","900"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Personal or private sector occupational scheme","Total","Total","4300"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Personal or private sector occupational scheme","Total","Total","3600"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Personal or private sector occupational scheme","Total","Total","3100"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Total","Defined benefit","Total","2500"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Total","Defined benefit","Total","0"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Total","Defined benefit","Total","200"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Total","Defined benefit","Total","7200"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Total","Defined benefit","Total","0"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Total","Defined benefit","Total","600"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Total","Defined contribution","Total","1100"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Total","Defined contribution","Total","1200"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Total","Defined contribution","Total","800"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Total","Defined contribution","Total","3300"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Total","Defined contribution","Total","3800"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Total","Defined contribution","Total","2800"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Total","Total","Main Rate","2800"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Total","Total","Main Rate","900"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Total","Total","Main Rate","600"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Total","Total","Main Rate","10600"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Total","Total","Main Rate","3800"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Total","Total","Main Rate","3400"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Primary (employee)","Total","Total","Additional Rate","800"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Primary (employee)","Total","Total","Additional Rate","300"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Primary (employee)","Total","Total","Additional Rate","300"
+"2024 to 2025","NICs","Employer contributions to net pay arrangements and on employer deficit reduction contributions","Class 1 Secondary (employer)","Total","Total","Additional Rate","[z]"
+"2024 to 2025","NICs","Employer contributions to relief at source schemes","Class 1 Secondary (employer)","Total","Total","Additional Rate","[z]"
+"2024 to 2025","NICs","Salary sacrificed contributions","Class 1 Secondary (employer)","Total","Total","Additional Rate","[z]"
diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 9552aeea2..0c374dbf8 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -386,9 +386,7 @@ def _compute_column(target: Target, ctx: _SimContext, year: int) -> np.ndarray |
     # Salary sacrifice NI relief
     if name in (
         "hmrc/salary_sacrifice_employee_nics_relief",
-        "obr/salary_sacrifice_employee_ni_relief",
         "hmrc/salary_sacrifice_employer_nics_relief",
-        "obr/salary_sacrifice_employer_ni_relief",
     ):
         return compute_ss_ni_relief(target, ctx)
 
diff --git a/policyengine_uk_data/targets/compute/income.py b/policyengine_uk_data/targets/compute/income.py
index f5be3e2bb..d96c9234e 100644
--- a/policyengine_uk_data/targets/compute/income.py
+++ b/policyengine_uk_data/targets/compute/income.py
@@ -20,34 +20,66 @@ def compute_income_band(target, ctx) -> np.ndarray:
         return ctx.household_from_person(income_df[variable] * in_band)
 
 
-def compute_ss_it_relief(target, ctx) -> np.ndarray:
-    """Compute salary sacrifice IT relief by tax band."""
-    it_base = ctx.sim.calculate("income_tax")
-    it_cf = ctx.counterfactual_sim.calculate("income_tax", ctx.time_period)
-    it_relief = it_cf - it_base
-
-    adj_net_income_cf = ctx.counterfactual_sim.calculate(
-        "adjusted_net_income", ctx.time_period
-    )
+# Income tax on earned income in each rUK band (rates.uk thresholds on
+# earned_taxable_income), keyed by the band word in the target name. Scottish
+# taxpayers are split on the same rUK boundaries, an approximation: HMRC
+# groups their relief by Scottish marginal rate (starter and intermediate
+# with basic), and the Scottish band thresholds differ from the rUK ones.
+_BAND_TAX_VARIABLES = {
+    "basic": "basic_rate_earned_income_tax",
+    "higher": "higher_rate_earned_income_tax",
+    "additional": "add_rate_earned_income_tax",
+}
+
+
+def split_relief_by_band(
+    relief: np.ndarray, band_tax_cf: dict, band_tax_base: dict
+) -> dict:
+    """Share each person's income tax relief across the bands it is given in.
+
+    HMRC's Table 6.1 models contributions that straddle bands ("not all be
+    relievable at the same rate"; private pension statistics, background and
+    methodology), so a person's relief is split in proportion to the fall in
+    their earned-income tax within each band. Relief with no fall in any
+    earned band (it comes through savings or dividend bands) counts as basic
+    rate. The shares sum to one, so the bands add up to ``relief``.
+    """
+    drops = {
+        band: np.maximum(np.asarray(band_tax_cf[band]) - band_tax_base[band], 0)
+        for band in _BAND_TAX_VARIABLES
+    }
+    total = sum(drops.values())
+    has_drop = total > 0
+    safe_total = np.where(has_drop, total, 1)
+    relief = np.asarray(relief)
+    return {
+        band: relief
+        * np.where(has_drop, drop / safe_total, 1.0 if band == "basic" else 0.0)
+        for band, drop in drops.items()
+    }
 
-    params = ctx.sim.tax_benefit_system.parameters.gov.hmrc.income_tax.rates.uk
-    basic_thresh = params[0].threshold(ctx.time_period)
-    higher_thresh = params[1].threshold(ctx.time_period)
-    additional_thresh = params[2].threshold(ctx.time_period)
 
-    name = target.name
-    if "basic" in name:
-        mask = (adj_net_income_cf > basic_thresh) & (adj_net_income_cf <= higher_thresh)
-    elif "higher" in name:
-        mask = (adj_net_income_cf > higher_thresh) & (
-            adj_net_income_cf <= additional_thresh
-        )
-    elif "additional" in name:
-        mask = adj_net_income_cf > additional_thresh
-    else:
-        mask = np.ones_like(it_relief, dtype=bool)
-
-    return ctx.household_from_person(it_relief * mask)
+def compute_ss_it_relief(target, ctx) -> np.ndarray:
+    """Compute salary sacrifice IT relief, in total or for one rate band."""
+    period = ctx.time_period
+    cf, base = ctx.counterfactual_sim, ctx.sim
+    relief = np.asarray(cf.calculate("income_tax", period)) - np.asarray(
+        base.calculate("income_tax", period)
+    )
+    band = next((b for b in _BAND_TAX_VARIABLES if b in target.name), None)
+    if band is not None:
+        relief = split_relief_by_band(
+            relief,
+            {
+                b: np.asarray(cf.calculate(v, period))
+                for b, v in _BAND_TAX_VARIABLES.items()
+            },
+            {
+                b: np.asarray(base.calculate(v, period))
+                for b, v in _BAND_TAX_VARIABLES.items()
+            },
+        )[band]
+    return ctx.household_from_person(relief)
 
 
 def compute_ss_contributions(target, ctx) -> np.ndarray:
diff --git a/policyengine_uk_data/targets/sources.yaml b/policyengine_uk_data/targets/sources.yaml
index 5d1cd89ba..973e39567 100644
--- a/policyengine_uk_data/targets/sources.yaml
+++ b/policyengine_uk_data/targets/sources.yaml
@@ -11,7 +11,9 @@ hmrc:
   spi_geography: "https://assets.publishing.service.gov.uk/media/69f1f17cc42061e837e3ac3b/Collated_Tables_3_12_to_3_15a_2324.ods"
   income_tax_liabilities: "https://www.gov.uk/government/statistics/income-tax-liabilities-statistics-tax-year-2022-to-2023-to-tax-year-2025-to-2026"
   capital_gains_statistics: "https://www.gov.uk/government/statistics/capital-gains-tax-statistics"
-  salary_sacrifice_table_6: "https://assets.publishing.service.gov.uk/media/687a294e312ee8a5f0806b6d/Tables_6_1_and_6_2.csv"
+  # Private pension statistics, July 2026 (tax year 2024-25). Refresh with
+  # storage/hmrc_pension_relief_tables_6_1_6_2_*.csv (hmrc_salary_sacrifice.py).
+  salary_sacrifice_table_6: "https://assets.publishing.service.gov.uk/media/6a673e0e5e87122783093901/Tables_6_1_and_6_2.csv"
 
 dwp:
   stat_xplore_api: "https://stat-xplore.dwp.gov.uk/webapi/rest/v1"
diff --git a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
index f9865e6f7..828d48688 100644
--- a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
+++ b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
@@ -1,13 +1,23 @@
 """HMRC salary sacrifice income tax and NICs relief targets.
 
-Downloads Table 6.2 CSV from HMRC to get salary sacrifice IT relief
-by tax rate band and NICs relief (employee + employer).
-
-Source: https://assets.publishing.service.gov.uk/media/687a294e312ee8a5f0806b6d/Tables_6_1_and_6_2.csv
+Reads HMRC private pension statistics Tables 6.1 and 6.2 (tidy CSV): income
+tax relief on salary-sacrificed pension contributions by marginal rate, and
+the Class 1 primary (employee) and secondary (employer) NICs relief on them.
+
+GOV.UK withdraws a release's assets when HMRC publishes the next one: the
+July 2025 CSV has returned 410 Gone since the July 2026 release, and the
+broad ``except`` this module used to have turned that into calibration
+builds that silently lacked these targets. A failed download now falls back
+to the copy committed in storage (the same release as ``sources.yaml``), and
+a table that no longer has the expected rows raises instead of returning
+fewer targets.
+
+Source: https://www.gov.uk/government/statistics/personal-and-stakeholder-pensions-statistics
 """
 
 import io
 import logging
+import re
 
 import pandas as pd
 import requests
@@ -15,110 +25,135 @@
 from policyengine_uk_data.targets.schema import Target, Unit
 from policyengine_uk_data.targets.sources._common import (
     HEADERS,
+    STORAGE,
     load_config,
     to_float,
 )
 
 logger = logging.getLogger(__name__)
 
+# Tables 6.1 and 6.2 from the July 2026 release (tax year 2024-25), as
+# downloaded from the sources.yaml URL. Replace both together.
+FALLBACK_CSV = STORAGE / "hmrc_pension_relief_tables_6_1_6_2_2024_25.csv"
+
 # Uprate 3% pa for wage growth from the base year
 _GROWTH = 1.03
-_BASE_YEAR = 2024  # 2023-24 tax year → calendar 2024
+_LAST_YEAR = 2031
+
+# Table 6.1 rate rows → target name suffix. HMRC counts Scottish starter
+# and intermediate rate relief as basic rate.
+_IT_RATES = {
+    "Basic Rate": "basic_rate",
+    "Higher Rate": "higher_rate",
+    "Additional Rate": "additional_rate",
+    "Total": "total",
+}
+# Table 6.2 NICs classes → (target name, PolicyEngine variable)
+_NICS_CLASSES = {
+    "Class 1 Primary (employee)": (
+        "hmrc/salary_sacrifice_employee_nics_relief",
+        "ni_employee",
+    ),
+    "Class 1 Secondary (employer)": (
+        "hmrc/salary_sacrifice_employer_nics_relief",
+        "ni_employer",
+    ),
+}
+
+# Total salary sacrifice contributions (SPP Review 2025: £24bn base)
+_SS_CONTRIBUTIONS_BASE_YEAR = 2024
+
+
+def _read_table(url: str) -> pd.DataFrame:
+    try:
+        r = requests.get(url, headers=HEADERS, allow_redirects=True, timeout=30)
+        r.raise_for_status()
+        text = r.content.decode("utf-8-sig")
+    except requests.RequestException as e:
+        logger.warning(
+            "HMRC Tables 6.1 and 6.2 download failed (%s); using %s",
+            e,
+            FALLBACK_CSV.name,
+        )
+        text = FALLBACK_CSV.read_text(encoding="utf-8-sig")
+    return pd.read_csv(io.StringIO(text), dtype=str)
+
+
+def _base_year(df: pd.DataFrame) -> int:
+    """Map the table's tax year to a PolicyEngine year.
+
+    PolicyEngine UK's year N is tax year N to N+1 (a year-N simulation reads
+    the parameter values in force from 6 April N), so "2024 to 2025" is 2024.
+    """
+    years = df["tax_year"].unique()
+    match = re.fullmatch(r"(\d{4}) to (\d{4})", years[0]) if len(years) == 1 else None
+    if match is None or int(match[2]) != int(match[1]) + 1:
+        raise ValueError(f"Expected one tax year in Tables 6.1/6.2, got {years}")
+    return int(match[1])
+
+
+def _relief(rows: pd.DataFrame, column: str, label: str) -> float:
+    """The single positive £m value in ``rows`` whose ``column`` is ``label``."""
+    values = rows.loc[rows[column] == label, "value_of_relief"].map(to_float)
+    if len(values) != 1 or values.iloc[0] <= 0:
+        raise ValueError(
+            f"HMRC salary sacrifice relief: expected one positive value for "
+            f"{label!r}, got {values.tolist()}"
+        )
+    return values.iloc[0] * 1e6
+
+
+def _relief_targets(df: pd.DataFrame, reference_url: str) -> list[Target]:
+    base_year = _base_year(df)
+    ss = df[
+        (df["contribution_type"] == "Salary sacrificed contributions")
+        & (df["sector_scheme"] == "Total")
+        & (df["scheme_type"] == "Total")
+    ]
+    income_tax = ss[ss["income_tax_nics"] == "Income Tax"]
+    nics = ss[(ss["income_tax_nics"] == "NICs") & (ss["tax_rate"] == "Total")]
+
+    specs = [
+        (
+            f"hmrc/salary_sacrifice_it_relief_{suffix}",
+            "income_tax",
+            _relief(income_tax, "tax_rate", rate),
+        )
+        for rate, suffix in _IT_RATES.items()
+    ] + [
+        (name, variable, _relief(nics, "nics_relief_class", nics_class))
+        for nics_class, (name, variable) in _NICS_CLASSES.items()
+    ]
+    return [
+        Target(
+            name=name,
+            variable=variable,
+            source="hmrc",
+            unit=Unit.GBP,
+            values={
+                y: base * _GROWTH ** (y - base_year)
+                for y in range(base_year, _LAST_YEAR + 1)
+            },
+            reference_url=reference_url,
+        )
+        for name, variable, base in specs
+    ]
 
 
 def get_targets() -> list[Target]:
-    config = load_config()
-    ref = config["hmrc"]["salary_sacrifice_table_6"]
-    targets = []
+    ref = load_config()["hmrc"]["salary_sacrifice_table_6"]
+    targets = _relief_targets(_read_table(ref), ref)
 
-    try:
-        r = requests.get(ref, headers=HEADERS, allow_redirects=True, timeout=30)
-        r.raise_for_status()
-        df = pd.read_csv(io.StringIO(r.content.decode("utf-8-sig")))
-
-        ss = df[df["contribution_type"] == "Salary sacrificed contributions"]
-
-        # IT relief by tax band
-        ss_it = ss[
-            (ss["income_tax_nics"] == "Income Tax")
-            & (ss["sector_scheme"] == "Total")
-            & (ss["scheme_type"] == "Total")
-        ]
-        for _, row in ss_it.iterrows():
-            rate = row["tax_rate"]
-            val = to_float(row["value_of_relief"])
-            if val <= 0:
-                continue
-            rate_key = rate.lower().replace(" ", "_")
-            base = val * 1e6
-            targets.append(
-                Target(
-                    name=f"hmrc/salary_sacrifice_it_relief_{rate_key}",
-                    variable="income_tax",
-                    source="hmrc",
-                    unit=Unit.GBP,
-                    values={
-                        y: base * _GROWTH ** max(0, y - _BASE_YEAR)
-                        for y in range(_BASE_YEAR, 2032)
-                    },
-                    reference_url=ref,
-                )
-            )
-
-        # NICs relief (employee + employer)
-        ss_nics = ss[
-            (ss["income_tax_nics"] == "NICs")
-            & (ss["sector_scheme"] == "Total")
-            & (ss["scheme_type"] == "Total")
-        ]
-        for _, row in ss_nics.iterrows():
-            nics_class = row["nics_relief_class"]
-            val = to_float(row["value_of_relief"])
-            if val <= 0:
-                continue
-            if "employee" in str(nics_class).lower():
-                name = "hmrc/salary_sacrifice_employee_nics_relief"
-                variable = "ni_employee"
-            elif "employer" in str(nics_class).lower():
-                name = "hmrc/salary_sacrifice_employer_nics_relief"
-                variable = "ni_employer"
-            else:
-                continue
-
-            # Only take the first (Total scheme) row for each class
-            existing = {t.name for t in targets}
-            if name in existing:
-                continue
-
-            base = val * 1e6
-            targets.append(
-                Target(
-                    name=name,
-                    variable=variable,
-                    source="hmrc",
-                    unit=Unit.GBP,
-                    values={
-                        y: base * _GROWTH ** max(0, y - _BASE_YEAR)
-                        for y in range(_BASE_YEAR, 2032)
-                    },
-                    reference_url=ref,
-                )
-            )
-
-    except Exception as e:
-        logger.error("Failed to download/parse HMRC salary sacrifice CSV: %s", e)
-
-    # Total salary sacrifice contributions (SPP Review 2025: £24bn base)
-    _SS_CONTRIBUTIONS = {
-        y: 24e9 * _GROWTH ** max(0, y - _BASE_YEAR) for y in range(_BASE_YEAR, 2030)
-    }
     targets.append(
         Target(
             name="hmrc/salary_sacrifice_contributions",
             variable="pension_contributions_via_salary_sacrifice",
             source="hmrc",
             unit=Unit.GBP,
-            values=_SS_CONTRIBUTIONS,
+            values={
+                y: 24e9 * _GROWTH ** (y - _SS_CONTRIBUTIONS_BASE_YEAR)
+                for y in range(_SS_CONTRIBUTIONS_BASE_YEAR, 2030)
+            },
             reference_url=(
                 "https://assets.publishing.service.gov.uk/media/"
                 "67ce0e7c08e764d17a5d3c21/2025_SPP_Review.pdf"
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index ad4e3cf17..82909c0ca 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -617,9 +617,7 @@ def _parse_tv_licence(wb: openpyxl.Workbook) -> list[Target]:
 # ISC census: private school students (roughly constant at ~557k)
 _PRIVATE_SCHOOL = {y: 557_000 for y in range(2018, 2032)}
 
-# SPP Review: salary sacrifice NI relief (uprated 3% pa from 2024 base)
-_SS_EMPLOYEE_NI = {y: 1.2e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)}
-_SS_EMPLOYER_NI = {y: 2.9e9 * 1.03 ** max(0, y - 2024) for y in range(2024, 2032)}
+# Salary sacrifice NICs relief comes from HMRC Table 6.2 (hmrc_salary_sacrifice.py).
 
 # Salary sacrifice headcount: 7.7m total (3.3m above £2k, 4.3m below)
 # OBR para 1.7: SS population grows 0.9% faster than employees (~2.4%/yr)
@@ -666,26 +664,6 @@ def get_targets() -> list[Target]:
             reference_url="https://www.isc.co.uk/research/annual-census/",
         )
     )
-    targets.append(
-        Target(
-            name="obr/salary_sacrifice_employee_ni_relief",
-            variable="ni_employee",
-            source="obr",
-            unit=Unit.GBP,
-            values=_SS_EMPLOYEE_NI,
-            reference_url="https://assets.publishing.service.gov.uk/media/67ce0e7c08e764d17a5d3c21/2025_SPP_Review.pdf",
-        )
-    )
-    targets.append(
-        Target(
-            name="obr/salary_sacrifice_employer_ni_relief",
-            variable="ni_employer",
-            source="obr",
-            unit=Unit.GBP,
-            values=_SS_EMPLOYER_NI,
-            reference_url="https://assets.publishing.service.gov.uk/media/67ce0e7c08e764d17a5d3c21/2025_SPP_Review.pdf",
-        )
-    )
 
     # Salary sacrifice headcount targets
     _SS_REF = (
diff --git a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
new file mode 100644
index 000000000..801e86a9f
--- /dev/null
+++ b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
@@ -0,0 +1,178 @@
+"""Tests for the HMRC salary sacrifice relief targets (Tables 6.1 and 6.2).
+
+The July 2025 CSV returned 410 Gone once HMRC published the July 2026
+release, and the source module logged the error and returned without the
+relief targets, so builds calibrated without them. These tests pin: the
+committed table parses to every relief target, a failed download uses it, a
+changed table raises, and the rate-band columns split relief the way HMRC
+does (across the bands a contribution straddles).
+"""
+
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+import requests
+
+from policyengine_uk_data.targets.compute.income import (
+    compute_ss_it_relief,
+    split_relief_by_band,
+)
+from policyengine_uk_data.targets.schema import Target, Unit
+from policyengine_uk_data.targets.sources import hmrc_salary_sacrifice as hmrc_ss
+
+# Tables 6.1 and 6.2, tax year 2024-25 (£m), from the committed CSV.
+RELIEF_2024_25 = {
+    "hmrc/salary_sacrifice_it_relief_total": 8_800,
+    "hmrc/salary_sacrifice_it_relief_basic_rate": 1_600,
+    "hmrc/salary_sacrifice_it_relief_higher_rate": 5_500,
+    "hmrc/salary_sacrifice_it_relief_additional_rate": 1_800,
+    "hmrc/salary_sacrifice_employee_nics_relief": 1_000,
+    "hmrc/salary_sacrifice_employer_nics_relief": 3_400,
+}
+
+
+def _committed_table() -> pd.DataFrame:
+    return pd.read_csv(hmrc_ss.FALLBACK_CSV, dtype=str, encoding="utf-8-sig")
+
+
+def test_committed_table_gives_every_relief_target():
+    targets = {t.name: t for t in hmrc_ss._relief_targets(_committed_table(), "x")}
+    assert set(targets) == set(RELIEF_2024_25)
+    for name, millions in RELIEF_2024_25.items():
+        values = targets[name].values
+        # Tax year 2024-25 is PolicyEngine year 2024.
+        assert min(values) == 2024
+        assert values[2024] == pytest.approx(millions * 1e6)
+        assert values[2025] == pytest.approx(millions * 1e6 * 1.03)
+    assert targets["hmrc/salary_sacrifice_employee_nics_relief"].variable == (
+        "ni_employee"
+    )
+    assert targets["hmrc/salary_sacrifice_employer_nics_relief"].variable == (
+        "ni_employer"
+    )
+
+
+def _gone(*args, **kwargs):
+    response = requests.Response()
+    response.status_code = 410
+    response.url = args[0]
+    return response
+
+
+def _offline(*args, **kwargs):
+    raise requests.ConnectionError("offline")
+
+
+@pytest.mark.parametrize("get", [_gone, _offline], ids=["410", "offline"])
+def test_failed_download_uses_committed_table(get):
+    with patch.object(hmrc_ss.requests, "get", side_effect=get):
+        names = {t.name for t in hmrc_ss.get_targets()}
+    assert set(RELIEF_2024_25) <= names
+    assert "hmrc/salary_sacrifice_contributions" in names
+
+
+@pytest.mark.parametrize(
+    "change",
+    [
+        lambda df: df[df["tax_rate"] != "Higher Rate"],
+        lambda df: df[df["nics_relief_class"] != "Class 1 Secondary (employer)"],
+        lambda df: pd.concat(
+            [df.iloc[:-1], df.iloc[-1:].assign(tax_year="2025 to 2026")]
+        ),
+        lambda df: df.assign(tax_year="2024 to 2026"),
+        lambda df: df.drop(columns="sector_scheme"),
+    ],
+    ids=["no-higher-rate", "no-employer-nics", "two-years", "bad-year", "no-column"],
+)
+def test_changed_table_raises(change):
+    with pytest.raises((ValueError, KeyError)):
+        hmrc_ss._relief_targets(change(_committed_table()), "x")
+
+
+BANDS = ("basic", "higher", "additional")
+
+
+def test_split_straddling_relief():
+    # £4k sacrificed from £52k pay (2025-26 rUK): earned taxable income falls
+    # from £39,430 to £35,430, so £1,730 leaves the higher band and £2,270
+    # the basic band.
+    split = split_relief_by_band(
+        np.array([1_146.0]),
+        {"basic": [7_540.0], "higher": [692.0], "additional": [0.0]},
+        {"basic": [7_086.0], "higher": [0.0], "additional": [0.0]},
+    )
+    assert split["basic"] == pytest.approx([454.0])
+    assert split["higher"] == pytest.approx([692.0])
+    assert split["additional"] == pytest.approx([0.0])
+
+
+def test_split_invariants_on_random_inputs():
+    """Bands add up to the relief, keep its sign, and default to basic rate."""
+    rng = np.random.default_rng(0)
+    n = 5_000
+    relief = rng.normal(500, 1_000, n) * rng.integers(0, 2, n)
+    base = {b: rng.uniform(0, 20_000, n) * rng.integers(0, 2, n) for b in BANDS}
+    # A smaller sacrifice never raises band tax; some people see no change.
+    cf = {b: base[b] + rng.uniform(0, 3_000, n) * rng.integers(0, 2, n) for b in BANDS}
+    split = split_relief_by_band(relief, cf, base)
+
+    np.testing.assert_allclose(sum(split.values()), relief, rtol=1e-12, atol=1e-9)
+    for band in BANDS:
+        assert np.all(split[band] * np.sign(relief) >= -1e-9)
+    no_drop = sum(cf[b] - base[b] for b in BANDS) == 0
+    assert no_drop.any()
+    np.testing.assert_array_equal(split["basic"][no_drop], relief[no_drop])
+
+
+class _Ctx:
+    """The parts of build_loss_matrix._SimContext the compute function reads."""
+
+    time_period = 2025
+
+    def __init__(self, base_pay, sacrifice):
+        from policyengine_uk import Simulation
+
+        def sim(pay):
+            return Simulation(
+                situation={
+                    "people": {
+                        "a": {"age": {2025: 40}, "employment_income": {2025: pay}}
+                    },
+                    "benunits": {"b": {"members": ["a"]}},
+                    "households": {"h": {"members": ["a"]}},
+                }
+            )
+
+        # The counterfactual adds the sacrifice back to pay, as
+        # _SimContext.counterfactual_sim does.
+        self.sim = sim(base_pay)
+        self.counterfactual_sim = sim(base_pay + sacrifice)
+
+    @staticmethod
+    def household_from_person(values):
+        return np.asarray(values)
+
+
+def _relief(ctx, suffix):
+    target = Target(
+        name=f"hmrc/salary_sacrifice_it_relief_{suffix}",
+        variable="income_tax",
+        source="hmrc",
+        unit=Unit.GBP,
+        values={2025: 1.0},
+    )
+    return float(compute_ss_it_relief(target, ctx)[0])
+
+
+def test_compute_splits_relief_for_a_basic_rate_taxpayer_near_the_threshold():
+    """Pay of £48k after a £4k sacrifice: 40% relief only above £50,270."""
+    ctx = _Ctx(base_pay=48_000, sacrifice=4_000)
+    by_band = {
+        s: _relief(ctx, s) for s in ("basic_rate", "higher_rate", "additional_rate")
+    }
+    assert _relief(ctx, "total") == pytest.approx(1_146)
+    assert by_band == pytest.approx(
+        {"basic_rate": 454, "higher_rate": 692, "additional_rate": 0}
+    )
diff --git a/policyengine_uk_data/tests/test_target_registry.py b/policyengine_uk_data/tests/test_target_registry.py
index 22cf7b2d0..59d0b09f5 100644
--- a/policyengine_uk_data/tests/test_target_registry.py
+++ b/policyengine_uk_data/tests/test_target_registry.py
@@ -81,6 +81,25 @@ def pe_count(variable):
     assert _compute_column(target, DummyCtx(), 2025) == [1, 0, 1]
 
 
+def test_hmrc_salary_sacrifice_relief_targets():
+    """HMRC Table 6.1/6.2 relief targets reach the registry (uk-data: 410 Gone
+    on the old CSV dropped them from every build without failing it)."""
+    targets = {t.name: t for t in get_all_targets(year=2025)}
+    for name in (
+        "hmrc/salary_sacrifice_it_relief_total",
+        "hmrc/salary_sacrifice_it_relief_basic_rate",
+        "hmrc/salary_sacrifice_it_relief_higher_rate",
+        "hmrc/salary_sacrifice_it_relief_additional_rate",
+        "hmrc/salary_sacrifice_employee_nics_relief",
+        "hmrc/salary_sacrifice_employer_nics_relief",
+    ):
+        assert name in targets, f"{name} missing from the target registry"
+    # One NICs relief target per class: the OBR-labelled copies of the
+    # 2023-24 figures are gone.
+    assert "obr/salary_sacrifice_employee_ni_relief" not in targets
+    assert "obr/salary_sacrifice_employer_ni_relief" not in targets
+
+
 def test_voa_council_tax_targets():
     """VOA council tax band targets should exist."""
     targets = get_all_targets(year=2025)

From dfe8dcaa175cd7df576c08bca1fc483ba016439b Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 17:22:26 -0400
Subject: [PATCH 44/70] Use TOTCAPB4, model-derived uprating rows and core
 3.32.13 (review of #513)

- TOTCAPB4 (DWP's below-average-resources measure since 2019/20) in place of
  TOTCAPB3, which stays as the fallback for earlier survey years. On #510
  build D plus the column: the same 87.7k non-entitled reporters as with
  TOTCAPB3, GB entitled units 2,080.7k vs 2,083.5k, gate claims 1,290.0k vs
  1,291.8k. The docstring now calls it an approximation (financial assets
  only; no Sch V disregard or reg 19 valuation).
- uprating_factors.csv and uprating_growth_factors.csv rows recomputed from
  policyengine-uk 2.107.0's per-capita GDP index (2024 1.204, 2025 1.256),
  not copied from the stale savings row, so calibration and runtime grow
  the capital alike; the test now checks the rows against the model.
- policyengine-core 3.32.13 in uv.lock (3.32.12 leaks memory per simulation).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../frs-pc-reported-capital.changed.md        |  2 +-
 policyengine_uk_data/datasets/frs.py          | 42 +++++++------
 .../storage/uprating_factors.csv              |  2 +-
 .../storage/uprating_growth_factors.csv       |  2 +-
 .../test_pension_credit_reported_capital.py   | 59 ++++++++++++++-----
 uv.lock                                       |  6 +-
 6 files changed, 73 insertions(+), 40 deletions(-)

diff --git a/changelog.d/frs-pc-reported-capital.changed.md b/changelog.d/frs-pc-reported-capital.changed.md
index 7986dc739..d6433759f 100644
--- a/changelog.d/frs-pc-reported-capital.changed.md
+++ b/changelog.d/frs-pc-reported-capital.changed.md
@@ -1 +1 @@
-Carry each benefit unit's FRS total capital (TOTCAPB3) into policyengine-uk's `pension_credit_reported_capital`, so Pension Credit's capital test uses the survey's own benefit-unit capital instead of imputed household wealth.
+Carry each benefit unit's FRS total capital (TOTCAPB4, DWP's current benefit-unit savings and investments measure; TOTCAPB3 for earlier survey years) into policyengine-uk's `pension_credit_reported_capital`, so Pension Credit's capital test uses the survey's own benefit-unit capital instead of imputed household wealth.
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 5cdb760c0..4f69a1262 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -573,25 +573,31 @@ def validate_frs_survey_year(raw_frs_folder, year: int) -> None:
 def derive_pension_credit_reported_capital(benunit: pd.DataFrame) -> np.ndarray:
     """Each benefit unit's capital as the FRS records it, for Pension Credit.
 
