Kleinanzeigen is where German private landlords post rooms and flats that never reach the paid property portals, priced by negotiation. We turn those adverts into structured rows.
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Kleinanzeigen needs no login to browse, but it blocks by IP range without warning. One request during research returned a page reading IP-Bereich vorübergehend gesperrt - IP range temporarily blocked - with a reference code and the calling IP; the same URL answered normally on retry. Handling that is our side of the job: rotating proxies, low request rates, CAPTCHA solving where challenges appear, and adaptive crawling when markup shifts. We stay out of anything behind a login.
Almost every seller here is a private individual. Output is limited to advert and commercial data. Seller names, profile links, phone numbers and message routes are excluded by default rather than on request, which keeps the delivered dataset clear of personal data under the GDPR.
The fields below were read from a live Mietwohnung advert in Berlin on 4 September 2026. This is what the pages render, not a wish list.
Kaufpreis (purchase price) adverts under Häuser zum Kauf carry a different attribute block, and WG-Zimmer (rooms in a shared flat) different again. We map each category to its own schema instead of forcing one shape over all of them.
Some of the difficulty here is not in any field. It is in how the site answers a crawler.
The result cap is real and low. A search for rental flats in Berlin reported 7,141 matches on 4 September 2026. Results come 25 to a page, and paging stops at page 50 - a request for page 51 lands back on page 50. That is 1,250 adverts reachable out of 7,141. Full coverage means splitting one query into many narrower ones - by PLZ, by Umkreis (search radius), by price band, by room count - until every slice fits under the cap and the slices reassemble without gaps or duplicates. That partitioning is most of the engineering on this site.
A dead advert does not simply disappear. An unknown advert id redirects to a category search page rather than showing a not-found page. A crawler that follows redirects without reading the page will store a search page as though it were a listing. Expired adverts have to be detected from the page content itself.
Search cards carry relative dates. They show strings such as Heute, 20:11 rather than a date, so they only mean anything once resolved against the moment of the crawl.
URLs are stable enough to build on. An advert sits under /s-anzeige/ with a cosmetic slug followed by a numeric advert id, a category code and a subcode. The numeric id is what identifies a listing across runs.
The site traded as eBay Kleinanzeigen for years. Adevinta completed its acquisition of eBay Classifieds Group in June 2021, and in May 2023 the brand dropped the eBay name and moved to kleinanzeigen.de. Adevinta's own announcement said the previous domain would stay reachable for several more years, which is why old links and archived references still resolve to live pages today.
For data work the ownership matters less than the shape of the inventory. This is a classifieds board, not a retail marketplace. There is no catalogue and no SKU. Each advert is one physical thing in one place, typed by hand, usually by a private individual rather than a shop. Sellers expect to negotiate and expect the buyer to turn up in person, so Abholung - collection by the buyer - runs through the whole site and shapes how adverts are written.
The Immobilien (real estate) section is substantial on its own. A request to the Immobilien root on 4 September 2026 reported 572,432 adverts across Germany, with 109,998 of those in Mietwohnungen (rental flats). Those counts move daily.
One practical wrinkle: the site is mid-migration between frontends. Real estate results still render in older server-side markup, while the Zu verschenken section is served by a newer Astro build with entirely different class names. A single parser does not cover both, and we maintain them separately.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →The reason to scrape Kleinanzeigen is that much of its housing supply is not anywhere else. A private landlord with one spare room posts here because it is free and takes five minutes, and often never pays to appear on a dedicated property portal. A market picture built only from the paid portals is missing that private tail, and scraping the public pages is what fills it in.
Three things become measurable. Asking rents by postcode, including negotiable ones, once VB is kept as a flag rather than discarded. The spread between Kaltmiete and Warmmiete, which is where German service charges sit and where cost pressure tends to surface before headline rents move. And supply turnover - how long a room in a given district stays listed before it disappears.
WG-Zimmer deserve their own series. Shared-flat rooms have shorter listing lifetimes, smaller deposits and heavy seasonality around the start of university terms. Averaged into a general rent index, they quietly distort it.
The non-property categories are collectable through the same pipeline - Auto, Elektronik, Möbel, Zu verschenken - and clients do ask for them. We will not pretend they answer housing questions, but the extraction work is the same.
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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.”
Six jobs that scraped property data does, each backed by a ScrapeIt project: seller leads, listing monitoring, image datasets, weekly market supply, sold records and unit-level rentals. For every use case - the fields it needs and how often to refresh them.
ScrapeIt is a managed service. We build the crawler, run it on your schedule, watch it when Kleinanzeigen changes, and hand over the output as CSV, JSON, XLSX or an API endpoint. You name the categories, the regions and the frequency. We extract a sample first so you can check the fields before the full run, then keep the pipeline healthy as the site moves. No tools for you to learn and no scripts to maintain. We reply to enquiries within one business day.
Yes. The ScrapeIt Kleinanzeigen API returns Miete, Kaufpreis, Wohnfläche, Nebenkosten, Kaution and the VB flag as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
As flags, not as numbers. VB (Verhandlungsbasis) marks a price as a negotiating position, so we keep the figure and a separate boolean beside it. Zu verschenken adverts render an empty price element rather than a zero, so we record them as giveaways and leave the price null. Folding either into a plain price column produces averages that do not describe anything real, which is the most common defect we see in Kleinanzeigen data collected elsewhere.
No, because Kleinanzeigen does not publish it. An advert shows postcode, city and district - for example 12489 Berlin - Köpenick - and nothing finer. Anything more precise would have to be read out of the free-text description, where a seller may or may not have typed it, and we do not present that as a structured address field. You get the location the portal actually renders.
Not from one search, because pagination is capped. A Berlin rental search reported 7,141 matches but stops at page 50 of 25 results, which is 1,250 adverts; page 51 redirects back to page 50. We reach the remainder by splitting the query into narrower slices - postcode, radius, price band, room count - so each slice returns fewer results than the cap, then deduplicating by advert id. You get the full set, assembled from many requests rather than one.
As fresh as the schedule you choose. We run scheduled crawls - daily, weekly, or several times a day - rather than a live feed, and every row carries the timestamp of the run that produced it. For rental stock in a busy city a daily run catches most adverts inside their listing life. Because search cards show relative dates such as Heute, we resolve those against the crawl time before delivery so the dates mean something later.
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.
Kleinanzeigen data from €169/month. Free project assessment, reply within 1 business day.
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