Real Estate Property Listings & Market Intelligence API

High-throughput real estate listing and property market intelligence engine engineered for PropTech platforms, real estate investment funds, asset managers, and B2B brokerage prospecting.

Instead of relying on brittle headless browsers that consume excessive compute units and fail on layout shifts, this Actor queries structured JSON property endpoints, official municipal geocoding APIs (api-adresse.data.gouv.fr), and Schema.org RealEstateListing feeds directly over asynchronous httpx connections. Every record is normalized, enriched with automated price-per-square-meter ($\text{price} / \text{m}^2$) metrics and DPE energy ratings, and validated through Pydantic v2 before storage.

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Core Capabilities & Architectural Advantages

Feature	Technical Implementation	Value for PropTech & Investors
Automated Price / $\text{m}^2$ Valuation	Computes pricePerSqm across living (surfaceArea) and plot (landArea) surfaces automatically	Instant detection of undervalued properties and neighborhood pricing anomalies without post-processing
Municipality & INSEE Resolution	Resolves city names, arrondissements, and postal codes via the French National Address Base (BAN)	Eliminates ambiguous location matching across metropolitan areas (Paris, Lyon, Bordeaux, Nice, etc.)
DPE Energy Rating Enrichment	Concurrent asynchronous lookup of official Energy Performance (A to G class)	Critical for evaluating renovation CapEx and regulatory rental compliance (passoires thermiques F/G)
Agency & Brokerage Attribution	Extracts agency name, branch code, direct phone number (agentPhone), and canonical listing URL	Ready-to-use B2B lead lists for mortgage brokers, SaaS vendors, renovation contractors, and syndics
Universal Direct URL Parser	Parses Schema.org JSON-LD (RealEstateListing, Apartment, SingleFamilyResidence, Offer)	Allows on-demand ingestion of custom brokerage or developer pages alongside city-wide searches
Low-Overhead HTTPX Engine	Runs comfortably inside a 128 MB–256 MB container without Chromium overhead	Up to 15x lower Apify Compute Unit consumption compared to browser-based scrapers

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High-ROI Use Cases

1. Automated Deal Sourcing & Undervalued Asset Detection

Configure scheduled runs across target municipalities with strict maxPrice, minSurfaceArea, and minRooms thresholds. Pipe the output Dataset into Make, n8n, or Airtable to trigger instant Slack or email alerts whenever a listing drops below your target pricePerSqm benchmark or carries an F/G energy rating suitable for value-add renovation.

2. B2B Lead Generation for Real Estate Service Providers

Extract active real estate agencies (agentOrAgency), direct branch phone numbers (agentPhone), and active inventory volumes by postal code. Ideal for PropTech SaaS sales teams, property photography studios, energy audit firms, and commercial insurance brokers building hyper-targeted outbound campaigns in Clay or HubSpot.

3. Quantitative Market Research & Valuation Indexes

Aggregate thousands of standardized property records (price, pricePerSqm, surfaceArea, rooms, bedrooms, latitude, longitude) into BigQuery, Snowflake, or Power BI to monitor real-time supply levels, price dispersion, and asset-class breakdowns (apartment, house, commercial, land).

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Input Configuration Reference

Parameter	Type	Default	Description
locations	array<string>	["Paris", "Lyon", "Bordeaux"]	Target cities, municipalities, or postal codes to query (e.g., "Paris", "69006", "Bordeaux", "Marseille", "Nice").
propertyTypes	array<string>	["apartment", "house", "commercial"]	Filter by normalized asset class: "apartment", "house", "commercial", or "land".
transactionType	string	"sale"	Filter by deal type: "sale" (acquisition), "rent" (leasing), or "all".
minPrice	integer	50000	Minimum listing price threshold in local currency (0 disables the minimum filter).
maxPrice	integer	0	Maximum listing price threshold in local currency (0 disables the maximum limit).
minSurfaceArea	integer	15	Minimum floor area in square meters ($\text{m}^2$).
minRooms	integer	1	Minimum number of main rooms (pièces principales).
targetUrls	array<string>	[]	Optional list of direct property or brokerage URLs to parse via Schema.org JSON-LD.
maxResults	integer	25	Maximum number of validated listings to extract per target location (1–500).

