Funda

Python API for Funda.nl real estate listings.

Installation

pip install -r requirements.txt

Quick Start

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'])

How It Works

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.

Discovery Process

The API was reverse engineered by intercepting and analyzing HTTPS traffic from the official Funda Android app:

1. Configured an Android device to route traffic through an intercepting proxy
2. Used the Funda app normally - browsing listings, searching, opening shared URLs
3. Identified the *.funda.io API infrastructure separate from the www.funda.nl website
4. Analyzed request/response patterns to understand the query format and available filters
5. Discovered how the app resolves URL-based IDs (tinyId) to internal IDs (globalId)

API Architecture

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

ID System

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 API

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).

Required Headers

User-Agent: Dart/3.9 (dart:io)
X-Funda-App-Platform: android
Content-Type: application/json

Response Data

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

API Reference

Funda

Main entry point for the API.

from funda import Funda

f = Funda(timeout=30)

get_listing(listing_id)

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_listing(location, ...)

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'])

Listing

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 text

Dates:

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 link

Media:

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 scheduled

Stats & 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 characteristics

Key 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

Methods

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 ID

Examples

Find apartments in Amsterdam under €400k

from 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()

Get detailed listing information

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}")

Search rentals in multiple cities

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")

Find energy-efficient homes with a garden

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']}")

Download listing photos

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_urls

Search by radius from postcode

from 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']:,}")

License

MIT