A large share of WG-Gesucht adverts are fixed-term sublets, so we capture the frei bis end date next to the layered German rent and deliver records showing when each room stops being free.
We deliver CSV, JSON, XLSX or a REST API on the schedule the data needs - daily for a live supply feed, weekly or monthly for a rent series. Records are keyed on the advert id, so runs join cleanly and changes are visible between them.
Anti-bot handling is our side of the job. Public search and advert pages are open to automated clients, but requests are refused intermittently and robots.txt names several crawlers and disallows them outright. Sustained collection therefore needs proxy rotation, request pacing, session handling and CAPTCHA solving, and all of that sits inside the managed service. We adapt the crawler when the markup changes. We do not promise a perfect run, and we do not collect anything behind a login.
Each advert page carries a Kosten (costs) block, a Verfügbarkeit (availability) block and a WG-Details block. We flatten all three into one row per advert. A value the advertiser left empty renders as n.a. on the page, and we keep it null rather than guessing a zero.
Keeping the rent split matters. A headline 690 EUR can be a full Gesamtmiete or a Kaltmiete with Nebenkosten still to come, and a WG-Gesucht rent series that mixes the two is not comparable across adverts or across districts.
The WG-Details panel is a layer an ordinary rental site does not have. It gives household size and gender make-up in a compact form such as 2er WG (1 Mann) or 3er WG (2 Frauen), plus tags: Studenten-WG, Berufstätigen-WG, Frauen-WG, Männer-WG, keine Zweck-WG, Mehrgenerationen, LGBTQIA+ and Internationals welcome. Adverts also state languages spoken, pets, and smoking rules such as Rauchen nicht erwünscht. Many carry a Gesucht wird line naming who the household wants, sometimes with an age limit.
This is personal data, and part of it touches special categories under the GDPR. We handle it conservatively. Output is limited to advert and commercial data. Advertiser names, profile links, photographs and contact routes are excluded by default rather than on request - the listing markup does expose an advertiser first name, and we drop it before delivery. We do not build person-level records or join adverts into profiles of people.
Household attributes are the part we agree before the first crawl. Room size, flat size, household size and smoking rules are usually enough. Gender make-up, age preferences and orientation tags are included only where a buyer can explain the purpose.
Anmeldung (address registration) is a separate problem. Whether the address can be registered decides if a room suits a resident rather than a visitor, but there is no field for it. Advertisers write it into the title or description, in German or English, so we extract it by string match and flag it as derived rather than declared.
WG-Gesucht.de is a German-language marketplace for shared flats and rooms. Adverts split into Angebote (offers) and Gesuche (wanted), and each side runs across four categories: WG-Zimmer (a room in a shared flat), 1-Zimmer-Wohnung (a studio), Wohnung (a whole flat) and Haus.
Search pages are addressed by city name plus a numeric city id. Berlin is 8, so the WG room search sits at /wg-zimmer-in-Berlin.8.0.1.0.html, studios at /1-zimmer-wohnungen-in-Berlin.8.1.1.0.html and whole flats at /wohnungen-in-Berlin.8.2.1.0.html. The digit after the city id picks the category and the last digit is a zero-indexed page number, which makes city and district coverage easy to plan before a crawl starts.
Volume is city-scale rather than national-scale. On 4 September 2026 the Berlin WG-Zimmer search reported 3120 WG-Zimmer and 5315 adverts across all categories in that city. Those counts move daily, so we treat them as a measurement with a date attached rather than a constant.
Almost everything that makes a record useful is typed by a private advertiser instead of picked from a controlled list. That is the central problem in any WG-Gesucht data project, and it is where most of our work goes.
Get a Quote
Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€199 / one-time
setup fee - included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
The reason to scrape WG-Gesucht is that its supply behaves differently from an ordinary rental portal. A large share of adverts are Zwischenmiete (interim sublet), fixed-term lets with a stated start and end date, and the site treats that as a first-class distinction. Its rent-type filter offers exactly three values: unbefristet (open-ended), befristet (fixed term) and Übernachtung (overnight).
