MaxGhenis · Oct 8, 2026 · Oct 11, 2026 · Oct 16, 2026 · Oct 16, 2026
diff --git a/.gitignore b/.gitignore
@@ -26,3 +26,6 @@ policyengine_uk_data/storage/local_geography_weights.csv.gz
 
 # Hypothesis example database (property-based tests)
 .hypothesis/
+
+# Local build-job evidence and Git metadata
+.job/
diff --git a/changelog.d/frs-benunit-reported-capital.changed.md b/changelog.d/frs-benunit-reported-capital.changed.md
@@ -0,0 +1 @@
+- Fill policyengine-uk's `benunit_reported_capital` with each benefit unit's FRS total capital (TOTCAPB4; TOTCAPB3 for earlier survey years) in place of `pension_credit_reported_capital`. policyengine-uk's Universal Credit and Pension Credit reported capital both default to it, so Universal Credit's capital test now uses the FRS's own benefit-unit capital instead of the household wealth imputation, as Pension Credit already did. SPI-synthetic copies still get -1 (household proxy).
diff --git a/policyengine_uk_data/datasets/frs.py b/policyengine_uk_data/datasets/frs.py
@@ -790,25 +790,31 @@ 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.
+def derive_benunit_reported_capital(benunit: pd.DataFrame) -> np.ndarray:
+    """Each benefit unit's capital as the FRS records it.
 
     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.
+    fallback for earlier survey years. This fills policyengine-uk's
+    ``benunit_reported_capital``, an input that records the observed figure
+    only. Each means test derives its own capital from it: Universal Credit
+    (Welfare Reform Act 2012 s. 5) and Pension Credit (State Pension Credit
+    Act 2002 s. 5) both assess the claimant's and partner's capital together,
+    which this benefit-unit measure covers. As a means-test figure it is an
+    approximation, not the assessed amount: it covers financial assets only
+    (second homes and land, which both tests also count, are not in it), and
+    no disregard or valuation rule 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 shares
+    it out between the household's benefit units.
+
+    Both source fields are derived pound amounts: DWP has already estimated
+    banded responses within those totals. No new band midpoint is applied
+    here. A finite, non-negative TOTCAPB4 takes precedence, with a row-wise
+    TOTCAPB3 fallback. If neither is usable, -1 tells policyengine-uk to use
+    the household proxy; an observed zero is kept.
     """
     capital = np.full(len(benunit), np.nan)
     for column in ("totcapb3", "totcapb4"):  # later columns take precedence
@@ -1843,12 +1849,11 @@ def determine_education_level(fted_val, typeed2_val, age_val):
 
     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)
-    )
+    # The benefit unit's capital as the FRS records it. policyengine-uk's
+    # Universal Credit and Pension Credit capital (`uc_reported_capital`,
+    # `pension_credit_reported_capital`) default to it in place of the
+    # household wealth proxy.
+    pe_benunit["benunit_reported_capital"] = derive_benunit_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/datasets/imputations/income.py b/policyengine_uk_data/datasets/imputations/income.py
@@ -461,16 +461,24 @@ def impute_over_incomes(
     return dataset
 
 
+REPORTED_CAPITAL_COLUMNS = (
+    "benunit_reported_capital",
+    "pension_credit_reported_capital",
+    "uc_reported_capital",
+)
+
+
 def clear_frs_reported_capital(dataset: UKSingleYearDataset) -> UKSingleYearDataset:
-    """Set ``pension_credit_reported_capital`` to -1 (none recorded).
+    """Set the FRS-recorded capital columns 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.
+    uses the household capital proxy for these rows in every means test.
     """
-    if "pension_credit_reported_capital" in dataset.benunit.columns:
-        dataset.benunit["pension_credit_reported_capital"] = -1.0
+    for column in REPORTED_CAPITAL_COLUMNS:
+        if column in dataset.benunit.columns:
+            dataset.benunit[column] = -1.0
     return dataset
 
