Pricing

$4.00 / 1,000 results

Airbnb Listing Reviews Scraper

Guest reviews for one Airbnb listing — reviewer, star rating, date and the full review text, with a stable review id for deduping. No login. Pay per result ($4/1k).

Pricing

$4.00 / 1,000 results

Rating

0.0

(0)

Developer

Danny

Actor stats

Bookmarked

Total users

Monthly active users

4 days ago

Last modified

What you get

Every review returns:

Field	Description
`review_id`	Stable Airbnb review id (dedup key)
`reviewer`	Reviewer's first name
`rating`	Star rating the guest gave, 1–5
`date`	When the review was written (e.g. "June 2026", "1 week ago")
`text`	The full review text

Input

Field	Required	Description
`url`	✓	Airbnb listing URL (`airbnb.com/rooms/<id>`) or a bare room id
`max_reviews`		How many reviews to return (default 6, up to 12)

{ "url": "https://www.airbnb.com/rooms/22712551", "max_reviews": 6 }

Example output

{
  "review_id": "1726754688535307608",
  "reviewer": "Manpreet",
  "rating": 5,
  "date": "1 week ago",
  "text": "We had a wonderful stay at Priscilla's apartment. Priscilla was very helpful and shared a lot of local knowledge which was great to explore local areas and restaurants. The listing matched the description and would highly recommend."
}

Notes & limits

Airbnb renders the most recent ~6 reviews on the listing page; max_reviews caps how many are returned (up to 12). This surface targets recent reviews, not a listing's entire review history.
Reviews come back most-recent-first and are deduped by review_id.
The URL must be a specific listing page (airbnb.com/rooms/<id>). A listing with no reviews simply returns nothing.
For the listing itself (amenities, host, description, photos, ratings) use the Airbnb Listing Detail Scraper; to find listings, use the Airbnb Search Scraper.

Use it from Python

from apify_client import ApifyClient
client = ApifyClient("<APIFY_TOKEN>")
run = client.actor("<THIS_ACTOR_ID>").call(
    run_input={"url": "https://www.airbnb.com/rooms/22712551", "max_reviews": 6})
for r in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(r["rating"], r["reviewer"], r["text"][:80])

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