The only working real Python API for Funda (funda.nl) — the Netherlands' largest real estate platform.
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pip install pyfundaBecause it simply works.
Funda has no public API. If you want Dutch real estate data programmatically, your options are limited:
| Library | Approach | Limitations |
|---|---|---|
| whchien/funda-scraper | HTML scraping | Listing dates blocked since Q4 2023 (requires login). Breaks when Funda changes frontend. |
| khpeek/funda-scraper | Scrapy | Last updated 2016. No longer maintained. |
| joostboon/Funda-Scraper | Selenium | Requires manual CAPTCHA solving. Slow browser automation. |
| Official API | — | Only available to registered brokers. Not accessible to developers. |
pyfunda takes a different approach: it uses Funda's internal mobile app API, reverse-engineered from the official Android app.
- Pure Python, no browser or Selenium needed
- No CAPTCHAs or anti-bot blocks
- 70+ fields including photos, floorplans, coordinates, and listing dates
- Stable mobile API that doesn't break when the website changes
from funda import Funda
f = Funda()
# Get a listing by ID
listing = f.get_listing(43117443)
print(listing['title'], listing['city'])
# Reehorst 13 Luttenberg
# Get a listing by URL
listing = f.get_listing('https://www.funda.nl/detail/koop/amsterdam/appartement-123/43117443/')
# Search listings
results = f.search_listing('amsterdam', price_max=500000)
for r in results:
print(r['title'], r['price'])This library uses Funda's undocumented mobile app API, which provides clean JSON responses unlike the website that embeds data in Nuxt.js/JavaScript bundles.
The API was reverse engineered by intercepting and analyzing HTTPS traffic from the official Funda Android app:
- Configured an Android device to route traffic through an intercepting proxy
- Used the Funda app normally - browsing listings, searching, opening shared URLs
- Identified the
*.funda.ioAPI infrastructure separate from thewww.funda.nlwebsite - Analyzed request/response patterns to understand the query format and available filters
- Discovered how the app resolves URL-based IDs (
tinyId) to internal IDs (globalId)
The mobile app communicates with a separate API at *.funda.io:
| Endpoint | Method | Purpose |
|---|---|---|
listing-detail-page.funda.io/api/v4/listing/object/nl/{globalId} |
GET | Fetch listing by internal ID |
listing-detail-page.funda.io/api/v4/listing/object/nl/tinyId/{tinyId} |
GET | Fetch listing by URL ID |
listing-search-wonen.funda.io/_msearch/template |
POST | Search listings |
Funda uses two ID systems:
- globalId: Internal numeric ID (7 digits), used in the database
- tinyId: Public-facing ID (8-9 digits), appears in URLs like
funda.nl/detail/koop/amsterdam/.../{tinyId}/
The tinyId endpoint was key - it allows fetching any listing directly from a Funda URL without needing to know the internal ID.
Search uses Elasticsearch's Multi Search Template API with NDJSON format:
{"index":"listings-wonen-searcher-alias-prod"}
{"id":"search_result_20250805","params":{...}}
Search parameters:
| Parameter | Description | Example |
|---|---|---|
selected_area |
Location filter | ["amsterdam"] |
radius_search |
Radius from location | {"index": "geo-wonen-alias-prod", "id": "1012AB-0", "path": "area_with_radius.10"} |
offering_type |
Buy or rent | "buy" or "rent" |
price.selling_price |
Price range (buy) | {"from": 200000, "to": 500000} |
price.rent_price |
Price range (rent) | {"from": 500, "to": 2000} |
object_type |
Property types | ["house", "apartment"] |
floor_area |
Living area m² | {"from": 50, "to": 150} |
plot_area |
Plot area m² | {"from": 100, "to": 500} |
energy_label |
Energy labels | ["A", "A+", "A++"] |
sort |
Sort order | {"field": "publish_date_utc", "order": "desc"} |
page.from |
Pagination offset | 0, 15, 30... |
Results are paginated with 15 listings per page.
Valid radius values: 1, 2, 5, 10, 15, 30, 50 km (other values are not indexed).
