Booking.com is one of the biggest platforms for booking hotels, flights, and rental cars. Most of this data is public which makes it an attractive target for web scraping.
Booking.com is using a proprietary web scraping protection mechanism that is constantly evolving. This makes it difficult to scrape Booking data reliably and this is where web scraping APIs come in handy.
Overall, most web scraping APIs we've tested through our benchmarks perform well for all Booking.com pages (hotels listings, reviews, search system) at $2.74 per 1,000 scrape requests on average.
Booking.com scraping API benchmarks
Scrapeway runs weekly benchmarks for Booking Hotel Data for the most popular web scraping APIs. Here's the table for this week:
| Service | Success % | Speed | Cost $/1000 | |
|---|---|---|---|---|
|
1
 |
100%
+21
|
5.4s
-0.3
|
$2.35
+0.54
|
|
|
2
 |
100%
+12
|
6.6s
-0.8
|
$1.89
+0.54
|
|
|
3
 |
100%
+9
|
26.8s
+3.2
|
$4.0
+0.05
|
|
|
4
 |
99%
+15
|
4.8s
+0.1
|
$2.76
=
|
|
|
5
 |
97%
+6
|
6.9s
-0.8
|
$4.9
=
|
|
|
6
 |
96%
+4
|
3.3s
+=
|
$3.27
=
|
|
|
7
 |
0%
|
-
|
-
|
How to scrape booking.com?
Booking.com is relatively easy to scrape as it's mostly static content with a few dynamic elements so headless browser use is not required.
That being said, Booking.com employs a lot of anti-scraping mechanisms, so it's recommended to use a reliable web scraping service that can bypass the constantly changing anti-scraping measures. See benchmarks for the most up-to-date results.
Booking HTML datasets can be overwhelming to parse using traditional HTML parsing tools but since Booking.com uses a lot of structured data is hidden inside of the page source as JSON datasets - making data parsing often trivial in practice.
Booking.com uses a lot of javascript to render dynamic parts of the page like review pagination and pricing calculations so headless browser scraping is recommended for extracting dynamic datapoints.
from parsel import Selector
# webscrapingapi has a Python SDK but it's not great, use httpx instead:
# `pip install httpx`
import httpx
# create an API client instance
client = httpx.Client(timeout=180)
# create scrape function that returns HTML parser for a given URL
def scrape(url: str, country: str="", render_js=False, headers: dict=None) -> Selector:
api_result = client.get(
url,
headers=headers,
params={
"url": url,
"api_key": "YOUR API KEY", # NOTE: add your API KEY here!
"timeout": 60_000,
"render_js": "0",
},
)
assert api_result.status_code == 200, api_result.reason_phrase
return Selector(api_result.text)
url = "https://www.booking.com/hotel/us/zephyr-san-francisco.en-gb.html"
selector = scrape(url)
data = {
"url": url,
"title": selector.css("h2::text").get(),
"description": '\n'.join(selector.css("div#property_description_content ::text").getall()).strip(),
"address": selector.css(".hp_address_subtitle::text").get("").strip(),
"images": selector.css("a.bh-photo-grid-item>img::attr(src)").getall(),
# ...
}
from pprint import pprint
pprint(data)
{'address': "250 Beach Street, Fisherman's Wharf, San Francisco, CA 94133, "
'United States',
'description': 'Offering a fitness centre, Hotel Zephyr San Francisco is '
"located a short 300 metres from Pier 39 Fisherman's Wharf.\n"
'...'
'images': ['https://cf.bstatic.com/xdata/images/hotel/max1024x768/43125679.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max500/51805946.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max500/84087974.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max300/51805897.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max300/51805972.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max300/51805937.jpg',
'https://cf.bstatic.com/xdata/images/hotel/max300/51805909.jpg'],
'title': 'Hotel Zephyr San Francisco',
'url': 'https://www.booking.com/hotel/us/zephyr-san-francisco.en-gb.html'}
Why scrape Booking Hotel Data?
Booking.com is a popular target for web scraping because it has a large amount of hospitality data. Hotel listing details, reviews, and prices are all valuable data points for price comparison, market research, and competitor analysis.
With price monitoring scraping we can keep track of travel prices and take advantage of market fluctuations and trends. Primarily, this sort of web scraping is used in dynamic pricing strategies like identifying the best time to increase or decrease prices.
Market research scraping, and especially booking.com hotel review scraping, can help us understand customer preferences through sentiment analysis and identify trends through statistics. This sort of scraping is used in early stages of product development to understand customer needs and preferences.
Booking.com is also often scraped by venues listed on Booking.com themselves to monitor competition and adjust their pricing strategy.
Finally, Booking.com contains so much varied data points and user-generated content that it can be used in AI and machine learning models.