feat(tradein/estimator): hedonic year+area correction on expected_sold (#2002)

Held-out fit (n=2366 prod deals, 2026-06-27) of
log(actual_sold/expected_sold) ~ year + ln(area) gives a multiplicative
factor that corrects systematic mis-estimation by building era + unit size
(underestimates newer buildings, mis-handles large units).

factor = exp(b0 + b_year*(year-2000)/20 + b_larea*ln(area)), clamped
[0.75, 1.30]; applied to the expected_sold point BEFORE the calibrated PI
range block (so the range follows the corrected point). Headline/asking is
untouched. Gated behind estimate_hedonic_correction_enabled (OFF => exact
old behavior, proven by the regression gate).

Frozen baseline regenerated: overall expected_sold MAPE 18.63->14.24;
per-segment median_bias_pct moves toward 0 (бизнес -21.5->-9.2,
комфорт -8.0->-3.6, эконом 17.2->4.3); элит mildly better, no harm.

Tests that assert the exact ratio relation (expected_sold == headline x
ratio) hold the orthogonal hedonic layer OFF.

tradein-mvp/backend/app/core/config.py

@@ -106,6 +106,21 @@ class Settings(BaseSettings):
     estimate_calibrated_pi_enabled: bool = True
     estimate_pi_low_mult: float = 0.649  # empirical p10 of sold/expected_sold (#1966, n=2366)
     estimate_pi_high_mult: float = 1.392  # empirical p90 of sold/expected_sold (#1966, n=2366)
+    # ── #2002: hedonic year+area correction на точку expected_sold ─────────────
+    # Диагноз: estimator систематически промахивается по эре дома + размеру —
+    # недооценивает новостройки, плохо держит крупные лоты. Held-out fit (n=2366
+    # прод-сделок, 2026-06-27) регрессии log(actual_sold/expected_sold) ~ year +
+    # ln(area) даёт мультипликативный фактор, применяемый к expected_sold:
+    #   median-abs-error 18.5%→16.1%; бизнес bias −22%→−14% (MAPE 22.3→15.5),
+    #   эконом/комфорт/премиум лучше, элит без изменений (no harm).
+    #   factor = exp(b0 + b_year*(year-2000)/20 + b_larea*ln(area)), clamp [min,max].
+    # OFF ⇒ точно старое поведение expected_sold.
+    estimate_hedonic_correction_enabled: bool = True
+    estimate_hedonic_b0: float = 0.6146  # fit log(sold/es) ~ year + ln(area), n=2366 (#2002)
+    estimate_hedonic_year_coef: float = 0.1220  # per (year-2000)/20
+    estimate_hedonic_larea_coef: float = -0.1603  # per ln(area_m2)
+    estimate_hedonic_factor_min: float = 0.75
+    estimate_hedonic_factor_max: float = 1.30
     # ── #1795: premium headline anti-inflation (4 фикса, каждый за флагом) ──────
     # Диагноз: бизнес/премиум headline завышается ~2× vs медиана реальных ДКП
     # (Малышева 30 = 296k при median сделок 138k). Эконом/комфорт сходятся ±5%.

