feat(realestate): 5축 점수 breakdown + 대시보드 pass_count

- matcher: _compute_score()에 score_breakdown {region/type/area/price/eligibility} 반환
- matcher: run_matching() DB INSERT에 score_breakdown JSON 저장
- db: match_results에 score_breakdown 컬럼 마이그레이션
- db: _enrich_items / get_matches에서 score_breakdown 파싱 포함
- db: get_matches에 a.district 컬럼 추가
- db: get_dashboard()에 pass_count (min_match_score 임계값 통과 건수) 추가

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

realestate-lab/app/matcher.py

@@ -113,27 +113,33 @@ def _compute_score(
     preferred_types = profile.get("preferred_types") or []
     house_secd = ann.get("house_secd") or ""
     type_name = _HOUSE_TYPE_MAP.get(house_secd, house_secd)
+    type_score = 0
     if type_name and type_name in preferred_types:
+        type_score = 10
         score += 10
         reasons.append(f"선호 유형 일치: {type_name}")
 
     # 3. 면적 (15점) — binary, 범위 안 모델 1개라도 있으면 통과
     min_area = profile.get("min_area")
     max_area = profile.get("max_area")
+    area_score = 0
     if min_area is not None and max_area is not None and models:
         for m in models:
             supply_area = m.get("supply_area")
             if supply_area is not None and min_area <= supply_area <= max_area:
+                area_score = 15
                 score += 15
                 reasons.append(f"희망 면적 범위 내 모델 존재 ({supply_area}㎡)")
                 break
 
     # 4. 가격 (15점) — binary, 예산 이하 모델 1개라도 있으면 통과
     max_price = profile.get("max_price")
+    price_score = 0
     if max_price is not None and models:
         for m in models:
             top_amount = m.get("top_amount")
             if top_amount is not None and top_amount <= max_price:
+                price_score = 15
                 score += 15
                 reasons.append(f"예산 범위 내 모델 존재 (최고가 {top_amount:,}만원)")
                 break
@@ -149,6 +155,13 @@ def _compute_score(
         "match_score": score,
         "match_reasons": reasons,
         "eligible_types": eligible_types,
+        "score_breakdown": {
+            "region": region_score,
+            "type": type_score,
+            "area": area_score,
+            "price": price_score,
+            "eligibility": elig_score,
+        },
     }
 
 
@@ -178,18 +191,20 @@ def run_matching():
             result = _compute_score(profile, ann, model_list)
             if result["match_score"] > 0:
                 conn.execute("""
-                    INSERT INTO match_results (announcement_id, model_id, match_score, match_reasons, eligible_types, is_new)
-                    VALUES (?, ?, ?, ?, ?, 1)
+                    INSERT INTO match_results (announcement_id, model_id, match_score, match_reasons, eligible_types, score_breakdown, is_new)
+                    VALUES (?, ?, ?, ?, ?, ?, 1)
                     ON CONFLICT(announcement_id, model_id) DO UPDATE SET
                         match_score=excluded.match_score,
                         match_reasons=excluded.match_reasons,
-                        eligible_types=excluded.eligible_types
+                        eligible_types=excluded.eligible_types,
+                        score_breakdown=excluded.score_breakdown
                 """, (
                     ann["id"],
                     None,
                     result["match_score"],
                     json.dumps(result["match_reasons"]),
                     json.dumps(result["eligible_types"]),
+                    json.dumps(result["score_breakdown"]),
                 ))
 
         # Clean up stale match results for completed announcements