Paste a StreetEasy for-sale or for-rent search URL and get every listing on it back as rows: the address with and without its unit, the price, the building type, beds and baths, the size, the neighbourhood and postcode, latitude and longitude, and links to the listing and its photo.
Every filter you set - borough, price band, bed count - is already encoded in the URL. Copy it from the bar and the run reproduces that search.
One row per listing, tagged with the query it came from and numbered in the order StreetEasy returned it. StreetEasy blocks datacenter IPs, so live runs need a residential proxy.
Latitude and longitude arrive as their own columns, so the export is mappable the moment it lands - no geocoding pass, no address matching, no per-row API bill just to put pins on a map.
name carries the unit - 80 Elizabeth Street #2K - while address is the building without it. Group on one to count buildings, on the other to count units, without splitting strings yourself.
Rows arrive numbered in the order StreetEasy returned them, so what the site ranked first for your filters is recoverable afterwards. Re-run the same search later and the movement is the interesting part.
What a StreetEasy result carries: where the property is, what it costs, what kind of building it sits in, and how big it is.
One column will bite you if you miss it. price is a formatted string, not a number - it arrives as $820,000, dollar sign and thousands commas included. Cast that column straight to a number and you get NaN for every row; strip the $ and the commas first. Everything else numeric is a string too, including bedrooms, bathrooms, size_sqft and the two coordinates.
Two more worth knowing before you filter on them. building_type is an enum, and one of its values is UNKNOWN - we have seen CONDO, CO_OP, HOUSE, TWOFAMILY and UNKNOWN, and the naming is not consistent between them, so match on the exact string rather than a tidied-up guess. And bathrooms carries halves: 1.5 and 2.5 both occur, so an integer parse will quietly round your data.
One limit we would rather state than let you assume. The scraper accepts both for-sale and for-rent searches, but the populated export we hold came from a for-sale search - so the $NNN,NNN price shape is confirmed for sales, and worth checking on your first rental run rather than assumed. And because StreetEasy blocks datacenter IPs, a live run needs a residential proxy.
The export's header row, in order. Where a field is marked optional below, that describes what a StreetEasy listing published - not the scrape.
80 Elizabeth Street #2K. Where a property has no unit, this matches address exactly.$820,000, with a dollar sign and thousands commas. Strip both before casting to a number - see the note below.CONDO, CO_OP, HOUSE, TWOFAMILY and UNKNOWN. Note the inconsistent naming, and treat the vocabulary as open - match the exact string.0 occurs, so do not treat a zero here as a missing value.1.5 and 2.5 both occur, so parse as a decimal rather than an integer.Little Italy or East New York.latitude this makes the export mappable without a geocoding step./sale/<id> permalink and a /building/<slug>/<unit> path.One trap, and it is the first column you will reach for. price is a formatted string - $820,000 - so casting it without stripping the dollar sign and commas gives you NaN across the board. Every other numeric field is a string too, including both coordinates, so cast before you plot or sort. Watch two enums: building_type includes a literal UNKNOWN and its members are named inconsistently, so match exact strings; and bathrooms carries halves, so parse it as a decimal. And name is not address - the first carries the unit, the second does not, which is what lets you count buildings and units separately. position exists only in the JSON, so a CSV or XLSX export has fifteen columns rather than sixteen.
The input is the StreetEasy URL from your address bar, with your filters already baked into it. Both search types come back in the same fifteen columns, one row per listing.
A few examples of what a property search is worth once it is a table with coordinates attached.
Pull a whole search and group by neighborhood and building_type. Strip the price formatting once and you can compare a co-op against a condo on the same block instead of eyeballing a results page.
Latitude and longitude arrive per row, so the file goes straight into a map. That removes the geocoding step entirely - which is usually the slowest and most expensive part of turning listings into a picture.
Re-run the same search on a schedule and keep the files. Comparing the url column between runs tells you what is new and what has gone - and the position column tells you how the ranking shifted.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Per-query limits, file upload and every export format included.
Roughly $2 per 1,000 listings. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for tracking many searches on a schedule. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.StreetEasy is New York. These three cover the ground around it.
The same job on the German property portal - listings, prices and addresses as rows. Useful if your research spans more than one country's market.
The other side of the New York rental market - the listings that never reach a brokerage site. Different inventory, same city, and worth holding next to this.
What is actually around a coordinate - schools, transit, groceries. Since this export already carries latitude and longitude, the join between the two is trivial.
Short answer: these listings are public property advertisements - but StreetEasy is a Zillow property and takes a firm line on automated collection, so read this rather than skim it.
Everything collected here is shown on a public StreetEasy results page to any visitor, signed in or not: the address, the price, the building type, the bed and bath counts, the size, the neighbourhood, the coordinates and the photo. No login, no paywall, nothing behind an account. There is no agent name, no broker phone number and no seller contact anywhere in the export, and no column for one.
We would rather flag this than let you find out later. StreetEasy blocks datacenter IPs, which is a deliberate anti-automation measure rather than an accident, and their terms restrict automated collection. That does not make public listings secret, but it does mean this is a terms question with a real position behind it. If you are planning volume, or building a product on top of the output, take advice on your specific use rather than relying on a general rule about public data.
One data-protection note. These rows describe properties, not people - but a property address is capable of identifying whoever lives there, so if you combine this export with anything that names occupants you have moved into personal-data territory and the usual obligations follow. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
Your first 500 rows are free - no card, no subscription. After that it is $0.002 per row, flat.
StreetEasy is where New York property is listed, and a search on it is a filtered view you can only read one card at a time. The StreetEasy Scraper turns that view into rows. You submit a for-sale or for-rent search URL - with your borough, price band and bed count already encoded in it, one per line or as a CSV, XLSX, TXT or Parquet upload - and every listing comes back as a record with the address, the unit, the price, the building type, the bed and bath counts, the size in square feet, the neighbourhood, the ZIP code, latitude and longitude, and links to the listing and its photo. Fifteen columns in the file, sixteen in the JSON, one row per listing.
The coordinates are the part that changes what you can do with it. Because latitude and longitude arrive per row, the export is mappable the moment it lands - no geocoding pass, no address matching against a second service, no per-row API bill just to plot the inventory. Pair that with the neighbourhood label and the building type and a search becomes a picture of a submarket rather than a list.
One column needs care before you build on it. price is a formatted string, not a number: it arrives as $820,000 with the dollar sign and thousands commas, so casting it directly returns NaN for every row. Strip the symbol and the separators first. The same applies more quietly to the rest - bedrooms, bathrooms, size_sqft and both coordinates are all strings, and bathrooms carries halves like 1.5, so parse it as a decimal rather than an integer.
Two honest notes. building_type is an enum whose values include a literal UNKNOWN, and its members are named inconsistently, so match exact strings rather than a tidied guess. And StreetEasy blocks datacenter IPs, so live results need a residential proxy - stated here rather than left to be discovered. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.