 

diff --git a/policyengine_uk_data/storage/uprating_factors.csv b/policyengine_uk_data/storage/uprating_factors.csv
@@ -1,6 +1,7 @@
 Variable,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034
 afcs_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 alcohol_and_tobacco_consumption,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
+benunit_reported_capital,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
 bsp_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 bus_fare_spending,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 bus_fare_spending_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
@@ -65,7 +66,10 @@ petrol_spending,1.0,1.104066,1.63457,1.795584,1.530966,1.483159,1.452034,1.40618
 private_pension_income,1.0,1.003,1.05315,1.105808,1.161098,1.202781,1.252095,1.29091,1.329638,1.366867,1.406507,1.438856,1.47195,1.506835,1.542698
 private_pension_wealth,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
 private_transfer_income,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
+property_finance_costs,1.0,0.9774,1.120296,1.705538,2.17115,2.377844,2.567358,2.834363,2.969279,3.108241,3.280127,3.461518,3.65294,3.854948,4.068126
+property_finance_costs_brought_forward,1.0,0.9774,1.120296,1.705538,2.17115,2.377844,2.567358,2.834363,2.969279,3.108241,3.280127,3.461518,3.65294,3.854948,4.068126
 property_income,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
+property_rental_income,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
 recreation_consumption,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 restaurants_and_hotels_consumption,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 savings,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
@@ -80,6 +84,7 @@ statutory_sick_pay,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.34604
 student_loan_repayments,1.0,1.059,1.124023,1.193937,1.254828,1.321333,1.366259,1.399049,1.428429,1.459854,1.494891,1.544521,1.601823,1.661731,1.724379
 sublet_income,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
 transport_consumption,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
+uc_reported_capital,1.0,1.0892,1.200189,1.26404,1.311062,1.368486,1.408446,1.453939,1.499011,1.543382,1.591998,1.642146,1.693874,1.747231,1.802269
 universal_credit_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 winter_fuel_allowance_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
 working_tax_credit_reported,1.0,1.04,1.134328,1.217134,1.247562,1.289979,1.319649,1.346042,1.372963,1.400422,1.42843,1.456999,1.486139,1.515862,1.546179
diff --git a/policyengine_uk_data/storage/uprating_growth_factors.csv b/policyengine_uk_data/storage/uprating_growth_factors.csv
@@ -1,6 +1,7 @@
 Variable,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034
 afcs_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 alcohol_and_tobacco_consumption,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
+benunit_reported_capital,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
 bsp_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 bus_fare_spending,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 bus_fare_spending_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
@@ -65,7 +66,10 @@ petrol_spending,0,0.104066,0.4805,0.098505,-0.147372,-0.031227,-0.020986,-0.0315
 private_pension_income,0,0.003,0.05,0.05,0.05,0.0359,0.041,0.031,0.030001,0.027999,0.029001,0.023,0.023,0.0237,0.0238
 private_pension_wealth,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
 private_transfer_income,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
+property_finance_costs,0,-0.0226,0.1462,0.522399,0.273,0.0952,0.0797,0.104,0.0476,0.0468,0.0553,0.0553,0.0553,0.0553,0.0553
+property_finance_costs_brought_forward,0,-0.0226,0.1462,0.522399,0.273,0.0952,0.0797,0.104,0.0476,0.0468,0.0553,0.0553,0.0553,0.0553,0.0553
 property_income,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
+property_rental_income,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
 recreation_consumption,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 restaurants_and_hotels_consumption,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 savings,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
@@ -80,6 +84,7 @@ statutory_sick_pay,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.0
 student_loan_repayments,0,0.059,0.0614,0.0622,0.051,0.052999,0.034001,0.024,0.021,0.022,0.024,0.0332,0.0371,0.0374,0.0377
 sublet_income,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