-    ``TOTCAPB3`` is DWP's derived benefit-unit total of the adults' savings and
-    investments (current, savings and NS&I accounts, gilts, unit and investment
-    trusts, shares and bonds, ISAs, credit unions), the measure its FRS-based
-    National Statistics use. Pension Credit counts the claimant's capital and,
-    under the State Pension Credit Act 2002 s. 5, the partner's, which is what
-    this records. The household wealth imputation instead draws a household's
-    wealth from Wealth and Assets Survey households with similar income,
-    composition, tenure and region, with no information on means-tested
-    receipt, and policyengine-uk spreads it over the household's pension-age
-    adults.
-
-    ``TOTCAPB3`` covers financial assets only: second homes and land, which
-    Pension Credit also counts, are not in it. A missing or negative value gives
-    -1, so policyengine-uk falls back to the household proxy.
+    Uses ``TOTCAPB4``, DWP's derived benefit-unit total of the adults' savings
+    and investments, which its below-average-resources statistics use in place
+    of ``TOTCAPB3`` since it became available in 2019/20; ``TOTCAPB3`` is the
+    fallback for earlier survey years. Pension Credit counts the claimant's
+    capital and, under the State Pension Credit Act 2002 s. 5, the partner's,
+    and this is a benefit-unit measure. It is an approximation of Pension
+    Credit capital, not the assessed figure: it covers financial assets only
+    (second homes and land, which Pension Credit also counts, are not in it),
+    and no Schedule V disregard or reg. 19 valuation is applied to it. The
+    household wealth imputation instead draws a household's wealth from Wealth
+    and Assets Survey households with similar income, composition, tenure and
+    region, with no information on means-tested receipt, and policyengine-uk
+    spreads it over the household's pension-age adults.
+
+    A missing or negative value gives -1, so policyengine-uk falls back to the
+    household proxy.
     """
-    if "totcapb3" not in benunit.columns:
-        return np.full(len(benunit), -1.0)
-    capital = pd.to_numeric(benunit["totcapb3"], errors="coerce").to_numpy(dtype=float)
-    return np.where(np.isfinite(capital) & (capital >= 0), capital, -1.0)
+    capital = pd.Series(np.nan, index=benunit.index, dtype=float)
+    for column in ("totcapb3", "totcapb4"):  # later columns take precedence
+        if column in benunit.columns:
+            values = pd.to_numeric(benunit[column], errors="coerce")
+            valid = np.isfinite(values) & (values >= 0)
+            capital = capital.where(~valid, values)
+    values = capital.to_numpy(dtype=float)
+    return np.where(np.isfinite(values) & (values >= 0), values, -1.0)
 
 
 def create_frs(
diff --git a/policyengine_uk_data/storage/uprating_factors.csv b/policyengine_uk_data/storage/uprating_factors.csv
index 8453aaa97..149ef19ff 100644
--- a/policyengine_uk_data/storage/uprating_factors.csv
+++ b/policyengine_uk_data/storage/uprating_factors.csv
@@ -54,7 +54,7 @@ other_investment_income,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.
 other_residential_property_value,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 owned_land,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 pension_credit_reported,1.0,1.04,1.144,1.209,1.237,1.277,1.301,1.327,1.353,1.38,1.38,1.38,1.38,1.38,1.38
-pension_credit_reported_capital,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
+pension_credit_reported_capital,1.0,1.0,1.102,1.161,1.204,1.256,1.293,1.335,1.376,1.417,1.462,1.508,1.555,1.604,1.655
 pension_income,1.0,1.0,1.092,1.147,1.19,1.223,1.258,1.297,1.34,1.384,1.384,1.384,1.384,1.384,1.384
 personal_pension_contributions,1.0,1.059,1.127,1.205,1.261,1.308,1.337,1.365,1.396,1.431,1.431,1.431,1.431,1.431,1.431
 petrol_spending,1.0,1.104,1.635,1.796,1.531,1.483,1.452,1.406,1.363,1.305,1.237,1.237,1.237,1.237,1.237
diff --git a/policyengine_uk_data/storage/uprating_growth_factors.csv b/policyengine_uk_data/storage/uprating_growth_factors.csv
index 6722cb582..956f740d7 100644
--- a/policyengine_uk_data/storage/uprating_growth_factors.csv
+++ b/policyengine_uk_data/storage/uprating_growth_factors.csv
@@ -54,7 +54,7 @@ other_investment_income,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0
 other_residential_property_value,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 owned_land,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 pension_credit_reported,0,0.04,0.1,0.057,0.023,0.032,0.019,0.02,0.02,0.02,0.0,0.0,0.0,0.0,0.0
-pension_credit_reported_capital,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
+pension_credit_reported_capital,0,0.0,0.102,0.054,0.037,0.043,0.029,0.032,0.031,0.03,0.032,0.031,0.031,0.032,0.032
 pension_income,0,0.0,0.092,0.05,0.037,0.028,0.029,0.031,0.033,0.033,0.0,0.0,0.0,0.0,0.0
 personal_pension_contributions,0,0.059,0.064,0.069,0.046,0.037,0.022,0.021,0.023,0.025,0.0,0.0,0.0,0.0,0.0
 petrol_spending,0,0.104,0.481,0.099,-0.147,-0.031,-0.021,-0.032,-0.031,-0.043,-0.052,0.0,0.0,0.0,0.0
diff --git a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
index 0081769bb..2e93839de 100644
--- a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
+++ b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
@@ -1,11 +1,13 @@
 """`pension_credit_reported_capital` from the FRS benefit-unit capital
-measure (TOTCAPB3).
+measure (TOTCAPB4, falling back to TOTCAPB3).
 
 Invariants:
-1. A finite, non-negative TOTCAPB3 is carried over unchanged.
-2. A missing, non-numeric or negative value gives -1 (policyengine-uk's
-   "none recorded" sentinel), so the household proxy applies.
-3. Without a TOTCAPB3 column every benefit unit gets -1.
+1. A finite, non-negative TOTCAPB4 is carried over unchanged; where TOTCAPB4
+   is absent or invalid, a valid TOTCAPB3 is used.
+2. A missing, non-numeric or negative value in both gives -1
+   (policyengine-uk's "none recorded" sentinel), so the household proxy
+   applies.
+3. Without either column every benefit unit gets -1.
 4. The output is always -1 or a non-negative number, one per benefit unit.
 """
 
@@ -23,6 +25,17 @@ def test_values_carry_over_and_invalid_values_fall_back():
     assert result.tolist() == [0.0, 300.0, 2_900.0, 1_250_000.0, -1.0, -1.0, -1.0]
 
 
+def test_totcapb4_takes_precedence_with_totcapb3_fallback():
+    benunit = pd.DataFrame(
+        {
+            "totcapb3": [100.0, 200.0, 300.0, np.nan, -1.0],
+            "totcapb4": [150.0, np.nan, -5.0, 400.0, np.nan],
+        }
+    )
+    result = derive_pension_credit_reported_capital(benunit)
+    assert result.tolist() == [150.0, 200.0, 300.0, 400.0, -1.0]
+
+
 def test_missing_column_gives_sentinel():
     benunit = pd.DataFrame({"benunit_id": [101, 102, 201]})
     assert derive_pension_credit_reported_capital(benunit).tolist() == [-1.0] * 3
@@ -35,7 +48,7 @@ def test_output_is_sentinel_or_non_negative_for_random_inputs():
         values = rng.normal(5_000, 20_000, n)
         values[rng.random(n) < 0.1] = np.nan
         result = derive_pension_credit_reported_capital(
-            pd.DataFrame({"totcapb3": values})
+            pd.DataFrame({"totcapb4": values})
         )
         assert len(result) == n
         assert np.all((result == -1) | (result >= 0))
@@ -44,18 +57,32 @@ def test_output_is_sentinel_or_non_negative_for_random_inputs():
         assert np.all(result[~keep] == -1)
 
 
-def test_uprating_rows_match_savings():
-    """The column grows with ``savings`` (policyengine-uk uprates it with the
-    same per-capita GDP index), so the build's uprating keeps them in step."""
-    from policyengine_uk_data.storage import STORAGE_FOLDER
+def test_uprating_rows_match_the_model():
+    """The build uprates the column with these rows (calibration materialises
+    the calibration year from them), and policyengine-uk projects the saved
+    dataset with the variable's own uprating index at runtime. The rows must
+    equal what ``create_policyengine_uprating_factors_table`` derives from the
+    locked policyengine-uk, or a unit near the 10,000 pound deemed-income
+    disregard can be assessed differently in calibration and at runtime."""
+    from policyengine_uk.system import system
 
-    for name in ["uprating_factors.csv", "uprating_growth_factors.csv"]:
-        table = pd.read_csv(STORAGE_FOLDER / name).set_index("Variable")
-        pd.testing.assert_series_equal(
-            table.loc["pension_credit_reported_capital"],
-            table.loc["savings"],
-            check_names=False,
+    from policyengine_uk_data.storage import STORAGE_FOLDER
+    from policyengine_uk_data.utils.uprating import END_YEAR, START_YEAR
+
+    variable = system.variables["pension_credit_reported_capital"]
+    index = system.parameters.get_child(variable.uprating)
+    years = range(START_YEAR, END_YEAR + 1)
+    expected = {y: round(index(y) / index(START_YEAR), 3) for y in years}
+    factors = pd.read_csv(STORAGE_FOLDER / "uprating_factors.csv").set_index("Variable")
+    growth = pd.read_csv(STORAGE_FOLDER / "uprating_growth_factors.csv").set_index(
+        "Variable"
+    )
+    for y in years:
+        assert factors.loc["pension_credit_reported_capital", str(y)] == expected[y]
+        expected_growth = (
+            0 if y == START_YEAR else round(expected[y] / expected[y - 1] - 1, 3)
         )
+        assert growth.loc["pension_credit_reported_capital", str(y)] == expected_growth
 
 
 def test_spi_copy_records_no_capital():
diff --git a/uv.lock b/uv.lock
index ab6e353ec..b0b2249b7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1322,7 +1322,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-core"
-version = "3.32.12"
+version = "3.32.13"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dpath" },
@@ -1342,9 +1342,9 @@ dependencies = [
     { name = "standard-imghdr" },
     { name = "wheel" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/e3/cd/2e9c7e4a0c4395a0d556cebde1fa92b94c4c2c594815938e5bfd052b3b47/policyengine_core-3.32.12.tar.gz", hash = "sha256:1d163936b3e4b2704cf0fe33eee3b93832a84aa4bf027b552a745d6f6d35bad8", size = 451537, upload-time = "2026-10-02T04:58:58.42Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/40/0a/b6c27953e1083d3ab2c40330c49580130fff48a2fe9cbfc8d731e92882e6/policyengine_core-3.32.13.tar.gz", hash = "sha256:a870f7e212fdfa1b5fdad7df92aa2b325fd0280ede16a40aeeceea74d44fb864", size = 457899, upload-time = "2026-10-03T11:30:20.572Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/36/9e/f8f41e2f33454be51e9116c1b96db77ec9982d5d97d8691a0d8bd7b35b99/policyengine_core-3.32.12-py3-none-any.whl", hash = "sha256:44f0cdc100ff171f107671965bdbb41aa0c9965f632e400734e9eb2bf6ed80ee", size = 250582, upload-time = "2026-10-02T04:58:56.986Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/01/00ebf4c13152347bc5396b40e80445fa92ea96712b9ec0bfdca7c5d11413/policyengine_core-3.32.13-py3-none-any.whl", hash = "sha256:262543c3ac696207f304f55664711b72e894fbad7b566407afa9d30b16086a41", size = 251251, upload-time = "2026-10-03T11:30:19.171Z" },
 ]
 
 [[package]]

From 1cecceeaea9e701bb02b68207653dddae9f0bdfd Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sat, 3 Oct 2026 18:45:24 -0400
Subject: [PATCH 45/70] Check PolicyBench couples against claimant and partner
 (policyengine-uk 2.107)

policyengine-uk 2.107 (#1896) presumes a couple married when the dataset
does not say, and its couple is the claimant and partner rather than any two
adults: a member under 20 and 16+ years younger than the claimant is
presumed to be their child. The family-structure test compared is_married
with the adult count, which the lock bump to 2.107.0 made fail; it now
compares with the claimant-and-partner count. family_type still counts
is_adult, so that check is unchanged.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_policybench_transfer.py                 | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/policyengine_uk_data/tests/test_policybench_transfer.py b/policyengine_uk_data/tests/test_policybench_transfer.py
index 900b73d1f..9a8d587cb 100644
--- a/policyengine_uk_data/tests/test_policybench_transfer.py
+++ b/policyengine_uk_data/tests/test_policybench_transfer.py
@@ -205,6 +205,9 @@ def test_policybench_transfer_family_structure_matches_person_membership(
     person_benunit_ids = sim.calculate("person_benunit_id", map_to="person").values
     is_adult = sim.calculate("is_adult", map_to="person").values
     is_child = sim.calculate("is_child", map_to="person").values
+    is_claimant_or_partner = sim.calculate(
+        "is_claimant_or_partner", map_to="person"
+    ).values
     is_married = sim.calculate("is_married", map_to="benunit").values
     family_type = sim.calculate("family_type", map_to="benunit").values
 
@@ -213,7 +216,12 @@ def test_policybench_transfer_family_structure_matches_person_membership(
         adults = int(is_adult[member_mask].sum())
         children = int(is_child[member_mask].sum())
 
-        assert bool(married) == (adults == 2)
+        # policyengine-uk (from 2.107, #1896) presumes a couple married when
+        # the dataset does not say, and a couple is a claimant and partner,
+        # not any two adults: a member under 20 and 16+ years younger than
+        # the claimant is presumed to be their child.
+        claimant_and_partner = int(is_claimant_or_partner[member_mask].sum())
+        assert bool(married) == (claimant_and_partner == 2)
 
         if adults == 2 and children > 0:
             expected = "COUPLE_WITH_CHILDREN"

From 138c72ac246315cfeaefcf0402b0be76f95c45f4 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 00:35:22 -0400
Subject: [PATCH 46/70] Address review: rate-adjust NICs targets, relieve pay
 at rUK or Scottish bands
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Employer NICs relief targets follow the Class 1 rate in PolicyEngine's
  parameters (13.8% in 2024-25, 15% from April 2025), so the 2025 target
  is £3.81bn, not £3.50bn; employee targets weight the main and
  additional rates by HMRC's split (unchanged, 8% and 2%).
- Income tax relief is the rise in tax on earned income, allocated to
  HMRC's basic/higher/additional categories under the person's rUK or
  Scottish rates (starter and intermediate with basic, advanced with
  higher, top with additional). HMRC applies income tax rates to ASHE
  pay; the previous measure (change in income_tax) included the High
  Income Child Benefit Charge and savings and dividend tax, and split
  Scottish relief on rUK boundaries.
- Drop the IT relief total target: it is the sum of the bands, which
  HMRC rounds separately (£8.9bn vs £8.8bn in 2024-25).
- The table's tax year must equal _TAX_YEAR, which names the committed
  copy, so pointing sources.yaml at a new release without refreshing
  the copy fails instead of giving online and offline builds different
  targets.
- Test that every relief target produces a loss-matrix column on the
  built dataset (create_target_matrix skips with a warning otherwise).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../targets/compute/income.py                 | 103 +++++-----
 .../targets/sources/hmrc_salary_sacrifice.py  | 104 ++++++++--
 .../test_hmrc_salary_sacrifice_targets.py     | 188 +++++++++++++-----
 .../tests/test_target_registry.py             |   3 +-
 4 files changed, 269 insertions(+), 129 deletions(-)

diff --git a/policyengine_uk_data/targets/compute/income.py b/policyengine_uk_data/targets/compute/income.py
index d96c9234e..19c49ac71 100644
--- a/policyengine_uk_data/targets/compute/income.py
+++ b/policyengine_uk_data/targets/compute/income.py
@@ -20,66 +20,59 @@ def compute_income_band(target, ctx) -> np.ndarray:
         return ctx.household_from_person(income_df[variable] * in_band)
 
 
-# Income tax on earned income in each rUK band (rates.uk thresholds on
-# earned_taxable_income), keyed by the band word in the target name. Scottish
-# taxpayers are split on the same rUK boundaries, an approximation: HMRC
-# groups their relief by Scottish marginal rate (starter and intermediate
-# with basic), and the Scottish band thresholds differ from the rUK ones.
-_BAND_TAX_VARIABLES = {
-    "basic": "basic_rate_earned_income_tax",
-    "higher": "higher_rate_earned_income_tax",
-    "additional": "add_rate_earned_income_tax",
-}
-
-
-def split_relief_by_band(
-    relief: np.ndarray, band_tax_cf: dict, band_tax_base: dict
-) -> dict:
-    """Share each person's income tax relief across the bands it is given in.
-
-    HMRC's Table 6.1 models contributions that straddle bands ("not all be
-    relievable at the same rate"; private pension statistics, background and
-    methodology), so a person's relief is split in proportion to the fall in
-    their earned-income tax within each band. Relief with no fall in any
-    earned band (it comes through savings or dividend bands) counts as basic
-    rate. The shares sum to one, so the bands add up to ``relief``.
+def tax_by_band(income: np.ndarray, thresholds, rates) -> dict:
+    """Tax on ``income`` within each of HMRC's rate categories.
+
+    HMRC's Table 6.1 groups relief by marginal rate: basic (with the Scottish
+    starter and intermediate rates), higher and additional. Brackets taxed
+    below 30% are basic, brackets at the scale's top rate are additional (the
+    rUK additional rate, the Scottish top rate) and the rest are higher (the
+    Scottish higher and advanced rates, which cover the income range of the
+    rUK higher rate). The categories sum to the scale's tax on ``income``.
     """
-    drops = {
-        band: np.maximum(np.asarray(band_tax_cf[band]) - band_tax_base[band], 0)
-        for band in _BAND_TAX_VARIABLES
-    }
-    total = sum(drops.values())
-    has_drop = total > 0
-    safe_total = np.where(has_drop, total, 1)
-    relief = np.asarray(relief)
-    return {
-        band: relief
-        * np.where(has_drop, drop / safe_total, 1.0 if band == "basic" else 0.0)
-        for band, drop in drops.items()
-    }
+    income = np.asarray(income, dtype=float)
+    thresholds = [t for t, r in zip(thresholds, rates) if r is not None]
+    rates = [r for r in rates if r is not None]
+    uppers = thresholds[1:] + [np.inf]
+    bands = {"basic": 0.0, "higher": 0.0, "additional": 0.0}
+    for lower, upper, rate in zip(thresholds, uppers, rates):
+        band = (
+            "basic" if rate < 0.3 else "additional" if rate == rates[-1] else "higher"
+        )
+        bands[band] = bands[band] + rate * np.clip(income - lower, 0, upper - lower)
+    return bands
+
+
+def ss_it_relief_by_band(ctx) -> dict:
+    """Person-level salary sacrifice income tax relief in each rate category.
+
+    HMRC applies income tax rates to employees' pay (ASHE), so relief is the
+    rise in tax on earned income when the sacrifice is paid as salary, under
+    the person's rUK or Scottish rates. Contributions that straddle a band
+    boundary are relieved partly at each rate, as in HMRC's estimates.
+    """
+    period = ctx.time_period
+    rates = ctx.sim.tax_benefit_system.parameters(period).gov.hmrc.income_tax.rates
+    scottish = np.asarray(ctx.sim.calculate("pays_scottish_income_tax", period))
+    base = np.asarray(ctx.sim.calculate("earned_taxable_income", period))
+    cf = np.asarray(ctx.counterfactual_sim.calculate("earned_taxable_income", period))
+    relief = {}
+    for scale, in_scale in ((rates.uk, ~scottish), (rates.scotland.rates, scottish)):
+        band_cf = tax_by_band(cf, scale.thresholds, scale.rates)
+        band_base = tax_by_band(base, scale.thresholds, scale.rates)
+        for band in band_cf:
+            relief[band] = relief.get(band, 0) + in_scale * (
+                band_cf[band] - band_base[band]
+            )
+    return relief
 
 
 def compute_ss_it_relief(target, ctx) -> np.ndarray:
-    """Compute salary sacrifice IT relief, in total or for one rate band."""
-    period = ctx.time_period
-    cf, base = ctx.counterfactual_sim, ctx.sim
-    relief = np.asarray(cf.calculate("income_tax", period)) - np.asarray(
-        base.calculate("income_tax", period)
+    """Compute salary sacrifice income tax relief at one rate."""
+    band = target.name.removeprefix("hmrc/salary_sacrifice_it_relief_")
+    return ctx.household_from_person(
+        ss_it_relief_by_band(ctx)[band.removesuffix("_rate")]
     )
-    band = next((b for b in _BAND_TAX_VARIABLES if b in target.name), None)
-    if band is not None:
-        relief = split_relief_by_band(
-            relief,
-            {
-                b: np.asarray(cf.calculate(v, period))
-                for b, v in _BAND_TAX_VARIABLES.items()
-            },
-            {
-                b: np.asarray(base.calculate(v, period))
-                for b, v in _BAND_TAX_VARIABLES.items()
-            },
-        )[band]
-    return ctx.household_from_person(relief)
 
 
 def compute_ss_contributions(target, ctx) -> np.ndarray:
diff --git a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
index 828d48688..f90754fe0 100644
--- a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
+++ b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
@@ -9,15 +9,19 @@
 broad ``except`` this module used to have turned that into calibration
 builds that silently lacked these targets. A failed download now falls back
 to the copy committed in storage (the same release as ``sources.yaml``), and
-a table that no longer has the expected rows raises instead of returning
-fewer targets.
+a table for another tax year, or without the expected rows, raises instead
+of returning fewer targets.
+
+Targets grow 3% a year from the table's year. The NICs relief targets also
+follow the Class 1 rates in PolicyEngine's parameters, so the April 2025
+rise in the employer rate from 13.8% to 15% raises the employer target in
+the years the simulation charges 15%.
 
 Source: https://www.gov.uk/government/statistics/personal-and-stakeholder-pensions-statistics
 """
 
 import io
 import logging
-import re
 
 import pandas as pd
 import requests
@@ -32,21 +36,27 @@
 
 logger = logging.getLogger(__name__)
 
-# Tables 6.1 and 6.2 from the July 2026 release (tax year 2024-25), as
-# downloaded from the sources.yaml URL. Replace both together.
-FALLBACK_CSV = STORAGE / "hmrc_pension_relief_tables_6_1_6_2_2024_25.csv"
+# Tax year of the Tables 6.1 and 6.2 release in sources.yaml (July 2026,
+# 2024-25) and of its committed copy. Refresh the URL, the copy and this
+# together: a table for any other year raises.
+_TAX_YEAR = 2024
+FALLBACK_CSV = (
+    STORAGE
+    / f"hmrc_pension_relief_tables_6_1_6_2_{_TAX_YEAR}_{(_TAX_YEAR + 1) % 100:02d}.csv"
+)
 
 # Uprate 3% pa for wage growth from the base year
 _GROWTH = 1.03
 _LAST_YEAR = 2031
 
 # Table 6.1 rate rows → target name suffix. HMRC counts Scottish starter
-# and intermediate rate relief as basic rate.
+# and intermediate rate relief as basic rate. The Total row is not a target:
+# it is the sum of the bands, which HMRC rounds separately (2024-25: bands
+# £8.9bn, total £8.8bn), so targeting both would ask for two values.
 _IT_RATES = {
     "Basic Rate": "basic_rate",
     "Higher Rate": "higher_rate",
     "Additional Rate": "additional_rate",
-    "Total": "total",
 }
 # Table 6.2 NICs classes → (target name, PolicyEngine variable)
 _NICS_CLASSES = {
@@ -60,6 +70,13 @@
     ),
 }
 
+# Table 6.2 (class, rate row) → the PolicyEngine Class 1 rate it was relieved at
+_NICS_RATE_PARAMETERS = {
+    ("Class 1 Primary (employee)", "Main Rate"): "employee.main",
+    ("Class 1 Primary (employee)", "Additional Rate"): "employee.additional",
+    ("Class 1 Secondary (employer)", "Main Rate"): "employer",
+}
+
 # Total salary sacrifice contributions (SPP Review 2025: £24bn base)
 _SS_CONTRIBUTIONS_BASE_YEAR = 2024
 
@@ -79,17 +96,54 @@ def _read_table(url: str) -> pd.DataFrame:
     return pd.read_csv(io.StringIO(text), dtype=str)
 
 
-def _base_year(df: pd.DataFrame) -> int:
-    """Map the table's tax year to a PolicyEngine year.
+def _check_tax_year(df: pd.DataFrame) -> int:
+    """The table's PolicyEngine year, which must be ``_TAX_YEAR``.
 
     PolicyEngine UK's year N is tax year N to N+1 (a year-N simulation reads
     the parameter values in force from 6 April N), so "2024 to 2025" is 2024.
     """
-    years = df["tax_year"].unique()
-    match = re.fullmatch(r"(\d{4}) to (\d{4})", years[0]) if len(years) == 1 else None
-    if match is None or int(match[2]) != int(match[1]) + 1:
-        raise ValueError(f"Expected one tax year in Tables 6.1/6.2, got {years}")
-    return int(match[1])
+    years = list(df["tax_year"].unique())
+    expected = f"{_TAX_YEAR} to {_TAX_YEAR + 1}"
+    if years != [expected]:
+        raise ValueError(
+            f"HMRC Tables 6.1/6.2 cover {years}, but {FALLBACK_CSV.name} and "
+            f"_TAX_YEAR are {expected!r}: refresh the sources.yaml URL, the "
+            "committed copy and _TAX_YEAR together."
+        )
+    return _TAX_YEAR
+
+
+def _nics_rate_factors(rows: pd.DataFrame, nics_class: str, base_year: int) -> dict:
+    """Class 1 rate in each year relative to the table's year, by year.
+
+    Weighted by HMRC's split of the class's relief between the main and
+    additional rates (rows suppressed as [z] weigh nothing).
+    """
+    from policyengine_uk import CountryTaxBenefitSystem
+
+    parameters = CountryTaxBenefitSystem().parameters
+    weights = {}
+    for rate_row in ("Main Rate", "Additional Rate"):
+        values = rows.loc[
+            (rows["nics_relief_class"] == nics_class) & (rows["tax_rate"] == rate_row),
+            "value_of_relief",
+        ].map(to_float)
+        if values.sum() > 0:
+            path = _NICS_RATE_PARAMETERS[(nics_class, rate_row)]
+            weights[path] = values.sum()
+    if not weights:
+        raise ValueError(f"HMRC Table 6.2: no rate split for {nics_class!r}")
+
+    def rate(path, year):
+        return parameters.get_child(
+            f"gov.hmrc.national_insurance.class_1.rates.{path}"
+        )(str(year))
+
+    return {
+        year: sum(w * rate(p, year) / rate(p, base_year) for p, w in weights.items())
+        / sum(weights.values())
+        for year in range(base_year, _LAST_YEAR + 1)
+    }
 
 
 def _relief(rows: pd.DataFrame, column: str, label: str) -> float:
@@ -104,24 +158,32 @@ def _relief(rows: pd.DataFrame, column: str, label: str) -> float:
 
 
 def _relief_targets(df: pd.DataFrame, reference_url: str) -> list[Target]:
-    base_year = _base_year(df)
+    base_year = _check_tax_year(df)
     ss = df[
         (df["contribution_type"] == "Salary sacrificed contributions")
         & (df["sector_scheme"] == "Total")
         & (df["scheme_type"] == "Total")
     ]
     income_tax = ss[ss["income_tax_nics"] == "Income Tax"]
-    nics = ss[(ss["income_tax_nics"] == "NICs") & (ss["tax_rate"] == "Total")]
+    nics = ss[ss["income_tax_nics"] == "NICs"]
+    nics_total = nics[nics["tax_rate"] == "Total"]
+    unchanged = {y: 1.0 for y in range(base_year, _LAST_YEAR + 1)}
 
     specs = [
         (
             f"hmrc/salary_sacrifice_it_relief_{suffix}",
             "income_tax",
             _relief(income_tax, "tax_rate", rate),
+            unchanged,
         )
         for rate, suffix in _IT_RATES.items()
     ] + [
-        (name, variable, _relief(nics, "nics_relief_class", nics_class))
+        (
+            name,
+            variable,
+            _relief(nics_total, "nics_relief_class", nics_class),
+            _nics_rate_factors(nics, nics_class, base_year),
+        )
         for nics_class, (name, variable) in _NICS_CLASSES.items()
     ]
     return [
@@ -131,12 +193,12 @@ def _relief_targets(df: pd.DataFrame, reference_url: str) -> list[Target]:
             source="hmrc",
             unit=Unit.GBP,
             values={
-                y: base * _GROWTH ** (y - base_year)
-                for y in range(base_year, _LAST_YEAR + 1)
+                y: base * _GROWTH ** (y - base_year) * factor
+                for y, factor in factors.items()
             },
             reference_url=reference_url,
         )
-        for name, variable, base in specs
+        for name, variable, base, factors in specs
     ]
 
 
diff --git a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
index 801e86a9f..2e6aafdac 100644
--- a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
+++ b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
@@ -4,8 +4,9 @@
 release, and the source module logged the error and returned without the
 relief targets, so builds calibrated without them. These tests pin: the
 committed table parses to every relief target, a failed download uses it, a
-changed table raises, and the rate-band columns split relief the way HMRC
-does (across the bands a contribution straddles).
+changed table raises, the rate-band columns measure relief the way HMRC does
+(tax on pay, relieved at each rate a contribution straddles), and on a built
+dataset every relief target produces a loss-matrix column.
 """
 
 from unittest.mock import patch
@@ -15,37 +16,57 @@
 import pytest
 import requests
 
