fix(report): newbuild-consistent district median + obj_class dedup (#1953)

FIX A (#1955) «Что хорошо продаётся»: убран фантомный класс 'Comfort'.
- Миграция 172: v_bucket_success_score COALESCE(obj_class,'Comfort')
  → COALESCE(obj_class,'не указан'). Английский литерал заполнял 397 NULL
  и сливался отдельным классом от русского 'Комфорт' → визуальные дубли
  бакетов в UI. Источник уже канонически-русский (проверено на проде),
  synonym-mapping не нужен.
- parcels.py: obj_class протаскивается в success-ranking query + dict.
- TS SuccessRankingBucket.obj_class добавлен.

FIX B (#1960) «Медиана рынка» = 64k (квартальная росреестровская n=1 ДКП):
- district.median_price_per_m2 больше не COALESCE(median_12m, ekb_ref) в SQL.
  Basis-приоритет (newbuild-first): Objective по имени района →
  geo_radius (Objective в 3км) → ekb_districts reference →
  квартальная росреестровская медиана ТОЛЬКО при deals_count≥5.
  Для 66:41:0205010:287: 64k → 132690 (geo_radius, newbuild-consistent).
- median_price_basis добавлен в payload + TS type (nullable median).
- Frontend null-guards для нового nullable median.

Tests: +4 (geo_radius basis, objective-приоритет, deals-guard, obj_class
passthrough); обновлены district-моки в 9 analyze-тестах под новую
SQL-сигнатуру.

backend/app/api/v1/parcels.py

@@ -153,6 +153,12 @@ _GEO_PRICE_RADIUS_M: float = 3000.0  # 3 км — городской радиу
 _GEO_PRICE_MIN_LOTS: int = 10
 _GEO_PRICE_MIN_COMPLEXES: int = 2
 
+# #1960 «Медиана рынка»: минимум сделок квартальной росреестровской MV
+# (mv_quarter_price_per_m2.deals_count, окно 24 мес), чтобы её медиана вообще
+# могла служить последним fallback'ом для карточки district.median_price_per_m2.
+# Тонкие кварталы (1 ДКП → 64k) больше не «выигрывают» у newbuild-basis.
+_QUARTER_MEDIAN_MIN_DEALS: int = 5
+
 # Эмпирические пороги score для ЕКБ: средний диапазон 15-30, max редко >40.
 SCORE_THRESHOLDS: dict[str, float] = {"плохо": 5.0, "средне": 15.0, "хорошо": 25.0, "отлично": 40.0}
 SCORE_MAX_REFERENCE: float = 40.0
@@ -1646,13 +1652,21 @@ def analyze_parcel(
     geom_wkt: str = geom_row["wkt"]  # type: ignore[index]
 
     # 2) District context — ближайший район ЕКБ
-    # median_price_per_m2: предпочитаем median_12m из mv_quarter_price_per_m2 (12 мес),
-    # fallback на ekb_districts.median_price_per_m2 (24 мес).
-    district_row = (
+    # #1960: НЕ COALESCE'им median_12m в SQL. Раньше карточка «Медиана рынка» брала
+    # median_12m из mv_quarter_price_per_m2 — это РОСРЕЕСТРОВСКАЯ квартальная медиана
+    # (ДКП), которая для тонких кварталов считается по 1 сделке (=64k для
+    # 66:41:0205010:287) и противоречит остальной странице, построенной на NEWBUILD
+    # Objective/DOM.РФ basis (geo_radius ≈132k). Поэтому возвращаем компоненты по
+    # отдельности — финальный выбор basis делается в Python (см. блок «#1960» ниже),
+    # где доступны district_price_block (Objective по имени района) и geo_radius_price.
+    # ekb_districts.median_price_per_m2 — справочный DDU-fallback (24 мес).
+    district_row_raw = (
         db.execute(
             text("""
             SELECT d.district_name,
-                   COALESCE(mq.median_12m, d.median_price_per_m2) AS median_price_per_m2,
+                   d.median_price_per_m2          AS ekb_reference_median,
+                   mq.median_12m                  AS quarter_median_12m,
+                   mq.deals_count                 AS quarter_deals_count,
                    ST_Distance(
                        d.geom::geography,
                        ST_Centroid(ST_GeomFromText(:wkt, 4326))::geography
@@ -1674,6 +1688,19 @@ def analyze_parcel(
         .mappings()
         .first()
     )
+    # district_row сохраняет тот же контракт, что и раньше (district_name,
+    # median_price_per_m2, dist_to_center). median_price_per_m2 пока = справочный
+    # ekb-fallback; финальный newbuild-consistent basis проставляется ниже (#1960),
+    # когда посчитаны district_price_block + geo_radius_price.
+    district_row: dict[str, Any] | None
+    if district_row_raw is not None:
+        district_row = {
+            "district_name": district_row_raw["district_name"],
+            "median_price_per_m2": district_row_raw["ekb_reference_median"],
+            "dist_to_center": district_row_raw["dist_to_center"],
+        }
+    else:
+        district_row = None
 
