We extract flatshares, apartments, pricing signals, and availability dates from WG-Gesucht. Delivered as clean JSON, CSV, or Parquet to S3 or BigQuery 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 Flatshare Listings objects from wg-gesucht.de. All fields typed and schema-versioned.
"listing_id": "10482910", "title": "Bright room in Neukölln altbau", "city": "Berlin", "district": "Neukölln", "room_size_sqm": 18.5, "warm_rent": 550.0, "wg_size": 3, "available_from": "2026-09-01"
| # | listing_id | title | city | district | room_size_sqm | total_size_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Apartments objects from wg-gesucht.de. All fields typed and schema-versioned.
"listing_id": "9938201", "title": "Modern 2-room apartment near Hbf", "city": "Munich", "rent_warm": 1450.0, "rent_cold": 1200.0, "rooms": 2.0, "square_meters": 54.0
| # | listing_id | title | city | district | rent_warm | rent_cold |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Costs objects from wg-gesucht.de. All fields typed and schema-versioned.
"listing_id": "10482910", "base_rent": 450.0, "utility_costs": 70.0, "heating_costs": 30.0, "total_rent": 550.0, "deposit": 1350.0, "internet_included": true
| # | listing_id | listing_type | base_rent | utility_costs | heating_costs | total_rent |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities objects from wg-gesucht.de. All fields typed and schema-versioned.
"listing_id": "10482910", "balcony": true, "washing_machine": true, "dishwasher": false, "elevator": false, "furnished_status": "partially", "cellar": true
| # | listing_id | balcony | garden | washing_machine | dishwasher | cellar |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Advertiser Data objects from wg-gesucht.de. All fields typed and schema-versioned.
"listing_id": "10482910", "advertiser_name": "Julia M.", "advertiser_type": "private", "languages_spoken": "['German', 'English']", "account_age_days": 412, "verified_status": true
| # | listing_id | advertiser_name | advertiser_type | languages_spoken | account_age_days | response_rate |
|---|---|---|---|---|---|---|
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Our WG-Gesucht scraper parses complex rental formats, normalises pricing structures, and circumvents aggressive bot protection to deliver clean real estate data.
Title, description, room size, total apartment size, and exact availability dates parsed into structured fields.
Separation of Kaltmiete, Warmmiete, Nebenkosten, and Kaution to provide accurate total cost of living metrics.
Extract current flatmate counts, age ranges, gender distribution, and specific requirements for new tenants.
Capture city, district, and street level approximation for precise geographical market analysis.
High-frequency scraping pipelines designed to capture highly desirable listings before they are taken down.
Accurate tracking of befristet versus unbefristet rental contracts with specific start and end dates.
Structured extraction of features like balcony, EBK, washing machine, and furnished status.
Distinguish between private landlords, current tenants, and commercial agencies.
Monitor price drops, date changes, or availability status updates on existing listings.
Brief in. Clean data out.
Provide target cities, listing types, or specific filter parameters. We map the extraction requirements together.
We configure Playwright crawlers, residential proxy rotation, and Cloudflare bypass mechanisms for wg-gesucht.de.
Schema validation, rent outlier detection, and data normalisation checks before production launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake warehouse on your defined schedule.
The platform employs aggressive anti-bot measures and listings expire rapidly. Here is how we maintain pipeline stability.
WG-Gesucht uses strict Cloudflare protection to block automated traffic. We route requests through German residential proxies with full browser fingerprint spoofing to maintain access without triggering captchas.
In competitive markets like Munich or Berlin, well-priced listings expire within twenty minutes. Our distributed architecture polls target URLs at high frequency to capture data before the listing is deactivated.
Crucial details like Schufa requirements or exact transfer fees are often buried in free-text descriptions. We apply regex patterns and NLP to extract these hidden variables into structured columns.
The platform limits standard pagination depth. We utilise complex search parameter combinations and geographic bounding boxes to access the complete catalogue of active listings.
We maintain multiple fallback chains for CSS selectors and XPath queries. When the platform updates its frontend layout, our pipelines continue extracting data without interruption.
Market analysts track Kaltmiete trends across districts to build accurate, real-time rent indices.
Real estate platforms aggregate listings to provide users with a unified view of available housing.
Universities and private developers analyse WG demand and pricing to plan future student accommodation.
Real estate funds identify undervalued districts by tracking yield potential against current asking rents.
Municipalities monitor housing availability and demographic shifts at the district level.
Corporate relocation agencies automate the discovery of suitable temporary housing for new employees.
"WG-Gesucht holds the pulse of the German rental market, but its listings vanish in minutes. If your crawler is slow, you are analysing ghost data."
Most teams fail at scraping WG-Gesucht because they rely on slow polling. The best listings expire within twenty minutes. DataFlirt deploys distributed residential proxies and concurrent Playwright sessions to capture listings the second they go live, bypassing aggressive bot protection without triggering bans. We handle the infrastructure. You consume the data.
Everything supported by our wg-gesucht.de 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 orchestration and deduplication. Playwright manages JavaScript rendering and complex interaction flows to bypass anti-bot screens.
We route traffic through German residential ISP proxies. Rotation occurs per request to maintain high success rates against Cloudflare.
Pipelines run on Kubernetes clusters. Airflow handles scheduling and SLA alerting. All state is stored in managed Postgres databases.
Data delivered to where your team already works — no new tooling required.
About wg-gesucht.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt targets only public, non-authenticated listing data. We do not extract personal user data behind login walls or automate messaging. Clients must review platform terms of service and consult legal counsel for their specific use cases.
We utilise German residential ISP proxies combined with full Playwright browser sessions. Our systems mimic human browsing patterns and realistic fingerprints to solve challenges without triggering blocks.
Yes. For competitive markets like Berlin and Munich, we configure high-frequency polling pipelines that scan target parameters every few minutes to capture data before the listing expires.
We extract the city, district, and street name when provided. Exact house numbers are frequently hidden by advertisers until direct communication is established and cannot be scraped from the public listing.
Our parsers separate Kaltmiete, Nebenkosten, and Warmmiete. If an advertiser only provides a total price in the description, our NLP models extract and map the value to the correct structured field.
Yes. We capture the 'befristet' flag along with the exact available_from and available_to dates for every listing.
Engagements typically start with a defined set of target cities and delivery cadences. We price based on data volume and polling frequency. Contact us to scope your specific pipeline requirements.
Yes. We provide a sample extraction of recent listings for your target cities to validate field completeness and schema structure before pipeline commissioning.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily market snapshots or a real-time feed of new listings across Germany, we scope, build, and operate the pipeline. Tell us what you need.
