scoring and analyzsis

Dockerfile

@@ -10,8 +10,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libxslt1-dev \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy dependency files
+# Copy source files
 COPY pyproject.toml ./
+COPY finn_eiendom ./finn_eiendom
+COPY README.md ./
 
 # Create virtual environment and install dependencies
 RUN python -m venv /venv && \

fetch_trikk_coords.py

@@ -0,0 +1,238 @@
+"""
+fetch_trikk_coords.py
+Henter P625-koordinater fra Wikidata for alle Oslo-trikkeholdeplasser
+via Wikipedia sitelinks. Kjør: python3 fetch_trikk_coords.py
+Krever: pip install requests beautifulsoup4
+"""
+
+import requests
+from urllib.parse import unquote, urlparse, parse_qs
+from bs4 import BeautifulSoup
+
+# Alle Wikipedia-lenker fra trikkelinjene (inkl. redlinks)
+HTML = """
+<a href="/wiki/Majorstuen_(station)">Majorstuen</a>
+<a href="/wiki/Bogstadveien_tram_stop">Bogstadveien</a>
+<a href="/wiki/Rosenborg_tram_stop">Rosenborg</a>
+<a href="/w/index.php?title=Briskeby_tram_stop&action=edit&redlink=1">Briskeby</a>
+<a href="/w/index.php?title=Riddervolds_plass_tram_stop&action=edit&redlink=1">Riddervolds plass</a>
+<a href="/wiki/Inkognitogata_tram_stop">Inkognitogata</a>
+<a href="/wiki/Nationaltheatret_metro_station">Nationaltheatret</a>
+<a href="/wiki/%C3%98vre_Slottsgate_tram_stop">Øvre Slottsgate</a>
+<a href="/wiki/Dronningens_gate_tram_stop">Dronningens gate</a>
+<a href="/wiki/Jernbanetorget_(station)">Jernbanetorget</a>
+<a href="/wiki/Storgata_tram_stop">Storgata</a>
+<a href="/wiki/Nybrua_tram_stop">Nybrua</a>
+<a href="/wiki/Schous_plass_tram_stop">Schous plass</a>
+<a href="/wiki/Olaf_Ryes_plass">Olaf Ryes plass</a>
+<a href="/wiki/Birkelunden">Birkelunden</a>
+<a href="/wiki/Biermanns_gate_(station)">Biermanns gate</a>
+<a href="/w/index.php?title=Torshov_(station)&action=edit&redlink=1">Torshov</a>
+<a href="/w/index.php?title=Sandaker_senter_(station)&action=edit&redlink=1">Sandaker senter</a>
+<a href="/w/index.php?title=Grefsenveien_tram_stop&action=edit&redlink=1">Grefsenveien</a>
+<a href="/wiki/Storo_(station)">Storo</a>
+<a href="/wiki/Disen_tram_stop">Disen tram stop</a>
+<a href="/w/index.php?title=Doktor_Smiths_vei_tram_stop&action=edit&redlink=1">Doktor Smiths vei</a>
+<a href="/w/index.php?title=Glads_vei_tram_stop&action=edit&redlink=1">Glads vei</a>
+<a href="/w/index.php?title=Grefsenplat%C3%A5et_tram_stop&action=edit&redlink=1">Grefsenplatået</a>
+<a href="/w/index.php?title=Grefsen_stadion_tram_stop&action=edit&redlink=1">Grefsen stadion</a>
+<a href="/w/index.php?title=Kjels%C3%A5salleen_tram_stop&action=edit&redlink=1">Kjelsåsalleen</a>
+<a href="/wiki/Kjels%C3%A5s_tram_stop">Kjelsås</a>
+<a href="/wiki/Frogner_stadion_tram_stop">Frogner stadion</a>
+<a href="/w/index.php?title=Vigelandsparken_tram_stop&action=edit&redlink=1">Vigelandsparken</a>
+<a href="/w/index.php?title=Frogner_plass_tram_stop&action=edit&redlink=1">Frogner plass</a>
+<a href="/wiki/Elisenberg_tram_stop">Elisenberg</a>
+<a href="/w/index.php?title=Lille_Frogner_all%C3%A9_tram_stop&action=edit&redlink=1">Lille Frogner allé</a>
+<a href="/w/index.php?title=Niels_Juels_gate_tram_stop&action=edit&redlink=1">Niels Juels gate</a>
+<a href="/wiki/Solli_tram_stop">Solli</a>
+<a href="/w/index.php?title=Rusel%C3%B8kka_tram_stop&action=edit&redlink=1">Ruseløkka</a>
+<a href="/wiki/Aker_Brygge_tram_stop">Akerbrygge</a>
+<a href="/wiki/Kontraskj%C3%A6ret_tram_stop">Kontraskjæret</a>
+<a href="/wiki/Middelalderparken_tram_stop">Middelalderparken</a>
+<a href="/w/index.php?title=Bislett_tram_stop&action=edit&redlink=1">Bislett</a>
+<a href="/w/index.php?title=Dalsbergstien_tram_stop&action=edit&redlink=1">Dalsbergstien</a>
+<a href="/w/index.php?title=Welhavens_gate_tram_stop&action=edit&redlink=1">Welhavens gate</a>
+<a href="/w/index.php?title=Frydenlund_tram_stop&action=edit&redlink=1">Frydenlund</a>
+<a href="/w/index.php?title=Holbergs_plass_tram_stop&action=edit&redlink=1">Holbergs plass</a>
+<a href="/w/index.php?title=Tullin%C3%B8kka_(station)&action=edit&redlink=1">Tullinøkka</a>
+<a href="/wiki/Stortinget_(station)">Tinghuset</a>
+<a href="/wiki/Stortorvet_(station)">Stortorvet</a>
+<a href="/w/index.php?title=Heimdalsgata_tram_stop&action=edit&redlink=1">Heimdalsgata</a>
+<a href="/w/index.php?title=Lakkegata_skole_tram_stop&action=edit&redlink=1">Lakkegata skole</a>
+<a href="/w/index.php?title=Sofienberg_tram_stop&action=edit&redlink=1">Sofienberg</a>
+<a href="/wiki/Carl_Berners_plass_(station)">Carl Berners plass</a>
+<a href="/w/index.php?title=Rosenhoff_tram_stop&action=edit&redlink=1">Rosenhoff</a>
+<a href="/w/index.php?title=Sinsenterrassen_tram_stop&action=edit&redlink=1">Sinsenterrassen</a>
+<a href="/wiki/Sinsen_(station)">Sinsenkrysset</a>
+<a href="/wiki/Grefsen_Station">Grefsen stasjon</a>
+<a href="/wiki/Homansbyen_tram_stop">Homansbyen</a>
+"""
+
+# Redlinks har ingen Wikipedia-side — søk direkte på Wikidata label
+WIKIDATA_DIRECT = {
+    # Redlinks — ingen Wikipedia-side
+    "Briskeby_tram_stop": "Q11962293",
+    "Riddervolds_plass_tram_stop": "Q19386557",
+    "Grefsenveien_tram_stop": "Q17778424",
+    "Doktor_Smiths_vei_tram_stop": None,
+    "Glads_vei_tram_stop": "Q17776371",
+    "Grefsenplatået_tram_stop": "Q11972531",
+    "Grefsen_stadion_tram_stop": "Q11972525",
+    "Kjelsåsalleen_tram_stop": None,
+    "Vigelandsparken_tram_stop": "Q19398059",
+    "Frogner_plass_tram_stop": "Q11970372",
+    "Lille_Frogner_allé_tram_stop": "Q19379373",
+    "Niels_Juels_gate_tram_stop": "Q11991378",
+    "Ruseløkka_tram_stop": None,
+    "Bislett_tram_stop": "Q11961163",
+    "Dalsbergstien_tram_stop": "Q17764618",
+    "Welhavens_gate_tram_stop": "Q12010485",
+    "Frydenlund_tram_stop": "Q19373143",
+    "Holbergs_plass_tram_stop": "Q11975623",
+    "Tullinøkka_(station)": None,
+    "Heimdalsgata_tram_stop": None,
+    "Lakkegata_skole_tram_stop": "Q11982987",
+    "Sofienberg_tram_stop": None,
+    "Rosenhoff_tram_stop": None,
+    "Sinsenterrassen_tram_stop": None,
+    "Torshov_(station)": None,
+    "Sandaker_senter_(station)": None,
+    "Frogner_stadion_tram_stop": None,
+    # Wikipedia-redirect-sider — QID til redirect-målet
+    "Stortorvet_(station)": "Q7620354",  # → Stortorvet_tram_stop
+    "Stortinget_(station)": "Q188712",  # → Stortinget T-bane (Tinghuset tram er ved siden)
+    "Sinsen_(station)": "Q19388523",  # → Sinsenkrysset tram stop
+    "Biermanns_gate_(station)": "Q19363042",  # → Biermanns gate tram stop
+    "Carl_Berners_plass_(station)": "Q890592",  # → Carl Berners plass metro+tram
+    "Majorstuen_(station)": "Q686510",  # → Majorstuen T-bane
+    "Grefsen_Station": "Q728583",  # → Grefsen stasjon jernbane
+    "Kjelsås_tram_stop": "Q11981146",  # → Kjelsås
+    "Storo_(station)": "Q932133",  # → Storo T-bane
+    "Schous_plass_tram_stop": "Q12006491",  # → Schous plass
+    "Jernbanetorget_(station)": "Q841481",  # → Jernbanetorget T+tram
+    "Sandaker_senter_(station)": "Q12008217",  # → Sandaker senter
+}
+
+
+def extract_titles(html):
+    soup = BeautifulSoup(html, "html.parser")
+    titles = []
+    for a in soup.find_all("a"):
+        href = a.get("href", "")
+        label = a.get_text(strip=True)
+        if href.startswith("/wiki/"):
+            title = unquote(href.removeprefix("/wiki/"))
+            titles.append((title, label))
+        elif href.startswith("/w/index.php"):
+            qs = parse_qs(urlparse(href).query)
+            t = qs.get("title", [None])[0]
+            if t:
+                titles.append((unquote(t), label))
+    seen = {}
+    for title, label in titles:
+        seen.setdefault(title, label)
+    return seen  # {wiki_title: display_label}
+
+
+HEADERS = {"User-Agent": "finn-mcp-trikk-coords/1.0 (contact: ole@example.com)"}
+
+
+def get_qids_from_wikipedia(titles):
+    """Wikipedia API: article titles → Wikidata QIDs."""
+    url = "https://en.wikipedia.org/w/api.php"
+    result = {}
+    batch = [t for t in titles if t not in WIKIDATA_DIRECT]
+    for i in range(0, len(batch), 50):
+        chunk = batch[i : i + 50]
+        r = requests.get(
+            url,
+            params={
+                "action": "query",
+                "format": "json",
+                "redirects": "1",
+                "prop": "pageprops",
+                "ppprop": "wikibase_item",
+                "titles": "|".join(chunk),
+            },
+            headers=HEADERS,
+            timeout=30,
+        )
+        print(f"Status: {r.status_code}, len: {len(r.text)}, preview: {r.text[:200]!r}")
+        for page in r.json()["query"]["pages"].values():
+            t = page.get("title", "").replace(" ", "_")
+            qid = page.get("pageprops", {}).get("wikibase_item")
+            if qid:
+                result[t] = qid
+    # Merge known QIDs
+    for t, qid in WIKIDATA_DIRECT.items():
+        if qid:
+            result[t] = qid
+    return result
+
+
+def get_p625(qids):
+    """Wikidata API: QIDs → P625 coordinates."""
+    url = "https://www.wikidata.org/w/api.php"
+    result = {}
+    unique = list(set(qids.values()))
+    for i in range(0, len(unique), 50):
+        chunk = unique[i : i + 50]
+        r = requests.get(
+            url,
+            params={
+                "action": "wbgetentities",
+                "format": "json",
+                "ids": "|".join(chunk),
+                "props": "claims|labels",
+                "languages": "en|nb",
+            },
+            headers=HEADERS,
+            timeout=30,
+        )
+        for qid, entity in r.json()["entities"].items():
+            label = (
+                entity.get("labels", {}).get("en", {}).get("value")
+                or entity.get("labels", {}).get("nb", {}).get("value")
+                or qid
+            )
+            p625 = entity.get("claims", {}).get("P625", [])
+            coords = None
+            if p625:
+                v = p625[0]["mainsnak"]["datavalue"]["value"]
+                coords = (round(v["latitude"], 5), round(v["longitude"], 5))
+            result[qid] = {"label": label, "coords": coords}
+    return result
+
+
+def main():
+    title_to_label = extract_titles(HTML)
+    print(f"Extracted {len(title_to_label)} unique titles\n")
+
+    title_to_qid = get_qids_from_wikipedia(title_to_label)
+    print(f"Resolved {len(title_to_qid)} QIDs\n")
+
+    qid_to_data = get_p625(title_to_qid)
+
+    print(f"{'Wiki title':<45} {'QID':<12} {'Label':<35} {'Coords'}")
+    print("-" * 120)
+    no_coords = []
+    for title in sorted(title_to_label):
+        qid = title_to_qid.get(title, "—")
+        if qid == "—":
+            coords = "NO QID"
+            label = "?"
+        else:
+            d = qid_to_data.get(qid, {})
+            coords = str(d.get("coords") or "NO P625")
+            label = d.get("label", "?")
+        print(f"{title:<45} {qid:<12} {label:<35} {coords}")
+        if "NO" in str(coords):
+            no_coords.append(title)
+
+    print(f"\n\nMissing coords: {len(no_coords)}")
+    for t in no_coords:
+        print(f"  {t}")
+
+
+if __name__ == "__main__":
+    main()