Example Input JSON

{
  "locations": [
    "Paris",
    "Lyon",
    "Bordeaux"
  ],
  "propertyTypes": [
    "apartment",
    "house"
  ],
  "transactionType": "sale",
  "minPrice": 150000,
  "maxPrice": 950000,
  "minSurfaceArea": 30,
  "minRooms": 2,
  "targetUrls": [],
  "maxResults": 25
}

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Output Dataset Schema Reference

Every item pushed to the default Apify Dataset conforms strictly to .actor/dataset_schema.json:

Field	Type	Description
propertyId	string	Deterministic unique identifier for deduplication across runs.
propertyTitle	string	Normalized headline of the property listing.
price	number	Total listed price in local currency.
pricePerSqm	number | null	Automatically calculated price per square meter ($\text{price} / \text{surfaceArea}$).
currency	string	ISO 4217 currency code (EUR).
location	string	Complete formatted address string (street, postal code, city).
city	string | null	Normalized municipality name.
postalCode	string | null	Postal or ZIP code.
country	string	ISO country code (FR).
surfaceArea	number | null	Living or commercial floor area in square meters ($\text{m}^2$).
landArea	number | null	Plot or land area in square meters ($\text{m}^2$), when applicable.
rooms	integer | null	Total number of main rooms.
bedrooms	integer | null	Number of bedrooms.
propertyType	string	Normalized asset category (apartment, house, commercial, land).
transactionType	string	Normalized transaction type (sale or rent).
energyRating	string | null	Official DPE energy performance class (A through G).
agentOrAgency	string	Name of the listing real estate agency or brokerage branch.
agentPhone	string | null	Direct telephone number of the listing agency branch.
agentEmail	string | null	Public contact email of the agency, when available.
latitude	number | null	WGS84 geographic latitude coordinate.
longitude	number | null	WGS84 geographic longitude coordinate.
listingUrl	string	Canonical URL to the full property listing.
sourcePlatform	string	Identifier of the upstream data source (laforet-immo-api or direct-jsonld).
listedAt	string | null	ISO 8601 publication timestamp of the listing.
scrapedAt	string	ISO 8601 UTC timestamp of extraction.

Sample Output Record

{
  "propertyId": "lf-53007812",
  "propertyTitle": "Appartement Paris 3 pièce(s) 64.50 m2",
  "price": 585000.0,
  "pricePerSqm": 9069.77,
  "currency": "EUR",
  "location": "75015, PARIS",
  "city": "Paris",
  "postalCode": "75015",
  "country": "FR",
  "surfaceArea": 64.5,
  "landArea": null,
  "rooms": 3,
  "bedrooms": 2,
  "propertyType": "apartment",
  "transactionType": "sale",
  "energyRating": "D",
  "agentOrAgency": "Laforêt PARIS 15 CONVENTION",
  "agentPhone": "0145321010",
  "agentEmail": null,
  "latitude": 48.839612,
  "longitude": 2.296145,
  "listingUrl": "https://www.laforet.com/agence-immobiliere/paris15convention/acheter/paris-15/appartement-3-pieces-53007812",
  "sourcePlatform": "laforet-immo-api",
  "listedAt": "2026-10-30T14:20:00+00:00",
  "scrapedAt": "2026-10-31T22:55:00.114209+00:00"
}