     # 3) POI в радиусе 1 км — список с distance_m (straight-line, ST_Distance).
     # В dict, а не RowMapping (read-only) — чтобы при включённом OSRM (#39 A2)
@@ -2761,7 +2788,8 @@ def analyze_parcel(
                 success_rows = (
                     db.execute(
                         text("""
-                        SELECT bucket, success_score, n_deals, avg_price_per_m2, avg_area_m2,
+                        SELECT bucket, obj_class, success_score, n_deals,
+                               avg_price_per_m2, avg_area_m2,
                                velocity_z, price_z, area_z
                           FROM v_bucket_success_score
                          WHERE district_name = :dn
@@ -2784,6 +2812,10 @@ def analyze_parcel(
                         "ranking": [
                             {
                                 "bucket": r["bucket"],
+                                # #1955: obj_class теперь различает строки с одинаковым
+                                # area-label (Комфорт vs Типовой vs «не указан») — убирает
+                                # визуальные дубли бакетов и фантом english 'Comfort'.
+                                "obj_class": r["obj_class"],
                                 "success_score": round(float(r["success_score"]), 2),
                                 "n_deals": int(r["n_deals"]),
                                 "avg_price_per_m2": (
@@ -3290,6 +3322,50 @@ def analyze_parcel(
         logger.warning("financial_estimate bridge failed for %s: %s", cad_num, e)
         financial_estimate = None
 
+    # #1960: карточка «Медиана рынка» (district.median_price_per_m2) должна быть
+    # КОНСИСТЕНТНА с остальной страницей, построенной на NEWBUILD Objective/DOM.РФ
+    # basis (geo_radius_price.median, market_avg ≈197k), а не на загрязнённой
+    # квартальной росреестровской ДКП-медиане (median_12m, для 66:41:0205010:287 = 64k
+    # по 1 сделке). Раньше SQL делал COALESCE(median_12m, ekb_reference) → выигрывала
+    # тонкая квартальная медиана.
+    #
+    # Basis-приоритет (newbuild-first, с устойчивыми fallback'ами):
+    #   1. district_price_block.district_price_per_m2_median — Objective-лоты по ИМЕНИ
+    #      района (objective_lots.district). NULL для 5/9 районов ЕКБ без name-match.
+    #   2. geo_radius_price.median — Objective-лоты в 3 км радиусе вокруг центроида
+    #      (n≥10 лотов, ≥2 ЖК). Закрывает name-match-пробел; для 66:41:0205010:287 ≈132k.
+    #      Это ТОТ ЖЕ источник, что и market_pulse/financial_estimate → консистентно.
+    #   3. ekb_districts.median_price_per_m2 — справочная DDU-медиана района (24 мес).
+    #   4. mv_quarter_price_per_m2.median_12m — росреестровская квартальная медиана,
+    #      ТОЛЬКО если deals_count ≥ _QUARTER_MEDIAN_MIN_DEALS (5). Тонкие кварталы
+    #      (1 ДКП → 64k) сюда не проходят и медиана честно остаётся None.
+    if district_row is not None:
+        _newbuild_basis: float | None = None
+        _basis_source = "none"
+        _dpm = district_price_block.get("district_price_per_m2_median")
+        _geo_m = geo_radius_price.get("median")
+        _ekb_ref = district_row_raw["ekb_reference_median"] if district_row_raw else None
+        _q_median = district_row_raw["quarter_median_12m"] if district_row_raw else None
+        _q_deals = district_row_raw["quarter_deals_count"] if district_row_raw else None
+        if _dpm is not None:
+            _newbuild_basis = float(_dpm)
+            _basis_source = "objective_district"
+        elif _geo_m is not None:
+            _newbuild_basis = float(_geo_m)
+            _basis_source = "objective_geo_radius"
+        elif _ekb_ref is not None:
+            _newbuild_basis = float(_ekb_ref)
+            _basis_source = "ekb_districts_reference"
+        elif (
+            _q_median is not None
+            and _q_deals is not None
+            and (int(_q_deals) >= _QUARTER_MEDIAN_MIN_DEALS)
+        ):
+            _newbuild_basis = float(_q_median)
+            _basis_source = "quarter_rosreestr"
+        district_row["median_price_per_m2"] = _newbuild_basis
+        district_row["median_price_basis"] = _basis_source
+
     result_payload: dict[str, Any] = {
         "cad_num": cad_num,
         "source": source,

