Booking Reviews Scraper — by hotel URL or destination

What does Booking Reviews Scraper do?

Booking Reviews Scraper extracts structured guest reviews from booking.com — star rating, positive and negative review text, owner replies, guest photos, room type and stay dates, one row per review. Point it at hotel URLs or a destination, cap reviews per hotel, and run incrementally so recurring runs return only new and changed reviews.

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Key features

• 🏨 Hotel URL or destination — scrape reviews two ways: paste booking.com hotel URLs (/hotel/{cc}/{slug}.html) straight from your browser, or give a destination (e.g. "Amsterdam") and the scraper resolves its top hotels and pulls their reviews. Results dedupe by review ID.
• 💬 Owner replies & guest photos — every review carries the full guest text and rating plus owner replies and guest photos when present — the reputation-monitoring fields most competitors drop.
• ♻️ Incremental mode — recurring runs emit only listings whose ratings, reviews, or metadata changed — track reputation movement over time without re-processing the universe. Saves 80–95% on monitoring runs.
• 📦 Compact mode — compact mode — core fields only, ideal when you're feeding an LLM or building a comparison sheet across many companies.
• 🧹 Empty-field stripping — drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards that already handle missing fields gracefully.
• 📌 Change classification — each record carries a changeType of NEW / UPDATED / UNCHANGED. Default emits NEW + UPDATED; opt into UNCHANGED with emitUnchanged.
• 📤 Export anywhere — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
• 🔌 MCP connectors — export your results into Notion via Apify's MCP connectors — a clean run-summary page, no glue code. Opt-in via the App connector field; deterministic field-mapping, no AI. Built on Apify's connector framework, so more destinations open up as their catalog grows.

What data can you extract from booking.com?

Each result includes Core review fields (reviewId, hotelId, hotelName, hotelUrl, hotelCountryCode, reviewScore, title, and positiveText, and more). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Input

Configure the actor through the input schema in Apify Console.

Key parameters:

• hotelUrls — Booking.com hotel page URLs (e.g. https://www.booking.com/hotel/nl/estherea.html). Each hotel's reviews are scraped. Use this OR Destination. (default: [])
• destination — City or area name (e.g. "Amsterdam"). Scrapes reviews for its top hotels. Use this OR Hotel URLs.
• maxHotels — In Destination mode: maximum number of hotels to scrape (0 = unlimited). Ignored in Hotel-URL mode. (default: 10)
• sorter — How to order reviews before scraping. (default: "MOST_RELEVANT")
• maxReviewsPerHotel — Maximum reviews to scrape per hotel (0 = all). (default: 0)
• reviewLanguage — Optional. Keep only reviews written in this language (ISO code, e.g. "en"). Leave empty for all languages.
• incrementalMode — Only emit reviews that are new or changed since the previous run for the same inputs. stateKey is optional — it is derived automatically from the hotels/destination, sort order, and language. (default: false)
• stateKey — Optional. Stable identifier for the tracked review universe. Leave empty to auto-generate from inputs.
• emitUnchanged — When incremental, also emit reviews already seen and unchanged. Off by default. (default: false)
• compact — Core fields only (for AI-agent/MCP workflows). (default: false)
• excludeEmptyFields — Drop null, empty-string, and empty-array fields from each record. Smaller payloads for AI agents and dashboards. (default: false)
• appConnector — Optional. Pick a connected app under Settings → API & Integrations to receive your results. Best-effort across MCP connectors as Apify expands its catalog.

Input examples

Basic search — Targeted run scoped to a specific hotelUrls.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "hotelUrls": [
    "https://www.booking.com/hotel/nl/estherea.html"
  ]
}

Incremental tracking — Only emit reviews that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "hotelUrls": [
    "https://www.booking.com/hotel/nl/estherea.html"
  ],
  "incrementalMode": true,
  "stateKey": "booking-tracker"
}

Compact output for AI agents — Return only core fields for AI-agent and MCP workflows.

→ Small payload with the most important fields — ideal for piping into LLMs without token overhead.

{
  "hotelUrls": [
    "https://www.booking.com/hotel/nl/estherea.html"
  ],
  "compact": true
}

Output

Each run produces a dataset of structured review records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example review record

{
  "reviewId": "4f4cb5f2a4b8f650",
  "hotelId": 10427,
  "hotelName": "The Highlander Amsterdam",
  "hotelUrl": "https://www.booking.com/hotel/nl/citadel.html",
  "hotelCountryCode": "nl",
  "reviewScore": 7,
  "positiveText": "I have many positive things to say about the hotel, and overall we enjoyed our stay. The staff were welcoming, friendly, and always willing to help. We also really appreciated the complimentary snacks...",
  "negativeText": "There were, however, a few issues that affected our comfort:\n\n* The shower soap was very watery.\n* One of the towels was very old and torn.\n* The biggest issue was the stairs, which made it very diffi...",
  "reviewLanguage": "en",
  "reviewedDate": "2026-07-18T06:49:44.000Z",
  "guestName": "Mashael",
  "guestCountryCode": "sa",
  "guestCountryName": "Saudi Arabia",
  "guestType": "Solo traveler",
  "guestReviewCount": 5,
  "roomType": "Standard Double/Queen Room",
  "checkinDate": "2026-07-09",
  "checkoutDate": "2026-07-13",
  "numNights": 4,
  "stayStatus": "stayed",
  "helpfulVotesCount": 0,
  "photoUrls": [
    "https://q-xx.bstatic.com/xdata/images/xphoto/max1280x900/709020688.jpg?k=339ff12b30cbd3551e7c8c28622bf5747b844a3d822b57296ad165da27485f10&o="
  ],
  "isAnonymous": false,
  "source": "booking.com",
  "scrapedAt": "2026-07-18T21:02:21.410Z"
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED. Reviews are rarely removed from Booking.com (deletion is rare moderation, not a routine lifecycle event), and maxReviewsPerHotel / reviewLanguage mean a given run only fetches a bounded slice of a hotel's reviews — so a review missing from one run's slice can't be told apart from one that was merely outside this run's cap or language filter. This actor tracks new and changed reviews but never claims one is gone for good. Default output covers NEW / UPDATED; set emitUnchanged: true to opt into UNCHANGED.

How to scrape booking.com

1. Go to Booking Reviews Scraper in Apify Console.
2. Configure the input.
3. Set maxResults to control how many results you need.
4. Click Start and wait for the run to finish.
5. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract review data from booking.com for market research and competitive analysis.
• Monitor new and changed reviews on scheduled runs without processing the full dataset every time.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.
• Benchmark reputation and guest satisfaction using rating and review fields.

How much does it cost to scrape booking.com?

Booking Reviews Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.001 per review record

Example costs:

• 10 results: $0.015
• 25 results: $0.03
• 100 results: $0.11
• 200 results: $0.21
• 500 results: $0.51

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of reviews that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.26	$0.02	$0.24 (93%)	$0.53
15% — moderate broad query	$0.26	$0.04	$0.21 (83%)	$1.27
30% — high-volume aggregator	$0.26	$0.08	$0.17 (69%)	$2.40

Full re-scrape monthly cost at daily polling: $7.65. First month with incremental costs $0.76 / $1.49 / $2.57 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

FAQ

How many results can I get from booking.com?

The number of results depends on the search query and available reviews on booking.com. Use the maxResults parameter to control how many results are returned per run.

Does Booking Reviews Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed reviews on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Booking Reviews Scraper with other apps?

Yes. Booking Reviews Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Booking Reviews Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Booking Reviews Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape booking.com?

This actor extracts publicly available data from booking.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.