User-Agent: Dart/3.9 (dart:io)
X-Funda-App-Platform: android
Content-Type: application/json
Listing responses include:
- Identifiers - globalId, tinyId
- AddressDetails - title, city, postcode, province, neighbourhood, house number
- Price - numeric and formatted prices (selling or rental), auction flag
- FastView - bedrooms, living area, plot area, energy label
- Media - photos, floorplans, videos, 360° photos, brochure URL (all with CDN base URLs)
- KenmerkSections - detailed property characteristics (70+ fields)
- Coordinates - latitude/longitude
- ObjectInsights - view and save counts
- Advertising.TargetingOptions - boolean features (garden, balcony, solar panels, heat pump, parking, etc.), construction year, room counts
- Share - shareable URL
- GoogleMapsObjectUrl - direct Google Maps link
- PublicationDate - when the listing was published
- Tracking.Values.brokers - broker ID and association
Main entry point for the API.
from funda import Funda
f = Funda(timeout=30)Get a single listing by ID or URL.
# By numeric ID (tinyId or globalId)
listing = f.get_listing(43117443)
# By URL
listing = f.get_listing('https://www.funda.nl/detail/koop/city/house-name/43117443/')Search for listings with filters.
results = f.search_listing(
location='amsterdam', # City or area name
offering_type='buy', # 'buy' or 'rent'
price_min=200000, # Minimum price
price_max=500000, # Maximum price
area_min=50, # Minimum living area (m²)
area_max=150, # Maximum living area (m²)
plot_min=100, # Minimum plot area (m²)
plot_max=500, # Maximum plot area (m²)
object_type=['house'], # Property types (default: house, apartment)
energy_label=['A', 'A+'], # Energy labels to filter
sort='newest', # Sort order (see below)
page=0, # Page number (15 results per page)
)Radius search - search within a radius from a postcode or city:
results = f.search_listing(
location='1012AB', # Postcode or city
radius_km=10, # Search radius in km
price_max=750000,
)Note: Valid radius values are 1, 2, 5, 10, 15, 30, and 50 km. Other values are automatically mapped to the nearest valid radius.
Sort options:
| Sort Value | Description |
|---|---|
newest |
Most recently published first |
oldest |
Oldest listings first |
price_asc |
Lowest price first |
price_desc |
Highest price first |
area_asc |
Smallest living area first |
area_desc |
Largest living area first |
plot_desc |
Largest plot area first |
city |
Alphabetically by city |
postcode |
Alphabetically by postcode |
Multiple locations:
results = f.search_listing(['amsterdam', 'rotterdam', 'utrecht'])Get the highest listing ID currently in Funda's search index.
latest = f.get_latest_id() # e.g., 7852306Generator that polls for new listings by incrementing IDs.
for listing in f.poll_new_listings(
since_id=7852306, # Start from this ID + 1
max_consecutive_404s=20, # Stop after N consecutive 404s
offering_type='buy', # Filter: 'buy' or 'rent' (optional)
):
print(listing['title'])This bypasses ES search and queries the detail API directly, catching listings that haven't been indexed yet.
Listing objects support dict-like access with convenient aliases.
Basic info:
listing['title'] # Property title/address
listing['city'] # City name
listing['postcode'] # Postal code
listing['province'] # Province
listing['neighbourhood'] # Neighbourhood name
listing['municipality'] # Municipality (gemeente)
listing['house_number'] # House number
listing['house_number_ext'] # House number extension (e.g., "A", "II")Price & Status:
listing['price'] # Numeric price
listing['price_formatted'] # Formatted price string (e.g., "€ 450.000 k.k.")
listing['price_per_m2'] # Price per m² (from characteristics)
listing['status'] # "available" or "sold"
listing['offering_type'] # "Sale" or "Rent"Property details:
listing['object_type'] # House, Apartment, etc.
listing['house_type'] # Type of house (e.g., "Tussenwoning")
listing['construction_type'] # New or existing construction
listing['construction_year'] # Year built
listing['bedrooms'] # Number of bedrooms
listing['rooms'] # Total number of rooms
listing['living_area'] # Living area in m²
listing['plot_area'] # Plot area in m²
listing['energy_label'] # Energy label (A, B, C, etc.)
