Funda Property Scraper

Tracking the Dutch housing market usually means copying details off funda.nl one listing at a time: address, price, floor area, energy label, agent. This scraper does that for you. Paste a funda.nl search URL and every listing comes back as a clean row, with the price, location, size, and agent already split into fields and ready for a spreadsheet or a model. It's fast, cheap, and code-free, and you only pay for what you scrape.

What you get

Each listing comes back as one row with three kinds of data:

• Location — streetAddress, postCode, cityName, districtName, plus the listingUrl and propertyId
• Price and size — priceValue, priceLabel, priceTerms, livingAreaSqm, plotAreaSqm, roomCount, bedroomCount, energyRating, propertyType
• Listing and agent — listingStatus, dealType, publishedDate, summary, brokerName, brokerUrl, coverImage, imageLinks, collectedAt

Quick start

1. Open funda.nl, apply your filters (area, price, type), and copy the URL from the address bar.
2. Hit Try for free and paste that URL into Search URLs.
3. Set a Results limit to control how many listings you pull.
4. Press Start, then export the results as JSON, CSV, Excel, or XML.

Use cases

• Price tracking — watch asking prices across a city or district and spot reductions early
• Neighbourhood comparison — compare price per m², room counts, and energy labels between areas
• Investment research — build a dataset of for-sale or rental stock to score against your criteria
• Agent and brokerage analysis — see which brokers list what, where, and at what price
• Market dashboards — feed live funda.nl data into your own sheet, BI tool, or app

Input

Field	Type	Required	Description
targetUrls	array of strings	Yes	funda.nl search URLs to scrape; each one runs with full pagination. Prefilled with a Rotterdam for-sale search.
resultsLimit	integer	No	How many listings to pull per run. Default 20.
timeoutSeconds	integer	No	Seconds to wait on each request before it times out. Default 45.

Example input

{
    "targetUrls": [
        "https://www.funda.nl/zoeken/koop/?selected_area=%5B%22rotterdam%22%5D"
    ],
    "resultsLimit": 20,
    "timeoutSeconds": 45
}

Output

Every listing on the search results becomes one row, paginated automatically up to your limit. Fields that funda.nl does not publish for a given property come back absent rather than guessed.

Example output

{
    "propertyId": "43891204",
    "listingUrl": "https://www.funda.nl/detail/koop/rotterdam/appartement-westersingel-12-a/43891204/",
    "streetAddress": "Westersingel 12 A",
    "postCode": "3014 GN",
    "cityName": "Rotterdam",
    "districtName": "Cool",
    "priceValue": 395000,
    "priceLabel": "€ 395.000 k.k.",
    "priceTerms": "k.k.",
    "livingAreaSqm": 78,
    "plotAreaSqm": null,
    "roomCount": 3,
    "bedroomCount": 2,
    "propertyType": "Appartement",
    "energyRating": "B",
    "publishedDate": "2026-06-20",
    "coverImage": "https://cloud.funda.nl/valentina/previews/large/example-1.jpg",
    "imageLinks": [
        "https://cloud.funda.nl/valentina/previews/large/example-1.jpg",
        "https://cloud.funda.nl/valentina/previews/large/example-2.jpg"
    ],
    "brokerName": "Ruimzicht Makelaars",
    "brokerUrl": "https://www.funda.nl/makelaar/rotterdam/12345-ruimzicht-makelaars/",
    "listingStatus": "te koop",
    "dealType": "koop",
    "summary": "Light three-room apartment in the heart of Cool, a short walk from Witte de Withstraat and Rotterdam Centraal.",
    "collectedAt": "2026-06-29T12:00:00.000000+00:00"
}

Output fields

Field	Type	Description
propertyId	string	Funda object identifier for the listing
listingUrl	string	Direct link to the listing on funda.nl
streetAddress	string	Street name and house number
postCode	string	Dutch postal code in 1234 AB format
cityName	string	Town or city
districtName	string	District or neighbourhood within the city
priceValue	integer	Asking price in euros; null when not shown
priceLabel	string	Price as printed on funda.nl, e.g. € 425.000 k.k.
priceTerms	string	k.k. (buyer covers costs) or v.o.n. (seller covers costs)
livingAreaSqm	integer	Indoor living space in m²
plotAreaSqm	integer	Land area in m²; null for apartments
roomCount	integer	Total rooms
bedroomCount	integer	Number of bedrooms
propertyType	string	Category, e.g. Appartement, Tussenwoning, Hoekwoning
energyRating	string	Energy label A through G
publishedDate	string	ISO 8601 date the listing went live
coverImage	string	Link to the main listing photo
imageLinks	array	Every photo URL for the listing
brokerName	string	Estate agent or broker name
brokerUrl	string	Link to the broker profile on funda.nl
listingStatus	string	te koop, te huur, verkocht, verhuurd, or onder bod
dealType	string	koop (for sale) or huur (for rent)
summary	string	First 300 characters of the marketing text
collectedAt	string	ISO 8601 timestamp of when the row was captured

Tips for best results

• Filter on funda.nl first. The more specific your search URL (area, price band, property type), the cleaner and more relevant your dataset.
• Use resultsLimit for test runs. Start with 20 to confirm the output fits your pipeline, then raise it for the full pull.
• Both koop and huur work. A /koop URL returns for-sale stock and a /huur URL returns rentals; dealType and listingStatus tell them apart.
• Some fields are sparse on certain listings. plotAreaSqm is null for apartments, and a few listings hide the price — those rows still carry everything else.
• Raise timeoutSeconds if you see timeouts on large result sets.

How can I use Funda property data?

How can I use the Funda Property Scraper to track Dutch house prices? Paste a funda.nl search URL for your target area and run it on a schedule. Each row carries priceValue, priceLabel, streetAddress, and publishedDate, so you can chart asking prices over time and flag reductions the day they appear.

How can I compare neighbourhoods on funda.nl by price per m²? Pull listings for several areas, then divide priceValue by livingAreaSqm per row. With districtName, roomCount, and energyRating alongside, you get a clean side-by-side read on where the value sits.

How can I build a Netherlands real estate dataset for investment research? Feed in one or more search URLs, set resultsLimit to the volume you need, and export to CSV or Excel. The result is a structured property database (address, price, size, energy label, agent) you can score against your own buy criteria.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.