If you work in travel tech, an OTA, a hotel chain, or at an airport, you are in a price-and-availability arms race. Fares change by the hour, room inventory disappears in minutes, and competitors test new bundles and ancillaries constantly. You cannot track that manually — you need structured, fresh web data.
This guide walks through travel websites that are especially valuable to scrape in 2026 for teams in:
All 11 are already supported by ScrapeIt’s managed Hotel, Travel and Airline Data Scraping Service, so you do not have to build and maintain the scrapers yourself.
We will cover for each website:
Before diving into individual sites, it is worth aligning on a few principles that matter across the board:
A managed provider like ScrapeIt centralizes that complexity, so teams in revenue management, network planning, or product can focus on the data, not on bypassing CAPTCHAs.
Why Booking.com matters
Booking.com is one of the world’s largest accommodation platforms, with millions of listings spanning hotels, apartments, resorts, guesthouses, and more. It is where many travelers start their search, which makes it a near-real-time reflection of global lodging supply, pricing, and demand.
Best for:
Key data to extract
Typical fields scraped from Booking.com:
Scraping challenges & nuances
As one GitHub maintainer notes about scraping hotel sites:
“For JavaScript-heavy websites like Booking.com & Agoda, Playwright is the better choice.”
For teams who want structured Booking data without owning that engineering burden, ScrapeIt exposes it through a dedicated Booking.com scraper service tailored to your destinations, date ranges, and update frequency.
Why Expedia matters
Expedia is one of the largest global OTAs, covering hotels, flights, car rentals, cruises, and bundled packages in dozens of countries. Its data is particularly useful when you care about how full-trip offers are composed and priced (flight + hotel, hotel + car, etc.).
Best for:
Key data to extract
Scraping challenges & nuances
A line from ScrapeIt’s own description captures the reality nicely:
“When you scrape Expedia data, you transform complex travel information into clear, actionable insights.”
ScrapeIt’s managed Expedia scraper focuses on delivering those cleaned, analysis-ready datasets (CSV, Excel, JSON) without requiring you to maintain internal crawling infrastructure.
Why Hotels.com matters
Hotels.com is a hotel-specialized brand within Expedia Group, with a strong global footprint, localized domains (hotels.co.uk, hotels.fr, etc.) and a loyalty program that heavily influences traveler behavior.
Best for:
Key data to extract
Scraping challenges & nuances
ScrapeIt’s Hotels.com scraper is designed to capture not only prices, but also these promo and urgency layers that matter for demand modeling and price elasticity analysis.
Why Airbnb matters
Airbnb is a dominant platform for short-term rentals and alternative accommodations. For many urban and leisure markets, it is the most important supply source outside traditional hotels.
A scraping tutorial summarized the appeal well:
“Scraping Airbnb data with Python can give you a lot of insights into how the travel market is working currently.”
Best for:
Key data to extract
Scraping challenges & nuances
Because Airbnb data sits at the intersection of “travel” and “real estate fundamentals,” many teams combine it with property-portal data. ScrapeIt’s real estate scraping services are frequently used alongside Airbnb datasets for investors and banks who want a full yield and valuation picture.
Why Vrbo matters
Vrbo (“Vacation Rentals by Owner”) is one of the major global vacation-rental brands and is now part of Expedia Group. It tends to skew toward entire homes and family/group travel.
Best for:
Key data to extract
Scraping challenges & nuances
If your team is already collecting property-portal data, a good next step is cross-referencing Vrbo with ScrapeIt’s article “Real Estate Platforms That Actually Matter”, which shows how professional investors combine rental, sales, and distressed-asset feeds into a single analytics layer.
Why Tripadvisor matters
Tripadvisor is one of the most popular travel service portals worldwide, hosting extensive data on trips, hotels, restaurants, and attractions. It is a goldmine for competitive positioning and sentiment analysis.
As one technical guide puts it:
“TripAdvisor.com is one of the most popular service portals in the travel industry, containing data about trips, hotels and restaurants.”
Best for:
Key data to extract
Scraping challenges & nuances
Tripadvisor review data becomes particularly powerful when paired with NLP. ScrapeIt’s blog post on scraping web data for sentiment analysis outlines how travel teams can turn unstructured review text into structured, model-ready features (topics, polarity, and trend lines).
Why Kayak matters
Kayak is a leading travel metasearch engine and part of Booking Holdings; it aggregates offers from hundreds of airlines, OTAs, and hotel providers across more than 30 countries and 20+ languages.
ScrapeIt summarizes its role succinctly:
“Kayak is one of the world’s most recognised travel search engines, helping users compare flights, hotels, car rentals and vacation packages in one place.”
Best for:
Key data to extract
Scraping challenges & nuances
For teams that want Kayak as the “meta-layer” in their stack, ScrapeIt’s Kayak scraper focuses on capturing the complete view of options and prices for each search, not only the first screen of results.
