ImmoScout24 Scraper

Pulling property data off ImmoScout24 by hand is slow work: open each ad, copy the price, jot down the size, note the agent, repeat. This scraper skips all of that. Paste a search URL from immobilienscout24.de and every matching listing comes back as a clean row, with the price, floor area, location, photos, and contact details already split into separate fields. It pages through the results for you, so one search URL can fill a whole spreadsheet. Fast, cheap, no code, and you only pay for the listings you collect.

What you get

Each listing becomes one row with a steady shape, so your columns line up when you load the data into a sheet, a database, or a model. The fields fall into three groups:

• Property and price — propertyTitle, propertyCategory, priceAmount, currencyCode, pricePeriod, offerType, pricePerSquareMeter, floorAreaSqm
• Location — cityName, districtName, postalCode, addressLine, plus the listingLink and propertyId
• Advertiser and extras — advertiserName, advertiserPhone, privateSeller, commissionApplies, imageLinks, featureTags, publishedDate, updatedDate, sourceUrl, collectedAt

Quick start

1. Open immobilienscout24.de, run a search, and apply your filters (city, property type, price, size).
2. Copy the URL from your browser's address bar.
3. Hit Try for free and paste that URL into Search page URLs.
4. Set a Results limit to decide how many listings to pull.
5. Press Start, then export the results as JSON, CSV, Excel, or XML.

Use cases

• Rent and price tracking — follow asking prices across German cities and catch changes early
• Commercial space research — gather office or retail availability in a region into one list
• Market comparison — line up price per m², size, and property type across districts
• Investment screening — build a database of for-sale or for-rent stock to score against your criteria
• Agency and broker analysis — see who lists what, where, and at what price
• Dashboards and pipelines — feed live ImmoScout24 data into your own sheet, BI tool, or app

Input

Field	Type	Required	Description
targetUrls	array of strings	Yes	One or more ImmoScout24 search result URLs. Run a search, apply filters, and copy the address bar link. Prefilled with a Hamburg for-sale search.
resultsLimit	integer	No	Total listings to collect across all URLs. Default 30; max 1000.
timeoutSeconds	integer	No	Seconds to wait on each request before timing out. Default 45.

Example input

{
    "targetUrls": [
        "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen",
        "https://www.immobilienscout24.de/Suche/de/koeln/haus-mieten"
    ],
    "resultsLimit": 200,
    "timeoutSeconds": 45
}

Output

Every listing on the search results becomes one row, paginated automatically until your resultsLimit is reached or the pages run out. A field that ImmoScout24 does not publish for a given property comes back as null rather than a guess, so the dataset stays rectangular.

Example output

{
    "propertyId": "168447935",
    "listingLink": "https://www.immobilienscout24.de/expose/168447935",
    "propertyTitle": "Helle 3-Zimmer-Wohnung mit Balkon in Eppendorf",
    "propertyCategory": "Apartment",
    "priceAmount": 489000.0,
    "currencyCode": "EUR",
    "pricePeriod": "ONE_TIME_CHARGE",
    "offerType": "BUY",
    "pricePerSquareMeter": 5988.0,
    "floorAreaSqm": 81.65,
    "cityName": "Hamburg",
    "districtName": "Eppendorf",
    "postalCode": "20251",
    "addressLine": "Eppendorfer Weg, Hamburg",
    "advertiserName": "Nordstadt Immobilien GmbH",
    "advertiserPhone": "040 32819940",
    "privateSeller": false,
    "commissionApplies": true,
    "imageLinks": ["https://pictures.immobilienscout24.de/listings/xxx.jpg/ORIG/legacy_thumbnail/800x600/format/webp/quality/50"],
    "featureTags": ["Balkon", "Einbauküche"],
    "publishedDate": "2026-06-10T21:47:37.000+02:00",
    "updatedDate": "2026-06-10T21:47:50.729+02:00",
    "sourceUrl": "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen",
    "collectedAt": "2026-06-29T10:30:00+00:00",
    "errorMessage": null
}

Output fields

Field	Type	Description
propertyId	string	ImmoScout24's own identifier for the listing
listingLink	string	Direct link to the listing detail page
propertyTitle	string	Headline shown for the property
propertyCategory	string	Kind of property, such as Office, Apartment, or House
priceAmount	number	Monthly rent or sale price as a number
currencyCode	string	Currency the price is quoted in, for example EUR
pricePeriod	string	Billing period, such as MONTH for monthly rent
offerType	string	RENT or BUY
pricePerSquareMeter	number	Cost for each square meter of floor space
floorAreaSqm	number	Usable floor space in square meters
cityName	string	City where the property sits
districtName	string	District or neighbourhood within the city
postalCode	string	German postal code
addressLine	string	Full address text as printed on the listing
advertiserName	string	Agency company or private owner placing the ad
advertiserPhone	string	Phone number given for the advertiser
privateSeller	boolean	True for a private seller, false for an agency
commissionApplies	boolean	True when a broker commission (Courtage) is charged
imageLinks	array	Direct photo URLs at 800x600 resolution
featureTags	array	Highlight labels on the ad, such as Provisionsfrei or Aufzug
publishedDate	string	ISO date the listing first went online
updatedDate	string	ISO timestamp of the most recent change
sourceUrl	string	The search URL this listing was found through
collectedAt	string	ISO timestamp of when the row was captured
errorMessage	string	Reason a listing failed; null on success

Tips for best results

• Filter on the site first. A tighter search URL (city, type, price band, size) gives you a cleaner, more relevant dataset.
• Cap test runs with resultsLimit. Start at 20 to 50 to confirm the output fits your pipeline, then raise it for the full pull.
• Add several URLs at once. Drop in one URL per city or property type; the actor works through them in order until it hits your resultsLimit.
• Both rent and buy work. A rental search returns rentals and a sale search returns sale stock; offerType and pricePeriod tell them apart.
• Raise timeoutSeconds to around 60 if you see timeouts on large result sets or slow connections.

How can I use ImmoScout24 property data?

How can I use the ImmoScout24 Scraper to track German rental prices? Paste a search URL for your target city and run it on a schedule. Each row carries priceAmount, pricePerSquareMeter, floorAreaSqm, and publishedDate, so you can chart asking prices over time and flag changes as they land.

How can I pull ImmoScout24 listings without copying them by hand? Run a search on immobilienscout24.de, copy the address bar URL, and paste it into targetUrls. The scraper pages through the results and returns every listing as a structured row, with price, size, location, and agent contact already in separate fields.

How can I compare neighbourhoods on ImmoScout24 by price per square meter? Collect listings for several areas, then read pricePerSquareMeter straight from each row, or divide priceAmount by floorAreaSqm yourself. With districtName, propertyCategory, and offerType alongside, you get a clear side-by-side view of where the value sits.

How can I build a German real estate dataset for investment research? Feed in one or more search URLs, set resultsLimit to the volume you need, and export to CSV or Excel. The result is a structured property database (address, price, size, advertiser) you can score against your own buy or rent criteria.

Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

Support

Questions, feature requests, or a field you'd like added? Reach out at data.apify@proton.me and we'll get back to you.