backend/tests/api/v1/test_analyze_inline_weights.py

@@ -70,7 +70,9 @@ def _make_db_for_analyze(
         _make_mapping(
             {
                 "district_name": "Октябрьский",
-                "median_price_per_m2": 120000,
+                "ekb_reference_median": 120000,
+                "quarter_median_12m": None,
+                "quarter_deals_count": 0,
                 "dist_to_center": 1500.0,
             }
         )
@@ -91,7 +93,7 @@ def _make_db_for_analyze(
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             first_val = centroid_row

backend/tests/api/v1/test_analyze_market_price.py

@@ -53,11 +53,19 @@ def _make_db_for_analyze(
     geom_found: bool = True,
     district_found: bool = True,
     market_price_row: dict[str, Any] | None = None,
+    district_row_override: dict[str, Any] | None = None,
+    success_rows: list[dict[str, Any]] | None = None,
+    geo_radius_row: dict[str, Any] | None = None,
+    district_price_row: dict[str, Any] | None = None,
 ) -> MagicMock:
     """Сконструировать mock DB Session для analyze_parcel.
 
     market_price_row=None → имитирует "нет данных в MV" (mp_row is None → source='no_data').
     market_price_row={...} → имитирует найденную строку в MV.
+    district_row_override → переопределяет raw-district mapping (#1960 basis chain).
+    success_rows → строки v_bucket_success_score (#1955 obj_class passthrough).
+    geo_radius_row → строка geo_radius_price (objective lots в радиусе, #1960 basis #2).
+    district_price_row → строка district_price_block (objective по имени, #1960 basis #1).
     """
     db = MagicMock()
 
@@ -67,20 +75,20 @@ def _make_db_for_analyze(
         else None
     )
     wkt_row = _make_mapping({"wkt": _WKT}) if geom_found else None
-    district_row = (
-        _make_mapping(
-            {
-                "district_name": "Октябрьский",
-                "median_price_per_m2": 120000,
-                "dist_to_center": 1500.0,
-            }
-        )
-        if district_found
-        else None
-    )
+    _district_data = district_row_override or {
+        "district_name": "Октябрьский",
+        "ekb_reference_median": 120000,
+        "quarter_median_12m": None,
+        "quarter_deals_count": 0,
+        "dist_to_center": 1500.0,
+    }
+    district_row = _make_mapping(_district_data) if district_found else None
     centroid_row = _make_mapping({"lat": 56.84, "lon": 60.605})
 
     mp_mock = _make_mapping(market_price_row) if market_price_row is not None else None
+    success_mocks = [_make_mapping(r) for r in success_rows] if success_rows is not None else []
+    geo_mock = _make_mapping(geo_radius_row) if geo_radius_row is not None else None
+    dp_mock = _make_mapping(district_price_row) if district_price_row is not None else None
 
     def _execute_side_effect(*args: Any, **kwargs: Any) -> MagicMock:
         # Нормализуем SQL для сигнатурного матчинга (collapse whitespace).
@@ -94,13 +102,22 @@ def _make_db_for_analyze(
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             first_val = centroid_row
         # market-price (#33) — уникальная сигнатура трёх скользящих медиан.
         elif "median_6m" in sql and "median_12m" in sql and "median_24m" in sql:
             first_val = mp_mock
+        # #1955: success-ranking из v_bucket_success_score.
+        elif "v_bucket_success_score" in sql:
+            all_val = success_mocks
+        # #1960 basis #1: district_price_block (Objective по имени района).
+        elif "AS sample_size" in sql and "objective_lots" in sql:
+            first_val = dp_mock
+        # #1960 basis #2: geo_radius_price (Objective в радиусе).
+        elif "AS n_complexes" in sql and "objective_lots" in sql:
+            first_val = geo_mock
 