finn_eiendom/analysis.py

@@ -19,6 +19,12 @@ def _normalize_description(text: str | None) -> str:
     return text.lower() if text else ""
 
 
+def _is_resale_listing(url: str) -> bool:
+    """True for ordinary resale ads. Project / new-build ads use different URL
+    paths that fetch_ad_details cannot resolve (it builds a /homes/ URL)."""
+    return "/realestate/homes/" in url
+
+
 def _build_ad_summary(
     ad: FinnAd,
     enriched: EiendomUnit | None,
@@ -95,12 +101,17 @@ async def analyze_ad(
             if enriched is not None:
                 cache.save_eiendom_unit(conn, enriched)
 
-    if enriched and enriched.unit_vector:
+    if enriched:
-        similar_units = cache.get_similar_units(conn, enriched.unit_code, "RECENTLY_SOLD")
+        # EiendomUnit.unit_vector is NOT populated by get_unit / enrich -- the
-        if not similar_units:
+        # field comes back None. Reading enriched.unit_vector directly leaves
-            similar_units = await eiendom_no.get_similar_units(enriched.unit_vector)
+        # this block dead and similar_units permanently empty. Build the vector
-            if similar_units:
+        # from the unit fields instead (fall back to the field if a future
-                cache.save_similar_units(conn, enriched.unit_code, "RECENTLY_SOLD", similar_units)
+        # endpoint ever populates it).
+        vector = enriched.unit_vector or eiendom_no.build_unit_vector(enriched)
+        if vector:
+            # No dedicated cache table for similar units (per PRD) -- fetch
+            # fresh each call, consistent with service.get_or_fetch_similar_units.
+            similar_units = await eiendom_no.get_similar_units(vector)
 
     scores = scoring.score_ad(finn_ad, enriched, similar_units)
     categories = scoring.classify_ad(scores)
@@ -120,6 +131,26 @@ async def analyze_ad(
     return result
 
 
+async def _analyze_card(card, conn, *, include_eiendom_no: bool, client) -> dict:
+    """Fetch details + enrich a single search card. Raises on unrecoverable
+    errors; the caller is responsible for catching and skipping."""
+    finn_ad = cache.get_finn_ad(conn, card.finnkode, ttl_hours=FINN_CACHE_TTL_AD_HOURS)
+    if finn_ad is None:
+        finn_ad = await ad_module.fetch_ad_details(card.finnkode, client=client)
+
+    unit_code = None
+    if include_eiendom_no:
+        try:
+            matched_unit = await eiendom_no.search_unit_from_finn_url(card.url)
+            unit_code = matched_unit.unit_code if matched_unit else None
+        except Exception as exc:
+            # A failed unit resolution is non-fatal -- proceed without enrichment.
+            logger.warning("Eiendom.no unit search failed for %s: %s", card.finnkode, exc)
+            unit_code = None
+
+    return await analyze_ad(finn_ad, unit_code=unit_code)
+
+
 async def analyze_search(
     search_url: str,
     max_pages: int = FINN_MAX_SEARCH_PAGES,
@@ -139,21 +170,28 @@ async def analyze_search(
     )
     results = []
     enriched_count = 0
+    skipped_count = 0
 
     if fetch_details:
         for card in cards[:detail_limit]:
-            finn_ad = cache.get_finn_ad(conn, card.finnkode, ttl_hours=FINN_CACHE_TTL_AD_HOURS)
+            # Project / new-build ads are not resale listings and fetch_ad_details
-            if finn_ad is None:
+            # cannot resolve them -- skip up front rather than 404 mid-run.
-                finn_ad = await ad_module.fetch_ad_details(card.finnkode, client=client)
+            if not _is_resale_listing(card.url):
-            unit_code = None
+                logger.info("Skipping non-resale card %s (%s)", card.finnkode, card.url)
-            if include_eiendom_no:
+                skipped_count += 1
+                continue
+
+            # One bad card (stale finnkode, removed ad, transient network error)
+            # must not abort the whole search -- isolate each card.
             try:
-                    matched_unit = await eiendom_no.search_unit_from_finn_url(card.url)
+                result = await _analyze_card(
-                    unit_code = matched_unit.unit_code if matched_unit else None
+                    card, conn, include_eiendom_no=include_eiendom_no, client=client
+                )
             except Exception as exc:
-                    logger.warning("Eiendom.no unit search failed: %s", exc)
+                logger.warning("Skipping card %s: %s", card.finnkode, exc)
-                    unit_code = None
+                skipped_count += 1
-            result = await analyze_ad(finn_ad, unit_code=unit_code)
+                continue
+
             if result.get("eiendom_unit"):
                 enriched_count += 1
             results.append(result)
@@ -166,6 +204,7 @@ async def analyze_search(
         "summary": {
             "total_listings": len(cards),
             "analyzed_listings": len(results),
+            "skipped_listings": skipped_count,
             "eiendom_enriched": enriched_count,
         },
     }

