Booking Scraper

Booking Scraper

The Booking Scraper is a production-ready Apify actor that extracts structured hotel and apartment data from Booking.com at scale — including listings, prices, ratings, amenities, images, and more. It solves the hassle of manual copy-paste and inconsistent formats by turning public hotel pages and search results into clean JSON you can export via API or CSV. Built for marketers, developers, data analysts, and researchers, this booking.com scraper supports market research, hotel rate monitoring, and booking.com price tracking with robust proxy fallback and reliable, repeatable runs.

What data / output can you get?

Below are example fields this booking.com data extraction actor pushes to the dataset. Each result is one hotel:

Data type	Description	Example value
order	1-based index of the hotel in the run	1
url	Canonical Booking.com URL for the property	https://www.booking.com/hotel/us/trump-international-new-york.en-gb.html
source_url	The exact URL requested by the actor	https://www.booking.com/hotel/us/trump-international-new-york.en-gb.html
name	Hotel/property name	Trump International New York
type	Property type label	hotel
price	Lowest detected price for stay (if available)	3067.11
currency	Currency code for the detected price	USD
rating	Guest score	8.9
ratingLabel	Score label from Booking.com or inferred	Very good
reviews	Number of reviews	842
checkIn / checkOut	House rules time ranges (if available)	From 16:00 to 00:00 / From 00:00 to 12:00
checkInDate / checkOutDate	Dates used for the search (if provided)	2026-03-25 / 2026-03-31
location	Latitude/longitude coordinates	{"lat": 40.769, "lng": -73.981}
address	Structured address fields	{"full": "One Central Park West", "country": "US", "city": "New York"}
rooms	Room cards with ids, names, occupancy, and price (if available)	[{"id":"123","name":"Deluxe Room","price":450.0}]
images	Main image URLs	["https://cf.bstatic.com/..."]
roomImages	Per-room image groups	[{"roomId":"123","images":["https://cf.bstatic.com/..."]}]
facilities	Facility list with ids	[{"name":"Free Wifi","id":"42"}]
highlights	Highlighted amenities/features	["City view","Breakfast included"]
finePrint	Fine print text (if present)	City tax not included...
categoryReviews	Category-level review scores	[{"name":"Cleanliness","score":9.1}]
hotelChain	Chain name (if detected)	Marriott
licenseInfo	License number (if available)	12345/2024
hostInfo	Host or contact person (if applicable)	John Doe
traderInfo	Business/contact details including address	{"isBusiness":true,"email":"info@hotel.com",...}
breadcrumbs	Hierarchical navigation items	[{"name":"New York","url":"..."}]
hotelId	Internal Booking.com hotel id	123456
timeOfScrapeISO	UTC timestamp of scrape	2026-03-15T12:00:00.000Z
startUrlOrQuery	Search query or start URL for this item	New York

Bonus: You also get an “image” field (first image URL), “policies” (subset of highlights), and a “stars” field when present.

Export your dataset to CSV or JSON, or consume it directly via the Apify API for pipelines such as booking.com web scraping dashboards, booking data scraper API integrations, or travel site scraping tools.

Key features

• 🔎 Accurate hotel detail extraction Uses multiple sources on the page — Apollo state, schema.org JSON-LD, and HTML fallbacks — to populate fields consistently for the booking.com hotel listings scraper.

• 🌍 Search results to hotel pages Start from a destination query or Booking.com search URL to gather hotel links, then scrape each detail page for complete booking.com data extraction.

• 🛡️ Smart proxy fallback No proxy by default; automatic cascade to DATACENTER then RESIDENTIAL with up to 3 retries on residential when blocks occur. Clear [PROXY] logs throughout — ideal for OTA scraping for hotels at scale.

• 💾 Live dataset streaming Each hotel is pushed as soon as it’s scraped, ensuring partial results are saved even if a run is interrupted — perfect for booking.com price tracking and hotel rate monitoring scraper workflows.

