Is it legal to scrape Booking.com?

Publicly available data like hotel names and prices are generally scrapable for personal research or market analysis. However, you must comply with Booking.com's Terms of Service, which often prohibit automated access, and ensure you are not violating copyright by republishing their original content like descriptions and photos.

Does Booking.com have an official API for developers?

Yes, Booking.com offers a Connectivity API and a Demand API, but these are primarily for official partners, affiliates, and property managers. They are not open public APIs for general data scraping and require a formal application and approval process.

How do I avoid getting blocked by Booking.com while scraping?

To minimize the risk of blocking, use residential proxies to rotate your IP address and implement a headless browser like Playwright or Puppeteer to solve JavaScript challenges. Additionally, mimic human behavior by adding random delays between requests and rotating User-Agent strings.

Can I scrape reviews and ratings from Booking.com?

Yes, reviews and ratings are part of the public property page and can be extracted. However, because reviews are often loaded dynamically via AJAX, you will need a scraping tool that can execute JavaScript to capture all review pages.

What is the best format to save scraped Booking.com data?

For simple analysis, CSV or Excel files are standard and easy to use. For larger datasets or integration with other applications, JSON is preferred due to its ability to handle nested structures like amenity lists and room types.

Why do prices change based on the proxy location?

Booking.com uses localized pricing, meaning a user in the UK might see a different price than a user in the USA for the same hotel. To get consistent data, you should use proxies from the target market's country and set the 'currency' parameter in the URL.

How often should I scrape Booking.com for price updates?

Since hotel prices are highly dynamic, scraping once or twice a day is sufficient for most market research. For real-time price comparison, you may need more frequent intervals, but this increases the risk of being flagged for aggressive behavior.

Can I scrape Booking.com without coding?

Yes, no-code scraping tools like Automatio allow you to build scrapers visually. They handle complex tasks like clicking pagination buttons, rendering JavaScript, and rotating proxies automatically without requiring you to write a single line of code.

How to Scrape Booking.com: A Comprehensive Web Scraping Guide

Learn how to scrape Booking.com for hotel prices, availability, reviews, and amenities. Discover the best tools and strategies to extract valuable travel data.

Start Scraping Free

booking.comHard

Coverage:GlobalEuropeNorth AmericaAsiaSouth AmericaOceania

Available Data8 fields

TitlePriceLocationDescriptionImagesSeller InfoCategoriesAttributes

All Extractable Fields

Hotel NamePrice per NightAddressCityCountryLatitude & LongitudeReview ScoreNumber of ReviewsRoom TypeAmenitiesHotel DescriptionImage URLsAvailability DatesStar RatingDistance from Center

Technical Requirements

JavaScript Required

No Login

Has Pagination

Official API Available

Anti-Bot Protection Detected

Akamai Bot ManagerPerimeterXreCAPTCHARate LimitingIP BlockingCloudflare

View API Documentation

About Booking.com

Learn what Booking.com offers and what valuable data can be extracted from it.

The Global Travel Leader

Booking.com is one of the world's leading digital travel companies, providing a platform for users to book accommodations, flights, car rentals, and attractions. It operates in over 40 languages and offers more than 28 million listings, making it the primary repository for global travel data. From luxury hotels to unique homes and apartments, the platform covers virtually every destination on Earth.

Wealth of Structured Data

The website contains a massive amount of structured information including property names, real-time pricing, geographical coordinates, user reviews, and detailed amenity lists. This data is updated constantly, reflecting the highly dynamic nature of the travel industry. For researchers and businesses, Booking.com serves as a primary source for market intelligence and consumer behavior analysis.

Business Value of Booking Data

Scraping this data is invaluable for competitive benchmarking, pricing optimization, and sentiment analysis. By extracting hotel rates and availability across different regions, companies can build predictive models for travel demand or create aggregator services that help travelers find the best deals in real-time.

Why Scrape Booking.com?

Discover the business value and use cases for extracting data from Booking.com.