+from policyengine_uk_data.datasets.frs_release import CURRENT_FRS_RELEASE
+from policyengine_uk_data.targets.build_loss_matrix import (
+    _compute_column,
+    _resolve_value,
+    _SimContext,
+)
 from policyengine_uk_data.targets.compute.income import (
     compute_ss_it_relief,
-    split_relief_by_band,
+    tax_by_band,
 )
 from policyengine_uk_data.targets.schema import Target, Unit
 from policyengine_uk_data.targets.sources import hmrc_salary_sacrifice as hmrc_ss
 
 # Tables 6.1 and 6.2, tax year 2024-25 (£m), from the committed CSV.
 RELIEF_2024_25 = {
-    "hmrc/salary_sacrifice_it_relief_total": 8_800,
     "hmrc/salary_sacrifice_it_relief_basic_rate": 1_600,
     "hmrc/salary_sacrifice_it_relief_higher_rate": 5_500,
     "hmrc/salary_sacrifice_it_relief_additional_rate": 1_800,
     "hmrc/salary_sacrifice_employee_nics_relief": 1_000,
     "hmrc/salary_sacrifice_employer_nics_relief": 3_400,
 }
+# Class 1 secondary (employer) rate: 13.8% in 2024-25, 15% from 6 April 2025
+# (National Insurance Contributions (Secondary Class 1 Contributions) Act
+# 2025). The primary rates (8% main, 2% additional) did not change.
+EMPLOYER_RATE_RISE = 0.15 / 0.138
 
 
 def _committed_table() -> pd.DataFrame:
     return pd.read_csv(hmrc_ss.FALLBACK_CSV, dtype=str, encoding="utf-8-sig")
 
 
+def _offline(*args, **kwargs):
+    raise requests.ConnectionError("offline")
+
+
+def _offline_targets() -> list[Target]:
+    with patch.object(hmrc_ss.requests, "get", side_effect=_offline):
+        return hmrc_ss.get_targets()
+
+
 def test_committed_table_gives_every_relief_target():
     targets = {t.name: t for t in hmrc_ss._relief_targets(_committed_table(), "x")}
     assert set(targets) == set(RELIEF_2024_25)
     for name, millions in RELIEF_2024_25.items():
         values = targets[name].values
+        rate_rise = EMPLOYER_RATE_RISE if "employer" in name else 1
         # Tax year 2024-25 is PolicyEngine year 2024.
         assert min(values) == 2024
         assert values[2024] == pytest.approx(millions * 1e6)
-        assert values[2025] == pytest.approx(millions * 1e6 * 1.03)
+        assert values[2025] == pytest.approx(millions * 1e6 * 1.03 * rate_rise)
+        assert values[2026] == pytest.approx(millions * 1e6 * 1.03**2 * rate_rise)
     assert targets["hmrc/salary_sacrifice_employee_nics_relief"].variable == (
         "ni_employee"
     )
@@ -54,6 +75,12 @@ def test_committed_table_gives_every_relief_target():
     )
 
 
+def test_fallback_file_matches_the_tax_year_it_is_checked_against():
+    assert list(_committed_table()["tax_year"].unique()) == [
+        f"{hmrc_ss._TAX_YEAR} to {hmrc_ss._TAX_YEAR + 1}"
+    ]
+
+
 def _gone(*args, **kwargs):
     response = requests.Response()
     response.status_code = 410
@@ -61,10 +88,6 @@ def _gone(*args, **kwargs):
     return response
 
 
-def _offline(*args, **kwargs):
-    raise requests.ConnectionError("offline")
-
-
 @pytest.mark.parametrize("get", [_gone, _offline], ids=["410", "offline"])
 def test_failed_download_uses_committed_table(get):
     with patch.object(hmrc_ss.requests, "get", side_effect=get):
@@ -78,52 +101,71 @@ def test_failed_download_uses_committed_table(get):
     [
         lambda df: df[df["tax_rate"] != "Higher Rate"],
         lambda df: df[df["nics_relief_class"] != "Class 1 Secondary (employer)"],
+        lambda df: df[
+            ~(
+                (df["nics_relief_class"] == "Class 1 Secondary (employer)")
+                & (df["tax_rate"] == "Main Rate")
+            )
+        ],
         lambda df: pd.concat(
             [df.iloc[:-1], df.iloc[-1:].assign(tax_year="2025 to 2026")]
         ),
-        lambda df: df.assign(tax_year="2024 to 2026"),
+        lambda df: df.assign(tax_year="2025 to 2026"),
         lambda df: df.drop(columns="sector_scheme"),
     ],
-    ids=["no-higher-rate", "no-employer-nics", "two-years", "bad-year", "no-column"],
+    ids=[
+        "no-higher-rate",
+        "no-employer-nics",
+        "no-employer-rate-split",
+        "two-years",
+        "next-release",
+        "no-column",
+    ],
 )
 def test_changed_table_raises(change):
     with pytest.raises((ValueError, KeyError)):
         hmrc_ss._relief_targets(change(_committed_table()), "x")
 
 
-BANDS = ("basic", "higher", "additional")
+def _scales(year):
+    from policyengine_uk import CountryTaxBenefitSystem
 
-
-def test_split_straddling_relief():
-    # £4k sacrificed from £52k pay (2025-26 rUK): earned taxable income falls
-    # from £39,430 to £35,430, so £1,730 leaves the higher band and £2,270
-    # the basic band.
-    split = split_relief_by_band(
-        np.array([1_146.0]),
-        {"basic": [7_540.0], "higher": [692.0], "additional": [0.0]},
-        {"basic": [7_086.0], "higher": [0.0], "additional": [0.0]},
-    )
-    assert split["basic"] == pytest.approx([454.0])
-    assert split["higher"] == pytest.approx([692.0])
-    assert split["additional"] == pytest.approx([0.0])
+    rates = CountryTaxBenefitSystem().parameters(year).gov.hmrc.income_tax.rates
+    return {"uk": rates.uk, "scotland": rates.scotland.rates}
 
 
-def test_split_invariants_on_random_inputs():
-    """Bands add up to the relief, keep its sign, and default to basic rate."""
+@pytest.mark.parametrize("year", ["2023", "2024", "2025"])
+@pytest.mark.parametrize("schedule", ["uk", "scotland"])
+def test_tax_by_band_adds_up_to_the_schedule_and_rises_with_income(year, schedule):
+    """Differential against PolicyEngine's own scale, plus monotonicity."""
+    scale = _scales(year)[schedule]
     rng = np.random.default_rng(0)
-    n = 5_000
-    relief = rng.normal(500, 1_000, n) * rng.integers(0, 2, n)
-    base = {b: rng.uniform(0, 20_000, n) * rng.integers(0, 2, n) for b in BANDS}
-    # A smaller sacrifice never raises band tax; some people see no change.
-    cf = {b: base[b] + rng.uniform(0, 3_000, n) * rng.integers(0, 2, n) for b in BANDS}
-    split = split_relief_by_band(relief, cf, base)
+    income = np.concatenate(
+        [rng.uniform(-1_000, 200_000, 5_000), np.asarray(scale.thresholds[1:])]
+    )
+    bands = tax_by_band(income, scale.thresholds, scale.rates)
+    np.testing.assert_allclose(sum(bands.values()), scale.calc(income), atol=1e-6)
+    more = tax_by_band(
+        income + rng.uniform(0, 20_000, income.size), scale.thresholds, scale.rates
+    )
+    for band in bands:
+        assert np.all(more[band] >= bands[band] - 1e-9)
 
-    np.testing.assert_allclose(sum(split.values()), relief, rtol=1e-12, atol=1e-9)
-    for band in BANDS:
-        assert np.all(split[band] * np.sign(relief) >= -1e-9)
-    no_drop = sum(cf[b] - base[b] for b in BANDS) == 0
-    assert no_drop.any()
-    np.testing.assert_array_equal(split["basic"][no_drop], relief[no_drop])
+
+def test_tax_by_band_groups_scottish_rates_into_hmrc_categories():
+    # 2025-26 Scottish taxable-income bands: intermediate (21%) up to £31,092,
+    # higher (42%) to £62,430, advanced (45%) to £112,570, then top (48%).
+    scale = _scales("2025")["scotland"]
+    bands = tax_by_band(
+        np.array([20_000, 50_000, 100_000, 150_000]), scale.thresholds, scale.rates
+    )
+    assert bands["higher"][0] == 0 and bands["additional"][0] == 0
+    assert bands["higher"][1] > 0 and bands["additional"][1] == 0
+    # The advanced rate counts as higher rate.
+    assert bands["higher"][2] == pytest.approx(
+        0.42 * (62_430 - 31_092) + 0.45 * (100_000 - 62_430)
+    )
+    assert bands["additional"][3] == pytest.approx(0.48 * (150_000 - 112_570))
 
 
 class _Ctx:
@@ -131,7 +173,7 @@ class _Ctx:
 
     time_period = 2025
 
-    def __init__(self, base_pay, sacrifice):
+    def __init__(self, base_pay, sacrifice, region="LONDON"):
         from policyengine_uk import Simulation
 
         def sim(pay):
@@ -141,7 +183,7 @@ def sim(pay):
                         "a": {"age": {2025: 40}, "employment_income": {2025: pay}}
                     },
                     "benunits": {"b": {"members": ["a"]}},
-                    "households": {"h": {"members": ["a"]}},
+                    "households": {"h": {"members": ["a"], "region": {2025: region}}},
                 }
             )
 
@@ -155,9 +197,9 @@ def household_from_person(values):
         return np.asarray(values)
 
 
-def _relief(ctx, suffix):
+def _relief(ctx, band):
     target = Target(
-        name=f"hmrc/salary_sacrifice_it_relief_{suffix}",
+        name=f"hmrc/salary_sacrifice_it_relief_{band}_rate",
         variable="income_tax",
         source="hmrc",
         unit=Unit.GBP,
@@ -166,13 +208,55 @@ def _relief(ctx, suffix):
     return float(compute_ss_it_relief(target, ctx)[0])
 
 
-def test_compute_splits_relief_for_a_basic_rate_taxpayer_near_the_threshold():
-    """Pay of £48k after a £4k sacrifice: 40% relief only above £50,270."""
-    ctx = _Ctx(base_pay=48_000, sacrifice=4_000)
-    by_band = {
-        s: _relief(ctx, s) for s in ("basic_rate", "higher_rate", "additional_rate")
-    }
-    assert _relief(ctx, "total") == pytest.approx(1_146)
-    assert by_band == pytest.approx(
-        {"basic_rate": 454, "higher_rate": 692, "additional_rate": 0}
+@pytest.mark.parametrize(
+    "base_pay, sacrifice, region, expected",
+    [
+        # rUK, £48k after a £4k sacrifice: taxable income £35,430 → £39,430,
+        # 40% only above £37,700.
+        (48_000, 4_000, "LONDON", {"basic": 454, "higher": 692, "additional": 0}),
+        # Scotland, £42k after £4k: taxable £29,430 → £33,430, 21% to £31,092
+        # (basic category), 42% above.
+        (
+            42_000,
+            4_000,
+            "SCOTLAND",
+            {"basic": 349.02, "higher": 981.96, "additional": 0},
+        ),
+        # rUK, £100k after £10k: £5k of personal allowance is withdrawn, so
+        # relief is 60% of the sacrifice, all in the higher band.
+        (100_000, 10_000, "LONDON", {"basic": 0, "higher": 6_000, "additional": 0}),
+    ],
+    ids=["ruk-straddle", "scotland-straddle", "allowance-taper"],
+)
+def test_compute_relieves_each_slice_at_its_rate(base_pay, sacrifice, region, expected):
+    ctx = _Ctx(base_pay, sacrifice, region)
+    by_band = {band: _relief(ctx, band) for band in expected}
+    assert by_band == pytest.approx(expected, abs=0.01)
+    # With pay as the only income, the bands add up to the fall in income tax.
+    income_tax = [
+        float(s.calculate("income_tax", 2025)[0])
+        for s in (ctx.counterfactual_sim, ctx.sim)
+    ]
+    assert sum(by_band.values()) == pytest.approx(
+        income_tax[0] - income_tax[1], abs=0.01
     )
+
+
+def test_relief_targets_produce_loss_matrix_columns(enhanced_frs):
+    """create_target_matrix skips a target, with only a warning, when
+    _resolve_value or _compute_column raises or returns None. On the built
+    dataset neither may happen for these targets."""
+    from policyengine_uk import Microsimulation
+
+    year = CURRENT_FRS_RELEASE.calibration_year
+    sim = Microsimulation(dataset=enhanced_frs)
+    sim.default_calculation_period = year
+    ctx = _SimContext(sim, year, enhanced_frs, None)
+    weights = np.asarray(sim.calculate("household_weight", year))
+    targets = [t for t in _offline_targets() if t.name in RELIEF_2024_25]
+    assert len(targets) == len(RELIEF_2024_25)
+    for target in targets:
+        assert _resolve_value(target, year) is not None, target.name
+        column = np.asarray(_compute_column(target, ctx, year), dtype=float)
+        assert np.isfinite(column).all(), target.name
+        assert column @ weights > 0, target.name
diff --git a/policyengine_uk_data/tests/test_target_registry.py b/policyengine_uk_data/tests/test_target_registry.py
index 59d0b09f5..c2a1c1c37 100644
--- a/policyengine_uk_data/tests/test_target_registry.py
+++ b/policyengine_uk_data/tests/test_target_registry.py
@@ -86,7 +86,6 @@ def test_hmrc_salary_sacrifice_relief_targets():
     on the old CSV dropped them from every build without failing it)."""
     targets = {t.name: t for t in get_all_targets(year=2025)}
     for name in (
-        "hmrc/salary_sacrifice_it_relief_total",
         "hmrc/salary_sacrifice_it_relief_basic_rate",
         "hmrc/salary_sacrifice_it_relief_higher_rate",
         "hmrc/salary_sacrifice_it_relief_additional_rate",
@@ -94,6 +93,8 @@ def test_hmrc_salary_sacrifice_relief_targets():
         "hmrc/salary_sacrifice_employer_nics_relief",
     ):
         assert name in targets, f"{name} missing from the target registry"
+    # The bands, not HMRC's separately rounded total, are the targets.
+    assert "hmrc/salary_sacrifice_it_relief_total" not in targets
     # One NICs relief target per class: the OBR-labelled copies of the
     # 2023-24 figures are gone.
     assert "obr/salary_sacrifice_employee_ni_relief" not in targets

From 585402d9d6784226faf862227e288908332ef819 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 00:49:29 -0400
Subject: [PATCH 47/70] Update the changelog fragment for the review changes

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md b/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
index 46b3fb23a..f7fb4a92f 100644
--- a/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
+++ b/changelog.d/hmrc-salary-sacrifice-relief-targets.fixed.md
@@ -1 +1 @@
-Restore the HMRC salary sacrifice income tax and NICs relief calibration targets, lost to a withdrawn (410 Gone) HMRC CSV since the July 2026 private pension statistics release. They now use the 2024-25 Tables 6.1 and 6.2, mapped to year 2024, with a committed copy used when the download fails; a changed table fails the build. The rate-band targets split each person's relief across the bands it is given in (they had been assigned by adjusted net income against taxable-income thresholds), and the OBR-labelled NICs relief targets, which repeated the 2023-24 HMRC figures, are removed.
+Restore the HMRC salary sacrifice income tax and NICs relief calibration targets. They had been lost since the July 2026 private pension statistics release, when the old HMRC CSV was withdrawn (410 Gone). The targets now come from the 2024-25 Tables 6.1 and 6.2, mapped to year 2024. A committed copy is used when the download fails, and a table for another tax year or without the expected rows fails the build. Income tax relief is the rise in tax on pay under the person's rUK or Scottish rates, relieved at each rate the sacrifice straddles; it had been assigned whole to one band by comparing adjusted net income with taxable-income thresholds. The NICs targets follow the Class 1 rates, including the employer rate rise to 15% from April 2025. The OBR-labelled NICs relief targets, which repeated HMRC's 2023-24 figures, and the separately rounded income tax relief total are removed.

From b22417123f45824a8cf4b72ab6fa95ab2488440a Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 01:58:03 -0400
Subject: [PATCH 48/70] Address review r2 nits: blank cells raise, pin relief
 to pay, unpin a PE threshold
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- _relief rejects a blank (NaN) cell instead of returning a NaN target.
- Test that dividends pushed into a higher band by the sacrifice do not
  count as relief (a revert to the change in income_tax passed every
  test before).
- The Scottish category test reads the thresholds from PolicyEngine's
  schedule instead of stating its top-rate threshold (£112,570) as law
  (policyengine-uk#2130: the statutory threshold is £125,140).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../targets/sources/hmrc_salary_sacrifice.py  |  2 +-
 .../test_hmrc_salary_sacrifice_targets.py     | 48 +++++++++++++++----
 2 files changed, 40 insertions(+), 10 deletions(-)

diff --git a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
index f90754fe0..fd21f15c6 100644
--- a/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
+++ b/policyengine_uk_data/targets/sources/hmrc_salary_sacrifice.py
@@ -149,7 +149,7 @@ def rate(path, year):
 def _relief(rows: pd.DataFrame, column: str, label: str) -> float:
     """The single positive £m value in ``rows`` whose ``column`` is ``label``."""
     values = rows.loc[rows[column] == label, "value_of_relief"].map(to_float)
-    if len(values) != 1 or values.iloc[0] <= 0:
+    if len(values) != 1 or not values.iloc[0] > 0:  # also rejects a blank cell
         raise ValueError(
             f"HMRC salary sacrifice relief: expected one positive value for "
             f"{label!r}, got {values.tolist()}"
diff --git a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
index 2e6aafdac..d70f9384f 100644
--- a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
+++ b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
@@ -112,6 +112,9 @@ def test_failed_download_uses_committed_table(get):
         ),
         lambda df: df.assign(tax_year="2025 to 2026"),
         lambda df: df.drop(columns="sector_scheme"),
+        lambda df: df.assign(
+            value_of_relief=df["value_of_relief"].where(df["tax_rate"] != "Basic Rate")
+        ),
     ],
     ids=[
         "no-higher-rate",
@@ -120,6 +123,7 @@ def test_failed_download_uses_committed_table(get):
         "two-years",
         "next-release",
         "no-column",
+        "blank-cell",
     ],
 )
 def test_changed_table_raises(change):
@@ -153,19 +157,25 @@ def test_tax_by_band_adds_up_to_the_schedule_and_rises_with_income(year, schedul
 
 
 def test_tax_by_band_groups_scottish_rates_into_hmrc_categories():
-    # 2025-26 Scottish taxable-income bands: intermediate (21%) up to £31,092,
-    # higher (42%) to £62,430, advanced (45%) to £112,570, then top (48%).
+    """Starter, basic and intermediate → basic; higher and advanced → higher;
+    top → additional. Thresholds come from PolicyEngine's 2025 schedule
+    (policyengine-uk#2130: its top-rate threshold is £112,570 rather than
+    the statutory £125,140)."""
     scale = _scales("2025")["scotland"]
-    bands = tax_by_band(
-        np.array([20_000, 50_000, 100_000, 150_000]), scale.thresholds, scale.rates
+    (_, _, _, higher, advanced, top) = scale.thresholds
+    assert list(scale.rates) == [0.19, 0.20, 0.21, 0.42, 0.45, 0.48]
+    income = np.array(
+        [higher - 1, (higher + advanced) / 2, (advanced + top) / 2, top + 10_000]
     )
+    bands = tax_by_band(income, scale.thresholds, scale.rates)
     assert bands["higher"][0] == 0 and bands["additional"][0] == 0
-    assert bands["higher"][1] > 0 and bands["additional"][1] == 0
+    assert bands["higher"][1] == pytest.approx(0.42 * (income[1] - higher))
     # The advanced rate counts as higher rate.
     assert bands["higher"][2] == pytest.approx(
-        0.42 * (62_430 - 31_092) + 0.45 * (100_000 - 62_430)
+        0.42 * (advanced - higher) + 0.45 * (income[2] - advanced)
     )
-    assert bands["additional"][3] == pytest.approx(0.48 * (150_000 - 112_570))
+    assert bands["additional"][2] == 0
+    assert bands["additional"][3] == pytest.approx(0.48 * 10_000)
 
 
 class _Ctx:
@@ -173,14 +183,18 @@ class _Ctx:
 
     time_period = 2025
 
-    def __init__(self, base_pay, sacrifice, region="LONDON"):
+    def __init__(self, base_pay, sacrifice, region="LONDON", dividends=0):
         from policyengine_uk import Simulation
 
         def sim(pay):
             return Simulation(
                 situation={
                     "people": {
-                        "a": {"age": {2025: 40}, "employment_income": {2025: pay}}
+                        "a": {
+                            "age": {2025: 40},
+                            "employment_income": {2025: pay},
+                            "dividend_income": {2025: dividends},
+                        }
                     },
                     "benunits": {"b": {"members": ["a"]}},
                     "households": {"h": {"members": ["a"], "region": {2025: region}}},
@@ -260,3 +274,19 @@ def test_relief_targets_produce_loss_matrix_columns(enhanced_frs):
         column = np.asarray(_compute_column(target, ctx, year), dtype=float)
         assert np.isfinite(column).all(), target.name
         assert column @ weights > 0, target.name
+
+
+def test_relief_is_tax_on_pay_not_on_other_income():
+    """HMRC applies income tax rates to pay. Paying the sacrifice as salary
+    also pushes £10k of dividends from the basic into the higher dividend
+    band; that extra dividend tax is not salary sacrifice relief."""
+    ctx = _Ctx(48_000, 4_000, dividends=10_000)
+    by_band = {band: _relief(ctx, band) for band in ("basic", "higher", "additional")}
+    assert by_band == pytest.approx(
+        {"basic": 454, "higher": 692, "additional": 0}, abs=0.01
+    )
+    income_tax = [
+        float(s.calculate("income_tax", 2025)[0])
+        for s in (ctx.counterfactual_sim, ctx.sim)
+    ]
+    assert income_tax[0] - income_tax[1] > sum(by_band.values()) + 100

From 0863a6aa540be49dac65cfd73c17ebe82b17eecd Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 03:02:09 -0400
Subject: [PATCH 49/70] Correct the dividend test's docstring (review r3 T1)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Only the dividends taxed in the basic rate band move to the higher band,
not the whole £10k.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_hmrc_salary_sacrifice_targets.py              | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
index d70f9384f..ec01d2801 100644
--- a/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
+++ b/policyengine_uk_data/tests/test_hmrc_salary_sacrifice_targets.py
@@ -278,8 +278,9 @@ def test_relief_targets_produce_loss_matrix_columns(enhanced_frs):
 
 def test_relief_is_tax_on_pay_not_on_other_income():
     """HMRC applies income tax rates to pay. Paying the sacrifice as salary
-    also pushes £10k of dividends from the basic into the higher dividend
-    band; that extra dividend tax is not salary sacrifice relief."""
+    also moves the dividends that were taxed in the basic rate band into the
+    higher dividend band; that extra dividend tax is not salary sacrifice
+    relief."""
     ctx = _Ctx(48_000, 4_000, dividends=10_000)
     by_band = {band: _relief(ctx, band) for band in ("basic", "higher", "additional")}
     assert by_band == pytest.approx(

From 1c5b20c02a2f0d3509fff90cc679f302b8225c0e Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 06:27:07 -0400
Subject: [PATCH 50/70] Docs: 10 clones is the default, not what release builds
 use

Review nit: create_datasets.py defaults to 10 clones (2 with TESTING=1),
and PE_UK_DATA_OA_CLONES overrides it; push.yaml and pull_request.yaml set
it to 1.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 docs/oa_calibration_pipeline.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/oa_calibration_pipeline.md b/docs/oa_calibration_pipeline.md
index eb2a1f7bd..a8722826c 100644
--- a/docs/oa_calibration_pipeline.md
+++ b/docs/oa_calibration_pipeline.md
@@ -42,14 +42,14 @@ Clone each FRS household N times and assign each clone a different OA.
 
 **Deliverables:**
 - `policyengine_uk_data/calibration/clone_and_assign.py` — clones all three entity tables (household, person, benunit), remaps IDs, divides weights by N, attaches OA geography columns
-- `datasets/create_datasets.py` — clone step inserted after imputations, before uprating/calibration (N=10 production, N=2 testing)
+- `datasets/create_datasets.py` — clone step inserted after imputations, before uprating/calibration (N defaults to 10, or 2 with `TESTING=1`; `PE_UK_DATA_OA_CLONES` overrides it)
 - `tests/test_clone_and_assign.py` — 14 tests covering dimensions, weight preservation, ID uniqueness, FK integrity, country constraints, data preservation
 
 **Key design:**
-- N=10 clones in production, N=2 in testing mode
+- N defaults to 10 clones (2 with `TESTING=1`); the `PE_UK_DATA_OA_CLONES` environment variable overrides it, and the release (`push.yaml`) and pull request workflows set it to 1
 - Constituency collision avoidance: each clone gets a different constituency where possible
 - Region constraint: each clone's OA is drawn from the household's own FRS region (Wales and Scotland are one region each), so `region_code_oa`, `la_code_oa` and `constituency_code_oa` never contradict `region`. A household with no region below the country falls back to its country. No LA or constituency straddles a region, so every OA stays reachable
-- Collision avoidance draws from the same region; the smallest (North East) has 27 constituencies, more than the 10 production clones
+- Collision avoidance draws from the same region; the smallest (North East) has 27 constituencies, more than the default 10 clones
 - Weights divided by N so population totals are preserved
 - Pure pandas/numpy operations — no simulation required, fast execution
 

From a1690afaa72595b2d5a847ac6581b692be1fbf14 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 06:32:29 -0400
Subject: [PATCH 51/70] Keep the TOTCAPB3 fallback with nullable inputs (review
 of #513)

With pandas nullable Float64 columns, a missing TOTCAPB4 (pd.NA) made the
validity mask NA and wiped a valid TOTCAPB3. Convert each column to plain
float64 first so the mask has no missing entries. The FRS reader uses plain
NumPy dtypes, so this release's input is unaffected.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/datasets/frs.py              | 15 ++++++++++-----
 .../tests/test_pension_credit_reported_capital.py | 13 +++++++++++++
 2 files changed, 23 insertions(+), 5 deletions(-)

diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 4f69a1262..77e6f3391 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -590,14 +590,19 @@ def derive_pension_credit_reported_capital(benunit: pd.DataFrame) -> np.ndarray:
     A missing or negative value gives -1, so policyengine-uk falls back to the
     household proxy.
     """
-    capital = pd.Series(np.nan, index=benunit.index, dtype=float)
+    capital = np.full(len(benunit), np.nan)
     for column in ("totcapb3", "totcapb4"):  # later columns take precedence
         if column in benunit.columns:
-            values = pd.to_numeric(benunit[column], errors="coerce")
+            # Plain float64, so nullable (pd.NA) inputs become NaN and the
+            # validity mask is a plain bool array with no missing entries.
+            values = (
+                pd.to_numeric(benunit[column], errors="coerce")
+                .astype("float64")
+                .to_numpy(dtype=float, na_value=np.nan)
+            )
             valid = np.isfinite(values) & (values >= 0)
-            capital = capital.where(~valid, values)
-    values = capital.to_numpy(dtype=float)
-    return np.where(np.isfinite(values) & (values >= 0), values, -1.0)
+            capital = np.where(valid, values, capital)
+    return np.where(np.isfinite(capital) & (capital >= 0), capital, -1.0)
 
 
 def create_frs(
diff --git a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
index 2e93839de..d19185811 100644
--- a/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
+++ b/policyengine_uk_data/tests/test_pension_credit_reported_capital.py
@@ -36,6 +36,19 @@ def test_totcapb4_takes_precedence_with_totcapb3_fallback():
     assert result.tolist() == [150.0, 200.0, 300.0, 400.0, -1.0]
 
 
+def test_nullable_inputs_keep_the_totcapb3_fallback():
+    """Nullable (pd.NA) columns must not wipe a valid TOTCAPB3 where TOTCAPB4
+    is missing."""
+    benunit = pd.DataFrame(
+        {
+            "totcapb3": pd.array([0.0, 250.0, pd.NA, -3.0], dtype="Float64"),
+            "totcapb4": pd.array([pd.NA, pd.NA, 400.0, pd.NA], dtype="Float64"),
+        }
+    )
+    result = derive_pension_credit_reported_capital(benunit)
+    assert result.tolist() == [0.0, 250.0, 400.0, -1.0]
+
+
 def test_missing_column_gives_sentinel():
     benunit = pd.DataFrame({"benunit_id": [101, 102, 201]})
     assert derive_pension_credit_reported_capital(benunit).tolist() == [-1.0] * 3

From 503b00ce7e82ebf0061828a91082005564b5a2d9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 11:49:46 -0400
Subject: [PATCH 52/70] Fall back to the committed OBR workbooks when obr.uk
 answers 200 with a non-workbook page

obr.uk can answer the EFO URLs with HTTP 200 and an HTML "No Access" page
instead of the xlsx. openpyxl then raised zipfile.BadZipFile, which is not a
requests.RequestException, so it escaped _download_workbook before the
committed-workbook fallback ran, and get_targets() dropped the nine receipts
and NICs targets (34 -> 25).

A body that openpyxl cannot read (BadZipFile, InvalidFileException, KeyError,
OSError, ValueError) is now a permanent download failure: it is not retried,
control reaches the fallback, and the warning names the URL, the status, the
Content-Type and the parse error.

