How to Use Real Estate Web Scraping to Gain Valuable Insights
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
Scrape Kamernet data through ScrapeIt — get structured rental listings from across the Netherlands, filtered and ready to deploy.
Kamernet wasn’t built with data export in mind — but that’s where we come in. We create a custom extractor that pulls exactly what you need: rooms, studios, shared flats, or full apartments. You don’t have to deal with the platform itself — we handle the extraction, formatting, and delivery. The final dataset is filtered, clean, and fully compatible with your system, whether you're tracking listings across Dutch cities or building your own rental Database.
The Kamernet scraper extracts and structures every listing, providing consistent data for comparison, filtering, or integration. From each offer, we gather detailed rental information — type of space (room, studio, apartment), full address with postal code, rent in euros, deposit, availability date, and minimum rental period. We also include listing descriptions, furnished status, surface area, number of housemates (if applicable), and details about the landlord or advertiser.
Kamernet listings often contain subtle but valuable context. When we scrape Kamernet data, we extract user type (landlord, tenant, agency), listing visibility (public or profile-restricted), and tags like “available now” or “only for students.” Many ads also feature social details — such as preferred roommate age or gender — and extra criteria like registration possibilities or smoking rules. These overlooked details help you segment listings more precisely and build a dataset tailored to specific rental profiles or policies.
Kamernet is the Netherlands’ most trusted rental platform for students, expats, and young professionals. Live since 2000, it focuses on rooms, studios, and shared apartments, especially in cities like Amsterdam, Rotterdam, Utrecht, and Leiden. The platform — available at kamernet.nl — operates in Dutch, with all prices listed in euros (EUR).
With over 7,000 new listings per month, Kamernet helps thousands find housing — including more than 2,600 successful tenant matches monthly. Listings come from a mix of private landlords and verified advertisers, clearly labeled with a trust badge. A combination of manual and automated screening helps ensure quality and reduce fraudulent posts.
Beyond standard search, Kamernet offers a full-featured experience: users can create roommate profiles, receive real-time alerts, access landlord dashboards, and even participate in virtual viewings. Search filters include lifestyle preferences, making the platform ideal for mobile, community-driven rental scenarios — whether you're new to the Netherlands or switching cities for university or work.
Get a QuoteDevelopers
Customers
Pages extracted
Hours saved for our clients
Customized scraping setup for Kamernet — faster and cheaper than building a solution from scratch.
Data limits (rows): up to 10%
Iterations: up to 3
Custom requirements: Yes
Data lifetime: up-to-date
Data quality checks: Yes
Delivery deadline: 1-2 working days
Output formats: CSV, JSON, XLSX
Delivery options: e-mail
Kamernet scraper isn’t just about pulling listings — it unlocks the fastest-moving segment of the Dutch rental market, especially for student and mid-term housing. Scraping Kamernet gives you access to one of the most fluid and dynamic housing ecosystems in the Netherlands. You can track how often listings appear in certain cities, monitor average rental periods, or analyze which types of rooms are in highest demand. Whether you’re mapping housing supply across regions or building a lead generation engine for your platform, structured data from Kamernet helps you act on trends as they happen.
Learn how to use web scraping to solve data problems for your organization
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
Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.
The use of sentiment analysis tools in business benefits not only companies but also their customers by allowing them to improve products and services, identify the strengths and weaknesses of competitors' products, and create targeted advertising.
At ScrapeIt, we focus on turning complex real estate websites into simple, usable datasets — with zero effort on your side. For platforms like Kamernet, we build tailored scrapers that match your exact needs: location, property type, availability window, or advertiser type. Whether you're looking to scrape Kamernet data for research, aggregation, or internal analysis — we deliver it clean, organized, and ready to plug into your systems or tools.
Yes — we include all visible features from the listing, such as whether the room is furnished, how many people live there, and when it's available.
Depending on your needs, yes. In most cases we can configure the setup to navigate login walls or member-only listings.
If it’s shown, we capture advertiser type (private owner or agency), profile description, and verification status.
That’s entirely up to you. We can set up daily syncs, weekly refreshes, or run it on a fixed schedule.
Absolutely. Whether you're targeting Amsterdam, Groningen, or smaller towns, we’ll filter the listings accordingly.
1. Make a request
You tell us which website(s) to scrape, what data to capture, how often to repeat etc.
2. Analysis
An expert analyzes the specs and proposes a lowest cost solution that fits your budget.
3. Work in progress
We configure, deploy and maintain jobs in our cloud to extract data with highest quality. Then we sample the data and send it to you for review.
4. You check the sample
If you are satisfied with the quality of the dataset sample, we finish the data collection and send you the final result.
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