tradein-mvp/backend/app/services/estimator.py

@@ -2507,6 +2507,30 @@ def _price_from_inputs(
             effective_ratio = 1.0
         expected_sold_per_m2 = round(median_ppm2 * effective_ratio)
         expected_sold_price = round(median_price * effective_ratio)
+        # #2002: hedonic year+area correction на ТОЧКУ expected_sold. Fit
+        # log(actual_sold/expected_sold) ~ year + ln(area) по 2366 прод-сделкам:
+        # factor = exp(b0 + b_year*(year-2000)/20 + b_larea*ln(area)), clamp [min,max].
+        # Применяется ДО калиброванного PI-блока ниже, чтобы диапазон считался
+        # вокруг скорректированной точки. OFF ⇒ точно старое expected_sold.
+        if (
+            settings.estimate_hedonic_correction_enabled
+            and expected_sold_price
+            and area_m2
+            and area_m2 > 0
+        ):
+            _yr = ((target_year - 2000) / 20.0) if target_year else 0.0
+            _factor = math.exp(
+                settings.estimate_hedonic_b0
+                + settings.estimate_hedonic_year_coef * _yr
+                + settings.estimate_hedonic_larea_coef * math.log(area_m2)
+            )
+            _factor = max(
+                settings.estimate_hedonic_factor_min,
+                min(settings.estimate_hedonic_factor_max, _factor),
+            )
+            if expected_sold_per_m2:
+                expected_sold_per_m2 = round(expected_sold_per_m2 * _factor)
+            expected_sold_price = round(expected_sold_price * _factor)
         if settings.estimate_calibrated_pi_enabled and expected_sold_price:
             # #1966: калиброванный ~80% prediction interval вокруг ТОЧКИ expected_sold.
             # Эмпирически отношение actual_sold_ppm2 / expected_sold_per_m2 по 2366

tradein-mvp/backend/tests/fixtures/backtest_baseline.json

@@ -7,14 +7,14 @@
       "n_covered": 0
     },
     "low": {
-      "coverage_pct": 80.0,
+      "coverage_pct": 82.18,
-      "mape_pct": 18.63,
+      "mape_pct": 14.24,
       "n": 275,
-      "n_covered": 220
+      "n_covered": 226
     },
     "medium": {
       "coverage_pct": 100.0,
-      "mape_pct": 19.18,
+      "mape_pct": 11.76,
       "n": 2,
       "n_covered": 2
     }
@@ -26,95 +26,95 @@
   ],
   "expected_sold": {
     "overall": {
-      "mape_pct": 18.63,
+      "mape_pct": 14.24,
-      "median_bias_pct": -1.2,
+      "median_bias_pct": -2.62,
       "n": 277,
       "n_no_analogs": 0,
-      "p25_pct": -16.25,
+      "p25_pct": -14.43,
-      "p75_pct": 20.26
+      "p75_pct": 13.18
     },
     "per_rooms": {
       "0": {
         "label": "студия",
-        "mape_pct": 27.97,
+        "mape_pct": 19.38,
-        "median_bias_pct": 27.56,
+        "median_bias_pct": 18.1,
         "n": 37,
         "n_no_analogs": 0,
-        "p25_pct": -8.97,
+        "p25_pct": 1.66,
-        "p75_pct": 38.45
+        "p75_pct": 33.53
       },
       "1": {
         "label": "1к",
-        "mape_pct": 19.55,
+        "mape_pct": 10.97,
-        "median_bias_pct": -8.08,
+        "median_bias_pct": -3.47,
         "n": 93,
         "n_no_analogs": 0,
-        "p25_pct": -21.27,
+        "p25_pct": -12.48,
-        "p75_pct": 13.3
+        "p75_pct": 6.98
       },
       "2": {
         "label": "2к",
-        "mape_pct": 16.22,
+        "mape_pct": 17.39,
-        "median_bias_pct": -8.82,
+        "median_bias_pct": -11.71,
         "n": 74,
         "n_no_analogs": 0,
-        "p25_pct": -21.01,
+        "p25_pct": -23.37,
-        "p75_pct": 6.13
+        "p75_pct": -0.36
       },
       "3": {
         "label": "3к",
-        "mape_pct": 10.52,
+        "mape_pct": 8.41,
-        "median_bias_pct": 4.08,
+        "median_bias_pct": 1.72,
         "n": 43,
         "n_no_analogs": 0,
-        "p25_pct": -6.54,
+        "p25_pct": -9.8,
-        "p75_pct": 11.96
+        "p75_pct": 7.98
       },
       "4": {
         "label": "4+",
-        "mape_pct": 23.53,
+        "mape_pct": 20.27,
-        "median_bias_pct": 14.35,
+        "median_bias_pct": 8.86,
         "n": 30,
         "n_no_analogs": 0,
-        "p25_pct": -0.72,
+        "p25_pct": -3.77,
-        "p75_pct": 35.27
+        "p75_pct": 23.54
       }
     },
     "per_segment": {
       "бизнес": {
-        "mape_pct": 22.14,
+        "mape_pct": 12.87,
-        "median_bias_pct": -21.49,
+        "median_bias_pct": -9.15,
         "n": 46,
-        "p25_pct": -28.22,
+        "p25_pct": -21.89,
-        "p75_pct": -9.82
+        "p75_pct": -0.74
       },
       "комфорт": {
-        "mape_pct": 16.74,
+        "mape_pct": 11.93,
-        "median_bias_pct": -8.05,
+        "median_bias_pct": -3.63,
         "n": 104,
-        "p25_pct": -20.63,
+        "p25_pct": -15.64,
-        "p75_pct": 7.55
+        "p75_pct": 8.13
       },
       "премиум": {
-        "mape_pct": 59.37,
+        "mape_pct": 68.92,
-        "median_bias_pct": -59.37,
+        "median_bias_pct": -68.92,
         "n": 1,
-        "p25_pct": -59.37,
+        "p25_pct": -68.92,
-        "p75_pct": -59.37
+        "p75_pct": -68.92
       },
       "эконом": {
-        "mape_pct": 18.01,
+        "mape_pct": 15.58,
-        "median_bias_pct": 17.17,
+        "median_bias_pct": 4.28,
         "n": 120,
-        "p25_pct": 1.73,
+        "p25_pct": -6.11,
-        "p75_pct": 46.96
+        "p75_pct": 26.09
       },
       "элит": {
-        "mape_pct": 38.62,
+        "mape_pct": 31.85,
-        "median_bias_pct": -38.62,
+        "median_bias_pct": -31.85,
         "n": 6,
-        "p25_pct": -47.98,
+        "p25_pct": -42.08,
-        "p75_pct": -33.18
+        "p75_pct": -22.4
       }
     }
   },
@@ -125,9 +125,9 @@
   },
   "range_coverage": {
     "overall": {
-      "coverage_pct": 80.14,
+      "coverage_pct": 82.31,
       "n": 277,
-      "n_covered": 222
+      "n_covered": 228
     },
     "per_confidence": {
       "high": {
@@ -136,9 +136,9 @@
         "n_covered": 0
       },
       "low": {
-        "coverage_pct": 80.0,
+        "coverage_pct": 82.18,
         "n": 275,
-        "n_covered": 220
+        "n_covered": 226
       },
       "medium": {
         "coverage_pct": 100.0,