Tests run every failure kind (200 HTML/empty/truncated zip/zip without xlsx
manifest/zip without workbook part, 403, 404, 500 until retries run out,
connection error) against both EFO URLs and get_targets(), with a positive
control that a real workbook body is used without the fallback.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../obr-fallback-non-workbook.fixed.md        |   1 +
 policyengine_uk_data/targets/sources/obr.py   |  31 +++-
 .../tests/test_obr_efo_fallback.py            | 143 +++++++++++++++++-
 3 files changed, 170 insertions(+), 5 deletions(-)
 create mode 100644 changelog.d/obr-fallback-non-workbook.fixed.md

diff --git a/changelog.d/obr-fallback-non-workbook.fixed.md b/changelog.d/obr-fallback-non-workbook.fixed.md
new file mode 100644
index 000000000..5e3e78c6f
--- /dev/null
+++ b/changelog.d/obr-fallback-non-workbook.fixed.md
@@ -0,0 +1 @@
+Fall back to the committed OBR EFO workbooks when obr.uk answers 200 with a page that is not a workbook (such as its HTML "No Access" page), not only when the download fails with an HTTP error. The parse error used to escape the fallback, and `get_targets()` silently dropped the nine OBR receipts and NICs targets. A body that is not a readable xlsx is now treated as a permanent failure: it is not retried, and a warning names the URL, the status and the parse error.
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index ad4e3cf17..69e75c229 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -12,10 +12,12 @@
 import io
 import logging
 import time
+import zipfile
 from functools import lru_cache
 
 import openpyxl
 import requests
+from openpyxl.utils.exceptions import InvalidFileException
 
 from policyengine_uk_data.targets.schema import Target, Unit
 from policyengine_uk_data.targets.sources._common import (
@@ -44,6 +46,20 @@
 _DOWNLOAD_MAX_ATTEMPTS = 4
 _DOWNLOAD_RETRY_STATUSES = {429, 500, 502, 503, 504}
 
+# obr.uk can also answer 200 with an HTML "No Access" page instead of the
+# workbook (observed 2026-10-04 from a local machine). These are the errors
+# openpyxl raises on a body that is not a readable xlsx: not a zip
+# (BadZipFile), a zip without the xlsx manifest (KeyError) or workbook part
+# (OSError), or malformed XML inside (ValueError). Such a body will not turn
+# into a workbook on retry, so it is treated as permanent, like a 403.
+_WORKBOOK_PARSE_ERRORS = (
+    zipfile.BadZipFile,
+    InvalidFileException,
+    KeyError,
+    OSError,
+    ValueError,
+)
+
 
 # obr.uk serves 403 Forbidden to GitHub Actions runner IPs (observed on the
 # 2026-07-21 push builds: every attempt 403'd while the same URLs work from
@@ -78,7 +94,8 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
     Retries transient HTTP errors (429/5xx) and connection failures with
     exponential backoff, honouring a numeric Retry-After header when
     present. Falls back to the committed workbook in storage/obr_efo/ when
-    the download ultimately fails (obr.uk 403s CI runner IPs).
+    the download ultimately fails: obr.uk 403s CI runner IPs, and can answer
+    200 with an HTML page that is not a workbook. Neither is retried.
     """
     last_error: Exception | None = None
     for attempt in range(_DOWNLOAD_MAX_ATTEMPTS):
@@ -89,7 +106,17 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
             last_error = e  # connection/timeout — retryable
         else:
             if r.status_code < 400:
-                return openpyxl.load_workbook(io.BytesIO(r.content), data_only=False)
+                try:
+                    return openpyxl.load_workbook(
+                        io.BytesIO(r.content), data_only=False
+                    )
+                except _WORKBOOK_PARSE_ERRORS as e:
+                    last_error = ValueError(
+                        f"{r.status_code} for url: {url}, but the body "
+                        f"({r.headers.get('Content-Type', 'no Content-Type')}) "
+                        f"is not an xlsx workbook ({type(e).__name__}: {e})"
+                    )
+                    break
             last_error = requests.HTTPError(
                 f"{r.status_code} for url: {url}", response=r
             )
diff --git a/policyengine_uk_data/tests/test_obr_efo_fallback.py b/policyengine_uk_data/tests/test_obr_efo_fallback.py
index 309e8d7c4..ff6cace65 100644
--- a/policyengine_uk_data/tests/test_obr_efo_fallback.py
+++ b/policyengine_uk_data/tests/test_obr_efo_fallback.py
@@ -2,11 +2,17 @@
 
 obr.uk serves 403 Forbidden to GitHub Actions runner IPs, and losing the
 workbooks silently drops 28 OBR targets and calibrates a degraded dataset
-(observed on the 2026-07-21 push builds). These tests pin: the fallback
-workbooks are committed and parseable, a failed download uses them instead
-of raising, and a 403 does not burn the retry budget.
+(observed on the 2026-07-21 push builds). obr.uk can also answer 200 with
+an HTML "No Access" page, which once escaped the fallback as a BadZipFile
+and dropped the nine receipts and NICs targets (observed 2026-10-04). These
+tests pin: the fallback workbooks are committed and parseable, every kind of
+failed download uses them instead of raising, and permanent failures do not
+burn the retry budget.
 """
 
+import io
+import zipfile
+from contextlib import contextmanager
 from types import SimpleNamespace
 from unittest.mock import patch
 
@@ -15,6 +21,7 @@
 
 from policyengine_uk_data.storage import STORAGE_FOLDER
 from policyengine_uk_data.targets.sources import obr
+from policyengine_uk_data.targets.sources._common import load_config
 
 
 @pytest.fixture(autouse=True)
@@ -28,6 +35,102 @@ def _forbidden(*args, **kwargs):
     return SimpleNamespace(status_code=403, headers={}, content=b"")
 
 
+def _response(status_code, content=b"", content_type=None):
+    headers = {"Content-Type": content_type} if content_type else {}
+    return SimpleNamespace(status_code=status_code, headers=headers, content=content)
+
+
+def _zip(members: dict[str, str]) -> bytes:
+    buffer = io.BytesIO()
+    with zipfile.ZipFile(buffer, "w") as archive:
+        for name, data in members.items():
+            archive.writestr(name, data)
+    return buffer.getvalue()
+
+
+def _connection_error():
+    raise requests.ConnectionError("no route to obr.uk")
+
+
+_NO_ACCESS_PAGE = (
+    b"<!DOCTYPE html><html><head>"
+    b"<title>No Access - Office for Budget Responsibility</title>"
+    b"</head><body><h1>No Access</h1></body></html>"
+)
+
+# Every way the download can fail, with the number of requests it should
+# cost: transient failures use the full retry budget, permanent ones one.
+_FAILED_DOWNLOADS = {
+    "200-html-page": (
+        lambda: _response(200, _NO_ACCESS_PAGE, "text/html; charset=UTF-8"),
+        1,
+    ),
+    "200-empty-body": (lambda: _response(200), 1),
+    "200-truncated-zip": (lambda: _response(200, b"PK\x03\x04garbage"), 1),
+    "200-zip-without-xlsx-manifest": (
+        lambda: _response(200, _zip({"readme.txt": "not a workbook"})),
+        1,
+    ),
+    "200-zip-without-workbook-part": (
+        lambda: _response(
+            200,
+            _zip(
+                {
+                    "[Content_Types].xml": '<Types xmlns="http://schemas.'
+                    'openxmlformats.org/package/2006/content-types"/>'
+                }
+            ),
+        ),
+        1,
+    ),
+    "403": (lambda: _response(403), 1),
+    "404": (lambda: _response(404), 1),
+    "500-until-retries-run-out": (
+        lambda: _response(500),
+        obr._DOWNLOAD_MAX_ATTEMPTS,
+    ),
+    "connection-error": (_connection_error, obr._DOWNLOAD_MAX_ATTEMPTS),
+}
+
+_EFO_URL_KEYS = ["efo_receipts", "efo_expenditure"]
+
+_RECEIPTS_AND_NICS_TARGETS = {
+    "obr/income_tax",
+    "obr/ni",
+    "obr/vat",
+    "obr/fuel_duties",
+    "obr/capital_gains_tax",
+    "obr/sdlt",
+    "obr/ni_employee",
+    "obr/ni_employer",
+    "obr/ni_self_employed",
+}
+
+
+@contextmanager
+def _obr_answering(make_response):
+    """Answer every requests.get with make_response(), skip retry sleeps and
+    spy on the fallback. Yields the request and fallback call logs."""
+    requests_made, fallbacks_used = [], []
+    real_fallback = obr._fallback_workbook
+
+    def get(url, *args, **kwargs):
+        requests_made.append(url)
+        return make_response()
+
+    def fallback(url):
+        wb = real_fallback(url)
+        fallbacks_used.append((url, wb))
+        return wb
+
+    with (
+        patch.object(obr.requests, "get", side_effect=get),
+        patch.object(obr.time, "sleep", lambda s: None),
+        patch.object(obr, "_fallback_workbook", side_effect=fallback),
+    ):
+        yield requests_made, fallbacks_used
+
+
 def test_fallback_workbooks_are_committed_and_parseable():
     for filename in obr._EFO_FALLBACKS.values():
         path = STORAGE_FOLDER / "obr_efo" / filename
@@ -94,3 +197,37 @@ def get(*args, **kwargs):
         "obr/vat",
     } <= names
     assert len(names) >= 30
+
+
+@pytest.mark.parametrize("url_key", _EFO_URL_KEYS)
+@pytest.mark.parametrize("kind", list(_FAILED_DOWNLOADS))
+def test_every_failed_download_uses_committed_workbook(kind, url_key):
+    make_response, expected_requests = _FAILED_DOWNLOADS[kind]
+    url = load_config()["obr"][url_key]
+    with _obr_answering(make_response) as (requests_made, fallbacks_used):
+        wb = obr._download_workbook(url)
+    assert fallbacks_used == [(url, wb)], "the committed workbook was not returned"
+    assert len(requests_made) == expected_requests
+
+
+@pytest.mark.parametrize("kind", list(_FAILED_DOWNLOADS))
+def test_every_failed_download_keeps_receipts_and_nics_targets(kind):
+    make_response, _ = _FAILED_DOWNLOADS[kind]
+    with _obr_answering(make_response):
+        names = {t.name for t in obr.get_targets()}
+    assert _RECEIPTS_AND_NICS_TARGETS <= names, sorted(
+        _RECEIPTS_AND_NICS_TARGETS - names
+    )
+
+
+def test_workbook_response_is_parsed_without_fallback():
+    """Positive control: a real xlsx body is used as served."""
+    body = (STORAGE_FOLDER / "obr_efo" / "efo_receipts.xlsx").read_bytes()
+    with _obr_answering(lambda: _response(200, body)) as (
+        requests_made,
+        fallbacks_used,
+    ):
+        wb = obr._download_workbook(load_config()["obr"]["efo_receipts"])
+    assert fallbacks_used == []
+    assert len(requests_made) == 1
+    assert obr._find_receipts_sheet(wb) is not None

From cc7550101d289fda4d9cde3f92fa68e78982820f Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 12:26:38 -0400
Subject: [PATCH 53/70] Catch every openpyxl failure on a non-workbook 200
 body, per review

A zip with malformed XML inside raised xml.etree.ElementTree.ParseError (a
SyntaxError), which escaped the named-exception tuple; zlib.error,
NotImplementedError, RuntimeError and TypeError could too. Catch Exception
around the single load_workbook call on the response body: a genuine openpyxl
break still surfaces, because the fallback is parsed by the same call outside
any try. Chain the parse error as the cause of the reported ValueError.

Tests: add a malformed-XML row; pin the fallback warning text and the
ValueError raised for a URL with no committed workbook; fold the 403 and
connection-error tests into the response-kind table they duplicated.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../obr-fallback-non-workbook.fixed.md        |  2 +-
 policyengine_uk_data/targets/sources/obr.py   | 24 +++----
 .../tests/test_obr_efo_fallback.py            | 62 ++++++++-----------
 3 files changed, 33 insertions(+), 55 deletions(-)

diff --git a/changelog.d/obr-fallback-non-workbook.fixed.md b/changelog.d/obr-fallback-non-workbook.fixed.md
index 5e3e78c6f..c36e86429 100644
--- a/changelog.d/obr-fallback-non-workbook.fixed.md
+++ b/changelog.d/obr-fallback-non-workbook.fixed.md
@@ -1 +1 @@
-Fall back to the committed OBR EFO workbooks when obr.uk answers 200 with a page that is not a workbook (such as its HTML "No Access" page), not only when the download fails with an HTTP error. The parse error used to escape the fallback, and `get_targets()` silently dropped the nine OBR receipts and NICs targets. A body that is not a readable xlsx is now treated as a permanent failure: it is not retried, and a warning names the URL, the status and the parse error.
+Fall back to the committed OBR EFO workbooks when obr.uk answers 200 with a body that is not a workbook (such as its HTML "No Access" page), not only when the request fails. The parse error used to escape the fallback, and `get_targets()` dropped the nine OBR receipts and NICs targets, logging the error while the build carried on. A body openpyxl cannot read is now treated as a permanent failure: it is not retried, and a warning names the URL, the status and the parse error.
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index 69e75c229..5a1286b7f 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -12,12 +12,10 @@
 import io
 import logging
 import time
-import zipfile
 from functools import lru_cache
 
 import openpyxl
 import requests
-from openpyxl.utils.exceptions import InvalidFileException
 
 from policyengine_uk_data.targets.schema import Target, Unit
 from policyengine_uk_data.targets.sources._common import (
@@ -46,20 +44,6 @@
 _DOWNLOAD_MAX_ATTEMPTS = 4
 _DOWNLOAD_RETRY_STATUSES = {429, 500, 502, 503, 504}
 
-# obr.uk can also answer 200 with an HTML "No Access" page instead of the
-# workbook (observed 2026-10-04 from a local machine). These are the errors
-# openpyxl raises on a body that is not a readable xlsx: not a zip
-# (BadZipFile), a zip without the xlsx manifest (KeyError) or workbook part
-# (OSError), or malformed XML inside (ValueError). Such a body will not turn
-# into a workbook on retry, so it is treated as permanent, like a 403.
-_WORKBOOK_PARSE_ERRORS = (
-    zipfile.BadZipFile,
-    InvalidFileException,
-    KeyError,
-    OSError,
-    ValueError,
-)
-
 
 # obr.uk serves 403 Forbidden to GitHub Actions runner IPs (observed on the
 # 2026-07-21 push builds: every attempt 403'd while the same URLs work from
@@ -106,16 +90,22 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
             last_error = e  # connection/timeout — retryable
         else:
             if r.status_code < 400:
+                # obr.uk can answer 200 with an HTML "No Access" page instead
+                # of the workbook (observed 2026-10-04). A body openpyxl cannot
+                # read will not become a workbook on retry, so it is permanent,
+                # like a 403. Catching broadly cannot hide an openpyxl break:
+                # the fallback is parsed by the same call outside any try.
                 try:
                     return openpyxl.load_workbook(
                         io.BytesIO(r.content), data_only=False
                     )
-                except _WORKBOOK_PARSE_ERRORS as e:
+                except Exception as e:
                     last_error = ValueError(
                         f"{r.status_code} for url: {url}, but the body "
                         f"({r.headers.get('Content-Type', 'no Content-Type')}) "
                         f"is not an xlsx workbook ({type(e).__name__}: {e})"
                     )
+                    last_error.__cause__ = e
                     break
             last_error = requests.HTTPError(
                 f"{r.status_code} for url: {url}", response=r
diff --git a/policyengine_uk_data/tests/test_obr_efo_fallback.py b/policyengine_uk_data/tests/test_obr_efo_fallback.py
index ff6cace65..ecc25c9a4 100644
--- a/policyengine_uk_data/tests/test_obr_efo_fallback.py
+++ b/policyengine_uk_data/tests/test_obr_efo_fallback.py
@@ -11,6 +11,7 @@
 """
 
 import io
+import logging
 import zipfile
 from contextlib import contextmanager
 from types import SimpleNamespace
@@ -31,10 +32,6 @@ def _clear_workbook_cache():
     obr._download_workbook.cache_clear()
 
 
-def _forbidden(*args, **kwargs):
-    return SimpleNamespace(status_code=403, headers={}, content=b"")
-
-
 def _response(status_code, content=b"", content_type=None):
     headers = {"Content-Type": content_type} if content_type else {}
     return SimpleNamespace(status_code=status_code, headers=headers, content=content)
@@ -83,6 +80,10 @@ def _connection_error():
         ),
         1,
     ),
+    "200-zip-with-malformed-xml": (
+        lambda: _response(200, _zip({"[Content_Types].xml": "<Types"})),
+        1,
+    ),
     "403": (lambda: _response(403), 1),
     "404": (lambda: _response(404), 1),
     "500-until-retries-run-out": (
@@ -141,39 +142,14 @@ def test_fallback_workbooks_are_committed_and_parseable():
     assert obr._find_receipts_sheet(receipts) is not None
 
 
-def test_403_uses_fallback_without_retrying():
-    calls = []
-
-    def get(*args, **kwargs):
-        calls.append(args)
-        return _forbidden()
-
-    with patch.object(obr.requests, "get", side_effect=get):
-        wb = obr._download_workbook(
-            "https://obr.uk/download/whatever-forecast-tables-receipts/"
-        )
-    assert wb is not None
-    assert len(calls) == 1, "403 is permanent; it should not be retried"
-
-
-def test_connection_failure_uses_fallback():
-    def get(*args, **kwargs):
-        raise requests.ConnectionError("no route to obr.uk")
-
-    with (
-        patch.object(obr.requests, "get", side_effect=get),
-        patch.object(obr.time, "sleep", lambda s: None),
-    ):
-        wb = obr._download_workbook(
-            "https://obr.uk/download/whatever-forecast-tables-expenditure/"
-        )
-    assert wb is not None
-
-
-def test_unknown_url_with_failed_download_still_raises():
-    with patch.object(obr.requests, "get", side_effect=_forbidden):
-        with pytest.raises(requests.HTTPError):
-            obr._download_workbook("https://obr.uk/download/some-other-file/")
+@pytest.mark.parametrize(
+    "kind, error", [("403", requests.HTTPError), ("200-html-page", ValueError)]
+)
+def test_unknown_url_with_failed_download_still_raises(kind, error):
+    url = "https://obr.uk/download/some-other-file/"
+    with _obr_answering(_FAILED_DOWNLOADS[kind][0]):
+        with pytest.raises(error, match=f"for url: {url}"):
+            obr._download_workbook(url)
 
 
 def test_full_target_set_available_offline():
@@ -231,3 +207,15 @@ def test_workbook_response_is_parsed_without_fallback():
     assert fallbacks_used == []
     assert len(requests_made) == 1
     assert obr._find_receipts_sheet(wb) is not None
+
+
+def test_non_workbook_200_warning_names_url_status_and_parse_error(caplog):
+    caplog.set_level(logging.WARNING, logger=obr.logger.name)
+    url = load_config()["obr"]["efo_receipts"]
+    with _obr_answering(_FAILED_DOWNLOADS["200-html-page"][0]):
+        obr._download_workbook(url)
+    assert (
+        f"200 for url: {url}, but the body (text/html; charset=UTF-8) is not "
+        "an xlsx workbook (BadZipFile: File is not a zip file)); using "
+        "committed workbook fallback"
+    ) in caplog.text

From 7605e63efb57b1dce40df91ea459aa7da9485cc6 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 12:50:01 -0400
Subject: [PATCH 54/70] Tighten the fallback comment, message and changelog;
 pin cause chaining

Second review round: the broad catch only guarantees that an openpyxl break
on every workbook still surfaces, so the comment now says that; the message
says the body "could not be read as an xlsx workbook", which also covers a
served file openpyxl rejects. The changelog says the defect drops every
target from the affected workbook (nine in the observed receipts case), and
the no-fallback test asserts the parse error is chained as the cause.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/obr-fallback-non-workbook.fixed.md  |  2 +-
 policyengine_uk_data/targets/sources/obr.py     |  7 ++++---
 .../tests/test_obr_efo_fallback.py              | 17 +++++++++++------
 3 files changed, 16 insertions(+), 10 deletions(-)

diff --git a/changelog.d/obr-fallback-non-workbook.fixed.md b/changelog.d/obr-fallback-non-workbook.fixed.md
index c36e86429..4ec774889 100644
--- a/changelog.d/obr-fallback-non-workbook.fixed.md
+++ b/changelog.d/obr-fallback-non-workbook.fixed.md
@@ -1 +1 @@
-Fall back to the committed OBR EFO workbooks when obr.uk answers 200 with a body that is not a workbook (such as its HTML "No Access" page), not only when the request fails. The parse error used to escape the fallback, and `get_targets()` dropped the nine OBR receipts and NICs targets, logging the error while the build carried on. A body openpyxl cannot read is now treated as a permanent failure: it is not retried, and a warning names the URL, the status and the parse error.
+Fall back to the committed OBR EFO workbooks when obr.uk answers 200 with a body that is not a workbook (such as its HTML "No Access" page), not only when the request fails. The parse error used to escape the fallback, and `get_targets()` dropped every target parsed from that workbook (the nine OBR receipts and NICs targets in the case observed), logging the error while the build carried on. A body openpyxl cannot read is now treated as a permanent failure: it is not retried, and a warning names the URL, the status and the parse error.
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index 5a1286b7f..8a87c56c1 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -93,8 +93,8 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
                 # obr.uk can answer 200 with an HTML "No Access" page instead
                 # of the workbook (observed 2026-10-04). A body openpyxl cannot
                 # read will not become a workbook on retry, so it is permanent,
-                # like a 403. Catching broadly cannot hide an openpyxl break:
-                # the fallback is parsed by the same call outside any try.
+                # like a 403. An openpyxl break on every workbook still
+                # surfaces, as the fallback goes through the same load_workbook.
                 try:
                     return openpyxl.load_workbook(
                         io.BytesIO(r.content), data_only=False
@@ -103,7 +103,8 @@ def _download_workbook(url: str) -> openpyxl.Workbook:
                     last_error = ValueError(
                         f"{r.status_code} for url: {url}, but the body "
                         f"({r.headers.get('Content-Type', 'no Content-Type')}) "
-                        f"is not an xlsx workbook ({type(e).__name__}: {e})"
+                        "could not be read as an xlsx workbook "
+                        f"({type(e).__name__}: {e})"
                     )
                     last_error.__cause__ = e
                     break
diff --git a/policyengine_uk_data/tests/test_obr_efo_fallback.py b/policyengine_uk_data/tests/test_obr_efo_fallback.py
index ecc25c9a4..07169a88a 100644
--- a/policyengine_uk_data/tests/test_obr_efo_fallback.py
+++ b/policyengine_uk_data/tests/test_obr_efo_fallback.py
@@ -143,13 +143,18 @@ def test_fallback_workbooks_are_committed_and_parseable():
 
 
 @pytest.mark.parametrize(
-    "kind, error", [("403", requests.HTTPError), ("200-html-page", ValueError)]
+    "kind, error, cause",
+    [
+        ("403", requests.HTTPError, type(None)),
+        ("200-html-page", ValueError, zipfile.BadZipFile),
+    ],
 )
-def test_unknown_url_with_failed_download_still_raises(kind, error):
+def test_unknown_url_with_failed_download_still_raises(kind, error, cause):
     url = "https://obr.uk/download/some-other-file/"
     with _obr_answering(_FAILED_DOWNLOADS[kind][0]):
-        with pytest.raises(error, match=f"for url: {url}"):
+        with pytest.raises(error, match=f"for url: {url}") as raised:
             obr._download_workbook(url)
+    assert isinstance(raised.value.__cause__, cause)
 
 
 def test_full_target_set_available_offline():
@@ -215,7 +220,7 @@ def test_non_workbook_200_warning_names_url_status_and_parse_error(caplog):
     with _obr_answering(_FAILED_DOWNLOADS["200-html-page"][0]):
         obr._download_workbook(url)
     assert (
-        f"200 for url: {url}, but the body (text/html; charset=UTF-8) is not "
-        "an xlsx workbook (BadZipFile: File is not a zip file)); using "
-        "committed workbook fallback"
+        f"200 for url: {url}, but the body (text/html; charset=UTF-8) could "
+        "not be read as an xlsx workbook (BadZipFile: File is not a zip "
+        "file)); using committed workbook fallback"
     ) in caplog.text

From 304baad04de821caa82a605df52951506b598ab2 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 13:17:07 -0400
Subject: [PATCH 55/70] Calibrate working-age Housing Benefit net of supported
 and temporary accommodation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Max ruled on d703 that working-age Housing Benefit should be calibrated too.
policyengine-uk pays no Housing Benefit for specified (supported) or temporary
accommodation, so the working-age awards it can pay are general-needs ones.
DWP's working-age line includes both kinds, and DWP splits accommodation type
only across all ages. The calibrated working-age target is therefore DWP's
working-age line less all of its supported and temporary accommodation:
£584.8m and 107k claims in 2025-26, a lower bound on working-age general-needs
Housing Benefit. It exists for 2024-25 and 2025-26 only; from 2026-27 the
all-age accommodation lines exceed the whole working-age line.

Seeded calibrations of one saved 2025 input compared the options. Against the
full working-age line, uncapped calibration loaded it onto about three
effective records. Capping household weights at 20-40 times their prior
reached 54-60% of DWP's spending and raised income-related ESA claimants to
1.6-2.0 times DWP's count. Against the net figure, the calibration meets it
(1.04x) and no other national target crosses the 10% line compared with
calibrating pension-age figures only.

Tests: DWP's accommodation lines reconcile with its age split in every year;
the net figure equals all-age general needs less pension-age Housing Benefit,
the same quantity computed the other way; a property test of the netting; the
calibrated working-age column equals the full line's.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../targets/sources/dwp_housing_benefit.py    | 115 +++++++++++++++---
 .../tests/test_dwp_housing_benefit_targets.py | 106 +++++++++++++++-
 2 files changed, 203 insertions(+), 18 deletions(-)

diff --git a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
index eff5309f8..4ed28076e 100644
--- a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
+++ b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
@@ -29,16 +29,30 @@
 older group; DWP does not publish its rule for them, and this assumes they
 sit in its Pension Credit and State Pension groups.
 
-Only the older group is calibrated. policyengine-uk pays working-age
-Housing Benefit only as a continuing award to families that report it and
-do not claim Universal Credit: 267 of the 770 working-age records that
-report it, about 17,000 weighted claims in 2025-26 against DWP's 460,000.
-DWP's working-age figure also includes temporary and supported
-accommodation (together £5.2bn of Housing Benefit in 2025-26, not split by
-age), which the FRS barely samples. A test build on 2026-09-30 that also
-targeted the younger group reached DWP's £5.8bn by loading it onto about
-three effective records, and fitted the other targets no better. The
-younger group's figures stay here for diagnostics and tests.
+Working-age Housing Benefit is calibrated net of supported and temporary
+accommodation. policyengine-uk pays no Housing Benefit for specified
+(supported) or temporary accommodation (``housing_benefit_eligible``: such
+claims "are not modelled"; policyengine-uk#1911). So every working-age award
+it pays is general-needs Housing Benefit: a continuing award to a family that
+reports it and does not claim Universal Credit. DWP's working-age line
+includes both kinds, but DWP splits accommodation type only across all ages
+(Housing Benefit by Accomodation Type). The calibrated working-age figure is
+therefore DWP's working-age line less all of its supported and temporary
+accommodation: £584.8m and 107k claims in 2025-26, against the full line's
+£5,778.1m and 460k. Part of that accommodation is pension-age, so the figure
+is a lower bound on working-age general-needs Housing Benefit. It exists only
+for 2024-25 and 2025-26. From 2026-27, DWP's all-age supported and temporary
+accommodation exceeds its whole working-age line.
+
+Seeded test calibrations on 2026-10-04 tried the full working-age line too.
+With only the pension-age figures calibrated, the model pays about 17,000
+working-age claims in 2025-26. Calibrated to the full line, it came within
+12% of DWP's spending by loading it onto about three effective records. With
+household weights capped at 20-40 times their prior, it reached only 54-60%
+of that spending, and income-related ESA claimants rose to 1.6-2.0 times
+DWP's count. Against the net figure, no other national target crosses the
+10% line compared with calibrating the pension-age figures only. The full
+working-age lines stay here (group "under") for tests and diagnostics.
 
