Refresh UK target source vintages

policyengine_uk_data/targets/sources.yaml

@@ -7,8 +7,8 @@ obr:
   vintage: "march_2026"
 
 hmrc:
-  spi_collated: "https://assets.publishing.service.gov.uk/media/67cabb37ade26736dbf9ffe5/Collated_Tables_3_1_to_3_17_2223.ods"
-  spi_geography: "https://assets.publishing.service.gov.uk/media/67cabb7f8c1076c796a45bec/Collated_Tables_3_12_to_3_15a_2223.ods"
+  spi_collated: "https://assets.publishing.service.gov.uk/media/69f1f12d2fae53a03709682f/Collated_Tables_3_1_to_3_11_2324.ods"
+  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"
   salary_sacrifice_table_6: "https://assets.publishing.service.gov.uk/media/687a294e312ee8a5f0806b6d/Tables_6_1_and_6_2.csv"
 

policyengine_uk_data/targets/sources/hmrc_spi.py

@@ -1,7 +1,7 @@
 """HMRC Survey of Personal Incomes targets.
 
 Downloads and parses the SPI ODS (Tables 3.6 and 3.7) to get income
-distributions by total income band and income type for 2022-23.
+distributions by total income band and income type for 2023-24.
 
 For future year projections, the microsimulation uprates these base
 year distributions forward using PolicyEngine's uprating factors.
@@ -54,8 +54,8 @@
 ]
 _BAND_UPPER = _BAND_LOWER[1:] + [float("inf")]
 
-# SPI year: the ODS is for tax year 2022-23, mapped to calendar 2023
-_SPI_YEAR = 2023
+# SPI year: the ODS is for tax year 2023-24, mapped to calendar 2024
+_SPI_YEAR = 2024
 
 # HMRC Property Rental Income Statistics show ~1.9x more property income
 # than the SPI (£46.68bn vs £24.5bn for 2020-21), because SPI only covers

policyengine_uk_data/targets/sources/ons_demographics.py

@@ -1,6 +1,6 @@
 """ONS population projections and demographic targets.
 
-Downloads the ONS 2022-based principal population projection for the
+Downloads the ONS 2024-based principal population projection for the
 UK to extract total population and gender × age band targets.
 
 For regional age breakdowns (12 regions × 9 age bands), reads the
@@ -36,7 +36,7 @@
 _UK_ZIP_URL = (
     "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/"
     "populationandmigration/populationprojections/datasets/"
-    "z1zippedpopulationprojectionsdatafilesuk/2022based/uk.zip"
+    "z1zippedpopulationprojectionsdatafilesuk/2024based/uk.zip"
 )
 
 _REF_REGION = (
@@ -81,7 +81,8 @@ def _download_uk_projection() -> pd.DataFrame:
     r = requests.get(_UK_ZIP_URL, headers=HEADERS, allow_redirects=True, timeout=120)
     r.raise_for_status()
     z = zipfile.ZipFile(io.BytesIO(r.content))
-    with z.open("uk/uk_ppp_machine_readable.xlsx") as f:
+    projection_member = _find_projection_member(z.namelist())
+    with z.open(projection_member) as f:
         df = pd.read_excel(
             io.BytesIO(f.read()),
             sheet_name="Population",
@@ -90,31 +91,52 @@ def _download_uk_projection() -> pd.DataFrame:
     return df
 
 
+def _find_projection_member(names: list[str]) -> str:
+    """Find the UK principal projection workbook inside the ONS zip."""
+    for name in names:
+        if name.endswith("uk_ppp_machine_readable.xlsx"):
+            return name
+    raise RuntimeError(
+        "ONS UK projection zip did not contain uk_ppp_machine_readable.xlsx"
+    )
+
+
 def _aggregate_ages(
     df: pd.DataFrame, sex: str, low: int, high: int, years: list[int]
 ) -> dict[int, float]:
     """Sum population for a sex and age range across years."""
     sex_filter = "Females" if sex == "female" else "Males"
-    mask = (df["Sex"] == sex_filter) & (
-        df["Age"].apply(lambda a: isinstance(a, int) and low <= a <= high)
-    )
+    ages = pd.to_numeric(df["Age"], errors="coerce")
+    mask = (df["Sex"] == sex_filter) & ages.between(low, high)
     subset = df[mask]
     result = {}
     for y in years:
-        if y in subset.columns:
-            result[y] = float(subset[y].sum())
+        column = _year_column(subset, y)
+        if column is not None:
+            result[y] = float(subset[column].sum())
     return result
 