• ⚙️ Configurable search & filters Control currency, language, property type, star rating filter, price range, date flexibility, and more directly from input parameters.

• 🔌 Developer-friendly API Built on the Apify platform with clean, structured outputs for downstream workflows, integrations, and “booking.com scraper Python” use via the Apify API or SDK.

• 🚀 Production-ready reliability Headless Chromium with tuned timeouts, retries, and optional browser-like HTTP fallback on search pages helps reduce blocks for steady booking.com web scraping.

How to use Booking Scraper - step by step

1. Create or log in to your Apify account at https://console.apify.com.
2. Open Actors and find “BookingScraper”, then click Try or Run.
3. Choose how to start:
   • To discover hotels from a destination, set “search” (e.g., New York).
   • Or paste Booking.com URLs into “startUrls” (hotel detail pages or search results pages).
4. Configure key settings:
   • “maxItems” to limit results per destination or per start URL (default 10, up to 100).
   • Dates via “checkIn” and “checkOut” as YYYY-MM-DD or relative (e.g., 2 weeks). “flexWindow” can add ± flexibility.
   • Filters like “propertyType”, “starsCountFilter”, “minMaxPrice”, “currency”, and “language”.
5. Set proxy behavior (optional):
   • Leave “proxyConfiguration” with useApifyProxy=false to start without proxy. If blocked, the actor automatically falls back to datacenter then residential proxy.
6. Start the run:
   • Click Start. Watch the console logs for progress and [PROXY] messages if fallback is triggered.
7. Review and export results:
   • Open the Dataset to view items as a table or JSON. Export to JSON or CSV, or query via the Apify API.

Pro tip: You can enter relative dates like “2 weeks” for check-in/out. The actor resolves them to absolute dates automatically, making recurring booking.com data extraction easier to schedule.

Use cases

Use case name	Description
Market research for travel	Analyze cities or regions by scraping Booking.com public data — names, prices, ratings, facilities — to quantify supply and demand.
Competitor price benchmarking	Monitor competitor rates and rating trends using a booking.com competitor price scraper to inform pricing strategy.
Hotel rate monitoring	Automate hotel rate monitoring scraper runs with date filters to track price movements by season or event windows.
Travel app inventory	Feed your travel site scraping tool with structured hotel details and images to power search and discovery.
Data enrichment for analytics	Blend booking.com data extraction with internal datasets for dashboards on occupancy proxies, review scores, and property types.
Academic & policy studies	Collect standardized samples for tourism research (e.g., star ratings, facilities, pricing bands) across geographies.
Booking.com API alternative	Use this booking data scraper API approach to avoid official API constraints when working with public web data.

Why choose Booking Scraper?

This Booking Scraper prioritizes precision, resilience, and clean outputs for professional data workflows.

• ✅ Accurate multi-source parsing for robust results (Apollo state, schema.org, HTML).
• ⚡ Scalable scraping from destination queries or bulk start URLs.
• 🔁 Resilient proxy strategy with automatic fallbacks and retries when blocked.
• 🔗 API-first outputs for pipelines and booking.com scraper Python integrations.
• 🧭 Clear logs and progress messages for transparent monitoring.
• 🔒 No login or cookies required — extracts only public Booking.com pages.
• 📦 Export-ready dataset structure for CSV/JSON/API consumption.

Compared to unstable browser extensions, this production-grade booking.com scraper delivers repeatable, structured results with infrastructure designed for scale and reliability.

Is it legal / ethical to use Booking Scraper?

Yes — when used responsibly. This actor scrapes publicly available information on Booking.com hotel pages and does not access private or authenticated content. You are responsible for ensuring compliant use in your jurisdiction.

Guidelines:

• Only collect public data and avoid personal or private information.
• Understand and respect Booking.com’s terms and applicable laws (e.g., GDPR, CCPA).
• Use reasonable limits and avoid abuse that could harm service availability.
• Consult your legal team for edge cases or commercial redistribution.

