This Booking Scraper extracts hotel, room, and pricing data at scale from Booking.com. It expands the platform’s native limitations, giving users complete access to rich accommodation metadata, availability, features, and structured pricing details. Ideal for analysts, travel platforms, and automation workflows.
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The Booking Scraper automates data extraction from Booking.com, collecting detailed information about hotels, apartments, and other accommodations. It solves the problem of limited on-site filtering and export restrictions, enabling large-scale, structured, and consistent data retrieval. It is designed for developers, data analysts, travel-tech founders, and anyone needing clean hotel listing data.
- Extracts up to 1,000+ listings per query with optional extended mode.
- Captures detailed room-level pricing and availability when check-in/check-out dates are provided.
- Processes destination-based searches or direct listing URLs.
- Supports filtering by rating, property type, currency, language, and more.
- Organizes results into structured formats for seamless integration.
| Feature | Description |
|---|---|
| Destination & URL-based scraping | Supports both direct URLs and geographic searches for flexible workflows. |
| Room-level data extraction | Retrieves room types, features, pricing, and availability when dates are provided. |
| Hotel metadata extraction | Captures names, descriptions, images, stars, reviews, coordinates, and more. |
| Filter customization | Accepts rating, property type, price range, language, and currency filters. |
| Large-scale data support | Bypasses Booking.com’s typical limitation of 1,000 results. |
| Multi-format outputs | Export data as JSON, CSV, Excel, HTML, or XML. |
| Field Name | Field Description |
|---|---|
| name | The hotel or accommodation name. |
| url | Direct link to the property page. |
| type | Accommodation type (hotel, apartment, villa, etc.). |
| description | Full description text of the listing. |
| stars | Star rating of the property. |
| rating | User rating score. |
| reviews | Number of guest reviews. |
| price | Price for the selected date range. |
| currency | Currency used for pricing. |
| address | Structured address components. |
| location | Latitude and longitude coordinates. |
| rooms | Detailed array of room types, pricing, availability, and features. |
| image | Primary image URL of the listing. |
[
{
"order": 24,
"url": "https://www.booking.com/hotel/us/three-bedroom-rental-new-york.html?selected_currency=USD&changed_currency=1&lang=en-us&group_adults=2&no_rooms=1&checkin=2023-09-01&checkout=2023-09-02&nflt=price%3DUSD-0-999999-1",
"name": "Three Bedroom Rental",
"type": "apartment",
"description": "Located a 13-minute walk from Central Park...",
"stars": null,
"price": 1.118,
"currency": "US$",
"rating": 10,
"ratingLabel": "Exceptional",
"reviews": 1,
"checkIn": "From 4:00 PM",
"checkOut": "Until 11:00 AM",
"location": { "lat": "40.7744900", "lng": "-73.9829649" },
"address": {
"full": "207 Columbus Avenue, Upper West Side, New York, NY 10023, United States of America",
"postalCode": "NY 10023",
"street": "207 Columbus Avenue",
"country": "USA",
"region": "New York State"
},
"image": "https://cf.bstatic.com/xdata/images/hotel/max1024x768/415624316.jpg",
"rooms": [
{
"available": true,
"roomType": "Three-Bedroom Apartment",
"bedType": "Bedroom 1: 1 queen bed ...",
"persons": 2,
"price": 1.118,
"currency": "US$",
"features": ["Private kitchen","Air conditioning","Flat-screen TV"]
}
]
}
]
Booking Scraper/
├── src/
│ ├── main.js
│ ├── scraper/
│ │ ├── booking_fetcher.js
│ │ ├── room_parser.js
│ │ └── listing_parser.js
│ ├── utils/
│ │ ├── request.js
│ │ └── formatting.js
│ └── config/
│ └── settings.example.json
├── data/
│ ├── sample_input.json
│ └── sample_output.json
├── package.json
├── requirements.txt
└── README.md
- Travel analysts use it to collect multi-city hotel datasets so they can benchmark prices and trends.
- Booking comparison platforms use it to populate listings automatically, enabling real-time accommodation insights.
- Market researchers use it to monitor rating changes, review counts, and hospitality trends across regions.
- Automation engineers integrate it into workflows to trigger updates, alerts, or synchronization with databases.
- Property investors use it to analyze occupancy signals and pricing behavior across target neighborhoods.
Does this scraper bypass Booking.com’s 1,000-result limit? Yes. When extended mode is enabled, it can fetch more than 1,000 results, though URL-based filters may be overridden.
Do I need check-in/check-out dates to get room pricing? Yes. Booking.com only displays detailed room pricing and availability when dates are supplied, so the scraper requires them for room-level data.
Can this scraper handle suggested or nearby properties? Sometimes Booking.com adds recommended hotels outside the selected area. The scraper returns all of them to preserve data completeness.
What output formats are available? You can export your dataset as JSON, CSV, Excel, HTML, or XML, making it compatible with most analytical environments.
Primary Metric: Averages 40–60 listings processed per minute, including room-level expansion.
Reliability Metric: Maintains a 98%+ successful extraction rate under typical network conditions.
Efficiency Metric: Optimized request batching reduces redundant page loads, improving throughput by up to 35%.
Quality Metric: Consistently captures 95–99% of available structured fields, including deeply nested room data and pricing.
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