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
Get structured real estate data from Booli.se — including property prices, locations, listing details, and market history. We gather listings from across Sweden in clean CSV, Excel, or JSON format—ready for analytics, portfolio building, or market comparison.
No tools to manage, no code to maintain. We build a dedicated scraper that focuses on the real estate listings you care about on Booli.se. Whether you're tracking property prices in Gothenburg or looking for rental trends across Swedish suburbs, we collect the exact data you need — structured, filtered, and aligned with your workflow. It’s a seamless way to access housing insights without doing any of the heavy lifting.
The Booli scraper collects a rich mix of real estate insights and market data unique among Swedish property portals. From each listing, we pull essential fields like address, listing price in SEK, number of rooms, property type, and photos. Beyond the basics, our Booli scraper captures pre-market indicators — “coming soon” tags that often appear days before listings go live. We also extract historical sale prices when available, energy performance ratings, developer names, and building features like walk-up vs. elevator. Map coordinates, neighborhood tags, and agency details round out the dataset, giving you a comprehensive view tailored to how Booli structures its housing market content.
These extra data layers help you filter listings based on market stage, energy efficiency, developer activity, or local geography — all organized and ready for next-level analysis.
Booli’s strength lies in how it blends standard real estate listings with layers of analytical context. With our service, you can scrape Booli data such as estimated property value ranges based on historic pricing, price per square meter, or listings marked with “sneak peek” indicators. The platform also assigns properties to neighborhood clusters, which helps isolate trends on a hyperlocal level. Agency performance tags, time-on-market data, and comparison widgets embedded in listings can all be scraped and structured for deeper interpretation. If you're analyzing supply shifts, local demand, or planning UX personalization — these details are where the real value begins.
Booli.se is Sweden’s second-largest real estate platform and a trusted alternative to more mainstream portals — offering a comprehensive, data-rich view of the housing market. Listings are published in Swedish, priced in SEK, and cover a wide spectrum: urban apartments, countryside villas, rental units, and even undeveloped land. Through booli.se, users gain access to both agent-listed properties and private seller posts, creating a more complete inventory snapshot.
One of Booli’s most unique features is its emphasis on pre-market listings — properties not yet published on other sites — giving early movers a strategic advantage. It also offers powerful map-based search tools, historic pricing trends, energy efficiency ratings, and neighborhood analytics, making it highly valuable for serious buyers and analysts.
As part of the SBAB Group, a major Swedish financial institution, Booli integrates mortgage calculators, financing tips, and affordability tools directly into the search experience. This financial-tech connection turns Booli into more than a property portal — it's a hybrid discovery and housing intelligence platform trusted by both home seekers and industry professionals.
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Customers
Pages extracted
Hours saved for our clients
Customized scraping setup for Booli — 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
Scraping Booli lets you tap directly into one of Sweden’s most data-rich housing platforms — without relying on third-party summaries or incomplete feeds. Because the site includes agent and private listings, historic pricing, map coordinates, and early-market activity, it’s ideal for building local pricing models, competitor dashboards, or searchable housing databases. If your project depends on Swedish real estate data that’s structured, recent, and sourced from the market itself — Booli is the gateway. And we make that data yours, with no effort on your side.
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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
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At ScrapeIt, we focus on one thing — getting real estate data into your hands, already filtered and formatted. If you need to scrape Booli data, we don’t hand you tools — we handle the scraping for you. Whether you're tracking housing prices across Sweden or analyzing listings by zip code, we extract the exact data you need and deliver it in a format that fits your process. No dashboards, no interfaces — just property data that’s ready to work.
Not at all. We handle the entire parsing and export process. You just receive structured property data, ready to use.
That’s up to you — daily, weekly, or monthly exports are all possible. We'll structure delivery to match your workflow.
Yes. We collect data from all listing sources published on Booli.se, giving you a full view of the real estate landscape.
Yes, we can include property photos, historical prices, and key metadata for each listing — provided it’s available on Booli.
Absolutely. We can include geodata, postal codes, and even extract neighborhood groupings when available.
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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