Real-time competitive price monitoring for hotels and rentals

Analyzing global travel market trends and seasonal demand

Aggregating customer reviews for large-scale sentiment analysis

Building travel meta-search engines and comparison tools

Historical pricing analysis for predictive modeling and ROI forecasting

Lead generation for travel insurance and local tour services

Scraping Challenges

Technical challenges you may encounter when scraping Booking.com.

Advanced anti-bot protection mechanisms like Akamai and PerimeterX

Heavy reliance on JavaScript for rendering prices and dynamic elements

Localized pricing and currency formats based on the scraper's IP address

Frequent changes in CSS class names and internal HTML structure

Aggressive rate limiting on search result and property detail pages

Scrape Booking.com with AI

No coding required. Extract data in minutes with AI-powered automation.

How It Works

Describe What You Need

Tell the AI what data you want to extract from Booking.com. Just type it in plain language — no coding or selectors needed.

AI Extracts the Data

Our artificial intelligence navigates Booking.com, handles dynamic content, and extracts exactly what you asked for.

Get Your Data

Receive clean, structured data ready to export as CSV, JSON, or send directly to your apps and workflows.

Why Use AI for Scraping

Effortlessly bypasses advanced anti-bot detection systems

Handles complex JavaScript rendering without manual script writing

Provides a no-code interface for rapid scraper deployment

Automates multi-page extraction and pagination handling

Offers built-in proxy rotation to avoid IP-based blocking

Start Scraping Free

No credit card requiredFree tier availableNo setup needed

No-Code Web Scrapers for Booking.com

Point-and-click alternatives to AI-powered scraping

Several no-code tools like Browse.ai, Octoparse, Axiom, and ParseHub can help you scrape Booking.com. These tools use visual interfaces to select elements, but they come with trade-offs compared to AI-powered solutions.

Typical Workflow with No-Code Tools

Install browser extension or sign up for the platform

Navigate to the target website and open the tool

Point-and-click to select data elements you want to extract

Configure CSS selectors for each data field

Set up pagination rules to scrape multiple pages

Handle CAPTCHAs (often requires manual solving)

Configure scheduling for automated runs

Export data to CSV, JSON, or connect via API

Common Challenges

Learning curve

Understanding selectors and extraction logic takes time

Selectors break

Website changes can break your entire workflow

Dynamic content issues

JavaScript-heavy sites often require complex workarounds

CAPTCHA limitations

Most tools require manual intervention for CAPTCHAs

IP blocking

Aggressive scraping can get your IP banned

Code Examples

import requests
from bs4 import BeautifulSoup

# Booking.com blocks simple requests; headers and cookies are critical.
url = 'https://www.booking.com/searchresults.html?ss=London'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept-Language': 'en-US,en;q=0.9',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8'
}

try:
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    soup = BeautifulSoup(response.content, 'html.parser')
    # Selectors may change frequently; data-testid is usually more stable
    hotels = soup.find_all('div', {'data-testid': 'property-card'})
    for hotel in hotels:
        name = hotel.find('div', {'data-testid': 'title'}).text.strip()
        print(f'Hotel Found: {name}')
except Exception as e:
    print(f'Error occurred during scraping: {e}')

When to Use

Best for static HTML pages where content is loaded server-side. The fastest and simplest approach when JavaScript rendering isn't required.

Advantages

●Fastest execution (no browser overhead)
●Lowest resource consumption
●Easy to parallelize with asyncio
●Great for APIs and static pages

Limitations

●Cannot execute JavaScript
●Fails on SPAs and dynamic content
●May struggle with complex anti-bot systems

import asyncio
from playwright.async_api import async_playwright

async def scrape_booking():
    async with async_playwright() as p:
        # Use a non-headless browser or stealth plugins to avoid detection
        browser = await p.chromium.launch(headless=True)
        context = await browser.new_context(user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...')
        page = await context.new_page()
        
        await page.goto('https://www.booking.com/searchresults.html?ss=Paris', wait_until='networkidle')
        
        # Wait for the property cards to load dynamically
        await page.wait_for_selector('[data-testid="property-card"]')
        
        hotels = await page.query_selector_all('[data-testid="property-card"]')
        for hotel in hotels:
            title_el = await hotel.query_selector('[data-testid="title"]')
            title = await title_el.inner_text() if title_el else 'N/A'
            print(f'Name: {title}')
            
        await browser.close()

asyncio.run(scrape_booking())

When to Use

Use when content loads dynamically via JavaScript, or when you need to interact with the page (clicks, scrolls, form fills). Handles modern anti-bot detection better.

