feat(epc-prediction): cohort-median floor-area estimate (#1223)

Per-component method, not a global template change: the predicted floor
area is now the cohort median (the MAD-minimising point estimate of the
target's size) rather than whichever structural template's own area. The
calculator derives heat loss from building-part geometry, not this scalar,
so decoupling them is safe and the scalar becomes a better size estimate.

floor_area mean|.|: corpus (150pc/514 targets) 10.62 -> 10.48; fixture
12.2175 -> 11.8983 (ceiling ratcheted down). No other component moves.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

domain/epc_prediction/epc_prediction.py

@@ -56,10 +56,13 @@ class EpcPrediction:
         self, target: PredictionTarget, comparables: ComparableProperties
     ) -> EpcPropertyData:
         """Predict the target's EPC picture: copy a representative template's
-        structure (coherent for the calculator), then set the homogeneous
-        categoricals to the cohort mode."""
+        structure (coherent for the calculator), set the predicted floor area to
+        the cohort median (the best point estimate of the target's size, decoupled
+        from the one template's own area), then set the homogeneous categoricals
+        to the cohort mode."""
         template: Comparable = self._template(comparables)
         predicted: EpcPropertyData = copy.deepcopy(template.epc)
+        predicted.total_floor_area_m2 = _median_floor_area(comparables.members)
         self._apply_categorical_modes(predicted, comparables)
         self._apply_overrides(predicted, target)
         return predicted
@@ -213,6 +216,15 @@ def _main_floor_attr(comparable: Comparable, attr: str) -> Optional[int]:
     return value
 
 
+def _median_floor_area(members: tuple[Comparable, ...]) -> float:
+    """The cohort's median floor area — the point estimate of the target's size.
+    The median minimises mean absolute deviation, so it is the best single guess
+    for an unknown neighbour's area; it is set independently of the structural
+    template (the calculator derives heat loss from the building-part geometry,
+    not this scalar, so the two need not agree)."""
+    return statistics.median(c.epc.total_floor_area_m2 for c in members)
+
+
 def _age_band_index(comparable: Comparable) -> Optional[int]:
     """The main building part's construction-age-band position (A=0 … L=11), or
     None when no recognisable band is lodged."""

tests/domain/epc_prediction/test_component_accuracy_gate.py

@@ -57,7 +57,7 @@ _RATE_FLOORS: dict[str, float] = {
 # the predicted picture clusters at a mapper-default 4 windows while actuals
 # spread 1-21, yet total_window_area (the SAP-relevant signal) stays tight.
 _RESIDUAL_CEILINGS: dict[str, float] = {
-    "floor_area": 12.2175,
+    "floor_area": 11.8983,
     "total_window_area": 4.4067,
     "building_parts": 0.3333,
     "door_count": 0.6389,

tests/domain/epc_prediction/test_epc_prediction.py

@@ -226,6 +226,29 @@ def test_recency_weights_roof_insulation_mode() -> None:
     assert predicted.sap_building_parts[0].roof_insulation_thickness == 300
 
 
+def test_floor_area_is_the_cohort_median_not_the_templates_own_area() -> None:
+    # Arrange — an even-sized cohort whose median (70) falls between members, so
+    # the size-representative template (the first member closest to the median,
+    # 60 m²) does not itself sit on the median. The predicted floor area is a
+    # point estimate of the target's size, best served by the cohort median (the
+    # MAD-minimising estimator), decoupled from whichever template seeds the
+    # structure.
+    cohort = _cohort(
+        _epc(floor_area=40.0),
+        _epc(floor_area=60.0),
+        _epc(floor_area=80.0),
+        _epc(floor_area=100.0),
+    )
+
+    # Act
+    predicted: EpcPropertyData = EpcPrediction().predict(
+        PredictionTarget(postcode="LS6 1AA", property_type="2"), cohort
+    )
+
+    # Assert — the floor area is the cohort median (70), not the template's 60.
+    assert predicted.total_floor_area_m2 == 70.0
+
+
 def test_categorical_mode_leans_on_size_similar_neighbours() -> None:
     # Arrange — a count majority (three) carries wall-insulation 9, but two of
     # them are 400 m² size outliers; the cohort centre (median 100 m²) holds