Learning how to scrape Google Hotels opens up opportunities to gain a competitive edge for your business. When you scrape this specialized search engine, you gain access to valuable pricing and availability data that can transform your competitive analysis. By using targeted scraping methods, you can collect all the hotel data that fuels market research, tracks pricing changes in real time, and supports strategic decisions.
However, even experienced developers struggle to scrape Google Hotels without getting blocked. IP blocks, CAPTCHAs, and JavaScript rendering issues create significant hurdles when trying to extract hotel data. But don’t worry – our powerful web scraping API helps you overcome these challenges.
In this guide, I will show you how to extract hotel names, hotel prices, ratings, and hotel link information using ScrapingBee. With our solution and this tutorial at hand, you’ll spend less time troubleshooting scrapers and more time exploring pricing trends and market gaps
Quick Answer (TL;DR)
ScrapingBee is powered by an efficient Google Hotels API. With just one API call, it handles proxies, JavaScript rendering, and anti-bot measures automatically.
Once your Python environment is set up, use this complete code to begin scraping:
from scrapingbee import ScrapingBeeClient
import json
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
url = 'https://www.google.com/travel/search?q=hotels%20in%20paris'
response = client.get(
url,
params={
'country_code': 'us',
'extract_rules': '{"hotels": {"selector": "div.PVOOXe", "type": "list", "output": {"name": "h2", "price": "div.YQIgUd", "rating": "span.KFi5wf"}}}'
}
)
if response.status_code == 200:
data = json.loads(response.content)
print(f"Successfully extracted {len(data['hotels'])} hotels")
else:
print(f"Error: {response.status_code}")
print(response.content)
That’s it! This script extracts hotel results from Google Hotels, giving you structured data to compare prices and analyze hotel listings. Now, let me walk you through the full tutorial.
How to Scrape Google Hotels with ScrapingBee
Before diving into the web scraping process, let’s review how our solution overcomes the complexities of extracting hotel data from Google. The Google Hotels API scrapes public data from Google Hotels by removing the technical challenges of web scraping. Our platform manages browser automation, proxy rotation, and JavaScript rendering automatically, making it ideal for web scraping dynamic content like Google Hotels.
With ScrapingBee handling all the heavy lifting, you only need basic coding skills to scrape Google Hotels. If you're new to this, the following tutorial will show you how to set up ScrapingBee, make your first API request to the search engine, and parse the results to extract valuable hotel information. By following these steps, you’ll be able to reliably collect hotel names, hotel prices, ratings, and other hotel details for your market research or price comparison needs.
For more detailed information about all available parameters and options, check out the ScrapingBee Documentation, which provides comprehensive guidance on using the platform effectively.
Set Up Your Environment
Let's get things started. First, you’ll need to set up your Python environment and install the ScrapingBee SDK. This process is straightforward and only takes a few minutes:
Create a Python virtual environment (optional but recommended):
python -m venv scraping_env
source scraping_env/bin/activate # On Windows: scraping_env\Scripts\activate
Now, install the ScrapingBee Python client:
pip install scrapingbee
You're ready to sign up for a ScrapingBee account atscrapingbee.com and get your API key from the dashboard.
Then, create a new Python file for your web scraping project (e.g., google_hotels_scraper.py).
Make Your First API Request
Now that your environment is set up, let’s make your first request to scrape hotel card data. The following code demonstrates how to use ScrapingBee to extract hotel information from Google:
from scrapingbee import ScrapingBeeClient
import json
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
url = 'https://www.google.com/travel/search?q=hotels%20in%20paris'
response = client.get(
url,
params={
'country_code': 'us',
'extract_rules': '{"hotels": {"selector": "div.PVOOXe", "type": "list", "output": {"name": "h2", "price": "div.YQIgUd", "rating": "span.KFi5wf"}}}'
}
)
if response.status_code == 200:
data = json.loads(response.content)
print(f"Successfully extracted {len(data['hotels'])} hotels")
else:
print(f"Error: {response.status_code}")
print(response.content)
This request leverages JavaScript rendering and CSS selectors to return structured hotel data, even from dynamically loaded content. The extract_rules parameter defines what data points to extract and how to structure them. You can customize this to extract additional hotel details, such as amenities, location, and more.