finn_eiendom/mcp_server.py

@@ -75,7 +75,7 @@ async def finn_analyze_search(
             detail_limit=detail_limit,
             include_eiendom_no=include_eiendom_no,
         )
-        return json.dumps(result)
+        return json.dumps(result, default=str)
     except Exception as e:
         logger.error(f"Error analyzing search: {e}")
         return json.dumps({"error": True, "message": str(e)})
@@ -164,7 +164,7 @@ async def finn_get_similar_units(unit_vector: str, listing_status: str = "RECENT
     """Fetch similar units from Eiendom.no."""
     try:
         units = await get_similar_units(unit_vector, listing_status)
-        return json.dumps([unit.model_dump() for unit in units])
+        return json.dumps([unit.model_dump() for unit in units], default=str)
     except Exception as e:
         logger.error(f"Error fetching similar units: {e}")
         return json.dumps({"error": True, "message": str(e)})
@@ -222,7 +222,7 @@ async def finn_analyze_ad(
             include_eiendom_no=include_eiendom_no,
             include_similar_units=include_similar_units,
         )
-        return render_ad(result.get("ad", {}), "json")
+        return json.dumps(result, default=str)
     except Exception as e:
         logger.error(f"Error analyzing ad {finnkode}: {e}")
         return json.dumps({"error": True, "message": str(e)})

finn_eiendom/scoring.py

@@ -1,146 +1,771 @@
-"""Scoring engine for FINN listings enriched with Eiendom.no data."""
+"""Scoring engine tuned for Ole & partner's apartment search criteria.
+
+Priority hierarchy (stated):
+  MUST    : balcony, ≥80 m² main unit, 2-3 bedrooms, T-bane/trikk access
+  HIGH    : preferred neighbourhoods, view (sea/panorama > rooftop > general),
+            quiet setting, hybel with own bath + kitchen
+  MEDIUM  : sameie economy, green areas / walking terrain, price vs market
+  BONUS   : renovation upside (acceptable, not required)
+
+Dimension caps (non-risk total max ≈ 105, clamped to 100):
+  floor             -15..0  – ground floor penalty only; etasje alene uten bygghøyde = ingen info
+  neighbourhood     25     – preferred area anchors, distance-based
+  view_and_quiet    20     – view quality + quiet setting; 0 if no balcony
+  area_and_layout   15     – sqm + bedroom count; hard penalty < 80 m²
+  hybel             12     – hybel with own bath + kitchen
+  transport         10     – walking distance to T-bane / trikk
+  economy            8     – listing price vs Eiendom.no estimate
+  comparable_sales   8     – listing kr/m² vs median sold kr/m² of comps
+  building_health    7     – sameie/borettslag economy signals
+  green_areas        5     – parks, tur, marka keywords
+  renovation         3     – minor bonus (they accept renovation objects)
+  risk            0..-30   – stale listing, high costs, missing data
+"""
 
 import logging
+import math
 from typing import Any
 
 from .models import EiendomUnit, SimilarUnit
 
 logger = logging.getLogger(__name__)
 
-
+# ---------------------------------------------------------------------------
-def _clamp(value: float, min_value: float, max_value: float) -> float:
+# Geometry helpers
-    return max(min_value, min(max_value, value))
+# ---------------------------------------------------------------------------
 
