- By Web Screen Scraping
Hotel Price Scraping: Extract Rates from Booking.com, Expedia & Airbnb
Hotel price scraping made simple. Extract rates from Booking.com, Expedia, and Airbnb using ethical scraping, clean data, and smart price monitoring.
Table of Contents
Introduction
Hotel prices are constantly changing; for example, one hotel can be listed on different websites at different prices, often several times a day. If you are a hotel owner, work with a travel startup, or are conducting market research, you must have a reliable way to track these price changes on an ongoing basis through hotel price scraping.
In a nutshell, hotel price scraping means collecting and analyzing publicly accessible pricing information from online travel agencies or travel websites, so you can do things like compare prices between hotels that are local competitors for the same date; or compare hotels’ prices on weekends vs weekdays; and identify pricing patterns as they rise due to increased demand. Organizations utilize this information for a variety of reasons, including revenue management, competitor analysis, and customer insights.
However, scraping isn’t simply “copy and paste” but on a larger scale. Each online platform displays pricing differently, has its own rules regarding taxes and fees, and generally displays results tailored to your geographic area, the device you are using, or your browsing history (i.e., cookies). Therefore, a good scraping strategy should include three key components: accuracy, consistency, and compliance.
In this guide, you will learn how to properly scrape hotel accommodation rates from Booking.com, Expedia, and Airbnb, clean and standardize your data, and make use of that data to gain value for your organization.
What Do Hotel "Rates" Really Include and Why is this Important?
Searching and scraping data from travel sites may be more difficult than one thinks! First, one must define what they consider a “rate.” Many of us assume it is the first number we see on the page. However, this can be misleading, as your definition of “rate” could exclude charges like these: hotel base price per night (the most common), taxes (local, county, etc.), service fees (cleaning, resort, occupancy, etc.), processing fees (credit card provider), and, in some cases, costs associated with payments and processing.
For an Airbnb listing, there is a nightly total. However, it can fluctuate based on the cleaning fee, service fee, number of guests, and length-of-stay discount. For sites like Booking.com and Expedia, the price displayed may or may not include taxes depending on your account settings, your location, or how the property has been set up.
To effectively scrape hotel pricing from travel sites, identify the type of price to store: to compare nightly base rates for ease of comparison (and might exclude fees); total amount of the stay (best to compare costs between customers); all-inclusive nightly price (the amount divided by the number of nights; this may be helpful).
You should also record the currency, whether breakfast is included, the cancellation policy, the room type, the occupancy, and the refundable/non-refundable status. Without these details, you might compare two prices for different products.
Who Uses Hotel Price Scraping and What are the Main Use Cases?
There are many reasons businesses, beyond large travel companies, might scrape hotel pricing data to gain valuable insights.
For example, hotels and property managers may use pricing data from other hotels to make accurate pricing adjustments and remain competitive. In addition, if a local competitor were to lower their prices on a weekend and you’re planning to offer the same rates, you’d have time to react. Furthermore, it will enable you to identify online travel agencies (OTAs) that are discounting your property and violating your rate-parity agreements. It allows you to respond quickly, so you do not lose any sales.
Travel agencies and aggregators may scrape pricing data from competing hotels to create price-comparison tools; other agencies may use scraped rates to enhance their own information databases. All companies rely on scraping hotel pricing to stay informed about supply and demand, price patterns, and availability across many cities.
Market researchers can use historical price trend data to identify event influences on hotel pricing, demand spikes, and seasonal trends, thereby placing the seasonal influences on hotel revenues. Examples would include Festivals, Conferences, Sporting Events, etc.
Companies can enhance their forecasting and customer recommendation engine development by correlating scraped hotel pricing data with weather patterns, airfare pricing trends, holiday calendars, and other factors.
The most significant value of scraping hotel pricing data will be a consistent, long-term source. Although obtaining a one-time data source may be helpful, daily or hourly datasets will more accurately depict pricing trends and drive profitable business decisions.
How can you Extract Accurate Rates from Booking.com?
Booking.com is an excellent resource for booking hotels online. Because Booking.com has several different room types and rates to choose from, you’ll want to carefully scrape the data you pull from Booking.com with respect to which room types/rates actually match.
When you scrape Booking.com, be sure to retrieve the property’s basic data (name, location, review score, number of reviews) first. Once you have that, you’ll also want to get the search criteria you used for the rates (check-in/check-out dates, number of guests, number of rooms), as those variables can significantly impact the rate you find.