Advantages

●Executes JavaScript like a real browser
●Handles SPAs and dynamic content
●Better anti-bot evasion with stealth plugins
●Can take screenshots and PDFs

Limitations

●Slower than HTTP requests
●Higher memory/CPU usage
●More complex to set up

import scrapy

class BookingSpider(scrapy.Spider):
    name = 'booking'
    allowed_domains = ['booking.com']
    start_urls = ['https://www.booking.com/searchresults.html?ss=New+York']

    custom_settings = {
        'USER_AGENT': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
        'DOWNLOAD_DELAY': 2
    }

    def parse(self, response):
        for hotel in response.css('[data-testid="property-card"]'):
            yield {
                'name': hotel.css('[data-testid="title"]::text').get(),
                'price': hotel.css('[data-testid="price-and-discounted-price"] span::text').get(),
                'score': hotel.css('[data-testid="review-score-badge"]::text').get()
            }
        
        # Pagination handling
        next_page = response.css('button[aria-label="Next page"]::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

When to Use

Ideal for large-scale crawling projects that need to scrape thousands of pages. Built-in support for rate limiting, retries, and data pipelines.

Advantages

●Built for scale (millions of pages)
●Automatic request throttling
●Built-in data export pipelines
●Middleware system for proxies/headers

Limitations

●Steeper learning curve
●Overkill for small projects
●No native JavaScript rendering

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Setting a realistic User-Agent is essential
  await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36');
  
  await page.goto('https://www.booking.com/searchresults.html?ss=Berlin', { waitUntil: 'networkidle2' });
  
  await page.waitForSelector('[data-testid="property-card"]');
  
  const results = await page.evaluate(() => {
    const items = Array.from(document.querySelectorAll('[data-testid="property-card"]'));
    return items.map(item => ({
      name: item.querySelector('[data-testid="title"]')?.innerText,
      price: item.querySelector('[data-testid="price-and-discounted-price"]')?.innerText
    }));
  });
  
  console.log(results);
  await browser.close();
})();

When to Use

Choose this if you're in a Node.js/JavaScript ecosystem or need tight integration with frontend tools. Similar capabilities to Playwright.

Advantages

●Native JavaScript/TypeScript support
●Chrome DevTools Protocol access
●Large ecosystem and community
●Good for JS-heavy projects

Limitations

●Chrome-only (vs Playwright's multi-browser)
●Similar overhead to Playwright
●Less mature stealth options

How to Scrape Booking.com with Code

Python + Requests

import requests
from bs4 import BeautifulSoup

# Booking.com blocks simple requests; headers and cookies are critical.
url = 'https://www.booking.com/searchresults.html?ss=London'
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
    'Accept-Language': 'en-US,en;q=0.9',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8'
}

try:
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    soup = BeautifulSoup(response.content, 'html.parser')
    # Selectors may change frequently; data-testid is usually more stable
    hotels = soup.find_all('div', {'data-testid': 'property-card'})
    for hotel in hotels:
        name = hotel.find('div', {'data-testid': 'title'}).text.strip()
        print(f'Hotel Found: {name}')
except Exception as e:
    print(f'Error occurred during scraping: {e}')

Python + Playwright

import asyncio
from playwright.async_api import async_playwright

async def scrape_booking():
    async with async_playwright() as p:
        # Use a non-headless browser or stealth plugins to avoid detection
        browser = await p.chromium.launch(headless=True)
        context = await browser.new_context(user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) ...')
        page = await context.new_page()
        
        await page.goto('https://www.booking.com/searchresults.html?ss=Paris', wait_until='networkidle')
        