 Source: https://www.gov.uk/government/publications/benefit-expenditure-and-caseload-tables-2026
 """
@@ -116,6 +130,75 @@
 }
 
 
+# Housing benefits sheet, "Housing Benefit by Accomodation Type", rows "of
+# which Supported Accomodation" and "of which Temporary Accomodation": all
+# ages, same units, first published for 2024-25.
+_SUPPORTED_GBP_M = {
+    2024: 3_262.8,
+    2025: 3_631.4,
+    2026: 3_936.0,
+    2027: 4_164.1,
+    2028: 4_399.2,
+    2029: 4_687.1,
+    2030: 4_892.2,
+}
+_TEMPORARY_GBP_M = {
+    2024: 1_431.1,
+    2025: 1_561.9,
+    2026: 1_643.7,
+    2027: 1_732.1,
+    2028: 1_819.1,
+    2029: 1_929.8,
+    2030: 2_006.6,
+}
+_SUPPORTED_THOUSANDS = {
+    2024: 235,
+    2025: 242,
+    2026: 246,
+    2027: 251,
+    2028: 256,
+    2029: 262,
+    2030: 267,
+}
+_TEMPORARY_THOUSANDS = {
+    2024: 104,
+    2025: 111,
+    2026: 117,
+    2027: 123,
+    2028: 129,
+    2029: 136,
+    2030: 143,
+}
+
+
+def net_of_supported_and_temporary(
+    working_age: dict, supported: dict, temporary: dict
+) -> dict:
+    """Working-age figures less all-age supported and temporary
+    accommodation, for the years with all three where the result is
+    positive."""
+    net = {}
+    for year in sorted(working_age.keys() & supported.keys() & temporary.keys()):
+        value = round(working_age[year] - supported[year] - temporary[year], 1)
+        if value > 0:
+            net[year] = value
+    return net
+
+
+_SPENDING_GBP_M["under_general_needs"] = net_of_supported_and_temporary(
+    _SPENDING_GBP_M["under"], _SUPPORTED_GBP_M, _TEMPORARY_GBP_M
+)
+_CASELOAD_THOUSANDS["under_general_needs"] = net_of_supported_and_temporary(
+    _CASELOAD_THOUSANDS["under"], _SUPPORTED_THOUSANDS, _TEMPORARY_THOUSANDS
+)
+
+_NAMES = {
+    "over": "dwp/housing_benefit/over_pension_credit_age",
+    "under": "dwp/housing_benefit/under_pension_credit_age",
+    "under_general_needs": "dwp/housing_benefit/under_pension_credit_age_general_needs",
+}
+
+
 def _over_pension_credit_age(ctx) -> np.ndarray:
     """Benefit units assessed under the pension-age Housing Benefit rules."""
     claimant = np.asarray(
@@ -135,7 +218,7 @@ def _over_pension_credit_age(ctx) -> np.ndarray:
 
 
 # Age groups the calibration targets; see the module docstring.
-_CALIBRATED_AGE_GROUPS = ("over",)
+_CALIBRATED_AGE_GROUPS = ("over", "under_general_needs")
 
 
 def _make_compute(age_group: str, count: bool):
@@ -144,7 +227,9 @@ def compute(ctx, target: Target, year: int) -> np.ndarray:
             ctx.sim.calculate("housing_benefit").values, dtype=float
         )
         in_group = _over_pension_credit_age(ctx)
-        if age_group == "under":
+        if age_group != "over":
+            # The model pays no supported or temporary accommodation Housing
+            # Benefit, so both working-age groups share one column.
             in_group = ~in_group
         value = (housing_benefit > 0) if count else housing_benefit
         return np.asarray(ctx.household_from_family(value * in_group), dtype=float)
@@ -156,11 +241,13 @@ def get_targets() -> list[Target]:
     return build_targets(_CALIBRATED_AGE_GROUPS)
 
 
-def build_targets(age_groups=("over", "under")) -> list[Target]:
+def build_targets(
+    age_groups=("over", "under", "under_general_needs"),
+) -> list[Target]:
     """Housing Benefit spending and claims targets for these age groups."""
     targets = []
     for age_group in age_groups:
-        name = f"dwp/housing_benefit/{age_group}_pension_credit_age"
+        name = _NAMES[age_group]
         targets.append(
             Target(
                 name=name,
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index d9a805f14..bc140334c 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -39,9 +39,32 @@
     2030: 1_414,
 }
 
+# Same sheet, "Housing Benefit by Accomodation Type: of which General
+# Needs" (£ million and thousands, all ages), transcribed separately.
+_DWP_GENERAL_NEEDS_GBP_M = {
+    2024: 10_760.6,
+    2025: 7_699.5,
+    2026: 6_894.5,
+    2027: 6_938.0,
+    2028: 7_034.6,
+    2029: 7_282.5,
+    2030: 7_448.0,
+}
+_DWP_GENERAL_NEEDS_K = {
+    2024: 1_734,
+    2025: 1_216,
+    2026: 1_047,
+    2027: 1_021,
+    2028: 1_004,
+    2029: 1_000,
+    2030: 1_004,
+}
+
 _CALIBRATED = {
     "dwp/housing_benefit/over_pension_credit_age",
     "dwp/housing_benefit/over_pension_credit_age_claims",
+    "dwp/housing_benefit/under_pension_credit_age_general_needs",
+    "dwp/housing_benefit/under_pension_credit_age_general_needs_claims",
 }
 _ALL = _CALIBRATED | {
     "dwp/housing_benefit/under_pension_credit_age",
@@ -50,7 +73,8 @@
 
 
 def _targets():
-    """Both age groups, including the one calibration leaves out."""
+    """Every group's targets, including the full working-age line that
+    calibration leaves out."""
     return {t.name: t for t in dwp_housing_benefit.build_targets()}
 
 
@@ -82,13 +106,72 @@ def test_age_split_reconciles_with_dwp_totals():
         assert abs((over_k[year] + under_k[year]) / 1e3 - total) <= 1, year
 
 
-def test_only_pension_age_housing_benefit_is_calibrated():
+def test_calibration_uses_working_age_net_of_supported_and_temporary():
     housing_benefit_targets = {
         t.name for t in get_all_targets() if t.variable == "housing_benefit"
     }
     assert housing_benefit_targets == _CALIBRATED
 
 
+def test_accommodation_types_reconcile_with_the_age_split():
+    """General needs + supported + temporary is all Housing Benefit, the
+    same total as over + under, in every year DWP splits it."""
+    h = dwp_housing_benefit
+    for year, general_needs in _DWP_GENERAL_NEEDS_GBP_M.items():
+        by_type = general_needs + h._SUPPORTED_GBP_M[year] + h._TEMPORARY_GBP_M[year]
+        by_age = h._SPENDING_GBP_M["over"][year] + h._SPENDING_GBP_M["under"][year]
+        assert abs(by_type - by_age) <= 0.2, year
+        by_type_k = (
+            _DWP_GENERAL_NEEDS_K[year]
+            + h._SUPPORTED_THOUSANDS[year]
+            + h._TEMPORARY_THOUSANDS[year]
+        )
+        by_age_k = (
+            h._CASELOAD_THOUSANDS["over"][year] + h._CASELOAD_THOUSANDS["under"][year]
+        )
+        assert abs(by_type_k - by_age_k) <= 1, year
+
+
+def test_working_age_general_needs_two_ways():
+    """Working-age less supported and temporary accommodation equals all-age
+    general needs less pension-age Housing Benefit, since the two splits
+    share one total; it is defined only where positive (2024-25, 2025-26)."""
+    h = dwp_housing_benefit
+    spending = h._SPENDING_GBP_M["under_general_needs"]
+    caseload = h._CASELOAD_THOUSANDS["under_general_needs"]
+    assert set(spending) == set(caseload) == {2024, 2025}
+    assert spending[2025] == 584.8 and caseload[2025] == 107
+    for year in spending:
+        other_way = _DWP_GENERAL_NEEDS_GBP_M[year] - h._SPENDING_GBP_M["over"][year]
+        assert abs(spending[year] - other_way) <= 0.2, year
+        other_way_k = _DWP_GENERAL_NEEDS_K[year] - h._CASELOAD_THOUSANDS["over"][year]
+        assert abs(caseload[year] - other_way_k) <= 1, year
+        assert spending[year] < h._SPENDING_GBP_M["under"][year]
+        assert caseload[year] < h._CASELOAD_THOUSANDS["under"][year]
+
+
+
+@settings(max_examples=200, deadline=None)
+@given(
+    st.dictionaries(st.integers(2020, 2035), st.floats(0, 1e4), max_size=8),
+    st.dictionaries(st.integers(2020, 2035), st.floats(0, 1e4), max_size=8),
+    st.dictionaries(st.integers(2020, 2035), st.floats(0, 1e4), max_size=8),
+)
+def test_net_of_supported_and_temporary(working_age, supported, temporary):
+    net = dwp_housing_benefit.net_of_supported_and_temporary(
+        working_age, supported, temporary
+    )
+    common = working_age.keys() & supported.keys() & temporary.keys()
+    assert set(net) <= common
+    for year in common:
+        difference = working_age[year] - supported[year] - temporary[year]
+        if year in net:
+            assert net[year] > 0
+            assert abs(net[year] - difference) <= 0.05 + 1e-9
+        else:
+            assert round(difference, 1) <= 0
+
+
 def test_obr_housing_benefit_row_is_not_parsed():
     wb = openpyxl.Workbook()
     ws = wb.active
@@ -231,6 +314,18 @@ def test_age_split_partitions_housing_benefit(population):
     under = _column(ctx, "dwp/housing_benefit/under_pension_credit_age")
     over_k = _column(ctx, "dwp/housing_benefit/over_pension_credit_age_claims")
     under_k = _column(ctx, "dwp/housing_benefit/under_pension_credit_age_claims")
+    # The model pays no supported or temporary accommodation HB, so the
+    # calibrated working-age group shares the full line's column.
+    np.testing.assert_array_equal(
+        _column(ctx, "dwp/housing_benefit/under_pension_credit_age_general_needs"),
+        under,
+    )
+    np.testing.assert_array_equal(
+        _column(
+            ctx, "dwp/housing_benefit/under_pension_credit_age_general_needs_claims"
+        ),
+        under_k,
+    )
     np.testing.assert_allclose(
         over + under, np.bincount(household, hb, minlength=n_households)
     )
@@ -280,7 +375,9 @@ def test_country_restriction_partitions_the_uk(rows):
 
 def test_calibrated_columns_build_on_the_dataset(baseline, enhanced_frs):
     """The loss matrix skips a target whose column raises, so check that the
-    calibrated columns build on a real simulation and land near DWP."""
+    calibrated columns build on a real simulation and land in range. The
+    working-age range is wider: a dataset calibrated without its target
+    carries about a sixth of it (17k of 107k claims in 2025-26)."""
     from policyengine_uk_data.targets.build_loss_matrix import _SimContext
 
     year = 2025
@@ -293,4 +390,5 @@ def test_calibrated_columns_build_on_the_dataset(baseline, enhanced_frs):
         )
         assert np.isfinite(column).all(), target.name
         ratio = (column * weight).sum() / target.values[year]
-        assert 0.5 < ratio < 2, (target.name, ratio)
+        low = 0.5 if "over_pension_credit_age" in target.name else 0.1
+        assert low < ratio < 2, (target.name, ratio)

From d156176f8a0285f609a253a7bdd1d7cc91036b98 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 14:37:31 -0400
Subject: [PATCH 56/70] Update the release note for the working-age Housing
 Benefit target

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/hb-dwp-age-targets.changed.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/changelog.d/hb-dwp-age-targets.changed.md b/changelog.d/hb-dwp-age-targets.changed.md
index 759b2a2ff..c98ce26b4 100644
--- a/changelog.d/hb-dwp-age-targets.changed.md
+++ b/changelog.d/hb-dwp-age-targets.changed.md
@@ -1 +1 @@
-Calibrate pension-age Housing Benefit to DWP's Great Britain spending and claims over Pension Credit qualifying age, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Modelled GB Housing Benefit for 2025-26 falls from about £11.7bn to £8.0bn (DWP: £12.9bn), because pension-age Housing Benefit was about 1.6 times DWP's figure while working-age, temporary and supported-accommodation Housing Benefit remain largely unmodelled. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
+Calibrate Housing Benefit to DWP's Great Britain spending and claims by age group, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Pension-age Housing Benefit is calibrated to DWP's figures over Pension Credit qualifying age. Working-age Housing Benefit is calibrated to DWP's figures under that age less all supported and temporary accommodation Housing Benefit, which policyengine-uk does not model (£585m and 107k claims in 2025-26, against £5.78bn and 460k in total). Modelled GB Housing Benefit for 2025-26 falls from about £11.7bn to £8.5bn (DWP: £12.9bn), because pension-age Housing Benefit was about 1.6 times DWP's figure while about £5.2bn of supported and temporary accommodation Housing Benefit remains unmodelled. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.

From 33092bfa07f8c5fbe58600db56dc44ffeb2a4ae9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Sun, 4 Oct 2026 14:40:47 -0400
Subject: [PATCH 57/70] Format the Housing Benefit target tests

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index bc140334c..5267cd241 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -150,7 +150,6 @@ def test_working_age_general_needs_two_ways():
         assert caseload[year] < h._CASELOAD_THOUSANDS["under"][year]
 
 
-
 @settings(max_examples=200, deadline=None)
 @given(
     st.dictionaries(st.integers(2020, 2035), st.floats(0, 1e4), max_size=8),

From 9f18a3d7c6530697e7a0797878b76b1d0e7e49e4 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Mon, 5 Oct 2026 12:10:49 -0400
Subject: [PATCH 58/70] Don't carry the net working-age HB targets past 2025-26
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

The loss matrix fills a year a target doesn't list with the latest earlier
value within three years (_resolve_value). That carried the 2025-26 net
working-age Housing Benefit targets (£584.8m, 107k claims) into 2026-27 to
2028-29, where DWP's lines give a negative net figure. Targets gain a
carry_forward flag (default True, so nothing else changes), and the two net
targets set it to False. The build calibrates 2025 only, so the dataset is
unchanged; any loss matrix built for 2026-28 now leaves the net targets out.

Also reword the docstring: policyengine-uk has no supported or temporary
accommodation rules, but the FRS can't identify those residents, so a
reported award there is modelled under the general-needs rules and treating
the model's working-age HB as general needs is an approximation. The
unmodelled supported and temporary accommodation HB (£5.2bn, all ages) is
recorded as a limitation (d946). "Within 12%" becomes "about 12% below".

Tests: the net targets resolve in 2024-25 and 2025-26 only while the other
HB targets still carry forward; a property test of _resolve_value for any
values, year and flag.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../targets/build_loss_matrix.py              |  7 ++-
 policyengine_uk_data/targets/schema.py        |  4 ++
 .../targets/sources/dwp_housing_benefit.py    | 44 ++++++++------
 .../tests/test_dwp_housing_benefit_targets.py | 57 ++++++++++++++++++-
 4 files changed, 91 insertions(+), 21 deletions(-)

diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 44b34272c..7ac3c997d 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -137,11 +137,14 @@ def restrict_to_countries(column, household_country, countries):
 def _resolve_value(target: Target, year: int) -> float | None:
     """Get the target value for a year, falling back to nearest year.
 
-    VOA council tax targets are population-uprated when extrapolating
-    from their base year (2024).
+    A missing year takes the latest earlier value within three years, unless
+    the target sets ``carry_forward=False``. VOA council tax targets are
+    population-uprated when extrapolating from their base year (2024).
     """
     if year in target.values:
         return target.values[year]
+    if not target.carry_forward:
+        return None
     available = sorted(target.values.keys())
     if not available:
         return None
diff --git a/policyengine_uk_data/targets/schema.py b/policyengine_uk_data/targets/schema.py
index 0a678a228..512ee284e 100644
--- a/policyengine_uk_data/targets/schema.py
+++ b/policyengine_uk_data/targets/schema.py
@@ -50,6 +50,10 @@ class Target(BaseModel):
     # values of the model's `country` variable. The loss matrix column only
     # counts households in these countries. None means the whole UK.
     countries: tuple[str, ...] | None = None
+    # Whether the loss matrix may carry the latest earlier value forward to a
+    # later year the target does not list (see _resolve_value). False for a
+    # figure that exists only in the years it lists.
+    carry_forward: bool = True
 
     # For targets needing custom simulation logic (UC splits,
     # counterfactuals). Excluded from serialisation.
diff --git a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
index 4ed28076e..d822251d6 100644
--- a/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
+++ b/policyengine_uk_data/targets/sources/dwp_housing_benefit.py
@@ -30,29 +30,36 @@
 sit in its Pension Credit and State Pension groups.
 
 Working-age Housing Benefit is calibrated net of supported and temporary
-accommodation. policyengine-uk pays no Housing Benefit for specified
+accommodation. policyengine-uk has no rules or input for specified
 (supported) or temporary accommodation (``housing_benefit_eligible``: such
-claims "are not modelled"; policyengine-uk#1911). So every working-age award
-it pays is general-needs Housing Benefit: a continuing award to a family that
-reports it and does not claim Universal Credit. DWP's working-age line
-includes both kinds, but DWP splits accommodation type only across all ages
+claims "are not modelled"; policyengine-uk#1911). It pays working-age
+Housing Benefit only as a continuing award to a family that reports it and
+does not claim Universal Credit, computed under the general-needs rules. The
+FRS does not identify those accommodation types, so a respondent in them who
+reports Housing Benefit is modelled the same way; treating the model's
+working-age Housing Benefit as general needs is an approximation. DWP's
+Spring 2026 forecast tables split accommodation type only across all ages
 (Housing Benefit by Accomodation Type). The calibrated working-age figure is
 therefore DWP's working-age line less all of its supported and temporary
 accommodation: £584.8m and 107k claims in 2025-26, against the full line's
 £5,778.1m and 460k. Part of that accommodation is pension-age, so the figure
-is a lower bound on working-age general-needs Housing Benefit. It exists only
-for 2024-25 and 2025-26. From 2026-27, DWP's all-age supported and temporary
-accommodation exceeds its whole working-age line.
+is a lower bound on working-age general-needs Housing Benefit. It exists
+only for 2024-25 and 2025-26, and the targets do not carry it forward
+(``carry_forward=False``): from 2026-27, DWP's all-age supported and
+temporary accommodation exceeds its whole working-age line, so these lines
+give no positive lower bound. That leaves DWP's supported and temporary
+accommodation Housing Benefit, £5.2bn across all ages in 2025-26, unmodelled
+as such: a known limitation.
 
 Seeded test calibrations on 2026-10-04 tried the full working-age line too.
 With only the pension-age figures calibrated, the model pays about 17,000
-working-age claims in 2025-26. Calibrated to the full line, it came within
-12% of DWP's spending by loading it onto about three effective records. With
-household weights capped at 20-40 times their prior, it reached only 54-60%
-of that spending, and income-related ESA claimants rose to 1.6-2.0 times
-DWP's count. Against the net figure, no other national target crosses the
-10% line compared with calibrating the pension-age figures only. The full
-working-age lines stay here (group "under") for tests and diagnostics.
+working-age claims in 2025-26. Calibrated to the full line, it came about
+12% below DWP's spending by loading it onto about three effective records.
+With household weights capped at 20-40 times their prior, it reached only
+54-60% of that spending, and income-related ESA claimants rose to 1.6-2.0
+times DWP's count. Against the net figure, no other national target crosses
+the 10% line compared with calibrating the pension-age figures only. The
+full working-age lines stay here (group "under") for tests and diagnostics.
 
 Source: https://www.gov.uk/government/publications/benefit-expenditure-and-caseload-tables-2026
 """
@@ -228,8 +235,9 @@ def compute(ctx, target: Target, year: int) -> np.ndarray:
         )
         in_group = _over_pension_credit_age(ctx)
         if age_group != "over":
-            # The model pays no supported or temporary accommodation Housing
-            # Benefit, so both working-age groups share one column.
+            # The model has no supported or temporary accommodation rules,
+            # so both working-age groups share one column (see the module
+            # docstring).
             in_group = ~in_group
         value = (housing_benefit > 0) if count else housing_benefit
         return np.asarray(ctx.household_from_family(value * in_group), dtype=float)
@@ -261,6 +269,7 @@ def build_targets(
                 reference_url=_REFERENCE_URL,
                 forecast_vintage=_VINTAGE,
                 countries=GREAT_BRITAIN,
+                carry_forward=age_group != "under_general_needs",
                 custom_compute=_make_compute(age_group, count=False),
             )
         )
@@ -278,6 +287,7 @@ def build_targets(
                 reference_url=_REFERENCE_URL,
                 forecast_vintage=_VINTAGE,
                 countries=GREAT_BRITAIN,
+                carry_forward=age_group != "under_general_needs",
                 custom_compute=_make_compute(age_group, count=True),
             )
         )
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index 5267cd241..bc7b739b0 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -9,8 +9,11 @@
 from hypothesis import strategies as st
 
 from policyengine_uk_data.targets import get_all_targets
-from policyengine_uk_data.targets.build_loss_matrix import restrict_to_countries
-from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Unit
+from policyengine_uk_data.targets.build_loss_matrix import (
+    _resolve_value,
+    restrict_to_countries,
+)
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Target, Unit
 from policyengine_uk_data.targets.sources import dwp_housing_benefit, obr
 
 # Other rows of the same DWP Housing benefits sheet, transcribed separately:
@@ -150,6 +153,56 @@ def test_working_age_general_needs_two_ways():
         assert caseload[year] < h._CASELOAD_THOUSANDS["under"][year]
 
 
+def test_net_working_age_targets_are_not_carried_forward():
+    """The net figure is negative from 2026-27, so the loss matrix must not
+    reuse 2025-26's value for later years. Other HB targets still resolve."""
+    targets = _targets()
+    for suffix in ("", "_claims"):
+        net = targets[
+            f"dwp/housing_benefit/under_pension_credit_age_general_needs{suffix}"
+        ]
+        assert not net.carry_forward
+        assert _resolve_value(net, 2024) == net.values[2024]
+        assert _resolve_value(net, 2025) == net.values[2025]
+        for year in (2023, 2026, 2027, 2028):
+            assert _resolve_value(net, year) is None, year
+        for group in ("over", "under"):
+            target = targets[f"dwp/housing_benefit/{group}_pension_credit_age{suffix}"]
+            assert target.carry_forward
+            for year in (2026, 2027, 2028):
+                assert _resolve_value(target, year) == target.values[year]
+            assert _resolve_value(target, 2031) == target.values[2030]
+
+
+@settings(max_examples=300, deadline=None)
+@given(
+    st.dictionaries(
+        st.integers(2015, 2035), st.floats(-1e9, 1e9), min_size=1, max_size=8
+    ),
+    st.integers(2010, 2040),
+    st.booleans(),
+)
+def test_resolve_value_carries_forward_only_when_allowed(values, year, carry):
+    target = Target(
+        name="test/target",
+        variable="housing_benefit",
+        source="test",
+        unit=Unit.GBP,
+        values=values,
+        carry_forward=carry,
+    )
+    resolved = _resolve_value(target, year)
+    if year in values:
+        assert resolved == values[year]
+        return
+    # Nearest listed year; an earlier year wins a tie.
+    closest = min(sorted(values), key=lambda y: abs(y - year))
+    if carry and closest < year and year - closest <= 3:
+        assert resolved == values[closest]
+    else:
+        assert resolved is None
+
+
 @settings(max_examples=200, deadline=None)
 @given(
     st.dictionaries(st.integers(2020, 2035), st.floats(0, 1e4), max_size=8),

From 954c22508761b9f218e6c26f0c2e835e0534559a Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Mon, 5 Oct 2026 13:36:44 -0400
Subject: [PATCH 59/70] Say what the model lacks for supported and temporary
 accommodation; describe the year resolver exactly

The changelog and a test comment still said the model pays no supported or
temporary accommodation Housing Benefit. It has no rules for it, and the FRS
can't identify those residents, so treating its working-age HB as general
needs is an approximation. The resolver docstring and the carry_forward
comment now describe _resolve_value as it works: the nearest listed year,
the earlier on a tie, used only if it is earlier and at most three years
away.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/hb-dwp-age-targets.changed.md                   | 2 +-
 policyengine_uk_data/targets/build_loss_matrix.py           | 5 +++--
 policyengine_uk_data/targets/schema.py                      | 6 +++---
 .../tests/test_dwp_housing_benefit_targets.py               | 2 +-
 4 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/changelog.d/hb-dwp-age-targets.changed.md b/changelog.d/hb-dwp-age-targets.changed.md
index c98ce26b4..c36ead1fc 100644
--- a/changelog.d/hb-dwp-age-targets.changed.md
+++ b/changelog.d/hb-dwp-age-targets.changed.md
@@ -1 +1 @@
-Calibrate Housing Benefit to DWP's Great Britain spending and claims by age group, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Pension-age Housing Benefit is calibrated to DWP's figures over Pension Credit qualifying age. Working-age Housing Benefit is calibrated to DWP's figures under that age less all supported and temporary accommodation Housing Benefit, which policyengine-uk does not model (£585m and 107k claims in 2025-26, against £5.78bn and 460k in total). Modelled GB Housing Benefit for 2025-26 falls from about £11.7bn to £8.5bn (DWP: £12.9bn), because pension-age Housing Benefit was about 1.6 times DWP's figure while about £5.2bn of supported and temporary accommodation Housing Benefit remains unmodelled. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
+Calibrate Housing Benefit to DWP's Great Britain spending and claims by age group, replacing the OBR Housing Benefit target that the model compared with UK-wide Housing Benefit. Pension-age Housing Benefit is calibrated to DWP's figures over Pension Credit qualifying age. Working-age Housing Benefit is calibrated to DWP's figures under that age less all supported and temporary accommodation Housing Benefit, for which policyengine-uk has no rules (£585m and 107k claims in 2025-26, against £5.78bn and 460k in total); treating the model's working-age Housing Benefit as general needs is an approximation. Modelled GB Housing Benefit for 2025-26 falls from about £11.7bn to £8.5bn (DWP: £12.9bn), because pension-age Housing Benefit was about 1.6 times DWP's figure while about £5.2bn of supported and temporary accommodation Housing Benefit remains unmodelled as such. Stop setting `would_claim_uc` for benefit units whose adults have all reached State Pension age, and build with policyengine-uk 2.102.5 so pension-age families can make new Housing Benefit claims.
diff --git a/policyengine_uk_data/targets/build_loss_matrix.py b/policyengine_uk_data/targets/build_loss_matrix.py
index 7ac3c997d..3942df2df 100644
--- a/policyengine_uk_data/targets/build_loss_matrix.py
+++ b/policyengine_uk_data/targets/build_loss_matrix.py
@@ -137,8 +137,9 @@ def restrict_to_countries(column, household_country, countries):
 def _resolve_value(target: Target, year: int) -> float | None:
     """Get the target value for a year, falling back to nearest year.
 
-    A missing year takes the latest earlier value within three years, unless
-    the target sets ``carry_forward=False``. VOA council tax targets are
+    A missing year takes the value of the nearest listed year (the earlier
+    on a tie) if that year is earlier and at most three years away, unless
+    the target sets ``carry_forward=False``; otherwise there is no value. VOA council tax targets are
     population-uprated when extrapolating from their base year (2024).
     """
     if year in target.values:
diff --git a/policyengine_uk_data/targets/schema.py b/policyengine_uk_data/targets/schema.py
index 512ee284e..73c584e2d 100644
--- a/policyengine_uk_data/targets/schema.py
+++ b/policyengine_uk_data/targets/schema.py
@@ -50,9 +50,9 @@ class Target(BaseModel):
     # values of the model's `country` variable. The loss matrix column only
     # counts households in these countries. None means the whole UK.
     countries: tuple[str, ...] | None = None
-    # Whether the loss matrix may carry the latest earlier value forward to a
-    # later year the target does not list (see _resolve_value). False for a
-    # figure that exists only in the years it lists.
+    # Whether the loss matrix may fill a year the target does not list with
+    # the nearest earlier listed year's value (see _resolve_value). False for
+    # a figure that exists only in the years it lists.
     carry_forward: bool = True
 
     # For targets needing custom simulation logic (UC splits,
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index bc7b739b0..4dccafd55 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -366,7 +366,7 @@ def test_age_split_partitions_housing_benefit(population):
     under = _column(ctx, "dwp/housing_benefit/under_pension_credit_age")
     over_k = _column(ctx, "dwp/housing_benefit/over_pension_credit_age_claims")
     under_k = _column(ctx, "dwp/housing_benefit/under_pension_credit_age_claims")
-    # The model pays no supported or temporary accommodation HB, so the
+    # The model has no supported or temporary accommodation rules, so the
     # calibrated working-age group shares the full line's column.
     np.testing.assert_array_equal(
         _column(ctx, "dwp/housing_benefit/under_pension_credit_age_general_needs"),

From f4d8f95c512ad47b902c2a2bb9c12f8853cce7b8 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Mon, 5 Oct 2026 13:42:15 -0400
Subject: [PATCH 60/70] Describe the year resolver exactly in the carry_forward
 comment

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 policyengine_uk_data/targets/schema.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/policyengine_uk_data/targets/schema.py b/policyengine_uk_data/targets/schema.py
index 73c584e2d..ebdaba9d0 100644
--- a/policyengine_uk_data/targets/schema.py
+++ b/policyengine_uk_data/targets/schema.py
@@ -51,8 +51,9 @@ class Target(BaseModel):
     # counts households in these countries. None means the whole UK.
     countries: tuple[str, ...] | None = None
     # Whether the loss matrix may fill a year the target does not list with
-    # the nearest earlier listed year's value (see _resolve_value). False for
-    # a figure that exists only in the years it lists.
+    # the nearest listed year's value (the earlier on a tie), used only when
+    # that year is earlier and at most three years away (see _resolve_value).
+    # False for a figure that exists only in the years it lists.
     carry_forward: bool = True
 
     # For targets needing custom simulation logic (UC splits,

From a7495fc3bd9c90b105120beb31a53f48d220314b Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 10:07:37 -0400
Subject: [PATCH 61/70] Solve Pension Credit take-up over entitled benefit
 units

The FRS build drew would_claim_pc against an unweighted count of every
benefit unit, so entitled non-reporters claimed at about the take-up rate
on top of every reporter. PolicyEngine/uk-triple-lock's coverage run
(data/results.json, Enhanced FRS 2024-25 on policyengine-uk 2.90.2) put
2026-27 Pension Credit claims at 1.82m UK-wide, against DWP's 1.31m for
Great Britain.

After the imputations, compute Pension Credit entitlement and solve the
claim probability that makes weighted take-up among entitled units equal
DWP's FYE 2024 caseload take-up (62%). Reporters claim. Every non-reporter,
entitled or not, is drawn at that probability, so units a reform makes
entitled claim at the same rate.

