We extract residential and commercial listings, price drops, energy labels, and broker intelligence from Funda.nl. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Listings objects from funda.nl. All fields typed and schema-versioned.
"funda_id": "42918374", "address": "Keizersgracht 123", "city": "Amsterdam", "postcode": "1015 CJ", "price": 1250000, "living_area_sqm": 142, "energy_label": "A", "status": "Beschikbaar"
| # | funda_id | url | title | address | city | postcode |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & History objects from funda.nl. All fields typed and schema-versioned.
"funda_id": "42918374", "current_price": 1250000, "original_price": 1300000, "price_per_sqm": 8802, "price_changes": -50000, "last_price_drop_date": "2023-10-12", "asking_price_type": "k.k."
| # | funda_id | current_price | original_price | price_per_sqm | price_changes | last_price_drop_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Broker & Agent Data objects from funda.nl. All fields typed and schema-versioned.
"broker_id": "9921", "broker_name": "Amsterdam Housing Makelaars", "rating": 9.2, "review_count": 142, "active_listings": 34, "nvm_member": true, "phone_number": "020-1234567"
| # | broker_id | broker_name | office_address | phone_number | website | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Media & Documents objects from funda.nl. All fields typed and schema-versioned.
"funda_id": "42918374", "main_image_url": "https://cloud.funda.nl/image1.jpg", "has_360_photos": true, "image_urls": "['url1', 'url2', 'url3']", "brochure_pdf": "https://cloud.funda.nl/brochure.pdf", "floorplan_urls": "['fp1', 'fp2']"
| # | funda_id | main_image_url | image_urls | floorplan_urls | video_urls | matterport_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Neighbourhood Stats objects from funda.nl. All fields typed and schema-versioned.
"neighbourhood_name": "Grachtengordel-West", "municipality": "Amsterdam", "population": 4200, "distance_to_train": 1.2, "distance_to_supermarket": 0.3, "distance_to_school": 0.5, "avg_income": 65000
| # | funda_id | neighbourhood_name | municipality | population | avg_income | age_distribution |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Funda scraper handles every layer of the platform: residential listings, commercial real estate, historical transactions, and broker intelligence. Built to bypass Datadome and pagination limits.
Address, price, living area, plot size, year built, energy label, and full description extracted accurately.
Monitor original asking price versus current asking price, timestamped per crawl.
Capture official energy labels (A++++ to G) and insulation details critical for modern valuations.
Extract selling agent details, office addresses, active listing counts, and NVM membership status.
Collect high resolution image URLs, floorplan links, and 360 virtual tour identifiers.
Track monthly VvE (Homeowners Association) contributions and registered maintenance statuses.
Scrape recently sold (verkocht) properties to build accurate valuation models.
Funda in Business extraction for office space, retail units, and industrial properties.
Hash based diffing ensures you only receive records when a property status or price changes.
Route requests through Netherlands based residential IPs to bypass regional blocking.
Brief in. Clean data out.
Provide target municipalities, listing types (koop/huur), or broker IDs. We design the schema.
We configure Scrapy crawlers, Dutch residential proxies, and Datadome bypass mechanisms.
Schema validation, null rate checks, and price outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or BigQuery dataset on agreed cadence.
Funda enforces strict rate limits and sophisticated bot protection. Here is how we maintain extraction reliability.
Funda uses Datadome for bot mitigation. We use TLS fingerprint spoofing, realistic browser headers, and human like interaction patterns.
Funda blocks or restricts non European traffic. We route all requests through high reputation Netherlands residential ISP proxies.
Funda caps search results at 15 pages (225 listings). We use sub grid bounding boxes and price bracket chunking to extract full market inventory.
Energy labels and interactive maps load via JavaScript. We use Playwright to hydrate the DOM before extraction.
Instead of redownloading 50 images per property every day, we maintain hash indexes to only push data when listing attributes change.
PropTech companies ingest sold prices, WOZ values, and square meter rates to train automated valuation algorithms.
Real estate funds track yield potential by correlating asking prices with rental market rates across specific postcodes.
Agencies monitor competitor market share, time on market, and price reduction frequencies.
Sustainability firms target properties with low energy labels (E, F, G) for retrofitting and solar panel lead generation.
Banks and macro analysts track listing inventory and status changes to gauge housing market health.
Mortgage advisors and moving companies track new listings to identify high intent consumer segments.
"Funda holds the absolute ground truth for the Dutch housing market, but extracting it requires navigating aggressive bot mitigation and pagination limits."
Most internal engineering teams fail at scraping Funda because they underestimate Datadome and rely on data center IPs. DataFlirt absorbs that complexity, providing clean, schema validated property records so your data science team can focus on modeling rather than managing proxy rotations.
Everything supported by our funda.nl scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and Datadome challenges.
We maintain pools of residential ISP proxies strictly localized to the Netherlands to ensure high success rates.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About funda.nl scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property data is generally permissible for non personal data. We extract public listing details and do not scrape personal user accounts. Clients should consult legal counsel regarding database rights.
Funda restricts search results to 15 pages. We use geographic bounding boxes and micro price brackets to chunk the search space, ensuring 100% coverage of available inventory.
We can extract properties marked as 'verkocht' (sold) that remain publicly accessible on the platform, capturing the original asking price and time on market.
Active listing pipelines typically run daily. We can configure higher frequency runs for specific high velocity urban markets like Amsterdam or Utrecht.
Yes. Our schema supports commercial real estate listings, including office space, retail units, and industrial properties.
We extract and deliver the direct CDN URLs for all media assets. If binary file delivery is required, we can sync these directly to your S3 bucket.
Our selector strategy uses multiple fallback chains. If a DOM change breaks extraction, our monitoring stack alerts us, and our engineers deploy a fix within hours.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of active listings or a historical dump of sold properties across the Netherlands, we scope, build, and operate the pipeline.