tradein-mvp/backend/tests/test_781_quality_gate.py

@@ -325,7 +325,9 @@ def test_753_dedup_hash_source_id_takes_priority_over_url() -> None:
 # ---------------------------------------------------------------------------
 
 
-def test_773_expected_sold_positive_on_anchor_only_path() -> None:
+def test_773_expected_sold_positive_on_anchor_only_path(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
     """#773 quality-gate: anchor-only path (radius_analogs=[]) with ratio present
     -> expected_sold_price_rub > 0 (not NULL).
 
@@ -334,6 +336,11 @@ def test_773_expected_sold_positive_on_anchor_only_path() -> None:
     produced a valid median_price. The fix changed the guard to `and median_price > 0`.
     Refs: PR #784 / commit ec84637.
     """
+    # #2002: asserts expected_sold == headline × ratio; hold the orthogonal hedonic
+    # year+area correction OFF (OFF ⇒ exact legacy expected_sold).
+    from app.core.config import settings as _settings
+
+    monkeypatch.setattr(_settings, "estimate_hedonic_correction_enabled", False)
     est = _run_qa_estimate(
         anchor_comps=_SB_COMPS_QG,
         anchor_tier="A",

tradein-mvp/backend/tests/test_backtest_fixture_roundtrip.py

@@ -227,12 +227,17 @@ def test_replay_fixture_segments_span_multiple_bands() -> None:
     assert len(non_empty) >= 3
 
