Funda.nl Scraper 🏠

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Funda.nl Scraper — Dutch Real Estate Data Extraction

Scrape residential property listings from Funda.nl, the Netherlands' largest real estate platform. Extract prices, photos, coordinates, property characteristics, neighborhood data, and more — powered by Funda's internal API for maximum speed and reliability.

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parsebird/funda-scraper on Apify. Call: ApifyClient("TOKEN").actor("parsebird/funda-scraper").call(run_input={...}), then client.dataset(run["defaultDatasetId"]).list_items().items for results. Key inputs: startUrls (array of {"url":"..."}, search or detail pages), searchTransactionType (string, "buy"/"rent", default "buy"), searchLocation (string, e.g. "amsterdam"), searchPriceMin (integer, EUR), searchPriceMax (integer, EUR), maxItems (integer, default 100), includeNeighborhoodData (boolean, default false), contentLanguage (string, "nl"/"en", default "en"), maxConcurrency (integer, default 3). Full actor spec: fetch build via GET https://api.apify.com/v2/acts/parsebird~funda-scraper (Bearer TOKEN). Get token: https://console.apify.com/account/integrations

What does Funda.nl Scraper do?

This Actor scrapes property listings from Funda.nl — the Netherlands' dominant real estate platform with 80%+ market share. It connects directly to Funda's internal listing API, bypassing the website entirely for fast, structured JSON data without browser rendering.

For each property listing, the scraper extracts:

• Price data — asking price, price per m², auction status, rental price
• Property characteristics — bedrooms, living area, plot size, energy label, construction year, building type
• Full descriptions — Dutch or English listing text with all property details
• Media — all photo URLs, floor plans, 360° photos, virtual tours, brochure links
• Location — GPS coordinates, neighborhood name/identifier, full address with postcode
• Broker info — broker ID, association (NVM/VBO/VastgoedPRO), contact details
• Property features — garden, balcony, solar panels, heat pump, parking, monument status
• Neighborhood data (optional) — local market insights from Funda's Market Insights API
• Status & metadata — publication date, sold/rented status, labels, views/saves count

How to use Funda.nl Scraper

1. Provide search URLs — paste Funda.nl search URLs (e.g. https://www.funda.nl/zoeken/koop?selected_area=["amsterdam"]) or individual listing detail URLs
2. Or use search parameters — set location, price range, and transaction type (buy/rent) without a URL
3. Configure limits — set max items, concurrency, and optional neighborhood data enrichment
4. Run — the scraper fetches listings via Funda's API, processes details concurrently, and outputs structured JSON

Input examples

Search by URL:

{
"startUrls":[
{"url":"https://www.funda.nl/zoeken/koop?selected_area=[\"amsterdam\"]"}
],
"maxItems":50
}

Search by parameters:

{
"searchLocation":"rotterdam",
"searchTransactionType":"buy",
"searchPriceMin":200000,
"searchPriceMax":450000,
"maxItems":100,
"includeNeighborhoodData":true
}

Single listing:

{
"startUrls":[
{"url":"https://www.funda.nl/detail/koop/amsterdam/appartement-wildeman-97/43279645/"}
]
}

Output

Each listing is output as a JSON object matching Funda's internal data structure:

{
"_id":"7998544",
"Price":{
"SellingPrice":"€ 595.000 k.k.",
"NumericSellingPrice":595000,
"IsAuction":false
},
"KenmerkSections":[...],
"Labels":[{"Text":"Nieuw","Type":"New"}],
"Aanbiedingstekst":"Full property description...",
"WoonOppervlakteSubTitle":"72 m²",
"KoopprijsSubTitle":"€ 595.000 k.k.",
"AddressTitle":"Waalstraat 122",
"AddressSubTitle":"1079 EC Amsterdam",
"NumberOfBedrooms":"1",
"ListingUrl":"https://www.funda.nl/detail/43343907",
"BuurtName":"Scheldebuurt-Midden",
"Coordinates":{"Latitude":52.343155,"Longitude":4.9018593},
"Media":{"HoofdfotoUrl":"...","Photos":[...],"FloorPlan":{...}},
"IsSoldOrRented":false,
"PublicationDate":"2026-05-12T00:00:00"
}

Use cases

• Real estate market research — analyze property prices, trends, and market dynamics across Dutch cities
• Investment analysis — find undervalued properties by comparing asking prices to neighborhood averages
• Property monitoring — track new listings matching specific criteria (area, price range, features)
• Portfolio valuation — compare similar properties for accurate real estate appraisals
• Academic research — study housing market patterns, gentrification, and urban development
• Data journalism — investigate property market trends and housing affordability

Pricing

This Actor uses pay-per-event pricing:

Platform costs (compute + proxy) are charged separately by Apify.

Limitations

• Funda's internal API has a pagination limit of ~10,000 results per search query. For larger datasets, use multiple searches with different filters.
• Neighborhood data enrichment adds an extra API call per listing, which may slow down large runs.
• Some sold/rented listings may have limited data available.

FAQ

Can I scrape sold properties?

Currently the scraper focuses on available listings. Sold properties may return limited data.

How often does the data update?

Funda updates listings in real-time. New listings appear within minutes of being published.

Is this legal?

This scraper uses publicly available API endpoints. Always ensure your use case complies with applicable laws and Funda's terms of service.