Custom-built data collection from Rentberry - rental listings, prices, deposits and landlord details from the markets you care about, delivered in the format your team already uses.
We start from your brief: which cities, which property types, which filters, how deep. From there we map Rentberry's search and listing structure, build the crawler around it, and add validation so broken or half-loaded records never reach your file. We run our own pacing, monitoring and retry logic so collection stays stable over months. Output lands as CSV, Excel or JSON, one-off or on a daily, weekly or monthly schedule.
We collect the listing card and the detail page behind it: listing URL and ID, title, address down to district, city and country, property type, monthly rent and currency, security deposit, bedroom and bathroom count, square footage, availability date, pet policy, description text, amenity list, photo URLs, coordinates, publication and update dates, and landlord or agent identity where the page shows it. Where offer or application activity is exposed, we capture that too, along with any price change between runs. Field lists are set per project - if the page carries something else you need, we add it.
A single scrape gives you a price. A repeated one gives you a market. Because we snapshot listings on every run, you see how long a unit sits before it disappears, whether it returns later at a different rent, and how the deposit-to-rent ratio shifts by district - a signal most rental datasets never expose. Landlord and agent identity, accumulated across runs, shows who controls what share of supply in a given city. Rentberry also publishes median rent figures for major cities and search trend sections grouped by the Americas, Asia Pacific and Europe, which we can pull as a demand-side complement to the listing feed.
Rentberry is a global long-term rental marketplace founded in 2015 and headquartered in San Francisco, with Alex Lubinsky as its CEO. It went to market in 2017 and, by its own count, carries listings in more than 90 countries, with New York, Los Angeles, Chicago, Berlin, London, Lisbon, Seoul, Barcelona and Birmingham among the cities featured on its own front page.
Inventory is split by property type rather than lumped into one feed. Apartments, houses, condos, duplexes, townhouses, lofts and rooms each get their own browsing section, so a single city can hold several distinct pools of supply that are worth tracking separately.
What sets Rentberry apart from a plain classifieds board is the custom offer. Tenants do not simply accept the asking rent - they submit their own monthly price and security deposit, and landlords compare the competing proposals side by side. Every listing therefore carries both a published price and a negotiation layer above it.
Search is filtered by price range, property type, bedroom and bathroom count, and amenities such as balcony, elevator or wheelchair access, with a map for narrowing down to a district and sorting by newest. A separate Flexible Living stream covers furnished homes rented for roughly one to twelve months, aimed at remote workers and digital nomads.
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Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€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
Rentberry data goes to property portals keeping their own database current, to rental operators and landlords benchmarking asking rents against real competing supply, to analysts watching how rents and deposits move across cities, and to relocation and proptech services that need live inventory in markets they do not physically cover. Doing it by hand does not scale: rental stock turns over in days, supply is spread across seven property types and dozens of countries, and a spreadsheet assembled manually is stale before it is finished. An automated feed hands you the same market every morning, in the same shape.
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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 scrapers to order - no dashboard to learn, no generic tool to fight. Rental property data is territory we know well: one of our projects put Rentberry under full listing monitoring alongside funda.nl, pararius.com, rentola.com and zimmo.be for a growing European property portal, and reduced five different site structures to a single analytics-ready format. Tell us which Rentberry markets you need and in what shape, and we handle the rest.
Yes. We scope collection by city, region or country, or by any filter combination the site supports - property type, price range, bedrooms, bathrooms, amenities. You get the segment you asked for and nothing else.
Collection is either one-off or scheduled - daily, weekly or monthly. Rental inventory turns over quickly, so clients tracking availability and price movement usually choose a daily run with changes flagged between snapshots.
CSV, Excel or JSON, whichever fits your pipeline. If you collect Rentberry alongside other rental portals, we normalize every source to one shared field structure so the files merge without extra cleanup on your side.
Yes. One project of ours ran full listing monitoring across five European property sites - Rentberry, funda.nl, pararius.com, rentola.com and zimmo.be - with every report reduced to a single common structure for the client's analytics.
It depends on scope. The five-site European rental monitoring project, Rentberry included, went from brief to running pipeline in three weeks. A single-site Rentberry collection is a considerably smaller job.
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.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
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