Parse and Save Data
After successfully retrieving the data, you’ll want to parse it and save it for analysis. Here’s how to extract hotel names, prices, and ratings from the response and save them to a CSV file:
import pandas as pd
# Assuming we have the data from the previous step
if response.status_code == 200:
data = json.loads(response.content)
# Create a DataFrame from the extracted data
hotels_df = pd.DataFrame(data['hotels'])
# Clean the data if needed
hotels_df['price'] = hotels_df['price'].str.replace('$', '').astype(float)
# Save to CSV
hotels_df.to_csv('google_hotels_data.csv', index=False)
print(f"Saved {len(hotels_df)} hotels to google_hotels_data.csv")
This code processes the extracted data and saves it in formats that are easy to work with for further analysis. The CSV file is ideal for importing into spreadsheet applications, while the JSON file preserves the hierarchical structure of the data.
For more advanced data extraction needs, you can use ScrapingBee’s data extraction rules to define complex extraction patterns, including nested data structures and conditional extraction.
Why Scrape Google Hotels?
If you're operating in the hospitality industry, Google Hotels is an invaluable source of real-time market intelligence. The hotel includes real-time pricing from multiple booking platforms, detailed hotel details, ratings, reviews, and availability for specific check-in and check-out date periods.
By scraping its data, you gain actionable insights for market research, competitive analysis, strategic pricing, demand forecasting, and more.
This information can provide critical insights for:
Understanding market positioning and competitive landscape
Identifying pricing opportunities and gaps
Tracking seasonal trends and demand patterns
Monitoring competitor pricing strategies
Analyzing customer preferences through review data
Informing revenue management decisions
For example, a hotel chain might use web scraping to ensure its properties are competitively priced across different booking platforms. A travel agency could use this data to offer clients the best deals. Market researchers might analyze pricing patterns to identify trends and forecast future pricing movements.
For businesses interested inautomated price scraping, ScrapingBee provides reliable access to this data without the technical challenges of building and maintaining custom code.
Use Cases
Here are concrete ways to leverage Google Hotels data points – like hotel links, prices, ratings, and amenities – extracted via ScrapingBee’s API token and simple HTTP requests.
After kicking off an https://www.google.com/travel/search and pulling each hotel card’s HTML content, you can save the structured output to a CSV file or JSON file for:
Price Monitoring: Track hotel prices across different booking platforms to identify the best deals for customers or ensure your pricing is competitive.
Market Analysis: Gather data points on hotel availability, pricing, and ratings in specific locations to understand market dynamics and identify opportunities.
Competitive Intelligence: Monitor competitors’ pricing strategies, promotions, and availability to inform your own business decisions.
Travel Research: Collect hotel details on options, amenities, and pricing for travel planning services or applications.
Dynamic Pricing Optimization: Analyze pricing trends to develop algorithms that optimize your pricing strategy based on market conditions.
Seasonal Trend Analysis: Identify patterns in pricing and availability across different seasons to plan marketing campaigns or special offers.
Machine Learning Models: Build predictive models using historical hotel data to forecast pricing trends and optimize revenue management.
Now that we’ve outlined these use cases, let’s explore the common challenges you might face when scraping Google Hotels – and how ScrapingBee helps you overcome them.
Challenges Without ScrapingBee
No matter how valuable scraping Google Hotels might be, it does come with some serious issues. Google Hotels uses sophisticated anti-scraping measures that can quickly detect and block conventional scrapers. These include IP-based rate limiting, browser fingerprinting, and CAPTCHA challenges that make consistent data extraction difficult.
Additionally, Google Hotels relies heavily on JavaScript to render content, meaning simple HTTP requests won’t capture the data you need. You’d need to implement a headless browser solution, which adds complexity and resource requirements to your scraping infrastructure.
This doesn't mean that web scraping without getting blocked is a thing of the past. ScrapingBee rotates proxies behind the scenes to avoid IP bans. It spins up real Chrome instances with a single render_js=true flag so you always get the complete, JavaScript-rendered page.
Also, genuine browser fingerprints bypass fingerprint checks, and our human-paced requests virtually eliminate CAPTCHAs. All of this lives behind one simple API call – no extra infrastructure required.
Best Practices for Scraping Google Hotels
When scraping hotel data from Google Hotels, following best practices ensures reliable data collection while respecting the platform’s resources. ScrapingBee handles many technical aspects automatically, but understanding these principles will help you optimize your web scraping operations.
Minimize API Usage
First, structure your requests efficiently to minimize API usage. For example, when searching for all the hotels, use specific location parameters and date ranges rather than broad queries. This not only reduces the amount of data processed but also provides more relevant search results for your analysis.