        # Wait for the property cards to load dynamically
        await page.wait_for_selector('[data-testid="property-card"]')
        
        hotels = await page.query_selector_all('[data-testid="property-card"]')
        for hotel in hotels:
            title_el = await hotel.query_selector('[data-testid="title"]')
            title = await title_el.inner_text() if title_el else 'N/A'
            print(f'Name: {title}')
            
        await browser.close()

asyncio.run(scrape_booking())

Python + Scrapy

import scrapy

class BookingSpider(scrapy.Spider):
    name = 'booking'
    allowed_domains = ['booking.com']
    start_urls = ['https://www.booking.com/searchresults.html?ss=New+York']

    custom_settings = {
        'USER_AGENT': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
        'DOWNLOAD_DELAY': 2
    }

    def parse(self, response):
        for hotel in response.css('[data-testid="property-card"]'):
            yield {
                'name': hotel.css('[data-testid="title"]::text').get(),
                'price': hotel.css('[data-testid="price-and-discounted-price"] span::text').get(),
                'score': hotel.css('[data-testid="review-score-badge"]::text').get()
            }
        
        # Pagination handling
        next_page = response.css('button[aria-label="Next page"]::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

Node.js + Puppeteer

const puppeteer = require('puppeteer');

(async () => {
  const browser = await puppeteer.launch({ headless: true });
  const page = await browser.newPage();
  
  // Setting a realistic User-Agent is essential
  await page.setUserAgent('Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36');
  
  await page.goto('https://www.booking.com/searchresults.html?ss=Berlin', { waitUntil: 'networkidle2' });
  
  await page.waitForSelector('[data-testid="property-card"]');
  
  const results = await page.evaluate(() => {
    const items = Array.from(document.querySelectorAll('[data-testid="property-card"]'));
    return items.map(item => ({
      name: item.querySelector('[data-testid="title"]')?.innerText,
      price: item.querySelector('[data-testid="price-and-discounted-price"]')?.innerText
    }));
  });
  
  console.log(results);
  await browser.close();
})();

What You Can Do With Booking.com Data

Explore practical applications and insights from Booking.com data.

Dynamic Price Optimization

Hotels and property managers can adjust their rates in real-time based on competitor pricing scraped daily from Booking.com.

How to implement:

1Identify top 10 local competitors on Booking.com.
2Schedule daily scrapes of prices for standard and deluxe rooms.
3Analyze the price gap between your property and competitors.
4Adjust your own pricing via channel manager API based on market average.

Use Automatio to extract data from Booking.com and build these applications without writing code.

More than just prompts

Supercharge your workflow with AI Automation

Automatio combines the power of AI agents, web automation, and smart integrations to help you accomplish more in less time.

AI Agents

Web Automation

Smart Workflows

Get Started Free

Pro Tips for Scraping Booking.com

Expert advice for successfully extracting data from Booking.com.

Use high-quality residential proxies to bypass Akamai and avoid IP blacklisting.

Always set the 'Accept-Language' header to ensure you get data in a consistent language regardless of proxy location.

Explicitly add 'selected_currency' and 'lang' parameters to the URL to force specific data formats.

Implement random 'human-like' delays and mouse movement simulations to stay under the radar of behavioral analysis.

Extract data from hidden JSON-LD scripts found in the page source for cleaner, more reliable metadata.

Check the 'robots.txt' file for specific crawl-delay requirements and disallowed paths to maintain ethical standards.

Testimonials

What Our Users Say

Join thousands of satisfied users who have transformed their workflow

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Automatio is one of the most used for RPA Tools both internally and externally. It saves us countless hours of work and we realized this could do the same for other startups and so we choose Automatio for most of our automation needs.

Mohammed Ibrahim

CEO, qannas.pro

I have used many tools over the past 5 years, Automatio is the Jack of All trades.. !! it could be your scraping bot in the morning and then it becomes your VA by the noon and in the evening it does your automations.. its amazing!