Replace the OBR Pension Credit target with DWP Spring 2026 Great Britain
spending and caseload targets.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/pc-takeup-over-eligible.fixed.md  |   1 +
 .../datasets/create_datasets.py               |  11 ++
 policyengine_uk_data/datasets/frs.py          |   2 +
 .../datasets/pension_credit_takeup.py         |  51 +++++++
 .../parameters/take_up/pension_credit.yaml    |   8 +-
 .../targets/sources/dwp_pension_credit.py     |  89 ++++++++++++
 policyengine_uk_data/targets/sources/obr.py   |   4 +-
 .../tests/test_dwp_housing_benefit_targets.py |   4 +-
 .../tests/test_pension_credit_takeup.py       | 136 ++++++++++++++++++
 policyengine_uk_data/utils/takeup.py          |  45 ++++++
 10 files changed, 346 insertions(+), 5 deletions(-)
 create mode 100644 changelog.d/pc-takeup-over-eligible.fixed.md
 create mode 100644 policyengine_uk_data/datasets/pension_credit_takeup.py
 create mode 100644 policyengine_uk_data/targets/sources/dwp_pension_credit.py
 create mode 100644 policyengine_uk_data/tests/test_pension_credit_takeup.py

diff --git a/changelog.d/pc-takeup-over-eligible.fixed.md b/changelog.d/pc-takeup-over-eligible.fixed.md
new file mode 100644
index 000000000..9e1605115
--- /dev/null
+++ b/changelog.d/pc-takeup-over-eligible.fixed.md
@@ -0,0 +1 @@
+Solve Pension Credit take-up over entitled benefit units, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter is drawn at the same probability, so units a reform newly entitles can claim. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
diff --git a/policyengine_uk_data/datasets/create_datasets.py b/policyengine_uk_data/datasets/create_datasets.py
index 29a73028b..a9707c548 100644
--- a/policyengine_uk_data/datasets/create_datasets.py
+++ b/policyengine_uk_data/datasets/create_datasets.py
@@ -100,6 +100,7 @@ def main():
             "Impute capital gains",
             "Impute salary sacrifice",
             "Impute student loan plan",
+            "Assign Pension Credit take-up",
             "Clone and assign OA geography",
             "Calibrate constituency weights",
             "Calibrate local authority weights",
@@ -202,6 +203,16 @@ def main():
             )
             update_dataset("Impute student loan plan", "completed")
 
+            # Pension Credit entitlement needs the imputed capital, so its
+            # take-up is solved here rather than in the FRS build.
+            update_dataset("Assign Pension Credit take-up", "processing")
+            from policyengine_uk_data.datasets.pension_credit_takeup import (
+                assign_pension_credit_takeup,
+            )
+
+            frs = assign_pension_credit_takeup(frs, year=frs_release.calibration_year)
+            update_dataset("Assign Pension Credit take-up", "completed")
+
             # Clone households and assign OA geography
             update_dataset("Clone and assign OA geography", "processing")
             from policyengine_uk_data.calibration.clone_and_assign import (
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
index 6b5b13b27..51e980ba0 100644
--- a/policyengine_uk_data/datasets/frs.py
+++ b/policyengine_uk_data/datasets/frs.py
@@ -1565,6 +1565,8 @@ def _reported_benunit_mask(person_column: str) -> np.ndarray:
     pe_benunit["child_benefit_opts_out"] = (
         generator.random(len(pe_benunit)) < child_benefit_opts_out_rate
     )
+    # The enhanced dataset redraws this once entitlement can be computed
+    # (datasets/pension_credit_takeup.py).
     pe_benunit["would_claim_pc"] = assign_takeup_with_reported_anchors(
         generator.random(len(pe_benunit)),
         pension_credit_rate,
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
new file mode 100644
index 000000000..0c144a541
--- /dev/null
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -0,0 +1,51 @@
+"""Pension Credit take-up solved over entitled benefit units.
+
+The FRS build first draws ``would_claim_pc`` against an unweighted count of
+every benefit unit, mostly units with no entitlement. Entitled non-reporters
+then claim at about the take-up rate on top of every reporter, so take-up
+among the entitled ends up well above it. This step redraws the flag once
+the imputations are in; entitlement depends on imputed capital, so it has
+to wait for them.
+
+For the calibration year it:
+- finds the benefit units with positive Pension Credit entitlement;
+- keeps reporters of Pension Credit as claimants;
+- solves the probability that makes weighted take-up among entitled units
+  equal DWP's caseload take-up rate;
+- applies that probability to every non-reporter, entitled or not, so a
+  unit that a reform makes entitled claims at the same rate.
+
+Weights are the survey grossing weights, before calibration.
+"""
+
+import numpy as np
+
+from policyengine_uk_data.parameters import load_take_up_rate
+from policyengine_uk_data.utils.takeup import assign_takeup_over_eligible
+
+# Separate from the FRS build's take-up generator, so redrawing here leaves
+# every other stochastic input unchanged.
+PENSION_CREDIT_TAKEUP_SEED = 1_792
+
+
+def assign_pension_credit_takeup(dataset, year: int):
+    """Return a copy of ``dataset`` with ``would_claim_pc`` solved for ``year``."""
+    from policyengine_uk import Microsimulation
+
+    sim = Microsimulation(dataset=dataset)
+    entitled = sim.calculate("pension_credit_entitlement", year).values > 0
+    reported = (
+        sim.calculate("pension_credit_reported", year, map_to="benunit").values > 0
+    )
+    weights = sim.calculate("benunit_weight", year).values
+    draws = np.random.default_rng(PENSION_CREDIT_TAKEUP_SEED).random(len(entitled))
+
+    dataset = dataset.copy()
+    dataset.benunit["would_claim_pc"] = assign_takeup_over_eligible(
+        draws,
+        load_take_up_rate("pension_credit", year),
+        weights,
+        entitled,
+        reported,
+    )
+    return dataset
diff --git a/policyengine_uk_data/parameters/take_up/pension_credit.yaml b/policyengine_uk_data/parameters/take_up/pension_credit.yaml
index dc31d8502..3abd278d4 100644
--- a/policyengine_uk_data/parameters/take_up/pension_credit.yaml
+++ b/policyengine_uk_data/parameters/take_up/pension_credit.yaml
@@ -1,4 +1,4 @@
-description: Share of eligible Pension Credit recipients that participate.
+description: Share of benefit units entitled to Pension Credit that receive it (caseload take-up, Great Britain).
 metadata:
   label: Pension Credit take-up rate
   name: PC_takeup
@@ -6,5 +6,11 @@ metadata:
   reference:
     - title: "Income-related benefits: estimates of take-up: financial year 2019 to 2020"
       href: https://www.gov.uk/government/statistics/income-related-benefits-estimates-of-take-up-financial-year-2019-to-2020/income-related-benefits-estimates-of-take-up-financial-year-2019-to-2020#pension-credit-2
+    - title: "Income-related benefits: estimates of take-up: financial year ending 2024"
+      href: https://www.gov.uk/government/statistics/income-related-benefits-estimates-of-take-up-financial-year-ending-2024/income-related-benefits-estimates-of-take-up-financial-year-ending-2024
 values:
   2015-01-01: 0.7
+  # FYE 2023 and FYE 2024 caseload take-up (65% and 62%) from the FYE 2024
+  # edition; expenditure take-up was 71% in FYE 2024.
+  2022-04-01: 0.65
+  2023-04-01: 0.62
diff --git a/policyengine_uk_data/targets/sources/dwp_pension_credit.py b/policyengine_uk_data/targets/sources/dwp_pension_credit.py
new file mode 100644
index 000000000..092a77fc6
--- /dev/null
+++ b/policyengine_uk_data/targets/sources/dwp_pension_credit.py
@@ -0,0 +1,89 @@
+"""DWP Pension Credit targets: spending and claims in Great Britain.
+
+From DWP's benefit expenditure and caseload tables for the Spring Forecast
+2026 (Pension Credit sheet: nominal £ million and thousands of claims,
+2022-23 to 2024-25 outturn, forecast after). Financial year 2025-26 is
+stored as 2025.
+
+DWP's figures cover Great Britain; Pension Credit in Northern Ireland is
+paid by the Department for Communities, so the model columns count GB
+households only. These targets replace OBR EFO table 4.9 "Pension credit",
+the same DWP spending line, which the model compared with UK-wide Pension
+Credit and which carries no caseload.
+
+Source: https://www.gov.uk/government/publications/benefit-expenditure-and-caseload-tables-2026
+"""
+
+import numpy as np
+
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Target, Unit
+
+_REFERENCE_URL = (
+    "https://www.gov.uk/government/publications/"
+    "benefit-expenditure-and-caseload-tables-2026"
+)
+_VINTAGE = "spring_2026"
+
+# "Total Pension Credit", £ million nominal.
+_SPENDING_GBP_M = {
+    2022: 4_935.31,
+    2023: 5_467.14,
+    2024: 6_007.70,
+    2025: 6_144.47,
+    2026: 6_054.90,
+    2027: 5_911.31,
+    2028: 5_783.32,
+    2029: 5_832.22,
+    2030: 5_821.77,
+}
+# "Pension Credit" caseload, thousands (annual average, rounded by DWP).
+_CASELOAD_THOUSANDS = {
+    2022: 1_374,
+    2023: 1_370,
+    2024: 1_376,
+    2025: 1_382,
+    2026: 1_312,
+    2027: 1_253,
+    2028: 1_188,
+    2029: 1_142,
+    2030: 1_133,
+}
+
+
+def _make_compute(count: bool):
+    def compute(ctx, target: Target, year: int) -> np.ndarray:
+        pension_credit = np.asarray(
+            ctx.sim.calculate("pension_credit").values, dtype=float
+        )
+        value = (pension_credit > 0) if count else pension_credit
+        return np.asarray(ctx.household_from_family(value), dtype=float)
+
+    return compute
+
+
+def get_targets() -> list[Target]:
+    return [
+        Target(
+            name="dwp/pension_credit",
+            variable="pension_credit",
+            source="dwp",
+            unit=Unit.GBP,
+            values={year: v * 1e6 for year, v in _SPENDING_GBP_M.items()},
+            reference_url=_REFERENCE_URL,
+            forecast_vintage=_VINTAGE,
+            countries=GREAT_BRITAIN,
+            custom_compute=_make_compute(count=False),
+        ),
+        Target(
+            name="dwp/pension_credit_claims",
+            variable="pension_credit",
+            source="dwp",
+            unit=Unit.COUNT,
+            values={year: v * 1e3 for year, v in _CASELOAD_THOUSANDS.items()},
+            is_count=True,
+            reference_url=_REFERENCE_URL,
+            forecast_vintage=_VINTAGE,
+            countries=GREAT_BRITAIN,
+            custom_compute=_make_compute(count=True),
+        ),
+    ]
diff --git a/policyengine_uk_data/targets/sources/obr.py b/policyengine_uk_data/targets/sources/obr.py
index bb4fc0ce6..3d88e3d48 100644
--- a/policyengine_uk_data/targets/sources/obr.py
+++ b/policyengine_uk_data/targets/sources/obr.py
@@ -484,7 +484,8 @@ def read_49(row_num: int) -> dict[int, float]:
 
     # "Housing benefit (not on JSA)" is not targeted: it is DWP-funded GB
     # spending only, and dwp_housing_benefit.py targets total GB Housing
-    # Benefit split by age instead.
+    # Benefit split by age instead. "Pension credit" is the same DWP GB line
+    # that dwp_pension_credit.py targets, with its caseload.
     benefit_rows = {
         "pip": (
             "Disability living allowance and personal independence p",
@@ -495,7 +496,6 @@ def read_49(row_num: int) -> dict[int, float]:
             "Attendance allowance",
             "attendance_allowance",
         ),
-        "pension_credit": ("Pension credit", "pension_credit"),
         "carers_allowance": ("Carer's allowance", "carers_allowance"),
         "statutory_maternity_pay": (
             "Statutory maternity pay",
diff --git a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
index 4dccafd55..53cd1c46e 100644
--- a/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
+++ b/policyengine_uk_data/tests/test_dwp_housing_benefit_targets.py
@@ -229,12 +229,12 @@ def test_obr_housing_benefit_row_is_not_parsed():
     ws = wb.active
     ws.title = "4.9"
     ws["B2"] = "Housing benefit (not on JSA)1"
-    ws["B3"] = "Pension credit"
+    ws["B3"] = "Attendance allowance"
     for row in (2, 3):
         for col in "CDEFGHI":
             ws[f"{col}{row}"] = 6.0
     names = {t.name for t in obr._parse_welfare(wb)}
-    assert "obr/pension_credit" in names
+    assert "obr/attendance_allowance" in names
     assert not any("housing_benefit" in name for name in names)
 
 
diff --git a/policyengine_uk_data/tests/test_pension_credit_takeup.py b/policyengine_uk_data/tests/test_pension_credit_takeup.py
new file mode 100644
index 000000000..794fd9e2e
--- /dev/null
+++ b/policyengine_uk_data/tests/test_pension_credit_takeup.py
@@ -0,0 +1,136 @@
+"""Pension Credit take-up solved over entitled units, and DWP targets."""
+
+import numpy as np
+import openpyxl
+from hypothesis import given, settings
+from hypothesis import strategies as st
+
+from policyengine_uk_data.parameters import load_take_up_rate
+from policyengine_uk_data.targets import get_all_targets
+from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Unit
+from policyengine_uk_data.targets.sources import dwp_pension_credit, obr
+from policyengine_uk_data.utils.takeup import (
+    assign_takeup_over_eligible,
+    solve_fill_probability,
+)
+
+_units = st.lists(
+    st.tuples(st.floats(0, 5_000), st.booleans(), st.booleans()),
+    min_size=1,
+    max_size=60,
+)
+
+
+@settings(max_examples=300, deadline=None)
+@given(_units, st.floats(0, 1))
+def test_fill_probability_meets_the_rate_or_hits_a_bound(units, rate):
+    weights = np.array([u[0] for u in units])
+    eligible = np.array([u[1] for u in units])
+    reported = np.array([u[2] for u in units])
+    p = solve_fill_probability(rate, weights, eligible, reported)
+    assert 0 <= p <= 1
+    target = rate * weights[eligible].sum()
+    reporting = weights[eligible & reported].sum()
+    remaining = weights[eligible & ~reported].sum()
+    expected = reporting + p * remaining
+    if remaining <= 0:
+        assert p == 0
+    elif 0 < p < 1:
+        assert np.isclose(expected, target, rtol=1e-9, atol=1e-6)
+    elif p == 0:
+        assert reporting >= target - 1e-6
+    else:
+        assert reporting + remaining <= target + 1e-6
+
+
+@settings(max_examples=200, deadline=None)
+@given(_units, st.floats(0, 1), st.integers(0, 2**32 - 1))
+def test_reporters_claim_and_every_non_reporter_shares_one_probability(
+    units, rate, seed
+):
+    weights = np.array([u[0] for u in units])
+    eligible = np.array([u[1] for u in units])
+    reported = np.array([u[2] for u in units])
+    draws = np.random.default_rng(seed).random(len(units))
+    result = assign_takeup_over_eligible(draws, rate, weights, eligible, reported)
+    p = solve_fill_probability(rate, weights, eligible, reported)
+    assert result[reported].all()
+    # Eligibility does not change a non-reporter's flag, so a unit a reform
+    # makes entitled claims at the same rate.
+    np.testing.assert_array_equal(result[~reported], (draws < p)[~reported])
+
+
+def test_weighted_take_up_among_eligible_matches_rate():
+    rng = np.random.default_rng(0)
+    n = 200_000
+    weights = rng.uniform(100, 3_000, n)
+    eligible = rng.random(n) < 0.2
+    reported = eligible & (rng.random(n) < 0.4)
+    result = assign_takeup_over_eligible(
+        rng.random(n), 0.62, weights, eligible, reported
+    )
+    take_up = weights[eligible & result].sum() / weights[eligible].sum()
+    assert abs(take_up - 0.62) < 0.005
+    # Non-entitled units are drawn at the same probability as entitled ones.
+    p = solve_fill_probability(0.62, weights, eligible, reported)
+    assert abs(result[~eligible].mean() - p) < 0.01
+
+
+def test_rate_is_dwp_fye_2024_caseload_take_up():
+    assert load_take_up_rate("pension_credit", 2025) == 0.62
+
+
+def test_targets_reconcile_with_dwp_components():
+    """Guarantee + savings credit spending and the three claim types sum to
+    the transcribed totals (DWP Pension Credit sheet, separate rows)."""
+    guarantee = {2022: 4_594.32, 2025: 5_787.44, 2030: 5_543.38}
+    savings = {2022: 340.99, 2025: 357.03, 2030: 278.38}
+    claims = {2022: (727, 448, 199), 2025: (807, 399, 176), 2030: (734, 285, 114)}
+    targets = {t.name: t for t in dwp_pension_credit.get_targets()}
+    spending = targets["dwp/pension_credit"]
+    caseload = targets["dwp/pension_credit_claims"]
+    for year in guarantee:
+        assert abs(spending.values[year] / 1e6 - guarantee[year] - savings[year]) < 0.02
+        assert abs(caseload.values[year] / 1e3 - sum(claims[year])) <= 1
+    for target in targets.values():
+        assert target.countries == GREAT_BRITAIN
+        assert target.unit == (Unit.COUNT if target.is_count else Unit.GBP)
+
+
+def test_pension_credit_is_targeted_only_by_dwp():
+    names = {t.name for t in get_all_targets() if t.variable == "pension_credit"}
+    assert names == {"dwp/pension_credit", "dwp/pension_credit_claims"}
+
+
+def test_obr_pension_credit_row_is_not_parsed():
+    wb = openpyxl.Workbook()
+    ws = wb.active
+    ws.title = "4.9"
+    ws["B2"] = "Pension credit"
+    for col in "CDEFGHI":
+        ws[f"{col}2"] = 6.0
+    assert not obr._parse_welfare(wb)
+
+
+def test_built_dataset_reporters_claim_and_caseload_is_near_dwp(baseline):
+    from policyengine_uk_data.targets.build_loss_matrix import (
+        _SimContext,
+        restrict_to_countries,
+    )
+
+    year = 2025
+    reported = (
+        baseline.calculate("pension_credit_reported", year, map_to="benunit").values > 0
+    )
+    would_claim = baseline.calculate("would_claim_pc", year).values.astype(bool)
+    assert would_claim[reported].all()
+
+    baseline.default_calculation_period = str(year)
+    ctx = _SimContext(baseline, str(year), None, None)
+    weight = baseline.calculate("household_weight", year).values
+    for target in dwp_pension_credit.get_targets():
+        column = restrict_to_countries(
+            target.custom_compute(ctx, target, year), ctx.country, target.countries
+        )
+        ratio = (column * weight).sum() / target.values[year]
+        assert 0.5 < ratio < 2, (target.name, ratio)
diff --git a/policyengine_uk_data/utils/takeup.py b/policyengine_uk_data/utils/takeup.py
index ab5f6241c..24e66cdd7 100644
--- a/policyengine_uk_data/utils/takeup.py
+++ b/policyengine_uk_data/utils/takeup.py
@@ -57,3 +57,48 @@ def assign_takeup_with_reported_anchors(
     adjusted_rate = remaining_needed / int(non_reporters.sum())
     result |= non_reporters & (draws < adjusted_rate)
     return result
+
+
+def solve_fill_probability(
+    rate: float,
+    weights: np.ndarray,
+    eligible: np.ndarray,
+    reported: np.ndarray,
+) -> float:
+    """Claim probability for eligible non-reporters that makes weighted
+    take-up among eligible entities equal ``rate`` in expectation.
+
+    Reporters always claim. If they alone exceed ``rate`` the probability is
+    0; if every eligible non-reporter claiming still falls short it is 1.
+    """
+    weights = np.asarray(weights, dtype=np.float64)
+    eligible = np.asarray(eligible, dtype=bool)
+    reported = np.asarray(reported, dtype=bool)
+    eligible_weight = weights[eligible].sum()
+    reporting_weight = weights[eligible & reported].sum()
+    remaining_weight = weights[eligible & ~reported].sum()
+    if remaining_weight <= 0:
+        return 0.0
+    needed = float(rate) * eligible_weight - reporting_weight
+    # A vanishing remaining weight sends the ratio to infinity; clip handles it.
+    with np.errstate(over="ignore", divide="ignore"):
+        probability = needed / remaining_weight
+    return float(np.clip(probability, 0.0, 1.0))
+
+
+def assign_takeup_over_eligible(
+    draws: np.ndarray,
+    rate: float,
+    weights: np.ndarray,
+    eligible: np.ndarray,
+    reported: np.ndarray,
+) -> np.ndarray:
+    """Take-up flags with ``rate`` met over eligible entities.
+
+    Reporters claim. Every non-reporter, eligible or not, claims with the
+    probability ``solve_fill_probability`` finds for eligible non-reporters,
+    so a unit a reform makes newly eligible claims at the same rate.
+    """
+    probability = solve_fill_probability(rate, weights, eligible, reported)
+    reported = np.asarray(reported, dtype=bool)
+    return reported | (np.asarray(draws, dtype=np.float64) < probability)

From 4ceeb5ce89226f166fccae7825840237dd34c158 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 13:59:25 -0400
Subject: [PATCH 62/70] Report Pension Credit take-up aggregates, including the
 GB reporter baseline

The take-up step returns weighted aggregates for the calibration year: the
solved probability, entitled units and reporters, and the Great Britain
reporter baseline before any fill, which the build's summary prints.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/create_datasets.py               |  5 +-
 .../datasets/pension_credit_takeup.py         | 70 +++++++++++++++----
 2 files changed, 60 insertions(+), 15 deletions(-)

diff --git a/policyengine_uk_data/datasets/create_datasets.py b/policyengine_uk_data/datasets/create_datasets.py
index a9707c548..881992da5 100644
--- a/policyengine_uk_data/datasets/create_datasets.py
+++ b/policyengine_uk_data/datasets/create_datasets.py
@@ -210,7 +210,9 @@ def main():
                 assign_pension_credit_takeup,
             )
 
-            frs = assign_pension_credit_takeup(frs, year=frs_release.calibration_year)
+            frs, pension_credit_takeup = assign_pension_credit_takeup(
+                frs, year=frs_release.calibration_year
+            )
             update_dataset("Assign Pension Credit take-up", "completed")
 
             # Clone households and assign OA geography
@@ -408,6 +410,7 @@ def main():
                 "long_geography_weights": "local_geography_weights.csv.gz",
                 "imputations_applied": "consumption, wealth, VAT, services, income, capital_gains, cgt_band_donors, salary_sacrifice, student_loan_plan",
                 "calibration": "national, LA and  constituency targets",
+                "pension_credit_take_up": pension_credit_takeup,
             },
         )
 
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
index 0c144a541..f106ff439 100644
--- a/policyengine_uk_data/datasets/pension_credit_takeup.py
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -21,7 +21,10 @@
 import numpy as np
 
 from policyengine_uk_data.parameters import load_take_up_rate
-from policyengine_uk_data.utils.takeup import assign_takeup_over_eligible
+from policyengine_uk_data.utils.takeup import (
+    assign_takeup_over_eligible,
+    solve_fill_probability,
+)
 
 # Separate from the FRS build's take-up generator, so redrawing here leaves
 # every other stochastic input unchanged.
@@ -29,23 +32,62 @@
 
 
 def assign_pension_credit_takeup(dataset, year: int):
-    """Return a copy of ``dataset`` with ``would_claim_pc`` solved for ``year``."""
+    """Return a copy of ``dataset`` with ``would_claim_pc`` solved for
+    ``year``, and a summary of weighted aggregates.
+
+    The summary's Great Britain reporter baseline (reporters only, before
+    any fill) is what DWP's GB targets are compared with: a fill can only
+    add claimants, so if reporters alone exceed DWP's caseload the fill is
+    zero and only calibration can bring the total down.
+    """
     from policyengine_uk import Microsimulation
 
     sim = Microsimulation(dataset=dataset)
-    entitled = sim.calculate("pension_credit_entitlement", year).values > 0
-    reported = (
-        sim.calculate("pension_credit_reported", year, map_to="benunit").values > 0
-    )
+    entitlement = sim.calculate("pension_credit_entitlement", year).values
+    entitled = entitlement > 0
+    reported_amount = sim.calculate(
+        "pension_credit_reported", year, map_to="benunit"
+    ).values
+    reported = reported_amount > 0
     weights = sim.calculate("benunit_weight", year).values
+    country = sim.calculate("country", year).values
+    in_ni = (
+        sim.map_result(
+            sim.map_result(
+                (country == "NORTHERN_IRELAND").astype(float), "household", "person"
+            ),
+            "person",
+            "benunit",
+        )
+        > 0
+    )
+    rate = load_take_up_rate("pension_credit", year)
+    probability = solve_fill_probability(rate, weights, entitled, reported)
     draws = np.random.default_rng(PENSION_CREDIT_TAKEUP_SEED).random(len(entitled))
 
     dataset = dataset.copy()
-    dataset.benunit["would_claim_pc"] = assign_takeup_over_eligible(
-        draws,
-        load_take_up_rate("pension_credit", year),
-        weights,
-        entitled,
-        reported,
-    )
-    return dataset
+    claims = assign_takeup_over_eligible(draws, rate, weights, entitled, reported)
+    dataset.benunit["would_claim_pc"] = claims
+
+    gb = ~in_ni
+
+    def total(values, mask, scale):
+        return round(float((weights * values)[mask].sum()) / scale, 3)
+
+    summary = {
+        "year": year,
+        "rate": rate,
+        "fill_probability": round(probability, 4),
+        "entitled_k": total(1, entitled, 1e3),
+        "reporters_k": total(1, reported, 1e3),
+        "entitled_reporters_k": total(1, entitled & reported, 1e3),
+        "gb_reporters_k": total(1, gb & reported, 1e3),
+        "gb_entitled_reporters_k": total(1, gb & entitled & reported, 1e3),
+        "gb_reported_pension_credit_bn": total(reported_amount, gb, 1e9),
+        "gb_reporters_modelled_pension_credit_bn": total(
+            entitlement, gb & reported, 1e9
+        ),
+        "gb_claims_after_fill_k": total(1, gb & entitled & claims, 1e3),
+        "gb_pension_credit_after_fill_bn": total(entitlement, gb & claims, 1e9),
+    }
+    return dataset, summary

From be2645d63c081384944479a150ebaab1d99ef540 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 09:34:38 -0400
Subject: [PATCH 63/70] Don't anchor Pension Credit take-up on SPI-synthetic
 reporters

The Pension Credit reports on SPI-synthetic households are imputed by the
second-stage QRF (imputations/frs_only.py) from their SPI incomes, not
observed claims. In build D, 154 such benefit units reported Pension Credit
and were all anchored as claimants. They are now drawn at the solved
probability like any non-reporter.

Most SPI-synthetic households have zero survey weight when the probability
is solved. The capital-gains band donors (imputations/capital_gains.py) are
copies of sampled households re-weighted to HMRC taxpayer counts, so a copy
of an SPI-synthetic household keeps the flag and has positive weight. The
solved probability still does not move in build D', because none of its
SPI-synthetic reporters is entitled.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/pc-takeup-over-eligible.fixed.md  |  2 +-
 .../datasets/pension_credit_takeup.py         | 25 +++++++++++++++--
 .../tests/test_pension_credit_takeup.py       | 28 +++++++++++++++++++
 3 files changed, 52 insertions(+), 3 deletions(-)

diff --git a/changelog.d/pc-takeup-over-eligible.fixed.md b/changelog.d/pc-takeup-over-eligible.fixed.md
index 9e1605115..58490defd 100644
--- a/changelog.d/pc-takeup-over-eligible.fixed.md
+++ b/changelog.d/pc-takeup-over-eligible.fixed.md
@@ -1 +1 @@
-Solve Pension Credit take-up over entitled benefit units, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter is drawn at the same probability, so units a reform newly entitles can claim. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
+Solve Pension Credit take-up over entitled benefit units, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which carry SPI-imputed incomes, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
index f106ff439..2419022fd 100644
--- a/policyengine_uk_data/datasets/pension_credit_takeup.py
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -9,7 +9,9 @@
 
 For the calibration year it:
 - finds the benefit units with positive Pension Credit entitlement;
-- keeps reporters of Pension Credit as claimants;
+- keeps reporters of Pension Credit as claimants, except on SPI-synthetic
+  households, which copy an FRS household's benefit reports but replace its
+  incomes with SPI-imputed ones;
 - solves the probability that makes weighted take-up among entitled units
   equal DWP's caseload take-up rate;
 - applies that probability to every non-reporter, entitled or not, so a
@@ -31,6 +33,21 @@
 PENSION_CREDIT_TAKEUP_SEED = 1_792
 
 
+def spi_synthetic_benunits(dataset) -> np.ndarray:
+    """Benefit units in households flagged ``household_is_spi_synthetic``."""
+    household = dataset.household
+    if "household_is_spi_synthetic" not in household.columns:
+        return np.zeros(len(dataset.benunit), dtype=bool)
+    synthetic = household.loc[
+        household["household_is_spi_synthetic"].astype(bool), "household_id"
+    ]
+    person = dataset.person
+    benunits = person.loc[
+        person["person_household_id"].isin(synthetic), "person_benunit_id"
+    ]
+    return dataset.benunit["benunit_id"].isin(benunits).to_numpy()
+
+
 def assign_pension_credit_takeup(dataset, year: int):
     """Return a copy of ``dataset`` with ``would_claim_pc`` solved for
     ``year``, and a summary of weighted aggregates.
@@ -48,7 +65,8 @@ def assign_pension_credit_takeup(dataset, year: int):
     reported_amount = sim.calculate(
         "pension_credit_reported", year, map_to="benunit"
     ).values
-    reported = reported_amount > 0
+    spi_synthetic = spi_synthetic_benunits(dataset)
+    reported = (reported_amount > 0) & ~spi_synthetic
     weights = sim.calculate("benunit_weight", year).values
     country = sim.calculate("country", year).values
     in_ni = (
@@ -80,6 +98,9 @@ def total(values, mask, scale):
         "fill_probability": round(probability, 4),
         "entitled_k": total(1, entitled, 1e3),
         "reporters_k": total(1, reported, 1e3),
+        "spi_synthetic_units_not_anchored": int(
+            (spi_synthetic & (reported_amount > 0)).sum()
+        ),
         "entitled_reporters_k": total(1, entitled & reported, 1e3),
         "gb_reporters_k": total(1, gb & reported, 1e3),
         "gb_entitled_reporters_k": total(1, gb & entitled & reported, 1e3),
diff --git a/policyengine_uk_data/tests/test_pension_credit_takeup.py b/policyengine_uk_data/tests/test_pension_credit_takeup.py
index 794fd9e2e..7259f138a 100644
--- a/policyengine_uk_data/tests/test_pension_credit_takeup.py
+++ b/policyengine_uk_data/tests/test_pension_credit_takeup.py
@@ -134,3 +134,31 @@ def test_built_dataset_reporters_claim_and_caseload_is_near_dwp(baseline):
         )
         ratio = (column * weight).sum() / target.values[year]
         assert 0.5 < ratio < 2, (target.name, ratio)
+
+
+def test_spi_synthetic_benefit_units_are_found_through_their_household():
+    from types import SimpleNamespace
+
+    import pandas as pd
+
+    from policyengine_uk_data.datasets.pension_credit_takeup import (
+        spi_synthetic_benunits,
+    )
+
+    dataset = SimpleNamespace(
+        household=pd.DataFrame(
+            {"household_id": [1, 2, 3], "household_is_spi_synthetic": [0, 1, 0]}
+        ),
+        person=pd.DataFrame(
+            {
+                "person_household_id": [1, 2, 2, 3],
+                "person_benunit_id": [10, 20, 21, 30],
+            }
+        ),
+        benunit=pd.DataFrame({"benunit_id": [30, 21, 20, 10]}),
+    )
+    np.testing.assert_array_equal(
+        spi_synthetic_benunits(dataset), [False, True, True, False]
+    )
+    dataset.household = dataset.household.drop(columns="household_is_spi_synthetic")
+    assert not spi_synthetic_benunits(dataset).any()

From 2a7c02bf3abf15ba5b15296939acd27a24467db9 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Thu, 1 Oct 2026 15:48:31 -0400
Subject: [PATCH 64/70] Solve Pension Credit take-up over Great Britain only

DWP's 62% caseload take-up covers Great Britain, but the solve counted
Northern Ireland's entitled units and reporters too, so NI's reporter mix
moved GB take-up away from 62%. Solve over GB entitled units and draw every
non-reporter, NI included, at that probability. A property test checks that
NI composition cannot change the GB probability or any GB flag.

