How to Scrape Google Hotels with Python?

Extracting information from platforms like Google Hotels opens up huge opportunities: you can easily access the latest aggregated hotel data (including prices, availability, reviews, and location) to power your market research or compare prices to make better decisions.

Seasonality, demand changes, and promotions cause hotel prices to fluctuate frequently, so it's almost impossible to keep up to date by manually collecting this information. Instead, you can automate (and scale) this process by scraping travel websites and platforms.

This tutorial will show you how to do this using Python's Scraping API. It walks you through how to scrape data from Google Hotels step by step.

Why We Scrape Google Hotels?

If you search for hotel-related keywords, Google will generate its own hotel-centric section with names, images, addresses, ratings, and prices for thousands of hotels. That’s because Google has access to millions of travel and hotel websites and aggregates all of that information into one place.

What Data Could You Scrape from Google Hotels?

🏨 Hotel Names and Descriptions

💰 Pricing Information (e.g., nightly rates, discounts, taxes)

🗺️ Location Data (e.g., address, proximity to landmarks)

🛜 Amenities and Features (e.g., free Wi-Fi, pool, breakfast)

🌟 Reviews and Ratings (e.g., average score, number of reviews)

🔔 Availability and Booking Options

Challenge of Scraping Google Hotels

Google Hotels relies heavily on JavaScript to render its content dynamically. This means that the data you see on the page (e.g., hotel names, prices, reviews) is not available in the raw HTML source. Google Hotels displays a wealth of information in a highly structured yet nested format.

Meanwhile, Google also employs sophisticated anti-scraping mechanisms to prevent automated access to its platforms.

• CAPTCHAs
• IP Blocking
• Rate Limiting
• Behavioral Analysis

Besides, Google frequently updates its platforms to improve user experience and add new features. These updates often involve changes to the HTML structure, CSS classes, or JavaScript behavior.

Why use the API to scrape Google Hotels?

• No need to create a parser from scratch and maintain it.
• Bypass Google's blocking: can automatically solve CAPTCHA or solve IP blocking.
• No need to pay for proxies and CAPTCHA solvers additionally.
• No need to use browser automation.

Scrapeless Google Hotels API can easily handle all of the above problems, with a short response time of ~2.33 seconds per request (~1.47 seconds is amazingly fast). Users only need one API call to get accurate scraped data, which we display using well-structured JSON.

How to Scrape Google Hotels?

Our Google Hotels API allows you to scrape hotel and vacation rental results from Google Hotels. You can visit the Scrapeless Playground for more details.

Why do businesses choose Scrapeless?

🔴 Cost-saving: Google Shopping API only needs $0.80. After subscription, you can get a 10% discount!

🔴 Accurate Data: Our developers constantly analyze Google's scraping algorithms and restrictions to ensure the API is updated and optimized.

🔴 Stable and High Success Rate: Scrapeless guarantees a 99% success rate and reliability. The stability and accuracy of Google Trends scraping have reached nearly 100%! Currently, the average response time is around 1-2 seconds, significantly faster than most API providers. Moreover, data is returned in a standardized JSON format, making it ready for immediate use.

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Google Hotels API guide

Step 1. Obtain Your API Key

To get started, you’ll need to obtain your API Key from the Scrapeless Dashboard:

• Log in to the Scrapeless Dashboard.
• Navigate to API Key Management.
• Click Create to generate your unique API Key.
• Once created, simply click on the API Key to copy it.

Step 2: Use Your API Key in the Code

You can now use your API Key to integrate Scrapeless into your project. Follow these steps to test and implement the API.

1. Visit the API Documentation.
2. Click "Try it out" for the desired endpoint.
3. Configure the parameters you need in the code body.

Here is my body request:

{
  "actor": "scraper.google.hotels",
  "input": {
    "engine": "google_hotels",
    "q": "Bali Resorts",
    "check_in_date": "2025-03-18",
    "check_out_date": "2025-03-28"
  }
}

• Replace the keyword q with the one you want to query.
• The engine parameter is mandatory, and its value must be google_hotels. However, you can add more specific parameters, such as google_scholar_author.
• Common parameters:

Parameter	Required	Description
engine	TRUE	Set to google_hotels to use this API.
q	TRUE	Search query (e.g., Bali Resorts).
hl	FALSE	Language setting (default: en).
currency	FALSE	The currency of the returned prices.
check_in_date	TRUE	The parameter defines the check-in date. The format is YYYY-MM-DD. e.g. 2025-03-05.
check_out_date	TRUE	The parameter defines the check-out date. The format is YYYY-MM-DD. e.g. 2025-03-06.

