Managed extraction from Ingatlan.com: listings, asking prices, m2 rates and district detail, delivered as CSV, JSON, XLSX or an API on the schedule you set.
Plans from €169/month · Free project assessment · Reply within 1 business day
You describe the scope: which transaction types, which kerület or settlements, which property types, and how often you want it refreshed. We build the crawler, run it, and maintain it when the site changes. Output arrives as CSV, JSON, XLSX or through an API endpoint, daily, weekly or monthly.
We collect only publicly available data - price, négyzetméterár, floor area, rooms, komfort, heating and kerület, everything a visitor can see on Ingatlan.com - and we collect it legally. Anti-bot handling, proxy rotation and CAPTCHA handling are part of the service.
Every Ingatlan.com scraper we run writes one row per advert, with the native Hungarian label kept next to an English gloss so nothing is lost in translation. A typical schema includes:
Hungary uses the forint, and a scraper that stores a bare number here produces nonsense. One listing page can show a sale price in millions of forint, a rent per month in forint, and a per-square-metre figure in Ft/m2 that reaches seven digits in central Budapest. Some Budapest rentals are advertised in euro. This is not a hypothetical we invented: the site's own search filter schema carries a separate euroPriceRange field alongside sellPriceRange and rentPriceRange. We store amount, unit, currency and period as separate columns, so no row is ambiguous about what its number means.
Accents are the second trap. Ingatlan.com serves UTF-8 and its labels carry real diacritics - eladó, kiadó, lakás, ház, fűtés, szobák, kerület. The URL slugs, by contrast, strip them: Győr becomes gyor, Pécs becomes pecs, Székesfehérvár becomes szekesfehervar. Matching on the slug and matching on the visible label are two different jobs, and conflating them drops rows silently.
Hungarian market programmes surface as their own flags in that same schema, including a fixed three percent loan compatibility marker, panel programme participation and a green home flag. These are the fields a Hungarian analyst asks about first, so we carry them through rather than flattening them away.
Ingatlan.com is the dominant property marketplace in Hungary. It carries stock from estate agencies and from private sellers side by side, which makes it a national supply picture rather than a single firm's inventory. ScrapeIt already publishes a Balla Ingatlan page for readers who need one agency's own listings; this page covers the marketplace those agencies advertise on.
The site is organised around two transaction words. eladó (for sale) and kiadó (for rent) appear in almost every URL and page title, paired with a property word: lakás (flat), ház (house), telek (plot), garázs (garage), nyaraló (holiday home), iroda (office) or üzlethelyiség (retail unit). A search landing path reads /budapest/elado+lakas. A single advert sits at a deeper path such as /i-ker/elado+lakas/tegla-lakas/ followed by a numeric identifier, so the district, the transaction, the property type and the construction subtype are all recoverable from the URL alone.
In August 2026, the search filter schema published inside the site's own front page markup exposes thirteen property types, running from flat and house through plot, garage, office, catering and agricultural land. That schema is a useful map of the source, because it defines what an Ingatlan.com scraper can reliably ask the site for.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →District is the unit Hungarian property analysis runs on. Budapest is divided into 23 numbered kerület, written in Roman numerals from I to XXIII, and they appear in Ingatlan.com URLs as slugs from i-ker through to xxiii-ker. A flat in the V. kerület and a flat of the same size in the XXIII. kerület are not comparable assets, so an Ingatlan.com price series that ignores district is close to meaningless. Every row we hand over is keyed to its district.
The usual reasons buyers come to us: valuation and automated valuation models that need current asking prices per m2; rental yield work that pairs kiadó rents against eladó prices in the same district; supply tracking for újépítésű (newly built) stock; portfolio and competitor monitoring for agencies; and mortgage or insurance teams assembling a Hungarian reference set.
Repeat collection matters more here than any single snapshot. Ingatlan.com does not publish a sold-price archive, and its help centre states that an advert left unedited can be deactivated and later deleted, with inactive adverts not displayed on the site. A property that vanishes has been sold, withdrawn or simply left to lapse, and the only way to tell those apart is to watch the same identifier across runs.
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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.”
Six jobs that scraped property data does, each backed by a ScrapeIt project: seller leads, listing monitoring, image datasets, weekly market supply, sold records and unit-level rentals. For every use case - the fields it needs and how often to refresh them.
ScrapeIt is a managed web scraping agency. We are not selling a library, a proxy pool or a browser plugin. You bring the requirement, we build the crawler, run it on your schedule, fix it when the source changes, and hand back clean structured files or an API you can query. Pricing follows scope and refresh frequency rather than a fixed tier. If Ingatlan.com sits alongside other Hungarian or regional sources you need, we scope them together so the schemas line up from the start.
Yes. The ScrapeIt Ingatlan.com API returns price, négyzetméterár, alapterület, szobák, komfort and kerület as JSON from an endpoint we host, refreshed on the schedule you set. The same Ingatlan.com data also comes as CSV or XLSX files or straight into your database.
Yes. District is a first-class field in the output. Budapest has 23 kerület, numbered I to XXIII, and they appear in the site's URLs as slugs from i-ker through to xxiii-ker. We key every row to its district and store both the forint price and the négyzetméterár in Ft/m2, so you can aggregate asking prices by district and by property type without any post-processing on your side.
No. Private sellers advertise on Ingatlan.com alongside agencies, so output is limited to listing and commercial data by default: price, size, layout, location, attributes and the advert URL. Personal contact details of private individuals are excluded. Personal data of people in Hungary and the wider EU falls under the GDPR, and we set the default this way so you are not holding it without a reason to.
Daily, weekly or monthly, whichever the use case needs. Because we keep first seen and last seen timestamps against each listing identifier, repeat runs build a price history and a time-on-market measure for you. That matters on this source in particular, since the site shows current adverts and publishes no sold-price archive. When an advert disappears, you learn it from your own series rather than from the page.
A dataset, not a script. Delivery is CSV, JSON, XLSX or an API endpoint, against a fixed schema agreed before the first run. Hungarian labels such as eladó, kiadó, lakás and komfort are preserved with their accents in UTF-8 and paired with English glosses. Prices carry their unit and currency, so a forint figure is never confused with a euro one, and a monthly rent is never confused with a sale price.
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.
Ingatlan.com data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
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