A dataset that ignores the end date treats a three-month sublet as a normal tenancy. Average rent per district then blends two different products, and turnover reads as churn when contracts are simply reaching their agreed end. Carrying frei bis through to the output fixes both.
Buyers use WG-Gesucht data for rent benchmarking by district, for student and relocation housing supply, for measuring how long rooms stay listed, and for tracking seasonal pressure around semester start. Housing researchers use it because the shared-flat segment is thinly covered elsewhere.
The site publishes no bulk export, so the practical choice is a manual copy of a few hundred rows or a managed WG-Gesucht scraper. We build and run the second one.
Monitoring of real estate listings on funda.nl, pararius.com, rentberry.com, rentola.com, and zimmo.be to support the growth of a European property portal.
Learn More
Daily detection of new private property listings in Switzerland on Homegate.ch and ImmoScout24.ch, giving the agency first access to high-value leads.
Learn More
Scraping residential listings from Immobilienscout24.de in Germany and Mallorca, including complete data and resized images.
Learn MoreLearn how to use web scraping to solve data problems for your organization
Real estate teams work in a fragmented data landscape. The sites that drive demand in Boston look nothing like the ones that matter in Berlin, São Paulo, Dubai, or Mumbai.
Real estate teams are operating in a data environment that is bigger, faster, and more fragmented than ever. Listings go live and disappear in hours, price cuts happen quietly, and the portals that matter most in each country are rarely the same global “top 5.”
Real estate web scraping: a powerful tool for data collection and analysis. Learn how to choose the right data collection method and benefit from real estate web scraping
ScrapeIt is a managed web scraping agency. We build the crawler, run it on your schedule, maintain it when the site's markup changes, and hand over clean data as CSV, JSON, XLSX or an API. You do not maintain parsers or proxy pools.
A WG-Gesucht project usually starts with short scoping: which cities, which categories, which fields, how often, and which household attributes are in scope. We send a sample file from a real run before any commitment.
No. There is no public or documented API for third-party data access, and no bulk export on the site. Its robots.txt disallows /api/, and that path is not open to outside callers. Collecting WG-Gesucht data at scale therefore means crawling public pages, which is the part we build and maintain. We then deliver the result as CSV, JSON, XLSX or an API on our side, which is usually what people are after when they search for a WG-Gesucht API.
By the availability block. The words befristet and unbefristet appear in the search filter, but not on the advert page itself. There the distinction is carried by whether frei bis is present: adverts we read on 4 September 2026 showed frei ab 01.10.2026 with frei bis 31.10.2026 on fixed-term rooms, and no frei bis at all on open-ended ones. We output frei ab, frei bis and a derived fixed-term flag, so a three-month Zwischenmiete never looks like a permanent tenancy in your table.
Every row the Kosten block prints, kept separate: Miete or Kaltmiete, Nebenkosten, Sonstige Kosten, Kaution and any Ablösevereinbarung, plus the Gesamtmiete shown in the page header. One Berlin advert we read listed Miete 690 EUR and Kaution 700 EUR with n.a. in the other two rows. Empty fields stay null in the output. We do not sum or estimate a total the advertiser did not state, because the deposit is a one-off and the service charge is an advance rather than a fixed cost.
No, those are excluded by default. The listing markup does expose an advertiser first name, and we drop it before delivery. We do not collect profile links, photographs, phone numbers or message routes, and we do not build person-level records. Household descriptions such as gender make-up or age preferences are personal data and some of it is sensitive under the GDPR, so we agree the field list with you before the first run instead of shipping whatever the page shows. We are not lawyers and we do not advise on your use of the data.
Daily, hourly or weekly, whichever the use case needs. Adverts do not stay readable forever: a deleted advert shows a not-found page titled Angebot nicht vorhanden/gelöscht, and the content is gone rather than archived. A room has to be captured while it is live, so a listing history is only as good as the crawl that ran while the advert existed. We keep first-seen and last-seen timestamps against each advert id, so you can measure how long each room stayed on the market.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