 
-def score_market_position(unit: EiendomUnit | None) -> float:
+def _distance_km(lat1: float, lng1: float, lat2: float, lng2: float) -> float:
-    if unit is None or unit.estimated_selling_price is None or unit.listing_price is None:
+    """Flat-earth approximation — accurate enough within Oslo (~59.9°N).
-        return 0.0
+    1° lat ≈ 111 km, 1° lng ≈ 56 km at this latitude.
-    ratio = unit.listing_price / unit.estimated_selling_price
+    """
-    if ratio <= 0.9:
+    dlat = (lat2 - lat1) * 111.0
+    dlng = (lng2 - lng1) * 56.0
+    return math.sqrt(dlat**2 + dlng**2)
+
+
+def _clamp(value: float, lo: float, hi: float) -> float:
+    return max(lo, min(hi, value))
+
+
+def _median(values: list[float]) -> float:
+    s = sorted(values)
+    mid = len(s) // 2
+    return s[mid] if len(s) % 2 else (s[mid - 1] + s[mid]) / 2.0
+
+
+# ---------------------------------------------------------------------------
+# Preferred neighbourhood anchors
+# ---------------------------------------------------------------------------
+
+_PREFERRED_ANCHORS: list[tuple[str, float, float]] = [
+    # (label, lat, lng) — label used only for debug logging
+    ("Grünerløkka", 59.9240, 10.7573),
+    ("Torshov", 59.9340, 10.7620),
+    ("Rodeløkka", 59.9315, 10.7660),
+    ("Kampen", 59.9125, 10.7760),
+    ("Sagene", 59.9400, 10.7590),
+    ("Nydalen", 59.9520, 10.7540),
+    ("Storo", 59.9450, 10.7670),
+    ("Grefsen", 59.9580, 10.7720),
+    ("Fagerborg", 59.9280, 10.7300),
+    ("St. Hans Haugen", 59.9300, 10.7400),
+    ("Ullevål", 59.9400, 10.7270),
+    ("Majorstua", 59.9210, 10.7170),
+    ("Frogner", 59.9160, 10.7150),
+    ("Løren", 59.9310, 10.7960),
+    ("Torshovdalen", 59.9295, 10.7630),
+    ("Rosenhoff", 59.9255, 10.7775),
+]
+
+
+# ---------------------------------------------------------------------------
+# Transit network — all T-bane and trikk stops.
+#
+# TBANE_STOPS: exact coordinates from Wikipedia DMS data (all 101 stations).
+# TRIKK_STOPS: estimated coordinates (Wikipedia has no trikk coords).
+#
+# To extend search to new areas: no changes needed — all stops are already
+# here. score_transport automatically finds the nearest stop for any address.
+# ---------------------------------------------------------------------------
+
+TBANE_STOPS: dict[str, tuple[float, float]] = {
+    # All 101 stations — Wikipedia DMS converted to decimal degrees
+    "Ammerud": (59.957922, 10.871165),
+    "Avløs": (59.913859, 10.552926),
+    "Bekkestua": (59.918097, 10.588031),
+    "Berg": (59.951142, 10.744801),
+    "Bergkrystallen": (59.867091, 10.821206),
+    "Besserud": (59.957760, 10.673092),
+    "Bjørnsletta": (59.926902, 10.635458),
+    "Blindern": (59.940052, 10.716262),
+    "Bogerud": (59.875833, 10.841944),
+    "Borgen": (59.934548, 10.696000),
+    "Brattlikollen": (59.888076, 10.801191),
+    "Brynseng": (59.909169, 10.811834),
+    "Bøler": (59.884167, 10.845278),
+    "Carl Berners plass": (59.926592, 10.778360),
+    "Eiksmarka": (59.946431, 10.622320),
+    "Ekraveien": (59.950836, 10.635822),
+    "Ellingsrudåsen": (59.936311, 10.916634),
+    "Ensjø": (59.913364, 10.786986),
+    "Forskningsparken": (59.943513, 10.720425),
+    "Frognerseteren": (59.979018, 10.675857),
+    "Frøen": (59.934167, 10.709167),
+    "Furuset": (59.941578, 10.897247),
+    "Gaustad": (59.945625, 10.709814),
+    "Gjettum": (59.906221, 10.527155),
+    "Gjønnes": (59.918097, 10.579877),
+    "Godlia": (59.908523, 10.835352),
+    "Grorud": (59.961413, 10.881701),
+    "Grønland": (59.912912, 10.759563),
+    "Gråkammen": (59.954838, 10.701842),
+    "Gulleråsen": (59.955526, 10.696521),
+    "Hasle": (59.925302, 10.794454),
+    "Haslum": (59.915021, 10.563183),
+    "Hauger": (59.910957, 10.510713),
+    "Haugerud": (59.922592, 10.855350),
+    "Hellerud": (59.910079, 10.829953),
+    "Helsfyr": (59.911514, 10.803680),
+    "Holmen": (59.946296, 10.666609),
+    "Holmenkollen": (59.960489, 10.662446),
+    "Holstein": (59.960403, 10.740552),
+    "Hovseter": (59.946328, 10.654694),
+    "Høyenhall": (59.905769, 10.819860),
+    "Jar": (59.926592, 10.621762),
+    "Jernbanetorget": (59.912116, 10.751211),
+    "Kalbakken": (59.954553, 10.866750),
+    "Karlsrud": (59.880453, 10.805225),
+    "Kolsås": (59.914416, 10.501366),
+    "Kringsjå": (59.963690, 10.734930),
+    "Lambertseter": (59.873289, 10.810440),
+    "Lijordet": (59.940901, 10.616559),
+    "Lillevann": (59.980481, 10.653037),
+    "Lindeberg": (59.932979, 10.882087),
+    "Linderud": (59.940976, 10.839214),
+    "Løren": (59.929972, 10.790806),
+    "Majorstuen": (59.929904, 10.714931),
+    "Makrellbekken": (59.941957, 10.673845),
+    "Manglerud": (59.897957, 10.812435),
+    "Midtstuen": (59.961299, 10.682911),
+    "Montebello": (59.936806, 10.670471),
+    "Mortensrud": (59.849083, 10.828657),
+    "Munkelia": (59.868914, 10.812500),
+    "Nationaltheatret": (59.915045, 10.733039),
+    "Nydalen": (59.948864, 10.765250),
+    "Oppsal": (59.892866, 10.840201),
+    "Ringstabekk": (59.916182, 10.593696),
+    "Ris": (59.948069, 10.705147),
+    "Risløkka": (59.932355, 10.822713),
+    "Rommen": (59.962127, 10.908968),
+    "Romsås": (59.962272, 10.890777),
+    "Ryen": (59.895807, 10.805617),
+    "Røa": (59.946791, 10.643874),
+    "Rødtvet": (59.951416, 10.859535),
+    "Sinsen": (59.938085, 10.781343),
+    "Skogen": (59.975246, 10.647415),
+    "Skullerud": (59.866754, 10.839171),
+    "Skødalen": (59.961787, 10.690789),
+    "Skøyenåsen": (59.898866, 10.836516),
+    "Slemdal": (59.949896, 10.695662),
+    "Smestad": (59.937315, 10.683609),
+    "Sognsvann": (59.967127, 10.733943),
+    "Steinerud": (59.939083, 10.704345),
+    "Storo": (59.944545, 10.778768),
+    "Stortinget": (59.913047, 10.741469),
+    "Stovner": (59.962616, 10.923414),
+    "Trosterud": (59.927152, 10.864041),
+    "Tveita": (59.914354, 10.841961),
+    "Tøyen": (59.915214, 10.774670),
+    "Tåsen": (59.953270, 10.752439),
+    "Ullernåsen": (59.930635, 10.654796),
+    "Ullevål stadion": (59.946629, 10.732226),
+    "Ulsrud": (59.889970, 10.849428),
+    "Veitvet": (59.944700, 10.847304),
+    "Vestli": (59.972324, 10.929337),
+    "Vettakollen": (59.959913, 10.695705),
+    "Vinderen": (59.942803, 10.704761),
+    "Voksenkollen": (59.980076, 10.665193),
+    "Voksenlia": (59.966937, 10.655082),
+    "Vollebekk": (59.935865, 10.831039),
+    "Åsjordet": (59.928764, 10.646889),
+    "Økern": (59.928592, 10.804152),
+    "Østerås": (59.939445, 10.608587),
+    "Østhorn": (59.956944, 10.749779),
+}
+
+# Trikk stops — estimated coordinates (Wikipedia has no trikk coords).
+# Grouped by line corridor for readability.
+# Verified trikk stop coordinates — sourced from Wikidata P625, Wikipedia
+# DMS infoboxes, or OpenStreetMap. Keys match display names used in scoring.
+# Source tag format: Wikidata QID | "shared T-bane" | "OSM node <id>" | "Wikipedia"
+TRIKK_STOPS_VERIFIED: dict[str, tuple[float, float]] = {
+    # ── Briskeby-linjen (l11/19) ─────────────────────────────────────────
+    "Majorstuen": (59.929904, 10.714931),  # shared T-bane
+    "Bogstadveien": (59.92611, 10.72167),  # Q19372022
+    "Rosenborg": (59.92417, 10.72389),  # Q7899658
+    "Briskeby": (59.92048, 10.71767),  # Q11962293
+    "Riddervolds plass": (59.91896, 10.72026),  # Q19386557
+    "Inkognitogata": (59.91565, 10.72114),  # Q11977313
+    "Nationaltheatret": (59.91504, 10.73304),  # shared T-bane
+    # ── Sentrum (shared l11/12/13/17/18/19) ──────────────────────────────
+    "Øvre Slottsgate": (59.9118, 10.7417),  # Q31079249
+    "Dronningens gate": (59.91053, 10.74697),  # Q29828354
+    "Jernbanetorget": (59.912116, 10.751211),  # shared T-bane
+    "Storgata": (59.91396, 10.75141),  # Q109484341
+    "Nybrua": (59.91707, 10.75834),  # Q104867506
+    "Stortorvet": (59.91310, 10.74530),  # Q7620354
+    "Bjørvika": (59.90806, 10.75639),  # Wikipedia
+    # ── Grünerløkka–Torshov-linjen (l11/12/18) ───────────────────────────
+    "Schous plass": (59.92081, 10.75932),  # Q12006491-area / Wikipedia
+    "Olaf Ryes plass": (59.9231, 10.7592),  # Q4993079
+    "Birkelunden": (59.9271, 10.7601),  # Q4916412
+    "Biermanns gate": (59.93028, 10.76104),  # Wikipedia
+    "Sandaker senter": (59.93889, 10.76861),  # Wikipedia
+    "Grefsenveien": (59.94278, 10.77344),  # Q17778424
+    "Storo": (59.944545, 10.778768),  # shared T-bane
+    # ── Kjelsåslinjen (l11/12) ───────────────────────────────────────────
+    "Disen": (59.94627, 10.78729),  # Q11965753
+    "Glads vei": (59.95235, 10.78533),  # Q17776371
+    "Grefsenplatået": (59.9560, 10.78573),  # Q11972531
+    "Grefsen stadion": (59.96008, 10.78475),  # Q11972525
+    "Kjelsås": (59.96611, 10.78278),  # Wikipedia
+    # ── Frogner-linjen (l12) ─────────────────────────────────────────────
+    "Vigelandsparken": (59.92457, 10.70815),  # Q19398059
+    "Frogner plass": (59.92255, 10.70491),  # Q11970372 / OSM node 30560564
+    "Elisenberg": (59.91944, 10.70861),  # Q5361695
+    "Lille Frogner allé": (59.9180, 10.7120),  # Q19379373
+    "Niels Juels gate": (59.91634, 10.71520),  # Q11991378
+    "Solli": (59.91486, 10.71906),  # Q7558364
+    # ── Vika-linjen (l12) ────────────────────────────────────────────────
+    "Aker Brygge": (59.9110, 10.7299),  # Q4700639
+    "Kontraskjæret": (59.91087, 10.73592),  # Q11998807
+    # ── Lilleaker-linjen (l13) ───────────────────────────────────────────
+    "Lilleaker": (59.92074, 10.63580),  # Wikipedia
+    "Sollerud": (59.92104, 10.64309),  # Wikipedia
+    "Furulund": (59.91990, 10.65013),  # Wikipedia
+    "Ullern": (59.92429, 10.65858),  # Wikipedia
+    "Abbediengen": (59.92517, 10.66716),  # Wikipedia
+    "Hoff": (59.92500, 10.67488),  # Wikipedia
+    "Skøyen": (59.92384, 10.68034),  # Wikipedia
+    # ── Skøyen-linjen (l13) ──────────────────────────────────────────────
+    "Thune": (59.92186, 10.68742),  # Wikipedia
+    "Nobels gate": (59.91758, 10.69866),  # Wikipedia
+    "Skarpsno": (59.91430, 10.70234),  # Wikipedia
+    "Skillebekk": (59.91277, 10.71103),  # Wikipedia
+    # ── Ekeberg-linjen (l13/19) ──────────────────────────────────────────
+    "Middelalderparken": (59.90639, 10.76417),  # Q99971403
+    "Oslo Hospital": (59.9032, 10.7674),  # Wikipedia
+    "Ekebergparken": (59.8977, 10.7593),  # Wikipedia
+    "Jomfrubråten": (59.8883, 10.7706),  # Wikipedia
+    "Sportsplassen": (59.8860, 10.7736),  # Wikipedia
+    "Holtet": (59.88151, 10.78415),  # Wikipedia
+    "Sørli": (59.87493, 10.78709),  # Wikipedia
+    "Kastellet": (59.87106, 10.79036),  # Wikipedia
+    "Bråten": (59.86714, 10.79244),  # Wikipedia
+    "Sæter": (59.86102, 10.79870),  # Wikipedia
+    "Ljabru": (59.85335, 10.80089),  # Wikipedia
+    # ── Ullevål Hageby-linjen (l17/18) ───────────────────────────────────
+    "Rikshospitalet": (59.947768, 10.714716),  # Wikipedia
+    "Gaustadalleen": (59.9454, 10.7172),  # Wikipedia
+    "Forskningsparken": (59.943513, 10.720425),  # shared T-bane
+    "Universitetet Blindern": (59.9421, 10.7243),  # Wikipedia
+    "John Collets plass": (59.9403, 10.7290),  # Wikipedia
+    "Ullevål sykehus": (59.9361, 10.7318),  # Wikipedia
+    "Adamstuen": (59.9326, 10.7345),  # Wikipedia
+    "Stensgata": (59.92957, 10.73303),  # Q7607927
+    "Bislett": (59.92599, 10.73108),  # Q11961163
+    "Dalsbergstien": (59.92354, 10.73163),  # Q17764618
+    "Welhavens gate": (59.92131, 10.72968),  # Q12010485
+    "Frydenlund": (59.92086, 10.73317),  # Q19373143
+    "Holbergs plass": (59.91876, 10.73453),  # Q11975623
+    # ── Sinsen-linjen (l17) ──────────────────────────────────────────────
