Paste an OfferUp search, explore or item URL and get the listings back as rows: the title, the asking price, the city and state, a thumbnail and a link straight to the listing. One row per item, ranked in the order OfferUp showed them. No account, no proxy pool, no parser to keep alive.
The input is whatever OfferUp page you are looking at - a search you ran, a category you are browsing, or a single item.
One row per listing, each tagged with the query it came from - so a run across twenty searches still reconciles back to your input list.
A search you already ran, a category you are browsing, or a single item - paste whichever you have. There is no separate mode to pick and nothing to reformat before the run.
Every row carries the city and the state alongside the number, so a price comparison stays honest about geography. Second-hand pricing is local, and a national average hides that.
The JSON records each listing's position within its query, one to n, in the order OfferUp returned it. Sort the file however you like afterwards - the original ordering is still in there.
Each row carries the listing as OfferUp's results show it: the title, the asking price, the city and state, a thumbnail image and a permalink to the item itself.
Titles come back verbatim, exactly as the seller typed them, with no cleanup applied - which is what you want if the next step is matching listings to a catalogue. The column list below is the header row of real runs rather than a specification, and the useful part of it is which columns arrived empty on every single row of the sample. Those are named, not glossed over.
Taken from the header row of real runs, identical across them and identical to the column list the platform ships.
30), sometimes two decimals (24.99). Filled on every row.City, ST with a two-letter US state code. Filled on every row.LOCAL_PICKUP - no other value appeared, so treat a second value as possible but untested.https://offerup.com/item/detail/ plus the listing’s id. Filled on every row.Read this before you write code against it. position exists only in the JSON, so a CSV or XLSX export has ten columns rather than eleven. There is no seller and no posting date anywhere in this output, because the results OfferUp renders do not carry them. And the form takes an item-detail URL as well as search and explore URLs, which is worth exercising against your own list before you build on it.
A per-query limit decides what a run costs and how much lands in the file. It is chosen before the job starts, alongside how you hand over the list of URLs.
A few examples of how teams use second-hand marketplace listings to answer a question they actually have.
Run the same search across a handful of metros and read the asking prices with the city attached. What a used item fetches in Wichita and what it fetches in Portland are different numbers, and the location column is what lets you say so.
Used listings set the price a new unit competes against. Export the category on a schedule and you can see the resale floor move instead of guessing where it sits.
Explore URLs cover a category rather than a keyword, so a run returns what is actually listed near a place. Counts and prices by city turn into a picture of local supply that no single search gives you.
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 continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Retail apparel prices with the product detail attached. Clothing is one of the categories where a used listing and a new one sit closest, so the two exports read well side by side.
The new-goods price the used listing is measured against - title, price, rating and review count, so the resale floor sits next to the retail ceiling.
Short answer: yes for the public listing content - and this export carries no seller identity at all.
Listings on OfferUp are published to be found. The title, the asking price, the city and state, the thumbnail and the link are shown to anyone who runs the search, signed in or not. Collecting publicly visible listing data for price research is long-established practice, and nothing here touches a login, a message thread or a paywall.
One point is worth stating plainly rather than burying. There is no seller in this output - no name, no profile link, no rating, no avatar - and no posting date either. The columns are the item and where it is, nothing about the person selling it. That is a property of what the results pages render, not a filter we apply afterwards, and it means a price study built on this data has no personal data in it to begin with.
OfferUp's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the site, check it. 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.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's OfferUp Scraper turns marketplace URLs into listing data. You submit offerup.com search, explore or item-detail URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - cap the rows per query if you want to, and download the results as a clean CSV, Excel or JSON file.
Each row carries the listing as OfferUp's results show it: the title exactly as the seller typed it, the asking price as a bare number, the city and two-letter state code, a thumbnail from images.offerup.com and a permalink to the item. The JSON adds the listing's rank within its query, contiguous from one, so the original result ordering survives however you sort the file afterwards.
Resellers run the same search across several metros and compare asking prices with the city attached, because second-hand pricing is local and a national average hides that. Pricing teams export a category on a schedule to watch the second-hand floor their new units compete against. Researchers use explore URLs, which cover a category rather than a keyword, to map what is actually listed near a place.
Two properties of this dataset are worth knowing before you build on it. Three columns - condition, firm_price and miles - did not carry values in our testing, and flags carried the single value LOCAL_PICKUP; plan for them as empty rather than as filters. And there is no seller identity and no posting date anywhere in the output, because the results pages do not render them. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.