         r = MagicMock()
         r.mappings.return_value.first.return_value = first_val
@@ -254,3 +271,170 @@ def test_market_price_invalid_cad_returns_404() -> None:
     finally:
         app.dependency_overrides.clear()
         _stop_patches()
+
+
+# ── #1960: «Медиана рынка» = newbuild-consistent basis ──────────────────────────
+
+
+def test_district_median_uses_geo_radius_not_quarter_rosreestr() -> None:
+    """#1960: при тонкой росреестровской квартальной медиане (deals<5) карточка
+    district.median_price_per_m2 берёт newbuild geo_radius медиану, НЕ 64k.
+
+    Репродукция бага для 66:41:0205010:287: quarter_median_12m=63694 (1 ДКП),
+    geo_radius медиана=132690. Objective-по-имени отсутствует (name-match gap) →
+    basis должен упасть на geo_radius_price.
+    """
+    from app.core.db import get_db
+
+    db = _make_db_for_analyze(
+        district_row_override={
+            "district_name": "Железнодорожный",
+            "ekb_reference_median": 69687,
+            "quarter_median_12m": 63694,  # загрязнённая росреестровская квартальная
+            "quarter_deals_count": 6,  # 24-мес окно ≥5, но 12-мес — тонкое
+            "dist_to_center": 0.0,
+        },
+        # Objective по имени района отсутствует (5/9 районов ЕКБ без name-match).
+        district_price_row=None,
+        # geo_radius — newbuild basis (что и остальная страница).
+        geo_radius_row={"median": 132690.0, "n": 12085, "n_complexes": 14},
+    )
+    app.dependency_overrides[get_db] = _override_db(db)
+    _start_patches()
+    try:
+        client = TestClient(app)
+        resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
+        assert resp.status_code == 200, resp.text
+        body = resp.json()
+        district = body["district"]
+        assert district["median_price_basis"] == "objective_geo_radius"
+        assert district["median_price_per_m2"] == pytest.approx(132690.0)
+        # КЛЮЧЕВОЕ: загрязнённая 64k росреестровская медиана НЕ выигрывает.
+        assert district["median_price_per_m2"] != pytest.approx(63694.0)
+        # И значение в newbuild-диапазоне (~130-200k).
+        assert 130000 <= district["median_price_per_m2"] <= 200000
+    finally:
+        app.dependency_overrides.clear()
+        _stop_patches()
+
+
+def test_district_median_prefers_objective_district_when_available() -> None:
+    """#1960: если есть Objective-медиана по имени района — она имеет приоритет
+    над geo_radius и квартальной росреестровской."""
+    from app.core.db import get_db
+
+    db = _make_db_for_analyze(
+        district_row_override={
+            "district_name": "Октябрьский",
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": 64000,
+            "quarter_deals_count": 6,
+            "dist_to_center": 1500.0,
+        },
+        district_price_row={
+            "price_min": 90000,
+            "price_max": 250000,
+            "price_median": 165000,
+            "sample_size": 42,
+        },
+        geo_radius_row={"median": 132690.0, "n": 12085, "n_complexes": 14},
+    )
+    app.dependency_overrides[get_db] = _override_db(db)
+    _start_patches()
+    try:
+        client = TestClient(app)
+        resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
+        assert resp.status_code == 200, resp.text
+        district = resp.json()["district"]
+        assert district["median_price_basis"] == "objective_district"
+        assert district["median_price_per_m2"] == pytest.approx(165000.0)
+    finally:
+        app.dependency_overrides.clear()
+        _stop_patches()
+
+
+def test_district_median_quarter_rosreestr_gated_by_min_deals() -> None:
+    """#1960: квартальная росреестровская медиана может стать basis ТОЛЬКО как
+    последний fallback и ТОЛЬКО при deals_count ≥ 5. Тонкий квартал (deals<5)
+    → median None (не показываем загрязнённое значение)."""
+    from app.core.db import get_db
+
+    # Нет Objective-данных вообще, нет ekb_reference, тонкий квартал (1 сделка).
+    db = _make_db_for_analyze(
+        district_row_override={
+            "district_name": "Глухой",
+            "ekb_reference_median": None,
+            "quarter_median_12m": 64000,
+            "quarter_deals_count": 1,  # < 5 → не проходит guard
+            "dist_to_center": 3000.0,
+        },
+        district_price_row=None,
+        geo_radius_row=None,
+    )
+    app.dependency_overrides[get_db] = _override_db(db)
+    _start_patches()
+    try:
+        client = TestClient(app)
+        resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
+        assert resp.status_code == 200, resp.text
+        district = resp.json()["district"]
+        assert district["median_price_basis"] == "none"
+        assert district["median_price_per_m2"] is None
+    finally:
+        app.dependency_overrides.clear()
+        _stop_patches()
+
+
+# ── #1955: obj_class passthrough в success_recommendation ────────────────────────
+
+
+def test_success_recommendation_carries_obj_class() -> None:
+    """#1955: ranking-строки несут obj_class — UI может различать строки с
+    одинаковым area-label (Комфорт vs Типовой vs «не указан»)."""
+    from app.core.db import get_db
+
+    success = [
+        {
+            "bucket": "Студии 15-30",
+            "obj_class": "Комфорт",
+            "success_score": 1.2,
+            "n_deals": 4857,
+            "avg_price_per_m2": 194000,
+            "avg_area_m2": 25.0,
+            "velocity_z": 0.5,
+            "price_z": 0.3,
+            "area_z": -0.2,
+        },
+        {
+            "bucket": "Студии 15-30",
+            "obj_class": "не указан",
+            "success_score": 0.4,
+            "n_deals": 165,
+            "avg_price_per_m2": 142000,
+            "avg_area_m2": 26.0,
+            "velocity_z": 0.1,
+            "price_z": -0.1,
+            "area_z": 0.0,
+        },
+    ]
+    db = _make_db_for_analyze(success_rows=success)
+    app.dependency_overrides[get_db] = _override_db(db)
+    _start_patches()
+    try:
+        client = TestClient(app)
+        resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
+        assert resp.status_code == 200, resp.text
+        rec = resp.json()["success_recommendation"]
+        assert rec is not None
+        ranking = rec["ranking"]
+        assert len(ranking) == 2
+        assert ranking[0]["obj_class"] == "Комфорт"
+        assert ranking[1]["obj_class"] == "не указан"
+        # Один area-label, но разные классы → больше не визуально-дубль.
+        assert ranking[0]["bucket"] == ranking[1]["bucket"]
+        assert ranking[0]["obj_class"] != ranking[1]["obj_class"]
+        # И никакого английского фантома 'Comfort'.
+        assert all(r["obj_class"] != "Comfort" for r in ranking)
+    finally:
+        app.dependency_overrides.clear()
+        _stop_patches()