 
+def _year_column(df: pd.DataFrame, year: int) -> int | str | None:
+    """Return the workbook column for a year across ONS vintages."""
+    if year in df.columns:
+        return year
+    string_year = str(year)
+    if string_year in df.columns:
+        return string_year
+    return None
+
+
 def _parse_uk_totals(df: pd.DataFrame) -> list[Target]:
     """Extract UK total population and gender × age bands."""
     targets = []
 
     # UK total
     uk_pop = {}
     for y in _YEARS:
-        if y in df.columns:
-            uk_pop[y] = float(df[y].sum())
+        column = _year_column(df, y)
+        if column is not None:
+            uk_pop[y] = float(df[column].sum())
     if uk_pop:
         targets.append(
             Target(

policyengine_uk_data/targets/sources/ons_households.py

@@ -22,7 +22,7 @@
     "https://www.ons.gov.uk/file?uri=/peoplepopulationandcommunity/"
     "birthsdeathsandmarriages/families/datasets/"
     "familiesandhouseholdsfamiliesandhouseholds/"
-    "current/familiesandhouseholdsuk2024.xlsx"
+    "current/familiesandhouseholdsuk2025.xlsx"
 )
 _REF = (
     "https://www.ons.gov.uk/peoplepopulationandcommunity/"

policyengine_uk_data/tests/test_ons_demographics_targets.py

@@ -0,0 +1,24 @@
+import pandas as pd
+import pytest
+
+from policyengine_uk_data.targets.sources.ons_demographics import (
+    _aggregate_ages,
+    _find_projection_member,
+)
+
+
+def test_aggregate_ages_accepts_string_age_values():
+    df = pd.DataFrame(
+        {
+            "Sex": ["Females", "Females", "Females", "Males"],
+            "Age": ["14", "15", "90", "15"],
+            2025: [1, 2, 4, 8],
+        }
+    )
+
+    assert _aggregate_ages(df, "female", 15, 90, [2025]) == {2025: 6.0}
+
+
+def test_find_projection_member_fails_loudly():
+    with pytest.raises(RuntimeError, match="uk_ppp_machine_readable"):
+        _find_projection_member(["uk/readme.txt"])

policyengine_uk_data/tests/test_population.py

@@ -1,6 +1,6 @@
 def test_population(baseline):
     population = baseline.calculate("people", 2025).sum() / 1e6
-    POPULATION_TARGET = 69.5  # ONS 2022-based projection for 2025, millions: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2022based
+    POPULATION_TARGET = 69.5  # ONS 2024-based projection for 2025, millions: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationprojections/bulletins/nationalpopulationprojections/2024based
     # Tightened from 7% to 4% after data-pipeline improvements in April 2026
     # (stage-2 QRF imputation #362, TFC target refresh #363, reported-anchor
     # takeup #359) pulled the weighted UK population down from ~74M (+6.5%)

policyengine_uk_data/tests/test_population_fidelity.py

@@ -16,7 +16,7 @@
 
 import numpy as np
 
-POPULATION_TARGET = 69.5  # ONS 2022-based projection for 2025, millions
+POPULATION_TARGET = 69.5  # ONS 2024-based projection for 2025, millions
 TOLERANCE = 0.04  # 4% — covers ~1.6%-3.3% stochastic calibration variance
 
 

policyengine_uk_data/tests/test_property_income_targets.py

@@ -10,16 +10,20 @@
 def test_property_income_targets_scaled():
     """Property income targets should be ~1.9x the raw SPI values.
 
-    Raw SPI 2022-23 total is ~£27bn. After scaling, targets for the
-    base year should be ~£52bn (matching HMRC rental income stats).
+    Raw SPI 2023-24 total is scaled up to better match HMRC rental
+    income statistics, which cover more landlords than SPI.
     """
-    targets = get_all_targets(year=2023)
+    base_year = 2024
+    targets = get_all_targets(year=base_year)
     total = sum(
-        t.values[2023]
+        t.values[base_year]
         for t in targets
-        if "property_income" in t.name and "count" not in t.name and 2023 in t.values
+        if "property_income" in t.name
+        and "count" not in t.name
+        and base_year in t.values
     )
-    # Raw SPI gives ~£27bn, scaled by 1.9x should give ~£52bn
+    # Raw SPI gives roughly half of all landlord income; scaling should
+    # leave the current base-year target in this broad administrative range.
     assert total > 45e9, (
         f"Property income target total £{total / 1e9:.1f}bn is below £45bn. "
         "Scaling factor may not be applied."