+    "Lakkegata skole": (59.92055, 10.76834),  # Q11982987
+    "Carl Berners plass": (59.926592, 10.778360),  # shared T-bane
+    "Sinsenkrysset": (59.93911, 10.78340),  # Q19388523
+    "Grefsen stasjon": (59.94167, 10.78056),  # Wikipedia
+    # ── Homansbyen-linjen (l19) ───────────────────────────────────────────
+    "Homansbyen": (59.92278, 10.72639),  # Q5887760
+}
+
+# Estimated trikk stop coordinates — no Wikidata P625 found.
+# Derived from linear interpolation between verified neighbours,
+# or placed from map/street knowledge. Max error ~150-250 m.
+# To update: find Wikidata QID, fetch P625, move entry to TRIKK_STOPS_VERIFIED.
+TRIKK_STOPS_ESTIMATED: dict[str, tuple[float, float]] = {
+    # ── Sentrum ───────────────────────────────────────────────────────────
+    "Tinghuset": (59.9146, 10.7403),  # Ullevål Hageby-l ved Stortinget T
+    # ── Grünerløkka–Torshov-linjen ───────────────────────────────────────
+    "Torshov": (59.9332, 10.7643),  # interp Biermanns gate↔Sandaker
+    # ── Kjelsåslinjen ────────────────────────────────────────────────────
+    "Doktor Smiths vei": (59.9503, 10.7867),  # interp Disen↔Kjelsås t=0.20
+    "Kjelsåsalleen": (59.9641, 10.7833),  # interp Disen↔Kjelsås t=0.90
+    # ── Frogner-linjen ───────────────────────────────────────────────────
+    "Frogner stadion": (59.9167, 10.7038),  # Kirkeveien S for Vigelandsparken
+    # ── Vika-linjen ──────────────────────────────────────────────────────
+    "Ruseløkka": (59.9120, 10.7258),  # interp Solli↔Kontraskjæret
+    # ── Ullevål Hageby-linjen ─────────────────────────────────────────────
+    "Tullinøkka": (59.9163, 10.7349),  # interp Holbergs plass↔Tinghuset
+    # ── Sinsen-linjen ────────────────────────────────────────────────────
+    "Heimdalsgata": (59.9188, 10.7633),  # interp Nybrua↔Lakkegata skole
+    "Sofienberg": (59.9236, 10.7734),  # interp Lakkegata skole↔Carl Berners
+    "Rosenhoff": (59.9307, 10.7800),  # interp Carl Berners↔Sinsenkrysset t=0.33
+    "Sinsenterrassen": (59.9350, 10.7817),  # interp Carl Berners↔Sinsenkrysset t=0.67
+}
+
+# Merged — verified takes precedence if a key appears in both (shouldn't happen).
+TRIKK_STOPS: dict[str, tuple[float, float]] = {
+    **TRIKK_STOPS_ESTIMATED,
+    **TRIKK_STOPS_VERIFIED,
+}
+
+# ---------------------------------------------------------------------------
+# Transit helpers
+# ---------------------------------------------------------------------------
+
+_WALK_SPEED_KMH = 5.0  # avg walking speed
+
+
+def _nearest_stop(
+    lat: float, lng: float, stops: dict[str, tuple[float, float]]
+) -> tuple[str, float]:
+    """Return (stop_name, distance_km) for the nearest stop in a dict."""
+    best_name, best_dist = "", float("inf")
+    for name, (slat, slng) in stops.items():
+        d = _distance_km(lat, lng, slat, slng)
+        if d < best_dist:
+            best_dist, best_name = d, name
+    return best_name, best_dist
+
+
+def nearby_transit(
+    lat: float, lng: float, max_walk_min: float = 10.0
+) -> dict[str, list[tuple[str, float]]]:
+    """Return T-bane and trikk stops within max_walk_min minutes walk.
+
+    Returns:
+        {
+          "tbane": [("Carl Berners plass", 0.28), ...],   # sorted by distance
+          "trikk": [("Rosenhoff", 0.19), ...],
+        }
+    All distances in km.
+    """
+    max_km = (max_walk_min / 60.0) * _WALK_SPEED_KMH
+
+    tbane = sorted(
+        [
+            (n, _distance_km(lat, lng, la, lo))
+            for n, (la, lo) in TBANE_STOPS.items()
+            if _distance_km(lat, lng, la, lo) <= max_km
+        ],
+        key=lambda x: x[1],
+    )
+    trikk = sorted(
+        [
+            (n, _distance_km(lat, lng, la, lo))
+            for n, (la, lo) in TRIKK_STOPS.items()
+            if _distance_km(lat, lng, la, lo) <= max_km
+        ],
+        key=lambda x: x[1],
+    )
+    return {"tbane": tbane, "trikk": trikk}
+
+
+# ---------------------------------------------------------------------------
+# Dimension functions
+# ---------------------------------------------------------------------------
+
+
+def score_neighbourhood(
+    unit: EiendomUnit | None,
+    address: str | None = None,
+    district: str | None = None,
+) -> float:
+    """Distance to nearest preferred-area anchor. Max 25."""
+    if unit and unit.lat and unit.lng:
+        distances = [
+            (_distance_km(unit.lat, unit.lng, lat, lng), label)
+            for label, lat, lng in _PREFERRED_ANCHORS
+        ]
+        min_dist, nearest = min(distances)
+        logger.debug("Nearest anchor: %s at %.2f km", nearest, min_dist)
+        if min_dist < 0.5:
+            return 25.0
+        if min_dist < 1.0:
             return 20.0
-    if ratio <= 1.0:
+        if min_dist < 1.5:
-        return 16.0 + (1.0 - ratio) * 40.0
+            return 15.0
-    if ratio <= 1.1:
+        if min_dist < 2.5:
-        return 12.0 - (ratio - 1.0) * 40.0
+            return 10.0
+        if min_dist < 4.0:
             return 5.0
+        return 2.0
+
+    haystack = " ".join(filter(None, [address, district])).lower()
+    if "oslo" in haystack:
+        return 5.0
+    return 0.0
+
+
+def score_transport(unit: EiendomUnit | None) -> float:
+    """Walking distance to nearest T-bane or trikk stop. Max 10.
+
+    Searches ALL stops in TBANE_STOPS and TRIKK_STOPS — no manual
+    curation needed when adding new search areas.
+
+    Distance bands:
+      < 400 m  →  10 pts  (~5 min walk)
+      < 800 m  →   8 pts  (~10 min — stated threshold)
+      < 1200 m →   4 pts  (~15 min)
+      ≥ 1200 m →   0 pts
+
+    Falls back to 0 when no coordinates available.
+    """
+    if unit is None or unit.lat is None or unit.lng is None:
+        return 0.0
+
+    _, tbane_dist = _nearest_stop(unit.lat, unit.lng, TBANE_STOPS)
+    _, trikk_dist = _nearest_stop(unit.lat, unit.lng, TRIKK_STOPS)
+    min_dist = min(tbane_dist, trikk_dist)
+
+    logger.debug("Nearest T-bane: %.2f km, trikk: %.2f km", tbane_dist, trikk_dist)
+
+    if min_dist < 0.4:
+        return 10.0
+    if min_dist < 0.8:
+        return 8.0
+    if min_dist < 1.2:
+        return 4.0
+    return 0.0
+
+
+def score_view_and_quiet(ad: Any, description: str) -> float:
+    """View quality × quiet setting. Max 20. Returns 0 if no balcony."""
+    if not (ad.has_balcony or ad.has_terrace):
+        return 0.0
+
+    d = description.lower()
+
+    view = 0.0
+    if any(kw in d for kw in ["sjøutsikt", "fjordutsikt", "sjøglimt", "fjordglimt"]):
+        view = 15.0
+    elif any(kw in d for kw in ["panorama", "panoramautsikt", "vidt utsyn", "vidstrakt"]):
+        view = 13.0
+    elif any(kw in d for kw in ["over hustak", "hustak", "over takene"]):
+        view = 10.0
+    elif "utsikt" in d:
+        view = 7.0
+
+    quiet = 0.0
+    if any(
+        kw in d
+        for kw in [
+            "rolig",
+            "tilbaketrukket",
+            "skjermet",
+            "bakgård",
+            "gårdsrom",
+            "stille",
+            "blindvei",
+        ]
+    ):
+        quiet += 5.0
+    if any(kw in d for kw in ["støy", "bilvei", "trafikkert", "støyutsatt"]):
+        quiet -= 5.0
+
+    return float(_clamp(view + quiet, 0.0, 20.0))
+
+
+def score_area_and_layout(ad: Any, unit: EiendomUnit | None) -> float:
+    """Main unit size + bedroom count. Max 15."""
+    area = (unit.usable_area if unit else None) or ad.area_m2 or 0
+
+    if area < 60:
+        return 0.0
+    if area < 80:
+        return 3.0
+    if area < 90:
+        area_score = 8.0
+    elif area < 105:
+        area_score = 11.0
+    else:
+        area_score = 14.0
+
+    bedrooms = ad.bedrooms or 0
+    if bedrooms >= 3:
+        bedroom_bonus = 1.0
+    elif bedrooms == 2:
+        bedroom_bonus = 0.5
+    else:
+        bedroom_bonus = 0.0
+
+    return float(_clamp(area_score + bedroom_bonus, 0.0, 15.0))
+
+
+def score_hybel(description: str) -> float:
+    """Hybel with own bath + kitchen. Max 12."""
+    d = description.lower()
+
+    if "hybel" not in d and "sokkelleil" not in d and "utleiedel" not in d:
+        return 0.0
+
+    _POTENTIAL = [
+        "mulighet for hybel",
+        "mulighet til hybel",
+        "mulig hybel",
+        "kan etableres hybel",
+        "kan bygges om til hybel",
+        "tilrettelagt for hybel",
+        "potensial for hybel",
+        "hybelpotensial",
+    ]
+    is_potential = any(sig in d for sig in _POTENTIAL)
+    if not is_potential and "mulighet" in d and "hybel" in d:
+        for sentence in d.replace("!", ".").replace("?", ".").split("."):
+            if "mulighet" in sentence and "hybel" in sentence:
+                is_potential = True
+                break
+
+    if is_potential:
+        return 2.0
+
+    # Documented rental income → definitively real hybel
+    if "leieinntekt" in d or "skattefri" in d:
+        return 12.0
+
+    has_bath = any(
+        kw in d
+        for kw in [
+            "eget bad",
+            "eget wc",
+            "eget toalett",
+            "bad i hybel",
+            "dusj i hybel",
+            "eget dusj",
+        ]
+    )
+    has_kitch = any(
+        kw in d for kw in ["eget kjøkken", "kjøkken i hybel", "kjøkkenkrok", "tekjøkken"]
+    )
+    if not has_bath:
+        has_bath = "bad" in d or "dusj" in d
+    if not has_kitch:
+        has_kitch = "kjøkken" in d
+
+    if has_bath and has_kitch:
+        return 12.0
+    if has_bath or has_kitch:
+        return 7.0
+    return 4.0
+
+
+def score_floor(ad: Any, unit: EiendomUnit | None) -> float:
+    """Floor level. Binary signal: ground floor is bad, everything else neutral.
+
+    Rationale: "toppleilighet i 3-etgs blokk" og "8. etg i høyblokk" er begge
+    topp for sin bygning. Etasjenummer alene sier ingenting om utsikt eller lys
+    uten å kjenne byggets totale høyde. Eneste reelle signal er 1. etg (innsyn,
+    støy, lys) vs ikke-1. etg.
+
+    Scores:
+      ground floor (≤1)  → -15  (hard penalty: innsyn, støy, lys)
+      unknown            →   0  (no data → no penalty)
+      above ground       →   0  (etasjenummer uten bygghøyde = ingen info)
+    """
+    floor: int | None = None
+
+    if unit is not None and unit.floor is not None:
+        floor = unit.floor
+    elif ad.floor is not None:
+        try:
+            floor = int(str(ad.floor).strip().rstrip("."))
+        except (ValueError, TypeError):
+            floor = None
+
+    if floor is None:
+        return 0.0
+    if floor <= 1:
+        return -15.0
+    return 0.0
+
+
+def score_building_health(ad: Any, description: str) -> float:
+    """Sameie / borettslag economy signals. Max 7."""
+    score = 0.0
+    d = description.lower()
+
+    if ad.shared_debt == 0:
+        score += 3.0
+    elif ad.shared_debt is None:
+        score += 1.0
+
+    fk = ad.common_costs or 0
+    if fk == 0:
+        score += 0.0
+    elif fk <= 3500:
+        score += 4.0
+    elif fk <= 5000:
+        score += 2.0
+    elif fk <= 7000:
+        score += 0.0
+    else:
+        score -= 2.0
+
+    if any(kw in d for kw in ["veldrevet", "solid økonomi", "god økonomi", "ingen fellesgjeld"]):
+        score += 2.0
+
+    return float(_clamp(score, 0.0, 7.0))
+
+
+def score_green_areas(description: str) -> float:
+    """Parks, walking terrain, green surroundings. Max 5."""
+    d = description.lower()
+    keywords = ["park", "turområde", "turterreng", "marka", "skog", "grønt", "grønne", "friluft"]
+    hits = sum(1 for kw in keywords if kw in d)
+    if hits >= 2:
+        return 5.0
+    if hits == 1:
+        return 2.0
+    return 0.0
 
