We help you access data from Apartments.com - the largest online rental platform in the U.S. Using an Apartments.com scraper, we extract detailed rental property information from one of the most trusted sources for apartments, houses, and residential communities. You get everything you need for analytics or to power your own housing services.
This is a managed service. We design the URL matrix, build the crawler, run it and keep it working when markup or geo slugs change. You receive data, not code.
Output ships as CSV, JSON, XLSX or a JSON API, and we can push to S3, Google Cloud Storage, an SFTP drop or straight into your database. Schedules run daily, weekly or monthly, and change-only delivery is available, so each file carries new, changed and disappeared listings rather than a full re-dump. Scope is set by geography, property type, bed count and price band, since covering a large metro means splitting the search space and de-duplicating on the property code.
We keep the platform's own naming so the output lines up with what a leasing team sees on screen. One property expands into many rows, because Apartments.com models inventory as Models (floor plan types) and Units (individual apartments such as #2B), each priced separately.
resultPosition records where a listing sat in the results, but ordering follows the Diamond, Platinum, Gold and Silver ad packages, so it reads as a spend signal, not relevance.
A single snapshot of Apartments.com ages badly. Unit availability, rent and move-in specials change continuously, and a deactivated listing can sit on the public site for up to 24 hours after it is pulled. Anything you want to say about a market over time has to come from repeated crawls with change detection, keyed on the seven-character property code and the unit label.
Run on a schedule, the same crawl produces a different class of data. You see the date a Unit first appeared and the date it stopped being offered, which brackets time on market. You see rent move inside a single floor plan while the property's advertised range stays flat. You see a Rent Special appear and vanish two weeks later. You see a property shift between Diamond, Platinum, Gold and Silver placement, which reads as an advertising budget change. You see new construction enter a submarket as fresh listingIds with a recent Year Built.
One caveat belongs in every historical series. Apartments.com opened All-In Pricing to all US and Canadian properties in late January 2026; before that it was limited to markets with legal disclosure requirements, so Total Monthly Price coverage is not comparable across that boundary. We keep base rent and Total Monthly Price in separate columns for exactly this reason.
Apartments.com is the flagship rental marketplace of CoStar Group and the front door of the Apartments.com Network, which also carries ApartmentFinder.com, ApartmentHomeLiving.com, ForRent.com, Apartamentos.com, CorporateHousing.com and After55.com. Listings arrive inward, through ILS syndication feeds from Yardi RentCafe and Voyager, RealPage LeaseStar and Entrata, with Yardi moving to the MITS 5.0 data model for granular fee syndication. Nothing flows back out through a public read API.
Every property carries a stable identity. Detail URLs take the form /{slug}-{city}-{state}/{id}/, where the id is a seven-character lowercase alphanumeric property code - /arrive-hollywood-hollywood-ca/cmv3yrg/, /henry-on-the-park-apartments-philadelphia-pa/8fjme23/, /2630-n-hamlin-ave-chicago-il/kvl7tm9/. The slug carries the marketing name for a managed community and the street address for a single house, condo or townhome. That code is the join key for any dataset built here.
Search is path-based rather than query-string based. Geography becomes /{city}-{state}/, /{state}/ or /{neighborhood}-{city}-{state}/, pagination becomes /2/ and /3/, and filters compile into one hyphenated segment such as /ma/min-2-bedrooms-under-1500-pet-friendly-dog/. A crawl plan is therefore a generated URL matrix, not a form submission. Map-drawn searches switch to bb bounding-box parameters with sk and ss saved-search keys, the only reliable way to slice a metro below the canned geo slugs. Canada sits on the same domain with province codes in the slug, /toronto-on/ and /north-york-toronto-on/, so there is no separate .ca property to crawl.
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Rent comps are the usual starting point. An operator pulls every competing community inside a defined ring at unit level, then compares rent per square foot by floor plan instead of comparing advertised property ranges that hide the spread. Because Total Monthly Price separates required recurring fees from base rent, the same pull shows how much of a competitor's headline number is really fees.
Revenue management teams watch concessions. Rent Specials appear and disappear faster than rent itself, so a scheduled crawl of a specials filter path such as /philadelphia-pa/rent-specials/ works as a discounting index for a submarket.
Acquisition and underwriting teams screen assets on Year Built, Number of Units, management company and unit mix before a broker package exists. Proptech products use the same feed to seed coverage in a new metro. Sales teams build territory lists from publicly listed leasing office numbers and management company names, for property management software, insurance, smart-home hardware or renovation services. Housing researchers use repeated pulls to measure affordability and the spread of pet and admin fees across a city.
Monitoring of real estate listings on funda.nl, pararius.com, rentberry.com, rentola.com, and zimmo.be to support the growth of a European property portal.
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Daily detection of new private property listings in Switzerland on Homegate.ch and ImmoScout24.ch, giving the agency first access to high-value leads.
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Scraping residential listings from Immobilienscout24.de in Germany and Mallorca, including complete data and resized images.
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
We have run rental and real estate crawlers for years, on sites that are server-rendered, sites that sit behind a WAF and sites that change their markup without notice. On a project like this the hard part is not parsing a page. It is coverage planning, request pacing, de-duplication and noticing when a field quietly changes meaning. We handle that, monitor the pipeline and give you one person to talk to when a file looks wrong.
We collect only publicly rendered pages. Listing content on Apartments.com is not behind a login wall: an account gates Favorites, Saved Searches and alerts, the New Listings feed, the Renter Profile, rental applications and rent payments, while rent, floor plans, fees, amenities, reviews and leasing office phone numbers stay public. We do not create accounts, do not sign in and do not touch renter personal data. Legal positions differ by jurisdiction and by intended use, so we recommend reviewing your specific case with your own counsel.
No. There is no public read API for third parties. Data flows the other way: property managers syndicate listings to Apartments.com through ILS feeds from Yardi RentCafe and Voyager, RealPage LeaseStar and Entrata, with Yardi moving to the MITS 5.0 data model for granular fee syndication. Those feeds exist for suppliers pushing inventory in, not for consumers reading it out. For a third party that needs rent, floor plan or availability data, structured extraction from the public site is the only read path.
The domain sits behind Akamai. Responses carry Server: AkamaiGHost, akaalb_www_apartments_com_main load balancer cookies and Akamai-GRN headers, and requests from datacenter IP ranges receive a hard 403 Access Denied page with an errors.edgesuite.net reference number. Even /robots.txt is refused, and the refusal persists with a full Chrome header set or a Googlebot user agent. That is the real engineering content of the project: egress that is not a datacenter range, conservative pacing, session handling and monitoring. We do not sell CAPTCHA defeat or detection evasion. We build a slow, well-behaved crawler and keep it stable.
Yes, but never from one URL. Pagination on a single search exposes roughly 700 listings regardless of the true count; a large market page can headline several thousand rentals and still stop far short of that when you page through it. We split the space by geography (state, city, neighborhood), price band, bed count and property type using the hyphenated filter paths, and add map bounding-box searches where the canned geo slugs are too coarse. Results are then de-duplicated on the seven-character property code. Coverage becomes a function of how finely the URL matrix is cut.
Yes. Apartments.com models inventory as Models and Units, so we expand a property into one row per priced unit, with beds, baths, square footage, deposit and availability date. Where the property publishes All-In Pricing we capture Total Monthly Price next to base rent, and we keep application, admin, pet and parking fees in their own fields instead of folding them into a single number. Leasing office phone numbers and office hours are publicly rendered and are included in the same row set.
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.
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