Booking.com Hotel Scraper

What does it do?

Booking.com Hotel Scraper is an Apify actor that searches Booking.com for hotels by destination and extracts detailed accommodation data. It collects hotel names, prices, ratings, review counts, distances from city center, amenities, and direct booking URLs. The scraper handles Booking.com's dynamic JavaScript-rendered pages using Puppeteer and supports check-in and check-out date configuration for accurate pricing.

This actor is ideal for travel industry professionals, hotel aggregators, price comparison services, and researchers who need structured hotel data from one of the world's largest travel booking platforms. Rather than manually searching and recording hotel information, this tool automates the entire data collection process across multiple destinations in a single run.

Booking.com lists over 28 million accommodation options worldwide, making it the largest online travel agency. This scraper navigates through search results, extracting comprehensive property information and automatically handling pagination to collect the number of results you specify.

Why use this scraper?

• Multi-destination search: Search multiple cities or regions in a single run for comparative analysis
• Date-aware pricing: Configure check-in and check-out dates to get accurate pricing for specific travel periods
• Complete hotel data: Names, prices, ratings, review counts, distances, amenities, and direct booking URLs
• Automatic pagination: Collects results across multiple pages up to your configured limit
• Export ready: Download structured data as JSON, CSV, or Excel for immediate analysis

How to use it

1. Navigate to the Booking.com Hotel Scraper page on Apify Store.
2. Click Try for free to open the actor configuration.
3. Enter the destinations you want to search (e.g., "London", "Tokyo", "New York").
4. Set your check-in and check-out dates for accurate pricing.
5. Configure the maximum number of results per destination.
6. Click Start and wait for the data to be extracted.
7. Download the dataset in your preferred format from the Dataset tab.

You can also automate runs using the Apify API or integrate with external tools via webhooks and the Apify JavaScript client.

Input configuration

Field	Type	Description	Default
destinations	Array	Cities or regions to search on Booking.com	["London", "Tokyo"]
checkIn	String	Check-in date (YYYY-MM-DD)	"2026-04-01"
checkOut	String	Check-out date (YYYY-MM-DD)	"2026-04-03"
maxResults	Integer	Maximum hotels to collect per destination	200
proxyConfiguration	Object	Proxy settings for the scraper	Apify Proxy

Output data

Each hotel entry in the dataset contains:

{
  "destination": "London",
  "name": "The Savoy",
  "price": "$450",
  "rating": 9.2,
  "reviewCount": 5600,
  "distance": "0.3 km from center",
  "amenities": ["Free WiFi", "Spa", "Restaurant", "Pool"],
  "url": "https://www.booking.com/hotel/gb/the-savoy.html",
  "imageUrl": "https://cf.bstatic.com/...",
  "scrapedAt": "2026-02-18T12:00:00.000Z"
}

Cost of usage

This actor uses Pay-Per-Event pricing. Each result delivered to the dataset costs $0.0015. There is also a small charge per actor start. A typical run searching two destinations with 200 results each costs approximately $0.60. Use the maxResults parameter to control costs effectively.

The actor uses Apify Proxy which is included in the per-event pricing. Memory usage is typically 2048-4096 MB due to Puppeteer browser requirements.

Tips and tricks

• Start small: Test with a single destination and low maxResults before running large-scale data collection.
• Date selection: Use future dates for check-in and check-out to ensure pricing is available and current.
• Schedule recurring runs: Use Apify Schedules to track hotel prices over time and detect seasonal trends.
• Combine with other scrapers: Pair with TripAdvisor or Hotels.com scrapers on Apify Store for comprehensive hotel price comparisons.
• Data integration: Use Apify integrations to send results directly to Google Sheets, databases, or notification channels.
• Cost control: Reduce maxResults for initial test runs and increase once you have verified the output meets your needs.

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