 transport_consumption,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
+uc_reported_capital,0,0.0892,0.1019,0.053201,0.0372,0.0438,0.0292,0.0323,0.031,0.0296,0.0315,0.0315,0.0315,0.0315,0.0315
 universal_credit_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 winter_fuel_allowance_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
 working_tax_credit_reported,0,0.04,0.0907,0.073,0.025,0.034,0.023,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
diff --git a/policyengine_uk_data/tests/test_benunit_reported_capital.py b/policyengine_uk_data/tests/test_benunit_reported_capital.py
@@ -0,0 +1,140 @@
+"""`benunit_reported_capital` from the FRS benefit-unit capital measure
+(TOTCAPB4, falling back to TOTCAPB3). policyengine-uk's Universal Credit and
+Pension Credit reported capital default to it.
+
+Invariants:
+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.
+
+Uprating is checked against the engine for every row, including this
+column, in test_uprating_factors_table.py.
+"""
+
+import numpy as np
+import pandas as pd
+
+from policyengine_uk_data.datasets.frs import derive_benunit_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_benunit_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_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_benunit_reported_capital(benunit)
+    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_benunit_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_benunit_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_benunit_reported_capital(pd.DataFrame({"totcapb4": 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)
+
+
+def test_uprating_rows_match_the_model():
+    """Capital follows the engine's load-time GDP growth at calibration too."""
+    from policyengine_uk_data.storage import STORAGE_FOLDER
+    from policyengine_uk_data.utils.uprating import (
+        END_YEAR,
+        START_YEAR,
+        UPRATING_TABLE_DECIMALS,
+        policyengine_uk_load_time_index,
+    )
+
+    years = range(START_YEAR, END_YEAR + 1)
+    expected = policyengine_uk_load_time_index().loc["benunit_reported_capital"]
+    expected = expected.round(UPRATING_TABLE_DECIMALS)
+    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["benunit_reported_capital", str(y)] == expected[y]
+        expected_growth = (
+            0
+            if y == START_YEAR
+            else round(expected[y] / expected[y - 1] - 1, UPRATING_TABLE_DECIMALS)
+        )
+        assert growth.loc["benunit_reported_capital", str(y)] == expected_growth
+
+
+def test_programme_capital_defaults_to_the_recorded_input():
+    """Programme formulas and legacy stored inputs use the same capital index."""
+    from policyengine_uk.system import system
+
+    from policyengine_uk_data.storage import STORAGE_FOLDER
+
+    assert not system.variables["benunit_reported_capital"].formulas
+    factors = pd.read_csv(STORAGE_FOLDER / "uprating_factors.csv").set_index("Variable")
+    for name in ("pension_credit_reported_capital", "uc_reported_capital"):
+        assert system.variables[name].formulas
+        # The engine also projects older datasets that store programme
+        # inputs directly, so its load-time index keeps these rows.
+        np.testing.assert_array_equal(
+            factors.loc[name], factors.loc["benunit_reported_capital"]
+        )
+
+
+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(
+            {
+                "benunit_reported_capital": [0.0, 300.0, -1.0],
+                "uc_reported_capital": [5.0, -1.0, 7.0],
+            }
+        )
+    )
+    cleared = clear_frs_reported_capital(copy).benunit
+    assert cleared["benunit_reported_capital"].tolist() == [-1.0, -1.0, -1.0]
+    assert cleared["uc_reported_capital"].tolist() == [-1.0, -1.0, -1.0]
+    without = SimpleNamespace(benunit=pd.DataFrame({"benunit_id": [1, 2]}))
+    assert list(clear_frs_reported_capital(without).benunit.columns) == ["benunit_id"]
Original file line number	Diff line number	Diff line change
		@@ -0,0 +1 @@
		- Fill policyengine-uk's `benunit_reported_capital` with each benefit unit's FRS total capital (TOTCAPB4; TOTCAPB3 for earlier survey years) in place of `pension_credit_reported_capital`. policyengine-uk's Universal Credit and Pension Credit reported capital both default to it, so Universal Credit's capital test now uses the FRS's own benefit-unit capital instead of the household wealth imputation, as Pension Credit already did. SPI-synthetic copies still get -1 (household proxy).