You will want the breakdown of the rates, with the following information:
- Room Type (e.g., Deluxe Double Room) – Room type helps you identify what you get in your booking, and is also helpful in comparing properties.
- Rate Plan Name (e.g., “Breakfast Included”) – The rate plan name tells you about the plan associated with your booking room type, and may have additional special conditions related to the rate plan.
- Cancellation Policy (e.g., Free cancellation vs. Non-refundable) –
- Cancellation policies are essential to know, as some room types will have different policies.
- Base Price and Total Price (if shown) – The base price is the price associated with the booking; total prices include taxes/fees.
- Tax/Fees Breakdown (if shown) – In many regions around the world, taxes and fees are shown separately from the total price.
- Currency, and Discount Label/Applicant – You should have the currency used and, if there are promotional discounts associated with your rate.
Lastly, you may often see Booking.com display messages such as “Only X left” or “In high Demand” for accommodations. Monitoring these messages helps you track when a property is approaching a sellout (which can result in a price increase).
By and large, different countries/regions have different local views of the Booking.com site. Make sure you maintain consistency by scraping data for the same currency, language, and number of guests.
What Makes Expedia Hotel Price Scraping Different?
Bookings can differ significantly from Booking.com (as with other travel companies), as they are often sold as a complete package rather than just a room. The hotel’s “per night” price may vary considerably by region, and whether you are logged in.
Scrape the following data from the booking page of Expedia.com:
- Hotel Name, Number of Stars
- Guest Review, Number of Reviews
- Room Type, Bed Types (important for room types)
- Cancellation Policy, Payment Policy
- Base and Total Price
- Base and Total Price, including tax and fees (taxes and fees are significant for comparison purposes)
- Any special pricing for members or for using the app
Promotions are presented differently on Expedia than on other travel companies. The main thing to note is to store both the regular price and the discounted member price in separate fields for accurate reporting.
The device type and whether you are logged in may also affect the results you see when scraping from Expedia. Therefore, to obtain consistent results, you must use the same profile for scraping and maintain a consistent scraping process. If you are conducting market research, your goal is to obtain repeatable results rather than individualized results.
Why is Airbnb Price Scraping More Complex than Hotels?
Airbnb is different from a hotel. Because listings often include Apartments, Estates, Rooms, and Unique options, with variable pricing for cleaning fees, service fees, and weekly/monthly discounts. Also, there may be rules tied to the Guest Count.
When scraping Airbnb, always capture:
Title and type (entire Unit, Private Room), location as close as the platform allows, maximum number of guests in bedrooms, bathroom count, nightly cost shown in results, total cost for your timeframe shown, cleaning fee shown, service fee shown, minimum night for booking, extra Guest charge if applicable, cancellation policy category.
The “Total Cost” is the most critical number for Airbnb listings, as the exact nightly cost can result in different Total Costs due to Fees. Availability also significantly impacts: a listing could be booked for the dates in use and therefore hidden from view, or it could show alternate date suggestions.
When scraping Airbnb for reliable analysis, use a fixed date range (e.g., a 2-night weekend), the number of Guests, and repeat the same request at set time intervals. It will provide a way to compare listings over the same period.
Which Data Fields Should Be Standardized Across All Platforms?
When scraping sites like Booking.com, Expedia, and Airbnb, you’ll find that the same ideas are presented differently. To compare the different price offerings, you need to create a standard set of fields to allow for the comparison.
An example of a properly structured dataset would consist of:
- Platform (Booking.com / Expedia / Airbnb)
- Property/listing ID (unique identifier from the site)
- Property name
- Geo (city, area, latitude/longitude if available)
- Check-in, check-out, nights
- Occupancy (adults, children, rooms)
- Unit type (hotel room / entire home / private room)
- Room type or listing category
- Cancellation type (refundable / partially refundable / non-refundable)
- Meal inclusion (breakfast included, yes/no)
- Currency
- Scrape timestamp
By collecting the above data structure, you will be able to create dashboards and charts, compare prices across cities, compare prices between platforms over time, and see how a specific property has changed in price. Standardizing the data structure you collect will enable you to more easily spot outliers (for example, when a listing did not include taxes, or when the price of a listing changed drastically after the owner put a promotional offer on their website).
The ultimate objective is not to collect more data; rather, it is to obtain consistent data that accurately answers the questions your business poses.
What are the Common Challenges in Hotel Price Scraping?
Dynamic pages, dynamic scripts, and anti-bot technologies are typical of hotels. The reliability of the data collected through scraping cannot be guaranteed if the scraper experiences a significant failure. Common reasons for scrapers to fail include:
- Dynamic Rendering: In some cases, the information displayed (prices and options) may not have loaded by the time the page is fully loaded. If only an HTML page is fetched, critical elements may not be collected.