listing['description'] # Full description textDates:
listing['publication_date'] # When listed on Funda
listing['offered_since'] # "Offered since" date (from characteristics)
listing['acceptance'] # Acceptance terms (e.g., "In overleg")Location:
listing['coordinates'] # (lat, lng) tuple
listing['latitude'] # Latitude
listing['longitude'] # Longitude
listing['google_maps_url'] # Direct Google Maps linkMedia:
listing['photos'] # List of photo IDs
listing['photo_urls'] # List of full CDN URLs for photos
listing['photo_count'] # Number of photos
listing['floorplans'] # List of floorplan IDs
listing['floorplan_urls'] # List of full CDN URLs for floorplans
listing['videos'] # List of video IDs
listing['video_urls'] # List of video URLs
listing['photos_360'] # List of 360° photo dicts with name, id, url
listing['brochure_url'] # PDF brochure URL (if available)Property features (booleans):
listing['has_garden'] # Has garden
listing['has_balcony'] # Has balcony
listing['has_roof_terrace'] # Has roof terrace
listing['has_solar_panels'] # Has solar panels
listing['has_heat_pump'] # Has heat pump
listing['has_parking_on_site'] # Parking on property
listing['has_parking_enclosed'] # Enclosed parking
listing['is_energy_efficient'] # Energy efficient property
listing['is_monument'] # Listed/protected building
listing['is_fixer_upper'] # Fixer-upper (kluswoning)
listing['is_auction'] # Sold via auction
listing['open_house'] # Has open house scheduledStats & metadata:
listing['views'] # Number of views on Funda
listing['saves'] # Number of times saved
listing['highlight'] # Highlight text (blikvanger)
listing['global_id'] # Internal Funda ID
listing['tiny_id'] # Public ID (used in URLs)
listing['url'] # Full Funda URL
listing['share_url'] # Shareable URL
listing['broker_id'] # Broker ID
listing['broker_association'] # Broker association (e.g., "NVM")
listing['characteristics'] # Dict of all detailed characteristicsKey aliases - these all work:
| Alias | Canonical Key |
|---|---|
name, address |
title |
location, locality |
city |
area, size |
living_area |
type, property_type |
object_type |
images, pictures, media |
photos |
agent, realtor, makelaar |
broker |
zip, zipcode, postal_code |
postcode |
listing.summary() # Text summary of the listing
listing.to_dict() # Convert to plain dictionary
listing.keys() # List available keys
listing.get('key') # Get with default (like dict.get)
listing.getID() # Get listing IDfrom funda import Funda
f = Funda()
results = f.search_listing('amsterdam', price_max=400000)
for listing in results:
print(f"{listing['title']}")
print(f" Price: €{listing['price']:,}")
print(f" Area: {listing.get('living_area', 'N/A')}")
print(f" Bedrooms: {listing.get('bedrooms', 'N/A')}")
print()from funda import Funda
f = Funda()
listing = f.get_listing(43117443)
print(listing.summary())
# Access all characteristics
for key, value in listing['characteristics'].items():
print(f"{key}: {value}")from funda import Funda
f = Funda()
results = f.search_listing(
location=['amsterdam', 'rotterdam', 'den-haag'],
offering_type='rent',
price_max=2000,
)
print(f"Found {len(results)} rentals")from funda import Funda
f = Funda()
listing = f.get_listing(43117443)
# Check property features
if listing['has_garden'] and listing.get('has_solar_panels'):
print("Energy efficient with garden!")
if listing['is_energy_efficient']:
print(f"Energy label: {listing['energy_label']}")from funda import Funda
import requests
f = Funda()
listing = f.get_listing(43117443)
# Photo URLs are ready to use
for i, url in enumerate(listing['photo_urls'][:5]):
response = requests.get(url)
with open(f"photo_{i}.jpg", "wb") as file:
file.write(response.content)
# Also available: floorplan_urls, video_urlsfrom funda import Funda
f = Funda()
results = f.search_listing(
location='1012AB',
radius_km=15,
price_max=600000,
energy_label=['A', 'A+', 'A++'],
sort='newest',
)
for r in results:
print(f"{r['title']} - €{r['price']:,}")Funda's search index can lag behind the actual database by hours. Use poll_new_listings to find listings that search doesn't show yet:
from funda import Funda
f = Funda()
# Get starting point from search (one-time)
latest_id = f.get_latest_id() # e.g., 7852306
# Poll for new listings by incrementing IDs
for listing in f.poll_new_listings(since_id=latest_id):
print(f"New: {listing['title']}, {listing['city']}")
print(f" {listing['url']}")
# Filter by type
for listing in f.poll_new_listings(since_id=latest_id, offering_type="buy"):
print(f"New sale: {listing['title']}")The generator stops after 20 consecutive 404s (configurable via max_consecutive_404s).
MIT