 
 def score_economy(ad: Any, unit: EiendomUnit | None) -> float:
-    if ad.total_price is None:
+    """Listing price vs Eiendom.no estimated value. Max 8."""
+    if unit is None or unit.estimated_selling_price is None:
         return 0.0
-    if unit and unit.estimated_selling_price:
+    price = ad.total_price or ad.asking_price
-        ratio = ad.total_price / unit.estimated_selling_price
+    if price is None:
-        if ratio <= 0.95:
+        return 0.0
-            return 20.0
+    ratio = price / unit.estimated_selling_price
-        if ratio <= 1.0:
+    if ratio <= 0.92:
-            return 15.0
-        if ratio <= 1.05:
-            return 10.0
-        return 6.0
-    if ad.asking_price and ad.total_price <= ad.asking_price:
-        return 12.0
         return 8.0
+    if ratio <= 1.00:
+        return 5.0 + (1.0 - ratio) * 37.5
+    if ratio <= 1.08:
+        return 5.0 - (ratio - 1.0) * 37.5
+    return 1.0
 
 
-def score_comparable_sales(listings: list[SimilarUnit], listing_price: int | None) -> float:
+def score_comparable_sales(
-    if not listings or listing_price is None:
+    listings: list[SimilarUnit],
+    listing_sqm_price: int | float | None,
+) -> float:
+    """Listing kr/m² vs median sold kr/m² of comp units. Max 8."""
+    if not listings or listing_sqm_price is None:
         return 0.0
-    selling_prices = [unit.selling_price for unit in listings if unit.selling_price]
+    sqm_prices = [u.sqm_price for u in listings if u.sqm_price]
-    if not selling_prices:
+    if not sqm_prices:
         return 0.0
-    average = sum(selling_prices) / len(selling_prices)
+    med = _median(sqm_prices)
-    ratio = listing_price / average
+    ratio = listing_sqm_price / med
-    score = (1.0 - abs(ratio - 1.0)) * 20.0
+    return float(_clamp((1.0 - abs(ratio - 1.0)) * 8.0, 0.0, 8.0))
-    return float(_clamp(score, 0.0, 20.0))
 
 
-def score_location(address: str | None, district: str | None) -> float:
+def score_renovation(description: str) -> float:
-    if not address and not district:
+    """Minor bonus for renovation upside. Max 3."""
+    d = description.lower()
+    if any(kw in d for kw in ["renover", "oppusse", "potensial", "moderniser"]):
+        return 3.0
     return 0.0
-    if district and "oslo" in district.lower():
-        return 15.0
-    if address and "oslo" in address.lower():
-        return 12.0
-    return 7.0
 
 
-def score_layout_and_potential(description: str | None, rooms: int | None) -> float:
+def score_risk(ad: Any, unit: EiendomUnit | None) -> float:
-    score = 0.0
+    """Risk penalty. Returns 0 or negative."""
-    if rooms and rooms >= 4:
+    penalty = 0.0
-        score += 10.0
-    if description and "potensial" in description.lower():
-        score += 8.0
-    return float(_clamp(score, 0.0, 20.0))
 
-
-def score_outdoor_and_view(description: str | None) -> float:
-    if not description:
-        return 0.0
-    score = 5.0 if "utsikt" in description.lower() or "balkong" in description.lower() else 0.0
-    return float(_clamp(score, 0.0, 15.0))
-
-
-def score_rental_potential(description: str | None) -> float:
-    if not description:
-        return 0.0
-    score = 10.0 if "hybel" in description.lower() or "leie" in description.lower() else 0.0
-    return score
-
-
-def score_renovation_upside(description: str | None, asking_price: int | None) -> float:
-    score = 0.0
-    if description and "renover" in description.lower():
-        score += 10.0
-    if asking_price and asking_price > 0:
-        score += 5.0
-    return float(_clamp(score, 0.0, 15.0))
-
-
-def score_risk(description: str | None, unit: EiendomUnit | None) -> float:
     if unit is None:
-        return -10.0
+        penalty -= 8.0
-    if description and "usikker" in description.lower():
+
-        return -10.0
+    fk = ad.common_costs or 0
-    return 0.0
+    if fk > 8000:
+        penalty -= 10.0
+    elif fk > 6000:
+        penalty -= 5.0
+
+    if unit and unit.days_on_market:
+        if unit.days_on_market > 120:
+            penalty -= 10.0
+        elif unit.days_on_market > 60:
+            penalty -= 5.0
+
+    if "usikker" in (ad.listing_description or "").lower():
+        penalty -= 5.0
+
+    return penalty
 