Ensure Error Handling
Second, implement proper error handling in your scraping code. Network issues, temporary service disruptions, or changes to Google’s HTML structure can all cause web scraping failures. Your code should detect these issues and implement appropriate retry mechanisms with exponential backoff.
Cache Results
Make sure to cache results when appropriate to avoid unnecessary requests. If you’re monitoring hotel prices over time, you don’t need to scrape the entire hotel listing again—just the pricing data that changes frequently. This approach saves API credits and reduces the load on both your system and Google’s servers.
Use Pagination
Consider pagination when dealing with multiple pages of results. Google Hotels typically shows a limited number of hotel listings per page, with a “next page” button to view more. Your scraping solution should handle pagination to ensure you collect data from all desired pages.
Don't Disable Proxy Rotation
Proxy rotation is another best practice, or rather a requirement. Google quickly identifies and blocks IP addresses that make too many requests in a short period. This can be solved by rotating proxies for each request.
ScrapingBee does this by default, making your scraping activities appear as if they’re coming from several different users. While this feature uses additional credits, I recommend not disabling it as it's crucial to your project's success.
Render JavaScript
As I mentioned before, Google Hotels relies heavily on JavaScript to load and display hotel data. Without proper JavaScript rendering, you’ll miss critical information like prices, availability, and detailed hotel information.
It's not something you need to worry about with our tool. It ensures JavaScript rendering automatically with its headless browser infrastructure. Just like the proxy rotation, this feature is on by default.
For more information on handling JavaScript-heavy websites, see our guide onweb scraping with JavaScript, which covers various techniques and approaches.
Limit API Credits
Here's the final tip for your Google Hotels scraping project: limit your API credits. ScrapingBee uses a credit-based system, where each request consumes a certain number of API credits based on the features used. You can make the most of it by optimizing your credit usage:
Disable JavaScript rendering when scraping simple pages that don’t require it, as rendering consumes more credits.
Use the block_resources parameter to prevent loading unnecessary resources like images and stylesheets:
response = client.get( url, params={ 'render_js': True, 'block_resources': 'image,media,font,stylesheet' } )Implement caching in your application to store results and avoid making duplicate requests for data that changes infrequently.
Use the extract_rules parameter to extract only the specific data you need, reducing the size of the response and potentially saving bandwidth.
That’s it! You now know how to scrape data from Google Hotels effectively and professionally.
However, if you want to get the most out of your scraping project, you may need more advanced techniques. So, check out our guide on how to scrape Google Search results, which covers many principles that apply to Google Hotels as well.
Putting Data to Work
Mastering the scraping of Google Hotels eliminates technical challenges or blocked IPs, allowing you to gain real-time insights into pricing changes, availability updates, and guest sentiment. Now that you know the basics, it's your turn to convert raw data into strategic decisions.
Sign up for ScrapingBee, obtain your API key, and within minutes, you’ll be collecting valuable hotel insights. Whether you’re fine-tuning your pricing strategy, benchmarking against competitors, or identifying emerging travel trends, ScrapingBee enables you with data so you can concentrate on growth rather than infrastructure.
Frequently Asked Questions (FAQs)
Is scraping Google Hotels legal?
Scraping data from Google Hotels is generally permissible for personal or research purposes. However, you should review Google’s Terms of Service and robots.txt file. For commercial use, consult with a legal professional as laws vary by jurisdiction.
Can ScrapingBee handle JavaScript-heavy pages?
Yes, ScrapingBee excels at handling JavaScript-heavy pages like Google Hotels. It uses real Chrome browsers to render pages, ensuring all dynamic content loads properly. Simply set the render_js parameter to True in your API requests, and ScrapingBee will execute JavaScript before returning the content, giving you access to the fully rendered HTML.
How do I extract hotel ratings and prices?
You can extract hotel ratings and prices using ScrapingBee’s data extraction rules. Define CSS selectors that target these specific elements in the HTML structure. For Google Hotels, ratings are typically found in elements with class “KFi5wf” while prices are in “YQIgUd”. ScrapingBee’s extraction rules make it easy to pull this data into a structured format.
What does it cost to scrape Google Hotels?
ScrapingBee offers various pricing plans starting at $49/month for 250,000 API credits. Scraping Google Hotels typically requires JavaScript rendering, which uses 5-10 credits per request, depending on complexity. For regular monitoring of hotel prices, the Startup plan is usually sufficient.
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.