backend/tests/api/v1/test_analyze_osrm_distances.py

@@ -40,14 +40,14 @@ def _make_mapping(data: dict[str, Any]) -> MagicMock:
 def _make_db_for_analyze(poi_rows: list[Any]) -> MagicMock:
     db = MagicMock()
 
-    geom_row = _make_mapping(
-        {"geom_geojson": _GEOJSON, "geom_wkb": None, "source": "cad_quarter"}
-    )
+    geom_row = _make_mapping({"geom_geojson": _GEOJSON, "geom_wkb": None, "source": "cad_quarter"})
     wkt_row = _make_mapping({"wkt": _WKT})
     district_row = _make_mapping(
         {
             "district_name": "Октябрьский",
-            "median_price_per_m2": 120000,
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
             "dist_to_center": 1500.0,
         }
     )
@@ -63,7 +63,7 @@ def _make_db_for_analyze(poi_rows: list[Any]) -> MagicMock:
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             # И centroid_row для блока 6, и OSRM-origin SELECT (тот же shape).
@@ -170,9 +170,7 @@ def test_flag_on_osrm_replaces_distance(monkeypatch) -> None:
     monkeypatch.setattr(settings, "use_osrm_distances", True)
     _start_patches()
     try:
-        with patch(
-            "app.api.v1.parcels.get_road_distances_m", return_value=[700.0]
-        ) as mock_osrm:
+        with patch("app.api.v1.parcels.get_road_distances_m", return_value=[700.0]) as mock_osrm:
             client = TestClient(app)
             resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
             assert resp.status_code == 200, resp.text

backend/tests/api/v1/test_analyze_parcel_meta.py

@@ -65,7 +65,9 @@ def _make_db_for_analyze(
     district_row = _make_mapping(
         {
             "district_name": "Октябрьский",
-            "median_price_per_m2": 120000,
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
             "dist_to_center": 1500.0,
         }
     )

backend/tests/api/v1/test_analyze_recent_permits.py

@@ -64,7 +64,9 @@ def _make_db_for_analyze(
     district_row = _make_mapping(
         {
             "district_name": "Октябрьский",
-            "median_price_per_m2": 120000,
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
             "dist_to_center": 1500.0,
         }
     )
@@ -83,7 +85,7 @@ def _make_db_for_analyze(
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             first_val = centroid_row

backend/tests/api/v1/test_analyze_zoning_regulation.py

@@ -110,7 +110,9 @@ def _make_db_for_analyze() -> MagicMock:
     district_row = _make_mapping(
         {
             "district_name": "Октябрьский",
-            "median_price_per_m2": 120000,
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
             "dist_to_center": 1500.0,
         }
     )

backend/tests/api/v1/test_custom_pois.py

@@ -259,7 +259,9 @@ def _make_db_for_analyze() -> MagicMock:
     district_row = _make_mapping_analyze(
         {
             "district_name": "Октябрьский",
-            "median_price_per_m2": 120000,
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
             "dist_to_center": 1500.0,
         }
     )

backend/tests/api/v1/test_parcels_forecast.py

@@ -77,7 +77,13 @@ def _make_db_for_analyze(geom_found: bool = True) -> MagicMock:
     )
     wkt_row = _make_mapping({"wkt": _WKT}) if geom_found else None
     district_row = _make_mapping(
-        {"district_name": "Октябрьский", "median_price_per_m2": 120000, "dist_to_center": 1500.0}
+        {
+            "district_name": "Октябрьский",
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
+            "dist_to_center": 1500.0,
+        }
     )
     centroid_row = _make_mapping({"lat": 56.84, "lon": 60.605})
 
