Pull reviews from Airbnb accommodations as rows - ratings, comments, dates and reviewer profiles. And because Airbnb only lets guests review a stay they actually completed, the review record is the closest thing to a public booking log any rental platform gives you.
The input is the listing, not the review. You name the accommodations you want covered and the job returns what guests wrote about them.
One row per review, tagged with the accommodation it belongs to - so a run across a whole comp set stays sortable by property.
Anyone can review a product on a marketplace. Only a guest who finished a stay can review an Airbnb - which makes review counts a demand signal, not just a sentiment one.
Review dates cluster around when people actually stayed. Across a market that gives you a seasonality curve nobody publishes.
A 4.8 average tells you almost nothing. The written comments are where the noisy street, the broken aircon and the brilliant host actually show up.
Coverage spans the review as Airbnb publishes it: ratings, comments, dates and reviewer profile detail - tagged with the accommodation it belongs to so multi-property runs stay sortable.
Comment text arrives verbatim, with no preprocessing, so anything you run over it downstream reads the guest's own wording. We don't print a fixed column list here - travel platforms restructure often, and the header row of an actual run is always the authoritative answer. The free tier is partly for that: pull one property, read the columns, then model it.
Eight arrive on every row. The host reply and the listing name are usually there; one column never is.
2026-06-12T08:02:55Z.There is no reviewer profile here. reviewer is a first name and nothing else - no surname, no avatar, no profile link - and reviewer_location, though present as a column, was empty on all 205 rows. That is a limitation for attribution and a helpful one for data minimisation. Also worth knowing: comment and host_response come back in whatever language they were written in, with language alongside, and host replies can carry escaped line-break markup that needs cleaning before display.
This is the analytical property that makes Airbnb review data unusually valuable - and it doesn't hold on most platforms.
On Amazon or Google Maps, a review requires nothing but an account. On Airbnb it requires a completed stay: the platform only opens the review window to guests who actually checked in and out. That single rule changes what the data means. A listing's review history is a public, dated record of bookings that really happened.
Which is why the standard way to estimate occupancy in short-term rental research is review velocity - reviews per month, adjusted for the share of guests who bother to leave one. It's an estimate, not a ledger, and the review rate varies by market and host. But it is the only demand signal the platform exposes at all, and it's the reason this dataset gets used for investment screening rather than just reputation work.
Pair it with the Airbnb Search Scraper and you have both halves: search gives you supply and asking price, reviews give you evidence of what actually got booked.
A few examples of how teams use Airbnb review data.
Pull reviews across a market and count them by month per listing. Properties with steady review velocity are booking; ones with a long gap are not. That separates a market's asking prices from what it actually achieves.
Read the comments across a neighbourhood's listings as one corpus. If everyone's guests mention the same street noise or the same check-in friction, that's either a risk to price in or a gap you can win on.
Export your own reviews next to the properties competing with you. Rating alone won't show why a similar unit outperforms - the written comments usually will, and they name things you can fix.
No subscription, no minimum, no per-property licence. Your first 500 reviews 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 a handful of properties and test a velocity model.
Roughly $2 per 1,000 reviews. 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 review collection across many markets. Tell us your numbers and we'll quote.
LIVESCRAPER10 at checkout.The two Airbnb tools answer opposite halves of the same question.
Turn an accommodation search into rows - titles, prices, availability, ratings, amenities and host detail. That's the supply side; this page is the demand side.
If you also run a hotel or guesthouse with a Google Business Profile, the same reputation work applies there - every review, every location.
Short answer: yes for the public review content - and that is all we ever collect.
Reviews are published to be read. Airbnb shows the guest's display name, the comment, the rating and the date to anyone viewing a listing, signed in or not. Collecting publicly visible feedback for market and reputation research 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 reviews carry guest display names and profile detail, which makes the output partly personal data: analysing a market or a property is a defensible purpose, building a picture of an individual traveller is not.
We touch nothing behind a login, collect no booking or payment 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.
reviewer_location was empty on every row we measured. Comment text is preserved verbatim with no preprocessing. For the exact columns in your file, take the header row of a small free-tier run.500 one-time free reviews on every new account - no expiry. After that it's $0.002 per review, pay-as-you-go - no card on file until you say so.
Livescraper's Airbnb Reviews Scraper turns accommodation listings into review data. You add the properties you want covered, set the parameters offered, and download ratings, comments, dates and reviewer profile detail as CSV, JSON or Excel - one row per review, tagged with the accommodation it belongs to.
What makes this dataset different from other review data is the eligibility rule behind it. On most marketplaces a review needs only an account. On Airbnb it needs a completed stay - the platform opens the review window to guests who actually checked in and out. That turns a listing's review history into a public, dated record of bookings that really happened.
It's why review velocity is the standard occupancy estimate in short-term rental research, and why this data gets used for investment screening rather than only reputation work. It remains a proxy: not every guest reviews, and the response rate varies by market and host. But it is the only demand signal Airbnb exposes, and counted by month it also yields a seasonality curve nobody publishes.
Investors count reviews per listing per month to separate a market's asking prices from what it achieves. Operators read a neighbourhood's comments as one corpus to find the complaint everyone's guests share. Hosts benchmark their own reviews against the properties competing with them, because a rating average won't explain an underperforming unit but the written comments usually do. Start free - your first 500 reviews cost nothing and need no credit card.