Add main residence value to LA calibration

changelog.d/370.md

@@ -0,0 +1 @@
+Add LA-level main residence value calibration targets for all 360 UK local authorities, built from directly observed indicators (HMLR UK HPI × English Housing Survey ownership share × Census household count) and wired into the LA reweighter alongside the existing tenure and rent targets.

policyengine_uk_data/datasets/local_areas/local_authorities/loss.py

@@ -11,6 +11,7 @@
 - ONS income: ONS small area income estimates
 - Tenure: English Housing Survey
 - Private rent: VOA/ONS private rental market statistics
+- Main residence value: HMLR UK HPI × ownership share × household count
 """
 
 from policyengine_uk import Microsimulation
@@ -38,6 +39,7 @@
     load_tenure_data,
     load_private_rents,
 )
+from policyengine_uk_data.targets.sources.la_land import load_la_avg_prices
 
 
 def create_local_authority_target_matrix(
@@ -252,6 +254,47 @@ def create_local_authority_target_matrix(
         national_rent * la_household_share,
     )
 
+    # ── Main residence value (HMLR × ownership share × households) ─
+    # Derived proxy target: a product of three observed inputs, not a
+    # directly observed LA total. Mirrors the private-rent target's
+    # multiplicative shape. Lineage caveat: matrix col is WAS-imputed
+    # stock wealth; the target uses HMLR HPI transaction prices —
+    # different price concepts on the two sides of the constraint.
+    # See targets/sources/la_land.py for full provenance.
+    la_prices = load_la_avg_prices()
+    tenure_merged = tenure_merged.merge(
+        la_prices[["code", "avg_house_price"]], on="code", how="left"
+    )
+
+    matrix["housing/main_residence_value"] = sim.calculate(
+        "main_residence_value"
+    ).values
+
+    ownership_share_la = (
+        tenure_merged["owned_outright_pct"].fillna(0)
+        + tenure_merged["owned_mortgage_pct"].fillna(0)
+    ) / 100
+    tenure_merged["main_residence_value_target"] = (
+        tenure_merged["avg_house_price"]
+        * ownership_share_la
+        * tenure_merged["households"]
+    )
+
+    has_property = (
+        tenure_merged["avg_house_price"].notna()
+        & tenure_merged["owned_outright_pct"].notna()
+        & tenure_merged["households"].notna()
+    ).values
+    national_property = (
+        original_weights * matrix["housing/main_residence_value"].values
+    ).sum()
+
+    y["housing/main_residence_value"] = np.where(
+        has_property,
+        tenure_merged["main_residence_value_target"].values,
+        national_property * la_household_share,
+    )
+
     # ── Country mask ───────────────────────────────────────────────
     country_mask = create_country_mask(
         household_countries=sim.calculate("country").values,