Add housing implied admin targets

changelog_entry.yaml

@@ -0,0 +1,4 @@
+- bump: patch
+  changes:
+    fixed:
+    - Added mortgage and private rent targets.

policyengine_uk_data/datasets/imputations/income.py

@@ -153,14 +153,25 @@ def impute_over_incomes(
         DataFrame with imputed income components.
     """
     dataset = dataset.copy()
-    input_df = Microsimulation(dataset=dataset).calculate_dataframe(
-        ["age", "gender", "region"]
-    )
+    sim = Microsimulation(dataset=dataset)
+    input_df = sim.calculate_dataframe(["age", "gender", "region"])
+    original_income_total = dataset.person[IMPUTATIONS].copy().sum().sum()
     output_df = model.predict(input_df)
 
     for column in output_variables:
         dataset.person[column] = output_df[column].fillna(0).values
 
+    new_income_total = dataset.person[IMPUTATIONS].sum().sum()
+    adjustment_factor = new_income_total / original_income_total
+    # Adjust rent and mortgage interest and capital repayments proportionally
+    dataset.household["rent"] = dataset.household["rent"] * adjustment_factor
+    dataset.household["mortgage_interest_repayment"] = (
+        dataset.household["mortgage_interest_repayment"] * adjustment_factor
+    )
+    dataset.household["mortgage_capital_repayment"] = (
+        dataset.household["mortgage_capital_repayment"] * adjustment_factor
+    )
+
     return dataset
 
 

policyengine_uk_data/tests/microsimulation/reforms_config.yaml

@@ -16,7 +16,7 @@ reforms:
   parameters:
     gov.hmrc.child_benefit.amount.additional: 25
 - name: Reduce Universal Credit taper rate to 20%
-  expected_impact: -27.2
+  expected_impact: -30.9
   parameters:
     gov.dwp.universal_credit.means_test.reduction_rate: 0.2
 - name: Raise Class 1 main employee NICs rate to 10%

policyengine_uk_data/utils/calibrate.py

@@ -15,7 +15,7 @@ def calibrate_local_areas(
     area_count: int,
     weight_file: str,
     dataset_key: str = "2025",
-    epochs: int = 256,
+    epochs: int = 512,
     excluded_training_targets=[],
     log_csv=None,
     verbose: bool = False,

policyengine_uk_data/utils/loss.py

@@ -441,8 +441,6 @@ def pe_count(*variables):
     target_names.append("hmrc/salary_sacrifice_contributions")
     target_values.append(SS_CONTRIBUTIONS_2024 * uprating_factor)
 
-    print(target_names[-4:], target_values[-4:])
-
     # Add two-child limit targets.
     child_is_affected = (
         sim.map_result(
@@ -574,6 +572,30 @@ def pe_count(*variables):
     target_names.append("nts/households_two_plus_vehicles")
     target_values.append(total_households * NTS_TWO_PLUS_VEHICLE_RATE)
 
+    RENT_ESTIMATE = {
+        "private_renter": 1_400
+        * 12
+        * 4.7e6,  # https://www.ons.gov.uk/economy/inflationandpriceindices/bulletins/privaterentandhousepricesuk/january2025
+        "owner_mortgage": 1_100 * 12 * 7.5e6,
+    }
+
+    # Housing affordability targets
+    # Total mortgage payments (capital + interest)
+    mortgage_capital = pe("mortgage_capital_repayment")
+    mortgage_interest = pe("mortgage_interest_repayment")
+    total_mortgage = mortgage_capital + mortgage_interest
+    df["housing/total_mortgage"] = total_mortgage
+    target_names.append("housing/total_mortgage")
+    target_values.append(RENT_ESTIMATE["owner_mortgage"])
+
+    # Total rent by tenure type
+    rent = pe("rent")
+    tenure_type = sim.calculate("tenure_type", map_to="household").values
+
+    df["housing/rent_private"] = rent * (tenure_type == "RENT_PRIVATELY")
+    target_names.append("housing/rent_private")
+    target_values.append(RENT_ESTIMATE["private_renter"])
+
     combined_targets = pd.concat(
         [
             targets,