Booking.com Hotel Scraper

Scrape hotel listings from Booking.com for any destination worldwide. Extract prices, ratings, reviews, amenities, room types, availability, and more. Export data to JSON, CSV, or Excel.

Features

• Any destination — Search any city, region, or area that Booking.com supports
• Date-based pricing — Get actual prices for your check-in/check-out dates
• 36 currencies — Display prices in USD, EUR, GBP, JPY, and 32 more
• Multiple sort options — Sort by popularity, price, review score, distance, or stars
• Star rating filter — Filter to only 3-star, 4-star, 5-star, or any combination
• Rich data extraction — Hotel name, price, rating (out of 10), review count, star rating, address, distance from center, amenities, room type, images, and direct booking URL
• Free breakfast & cancellation flags — Instantly see which hotels offer these perks
• Smart pagination — Automatically follows pages to get all results
• Anti-detection — Rotates 10+ User-Agent strings with realistic browser headers (sec-fetch, sec-ch-ua, etc.)
• Deduplication — Automatically removes duplicate hotels across pages
• Proxy support — Works with Apify proxy for reliable large-scale scraping
• PPE pricing — Pay only for what you scrape (per hotel result)

Input Parameters

Parameter	Type	Default	Description
destination	string	"New York"	City or area to search (required)
checkIn	string	14 days from now	Check-in date (YYYY-MM-DD)
checkOut	string	15 days from now	Check-out date (YYYY-MM-DD)
adults	integer	2	Number of adult guests
rooms	integer	1	Number of rooms
maxResults	integer	25	Max hotels to scrape (0 = unlimited)
currency	string	"USD"	Price display currency (ISO 4217)
sortBy	string	"popularity"	Sort order: popularity, price, review_score, distance, stars
starRating	array	[]	Filter by star rating, e.g. [4, 5]
maxConcurrency	integer	3	Concurrent requests (lower = safer)
proxyConfiguration	object	none	Apify proxy settings

Example Input

{
    "destination": "Paris",
    "checkIn": "2026-05-01",
    "checkOut": "2026-05-04",
    "adults": 2,
    "rooms": 1,
    "maxResults": 50,
    "currency": "EUR",
    "sortBy": "review_score",
    "starRating": [4, 5]
}

Example Output

Each hotel in the dataset contains:

{
    "hotelName": "Hotel Le Marais",
    "price": 189,
    "currency": "EUR",
    "rating": 8.7,
    "reviewCount": 2451,
    "starRating": 4,
    "address": "3rd arr., Paris",
    "distance": "0.8 km from center",
    "amenities": ["Free WiFi", "Air conditioning", "Non-smoking rooms"],
    "imageUrl": "https://cf.bstatic.com/xdata/images/hotel/square200/12345.jpg",
    "bookingUrl": "https://www.booking.com/hotel/fr/le-marais.html",
    "roomType": "Superior Double Room",
    "freeBreakfast": true,
    "freeCancellation": true,
    "destination": "Paris",
    "checkIn": "2026-05-01",
    "checkOut": "2026-05-04",
    "nights": 3,
    "adults": 2,
    "rooms": 1,
    "scrapedAt": "2026-03-01T12:00:00.000Z"
}

Use Cases

Travel Price Comparison

Compare hotel prices across destinations or date ranges. Find the cheapest dates for your trip by running multiple scrapes with different check-in/check-out combinations.

Hospitality Market Research

Analyze hotel pricing strategies, occupancy indicators (via price fluctuations), and competitive positioning across markets. Track how prices change over time for revenue management insights.

Travel App Data Feed

Power your travel comparison app, hotel aggregator, or price alert service with structured Booking.com data. Combine with other OTA scrapers for comprehensive coverage.

Investment & Real Estate Analysis

Evaluate hotel market saturation, average daily rates (ADR), and guest satisfaction scores by area. Useful for hospitality investment decisions and market entry analysis.

Content & SEO

Generate data-driven travel content: "Top 10 Hotels in Barcelona Under $100", "Best Rated Hotels in Tokyo", etc. Use real pricing data for authentic, valuable content.

Tips for Best Results

1. Use proxies — Booking.com has aggressive anti-bot protection. Residential proxies (Apify RESIDENTIAL group) give the best results.
2. Keep concurrency low — Start with maxConcurrency: 2 for reliable scraping. Increase only if proxies handle it well.
3. Specific destinations — "Paris, France" works better than just "Paris". More specific = more accurate results.
4. Future dates only — Check-in must be a future date. If no dates are provided, the scraper defaults to 14 days from now.
5. Star filter — Use starRating: [4, 5] to focus on higher-end properties and reduce noise.

Data Fields Reference

Field	Type	Description
hotelName	string	Hotel property name
price	number	Price per night (numeric)
currency	string	Currency code (e.g., "USD")
rating	number	Guest review score (1.0 - 10.0)
reviewCount	number	Total number of guest reviews
starRating	number	Official star rating (1-5)
address	string	Hotel address or district
distance	string	Distance from city center
amenities	array	List of highlighted amenities
imageUrl	string	Hotel thumbnail image URL
bookingUrl	string	Direct link to hotel on Booking.com
roomType	string	Recommended room type shown
freeBreakfast	boolean	Whether breakfast is included
freeCancellation	boolean	Whether free cancellation is available
destination	string	Search destination used
checkIn	string	Check-in date (YYYY-MM-DD)
checkOut	string	Check-out date (YYYY-MM-DD)
nights	number	Number of nights
adults	number	Number of adult guests
rooms	number	Number of rooms
scrapedAt	string	ISO 8601 timestamp of when data was scraped

Limitations

• Booking.com uses aggressive anti-bot protection. Using residential proxies is strongly recommended for production use.
• Prices shown are the "starting from" prices displayed in search results — actual prices may vary on the booking page depending on room availability.
• Booking.com frequently updates their HTML structure. If the scraper stops working, please open a GitHub issue.
• Maximum practical limit is around 500-1000 results per run due to Booking.com pagination limits.

License

MIT

Integration — Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("sovereigntaylor/booking-scraper").call(run_input={
    "location": "Paris, France",
    "checkIn": "2026-04-01",
    "checkOut": "2026-04-05",
    "maxResults": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['hotelName']}: {item.get('price', 'N/A')} — {item.get('rating', 'N/A')} stars")

Integration — JavaScript

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('sovereigntaylor/booking-scraper').call({
    location: 'Paris, France',
    checkIn: '2026-04-01',
    checkOut: '2026-04-05',
    maxResults: 50
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(item.hotelName || item.name || 'N/A'));