Correct the docstrings: the fill is zero when entitled GB reporters already
make up the rate of entitled GB units, which is separate from calibration
against DWP's absolute targets. OBR's Pension Credit line is the same GB
series as DWP's, not identical figures. The summary now reports GB
entitled units.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/pc-takeup-over-eligible.fixed.md  |  2 +-
 .../datasets/pension_credit_takeup.py         | 40 ++++++++++++++-----
 .../targets/sources/dwp_pension_credit.py     |  5 ++-
 .../tests/test_pension_credit_takeup.py       | 38 ++++++++++++++++++
 4 files changed, 71 insertions(+), 14 deletions(-)

diff --git a/changelog.d/pc-takeup-over-eligible.fixed.md b/changelog.d/pc-takeup-over-eligible.fixed.md
index 58490defd..7bae6a1af 100644
--- a/changelog.d/pc-takeup-over-eligible.fixed.md
+++ b/changelog.d/pc-takeup-over-eligible.fixed.md
@@ -1 +1 @@
-Solve Pension Credit take-up over entitled benefit units, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which carry SPI-imputed incomes, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
+Solve Pension Credit take-up over entitled benefit units in Great Britain, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter, including in Northern Ireland, is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which carry SPI-imputed incomes, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
index 2419022fd..f4eba6380 100644
--- a/policyengine_uk_data/datasets/pension_credit_takeup.py
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -13,9 +13,11 @@
   households, which copy an FRS household's benefit reports but replace its
   incomes with SPI-imputed ones;
 - solves the probability that makes weighted take-up among entitled units
-  equal DWP's caseload take-up rate;
-- applies that probability to every non-reporter, entitled or not, so a
-  unit that a reform makes entitled claims at the same rate.
+  in Great Britain equal DWP's caseload take-up rate, which covers Great
+  Britain only;
+- applies that probability to every non-reporter, entitled or not and in
+  Northern Ireland too, so a unit that a reform makes entitled claims at the
+  same rate.
 
 Weights are the survey grossing weights, before calibration.
 """
@@ -48,14 +50,29 @@ def spi_synthetic_benunits(dataset) -> np.ndarray:
     return dataset.benunit["benunit_id"].isin(benunits).to_numpy()
 
 
+def pension_credit_takeup_flags(
+    draws, rate, weights, entitled, reported, great_britain
+):
+    """Claim flags and the fill probability solved over GB entitled units.
+
+    Only Great Britain enters the solve, because DWP's take-up rate covers
+    Great Britain. Every non-reporter, wherever it lives, claims when its
+    draw is below the probability.
+    """
+    eligible = np.asarray(entitled, dtype=bool) & np.asarray(great_britain, dtype=bool)
+    probability = solve_fill_probability(rate, weights, eligible, reported)
+    claims = assign_takeup_over_eligible(draws, rate, weights, eligible, reported)
+    return claims, probability
+
+
 def assign_pension_credit_takeup(dataset, year: int):
     """Return a copy of ``dataset`` with ``would_claim_pc`` solved for
     ``year``, and a summary of weighted aggregates.
 
-    The summary's Great Britain reporter baseline (reporters only, before
-    any fill) is what DWP's GB targets are compared with: a fill can only
-    add claimants, so if reporters alone exceed DWP's caseload the fill is
-    zero and only calibration can bring the total down.
+    The probability is zero when entitled GB reporters already make up the
+    take-up rate of entitled GB units. The summary's GB reporter baseline
+    (reporters only, before any fill) is for comparison with DWP's absolute
+    caseload and spending, which calibration targets later and separately.
     """
     from policyengine_uk import Microsimulation
 
@@ -79,16 +96,16 @@ def assign_pension_credit_takeup(dataset, year: int):
         )
         > 0
     )
+    gb = ~in_ni
     rate = load_take_up_rate("pension_credit", year)
-    probability = solve_fill_probability(rate, weights, entitled, reported)
     draws = np.random.default_rng(PENSION_CREDIT_TAKEUP_SEED).random(len(entitled))
+    claims, probability = pension_credit_takeup_flags(
+        draws, rate, weights, entitled, reported, gb
+    )
 
     dataset = dataset.copy()
-    claims = assign_takeup_over_eligible(draws, rate, weights, entitled, reported)
     dataset.benunit["would_claim_pc"] = claims
 
-    gb = ~in_ni
-
     def total(values, mask, scale):
         return round(float((weights * values)[mask].sum()) / scale, 3)
 
@@ -97,6 +114,7 @@ def total(values, mask, scale):
         "rate": rate,
         "fill_probability": round(probability, 4),
         "entitled_k": total(1, entitled, 1e3),
+        "gb_entitled_k": total(1, gb & entitled, 1e3),
         "reporters_k": total(1, reported, 1e3),
         "spi_synthetic_units_not_anchored": int(
             (spi_synthetic & (reported_amount > 0)).sum()
diff --git a/policyengine_uk_data/targets/sources/dwp_pension_credit.py b/policyengine_uk_data/targets/sources/dwp_pension_credit.py
index 092a77fc6..776875f33 100644
--- a/policyengine_uk_data/targets/sources/dwp_pension_credit.py
+++ b/policyengine_uk_data/targets/sources/dwp_pension_credit.py
@@ -8,8 +8,9 @@
 DWP's figures cover Great Britain; Pension Credit in Northern Ireland is
 paid by the Department for Communities, so the model columns count GB
 households only. These targets replace OBR EFO table 4.9 "Pension credit",
-the same DWP spending line, which the model compared with UK-wide Pension
-Credit and which carries no caseload.
+the same GB spending series (2025-26: £6,146.4m against DWP's £6,144.5m),
+which the model compared with UK-wide Pension Credit and which carries no
+caseload.
 
 Source: https://www.gov.uk/government/publications/benefit-expenditure-and-caseload-tables-2026
 """
diff --git a/policyengine_uk_data/tests/test_pension_credit_takeup.py b/policyengine_uk_data/tests/test_pension_credit_takeup.py
index 7259f138a..d4e81f5e7 100644
--- a/policyengine_uk_data/tests/test_pension_credit_takeup.py
+++ b/policyengine_uk_data/tests/test_pension_credit_takeup.py
@@ -162,3 +162,41 @@ def test_spi_synthetic_benefit_units_are_found_through_their_household():
     )
     dataset.household = dataset.household.drop(columns="household_is_spi_synthetic")
     assert not spi_synthetic_benunits(dataset).any()
+
+
+@settings(max_examples=200, deadline=None)
+@given(_units, _units, st.floats(0, 1), st.integers(0, 2**32 - 1))
+def test_northern_ireland_cannot_change_the_gb_solution(gb_units, ni_units, rate, seed):
+    """DWP's take-up rate covers Great Britain, so Northern Ireland's
+    entitlement, reporting and weights leave the probability and every GB
+    flag unchanged."""
+    from policyengine_uk_data.datasets.pension_credit_takeup import (
+        pension_credit_takeup_flags,
+    )
+
+    def arrays(units):
+        return (
+            np.array([u[0] for u in units]),
+            np.array([u[1] for u in units]),
+            np.array([u[2] for u in units]),
+        )
+
+    gw, ge, gr = arrays(gb_units)
+    nw, ne, nr = arrays(ni_units)
+    draws = np.random.default_rng(seed).random(len(gw) + len(nw))
+    gb_only, p_gb = pension_credit_takeup_flags(
+        draws[: len(gw)], rate, gw, ge, gr, np.ones(len(gw), dtype=bool)
+    )
+    combined, p_uk = pension_credit_takeup_flags(
+        draws,
+        rate,
+        np.concatenate([gw, nw]),
+        np.concatenate([ge, ne]),
+        np.concatenate([gr, nr]),
+        np.concatenate([np.ones(len(gw), bool), np.zeros(len(nw), bool)]),
+    )
+    assert p_uk == p_gb
+    np.testing.assert_array_equal(combined[: len(gw)], gb_only)
+    # Northern Ireland non-reporters are drawn at the GB probability.
+    ni_draws = draws[len(gw) :]
+    np.testing.assert_array_equal(combined[len(gw) :][~nr], (ni_draws < p_gb)[~nr])

From 2844ad4a049c13fcd62226c39cd1888469aa9b72 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Fri, 2 Oct 2026 09:36:30 -0400
Subject: [PATCH 65/70] Check survey reporters only; describe SPI reports as
 imputed

The built-dataset test asserted that every Pension Credit reporter claims,
which no longer holds for SPI-synthetic units, whose reports are imputed
and no longer anchor take-up. It now checks survey reporters. The module
docstring and changelog now say the SPI reports are predicted by the
second-stage imputation from SPI incomes.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/pc-takeup-over-eligible.fixed.md          |  2 +-
 .../datasets/pension_credit_takeup.py                 |  4 ++--
 .../tests/test_pension_credit_takeup.py               | 11 +++++++++--
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/changelog.d/pc-takeup-over-eligible.fixed.md b/changelog.d/pc-takeup-over-eligible.fixed.md
index 7bae6a1af..423800630 100644
--- a/changelog.d/pc-takeup-over-eligible.fixed.md
+++ b/changelog.d/pc-takeup-over-eligible.fixed.md
@@ -1 +1 @@
-Solve Pension Credit take-up over entitled benefit units in Great Britain, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter, including in Northern Ireland, is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which carry SPI-imputed incomes, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
+Solve Pension Credit take-up over entitled benefit units in Great Britain, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter, including in Northern Ireland, is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which are imputed from SPI incomes rather than observed, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
index f4eba6380..c3d759d3f 100644
--- a/policyengine_uk_data/datasets/pension_credit_takeup.py
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -10,8 +10,8 @@
 For the calibration year it:
 - finds the benefit units with positive Pension Credit entitlement;
 - keeps reporters of Pension Credit as claimants, except on SPI-synthetic
-  households, which copy an FRS household's benefit reports but replace its
-  incomes with SPI-imputed ones;
+  households, whose reports the second-stage imputation (frs_only.py)
+  predicts from their SPI incomes rather than observes;
 - solves the probability that makes weighted take-up among entitled units
   in Great Britain equal DWP's caseload take-up rate, which covers Great
   Britain only;
diff --git a/policyengine_uk_data/tests/test_pension_credit_takeup.py b/policyengine_uk_data/tests/test_pension_credit_takeup.py
index d4e81f5e7..d54b53ab6 100644
--- a/policyengine_uk_data/tests/test_pension_credit_takeup.py
+++ b/policyengine_uk_data/tests/test_pension_credit_takeup.py
@@ -112,7 +112,14 @@ def test_obr_pension_credit_row_is_not_parsed():
     assert not obr._parse_welfare(wb)
 
 
-def test_built_dataset_reporters_claim_and_caseload_is_near_dwp(baseline):
+def test_built_dataset_survey_reporters_claim_and_caseload_is_near_dwp(
+    baseline, enhanced_frs
+):
+    """Every survey reporter claims. SPI-synthetic reports are imputed, so
+    those units are drawn like non-reporters."""
+    from policyengine_uk_data.datasets.pension_credit_takeup import (
+        spi_synthetic_benunits,
+    )
     from policyengine_uk_data.targets.build_loss_matrix import (
         _SimContext,
         restrict_to_countries,
@@ -121,7 +128,7 @@ def test_built_dataset_reporters_claim_and_caseload_is_near_dwp(baseline):
     year = 2025
     reported = (
         baseline.calculate("pension_credit_reported", year, map_to="benunit").values > 0
-    )
+    ) & ~spi_synthetic_benunits(enhanced_frs)
     would_claim = baseline.calculate("would_claim_pc", year).values.astype(bool)
     assert would_claim[reported].all()
 

From 65656305498b1964ca000ecf1620097eec71c5ec Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Mon, 5 Oct 2026 12:18:49 -0400
Subject: [PATCH 66/70] Draw units not entitled in the calibration year at 37%
 Pension Credit take-up
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Until now every non-reporter claimed at the solved fill probability (about
13%), including units a reform newly entitles. That probability is a
residual: the share of entitled non-reporters that tops FRS reporters up to
DWP's 62%. Units with no entitlement in the calibration year are now drawn
at DWP's caseload take-up for Savings Credit-only awards, 37% in FYE 2024
(table PC1; 42% in FYE 2023), the group with the smallest awards (£9 a
week, against £87 and 69% take-up for Guarantee Credit). Reporters and the
solved fill for entitled non-reporters are unchanged, so is the solved
probability, and every entitled unit's flag.

The rate is a new take-up parameter, pension_credit_newly_entitled. The
generic assign_takeup_over_eligible helper, which applied one probability
to every non-reporter, is removed; pension_credit_takeup_flags applies the
two rates.

Tests: reporters claim, entitled non-reporters draw at the solved
probability and the rest at the new rate, and the new rate changes neither
the probability nor any entitled unit's flag (property test); weighted
take-up among entitled units still meets 62% and the rest claim at 37%
over 200,000 units; Northern Ireland follows the same split; the parameter
reads 37% for 2025 and 42% for 2022.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/pc-takeup-over-eligible.fixed.md  |  2 +-
 .../datasets/pension_credit_takeup.py         | 42 +++++++----
 .../pension_credit_newly_entitled.yaml        | 14 ++++
 .../tests/test_pension_credit_takeup.py       | 72 ++++++++++++-------
 policyengine_uk_data/utils/takeup.py          | 18 -----
 5 files changed, 90 insertions(+), 58 deletions(-)
 create mode 100644 policyengine_uk_data/parameters/take_up/pension_credit_newly_entitled.yaml

diff --git a/changelog.d/pc-takeup-over-eligible.fixed.md b/changelog.d/pc-takeup-over-eligible.fixed.md
index 423800630..ab1eb650d 100644
--- a/changelog.d/pc-takeup-over-eligible.fixed.md
+++ b/changelog.d/pc-takeup-over-eligible.fixed.md
@@ -1 +1 @@
-Solve Pension Credit take-up over entitled benefit units in Great Britain, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Every non-reporter, including in Northern Ireland, is drawn at the same probability, so units a reform newly entitles can claim. Reports on SPI-synthetic households, which are imputed from SPI incomes rather than observed, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
+Solve Pension Credit take-up over entitled benefit units in Great Britain, at DWP's FYE 2024 caseload take-up of 62%, after the imputations so entitlement reflects imputed capital. Entitled non-reporters, including in Northern Ireland, are drawn at the solved probability. Units with no entitlement in the calibration year are drawn at DWP's Savings Credit-only caseload take-up (37% in FYE 2024), the rate at which units a reform or a later year newly entitles claim. Reports on SPI-synthetic households, which are imputed from SPI incomes rather than observed, no longer anchor take-up. Calibrate Pension Credit to DWP's Great Britain spending and caseload, replacing the OBR target that the model compared with UK-wide Pension Credit.
diff --git a/policyengine_uk_data/datasets/pension_credit_takeup.py b/policyengine_uk_data/datasets/pension_credit_takeup.py
index c3d759d3f..0ba95aa7a 100644
--- a/policyengine_uk_data/datasets/pension_credit_takeup.py
+++ b/policyengine_uk_data/datasets/pension_credit_takeup.py
@@ -15,9 +15,14 @@
 - solves the probability that makes weighted take-up among entitled units
   in Great Britain equal DWP's caseload take-up rate, which covers Great
   Britain only;
-- applies that probability to every non-reporter, entitled or not and in
-  Northern Ireland too, so a unit that a reform makes entitled claims at the
-  same rate.
+- applies that probability to every entitled non-reporter, in Northern
+  Ireland too;
+- draws every unit with no entitlement in the calibration year at DWP's
+  take-up for Savings Credit-only awards (37% in FYE 2024), the rate at
+  which a unit claims once a reform or a later year entitles it. This step
+  finds no entitlement for them in the calibration year, so their rate only
+  matters under a reform, in a later year, or where the saved dataset's
+  calibration-year entitlement differs from this step's (#479).
 
 Weights are the survey grossing weights, before calibration.
 """
@@ -25,10 +30,7 @@
 import numpy as np
 
 from policyengine_uk_data.parameters import load_take_up_rate
-from policyengine_uk_data.utils.takeup import (
-    assign_takeup_over_eligible,
-    solve_fill_probability,
-)
+from policyengine_uk_data.utils.takeup import solve_fill_probability
 
 # Separate from the FRS build's take-up generator, so redrawing here leaves
 # every other stochastic input unchanged.
@@ -51,17 +53,21 @@ def spi_synthetic_benunits(dataset) -> np.ndarray:
 
 
 def pension_credit_takeup_flags(
-    draws, rate, weights, entitled, reported, great_britain
+    draws, rate, weights, entitled, reported, great_britain, newly_entitled_rate
 ):
     """Claim flags and the fill probability solved over GB entitled units.
 
     Only Great Britain enters the solve, because DWP's take-up rate covers
-    Great Britain. Every non-reporter, wherever it lives, claims when its
-    draw is below the probability.
+    Great Britain. Reporters claim. Every other entitled unit, wherever it
+    lives, claims when its draw is below the probability; every unit that
+    isn't entitled claims when its draw is below ``newly_entitled_rate``.
     """
-    eligible = np.asarray(entitled, dtype=bool) & np.asarray(great_britain, dtype=bool)
+    entitled = np.asarray(entitled, dtype=bool)
+    reported = np.asarray(reported, dtype=bool)
+    eligible = entitled & np.asarray(great_britain, dtype=bool)
     probability = solve_fill_probability(rate, weights, eligible, reported)
-    claims = assign_takeup_over_eligible(draws, rate, weights, eligible, reported)
+    claim_probability = np.where(entitled, probability, newly_entitled_rate)
+    claims = reported | (np.asarray(draws, dtype=np.float64) < claim_probability)
     return claims, probability
 
 
@@ -98,9 +104,10 @@ def assign_pension_credit_takeup(dataset, year: int):
     )
     gb = ~in_ni
     rate = load_take_up_rate("pension_credit", year)
+    newly_entitled_rate = load_take_up_rate("pension_credit_newly_entitled", year)
     draws = np.random.default_rng(PENSION_CREDIT_TAKEUP_SEED).random(len(entitled))
     claims, probability = pension_credit_takeup_flags(
-        draws, rate, weights, entitled, reported, gb
+        draws, rate, weights, entitled, reported, gb, newly_entitled_rate
     )
 
     dataset = dataset.copy()
@@ -113,6 +120,7 @@ def total(values, mask, scale):
         "year": year,
         "rate": rate,
         "fill_probability": round(probability, 4),
+        "newly_entitled_rate": newly_entitled_rate,
         "entitled_k": total(1, entitled, 1e3),
         "gb_entitled_k": total(1, gb & entitled, 1e3),
         "reporters_k": total(1, reported, 1e3),
@@ -128,5 +136,13 @@ def total(values, mask, scale):
         ),
         "gb_claims_after_fill_k": total(1, gb & entitled & claims, 1e3),
         "gb_pension_credit_after_fill_bn": total(entitlement, gb & claims, 1e9),
+        "gb_not_entitled_k": total(1, gb & ~entitled, 1e3),
+        "gb_not_entitled_flagged_share": round(
+            float(
+                weights[gb & ~entitled & claims].sum()
+                / max(weights[gb & ~entitled].sum(), 1)
+            ),
+            4,
+        ),
     }
     return dataset, summary
diff --git a/policyengine_uk_data/parameters/take_up/pension_credit_newly_entitled.yaml b/policyengine_uk_data/parameters/take_up/pension_credit_newly_entitled.yaml
new file mode 100644
index 000000000..6c1dc1bc1
--- /dev/null
+++ b/policyengine_uk_data/parameters/take_up/pension_credit_newly_entitled.yaml
@@ -0,0 +1,14 @@
+description: Probability that a benefit unit with no Pension Credit entitlement in the calibration year claims it once a reform or a later year entitles it. Set to DWP's caseload take-up of Savings Credit-only awards (Great Britain), the group with the smallest awards, which is closest to what a reform gives the units it newly entitles.
+metadata:
+  label: Pension Credit take-up for newly entitled units
+  name: PC_takeup_newly_entitled
+  unit: /1
+  reference:
+    - title: "Income-related benefits: estimates of take-up: financial year ending 2024"
+      href: https://www.gov.uk/government/statistics/income-related-benefits-estimates-of-take-up-financial-year-ending-2024/income-related-benefits-estimates-of-take-up-financial-year-ending-2024
+values:
+  # Savings Credit-only caseload take-up, FYE 2023 and FYE 2024 (42% and
+  # 37%, table PC1). Mean award claimed in FYE 2024: £9 a week, against £87
+  # for Guarantee Credit, whose take-up was 69%.
+  2022-04-01: 0.42
+  2023-04-01: 0.37
diff --git a/policyengine_uk_data/tests/test_pension_credit_takeup.py b/policyengine_uk_data/tests/test_pension_credit_takeup.py
index d54b53ab6..a7fc3db4b 100644
--- a/policyengine_uk_data/tests/test_pension_credit_takeup.py
+++ b/policyengine_uk_data/tests/test_pension_credit_takeup.py
@@ -9,10 +9,10 @@
 from policyengine_uk_data.targets import get_all_targets
 from policyengine_uk_data.targets.schema import GREAT_BRITAIN, Unit
 from policyengine_uk_data.targets.sources import dwp_pension_credit, obr
-from policyengine_uk_data.utils.takeup import (
-    assign_takeup_over_eligible,
-    solve_fill_probability,
+from policyengine_uk_data.datasets.pension_credit_takeup import (
+    pension_credit_takeup_flags,
 )
+from policyengine_uk_data.utils.takeup import solve_fill_probability
 
 _units = st.lists(
     st.tuples(st.floats(0, 5_000), st.booleans(), st.booleans()),
@@ -44,42 +44,61 @@ def test_fill_probability_meets_the_rate_or_hits_a_bound(units, rate):
 
 
 @settings(max_examples=200, deadline=None)
-@given(_units, st.floats(0, 1), st.integers(0, 2**32 - 1))
-def test_reporters_claim_and_every_non_reporter_shares_one_probability(
-    units, rate, seed
+@given(
+    _units, st.floats(0, 1), st.floats(0, 1), st.floats(0, 1), st.integers(0, 2**32 - 1)
+)
+def test_reporters_claim_entitled_units_share_the_solved_probability_others_the_new_rate(
+    units, rate, newly_entitled_rate, other_rate, seed
 ):
     weights = np.array([u[0] for u in units])
-    eligible = np.array([u[1] for u in units])
+    entitled = np.array([u[1] for u in units])
     reported = np.array([u[2] for u in units])
+    gb = np.ones(len(units), dtype=bool)
     draws = np.random.default_rng(seed).random(len(units))
-    result = assign_takeup_over_eligible(draws, rate, weights, eligible, reported)
-    p = solve_fill_probability(rate, weights, eligible, reported)
+    result, p = pension_credit_takeup_flags(
+        draws, rate, weights, entitled, reported, gb, newly_entitled_rate
+    )
+    assert p == solve_fill_probability(rate, weights, entitled, reported)
     assert result[reported].all()
-    # Eligibility does not change a non-reporter's flag, so a unit a reform
-    # makes entitled claims at the same rate.
-    np.testing.assert_array_equal(result[~reported], (draws < p)[~reported])
+    entitled_non_reporters = entitled & ~reported
+    np.testing.assert_array_equal(
+        result[entitled_non_reporters], (draws < p)[entitled_non_reporters]
+    )
+    others = ~entitled & ~reported
+    np.testing.assert_array_equal(result[others], (draws < newly_entitled_rate)[others])
+    # The newly entitled rate changes neither the probability nor any flag of
+    # an entitled unit, so the calibration year's take-up is unchanged.
+    other, p_other = pension_credit_takeup_flags(
+        draws, rate, weights, entitled, reported, gb, other_rate
+    )
+    assert p_other == p
+    np.testing.assert_array_equal(other[entitled], result[entitled])
 
 
 def test_weighted_take_up_among_eligible_matches_rate():
     rng = np.random.default_rng(0)
     n = 200_000
     weights = rng.uniform(100, 3_000, n)
-    eligible = rng.random(n) < 0.2
-    reported = eligible & (rng.random(n) < 0.4)
-    result = assign_takeup_over_eligible(
-        rng.random(n), 0.62, weights, eligible, reported
+    entitled = rng.random(n) < 0.2
+    reported = entitled & (rng.random(n) < 0.4)
+    result, _ = pension_credit_takeup_flags(
+        rng.random(n), 0.62, weights, entitled, reported, np.ones(n, bool), 0.37
     )
-    take_up = weights[eligible & result].sum() / weights[eligible].sum()
+    take_up = weights[entitled & result].sum() / weights[entitled].sum()
     assert abs(take_up - 0.62) < 0.005
-    # Non-entitled units are drawn at the same probability as entitled ones.
-    p = solve_fill_probability(0.62, weights, eligible, reported)
-    assert abs(result[~eligible].mean() - p) < 0.01
+    # Units with no entitlement claim at the newly entitled rate.
+    assert abs(result[~entitled].mean() - 0.37) < 0.01
 
 
 def test_rate_is_dwp_fye_2024_caseload_take_up():
     assert load_take_up_rate("pension_credit", 2025) == 0.62
 
 
+def test_newly_entitled_rate_is_dwp_savings_credit_only_take_up():
+    assert load_take_up_rate("pension_credit_newly_entitled", 2025) == 0.37
+    assert load_take_up_rate("pension_credit_newly_entitled", 2022) == 0.42
+
+
 def test_targets_reconcile_with_dwp_components():
     """Guarantee + savings credit spending and the three claim types sum to
     the transcribed totals (DWP Pension Credit sheet, separate rows)."""
@@ -177,9 +196,6 @@ def test_northern_ireland_cannot_change_the_gb_solution(gb_units, ni_units, rate
     """DWP's take-up rate covers Great Britain, so Northern Ireland's
     entitlement, reporting and weights leave the probability and every GB
     flag unchanged."""
-    from policyengine_uk_data.datasets.pension_credit_takeup import (
-        pension_credit_takeup_flags,
-    )
 
     def arrays(units):
         return (
@@ -192,7 +208,7 @@ def arrays(units):
     nw, ne, nr = arrays(ni_units)
     draws = np.random.default_rng(seed).random(len(gw) + len(nw))
     gb_only, p_gb = pension_credit_takeup_flags(
-        draws[: len(gw)], rate, gw, ge, gr, np.ones(len(gw), dtype=bool)
+        draws[: len(gw)], rate, gw, ge, gr, np.ones(len(gw), dtype=bool), 0.37
     )
     combined, p_uk = pension_credit_takeup_flags(
         draws,
@@ -201,9 +217,13 @@ def arrays(units):
         np.concatenate([ge, ne]),
         np.concatenate([gr, nr]),
         np.concatenate([np.ones(len(gw), bool), np.zeros(len(nw), bool)]),
+        0.37,
     )
     assert p_uk == p_gb
     np.testing.assert_array_equal(combined[: len(gw)], gb_only)
-    # Northern Ireland non-reporters are drawn at the GB probability.
+    # Entitled Northern Ireland non-reporters are drawn at the GB probability,
+    # the rest at the newly entitled rate.
     ni_draws = draws[len(gw) :]
-    np.testing.assert_array_equal(combined[len(gw) :][~nr], (ni_draws < p_gb)[~nr])
+    ni_flags = combined[len(gw) :]
+    np.testing.assert_array_equal(ni_flags[ne & ~nr], (ni_draws < p_gb)[ne & ~nr])
+    np.testing.assert_array_equal(ni_flags[~ne & ~nr], (ni_draws < 0.37)[~ne & ~nr])
diff --git a/policyengine_uk_data/utils/takeup.py b/policyengine_uk_data/utils/takeup.py
index 24e66cdd7..965c11aab 100644
--- a/policyengine_uk_data/utils/takeup.py
+++ b/policyengine_uk_data/utils/takeup.py
@@ -84,21 +84,3 @@ def solve_fill_probability(
     with np.errstate(over="ignore", divide="ignore"):
         probability = needed / remaining_weight
     return float(np.clip(probability, 0.0, 1.0))
-
-
-def assign_takeup_over_eligible(
-    draws: np.ndarray,
-    rate: float,
-    weights: np.ndarray,
-    eligible: np.ndarray,
-    reported: np.ndarray,
-) -> np.ndarray:
-    """Take-up flags with ``rate`` met over eligible entities.
-
-    Reporters claim. Every non-reporter, eligible or not, claims with the
-    probability ``solve_fill_probability`` finds for eligible non-reporters,
-    so a unit a reform makes newly eligible claims at the same rate.
-    """
-    probability = solve_fill_probability(rate, weights, eligible, reported)
-    reported = np.asarray(reported, dtype=bool)
-    return reported | (np.asarray(draws, dtype=np.float64) < probability)

From deb16e944e0ed00df43f6492344ebbc6b055657c Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Mon, 5 Oct 2026 14:23:51 -0400
Subject: [PATCH 67/70] Keep the QRF's council tax reduction draw on SPI rows
 (ruling d821)

Max approved #514 into the batched release with one change: CTR is no
longer zeroed on SPI-synthetic rows. In policyengine-uk 2.93.0 a CTR claim
needs a report, so zeroing it left those rows no claim route (-21% CTR in
the earlier build). SPI CTR stays the stage-two QRF draw, as on main, until
it is zeroed together with a household-level CTR imputation (#499).

