Property data API for surveyors.

# Step 1 — resolve address to UPRN (2 tokens)
curl "https://api.homedata.co.uk/address/find/?q=42+Victoria+Road+Leeds" \
  -H "Authorization: Api-Key YOUR_API_KEY"

# Step 2 — property record: type, age, beds, floor area (1 token)
curl "https://api.homedata.co.uk/properties/100032456789/" \
  -H "Authorization: Api-Key YOUR_API_KEY"

# Step 3 — EPC: current/potential rating, floor area, age band, certificate ID (1 token)
curl "https://api.homedata.co.uk/epc-checker/100032456789/" \
  -H "Authorization: Api-Key YOUR_API_KEY"

# Step 4 — flood risk: river and surface water (1 token)
curl "https://api.homedata.co.uk/risks/flood/?uprn=100032456789" \
  -H "Authorization: Api-Key YOUR_API_KEY"

# Step 5 — comparable sales. Arranged per customer rather than sold self-serve;
#          the same evidence is in the complete property profile.
curl "https://api.homedata.co.uk/comparables/100032456789/" \
  -H "Authorization: Api-Key YOUR_API_KEY"

import requests

API_KEY = "YOUR_API_KEY"
BASE    = "https://api.homedata.co.uk"
HEADERS = {"Authorization": f"Api-Key {API_KEY}"}

def prefill_survey_report(address: str) -> dict:
    # Step 1 — UPRN lookup
    r = requests.get(f"{BASE}/address/find/", params={"q": address}, headers=HEADERS)
    uprn = r.json()["suggestions"][0]["uprn"]

    # Steps 2-5 — all property data in parallel (or sequential)
    property_data = requests.get(f"{BASE}/properties/{uprn}/", headers=HEADERS).json()
    epc_data      = requests.get(f"{BASE}/epc-checker/{uprn}/", headers=HEADERS).json()
    flood_risk    = requests.get(f"{BASE}/risks/flood/", headers=HEADERS, params={"uprn": uprn}).json()
    # comparables is arranged per customer rather than sold self-serve
    comparables   = requests.get(f"{BASE}/comparables/{uprn}/", headers=HEADERS).json()

    return {
        "uprn": uprn,
        "property": {
            "type": property_data.get("property_type"),
            "bedrooms": property_data.get("bedrooms"),
            "floor_area": property_data.get("epc_floor_area"),
            "construction_age": property_data.get("construction_age_band"),
        },
        "epc": {
            "current_efficiency": epc_data.get("current_energy_efficiency"),
            "potential_efficiency": epc_data.get("potential_energy_efficiency"),
            "floor_area_m2": epc_data.get("epc_floor_area"),
            "construction_age": epc_data.get("construction_age_band"),
            "last_assessed": epc_data.get("last_epc_date"),
        },
        "flood_label": flood_risk["results"][0]["label"] if flood_risk.get("results") else "Unknown",
        "flood_desc":  flood_risk["results"][0]["properties"]["description"] if flood_risk.get("results") else "",
        "comparables_count": len(comparables.get("comparables", [])),
    }

report = prefill_survey_report("42 Victoria Road Leeds")
print(f"Property: {report['property']['type']}, {report['property']['bedrooms']} bed")
print(f"EPC: {report['epc']['current_efficiency']}/100 → {report['epc']['potential_efficiency']}/100")
print(f"Flood: {report['flood_label']} — {report['flood_desc']}")
print(f"Comps: {report['comparables_count']} found")