We extract rental apartments, houses for sale, viewing schedules, and agency intelligence from Flatfox. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
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 flatfox.ch. All fields typed and schema-versioned.
"listing_id": "184920", "url": "https://flatfox.ch/en/flat/184920/", "title": "Modern 3.5 room apartment in Zurich", "property_type": "apartment", "offer_type": "rent", "living_space_sqm": 85, "rooms": 3.5, "floor": 2, "availability_date": "2026-07-01", "language": "en"
| # | listing_id | url | title | property_type | offer_type | living_space_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from flatfox.ch. All fields typed and schema-versioned.
"listing_id": "184920", "net_rent": 2450.0, "extra_costs": 250.0, "gross_rent": 2700.0, "currency": "CHF", "deposit_amount": 5400.0, "price_per_sqm": 381.1, "price_timestamp": "2026-05-12T09:14:00Z"
| # | listing_id | net_rent | extra_costs | gross_rent | currency | deposit_amount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Building & Amenities objects from flatfox.ch. All fields typed and schema-versioned.
"listing_id": "184920", "build_year": 2018, "balcony": true, "elevator": true, "parking_available": true, "pets_allowed": false, "washing_machine": true, "minergie_certified": true
| # | listing_id | build_year | renovation_year | minergie_certified | balcony | elevator |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency & Contact objects from flatfox.ch. All fields typed and schema-versioned.
"listing_id": "184920", "agency_name": "Wincasa AG", "agency_id": "wincasa-zh", "contact_person": "Muller, Thomas", "contact_phone": "+41 44 000 00 00", "is_private_lister": false, "viewing_dates": "['2026-05-15T14:00:00Z', '2026-05-18T10:00:00Z']"
| # | listing_id | agency_name | agency_id | agency_url | contact_person | contact_phone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Geolocation Data objects from flatfox.ch. All fields typed and schema-versioned.
"listing_id": "184920", "street": "Badenerstrasse", "street_number": "120", "zip_code": "8004", "city": "Zurich", "canton": "ZH", "country": "CH", "latitude": 47.3742, "longitude": 8.5281
| # | listing_id | street | street_number | zip_code | city | canton |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Flatfox scraper extracts structured property data across all cantons, handling strict rate limits, multi-language schemas, and dynamic map layers automatically.
Title, description, rooms, floor, living space, and availability date extracted for every rental and sale listing.
Capture net rent, extra costs, gross rent, and deposit requirements. Track price adjustments over time.
Extract and normalise listing data across German, French, Italian, and English interfaces automatically.
Define bounding box coordinates to extract all properties within a specific radius or neighbourhood.
Extract agency names, contact persons, and portfolio sizes to track market share among Swiss real estate firms.
Extract available viewing dates and times to gauge property demand and listing velocity.
Capture high-resolution image URLs, floor plan PDFs, and links to 360-degree virtual tours.
Structured extraction of boolean amenities: balcony, elevator, Minergie certification, and parking availability.
Run continuous pipelines at daily cadences with change-detection diffing to track new listings and delistings.
Brief in. Clean data out.
Provide target cantons, ZIP codes, bounding boxes, or agency IDs. We design the extraction schema together.
We configure Scrapy crawlers, Swiss proxy rotation, and session management for flatfox.ch.
Schema validation, null-rate checks, and coordinate verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Swiss real estate portals requires local infrastructure and precise parsing. Here is how we maintain pipeline stability.
Swiss portals heavily restrict traffic from foreign data centres. Our crawlers use Swiss residential ISP proxies to ensure high success rates and avoid geographic blocking.
Map-based search results on Flatfox load dynamically via XHR. We intercept these API calls directly or use Playwright to render the map layers, ensuring no listings are missed in dense urban areas.
Listings on Flatfox can be in German, French, Italian, or English depending on the canton. Our pipeline normalises fields like amenities and property types into a single structured schema regardless of the source language.
For tracking market inventory, we maintain a hash index of active listings. Subsequent runs only push new properties, price changes, or delistings, reducing compute cost and downstream processing load.
We monitor total listing counts per canton. If a run returns 20 percent fewer listings than the historical baseline, the pipeline halts and alerts our engineering team to investigate potential site changes.
Banks and valuation firms use historical rent and sale prices to train automated valuation models for the Swiss market.
Institutional investors track gross rent versus purchase prices across cantons to identify high-yield investment zones.
Real estate agencies monitor competitor portfolios, listing velocity, and time-on-market metrics.
Meta-search engines and PropTech startups ingest structured Flatfox data to power their own consumer-facing applications.
Municipalities and researchers analyse housing supply, rent inflation, and vacancy rates at the neighbourhood level.
Corporate relocation agencies use real-time feeds to match incoming expats with available housing inventory instantly.
"Flatfox contains the most precise rental inventory in Switzerland, but extracting it requires navigating multi-language schemas and strict rate limits."
Most teams underestimate the investment required. Reliable Flatfox scraping requires Swiss residential proxies, full JavaScript rendering for map clusters, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our flatfox.ch 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 for dynamic map loads and XHR interception.
We maintain pools of residential ISP proxies specifically located in Switzerland to bypass geographic blocking and rate limits.
Pipelines run on AWS ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About flatfox.ch scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible for non-personal data. DataFlirt targets only public property details, pricing, and agency information. We do not circumvent authentication walls to access private user messages or applications. Clients should consult legal counsel for specific compliance requirements in Switzerland.
We route all requests through Swiss residential ISP proxies. This ensures our crawlers appear as legitimate local users, preventing the automatic blocks typically applied to foreign data centre IPs.
Yes. Flatfox listings can appear in German, French, Italian, or English. Our extraction schema normalises categorical fields like property types and amenities into a single language format while preserving the original description text.
Yes. You can provide bounding box coordinates or specific ZIP codes. Our pipeline interacts with the map API to extract all properties within the defined geographical area.
We support daily pipeline runs for full market snapshots. For specific tracking requirements, we can configure intraday runs to monitor new listings or price changes rapidly.
Yes. We provide a sample run of up to 500 listings from a specific canton during the scoping process. This allows you to validate schema fit and data quality before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off canton export or a continuous price-monitoring feed across Switzerland, we scope, build, and operate the pipeline.