@@ -88,7 +94,7 @@ def _make_db_for_analyze(geom_found: bool = True) -> MagicMock:
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             first_val = centroid_row
@@ -157,9 +163,7 @@ def test_horizon_default_12_and_enqueued() -> None:
     _start_patches()
     delay_mock = MagicMock()
     try:
-        with patch(
-            "app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock
-        ):
+        with patch("app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock):
             client = TestClient(app)
             resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
             assert resp.status_code == 200, resp.text
@@ -186,9 +190,7 @@ def test_horizon_valid_values_accepted() -> None:
         _start_patches()
         delay_mock = MagicMock()
         try:
-            with patch(
-                "app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock
-            ):
+            with patch("app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock):
                 client = TestClient(app)
                 resp = client.post(f"/api/v1/parcels/{_CAD}/analyze?horizon={h}")
                 assert resp.status_code == 200, f"horizon={h}: {resp.text}"
@@ -213,9 +215,7 @@ def test_horizon_invalid_returns_422() -> None:
         _start_patches()
         delay_mock = MagicMock()
         try:
-            with patch(
-                "app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock
-            ):
+            with patch("app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock):
                 client = TestClient(app)
                 resp = client.post(f"/api/v1/parcels/{_CAD}/analyze?horizon={h}")
                 assert resp.status_code == 422, f"horizon={h}: {resp.text}"
@@ -234,9 +234,7 @@ def test_enqueue_passes_created_by_header() -> None:
     _start_patches()
     delay_mock = MagicMock()
     try:
-        with patch(
-            "app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock
-        ):
+        with patch("app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock):
             client = TestClient(app)
             resp = client.post(
                 f"/api/v1/parcels/{_CAD}/analyze",
@@ -261,9 +259,7 @@ def test_enqueue_failure_returns_200_unavailable() -> None:
     _start_patches()
     delay_mock = MagicMock(side_effect=RuntimeError("redis down"))
     try:
-        with patch(
-            "app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock
-        ):
+        with patch("app.workers.tasks.forecast.forecast_site_finder_report.delay", delay_mock):
             client = TestClient(app)
             resp = client.post(f"/api/v1/parcels/{_CAD}/analyze")
             assert resp.status_code == 200, resp.text
@@ -314,9 +310,7 @@ def test_get_forecast_ready_when_run_present() -> None:
     try:
         # 1-й вызов — §22-ран ("1.0"); 2-й — analyze-ран для gate_caveat (#1740),
         # здесь None (gate не применяется).
-        with patch(
-            "app.api.v1.parcels.latest_run_for", side_effect=[fake_run, None]
-        ) as lrf:
+        with patch("app.api.v1.parcels.latest_run_for", side_effect=[fake_run, None]) as lrf:
             client = TestClient(app)
             resp = client.get(f"/api/v1/parcels/{_CAD}/forecast")
             assert resp.status_code == 200, resp.text
@@ -354,9 +348,7 @@ def test_get_forecast_db_error_returns_pending_not_500() -> None:
     db = MagicMock()
     app.dependency_overrides[get_db] = _override_db(db)
     try:
-        with patch(
-            "app.api.v1.parcels.latest_run_for", side_effect=RuntimeError("db gone")
-        ):
+        with patch("app.api.v1.parcels.latest_run_for", side_effect=RuntimeError("db gone")):
             client = TestClient(app)
             resp = client.get(f"/api/v1/parcels/{_CAD}/forecast")
             assert resp.status_code == 202, resp.text

backend/tests/api/v1/test_run_history_and_response_contract.py

@@ -54,7 +54,13 @@ def _make_db_for_analyze(geom_found: bool = True) -> MagicMock:
     )
     wkt_row = _make_mapping({"wkt": _WKT}) if geom_found else None
     district_row = _make_mapping(
-        {"district_name": "Октябрьский", "median_price_per_m2": 120000, "dist_to_center": 1500.0}
+        {
+            "district_name": "Октябрьский",
+            "ekb_reference_median": 120000,
+            "quarter_median_12m": None,
+            "quarter_deals_count": 0,
+            "dist_to_center": 1500.0,
+        }
     )
     centroid_row = _make_mapping({"lat": 56.84, "lon": 60.605})
 
@@ -65,7 +71,7 @@ def _make_db_for_analyze(geom_found: bool = True) -> MagicMock:
             first_val = geom_row
         elif "AS wkt" in sql:
             first_val = wkt_row
-        elif "AS median_price_per_m2" in sql and "district_name" in sql:
+        elif "AS ekb_reference_median" in sql and "district_name" in sql:
             first_val = district_row
         elif "AS lon" in sql and "AS lat" in sql:
             first_val = centroid_row