 
-def score_ad(
+# ---------------------------------------------------------------------------
-    ad: Any, unit: EiendomUnit | None, similar_units: list[SimilarUnit]
+# Orchestration
-) -> dict[str, float]:
+# ---------------------------------------------------------------------------
-    scores = {
+
+
+def score_ad(ad: Any, unit: EiendomUnit | None, similar_units: list[SimilarUnit]) -> dict[str, Any]:
+    description = ad.listing_description or ""
+
+    # Collect nearby transit for informational output (not used in scoring)
+    transit_nearby: dict | None = None
+    if unit and unit.lat and unit.lng:
+        transit_nearby = nearby_transit(unit.lat, unit.lng, max_walk_min=10.0)
+        if transit_nearby["tbane"] or transit_nearby["trikk"]:
+            logger.debug("Nearby transit: %s", transit_nearby)
+
+    scores: dict[str, Any] = {
+        "floor": score_floor(ad, unit),
+        "neighbourhood": score_neighbourhood(unit, ad.address, getattr(ad, "district", None)),
+        "view_and_quiet": score_view_and_quiet(ad, description),
+        "area_and_layout": score_area_and_layout(ad, unit),
+        "hybel": score_hybel(description),
+        "transport": score_transport(unit),
         "economy": score_economy(ad, unit),
-        "market_position": score_market_position(unit),
         "comparable_sales": score_comparable_sales(
-            similar_units, ad.total_price or ad.asking_price
+            similar_units,
+            unit.listing_sqm_price if unit else None,
         ),
-        "location": score_location(ad.address, ad.district),
+        "building_health": score_building_health(ad, description),
-        "layout": score_layout_and_potential(ad.listing_description, ad.rooms),
+        "green_areas": score_green_areas(description),
-        "outdoor": score_outdoor_and_view(ad.listing_description),
+        "renovation": score_renovation(description),
-        "rental_potential": score_rental_potential(ad.listing_description),
+        "risk": score_risk(ad, unit),
-        "renovation": score_renovation_upside(ad.listing_description, ad.asking_price),
-        "risk": score_risk(ad.listing_description, unit),
     }
-    scores["total"] = float(_clamp(sum(scores.values()), 0.0, 100.0))
+
+    # Numeric-only sum for total
+    numeric = {k: v for k, v in scores.items() if isinstance(v, (int, float))}
+    scores["total"] = float(_clamp(sum(numeric.values()), 0.0, 100.0))
+
+    # Attach nearby transit as metadata (non-scoring)
+    if transit_nearby is not None:
+        scores["nearby_transit"] = transit_nearby
+
     return scores
 
 
-def classify_ad(scores: dict[str, float]) -> list[str]:
+def classify_ad(scores: dict[str, Any]) -> list[str]:
     categories: list[str] = []
     total = scores.get("total", 0.0)
-    if total >= 70:
+
-        categories.append("bargain_candidate")
+    if total >= 75:
+        categories.append("top_match")
     if total >= 60:
-        categories.append("safe_candidate")
+        categories.append("strong_candidate")
-    if 50 <= total < 70:
+    if 45 <= total < 60:
-        categories.append("lifestyle_candidate")
+        categories.append("worth_viewing")
-    if scores.get("renovation", 0.0) >= 8:
-        categories.append("renovation_candidate")
-    if scores.get("rental_potential", 0.0) >= 5:
-        categories.append("hybel_candidate")
-    if scores.get("risk", 0.0) < 0:
-        categories.append("risk_object")
     if total < 30:
         categories.append("not_interesting")
-    if 30 <= total < 60:
+    if 30 <= total < 45:
         categories.append("manual_review_required")
+
+    if scores.get("hybel", 0.0) >= 7:
+        categories.append("has_hybel")
+    if scores.get("view_and_quiet", 0.0) >= 13:
+        categories.append("premium_view")
+    if scores.get("neighbourhood", 0.0) == 25:
+        categories.append("preferred_neighbourhood")
+    if scores.get("renovation", 0.0) > 0:
+        categories.append("renovation_candidate")
+    if scores.get("floor", 0.0) < 0:
+        categories.append("ground_floor")
+    if scores.get("risk", 0.0) < -5:
+        categories.append("risk_object")
+    if scores.get("area_and_layout", 0.0) <= 3:
+        categories.append("too_small")
+
     return categories

finn_eiendom/service.py

@@ -36,6 +36,43 @@ async def get_or_fetch_ad(finnkode: str, force_refresh: bool = False) -> FinnAd:
     return ad
 
 
+async def ensure_eiendom_unit_code(ad: FinnAd) -> str | None:
+    """Backfill ``ad.eiendom_unit_code`` by resolving it from the FINN URL.
+
+    ``fetch_ad_details`` never populates ``eiendom_unit_code`` -- only the
+    Eiendom.no resolver (``search_unit_from_finn_url``) can map a FINN listing
+    to an Eiendom.no unit. Every enrichment path gates on this field, so
+    without an explicit resolve step the gate is always falsy and enrichment
+    silently no-ops.
+
+    Resolves once, mutates the ad in place, and persists the backfill to the
+    cache so subsequent cache hits skip the network round trip.
+
+    IMPORTANT: callers must run this BEFORE serialising the ad with
+    ``model_dump()`` -- otherwise the dumped dict carries a stale
+    ``eiendom_unit_code: None`` even though enrichment succeeded.
+
+    Returns the unit_code, or ``None`` if the listing cannot be resolved
+    (e.g. new-build project ads, off-market addresses).
+    """
+    if ad.eiendom_unit_code:
+        return ad.eiendom_unit_code
+
+    unit = await search_unit_from_finn_url(ad.url)
+    if unit is None or not unit.unit_code:
+        logger.info("No Eiendom.no unit resolved for finnkode %s", ad.finnkode)
+        return None
+
+    ad.eiendom_unit_code = unit.unit_code
+    conn = init_db(FINN_CACHE_PATH)
+    save_finn_ad(conn, ad)  # persist backfill; do NOT cache `unit` here --
+    #                         the resolver returns a partial record (code +
+    #                         address + coords). The full unit comes from
+    #                         get_or_fetch_eiendom_unit -> get_unit().
+    logger.info("Resolved finnkode %s -> unit %s", ad.finnkode, unit.unit_code)
+    return ad.eiendom_unit_code
+
+
 async def get_or_fetch_eiendom_unit(
     unit_code: str, force_refresh: bool = False
 ) -> EiendomUnit | None:
@@ -84,7 +121,7 @@ async def resolve_eiendom_unit_from_finn_url(finn_url: str) -> EiendomUnit | Non
 
 
 # ============================================================================
-# Orchestration functions — delegate to analysis.py
+# Orchestration functions -- delegate to analysis.py
 # ============================================================================
 
 
@@ -96,7 +133,13 @@ async def analyze_search(
     include_details: bool = True,
     include_eiendom_no: bool = True,
 ) -> dict[str, Any]:
-    """Analyze a FINN search URL and return a ranked shortlist."""
+    """Analyze a FINN search URL and return a ranked shortlist.
+
+    NOTE: enrichment for search results lives in analysis.py. If that path
+    also reports `eiendom_enriched: 0`, it has the same root cause -- each
+    card's eiendom_unit_code must be resolved via ensure_eiendom_unit_code
+    (or search_unit_from_finn_url) before the enrichment gate.
+    """
     return await run_analysis_search(
         search_url,
         max_pages=max_pages,
@@ -114,15 +157,20 @@ async def analyze_ad(
 ) -> dict[str, Any]:
     """Fetch and enrich a single FINN ad with analysis."""
     ad = await get_or_fetch_ad(finnkode)
+
+    # Resolve BEFORE model_dump() so the serialised ad carries the backfilled
+    # eiendom_unit_code instead of a stale None.
+    unit_code = await ensure_eiendom_unit_code(ad) if include_eiendom_no else None
+
     result: dict[str, Any] = {
         "ad": ad.model_dump(),
     }
-    if include_eiendom_no and ad.eiendom_unit_code:
+    if unit_code:
-        unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
+        unit = await get_or_fetch_eiendom_unit(unit_code)
         if unit:
             result["eiendom_unit"] = unit.model_dump()
             if include_similar_units:
-                similar = await get_or_fetch_similar_units(ad.eiendom_unit_code)
+                similar = await get_or_fetch_similar_units(unit_code)
                 result["similar_units"] = [s.model_dump() for s in similar]
     return result
 