Input parameters & output format

Example JSON input

{
  "search": "New York",
  "startUrls": [
    { "url": "https://www.booking.com/searchresults.en-gb.html?ss=New%20York" }
  ],
  "maxItems": 10,
  "scrapeAdditionalHotelData": true,
  "propertyType": "none",
  "sortBy": "distance_from_search",
  "minimumRating": "",
  "starsCountFilter": "any",
  "currency": "USD",
  "language": "en-gb",
  "checkIn": "2 weeks",
  "checkOut": "3 weeks",
  "flexWindow": "0",
  "rooms": 1,
  "adults": 2,
  "children": 0,
  "minMaxPrice": "0-999999",
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Input field reference

• search (string, default: "New York", required: no) — City, region, or landmark to search for accommodation.
• startUrls (array, default: [], required: no) — Booking.com hotel detail or search results URLs. Accepts objects with “url” or strings.
• maxItems (integer, default: 10, min: 1, max: 100, required: no) — Max number of hotels to scrape per destination or per start URL.
• scrapeAdditionalHotelData (boolean, default: true, required: no) — Extract additional hotel data, including rooms, facilities, trader info, and images.
• propertyType (string, default: "none", required: no) — Filter by accommodation type. Options: none, hotels, apartments, hostels, guest houses, homestays, bed and breakfasts, holiday homes, boats, villas, motels, resorts, holiday parks, campsites, luxury tents.
• sortBy (string, default: "distance_from_search", required: no) — How to sort search results. Options: distance_from_search, price, review_score_and_price, review_score, star_rating.
• minimumRating (string, default: "", required: no) — Minimum guest rating (e.g., 7, 8, 9). Leave empty for no filter.
• starsCountFilter (string, default: "any", required: no) — Star rating filter. Options: any, 3, 4, 5, 1, 2.
• currency (string, default: "USD", required: no) — Preferred currency for displayed prices.
• language (string, default: "en-gb", required: no) — Interface language for the search.
• checkIn (string, default: "", required: no) — Absolute (YYYY-MM-DD) or relative date (e.g., 2 weeks). Leave empty for flexible search.
• checkOut (string, default: "", required: no) — Absolute (YYYY-MM-DD) or relative date (e.g., 1 week). Leave empty for flexible search.
• flexWindow (string, default: "0", required: no) — Date flexibility. Options: 0, 1, 2, 3, 7.
• rooms (integer, default: 1, required: no) — Number of rooms for the stay.
• adults (integer, default: 2, required: no) — Number of adult guests.
• children (integer, default: 0, required: no) — Number of children.
• minMaxPrice (string, default: "0-999999", required: no) — Min-max price filter, e.g., 50-200 or 100+ for minimum only.
• proxyConfiguration (object, default: {"useApifyProxy": false}, required: no) — By default no proxy is used. On blocks, the actor falls back to datacenter then residential proxy.