data/sql/172_fix_v_bucket_success_score_obj_class.sql

@@ -0,0 +1,124 @@
+-- 172_fix_v_bucket_success_score_obj_class.sql
+-- Issue #1955 (audit epic #1953) — «Что хорошо продаётся»: дубли бакетов + фантом Comfort.
+--
+-- Проблема:
+--   Старый view (86_v_bucket_success_score.sql) использовал
+--   `COALESCE(o.obj_class, 'Comfort')` — АНГЛИЙСКИЙ литерал, которым заполнялись
+--   NULL-классы (на проде: 397 объектов с obj_class IS NULL, регион 66). В итоге
+--   эти строки сливались в фантомный класс 'Comfort', отдельный от реального
+--   русского 'Комфорт' (723 объекта). В UI таблица проецирует только bucket-label,
+--   поэтому строки разных obj_class с одинаковым area-label визуально дублировались
+--   («Студии 15-30» = Комфорт 8256 И Comfort 150; «1-к 30-45» = Типовой И Бизнес).
+--
+-- Фикс:
+--   1. NULL obj_class → честный `'не указан'` (НЕ домысливаем comfort).
+--   2. obj_class уже присутствует в выходных колонках (был и раньше) — backend
+--      теперь протаскивает его в API (parcels.py success-ranking), чтобы UI мог
+--      различать классы и не схлопывать строки.
+--
+-- Нормализация синонимов: проверены distinct obj_class на проде (регион 66, latest
+--   snapshot per obj): только канонические русские значения —
+--   'Комфорт' (723), NULL (397), 'Типовой' (105), 'Бизнес' (84), 'Элит' (7),
+--   'Премиум' (6). Английских/нестандартных литералов в ИСТОЧНИКЕ нет (фантом
+--   'Comfort' рождался ИСКЛЮЧИТЕЛЬНО из COALESCE). Поэтому synonym-mapping не нужен —
+--   замена COALESCE-литерала полностью решает проблему. Если в будущем появятся
+--   реальные не-канонические значения (Comfort/Economy/Business/...) — добавить CASE
+--   normalization здесь.
+--
+-- Dependencies:
+--   domrf_kn_objects (obj_id, district_name, obj_class, region_cd, snapshot_date),
+--   domrf_kn_flats, domrf_kn_sale_graph.
+-- Idempotent: CREATE OR REPLACE VIEW (структура колонок не меняется — backend-safe).
+-- Deploy: auto-applied by deploy.yml via _schema_migrations tracking.
+
+BEGIN;
+
+CREATE OR REPLACE VIEW v_bucket_success_score AS
+WITH
+-- Latest snapshot per object (both tables are time-series)
+latest_obj AS (
+    SELECT DISTINCT ON (obj_id)
+        obj_id, district_name, obj_class, region_cd
+    FROM domrf_kn_objects
+    WHERE district_name IS NOT NULL
+    ORDER BY obj_id, snapshot_date DESC
+),
+
+bucket_aggs AS (
+    SELECT
+        o.district_name,
+        -- #1955: NULL obj_class → честный 'не указан' (НЕ английский 'Comfort').
+        -- Это убирает фантомный класс и сливание с реальным русским 'Комфорт'.
+        COALESCE(o.obj_class, 'не указан') AS obj_class,
+        CASE
+            WHEN f.rooms = 0 OR f.total_area < 30 THEN 'Студии 15-30'
+            WHEN f.total_area < 45              THEN '1-к 30-45'
+            WHEN f.total_area < 60              THEN '2-к 45-60'
+            WHEN f.total_area < 80              THEN '3-к 60-80'
+            ELSE '80+ м²'
+        END AS bucket,
+        AVG(NULLIF(f.price_per_m2, 0))   AS avg_price_per_m2,
+        AVG(NULLIF(f.total_area,   0))   AS avg_area_m2,
+        COUNT(*)                         AS n_deals,
+        -- Velocity proxy: avg monthly contracted units per object, over last 24 months
+        AVG(
+            (SELECT AVG(sg.contracted)
+               FROM domrf_kn_sale_graph sg
+              WHERE sg.obj_id = o.obj_id
+                AND sg.report_month > NOW() - INTERVAL '24 months')
+        ) AS avg_velocity_per_month
+    FROM domrf_kn_flats f
+    JOIN latest_obj o ON o.obj_id = f.obj_id
+    WHERE f.total_area BETWEEN 15 AND 200
+      AND f.price_per_m2 BETWEEN 30000 AND 500000
+      AND o.region_cd = 66
+      AND f.snapshot_date > CURRENT_DATE - INTERVAL '24 months'
+    GROUP BY 1, 2, 3
+    HAVING COUNT(*) >= 15  -- мин 15: weak-confidence (15-29), strong (≥30)
+),
+
+z_scores AS (
+    SELECT
+        district_name, obj_class, bucket,
+        n_deals, avg_price_per_m2, avg_area_m2, avg_velocity_per_month,
+        -- Z-score нормированный по группе (district × class)
+        (avg_velocity_per_month
+            - AVG(avg_velocity_per_month) OVER (PARTITION BY district_name, obj_class))
+            / NULLIF(STDDEV(avg_velocity_per_month) OVER (PARTITION BY district_name, obj_class), 0)
+            AS velocity_z,
+        (avg_price_per_m2
+            - AVG(avg_price_per_m2) OVER (PARTITION BY district_name, obj_class))
+            / NULLIF(STDDEV(avg_price_per_m2) OVER (PARTITION BY district_name, obj_class), 0)
+            AS price_z,
+        (avg_area_m2
+            - AVG(avg_area_m2) OVER (PARTITION BY district_name, obj_class))
+            / NULLIF(STDDEV(avg_area_m2) OVER (PARTITION BY district_name, obj_class), 0)
+            AS area_z
+    FROM bucket_aggs
+)
+
+SELECT
+    district_name,
+    obj_class,
+    bucket,
+    n_deals,
+    avg_price_per_m2,
+    avg_area_m2,
+    avg_velocity_per_month,
+    COALESCE(velocity_z, 0) * 0.5
+        + COALESCE(price_z,    0) * 0.3
+        - COALESCE(area_z,     0) * 0.2  AS success_score,
+    COALESCE(velocity_z, 0)              AS velocity_z,
+    COALESCE(price_z,    0)              AS price_z,
+    COALESCE(area_z,     0)              AS area_z
+FROM z_scores
+ORDER BY district_name, obj_class, success_score DESC;
+
+COMMENT ON VIEW v_bucket_success_score IS
+    'Success-driven bucket ranking per (district, class). '
+    'Z-scores: velocity (+) приоритет, price (+) приоритет, area (-) приоритет. '
+    'Min 15 сделок в группе (15-29 = weak confidence, ≥30 = strong). '
+    'NULL obj_class → ''не указан'' (#1955: убран фантом ''Comfort''). '
+    'Используется recommend_mix + analyze success_recommendation (issue #25, #1955).';
+
+COMMIT;

