Custom data extraction from Rentola's rental inventory, built and maintained by ScrapeIt for the markets, cities, and filters you actually need.
We start from your target: which country sites, which cities, which property types and price bands. From there we map Rentola's search and facet URLs, walk the result pages, open every listing and normalize the fields into one schema, so an ad in Berlin and an ad in Rotterdam arrive in the same shape. Collection is one-off or scheduled - daily, weekly or monthly - and delivery is CSV, Excel or JSON. Deduplication, retries, change tracking and monitoring for layout changes stay on our side, not yours.
Each run returns one structured row per listing: listing ID and URL, title and full description, property type (room, studio, apartment, house, student apartment), rent per month or week with currency, deposit and any stated utility or prepaid-rent terms, floor area, room and bedroom counts, furnished status, availability date and rental period, pets and smoking rules, location down to city, district, postcode and coordinates, photo URLs, advertiser type and landlord or agency name where shown, the originating source site for aggregated ads, plus first-seen and last-seen timestamps.
A single snapshot tells you asking rents. A time series tells you what the market does with them. Because every listing is logged on each run, you get days on market per property, price changes and their direction, relisting patterns that reveal which homes are not renting, and withdrawal rates by district. Advertiser fields let you separate private landlords from agencies and portfolio operators, and count how many listings each one runs at once. Since many ads reach Rentola from other portals, the source field shows which sites feed which markets and where the same property appears twice under different terms. Photo counts and description completeness give you a workable proxy for listing quality.
Rentola is a rental housing marketplace launched in 2020 and run from Copenhagen by Reva Group ApS, with related Danish entities such as Rentola UK ApS and Rentola Abroad ApS registered alongside it. The company traces its expertise to the Scandinavian market and its first launch to France, and today it operates localized domains including rentola.co.uk, rentola.de, rentola.it, rentola.nl, rentola.es and rentola.gr alongside the global rentola.com.
The platform is an aggregator as much as a listing site. Alongside ads placed directly by landlords, Rentola collects rental offers from a wide network of external sources - the company puts that number above 2,400 sites - and presents them in a single search. Its own materials describe coverage of 36 countries and more than a million properties, concentrated in Europe but reaching Latin America through rentola.mx, rentola.com.br and rentola.co.com.
Inventory is split into rooms, studios, apartments, houses and student apartments, and the focus is long-term renting rather than short stays. Filters cover location, property type, maximum rent per month or per week, bedroom count, free-text keywords and attributes such as pets allowed, with sorting by newest first, by rent in either direction, or by size. Facets are exposed directly in URLs, in patterns like /for-rent/apartment/{city}/pets-allowed.
Browsing is free, while landlord contact details sit behind a paid Premium subscription, and a saved-search agent alerts users as soon as new matching homes appear. The audience skews toward students, expats and relocating families, which makes Rentola a useful read on where mobile renters are looking and what they are willing to pay.
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Pages extracted
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€199 / one-time
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setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Rentola gives a cross-border view of long-term rental supply, which makes it valuable to anyone tracking prices across markets rather than inside a single city. Property portals use it to benchmark their own inventory and find listings they do not carry. Investors and build-to-rent operators use it for rent-per-square-meter curves by district and property type. Relocation agencies and student housing operators watch availability and lead times ahead of term starts. Analysts read listing churn as a demand signal. All of that needs full coverage and history, which page-by-page browsing simply cannot produce.
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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.”
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 builds and runs custom web scrapers as a service. Rentola is not new territory for us: it was one of five property sites we monitored for a growing European real estate portal, alongside funda.nl, pararius.com, rentberry.com and zimmo.be. That project went live in three weeks, and a large share of the work was reconciling five different report structures into a single schema the client's analytics pipeline could ingest. We keep the pipelines running afterward, so the data keeps arriving in the same shape.
Yes. The country domains share a common structure, so we can target rentola.co.uk, rentola.de, rentola.nl, rentola.es and the rest, or the global site, and merge everything into one file with a market column.
Rent, deposit, property type, floor area, rooms, furnished status, availability date, pets policy, location down to district and coordinates, photos, advertiser details, the original source site for aggregated ads, and first-seen and last-seen timestamps.
Collection can be one-off or scheduled - daily, weekly or monthly. Rental inventory moves fast, so teams tracking days on market or price changes usually choose daily runs on their priority cities.
CSV, Excel or JSON, whichever fits your pipeline. If you collect from several portals at once, we normalize the columns so every source lands in the same schema, exactly as we did on the five-site property project.
Access reliability is part of the service. Most European property sites, with a few exceptions, do not run advanced anti-bot systems. We keep request volume considerate and monitor every run, so layout or response changes get fixed before they cost you data.
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.
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