Turn an Airbnb search into structured rows - property titles and descriptions, prices, availability, ratings and reviews, amenities and host detail. Short-term rental supply is the hardest market data to hold onto, because it changes underneath you. This gets you a clean snapshot you can actually analyse.
You describe the search you'd otherwise run by hand, and the job returns the result set as rows instead of an endless scroll you can't export.
The tool shows the search inputs and parameters it accepts when you open it, and the estimator prices the run before it starts.
Scrolling a search and copying what you can reach gives you the first page. A run returns the result set as rows, so your analysis covers the market rather than the top of it.
Listings appear, get booked and vanish. Because a run is a point in time you can file and re-run, you get a series to compare instead of a impression that's already stale.
Price alone doesn't explain a market. Ratings, review counts, amenities and host detail come back with the listing, so you can see what commands a premium.
Coverage spans the detail Airbnb shows in and behind a search result: property titles and descriptions, prices, availability, ratings and reviews, amenities, and host information.
We don't print a fixed column list on this page. Travel marketplaces restructure their pages frequently, and a list that drifts out of date is worse than no list - the header row of an actual run is always the authoritative answer. The free tier is there partly for this: pull one search, read the columns, then decide how to model it.
This is the thing that catches people out when they move from product data to travel data.
An Amazon product has a price. An Airbnb listing has a price for a stay - and a different one for a different week, a different length, a different party size. Nightly rates move with season, demand, day of week and how far ahead you're looking. There is no single number to record.
Which means a search result set is only true for the search that produced it. Two exports of the same neighbourhood taken for different date windows aren't inconsistent; they're answering different questions. Note the search you ran alongside the file, and keep the windows fixed when you compare over time - otherwise you'll read your own parameter change as a market movement.
The same applies to supply. A listing missing from this week's run may be booked rather than delisted, which matters enormously if you're trying to measure occupancy.
A few examples of how teams use Airbnb search data.
If you manage properties, the listings competing with you are the ones a guest sees in the same search. Pull that result set, filter to genuinely comparable units, and you have a rate benchmark grounded in what's actually bookable.
Supply volume, price bands, ratings and amenity mix for a neighbourhood tell you whether a yield assumption is plausible. Far cheaper than finding out after completion that the area is saturated at a lower rate than your model assumed.
Re-run the same search on a fixed cadence with fixed date windows and you build a supply series - new entrants, exits, and how the price distribution shifts by season. That's a dataset no dashboard will hand you.
No subscription, no minimum, no per-market licence. Your first 500 rows are on us - after that, pay-as-you-go.
Every new account, one-time. No credit card required. All scrapers unlocked, full feature set - enough to pull one market and see how you want to model it.
Roughly $2 per 1,000 listings. The pre-flight estimator shows the count and credit cost before a run starts - no surprises, no compute units to translate.
Volume pricing, SLAs, dedicated workers and bespoke onboarding for continuous tracking across many markets. Tell us your numbers and we'll quote.
LIVESCRAPER10 at checkout.Accommodation supply is easier to read next to the neighbourhood around it.
Pull the hotels, restaurants and attractions around a market. Amenity density is a large part of why one neighbourhood commands a higher nightly rate than the next one over.
The demand side of the same market. Only guests who completed a stay can review, so review velocity is evidence of what actually got booked - not just what was listed.
Short answer: yes for the public listing data - and that is all we ever collect.
Search results are public. Airbnb shows listing titles, prices for the dates you asked about, ratings, amenities and host display names to anyone browsing, signed in or not. Collecting publicly visible facts about a market for research and pricing analysis is long-established practice, and as long as the data is publicly available and the process doesn't disrupt the site there are no federal laws prohibiting it.
Two caveats we'd rather state than bury. Airbnb's terms of service restrict automated access, so this is a terms question as much as a legal one - if you host on the platform or have a partner agreement, check it. And listings carry host display names and reviewer names, which makes the output partly personal data: analysing a market is a defensible purpose, building a profile of an individual host is not.
We touch nothing behind a login, collect no guest data, 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's $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Airbnb Search Scraper turns an accommodation search into structured rows. You define the search you'd otherwise run by hand, pick an output format, and download the result set as CSV, JSON or Excel - no parser to maintain and no scrolling through results you can't export.
Coverage spans the detail behind a search result: property titles and descriptions, prices, availability, ratings and reviews, amenities, and host information. Because travel marketplaces restructure their pages frequently, the header row of an actual run is the authoritative column reference - which is part of why the free tier exists.
Revenue managers use it to price against the listings a guest actually sees in the same search, rather than a comp set assembled by hand. Investors size up supply volume, price bands, ratings and amenity mix before committing to a market. Researchers re-run a fixed search on a cadence to build a supply series showing new entrants, exits and seasonal shifts in the price distribution.
One thing worth planning for: short-term rental data behaves differently from product data. A listing has no single price - only a price for a given stay window - and a listing missing from this week's run may be booked rather than delisted. Record the search alongside the results and hold your date windows constant when comparing over time. Start free: your first 500 rows cost nothing and need no credit card.