Booking.com Hotels Scraper — Prices, Reviews, Ratings & Geo

Scrape Booking.com hotels and accommodation in bulk and export name, property type, address, city, country, coordinates, review score, review count, star rating, price, image and link as structured data. Paste one or more Booking.com search URLs, or just type a destination with your dates, and get back clean JSON, CSV, Excel or XML.

What does Booking.com Hotels Scraper do?

Booking.com Hotels Scraper collects properties from Booking.com search results. Give it a city or a full Booking.com search URL — or a destination plus check-in/check-out dates — and it walks the results pages and returns one row per property: the name and type, full address with city and country, map coordinates, guest review score and count, official star rating, the displayed price and currency, a lead image and a direct link. Results stream into a dataset you can download or pull through the Apify API.

What data can you scrape from Booking.com?

Data point	Description
Name	Property name as shown on Booking.com
Type	Accommodation type, e.g. Hotel, Apartment, Resort, Guesthouse
Address	Street address of the property
City & country	City name and 2-letter country code
Coordinates	Latitude and longitude for mapping
Review score	Guest review score (out of 10)
Review count	Total number of guest reviews
Star rating	Official star rating of the property
Price	Displayed price for the stay, plus currency
Free cancellation	Whether free cancellation is offered
Image	Lead photo URL
URL	Direct link to the property page

What you get

One record per property:

{
  "name": "The Fullerton Hotel Singapore",
  "type": "Hotel",
  "address": "1 Fullerton Square",
  "city": "Singapore",
  "country": "SG",
  "latitude": 1.286476,
  "longitude": 103.853058,
  "reviewScore": 9.2,
  "reviewCount": 4141,
  "starRating": 5,
  "price": 385.4,
  "currency": "USD",
  "url": "https://www.booking.com/hotel/sg/the-fullerton-singapore.html",
  "image": "https://cf.bstatic.com/xdata/images/hotel/square600/....webp",
  "freeCancellation": true
}

Output schema

Field	Type	Description
name	string	Property name as shown in search results.
type	string	Accommodation type, e.g. Hotel, Apartment, Resort.
address	string	Street address of the property.
city	string	City the property is located in.
country	string	2-letter country code, e.g. SG, GB, US.
latitude	number	Property latitude coordinate.
longitude	number	Property longitude coordinate.
reviewScore	number	Guest review score out of 10.
reviewCount	number	Total number of guest reviews.
starRating	number	Official star rating of the property.
price	number	Displayed price for the selected stay.
currency	string	Currency code for the price, e.g. USD, GBP.
url	string (URL)	Direct link to the property page.
image	string (URL)	URL of the property's lead photo.
freeCancellation	boolean	True when free cancellation is offered.

Prices appear only when both check-in and check-out dates are set (in the URL or the date fields). A destination-only search still returns names, ratings, location and links.

Use cases

• Benchmark nightly and total prices across hotels in a market.
• Track review scores and review counts of competing properties over time.
• Map available inventory in a city or neighborhood using the coordinates.
• Build datasets for revenue-management and dynamic-pricing models.
• Compare star ratings, property types and cancellation policies.
• Enrich travel, real-estate and price-comparison apps with live hotel data.

How to scrape Booking.com hotels

1. Add Booking.com Hotels Scraper to your Apify account and open the input.
2. Either paste one or more Booking.com search URLs into searchUrls, or type a destination (e.g. "Singapore") with checkIn / checkOut dates and adults.
3. Set maxItems to cap how many properties you collect in total.
4. Optionally set a currency (e.g. USD, EUR, GBP, SGD).
5. Click Run and watch properties stream into the dataset.
6. Export the results as JSON, CSV, Excel or XML, or pull them through the Apify API.

Input

{
  "searchUrls": [
    "https://www.booking.com/searchresults.html?ss=Singapore&group_adults=2&no_rooms=1&group_children=0"
  ],
  "destination": "London",
  "checkIn": "2026-09-20",
  "checkOut": "2026-09-22",
  "adults": 2,
  "maxItems": 50,
  "currency": "USD"
}

Field	Description
searchUrls	Booking.com search-result URLs to scrape (dates/guests/filters in the URL are respected).
destination	A city or place to search when you have not pasted a URL.
checkIn / checkOut	Stay dates in YYYY-MM-DD; required for prices.
adults	Number of adult guests for the structured search.
maxItems	Maximum number of properties to collect in total.
currency	3-letter currency code for displayed prices.
proxyConfiguration	Proxy settings; the default is tuned for reliable results.

Using Booking.com Hotels Scraper with n8n and Make

The dataset is available over the Apify API, so you can wire it into no-code and automation tools. In n8n, use the Apify node (or an HTTP Request node) to start a run and read the dataset items, then push hotels into a database, spreadsheet or notification. The same works in Make, Zapier and any workflow that can call a REST API.

FAQ

How many hotels can I scrape? As many as the search results contain — set maxItems to control the total volume. Booking.com returns 25 properties per results page.

Why are some prices empty? Prices only appear when both check-in and check-out dates are provided. Add them to the search URL, or fill the checkIn and checkOut fields, to get pricing.

Can I scrape a specific city, region or filtered search? Yes. Open the search you want on Booking.com — set the destination, dates, guests and any filters — then copy the URL from the address bar into searchUrls.

What formats can I export? JSON, CSV, Excel and XML, or pull the data live through the Apify API.

Can I automate it on a schedule? Yes. Use Apify Schedules to run the actor at any interval and keep your hotel dataset fresh.

Related scrapers

• Airbnb Scraper
• Google Maps Scraper
• TripAdvisor-style travel data
• PropertyGuru Scraper
• 99.co Scraper

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Original clean-room implementation.