New York prices do not compare across a co-op and a condo until you have the monthly number beside them. Our StreetEasy scraper keeps both, on your schedule.
You describe the scope: boroughs, neighborhoods, ZIP codes, sales or rentals, co-op or condo, and how often it should refresh. We build the crawler, run it, watch it when the site changes, and deliver CSV, XLSX, JSON or an API endpoint on the schedule you set. StreetEasy is defensive - automated clients are turned away rather than served a page - so this is not a job to hand to a script and forget. We work only with what the site serves publicly, we respect robots.txt, which disallows /sale/*, /rental/*, building documents and floorplans, and we collect nothing behind a login. Sales and rentals stay separated in the export, so you receive only the type you asked for.
A StreetEasy listing page is a unit record. We extract the fields that survive comparison across boroughs, and we keep the ownership structure attached to every row.
Square footage is the field to watch. Many New York co-op listings omit it entirely, so a price per square foot column built without checking for nulls will quietly mislead. We return the field as published and empty where it is absent, rather than estimating it.
Buildings are first-class objects on StreetEasy, not a byproduct of listings. The public sitemap index splits building pages across eight files and off-market buildings across seventeen more, which tells you the building record outlives whatever happens to be for sale inside it. A building page carries the address, the ownership structure, year built, number of units and floors, the amenity set, and the sales and rentals recorded against it over time. The same index lists separate files for agents, new developments and search pages.
That persistence is what makes historical work possible. A unit that goes into contract or gets rented drops out of search results, but the building record remains and keeps reflecting activity at the address. Neighborhood pages sit under stable slugs, in the form /neighborhoods/bedford-stuyvesant/ or /neighborhoods/long-island-city/, and the same slugs appear inside search URLs, so a geographic scope defined once stays valid across runs.
StreetEasy does not publish user reviews of listings, so there is no review corpus to collect. What moves instead is price cuts, days on market, status and concession terms, and those exist as a series only if something records them on a schedule. Listings name a listing agent and brokerage; our output is limited to listing and commercial data, and agent contact details can be excluded on request.
StreetEasy covers New York City and a slice of northern New Jersey, and nothing else. It launched in 2006 and is part of Zillow Group, but the two sites do not run on one shared inventory. In New York most agents post directly to StreetEasy rather than arriving through an MLS feed, and listings posted there syndicate outward to Zillow, Trulia and HotPads. StreetEasy sits upstream, not downstream. Where the same brokerage advertises one unit for sale and for rent at the same time, only the sale listing carries onward to the other Zillow sites. Northern New Jersey runs on a separate direct feed that does not syndicate to Zillow at all. If you need New York inventory, you read it here.
The second thing to understand is ownership structure, because it governs everything downstream. A co-op sells shares in a corporation, and a board approves the buyer after an interview, references and financial tests. A condo sells real property, and its board rarely blocks a purchaser. A condop runs the residential units as a co-op while the commercial space is administered as a condo. That single field decides financing limits, sublet rights and how long a closing takes. A dataset that flattens all three into a single apartment type has discarded the most useful column in the file.
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setup fee €799
The reason to scrape StreetEasy is that a headline price in New York means nothing until the monthly number sits next to it. A co-op at one asking price with high maintenance and a condo at the same asking price with lower common charges and a separate tax bill are two different transactions. Any model, dashboard or feed that ranks on price alone will rank them wrongly. Pulling maintenance, common charges and ownership type into the same row is the entire point of the exercise.
Rentals carry their own version of the trap. When a landlord offers free months, the advertised figure may be the net effective rent, which spreads the lease total across every month including the free ones. The tenant pays the gross rent every month. StreetEasy publishes gross rent, net effective rent and the concession terms on listings that carry one, so both numbers are recoverable - but only if the extraction keeps them apart. We store them in separate columns and flag the concession, because collapsing them into a single price field destroys the comparison you were trying to make. The no-fee against broker-fee split is a third axis on the same listing, and it moves the true first-year cost of a rental well away from the rent line.
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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 is a managed web scraping company, not a tool. You do not write code, run infrastructure or debug a parser the morning a layout changes. Tell us which StreetEasy data you need and we handle extraction, cleaning and delivery in CSV, Excel, JSON or another format you name. One-time exports and recurring updates both work. Our team monitors the site between deliveries and keeps the pipeline running.
No. StreetEasy publishes no public API, there is no developer program and no key to request. An old /nyc/api path still surfaces in search results, but it is disallowed in the site's own robots.txt and is not a supported route for a data buyer. Everything we deliver is collected from publicly reachable pages as a managed service.
Both, in separate columns. When a rental carries a concession such as a free month, StreetEasy publishes the gross rent, the net effective rent and the terms of the concession. Net effective rent spreads the lease total across every month including the free ones, so it sits below the amount actually paid each month. We keep the two apart and flag the concession rather than merging them into one price field.
Yes. The extractor distinguishes rental listings from sale listings, so you export only the type relevant to your work. We can also split by ownership structure, so co-op, condo and condop rows stay identifiable in the same file.
Yes. We scope by ZIP code, borough, neighborhood or a set of areas you define. StreetEasy's neighborhood taxonomy uses stable slugs such as bedford-stuyvesant and long-island-city, and the same slugs appear in search URLs, so a scope defined once stays valid across later runs.
Yes. We run one-time exports and recurring updates on a schedule you choose, daily, weekly or monthly. Recurring runs are what turn a snapshot into a series, since price cuts, days on market and status changes only exist as history if something records them as they happen.
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.
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