@@ -132,16 +180,18 @@ async def analyze_ad_against_comps(
 ) -> dict[str, Any]:
     """Evaluate one listing against recent comparable sales."""
     ad = await get_or_fetch_ad(finnkode)
+
+    # Resolve before model_dump() -- see analyze_ad.
+    unit_code = await ensure_eiendom_unit_code(ad)
+
     result: dict[str, Any] = {
         "ad": ad.model_dump(),
     }
-    if ad.eiendom_unit_code:
+    if unit_code:
-        unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
+        unit = await get_or_fetch_eiendom_unit(unit_code)
         if unit:
             result["eiendom_unit"] = unit.model_dump()
-            comps = await get_or_fetch_similar_units(
+            comps = await get_or_fetch_similar_units(unit_code, listing_status=listing_status)
-                ad.eiendom_unit_code, listing_status=listing_status
-            )
             result["comparable_units"] = [c.model_dump() for c in comps]
     return result
 
@@ -152,17 +202,20 @@ async def find_similar_to_liked(
     """Find properties similar to a listing the user has liked."""
     # Requires that feedback.verdict = "liked" exists for this finnkode
     ad = await get_or_fetch_ad(finnkode)
-    if not ad.eiendom_unit_code:
+
+    unit_code = await ensure_eiendom_unit_code(ad)
+    if not unit_code:
         raise ValueError(
-            f"Finnkode {finnkode} has no Eiendom.no unit_code; cannot find similar properties"
+            f"Finnkode {finnkode} could not be resolved to an Eiendom.no unit; "
+            "cannot find similar properties"
         )
 
     # TODO: verify feedback verdict = "liked" exists
-    unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
+    unit = await get_or_fetch_eiendom_unit(unit_code)
     if not unit:
         raise ValueError(f"Cannot enrich finnkode {finnkode} with Eiendom.no data")
 
-    similar = await get_or_fetch_similar_units(ad.eiendom_unit_code, listing_status=listing_status)
+    similar = await get_or_fetch_similar_units(unit_code, listing_status=listing_status)
     return {
         "base_ad": ad.model_dump(),
         "similar_listings": [s.model_dump() for s in similar],
@@ -177,15 +230,18 @@ async def compare_ads(
     ads = []
     for finnkode in finnkoder:
         ad = await get_or_fetch_ad(finnkode)
-        ad_data = ad.model_dump()
 
-        if include_eiendom_no and ad.eiendom_unit_code:
+        # Resolve before model_dump() -- see analyze_ad.
-            unit = await get_or_fetch_eiendom_unit(ad.eiendom_unit_code)
+        unit_code = await ensure_eiendom_unit_code(ad) if include_eiendom_no else None
+
+        ad_data = ad.model_dump()
+        if unit_code:
+            unit = await get_or_fetch_eiendom_unit(unit_code)
             if unit:
                 ad_data["eiendom_unit"] = unit.model_dump()
                 if include_comps:
                     comps = await get_or_fetch_similar_units(
-                        ad.eiendom_unit_code, listing_status="RECENTLY_SOLD"
+                        unit_code, listing_status="RECENTLY_SOLD"
                     )
                     ad_data["comps"] = [c.model_dump() for c in comps]
 

validate_mcp_tools.py

@@ -1,152 +0,0 @@
-#!/usr/bin/env python3
-"""
-Validate that all MCP tool definitions correctly match their service layer functions.
-This catches parameter mismatches, missing arguments, and other integration issues.
-"""
-
-import inspect
-from typing import get_type_hints
-from finn_eiendom import mcp_server, service, eiendom_no
-
-# Define the mapping of MCP tools to their service/module functions
-TOOL_MAPPINGS = {
-    # Tool name: (service function, expected params to check)
-    "finn_analyze_search": (
-        service.analyze_search,
-        ["search_url", "max_pages", "detail_limit", "include_details", "include_eiendom_no"],
-    ),
-    "finn_get_ad": (service.get_or_fetch_ad, ["finnkode", "force_refresh"]),
-    "finn_resolve_eiendom_unit": (eiendom_no.search_unit_from_finn_url, ["finn_url"]),
-    "finn_get_eiendom_unit": (service.get_or_fetch_eiendom_unit, ["unit_code", "force_refresh"]),
-    "finn_analyze_unit_images": (service.get_unit_images, ["unit_code", "force_refresh"]),
-    "finn_get_similar_units": (eiendom_no.get_similar_units, ["unit_vector", "listing_status"]),
-    "finn_build_unit_vector": (
-        eiendom_no.get_unit,
-        ["unit_code"],
-    ),  # Uses get_unit, not build_unit_vector
-    "finn_decode_unit_vector": (eiendom_no.decode_unit_vector, ["unit_vector"]),
-    "finn_analyze_ad": (
-        service.analyze_ad,
-        ["finnkode", "include_eiendom_no", "include_similar_units"],
-    ),
-    "finn_analyze_ad_against_comps": (
-        service.analyze_ad_against_comps,
-        ["finnkode", "listing_status"],
-    ),
-    "finn_find_similar_to_liked_ad": (
-        service.find_similar_to_liked,
-        ["finnkode", "mode", "listing_status"],
-    ),
-    "finn_compare_ads": (service.compare_ads, ["finnkoder", "include_eiendom_no", "include_comps"]),
-    "finn_save_feedback": (service.save_feedback, ["finnkode", "verdict", "notes"]),
-    "finn_get_shortlist": (service.get_shortlist, ["run_id", "limit"]),
-    "finn_get_new_ads_since_last_run": (service.get_new_ads_since_last_run, ["search_url"]),
-}
-
-
-def get_function_params(func) -> dict:
-    """Extract parameter names and defaults from a function."""
-    sig = inspect.signature(func)
-    params = {}
-    for name, param in sig.parameters.items():
-        if name in ("self", "cls"):
-            continue
-        params[name] = {
-            "default": param.default,
-            "annotation": param.annotation,
-            "kind": param.kind.name,
-        }
-    return params
-
-
-def validate_tool_mapping(
-    tool_name: str, service_func, expected_params: list[str]
-) -> tuple[bool, list[str]]:
-    """Validate that an MCP tool correctly maps to its service function."""
-    errors = []
-
-    # Get the MCP tool function
-    mcp_tool = getattr(mcp_server, tool_name, None)
-    if not mcp_tool:
-        errors.append(f"MCP tool '{tool_name}' not found in mcp_server module")
-        return False, errors
-
-    # Get function signatures
-    mcp_params = get_function_params(mcp_tool)
-    service_params = get_function_params(service_func)
-
-    # Check that expected parameters exist in both
-    for param in expected_params:
-        if param not in mcp_params:
-            errors.append(f"  ✗ MCP tool missing parameter '{param}'")
-        if param not in service_params and param != "client":  # client is optional in service layer
-            errors.append(f"  ✗ Service function missing parameter '{param}'")
-
-    # Check that MCP tool doesn't pass unknown parameters
-    # (skip return annotation)
-    for param_name, param_info in mcp_params.items():
-        if param_name not in service_params and param_name not in ["return"]:
-            # This might be OK if it's a tool-specific parameter, but warn
-            pass
-
-    if errors:
-        return False, errors
-    return True, []
-
-
-async def validate_service_imports():
-    """Validate that all imported service functions exist and are callable."""
-    imported_funcs = [
-        ("analyze_ad", service.analyze_ad),
-        ("analyze_ad_against_comps", service.analyze_ad_against_comps),
-        ("analyze_search", service.analyze_search),
-        ("compare_ads", service.compare_ads),
-        ("find_similar_to_liked", service.find_similar_to_liked),
-        ("get_new_ads_since_last_run", service.get_new_ads_since_last_run),
-        ("get_or_fetch_ad", service.get_or_fetch_ad),
-        ("get_or_fetch_eiendom_unit", service.get_or_fetch_eiendom_unit),
-        ("get_shortlist", service.get_shortlist),
-        ("get_unit_images", service.get_unit_images),
-        ("save_feedback", service.save_feedback),
-    ]
-
-    errors = []
-    for name, func in imported_funcs:
-        if not callable(func):
-            errors.append(f"Service function '{name}' is not callable")
-
-    return errors
-
-
-def main():
-    """Run validation checks."""
-    print("=" * 80)
-    print("MCP Tool Parameter Validation")
-    print("=" * 80)
-
-    all_passed = True
-    total_checks = 0
-    passed_checks = 0
-
-    for tool_name, (service_func, expected_params) in TOOL_MAPPINGS.items():
-        total_checks += 1
-        passed, errors = validate_tool_mapping(tool_name, service_func, expected_params)
-
-        if passed:
-            print(f"✓ {tool_name}")
-            passed_checks += 1
-        else:
-            print(f"✗ {tool_name}")
-            for error in errors:
-                print(f"  {error}")
-            all_passed = False
-
-    print("\n" + "=" * 80)
-    print(f"Results: {passed_checks}/{total_checks} tools validated")
-    print("=" * 80)
-
-    return 0 if all_passed else 1
-
-
-if __name__ == "__main__":
-    exit(main())