Example JSON output

{
  "order": 1,
  "url": "https://www.booking.com/hotel/us/trump-international-new-york.en-gb.html",
  "startUrlOrQuery": "New York",
  "name": "Trump International New York",
  "type": "hotel",
  "description": "Located at the juncture of Columbus Circle and Broadway...",
  "stars": 5,
  "price": 3067.11,
  "currency": "USD",
  "rating": 8.9,
  "ratingLabel": "Very good",
  "reviews": 842,
  "breakfast": "Available",
  "checkIn": "From 16:00 to 00:00",
  "checkOut": "From 00:00 to 12:00",
  "checkInDate": "2026-03-25",
  "checkOutDate": "2026-03-31",
  "location": { "lat": 40.769, "lng": -73.981 },
  "address": {
    "full": "One Central Park West",
    "country": "US",
    "city": "New York"
  },
  "image": "https://cf.bstatic.com/images/hotel/max1024x768/123/123.jpg",
  "rooms": [
    { "id": "123", "name": "Deluxe Room", "description": "Spacious city view", "size": null, "occupancy": 2, "price": 450.0 }
  ],
  "highlights": ["City view", "Breakfast included", "Free Wifi"],
  "finePrint": "City tax not included.",
  "policies": ["City view", "Breakfast included", "Free Wifi"],
  "images": ["https://cf.bstatic.com/images/hotel/max1024x768/123/123.jpg"],
  "roomImages": [
    { "roomId": "123", "images": ["https://cf.bstatic.com/images/hotel/max1024x768/123/room1.jpg"] }
  ],
  "categoryReviews": [
    { "name": "Cleanliness", "score": 9.1 }
  ],
  "hotelChain": null,
  "licenseInfo": null,
  "hostInfo": null,
  "traderInfo": {
    "isBusiness": true,
    "email": "info@hotel.com",
    "phone": "+1 212-555-0100",
    "companyName": "Hotel LLC",
    "firstName": "John",
    "middleName": null,
    "lastName": "Doe",
    "registrationNumber": "AB-12345",
    "tradeRegisterName": "NYC",
    "address": {
      "street": "One Central Park West",
      "street2": null,
      "postalCode": "10023",
      "city": "New York",
      "countryCode": "US",
      "state": "NY"
    }
  },
  "breadcrumbs": [
    { "name": "New York", "url": "https://www.booking.com/city/us/new-york.html" }
  ],
  "hotelId": "123456",
  "facilities": [
    { "name": "Free Wifi", "id": "42" }
  ],
  "timeOfScrapeISO": "2026-03-15T12:00:00.000Z",
  "source_url": "https://www.booking.com/hotel/us/trump-international-new-york.en-gb.html"
}

Notes:

• Some fields may be null or empty when not present on the page (e.g., hotelChain, licenseInfo, hostInfo, price without availability).
• The actor sets “timeOfScrapeISO” at runtime and adds the original “source_url” for traceability.

FAQ

Do I need a proxy to use this booking.com scraper?

No. By default, the actor starts with no proxy. If Booking.com blocks requests, it automatically falls back to a datacenter proxy and then a residential proxy with up to three retries, logging each [PROXY] event.

Can I start from a destination instead of URLs?

Yes. Set the “search” field (for example, “Paris” or “New York”). The actor will build a Booking.com search URL, collect hotel links, and then scrape each hotel page up to “maxItems”.

How do I track prices by date range?

Provide “checkIn” and “checkOut” as absolute dates (YYYY-MM-DD) or relative strings like “2 weeks”. The actor resolves relative dates and attempts to extract room-level prices for the selected stay — ideal for booking.com price tracking and competitor monitoring.

Does it scrape availability and room details?

The actor extracts rooms and detects prices when available on the public hotel page for the given dates. If availability is limited or the page doesn’t expose prices, room price fields may be null, but room names and IDs are still included when present.

Can I integrate this with Python or use an API?

Yes. All outputs are stored in an Apify dataset, which you can access via the Apify API for pipelines or use from Python scripts — a practical booking data scraper API alternative to build automation workflows.

How many results can I get per run?

Use “maxItems” to control results per destination or per start URL. The default is 10 and the maximum is 100 per input unit, making it suitable for batch runs of a booking.com hotel listings scraper.

Which languages and currencies are supported?

Set “language” (e.g., en-gb, de, fr, es) and “currency” (e.g., USD, EUR, GBP, JPY) in the input. The actor will request pages accordingly to improve consistency for booking.com data extraction.

Is it legal to scrape Booking.com?

Yes — when done responsibly with public pages only. This tool accesses publicly available content. You’re responsible for complying with Booking.com’s terms and regulations like GDPR/CCPA in your use case.

Closing thoughts

The Booking Scraper is built to turn public Booking.com pages into structured, analysis-ready data. With accurate extraction, resilient proxy fallback, and export-ready JSON/CSV via API, it empowers marketers, developers, analysts, and researchers to automate booking.com web scraping for price monitoring, market research, and travel apps. Connect the dataset to your pipelines (API/SDK) or “booking.com scraper Python” workflows and start extracting smarter travel insights at scale.