Ben Bressington

CTO, AiChatSolutions

Automatio is fantastic and simple to use to extract data from any website. This allowed me to replace a developer and do tasks myself as they only take a few minutes to setup and forget about it. Automatio is a game changer!

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Jonathan Kogan

Co-Founder/CEO, rpatools.io

Mohammed Ibrahim

CEO, qannas.pro

Ben Bressington

CTO, AiChatSolutions

Sarah Chen

Head of Growth, ScaleUp Labs

We've tried dozens of automation tools, but Automatio stands out for its flexibility and ease of use. Our team productivity increased by 40% within the first month of adoption.

David Park

Founder, DataDriven.io

The AI-powered features in Automatio are incredible. It understands context and adapts to changes in websites automatically. No more broken scrapers!

Emily Rodriguez

Marketing Director, GrowthMetrics

Automatio transformed our lead generation process. What used to take our team days now happens automatically in minutes. The ROI is incredible.

Related Web Scraping

Frequently Asked Questions About Booking.com

Find answers to common questions about Booking.com

How to Scrape Booking.com: A Comprehensive Web Scraping Guide

About Booking.com

The Global Travel Leader

Wealth of Structured Data

Business Value of Booking Data

Why Scrape Booking.com?

Scraping Challenges

Scrape Booking.com with AI

How It Works

Why Use AI for Scraping

No-Code Web Scrapers for Booking.com

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

What You Can Do With Booking.com Data

Dynamic Price Optimization

Market Sentiment Analysis

Vacation Rental ROI Mapping

Travel Aggregator Maintenance

Competitive Amenity Benchmarking

Supercharge your workflow with AI Automation

Pro Tips for Scraping Booking.com

What Our Users Say

Related Web Scraping

How to Scrape Thrillophilia Tour Packages & Reviews

How to Scrape AirlineQuality.com (Skytrax) Reviews

How to Scrape Airbnb Listings and Prices (2025 Guide)

How to Scrape Cheapflights | Flight Data Web Scraper

Frequently Asked Questions About Booking.com

Is it legal to scrape Booking.com?

Does Booking.com have an official API for developers?

How do I avoid getting blocked by Booking.com while scraping?

Can I scrape reviews and ratings from Booking.com?

What is the best format to save scraped Booking.com data?

Why do prices change based on the proxy location?

How often should I scrape Booking.com for price updates?

Can I scrape Booking.com without coding?

How to Scrape Booking.com: A Comprehensive Web Scraping Guide

About Booking.com

The Global Travel Leader

Wealth of Structured Data

Business Value of Booking Data

Why Scrape Booking.com?

Scraping Challenges

Scrape Booking.com with AI

How It Works

Why Use AI for Scraping

How to scrape with AI:

Why use AI for scraping:

No-Code Web Scrapers for Booking.com

Typical Workflow with No-Code Tools

Common Challenges

No-Code Web Scrapers for Booking.com

Typical Workflow with No-Code Tools

Common Challenges

Code Examples

How to Scrape Booking.com with Code

Python + Requests

Python + Playwright

Python + Scrapy

Node.js + Puppeteer

What You Can Do With Booking.com Data

Dynamic Price Optimization

Market Sentiment Analysis

Vacation Rental ROI Mapping

Travel Aggregator Maintenance

Competitive Amenity Benchmarking

What You Can Do With Booking.com Data

Supercharge your workflow with AI Automation

Pro Tips for Scraping Booking.com

What Our Users Say

Related Web Scraping

How to Scrape Thrillophilia Tour Packages & Reviews

How to Scrape AirlineQuality.com (Skytrax) Reviews

How to Scrape Airbnb Listings and Prices (2025 Guide)

How to Scrape Cheapflights | Flight Data Web Scraper

Frequently Asked Questions About Booking.com

Is it legal to scrape Booking.com?

Does Booking.com have an official API for developers?

How do I avoid getting blocked by Booking.com while scraping?

Can I scrape reviews and ratings from Booking.com?

What is the best format to save scraped Booking.com data?

Why do prices change based on the proxy location?

How often should I scrape Booking.com for price updates?

Can I scrape Booking.com without coding?