1. Enter your API Key in the "Auth" field.
2. Click "Send" to get the scraping response.

You can also directly integrate our reference code into your program. Just replace your_token with the token you applied for:

import json
import requests


class Payload:
    def __init__(self, actor, input_data):
        self.actor = actor
        self.input = input_data


def send_request():
    host = "api.scrapeless.com"
    url = f"https://{host}/api/v1/scraper/request"
    token = your_token ## replace with your API Token

    headers = {
        "x-api-token": token
    }

    input_data = {
        "engine": "google_hotels",
        "q": "Bali Resorts",
        "check_in_date": "2025-03-18",
        "check_out_date": "2025-03-28"
    }

    payload = Payload("scraper.google.hotels", input_data)

    json_payload = json.dumps(payload.__dict__)

    response = requests.post(url, headers=headers, data=json_payload)

    if response.status_code != 200:
        print("Error:", response.status_code, response.text)
        return

    print("body", response.text)


if __name__ == "__main__":
    send_request()

Here you can see the reference JSON scraping result:

{
    "brands": [
        {
            "id": 37,
            "name": "Hyatt"
        },
        {
            "id": 180,
            "name": "Sol by Melia"
        },
        {
            "id": 402,
            "name": "Spot On"
        },
        {
            "id": 91,
            "name": "Mercure"
        },
        {
            "id": 174,
            "name": "Melia Hotels International"
        },
        {
            "id": 87,
            "name": "Hotel Indigo"
        },
        {
            "id": 135,
            "name": "Four Points by Sheraton"
        },
        {
            "id": 390,
            "name": "Capital O"
        },
        {
            "id": 154,
            "name": "Tribute Portfolio"
        },
        {
            "id": 325,
            "name": "Kempinski"
        },
        {
            "id": 90,
            "name": "Pullman Hotels and Resorts"
        },
        {
            "id": 137,
            "name": "W Hotels"
        },
        {
            "id": 53,
            "name": "Wyndham Hotels u0026 Resorts"
        },
        {
            "id": 67,
            "name": "Banyan Tree"
        },
        {
            "id": 134,
            "name": "Element"
        },
        {
            "id": 21,
            "name": "Ibis"
        },
        {
            "id": 2,
            "name": "InterContinental Hotels u0026 Resorts"
        },
        {
            "id": 117,
            "name": "Grand Hyatt"
        },

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Deep SerpApi will fully consider the needs of AI developers! We will simplify the process of integrating dynamic web information into AI-driven solutions and ultimately realize an ALL-in-One API that allows one-click search and extraction of web data. Moreover, we will maintain the lowest price in this field for a long time: $0.1-$0.3/1K queries.

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Use Cases for Scraped Google Hotels Data

Google Hotels is a treasure trove of data for travel tech developers, marketers, and analysts. Here’s why scraping this platform is worth your time:

1. Competitive Pricing Analysis: Track real-time price fluctuations across hotels to stay ahead of competitors.
2. Market Research: Identify trending destinations, popular amenities, and customer preferences.
3. Personalized Recommendations: Build apps that offer tailored hotel suggestions based on user preferences.
4. SEO and Content Creation: Use scraped data to create data-driven travel blogs or guides.
5. Dynamic Pricing Strategies: Businesses can discover pricing trends, adjust prices, and realize competitive pricing opportunities based on demand, availability, and competitor prices. This optimizes revenue and occupancy.
6. Customized Alerts: Monitor price drops to alert customers or for personal use.
7. Travel Aggregation Services: Provide users with a comprehensive view of hotel prices and options from a variety of sources.
8. Budgeting and Planning: Travelers can estimate accommodation costs and adjust plans accordingly.

Ending Thoughts

Congratulations, you have learned the easiest way to build a Google Hotel scraper! Just simple API calls are needed to complete complex data collection and extraction. Scraping Google Hotels data is of great value for pricing strategies, pricing trends, market research, sentiment analysis, predictive analysis, etc.

Scrapeless is gradually covering more in-depth Google Hotels information and more comprehensive Google scraping scenarios. We are committed to providing customers with a simple and fast API so that they can focus their resources on the core of their business.

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