🏡 Funda Scraper — Netherlands Property Data

Scrape Funda, the Netherlands' #1 property portal, to buy or rent — and get clean, structured listings: price, object type, bedrooms, rooms, floor area, energy label, full location (city, neighbourhood, district, province, postcode), estate agent name and link, photos and the listing URL. Search by any Dutch city.

Built for property research, makelaar (agent) lead generation, market analysis and price monitoring across the Netherlands. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

🔎 What is the Funda Scraper?

Give it Dutch cities (e.g. "amsterdam", "rotterdam") and choose buy (koop) or rent (huur), and it returns matching listings as structured rows — paginated to your requested limit. Perfect for building a clean NL property dataset or a list of active listings and the agents behind them.

What data does it extract?

• Price (and price condition, e.g. kosten koper) in EUR
• Object type (apartment, house…) and construction type
• Bedrooms, rooms and floor area (m²)
• Energy label
• Location: city, neighbourhood, district (wijk), municipality, province, postcode
• Estate agent (makelaar) name, association (e.g. NVM) and profile link
• Photo count, publish date and listing URL

⬇️ Input

Field	Type	Description
cities	array	Dutch city names, e.g. amsterdam.
listingType	string	koop (buy) or huur (rent).
maxResults	integer	Max listings per city. Default 50.

Example input

{
  "cities": ["amsterdam", "rotterdam"],
  "listingType": "koop",
  "maxResults": 100
}

⬆️ Output

One record per listing:

{
  "id": 8064870,
  "url": "https://www.funda.nl/detail/koop/amsterdam/appartement-ennemaborg-129/44419233/",
  "offering_type": "buy",
  "object_type": "apartment",
  "construction_type": "resale",
  "price": 359000,
  "price_currency": "EUR",
  "price_condition": "kosten_koper",
  "bedrooms": 1,
  "rooms": 2,
  "floor_area_m2": 36,
  "energy_label": "C",
  "city": "Amsterdam",
  "neighbourhood": "Gelderlandpleinbuurt",
  "district": "Buitenveldert-West",
  "municipality": "Amsterdam",
  "province": "Noord-Holland",
  "postcode": "1082SP",
  "agent": "FRIS Woningmakelaars Amsterdam",
  "agent_association": "NVM",
  "agent_url": "https://www.funda.nl/makelaar/24122-fris-woningmakelaars-amsterdam/",
  "photo_count": 23,
  "publish_date": "2026-06-29T14:08:36+02:00",
  "status": "none",
  "query": "amsterdam"
}

💡 Use cases

• 🎯 Makelaar lead generation — build a list of agents and their listings.
• 🏠 Market research — analyze asking prices by city, neighbourhood and type.
• 📈 Price monitoring — schedule runs and track the market over time.
• 🤖 Automation — push new listings to Slack/Sheets via Make/Zapier/n8n.

❓ FAQ

Do I need an API key or login? No — just provide Dutch cities.

Can I scrape rentals? Yes — set listingType to huur.

Do I get the agent (makelaar)? Yes — name, association and profile link.

Do I get the postcode and neighbourhood? Yes — full location detail.

Is the energy label included? Yes — where Funda lists it.

How many listings can I get? Set maxResults; it paginates automatically.

How does pricing work? Pay per listing returned. No subscription.

Is it legal? It collects publicly available listing data. Scrape responsibly, respect robots/ToS, and follow EU/NL GDPR for any personal data.

⚙️ How it works

Funda protects its data behind a bot challenge, so this actor uses a privacy-hardened stealth browser through NL residential IPs to load the site like a real visitor, then reads Funda's own embedded listing data — returning clean, structured JSON instead of fragile HTML. It paginates automatically and recovers from transient browser hiccups. The same input shape works for a quick check or a deep multi-city pull — only maxResults changes.

👥 Who uses Dutch property data?

Property data is valuable to investors, makelaars, proptech teams and analysts. An investor compares asking prices across neighbourhoods; an agency monitors competitor stock and the agents behind it; a proptech app enriches its map with fresh inventory; an analyst tracks supply and prices. Because every record is plain JSON with consistent fields, it drops straight into a spreadsheet, CRM, BI tool or LLM pipeline with no custom parsing.

📤 Export, schedule & integrate

Every run is saved to a dataset you can export to JSON, CSV, Excel, XML or RSS, or pull through the Apify API. Wire it into Make, Zapier, n8n, Google Sheets, Slack or your own database, run it on a schedule to catch new listings, and call it from AI agents through the Apify MCP server.

💡 Tips for best results

• Run koop and huur separately to keep two clean datasets.
• Schedule a daily run and diff by id for a new-listings alert.
• The agent name + profile link make every listing a warm lead.
• Combine several cities in one run to cover a whole region.

❓ More FAQ

How fresh is the data? It is fetched live on each run.

Can I run it automatically? Yes — use Apify Schedules (cron).

Are duplicates removed? Listings are de-duplicated within each run.

Which export formats? JSON, CSV, Excel, XML and RSS, plus the Apify API.

Can AI agents use it? Yes — via the Apify API and MCP server.

🔗 You might also like

• Rightmove Scraper — UK property data.
• Immobiliare.it Scraper — Italian property data.
• Real Estate Agent Lead Scraper — agent contact leads.

---

Keywords: funda scraper, funda api, netherlands property data, dutch real estate, funda listings, makelaar leads, property scraper netherlands, huizen te koop, woningen, funda data, nl property market, real estate netherlands, koopwoningen, huurwoningen