frontend/src/app/legacy/site-finder/page.tsx

@@ -257,7 +257,9 @@ function SiteFinderContent() {
   // (≈0 внутри района), а не до центра города (#1414).
   const distToCenterKm = data?.location?.distance_to_center_km;
   const districtLabel = data?.district
-    ? `${(data.district.median_price_per_m2 / 1000).toFixed(0)} тыс ₽/м²` +
+    ? (data.district.median_price_per_m2 != null
+        ? `${(data.district.median_price_per_m2 / 1000).toFixed(0)} тыс ₽/м²`
+        : "нет данных") +
       (distToCenterKm != null
         ? ` · ${distToCenterKm.toFixed(1)} км от центра`
         : "")

frontend/src/components/site-finder/OverviewTab.tsx

@@ -76,9 +76,11 @@ export function OverviewTab({ data, onIsochronesResult }: Props) {
           <div style={{ fontSize: 13, color: "#6b7280" }}>
             Медиана:{" "}
             <strong style={{ color: "#374151" }}>
-              {(data.district.median_price_per_m2 / 1000).toFixed(0)} тыс ₽/м²
+              {data.district.median_price_per_m2 != null
+                ? `${(data.district.median_price_per_m2 / 1000).toFixed(0)} тыс ₽/м²`
+                : "нет данных"}
             </strong>
-            <span className="text-xs text-slate-500 ml-1">(12 мес)</span>
+            <span className="text-xs text-slate-500 ml-1">(новостройки)</span>
             <span style={{ marginLeft: 12 }}>
               {(data.district.dist_to_center / 1000).toFixed(1)} км до центра
             </span>

frontend/src/types/site-finder.ts

@@ -57,7 +57,13 @@ export interface ParcelAnalysisCompetitor {
 
 export interface ParcelAnalysisDistrict {
   district_name: string;
-  median_price_per_m2: number;
+  /** #1960: newbuild-consistent медиана цены района (Objective/DOM.РФ basis).
+   * null, если ни один basis не разрешился. См. median_price_basis. */
+  median_price_per_m2: number | null;
+  /** #1960: какой источник дал median_price_per_m2 —
+   * objective_district | objective_geo_radius | ekb_districts_reference |
+   * quarter_rosreestr | none. */
+  median_price_basis?: string;
   dist_to_center: number;
 }
 
@@ -376,6 +382,9 @@ export interface GeometrySuitability {
 
 export interface SuccessRankingBucket {
   bucket: string;
+  /** #1955: класс ЖК ('Комфорт' / 'Типовой' / 'Бизнес' / 'не указан' / …) —
+   * различает строки с одинаковым area-label, убирает визуальные дубли бакетов. */
+  obj_class: string;
   success_score: number;
   n_deals: number;
   avg_price_per_m2: number | null;