diff --git a/realestate-lab/app/matcher.py b/realestate-lab/app/matcher.py
index e1cc6f6..3df8b2b 100644
--- a/realestate-lab/app/matcher.py
+++ b/realestate-lab/app/matcher.py
@@ -6,6 +6,44 @@ from .db import _conn, _profile_row_to_dict
 
 logger = logging.getLogger("realestate-lab")
 
+TIER_WEIGHTS = {"S": 1.00, "A": 0.80, "B": 0.60, "C": 0.40, "D": 0.20}
+
+
+def _region_score(profile: Dict[str, Any], ann: Dict[str, Any]) -> tuple[int, list[str]]:
+    """지역 점수 계산. 광역 10점 + 자치구 5티어 가중치 0~25점.
+    자치구 기준 미설정 시 광역 매칭만으로 35점 풀 점수(기존 호환).
+    """
+    region_name = ann.get("region_name") or ""
+    district = ann.get("district") or ""
+    preferred_regions = profile.get("preferred_regions") or []
+    preferred_districts = profile.get("preferred_districts") or {}
+
+    region_match = bool(region_name and any(r in region_name for r in preferred_regions))
+    if not region_match:
+        return 0, []
+
+    has_districts = any(preferred_districts.get(t) for t in TIER_WEIGHTS)
+    if not has_districts:
+        return 35, [f"선호 지역 일치: {region_name}"]
+
+    score = 10
+    reasons = [f"광역 일치: {region_name}"]
+    for tier, weight in TIER_WEIGHTS.items():
+        if district and district in (preferred_districts.get(tier) or []):
+            tier_score = round(25 * weight)
+            score += tier_score
+            reasons.append(f"자치구 {tier}티어: {district} (+{tier_score})")
+            break
+    return score, reasons
+
+
+def _eligibility_score(eligible_types: List[str]) -> int:
+    """자격 점수 0~25. 첫 자격 15점 + 추가 자격당 5점, 최대 +10."""
+    if not eligible_types:
+        return 0
+    return 15 + min((len(eligible_types) - 1) * 5, 10)
+
+
 # house_secd → 주택유형 이름 매핑
 _HOUSE_TYPE_MAP = {
     "01": "APT",
@@ -60,18 +98,18 @@ def _compute_score(
     ann: Dict[str, Any],
     models: List[Dict[str, Any]],
 ) -> Dict[str, Any]:
-    """매칭 점수(0-100)와 사유를 계산한다."""
+    """매칭 점수(0-100)와 사유를 계산한다.
+    배분: 지역 35 / 유형 10 / 면적 15 / 가격 15 / 자격 25.
+    """
     score = 0
     reasons: List[str] = []
 
-    # 1. 지역 (30점)
-    preferred_regions = profile.get("preferred_regions") or []
-    region_name = ann.get("region_name") or ""
-    if region_name and any(r in region_name for r in preferred_regions):
-        score += 30
-        reasons.append(f"선호 지역 일치: {region_name}")
+    # 1. 지역 (35점) — 광역 + 자치구 5티어
+    region_score, region_reasons = _region_score(profile, ann)
+    score += region_score
+    reasons.extend(region_reasons)
 
-    # 2. 주택유형 (10점)
+    # 2. 주택유형 (10점) — binary
     preferred_types = profile.get("preferred_types") or []
     house_secd = ann.get("house_secd") or ""
     type_name = _HOUSE_TYPE_MAP.get(house_secd, house_secd)
@@ -79,7 +117,7 @@ def _compute_score(
         score += 10
         reasons.append(f"선호 유형 일치: {type_name}")
 
-    # 3. 면적 (15점)
+    # 3. 면적 (15점) — binary, 범위 안 모델 1개라도 있으면 통과
     min_area = profile.get("min_area")
     max_area = profile.get("max_area")
     if min_area is not None and max_area is not None and models:
@@ -90,7 +128,7 @@ def _compute_score(
                 reasons.append(f"희망 면적 범위 내 모델 존재 ({supply_area}㎡)")
                 break
 
-    # 4. 가격 (15점)
+    # 4. 가격 (15점) — binary, 예산 이하 모델 1개라도 있으면 통과
     max_price = profile.get("max_price")
     if max_price is not None and models:
         for m in models:
@@ -100,11 +138,11 @@ def _compute_score(
                 reasons.append(f"예산 범위 내 모델 존재 (최고가 {top_amount:,}만원)")
                 break
 
