Funda.nl Scraper — Dutch Property Listings | $1.50/1K | No Proxy

For Dutch property investors, housing analysts, and real estate platforms who need structured data from Funda — the Netherlands' dominant portal with 95%+ of all residential listings.

$1.50 per 1,000 listings. First ~10 listings free for trial runs.

Scrape Dutch real estate listings from Funda.nl — the Netherlands #1 property portal. Extract structured data from any search URL: price, address, area, rooms, energy label, coordinates, and photos.

No proxy required. No authentication. Pay per listing.

What you get

Each listing record includes:

Field	Description
listing_id	Funda global object ID
title	Full address title
description	Listing description text
price	Display price (e.g. "€ 450.000 k.k.")
price_numeric	Numeric price in EUR
currency	Always EUR
address	Street address
city	City name
postcode	Dutch postcode (e.g. "1053 DM")
province	Province (e.g. "Noord-Holland")
neighborhood	Neighborhood name
bedrooms	Number of bedrooms
bathrooms	Number of bathrooms
property_type	"Apartment", "House", etc.
property_sub_type	More specific Dutch type
floor_area_m2	Living area in m²
energy_label	EU energy label (A–G)
construction_year	Year built
is_auction	True if property is auctioned
lat, lng	GPS coordinates
url	Full Funda listing URL
image_url	Main listing photo URL
images	All photo URLs
date_listed	Publication date (ISO 8601)
scraped_at	Scrape timestamp (ISO 8601 UTC)
parse_confidence	Data quality score 0.0–1.0
warnings	List of quality issue codes

How it works

Funda uses Nuxt4 SSR (Vue). Each search page exposes listing URLs via Schema.org ItemList (ld+json). Each detail page contains full property data in a dehydrated Nuxt4 state JSON array — no proxy, no JavaScript execution needed.

Flow: search page → extract listing URLs → detail page → parse Nuxt4 state array → normalize.

2 requests per listing (search page + detail page). This is normal and expected.

Input

{
  "searchUrl": "https://www.funda.nl/koop/amsterdam/",
  "maxItems": 50
}

searchUrl — paste any Funda search URL from your browser (koop/huur, area filters, price filters all work).

maxItems — limit total listings (0 = all pages).

Output sample

{
  "listing_id": 89234512,
  "title": "Kinkerstraat 141-A",
  "price": "€ 450.000 k.k.",
  "price_numeric": 450000,
  "currency": "EUR",
  "city": "Amsterdam",
  "postcode": "1053 DM",
  "province": "Noord-Holland",
  "bedrooms": 2,
  "floor_area_m2": 68.0,
  "property_type": "Apartment",
  "energy_label": "C",
  "construction_year": 1924,
  "is_auction": false,
  "lat": 52.3681,
  "lng": 4.8713,
  "url": "https://www.funda.nl/detail/koop/amsterdam/appartement-kinkerstraat-141-a/89234512/",
  "parse_confidence": 0.95,
  "warnings": []
}

Pricing example

Run size	Cost
50 listings	~$0.075
1,000 listings	~$1.50
10,000 listings	~$15.00
+ auction listing	+$2.00/1K
+ energy-label data	+$0.50/1K

FAQ

Do I need a Funda account or proxy? No account, no proxy. Works from Apify cloud IPs. Use /en/ URLs for English results.

What output formats are available? JSON, CSV, Excel, XML — all via Apify dataset download or API.

Can I filter by huur (rental) or only koop (sale)? Paste any Funda search URL — both /koop/ (sale) and /huur/ (rental) URLs work.

What if it returns empty results? Check that your searchUrl is a valid Funda search URL (e.g. https://www.funda.nl/koop/amsterdam/). The actor logs the issue and exits cleanly with 0 results rather than crashing.

Why this actor vs. competitors

Feature	This actor	epctex/funda-scraper ($3/1K)	Typical Funda scraper
parse_confidence field	Yes	No	No
Energy label (EU A–G)	Yes	No	Rarely
Construction year	Yes	No	Rarely
is_auction flag	Yes	No	No
GPS coordinates	Yes	Yes	Sometimes
Source: Nuxt4 embedded state	Yes (stable)	DOM	Usually DOM
Price per 1K listings	$1.50	$3.00	varies

Works with both Dutch (/koop/, /huur/) and English (/en/) Funda URLs.

EU compliance note: Netherlands has strict energy efficiency disclosure requirements. The energy_label field makes this actor useful for PropTech and investment firms tracking EU Green Deal compliance.

parse_confidence

Every listing includes a parse_confidence score (0–1). Score < 0.8 means one or more critical fields are missing — check the warnings array before relying on those records.

Use with AI agents (MCP)

Tag: MCP_SERVERS. Returns clean JSON — drop into any n8n / Make / LangChain workflow without post-processing.

{
  "mcpServer": "https://mcp.apify.com/?tools=bovi/funda-listings"
}

Competitive edge

• parse_confidence field on every record (no competitor has this)
• Extracts from Nuxt4 embedded state — not brittle CSS selectors
• Energy label, construction year, GPS coordinates included
• is_auction flag for high-intent investor filtering
• No proxy COGS — clean from Apify cloud IPs

Pricing

Pay-Per-Event (PPE):

Event	Price
listing-item (primary)	$1.50 / 1,000 listings
auction-listing (premium)	$2.00 / 1,000 auction listings
energy-label-data (premium)	$0.50 / 1,000 labelled listings

Auction listings and energy-labelled listings each fire their respective premium event in addition to the base charge. You only pay for actual results returned.

Integrations

Built for Dutch property investors and housing analysts tracking Funda listing prices, specs, and market availability — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.