Why Agoda matters
Agoda is a major OTA with especially strong coverage in Asia-Pacific hotels and flights. For travel teams with a focus on Asian destinations or outbound travel, it is often a primary pricing signal.
A scraping tutorial highlights its value in market research:
“Scraping Agoda helps you to analyze hotel prices, trends, and availability… for travel agencies, hotel managers, and competitors to optimize pricing.”
Best for:
Key data to extract
Scraping challenges & nuances
As one developer-facing guide reassures beginners:
“With the assistance of web scraping tools, even people unfamiliar with coding can extract data from Agoda effortlessly.”
Why Trip.com matters
Trip.com (part of Trip.com Group) is a major OTA with deep inventory in Asia and growing presence globally, across flights, hotels, and packages.
A scraping solutions provider describes it simply:
“Trip.com is one of the leading global OTAs, offering comprehensive travel services that include flights, hotels, activities, and package deals.”
Best for:
Key data to extract
Scraping challenges & nuances
For teams building “Asia first” travel products, Trip.com often sits alongside Skyscanner and Google Flights as a primary source for route and fare data to feed revenue and network models.
Why Skyscanner matters
Skyscanner is a British search aggregator and travel agency, part of Trip.com Group, known for powerful flight search and growing hotel coverage. It is particularly useful for market-level questions: “Which routes are showing demand?” and “Where are customers price-sensitive?”
A scraping API vendor sums up the use case:
“Collect public data at a large scale from Skyscanner, such as details on hotels, flights, and car rentals.”
Best for:
Key data to extract
Scraping challenges & nuances
Skyscanner datasets are often paired with Booking/Expedia/Trip.com hotel data to build holistic “origin-destination + stay” views for network planning and airport route development.
Why Google Flights & Hotels matter
Google’s travel surfaces — primarily Google Flights and Google Hotels — aggregate data from airlines, OTAs, and hotel providers and are embedded directly into search behavior.
A technical article on scraping Google Flights notes:
“Google Flights is one of the most comprehensive flight comparison platforms, showing real-time fares and schedules from hundreds of airlines and travel partners.”
And for hotels, an API provider describes their value:
“Scrape Google hotel data effortlessly. Access real-time pricing, availability, and reviews with a single API call.”
Best for:
Key data to extract
Scraping challenges & nuances
Most teams that rely on Google Flights/Hotels data do so via specialized scraping APIs or managed providers, and they treat this data as a complement — not a replacement — for first-party OTA/hotel data.
In practice, high-performing OTAs, airlines, airports, and hospitality groups rarely scrape just one site. They orchestrate a portfolio of sources tuned to specific questions:
ScrapeIt’s managed approach is designed exactly for this multi-source reality: you specify which platforms and fields you need, and the team handles anti-bot, maintenance, and delivery cadence. For a concrete example of this model applied to a large OTA, see the Booking.com end-to-end scraping case study, which shows how a single pipeline can keep complex, multi-vertical data in sync without dedicated in-house scraping engineers.
1. What is the main purpose of scraping travel websites for the travel and hospitality industry?
The main purpose is to stay competitive in a price-and-availability arms race. Teams need structured, fresh web data to track fares, monitor room inventory, and benchmark competitors' pricing, bundles, and product design, which cannot be done manually due to the constant changes.
2. Which segments of the travel industry can benefit most from scraping the recommended websites?
The primary beneficiaries are OTA aggregators and metasearch, Airlines and airports, Hotel chains, vacation rentals, and revenue management, and Travel analytics, consulting, and market research teams.
3. What are the key technical challenges in scraping modern travel websites?
Modern travel sites heavily rely on JavaScript, anti-bot systems, and A/B testing. This means many prices load via background API calls (not initial HTML), IP reputation and headless browser behavior matter, and layouts/API endpoints change regularly, making scraping an ongoing maintenance process.
4. How can I use data from websites like Booking.com, Airbnb, and Tripadvisor together?
High-performing teams orchestrate a portfolio of sources. For example, they combine Booking.com (for hotel pricing), Airbnb/Vrbo (for short-term rental supply), and Tripadvisor (for reputation and sentiment analysis) to build holistic views for pricing, inventory, and experience quality.
5. Which websites are most relevant for flight and route intelligence?
For flight and route intelligence, the most relevant metasearch and OTA websites to scrape are Skyscanner, Kayak, Trip.com, and Google Flights, as they provide aggregated data on fares, schedules, carriers, and demand trends for specific routes.
Let us take your work with data to the next level and outrank your competitors.
1. Make a request
You tell us which website(s) to scrape, what data to capture, how often to repeat etc.
2. Analysis
An expert analyzes the specs and proposes a lowest cost solution that fits your budget.
3. Work in progress
We configure, deploy and maintain jobs in our cloud to extract data with highest quality. Then we sample the data and send it to you for review.
4. You check the sample
If you are satisfied with the quality of the dataset sample, we finish the data collection and send you the final result.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
