Tap into Hungary’s residential and commercial property listings with ScrapeIt. We extract structured data from Balla Ingatlan — including pricing, location, property specs, and local insights — ready for real estate analysis or integration.
We tailor the data extraction process to match your real estate focus — from Budapest apartments to countryside land plots. With ScrapeIt, you can scrape Balla Ingatlan data effortlessly: we connect with public-facing listings, parse relevant property fields, and clean the output for easy export. You won’t deal with scripts, browser automation, or messy tables. Just tell us what you want — location filters, listing types, or pricing brackets — and we’ll deliver the dataset that fits your goals.
We tap into the full breadth of Balla Ingatlan’s listings to extract property data tailored for deep real estate insights. That includes pricing in HUF, complete addresses with district and postal codes, and property types — apartment, detached house, townhouse, land, garage, or commercial unit. You’ll receive structural details such as floor level, total floor area (m²), number of rooms, and age of the building. Equipment and condition info is also captured — for instance, heating type (gas boiler, radiator, underfloor), construction material (brick, panel, Ytong, clay), renovation state, and presence of balcony, terrace, private parking or garage.
We extract energy certification status and details when available, as well as listing metadata like posting date, unique ad ID, and whether it's listed by agent or private owner. High-quality image URLs are included, alongside descriptive text. Everything is parsed, deduplicated, and formatted, ready for export in CSV, JSON, or Excel formats.
With ScrapeIt’s Balla Ingatlan scraper, you don’t just get surface-level data — we extract the subtle elements that shape buyer and renter decisions on the platform. These include indicators like “new build,” “luxury,” or “price reduced,” as well as property condition labels such as “renovated,” “to be renovated,” or “in average condition.”
We also collect regional tags that reflect Budapest districts or named zones (e.g. Újbuda, Zugló, Kispest), helping to map out demand and pricing across neighborhoods. Additionally, we capture listing status cues like “exclusive offer” or “highlighted listing,” as well as whether a property is sold directly by a Balla agent or listed on behalf of a third-party partner. These extra signals offer deeper segmentation and allow for smarter filtering and trend analysis.
With more than 50 branches across Hungary, ballaingatlan.hu stands out as one of the country’s most established real estate brands. Based in Budapest and operating since the early 2000s, it combines national coverage with local insight — listing everything from studio flats in District VII to retail units in Debrecen or family homes in Szeged.
The platform supports listings in Hungarian and shows prices in forints (HUF). It’s not just a search engine — Balla Ingatlan also assists users with financing options, legal documentation, and full-cycle property transactions. Listings are agent-verified and typically include detailed specifications: heating type, renovation status, building structure, and even nearby metro lines.
Users can filter by neighborhood, proximity to public transport, or regional features — offering a precise, practical tool for renters, buyers, and professionals alike. Whether you’re relocating to Budapest or investing in secondary cities, Balla Ingatlan offers a structured and localized real estate experience.
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Developers
Customers
Pages extracted
Hours saved for our clients
Customized plans that grow with your data needs.
€199 / one-time
setup fee — included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Balla Ingatlan offers rich, localized real estate data — but it’s locked behind limited filters and non-exportable pages. A custom Balla Ingatlan scraper gives you direct, structured access to this data, bypassing UI restrictions and allowing for deep integration. Scraping the platform enables you to monitor market dynamics across Hungary, from Budapest’s districts to smaller regional hubs. Whether you’re analyzing pricing trends in specific neighborhoods, benchmarking building types, or aggregating inventory for your own portal, structured access to Balla’s listings gives you full visibility. ScrapeIt helps agencies, analysts, and proptech teams extract and organize the exact property data they need — at the scale and frequency that suits their workflow.
Learn how to use web scraping to solve data problems for your organization
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Whether you're building local housing dashboards or fueling a national property search tool, ScrapeIt simplifies the way you access real estate data. We scrape Balla Ingatlan data with custom-built extractors that match your criteria — by district, property category, condition, or price band. We handle the parsing, cleaning, and structuring so you receive data that's analysis-ready from the start. No need to wrestle with source code or browser automation — just tell us what you’re after, and we’ll supply it in the format that fits your system.
Yes — we can structure the output by district, city, or region, which is especially useful for regional market comparisons and heatmaps.
If publicly displayed on Balla Ingatlan, we’ll include it — typically agent names, office locations, and contact numbers.
Of course. We preserve original pricing, including HUF values and optional currency conversions if you need them.
Absolutely. We can filter listings by posting date, so you focus only on fresh entries or recent market changes.
Real estate portals, pricing analytics platforms, cross-border investment firms, and housing data aggregators use this service to stay ahead in the Hungarian market.
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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