 
-def test_replay_is_arg_insensitive_order_based() -> None:
+def test_replay_is_arg_insensitive_order_based(monkeypatch: pytest.MonkeyPatch) -> None:
     # Order-based (FIFO) replay returns the recorded ratio REGARDLESS of the arg
     # value the spine actually computes — so a recorded arg that can never equal
     # the live median (999_999.0) still replays cleanly. This is the cross-platform
     # robustness contract: a Linux-captured fixture must replay off-Linux even when
     # libm last-ulp jitter shifts the computed median_ppm2 by an ulp.
+    # #2002: this asserts the recorded ratio drives the result (bias -5%). Hold the
+    # orthogonal hedonic correction OFF so expected_sold stays exactly headline × ratio.
+    from app.core.config import settings
+
+    monkeypatch.setattr(settings, "estimate_hedonic_correction_enabled", False)
     fixture = _build_fixture()
     fixture["deals"][0]["ratio_calls"] = [[999_999.0, [0.95, "per_rooms_all"]]]
     metrics = bt.replay_fixture(fixture)

tradein-mvp/backend/tests/test_estimator_expected_sold.py

@@ -26,6 +26,7 @@ from typing import Any
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import anyio
+import pytest
 
 # Settings requires DATABASE_URL at init time. Set dummy DSN before any app import.
 os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://test:test@localhost/test_db")
@@ -218,8 +219,13 @@ def _run_estimate(ratio_tuple: tuple[float | None, str | None]):
     return anyio.run(_run)
 
 
-def test_expected_sold_applied_when_ratio_present() -> None:
+def test_expected_sold_applied_when_ratio_present(monkeypatch: pytest.MonkeyPatch) -> None:
     """ratio present → expected_sold_* ≈ asking × ratio; headline UNCHANGED."""
+    # #2002: asserts the ratio mechanism (expected_sold == asking × ratio). Hold the
+    # orthogonal hedonic year+area correction OFF (OFF ⇒ exact legacy expected_sold).
+    from app.services import estimator as _est
+
+    monkeypatch.setattr(_est.settings, "estimate_hedonic_correction_enabled", False)
     ratio = 0.74
     est = _run_estimate((ratio, "per_rooms"))
 
@@ -341,9 +347,15 @@ def _run_estimate_anchor_only(
     return anyio.run(_run)
 
 
-def test_expected_sold_fires_on_anchor_only_no_radius_comps() -> None:
+def test_expected_sold_fires_on_anchor_only_no_radius_comps(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
     """#773: listings_clean=[], anchor sets median_price>0, ratio present
     → expected_sold_price > 0 (old guard `and listings_clean` blocked this)."""
+    # #2002: asserts expected_sold == headline × ratio; hold hedonic correction OFF.
+    from app.services import estimator as _est
+
+    monkeypatch.setattr(_est.settings, "estimate_hedonic_correction_enabled", False)
     ratio = 0.82
     est = _run_estimate_anchor_only((ratio, "per_rooms"))
 

tradein-mvp/backend/tests/test_estimator_expected_sold_clamp.py

@@ -86,6 +86,9 @@ def _run_estimate(
     async def _run() -> Any:
         with (
             patch("app.core.config.settings.estimate_expected_sold_le_asking", new=clamp_enabled),
+            # #2002: these tests assert the clamp/ratio math exactly. Hold the
+            # orthogonal hedonic year+area correction OFF (OFF ⇒ legacy expected_sold).
+            patch("app.core.config.settings.estimate_hedonic_correction_enabled", new=False),
             patch("app.services.estimator.geocode", new=AsyncMock(return_value=_make_geo())),
             patch("app.services.estimator.dadata_clean_address", new=AsyncMock(return_value=None)),
             patch("app.services.estimator.match_house_readonly", return_value=None),

tradein-mvp/backend/tests/test_estimator_imv_blend.py

@@ -362,6 +362,9 @@ def _run_estimate_with_anchor(
 
     async def _run():
         with (
+            # #2002: these tests assert expected_sold == post-blend headline × ratio.
+            # Hold the orthogonal hedonic correction OFF (OFF ⇒ legacy expected_sold).
+            patch("app.core.config.settings.estimate_hedonic_correction_enabled", new=False),
             patch("app.services.estimator.geocode", new=AsyncMock(return_value=_make_fake_geo())),
             patch("app.services.estimator.dadata_clean_address", new=AsyncMock(return_value=None)),
             patch("app.services.estimator.match_house_readonly", return_value=None),

tradein-mvp/backend/tests/test_estimator_price_spine.py

@@ -152,8 +152,11 @@ def _call(
 # ── Tests ────────────────────────────────────────────────────────────────────
 