-    # 5. 자격 (30점)
+    # 5. 자격 (25점) — 첫 자격 15 + 추가당 5
     eligible_types = _check_eligible_types(profile, ann)
-    eligibility_score = min(len(eligible_types) * 10, 30)
-    if eligibility_score > 0:
-        score += eligibility_score
+    elig_score = _eligibility_score(eligible_types)
+    if elig_score > 0:
+        score += elig_score
         reasons.append(f"자격 유형 {len(eligible_types)}개: {', '.join(eligible_types)}")
 
     return {
diff --git a/realestate-lab/tests/test_matcher.py b/realestate-lab/tests/test_matcher.py
new file mode 100644
index 0000000..c757c64
--- /dev/null
+++ b/realestate-lab/tests/test_matcher.py
@@ -0,0 +1,83 @@
+def test_region_score_no_districts_full_when_region_match():
+    """자치구 미설정: 광역 일치 시 35점."""
+    from app.matcher import _region_score
+    profile = {"preferred_regions": ["서울"], "preferred_districts": {}}
+    ann = {"region_name": "서울특별시", "district": None}
+    score, _ = _region_score(profile, ann)
+    assert score == 35
+
+
+def test_region_score_no_districts_zero_when_region_mismatch():
+    from app.matcher import _region_score
+    profile = {"preferred_regions": ["서울"], "preferred_districts": {}}
+    ann = {"region_name": "부산광역시", "district": None}
+    score, _ = _region_score(profile, ann)
+    assert score == 0
+
+
+def test_region_score_s_tier_district():
+    """광역 매칭 + S티어 자치구: 10 + 25 = 35."""
+    from app.matcher import _region_score
+    profile = {
+        "preferred_regions": ["서울"],
+        "preferred_districts": {"S": ["강남구"], "A": [], "B": [], "C": [], "D": []},
+    }
+    ann = {"region_name": "서울특별시", "district": "강남구"}
+    score, _ = _region_score(profile, ann)
+    assert score == 35
+
+
+def test_region_score_a_tier_district():
+    """광역 매칭 + A티어 자치구: 10 + 20 = 30."""
+    from app.matcher import _region_score
+    profile = {
+        "preferred_regions": ["서울"],
+        "preferred_districts": {"S": [], "A": ["송파구"], "B": [], "C": [], "D": []},
+    }
+    ann = {"region_name": "서울특별시", "district": "송파구"}
+    score, _ = _region_score(profile, ann)
+    assert score == 30
+
+
+def test_region_score_d_tier_district():
+    """광역 매칭 + D티어 자치구: 10 + 5 = 15."""
+    from app.matcher import _region_score
+    profile = {
+        "preferred_regions": ["서울"],
+        "preferred_districts": {"S": [], "A": [], "B": [], "C": [], "D": ["도봉구"]},
+    }
+    ann = {"region_name": "서울특별시", "district": "도봉구"}
+    score, _ = _region_score(profile, ann)
+    assert score == 15
+
+
+def test_region_score_district_set_but_not_listed():
+    """광역 매칭 + 자치구 5티어 어디에도 없음: 10점만."""
+    from app.matcher import _region_score
+    profile = {
+        "preferred_regions": ["서울"],
+        "preferred_districts": {"S": ["강남구"], "A": [], "B": [], "C": [], "D": []},
+    }
+    ann = {"region_name": "서울특별시", "district": "강서구"}
+    score, _ = _region_score(profile, ann)
+    assert score == 10
+
+
+def test_eligibility_score_zero_when_empty():
+    from app.matcher import _eligibility_score
+    assert _eligibility_score([]) == 0
+
+
+def test_eligibility_score_one_type_returns_15():
+    from app.matcher import _eligibility_score
+    assert _eligibility_score(["일반1순위"]) == 15
+
+
+def test_eligibility_score_two_types_returns_20():
+    from app.matcher import _eligibility_score
+    assert _eligibility_score(["일반1순위", "특별-신혼부부"]) == 20
+
+
+def test_eligibility_score_caps_at_25():
+    from app.matcher import _eligibility_score
+    assert _eligibility_score(["a", "b", "c", "d", "e"]) == 25