- Geo-Location and Personalization: The price shown to users varies based on their location, the currency used to access the website, and whether they are logged in. For example, users in one country may see a different “all-in” price than users in another country.
- Rate Limits and Blocking Filtering: When scraping multiple pages simultaneously or making excessive requests to the same URL, an IP address may be blocked, and the scraper may be presented with a CAPTCHA or incomplete pages.
- Different Formulas for Pricing: Hotels price rooms using various formulas. Some hotels use “per night” pricing, while others have a “total price” listed on their site. Equally, some hotels include taxes; others do not.
- Booking Issues: One hotel offers numerous room options, yet only one may be available for the requested dates. Similarly, Airbnb may not have any properties available that meet your specified criteria.
Controlled testing and data validation are the best ways to mitigate failure when scraping hotel websites. When you scrape the same city and dates, if the output becomes narrow, it may indicate an area where the page structure changed.
Moreover, collection companies need to keep a record of the confidence score and instances of anomalies (such as incorrect currency notation or a price that is disproportionately higher than usual).
In terms of value, accuracy is far more advantageous than volume. You may obtain more value from a smaller, consistent price dataset than you will from a larger, yet dissimilar dataset full of different pricing formulas and methods for calculating pricing.
What Legal and Ethical Considerations Should You Follow?
To scrape data from travel sites, do so responsibly. While scraping data may appear public, do so in accordance with the travel site’s terms/conditions, rules, and technical policies. If you do not comply with these requirements, you may face legal issues or reputational damage, which could also affect business operations.
Factors for consideration:
- Use the terms/conditions: Many travel sites have terms and conditions prohibiting scraping, so be sure to read them for each site you plan to scrape before starting.
- Read the robots.txt file: It is not legally binding, but it indicates which sections of a travel site are allowed or prohibited for automated scraping.
- Do not collect personal data: Do not scrape for personal data. Only get scrape for price and listing data, which can be extracted from a travel site without linking to a specific individual.
- Don’t overload the travel sites and treat them fairly: Do not overload the travel site servers. Always follow the rate limits set by the travel site and use the most efficient scraping method when possible.
- Differentiate your use cases: The use cases of “internal” analytics vs. “public” republishing have different risk levels.
The safest route for businesses is to consult legal counsel to verify whether there are other, safer methods for accessing scraping (e.g., using official APIs, establishing a partnership, or working with a data provider). If scraping is necessary, you should adopt a civic approach, including defining the purpose of the scraping, limiting the amount of data searched, keeping an audit log, and establishing a process to remove the data if necessary.
Responsible scraping will also provide a company with more reliable data. By ensuring that scraping access is consistent over the long term, all trends will be more effectively monitored.
Which Tools and Architecture are Best for Scalable Rate Tracking?
To track hotel prices over time, you will need more than a basic script. Typically, stable systems consist of four layers: the collection layer, processing layer, storage layer, and reporting layer.
In the collection layer, you will scrape search results and hotel details. Depending on how a platform loads its content, it can either use simple HTTP requests or utilize browser automation; however, for reliable extraction, it is essential to do this under repeatable conditions.
The processing layer will allow you to organize the scraped content and convert it from one format to another (i.e., parse the content, extract fields, and normalize the newly created data into a standard schema). This layer will also compute derived metrics for you (i.e., total cost per night, date restrictions, and whether taxes are included in the total).
You will typically store processed and aggregated data in a relational database (e.g., PostgreSQL or MySQL) and raw page snapshots in an object store (e.g., S3) for future reference. Storing raw page snapshots is essential because the content of many hotel websites changes frequently.
Conclusion: turning scraped rates into business value
Collecting and scraping hotel pricing data enables a company to gain insight into the current hotel market landscape and develop a more effective pricing strategy based on what they have observed and learned over time. The actual intelligence in scraping Booking.com, Expedia, and Airbnb lies within the collection of comparative metrics (such as: the total price, taxes or fees, terms of cancellation, type of room/listing, guest information, etc.), standardizing or verification of these metrics will allow for the ability to conduct cross-platform benchmarking and generate insights from this type of amalgamated data. Responsible and compliant data scraping practices will be crucial to an organization’s long-term success; therefore, organizations should ensure that any data scraping practices comply with the terms of service and use fair crawling practices when gathering data from websites. Web Screen Scraping provides structured data extraction and ongoing maintenance of this data.