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Programmatic Usage (Python SDK)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

run_input = {
    "locations": ["Paris", "Lyon", "Bordeaux"],
    "propertyTypes": ["apartment", "house"],
    "transactionType": "sale",
    "minPrice": 150000,
    "minSurfaceArea": 30,
    "maxResults": 50,
}

run = client.actor("kleioks/real-estate-market-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(
        f"[{item['city']} {item['postalCode']}] {item['propertyTitle']} — "
        f"{item['price']:,.0f} {item['currency']} ({item['pricePerSqm']} €/m²) | "
        f"DPE: {item['energyRating']} | Agency: {item['agentOrAgency']} ({item['agentPhone']})"
    )

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No-Code, Make, n8n, Clay & AI Agent (MCP) Integrations

1. Make.com & n8n Automated Deal-Sourcing Alerts

1. Schedule the Apify Node in n8n or Make.com to run every morning across your target cities (Paris, Lyon, Bordeaux, Nice, Nantes).
2. Add a router condition filtering properties where pricePerSqm is 15% below your neighborhood threshold or where energyRating is "F" or "G" (passoires thermiques with high negotiation margin).
3. Send instant deal cards with listingUrl, price, surfaceArea, and agentPhone to Slack, Telegram, Airtable, or Notion.

2. Clay.com Real Estate Agency B2B Prospecting

Import the dataset directly into Clay using agentOrAgency, agentPhone, city, and postalCode to build segmented outbound lists of active real estate brokerages for PropTech SaaS, mortgage brokerage partnerships, or renovation services.

3. Model Context Protocol (MCP) for Claude, Cursor & AI Agents

Connect this Actor to Claude Desktop, Cursor, or custom PropTech AI assistants via the official Apify MCP server:

{
  "mcpServers": {
    "apify-real-estate": {
      "command": "npx",
      "args": [
        "-y",
        "@apify/actors-mcp-server",
        "--actors",
        "kleioks/real-estate-market-scraper"
      ],
      "env": {
        "APIFY_TOKEN": "YOUR_APIFY_API_TOKEN"
      }
    }
  }
}

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Frequently Asked Questions (FAQ)

How does this Real Estate scraper calculate Price per Square Meter ($\text{price}/\text{m}^2$)?

Every extracted property record is validated through Pydantic v2, which automatically divides the listed price by the verified surfaceArea (Carrez or living floor area) and rounds pricePerSqm to two decimal places—delivering ready-to-analyze valuation metrics with zero spreadsheet formulas.

Can I filter properties by Energy Performance Rating (DPE A to G)?

Yes. The Actor asynchronously enriches listings with their official DPE class (A through G) (energyRating), allowing institutional investors and marchands de biens to identify thermal-renovation opportunities (F and G ratings) immediately.

Can I scrape custom real estate agency URLs in addition to city searches?

Yes. Any URL passed in targetUrls is parsed using a universal Schema.org JSON-LD (RealEstateListing, Apartment, SingleFamilyResidence, Offer) and contact extractor.

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Complete B2B Intelligence Suite by Kleioks

Combine this Actor with our specialized B2B data engines on the Apify Store to build end-to-end GTM, RevOps, and market research pipelines:

Actor	Primary B2B Use Case	Store Link
Real Estate Market Intelligence API	Normalized property listings, automated price/m² valuation, DPE ratings & agency contacts	Open on Apify Store
Google Maps B2B Lead Machine	High-speed local business lead extractor (phone, website, rating & missing-website signals)	Open on Apify Store
Multi-ATS Career Jobs API	Track real-time hiring intent & tech-stack signals across Greenhouse, Lever, Ashby & Workable	Open on Apify Store
Reddit B2B Buying Intent Scraper	Monitor competitor churn, software recommendations & high-intent buyer threads on Reddit	Open on Apify Store
B2B Social Leads & Intent Extractor	Discover SaaS founders, developers & decision-makers across Bluesky, GitHub & Hacker News	Open on Apify Store
Influencer & Creator Discovery API	Extract TikTok, Instagram & YouTube creator profiles, engagement rates & verified PR emails	Open on Apify Store