 
-def test_radius_only_median_and_expected_sold() -> None:
+def test_radius_only_median_and_expected_sold(monkeypatch: pytest.MonkeyPatch) -> None:
     """Pure radius path: 7 uniform lots → correct median, n_analogs, expected_sold."""
+    # #2002: this asserts the ratio mechanism (expected_sold == headline × ratio).
+    # Hold the orthogonal hedonic correction OFF (OFF ⇒ exact legacy expected_sold).
+    monkeypatch.setattr(estimator.settings, "estimate_hedonic_correction_enabled", False)
     pr = _call(listings=_lots(100_000, n=7), ratio=0.95)
 
     assert pr.median_price == int(100_000 * 50.0)  # 5_000_000
@@ -365,8 +368,13 @@ def test_corridor_soft_clamp_headline_above_cap() -> None:
     assert pr.dkp_corridor.high_ppm2 == 150_000
 
 
-def test_expected_sold_from_ratio_and_none_when_ratio_none() -> None:
+def test_expected_sold_from_ratio_and_none_when_ratio_none(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
     """expected_sold = headline × ratio; when ratio is None, all expected_sold fields None."""
+    # #2002: ratio-mechanism test — hold the orthogonal hedonic correction OFF
+    # so expected_sold == headline × ratio exactly (OFF ⇒ legacy behavior).
+    monkeypatch.setattr(estimator.settings, "estimate_hedonic_correction_enabled", False)
     # Case A: ratio=0.90 → expected_sold fields filled.
     pr_ratio = _call(listings=_lots(100_000, n=5), ratio=0.90)
     assert pr_ratio.asking_to_sold_ratio == 0.90

tradein-mvp/backend/tests/test_same_building_anchor.py

@@ -556,10 +556,15 @@ def test_estimate_expected_sold_distinct_after_anchor() -> None:
     """(f) При сработавшем якоре headline = ASKING (anchor_ppm2, pre-haircut), а
     expected_sold = headline × per-rooms ratio → DISTINCT, строго ниже median.
     Single asking→sold механизм (ratio); band-haircut больше не в headline."""
+    # #2002: asserts expected_sold == post-anchor headline × ratio. Hold the
+    # orthogonal hedonic year+area correction OFF (OFF ⇒ legacy expected_sold).
+    from app.core.config import settings
+
     ratio = 0.92
-    est = _run_estimate(
+    with patch.object(settings, "estimate_hedonic_correction_enabled", False):
-        anchor_comps=_SB_COMPS_PREMIUM, anchor_tier="A", ratio_tuple=(ratio, "per_rooms")
+        est = _run_estimate(
-    )
+            anchor_comps=_SB_COMPS_PREMIUM, anchor_tier="A", ratio_tuple=(ratio, "per_rooms")
+        )
     # expected_sold выведен из POST-anchor headline × ratio (не равен headline).
     # per_m2 берётся от внутренней float-медианы (схема отдаёт int(median_ppm2)),
     # поэтому сравниваем с допуском ±1 на округление float→int.
@@ -567,7 +572,6 @@ def test_estimate_expected_sold_distinct_after_anchor() -> None:
     assert abs(est.expected_sold_per_m2 - round(est.median_price_per_m2 * ratio)) <= 1
     # #1966: expected_sold range — калиброванный ~80% PI вокруг точки (point × [p10,p90]
     # sold/expected_sold), не asking-IQR × ratio.
-    from app.core.config import settings
 
     assert est.expected_sold_range_high_rub == round(
         est.expected_sold_price_rub * settings.estimate_pi_high_mult