Moves council_tax_benefit_reported out of SPI_DONOR_ZEROED_PERSON_VARIABLES,
pins the set of reports kept as drawn, and tests that SPI CTR equals the
stage-two draw (not zeroed, not the donor's value).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../spi-synthetic-reported-benefits.fixed.md  |  2 +-
 .../datasets/imputations/frs_only.py          | 14 ++++---
 .../tests/test_spi_donor_benefit_rules.py     | 40 ++++++++++++++++++-
 3 files changed, 48 insertions(+), 8 deletions(-)

diff --git a/changelog.d/spi-synthetic-reported-benefits.fixed.md b/changelog.d/spi-synthetic-reported-benefits.fixed.md
index 1310079b8..a10ad0154 100644
--- a/changelog.d/spi-synthetic-reported-benefits.fixed.md
+++ b/changelog.d/spi-synthetic-reported-benefits.fixed.md
@@ -1 +1 @@
-SPI-synthetic rows no longer report income-related, out-of-work or Child Benefit receipt, take their industrial injuries, armed forces compensation and bereavement support from the FRS donor, and get UC, Pension Credit and `receives_benefits_in_own_right` flags from their own reports instead of the donor's.
+SPI-synthetic rows no longer report income-related (except council tax reduction, which keeps its imputed value for now), out-of-work or Child Benefit receipt, take their industrial injuries, armed forces compensation and bereavement support from the FRS donor, and get UC, Pension Credit and `receives_benefits_in_own_right` flags from their own reports instead of the donor's.
diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py
index 06b9b78b0..7c1d5cbea 100644
--- a/policyengine_uk_data/datasets/imputations/frs_only.py
+++ b/policyengine_uk_data/datasets/imputations/frs_only.py
@@ -117,10 +117,10 @@
 # - Income-related awards. Entitlement turns on the unit's joint means and
 #   make-up, which here come from the imputed incomes. UC and Pension Credit
 #   keep a route: their take-up flags are redrawn below. In
-#   policyengine-uk 2.93.0 the others (housing benefit, council tax
-#   reduction, income support, tax credits, income-related ESA and JSA) can
-#   only be claimed with a report, so these rows no longer receive them.
-#   Sure Start Maternity Grant needs one of these awards.
+#   policyengine-uk 2.93.0 the others (housing benefit, income support, tax
+#   credits, income-related ESA and JSA) can only be claimed with a report,
+#   so these rows no longer receive them. Sure Start Maternity Grant needs
+#   one of these awards. Council tax reduction is kept (below).
 # - Benefits paid only to people out of work or incapable of it: ESA and JSA
 #   (contributory), incapacity benefit and severe disablement allowance. On
 #   the 2024-25 build, 41% of SPI-row ESA (contributory) reporters by weight
@@ -137,7 +137,10 @@
 # Kept as drawn: state pension (paid as reported once over pension age),
 # winter fuel payment (not read by the model), the disability benefits and
 # carer's allowance, whose drawn rates sit below the FRS rates as the income
-# gradient implies.
+# gradient implies. Council tax reduction is also kept as drawn for now. In
+# 2.93.0 it too can only be claimed with a report, and zeroing it cut 2025
+# CTR by 21% on the 2024-25 build. It will be zeroed together with a
+# household-level CTR imputation (#499).
 #
 # Every column stays in the QRF chain above, so the values kept do not
 # change. They were drawn alongside the values later zeroed or restored.
@@ -145,7 +148,6 @@
     "universal_credit_reported",
     "pension_credit_reported",
     "housing_benefit_reported",
-    "council_tax_benefit_reported",
     "income_support_reported",
     "working_tax_credit_reported",
     "child_tax_credit_reported",
diff --git a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
index 799439038..1a3633fc2 100644
--- a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
+++ b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
@@ -59,7 +59,6 @@ def test_rule_sets_are_pinned():
         "universal_credit_reported",
         "pension_credit_reported",
         "housing_benefit_reported",
-        "council_tax_benefit_reported",
         "income_support_reported",
         "working_tax_credit_reported",
         "child_tax_credit_reported",
@@ -77,6 +76,20 @@ def test_rule_sets_are_pinned():
         "afcs_reported",
         "bsp_reported",
     }
+    # Kept as drawn: every report the QRF draws that no rule above touches.
+    drawn_reports = {c for c in FRS_ONLY_PERSON_VARIABLES if c.endswith("_reported")}
+    assert drawn_reports - RULED == {
+        "state_pension_reported",
+        "winter_fuel_allowance_reported",
+        "attendance_allowance_reported",
+        "dla_sc_reported",
+        "dla_m_reported",
+        "pip_m_reported",
+        "pip_dl_reported",
+        "carers_allowance_reported",
+        "maternity_allowance_reported",
+        "council_tax_benefit_reported",
+    }
     assert set(SPI_DONOR_REDRAWN_TAKEUP_FLAGS) == {"would_claim_uc", "would_claim_pc"}
     assert not set(SPI_DONOR_ZEROED_PERSON_VARIABLES) & set(
         SPI_DONOR_RESTORED_PERSON_VARIABLES
@@ -285,3 +298,28 @@ def test_stage_two_applies_the_rules_and_keeps_drawn_values(monkeypatch):
         ruled.person.drop(columns=flags), int(str(ruled.time_period)[:4])
     )
     pd.testing.assert_frame_equal(ruled.person[flags], recomputed[flags])
+
+
+def test_council_tax_reduction_keeps_the_stage_two_draw():
+    """SPI rows carry the QRF's CTR draw through unchanged, as before the
+    rules: not zeroed, not the donor's. Zeroing waits for #499."""
+    from policyengine_uk_data.tests.test_frs_only_imputation import _fake_dataset
+
+    train = _fake_dataset(person_rows=400, seed=0)
+    rng = np.random.default_rng(2)
+    train.person["council_tax_benefit_reported"] = np.where(
+        rng.random(400) < 0.3, 1_200.0, 0.0
+    )
+    target = _fake_dataset(person_rows=80, seed=1)
+    target.person["council_tax_benefit_reported"] = 999.0
+    outputs = [c for c in FRS_ONLY_PERSON_VARIABLES if c in target.person.columns]
+
+    # The draw alone, which is what stage two returned before the rules.
+    drawn = frs_only._impute_outputs(train, target.copy(), outputs).person
+    ruled = frs_only.impute_frs_only_variables(train, target).person
+
+    np.testing.assert_array_equal(
+        ruled.council_tax_benefit_reported, drawn.council_tax_benefit_reported
+    )
+    assert (drawn.council_tax_benefit_reported > 0).any()
+    assert (drawn.council_tax_benefit_reported != 999.0).any()

From 04581c5ca28acc6219349f570637111567184ed6 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 7 Oct 2026 00:15:52 -0400
Subject: [PATCH 68/70] Apply #490's pension-age UC exclusion to #514's
 SPI-donor redraw (I1)

The SPI-donor rules redraw would_claim_uc on SPI-synthetic rows, so a unit
whose claimant and any partner have all reached State Pension age could get
would_claim_uc there even though create_frs (#490) never gives it one.
apply_spi_donor_benefit_rules now takes an optional per-benefit-unit mask,
uc_pension_age_excluded, and clears would_claim_uc where it is set. Its one
pipeline caller always computes the mask on the real target dataset with the
same derivation as create_frs and passes it; a test checks that wiring.

Integration commit for the 10/8 uk-data release (d833).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../datasets/imputations/frs_only.py          |  50 ++++++-
 .../tests/test_spi_donor_benefit_rules.py     | 135 +++++++++++++++++-
 2 files changed, 179 insertions(+), 6 deletions(-)

diff --git a/policyengine_uk_data/datasets/imputations/frs_only.py b/policyengine_uk_data/datasets/imputations/frs_only.py
index 7c1d5cbea..d173612b0 100644
--- a/policyengine_uk_data/datasets/imputations/frs_only.py
+++ b/policyengine_uk_data/datasets/imputations/frs_only.py
@@ -35,7 +35,7 @@
 
 import numpy as np
 import pandas as pd
-from policyengine_uk.data import UKSingleYearDataset
+from policyengine_uk.data import UKMultiYearDataset, UKSingleYearDataset
 from policyengine_uk_data.datasets.disability_benefits import (
     add_disability_benefit_categories_from_reported_amounts,
     add_disability_benefit_flags_from_reported_amounts,
@@ -44,8 +44,10 @@
     BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS,
     REPORTED_TAKEUP_ANCHORS,
     assign_reported_takeup,
+    derive_all_claimants_over_state_pension_age,
     derive_receives_benefits_in_own_right,
 )
+from policyengine_uk_data.utils.benefit_units import claimant_or_partner_variable
 
 logger = logging.getLogger(__name__)
 
@@ -168,17 +170,39 @@
 
 # Take-up flags redrawn on SPI-donor rows. Whether a synthetic family claims
 # a means-tested benefit at its imputed income is unobserved, so these units
-# draw at the take-up rate. The Child Benefit flag keeps the donor's value:
-# the award doesn't depend on the replaced incomes, and the donor's claim is
-# for the same children.
+# draw at the take-up rate. As in create_frs, a unit whose claimant and any
+# partner have all reached State Pension age never gets would_claim_uc. The
+# Child Benefit flag keeps the donor's value: the award doesn't depend on the
+# replaced incomes, and the donor's claim is for the same children.
 SPI_DONOR_REDRAWN_TAKEUP_FLAGS = ("would_claim_uc", "would_claim_pc")
 # Seed for those draws; create_frs uses 100.
 SPI_DONOR_TAKEUP_SEED = 101
 
 
+def _all_claimants_over_state_pension_age(
+    dataset: UKSingleYearDataset, year: int
+) -> np.ndarray:
+    """``create_frs``'s pension-age Universal Credit exclusion for each of
+    ``dataset``'s benefit units, read through policyengine-uk the same way."""
+    from policyengine_uk import Microsimulation
+
+    # Only this dataset's year is needed. A multi-year container holding that
+    # one year avoids extending and uprating unrelated household inputs.
+    sim = Microsimulation(dataset=UKMultiYearDataset(datasets=[dataset.copy()]))
+    return derive_all_claimants_over_state_pension_age(
+        person_benunit_ids=sim.calculate("person_benunit_id", year).values,
+        is_claimant_or_partner=sim.calculate(
+            claimant_or_partner_variable(sim.tax_benefit_system.variables), year
+        ).values,
+        is_over_state_pension_age=sim.calculate("is_SP_age", year).values,
+        benunit_ids=dataset.benunit.benunit_id,
+    )
+
+
 def apply_spi_donor_benefit_rules(
     dataset: UKSingleYearDataset,
     donor_person: pd.DataFrame | None = None,
+    uc_pension_age_excluded: np.ndarray | None = None,
 ) -> UKSingleYearDataset:
     """Apply the SPI-donor benefit rules above to ``dataset``.
 
@@ -187,6 +211,13 @@ def apply_spi_donor_benefit_rules(
     ``receives_benefits_in_own_right`` and the redrawn take-up flags, which
     ``create_frs`` built from the donor's reports, are rebuilt from the rows'
     own reports.
+
+    ``uc_pension_age_excluded`` is an optional boolean array with one entry
+    per benefit unit, in the dataset's benefit-unit order. When given,
+    ``would_claim_uc &= ~uc_pension_age_excluded`` is applied after the redraw;
+    when ``None``, no units are excluded. The pipeline caller always computes
+    and passes this mask with ``_all_claimants_over_state_pension_age`` on the
+    real target dataset, using the same computation as ``create_frs``.
     """
     dataset = dataset.copy()
     person, benunit = dataset.person, dataset.benunit
@@ -213,6 +244,8 @@ def apply_spi_donor_benefit_rules(
         report_column = REPORTED_TAKEUP_ANCHORS[flag][1]
         if flag in benunit.columns and report_column in person.columns:
             benunit[flag] = assign_reported_takeup(person, benunit, flag, year, draws)
+    if uc_pension_age_excluded is not None and "would_claim_uc" in benunit.columns:
+        benunit["would_claim_uc"] &= ~uc_pension_age_excluded
     return dataset
 
 
@@ -323,7 +356,14 @@ def impute_frs_only_variables(
             "applying only the SPI-donor benefit rules."
         )
 
-    target_dataset = apply_spi_donor_benefit_rules(target_dataset, donor_person)
+    year = int(str(target_dataset.time_period)[:4])
+    target_dataset = apply_spi_donor_benefit_rules(
+        target_dataset,
+        donor_person,
+        uc_pension_age_excluded=_all_claimants_over_state_pension_age(
+            target_dataset, year
+        ),
+    )
     target_dataset.person = add_disability_benefit_categories_from_reported_amounts(
         target_dataset.person,
         int(str(target_dataset.time_period)[:4]),
diff --git a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
index 1a3633fc2..e162400d7 100644
--- a/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
+++ b/policyengine_uk_data/tests/test_spi_donor_benefit_rules.py
@@ -7,11 +7,15 @@
    donor's value.
 2. Nothing else changes except ``receives_benefits_in_own_right`` and the
    redrawn take-up flags, and the input is not mutated.
-3. A benefit unit with a member reporting UC or Pension Credit claims it.
+3. A benefit unit with a member reporting UC or Pension Credit claims it,
+   unless 6 applies.
 4. ``receives_benefits_in_own_right`` holds exactly when the person reports
    one of ``BENEFITS_IN_OWN_RIGHT_REPORTED_COLUMNS``.
 5. The rules are deterministic and idempotent, and no flag's draws depend on
    which other columns are present.
+6. With the pension-age exclusion mask supplied, a benefit unit whose
+   claimant and any partner have all reached State Pension age never gets
+   ``would_claim_uc``, even if it reports UC, as in ``create_frs``.
 
 The fixtures are synthetic, not survey records.
 """
@@ -256,6 +260,135 @@ def rate(name, year):
     assert years == [2031] * len(SPI_DONOR_REDRAWN_TAKEUP_FLAGS)
 
 
+def _people_dataset(people, seed=None) -> UKSingleYearDataset:
+    """One person per (benefit unit, age, claimant or partner) triple, every
+    unit reporting UC and entering with the donor's would_claim_uc. With a
+    ``seed``, both tables are shuffled."""
+    units, ages, claimants = (np.asarray(column) for column in zip(*people))
+    person = pd.DataFrame(
+        {
+            "person_id": np.arange(len(people)) * 5 + 11,
+            "person_benunit_id": units,
+            "person_household_id": units,
+            "age": ages,
+            "is_claimant_or_partner": claimants,
+            "universal_credit_reported": 1_000.0,
+            "pension_credit_reported": 0.0,
+        }
+    )
+    benunit_ids = np.unique(units)
+    benunit = pd.DataFrame(
+        {"benunit_id": benunit_ids, "would_claim_uc": True, "would_claim_pc": False}
+    )
+    if seed is not None:
+        rng = np.random.default_rng(seed)
+        person = person.iloc[rng.permutation(len(person))].reset_index(drop=True)
+        benunit = benunit.iloc[rng.permutation(len(benunit))].reset_index(drop=True)
+    household = pd.DataFrame({"household_id": benunit_ids, "household_weight": 0.0})
+    return UKSingleYearDataset(
+        person=person, benunit=benunit, household=household, fiscal_year=YEAR
+    )
+
+
+def _everyone_takes_up(monkeypatch):
+    monkeypatch.setattr(
+        "policyengine_uk_data.datasets.frs.load_take_up_rate", lambda name, year: 1.0
+    )
+
+
+def test_pension_age_units_never_get_would_claim_uc(monkeypatch):
+    """With every unit drawn to claim, exactly the units whose claimant and any
+    partner have all reached State Pension age lose would_claim_uc, judged by
+    the dataset's is_claimant_or_partner. Pension Credit is not affected."""
+    _everyone_takes_up(monkeypatch)
+    # (benefit unit, age, claimant or partner): a pensioner couple, a mixed-age
+    # couple, a single pensioner, a working-age single, a pensioner living with
+    # a younger adult who is neither, and a pensioner with a child.
+    people = [
+        (3, 70, True),
+        (3, 72, True),
+        (10, 70, True),
+        (10, 50, True),
+        (17, 80, True),
+        (24, 40, True),
+        (31, 75, True),
+        (31, 30, False),
+        (38, 68, True),
+        (38, 10, False),
+    ]
+    dataset = _people_dataset(people)
+    excluded = frs_only._all_claimants_over_state_pension_age(dataset, YEAR)
+    after = apply_spi_donor_benefit_rules(
+        dataset, uc_pension_age_excluded=excluded
+    ).benunit
+    assert dict(zip(after.benunit_id, after.would_claim_uc)) == {
+        3: False,
+        10: True,
+        17: False,
+        24: True,
+        31: False,
+        38: False,
+    }
+    assert after.would_claim_pc.all()
+
+
+@st.composite
+def units_clear_of_pension_age(draw):
+    """Units of one or two claimants, each clearly under (20 to 59) or over
+    (67 and up) State Pension age in 2024, plus up to two other members."""
+    clear_age = st.one_of(st.integers(20, 59), st.integers(67, 95))
+    people = []
+    for unit in range(draw(st.integers(1, 8))):
+        for _ in range(draw(st.integers(1, 2))):
+            people.append((unit * 7 + 3, draw(clear_age), True))
+        for _ in range(draw(st.integers(0, 2))):
+            people.append((unit * 7 + 3, draw(st.integers(0, 95)), False))
+    return people
+
+
+@settings(
+    max_examples=20,
+    deadline=None,
+    suppress_health_check=[HealthCheck.function_scoped_fixture],
+)
+@given(people=units_clear_of_pension_age(), seed=st.integers(0, 10**6))
+def test_uc_is_excluded_exactly_when_all_claimants_are_over_pension_age(
+    people, seed, monkeypatch
+):
+    _everyone_takes_up(monkeypatch)
+    dataset = _people_dataset(people, seed)
+    excluded = frs_only._all_claimants_over_state_pension_age(dataset, YEAR)
+    after = apply_spi_donor_benefit_rules(
+        dataset, uc_pension_age_excluded=excluded
+    ).benunit
+    for unit, claims in zip(after.benunit_id, after.would_claim_uc):
+        ages = [age for u, age, claimant in people if u == unit and claimant]
+        assert claims == (min(ages) < 67), (unit, ages)
+
+
+def test_stage_two_always_passes_the_pension_age_exclusion(monkeypatch):
+    """The pipeline computes the mask on its target and passes it to the rules."""
+    dataset = _people_dataset([(3, 70, True), (10, 40, True)])
+    expected = frs_only._all_claimants_over_state_pension_age(dataset, YEAR)
+    captured = []
+
+    def capture_rules(target, donor_person=None, *, uc_pension_age_excluded=None):
+        captured.append(uc_pension_age_excluded)
+        return target
+
+    # Keep this a caller wiring check without fitting a QRF.
+    monkeypatch.setattr(
+        frs_only, "_impute_outputs", lambda train, target, outputs: target
+    )
+    monkeypatch.setattr(frs_only, "apply_spi_donor_benefit_rules", capture_rules)
+    frs_only.impute_frs_only_variables(dataset, dataset)
+
+    assert len(captured) == 1
+    assert captured[0] is not None
+    np.testing.assert_array_equal(captured[0], expected)
+    np.testing.assert_array_equal(captured[0], [True, False])
+
+
 def test_stage_two_applies_the_rules_and_keeps_drawn_values(monkeypatch):
     """Through ``impute_frs_only_variables``: zeroed, restored, kept and flags."""
     from policyengine_uk_data.tests.test_frs_only_imputation import _fake_dataset

From 758d8c3e2e0de1059a3b70804fcfb1b6b0d20f87 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 7 Oct 2026 00:15:52 -0400
Subject: [PATCH 69/70] Relock to policyengine-uk 2.122.2 and policyengine-core
 3.32.13 (R)

#525 needs policyengine-uk 2.122.0 or later (first release with PE-UK
#2081). Floors become policyengine-uk>=2.122.0 and policyengine-core>=3.32.13;
the dev extras keep a single hypothesis>=6.168.3. The lock changes exactly
four packages: policyengine-uk 2.93.0 -> 2.122.2, policyengine-core 3.31.1 ->
3.32.13, hypothesis added (6.168.5), and the package's own entry 1.56.16 ->
1.57.4. uv lock --check passes.

Relock commit for the 10/8 uk-data release (d833).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 changelog.d/uk-data-batch-relock.changed.md |  1 +
 pyproject.toml                              |  5 +-
 uv.lock                                     | 89 ++++++++++++++++++---
 3 files changed, 83 insertions(+), 12 deletions(-)
 create mode 100644 changelog.d/uk-data-batch-relock.changed.md

diff --git a/changelog.d/uk-data-batch-relock.changed.md b/changelog.d/uk-data-batch-relock.changed.md
new file mode 100644
index 000000000..e9bbde552
--- /dev/null
+++ b/changelog.d/uk-data-batch-relock.changed.md
@@ -0,0 +1 @@
+Lock policyengine-uk 2.122.2 (from 2.93.0) and policyengine-core 3.32.13 (from 3.31.1); #525 needs policyengine-uk 2.122.0 or later.
diff --git a/pyproject.toml b/pyproject.toml
index f4f37b59b..453fbacf3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,7 @@ authors = [
 license = {file = "LICENSE"}
 requires-python = ">=3.13"
 dependencies = [
-    "policyengine-core>=3.19.4",
+    "policyengine-core>=3.32.13",
     "requests",
     "tqdm",
     "tabulate",
@@ -21,7 +21,7 @@ dependencies = [
     "policyengine",
     "google-cloud-storage",
     "google-auth",
-    "policyengine-uk>=2.107.0",
+    "policyengine-uk>=2.122.0",
     "microcalibrate>=0.18.0",
     "microimpute>=1.0.1",
     "ruff>=0.9.0",
@@ -38,7 +38,6 @@ dependencies = [
 dev = [
     "ruff>=0.9.0",
     "pytest",
-    "hypothesis",
     "torch",
     "l0-python>=0.4.0",
     "tables",
diff --git a/uv.lock b/uv.lock
index a3e9f44d9..5ae821352 100644
--- a/uv.lock
+++ b/uv.lock
@@ -577,6 +577,75 @@ wheels = [
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 ]
 
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+version = "6.168.5"
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+]
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+]
+
 [[package]]
 name = "idna"
 version = "3.11"
@@ -1322,7 +1391,7 @@ wheels = [
 
 [[package]]
 name = "policyengine-core"
-version = "3.31.1"
+version = "3.32.13"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "dpath" },
@@ -1342,14 +1411,14 @@ dependencies = [
     { name = "standard-imghdr" },
     { name = "wheel" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/9f/1e/cd58cb6947720a5b7850c06a988abbc0eef53e2be192ed49a17ed62d78d4/policyengine_core-3.31.1.tar.gz", hash = "sha256:3ca8f666c3e67b0c0ce992fa3dc9d1ff2c095a3860f3d719883588b2f9e44ec3", size = 500450, upload-time = "2026-08-28T05:06:43.16Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/40/0a/b6c27953e1083d3ab2c40330c49580130fff48a2fe9cbfc8d731e92882e6/policyengine_core-3.32.13.tar.gz", hash = "sha256:a870f7e212fdfa1b5fdad7df92aa2b325fd0280ede16a40aeeceea74d44fb864", size = 457899, upload-time = "2026-10-03T11:30:20.572Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/74/ce/995992049664b6b4cecd1e4db76b143e07aaee8239043057436f48ccbe75/policyengine_core-3.31.1-py3-none-any.whl", hash = "sha256:308200b28aa26bb79d7f0badebac391c0a81704d45ba4ef48ac79e88f0b21e6b", size = 245719, upload-time = "2026-08-28T05:06:41.769Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/01/00ebf4c13152347bc5396b40e80445fa92ea96712b9ec0bfdca7c5d11413/policyengine_core-3.32.13-py3-none-any.whl", hash = "sha256:262543c3ac696207f304f55664711b72e894fbad7b566407afa9d30b16086a41", size = 251251, upload-time = "2026-10-03T11:30:19.171Z" },
 ]
 
 [[package]]
 name = "policyengine-uk"
-version = "2.93.0"
+version = "2.122.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "microdf-python" },
@@ -1357,14 +1426,14 @@ dependencies = [
     { name = "pydantic" },
     { name = "tables" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/af/04/ab8fbb526c22d5c422eed374244d977b123241baa6f96a604c600876647a/policyengine_uk-2.93.0.tar.gz", hash = "sha256:e3b8eab824e7dad3a1fd61fa8503442a8b1b2285e05fff7ab60f93b6a9b22578", size = 1246786, upload-time = "2026-08-28T14:55:16.266Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/07/62/4378fd8e493df42fef30c58924dbb0f7ca8111c927302f6e60be862d67d0/policyengine_uk-2.122.2.tar.gz", hash = "sha256:0083793f1d398feef28a64db5508d5a64bcd7834feeade6cde1055d419d505ed", size = 1976779, upload-time = "2026-10-06T02:10:56.786Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6f/74/5e626f9f80b9485b10bcccbef8d93a635abe309bfdfad437b5de38a80fae/policyengine_uk-2.93.0-py3-none-any.whl", hash = "sha256:4f95feffa3b27fab927f9b903f8a9f1f0d63b2fba209d6b8ac010f0f07a5ec7c", size = 2046525, upload-time = "2026-08-28T14:55:14.114Z" },
+    { url = "https://files.pythonhosted.org/packages/63/11/09948c900ca6d58c1050c89b9c93d8f5884244aa6ac95661ef1a2ee9eb9a/policyengine_uk-2.122.2-py3-none-any.whl", hash = "sha256:14cefcedce43ce4a37d659a0ff17ad05695de0dc8e0c5f6e2259a69944cb5558", size = 3118697, upload-time = "2026-10-06T02:10:54.896Z" },
 ]
 
 [[package]]
 name = "policyengine-uk-data"
-version = "1.56.16"
+version = "1.57.4"
 source = { editable = "." }
 dependencies = [
     { name = "google-auth" },
@@ -1392,6 +1461,7 @@ dependencies = [
 dev = [
     { name = "build" },
     { name = "furo" },
+    { name = "hypothesis" },
     { name = "itables" },
     { name = "l0-python" },
     { name = "pytest" },
@@ -1410,6 +1480,7 @@ requires-dist = [
     { name = "google-auth" },
     { name = "google-cloud-storage" },
     { name = "huggingface-hub" },
+    { name = "hypothesis", marker = "extra == 'dev'", specifier = ">=6.168.3" },
     { name = "itables", marker = "extra == 'dev'" },
     { name = "l0-python", marker = "extra == 'dev'", specifier = ">=0.4.0" },
     { name = "microcalibrate", specifier = ">=0.18.0" },
@@ -1418,8 +1489,8 @@ requires-dist = [
     { name = "openpyxl" },
     { name = "pandas" },
     { name = "policyengine" },
-    { name = "policyengine-core", specifier = ">=3.19.4" },
-    { name = "policyengine-uk", specifier = ">=2.93.0" },
+    { name = "policyengine-core", specifier = ">=3.32.13" },
+    { name = "policyengine-uk", specifier = ">=2.122.0" },
     { name = "pydantic", specifier = ">=2.0" },
     { name = "pytest", marker = "extra == 'dev'" },
     { name = "pyyaml" },

From 57ce68ac14ebe2bb6da1965b30555a5a1a919488 Mon Sep 17 00:00:00 2001
From: Max Ghenis <mghenis@gmail.com>
Date: Wed, 7 Oct 2026 03:10:57 -0400
Subject: [PATCH 70/70] Compare the offline OBR target set with the served set,
 not a fixed count (F1)

test_full_target_set_available_offline (from #536) asserted at least 30 OBR
targets offline. The release combines four member PRs that each replace OBR
targets with better sources, which no single PR's CI saw together:
#490 swaps obr/housing_benefit for DWP Housing Benefit targets, #510 swaps
obr/pension_credit for DWP Pension Credit spend and caseload, #533 swaps the
two OBR salary-sacrifice NI relief targets for HMRC's, and #530 merges the
two OBR UC targets into obr/universal_credit. That leaves 29 OBR targets (34
on main), so release CI failed with 29 >= 30.

The test now checks what it was for: offline, the committed workbooks give
exactly the OBR targets that the same workbooks give when served, plus the
receipts and NICs names. A broken fallback still fails it.

Release-only fix for the 10/8 uk-data release (d833).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
---
 .../tests/test_obr_efo_fallback.py            | 44 ++++++++++++++-----
 1 file changed, 34 insertions(+), 10 deletions(-)

diff --git a/policyengine_uk_data/tests/test_obr_efo_fallback.py b/policyengine_uk_data/tests/test_obr_efo_fallback.py
index 07169a88a..5884642cc 100644
--- a/policyengine_uk_data/tests/test_obr_efo_fallback.py
+++ b/policyengine_uk_data/tests/test_obr_efo_fallback.py
@@ -157,18 +157,43 @@ def test_unknown_url_with_failed_download_still_raises(kind, error, cause):
     assert isinstance(raised.value.__cause__, cause)
 
 
-def test_full_target_set_available_offline():
-    """All 34 OBR targets must build from the committed workbooks alone."""
-
-    def get(*args, **kwargs):
-        raise requests.ConnectionError("offline")
-
+def _target_names(get):
     with (
         patch.object(obr.requests, "get", side_effect=get),
         patch.object(obr.time, "sleep", lambda s: None),
     ):
-        targets = obr.get_targets()
-    names = {t.name for t in targets}
+        return {t.name for t in obr.get_targets()}
+
+
+def test_full_target_set_available_offline():
+    """Offline, the committed workbooks yield every OBR target that the same
+    workbooks yield when obr.uk serves them.
+
+    This compares against the served set rather than a fixed count, because
+    other target sources replace OBR targets over time (the 10/8 release swaps
+    OBR's Housing Benefit, Pension Credit and salary-sacrifice NI relief
+    targets for DWP and HMRC ones, and merges its two UC targets into one).
+    """
+    config = load_config()["obr"]
+    bodies = {
+        config["efo_receipts"]: (
+            STORAGE_FOLDER / "obr_efo" / "efo_receipts.xlsx"
+        ).read_bytes(),
+        config["efo_expenditure"]: (
+            STORAGE_FOLDER / "obr_efo" / "efo_expenditure.xlsx"
+        ).read_bytes(),
+    }
+
+    def served(url, *args, **kwargs):
+        return _response(200, bodies[url])
+
+    def offline(*args, **kwargs):
+        raise requests.ConnectionError("offline")
+
+    served_names = _target_names(served)
+    obr._download_workbook.cache_clear()
+    offline_names = _target_names(offline)
+    assert offline_names == served_names
     assert {
         "obr/income_tax",
         "obr/ni_employee",
@@ -176,8 +201,7 @@ def get(*args, **kwargs):
         "obr/ni_self_employed",
         "obr/capital_gains_tax",
         "obr/vat",
-    } <= names
-    assert len(names) >= 30
+    } <= offline_names
 
 
 @pytest.mark.parametrize("url_key", _EFO_URL_KEYS)