Airbnb Reviews Scraper

Extract public guest reviews from Airbnb listing URLs. Add one or more room URLs and get structured review text, dates, reviewer metadata, listing ratings, review counts, languages, and host responses when Airbnb makes them public.

Quick start

Paste public Airbnb room URLs, choose a small per-listing cap, then run the Actor and inspect the dataset.

{
  "startUrls": [
    {
      "url": "https://www.airbnb.com/rooms/20669368"
    }
  ],
  "maxReviewsPerListing": 10,
  "sort": "recent"
}

Output example

Illustrative record based on the documented field shape; values and availability can change.

{
  "listingUrl": "https://www.airbnb.com/rooms/20669368",
  "listingId": "20669368",
  "listingTitle": "Little Country Houses - Finley's Fort + hot tub",
  "overallRating": 4.9,
  "reviewCount": 308,
  "reviewId": "1701461540622642748",
  "reviewerName": "Gabriella",
  "reviewerProfileUrl": "https://www.airbnb.com/users/show/755284424",
  "reviewerLocation": null,
  "reviewDate": "2026-06-05T13:40:21Z",
  "rating": null,
  "language": "en",
  "text": "Our stay here was Superb...",
  "translatedText": null,
  "responseText": null,
  "responseDate": null,
  "scrapedAt": "2026-06-26T00:00:00.000Z"
}

Input settings

What does Airbnb Reviews Scraper do?

Airbnb Reviews Scraper turns public Airbnb room pages into clean review datasets. It is built for teams that need recurring guest feedback, reputation monitoring, competitor research, or market intelligence without copying reviews by hand.

The actor accepts Airbnb room URLs such as https://www.airbnb.com/rooms/20669368 and saves one dataset item per review.

Who is it for?

• Property managers tracking guest sentiment across their own listings.
• Short-term rental analysts comparing competing stays in a destination.
• Reputation teams monitoring recent public guest feedback.
• Hospitality researchers building review datasets for market studies.
• Automation teams feeding reviews into BI, CRM, or AI workflows.

Why use this actor?

• Saves structured review rows instead of screenshots or copied text.
• Includes listing context with every review for easy exports.
• Supports multiple Airbnb URLs in one run.
• Lets you cap reviews per listing to control run size and cost.
• Filters saved reviews by date, rating, or a keyword without changing the original review data.
• Saves a run summary and a resumable pending-listing checkpoint when time runs short.
• Works through Apify datasets, API, webhooks, integrations, and MCP.

Filtering and batch limits

The current Actor accepts up to 100 unique listing URLs per run. Filters apply within the fetched maxReviewsPerListing batch; it does not keep searching until it saves that many matching reviews. With rating filters, reviews whose rating is null are excluded. With date filters, reviews whose date is missing or unreadable are excluded.

An empty filtered run can report that no reviews were found and still incur its start charge. PENDING_LISTINGS can restart a partly processed listing. Reviews fetched again in a separate run can be charged again. Compare listingId and reviewId downstream to avoid double-counting; use date filters or omit completed listing URLs where appropriate to limit repeat work.

Tips for best results

• Use direct /rooms/ URLs.
• Start with 5-10 reviews per listing for testing.
• Remove duplicate URLs before large runs.
• Split very large URL lists into smaller batches.
• Review the dataset sample before connecting automation.

Common workflows

Reputation monitoring

Run the actor weekly on your managed listings and send new rows to your dashboard or spreadsheet.

Market research

Collect reviews from competing listings in the same destination and compare guest language, sentiment, and frequency.

Guest experience analysis

Export text to your NLP or LLM pipeline to classify recurring topics such as cleanliness, check-in, location, host communication, and amenities.

Integrations

Apify datasets connect to:

• Google Sheets
• Make
• Zapier
• Webhooks
• BigQuery exports
• S3-compatible storage
• Custom API clients

Data quality notes

Airbnb may not expose every field for every review. For example, per-review star ratings, reviewer location, translated text, or host response can be missing. Missing fields are returned as null rather than invented.

Limitations

• Supports public Airbnb room/listing pages only.
• Does not access private account data.
• Does not bypass login-only, CAPTCHA, or blocked pages.
• Availability, prices, and booking calendars are outside this actor's scope.

Privacy and data handling

This Actor only requests the permissions needed to run the input you provide. It uses your input (such as URLs, search terms, identifiers, filters, and limits) only to fetch the requested public data from the relevant source site or API for this Actor, then writes results to your Apify dataset/key-value store.

Data may pass through Apify platform services and Apify Proxy during the run, and requests are sent only to the target site or public data provider required for this Actor's results. FetchCat does not send your inputs or outputs to advertising networks, data brokers, or model-training services, and does not retain run data outside Apify storage after the run except when you explicitly share run details for transient support debugging.

You are responsible for using this Actor lawfully, respecting the target site's terms, and avoiding unnecessary personal or sensitive data in inputs. Review the output before storing, sharing, or combining it with other data.

API usage

Use your Apify API token through the APIFY_TOKEN environment variable. Node.js and Python examples wait for the run and read its first dataset page; paginate the dataset for larger exports.

Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/airbnb-reviews-scraper').call({
  "startUrls": [
    {
      "url": "https://www.airbnb.com/rooms/20669368"
    }
  ],
  "maxReviewsPerListing": 10,
  "sort": "recent"
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import json
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run_input = json.loads('''{
  "startUrls": [
    {
      "url": "https://www.airbnb.com/rooms/20669368"
    }
  ],
  "maxReviewsPerListing": 10,
  "sort": "recent"
}''')
run = client.actor("fetch_cat/airbnb-reviews-scraper").call(run_input=run_input)
print(client.dataset(run["defaultDatasetId"]).list_items().items)

cURL

Save the quickstart JSON as input.json. This request starts a run asynchronously; use its returned run ID to check completion and its defaultDatasetId to retrieve results.

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~airbnb-reviews-scraper/runs" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  --data-binary @input.json

MCP and AI agents

Use the official Apify MCP server, not a separate custom server. The focused URL below selects this Actor. Authenticate with Apify when your client prompts you; configuration syntax and OAuth support depend on the client.

Claude Code

claude mcp add --transport http apify "https://mcp.apify.com?tools=fetch_cat/airbnb-reviews-scraper"

HTTP-capable MCP client configuration

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/airbnb-reviews-scraper"
    }
  }
}

Example prompt: "Export up to 10 recent reviews for each of these Airbnb listing URLs. Separate guest reviews from host replies and do not substitute listing ratings for missing review ratings."

Use the same input keys as the input table. Review the returned source URLs and any error or availability fields before using results in an automated summary.

Support

If a run fails or output looks wrong, open an issue from the Actor page. Include the Apify run ID or run URL, non-sensitive input JSON, expected output, actual output, and one reproducible public URL (or the exact search input). Do not share tokens, cookies, passwords, or private data.