Dataset

This dataset was created by Data Dives

Released under CC0: Public Domain

Contents

This dataset contains information on hotel bookings from two different types of hotels: city hotel and resort hotel. The dataset includes details about customer demographics, booking information, and reservation details. It is an excellent resource for businesses in the hospitality industry, researchers, and data scientists interested in analyzing customer behavior, booking patterns, and market trends for these two types of hotels.

*** This dataset contains detailed information about hotel bookings, including cancellations. It includes variables such as booking date, cancellation status, lead time, customer type, and hotel type (city or resort). The dataset is useful for analyzing trends in hotel booking cancellations, understanding customer behavior, and predicting cancellation likelihood. Ideal for data science projects involving classification, time series analysis, or building predictive models to minimize hotel cancellations and optimize booking strategies.***

The USA Hotels Dataset from Booking.com is a rich collection of data related to hotels across the United States, extracted from Booking.com. This dataset includes essential information about hotel listings, such as hotel names, locations, prices, star ratings, customer reviews, and amenities offered. It's an ideal resource for researchers, data analysts, and businesses looking to explore the hospitality industry, analyze customer preferences, and understand pricing patterns in the U.S. hotel market.

Access 3 million+ US hotel reviews — submit your request today.

Key Features:

• Hotel Information: Includes hotel names, addresses, star ratings, and descriptions.
• Pricing Data: Nightly rates, discounts, and price variations by room type and season.
• Customer Reviews: Aggregated ratings and detailed user feedback from verified guests.
• Amenities: Detailed list of amenities provided by each hotel (e.g., Wi-Fi, parking, spa, swimming pool).
• Geographical Information: Hotel locations including city, state, and proximity to major landmarks.

Use Cases:

• Sentiment Analysis: Analyze customer reviews to gauge hotel service quality and guest satisfaction.
• Price Analysis: Compare pricing across different hotels, locations, and time periods to identify trends.
• Recommendation Systems: Build recommendation engines based on customer ratings, reviews, and preferences.
• Tourism and Hospitality Research: Understand patterns in hotel demand and services across various U.S. cities.

The year-over-year monthly change in the number of hotel bookings worldwide dropped to *** percent in April 2020. The sharp change in hotel bookings was due to the impact of the coronavirus (COVID-19) pandemic on international travel and the hotel industry. Three years later, in April 2023, the monthly change in the number of hotel bookings was ** percent.

The Booking Hotel Listings Dataset provides a structured and in-depth view of accommodations worldwide, offering essential data for travel industry professionals, market analysts, and businesses. This dataset includes key details such as hotel names, locations, star ratings, pricing, availability, room configurations, amenities, guest reviews, sustainability features, and cancellation policies.

With this dataset, users can:

Analyze market trends to understand booking behaviors, pricing dynamics, and seasonal demand.
Enhance travel recommendations by identifying top-rated hotels based on reviews, location, and amenities.
Optimize pricing and revenue strategies by benchmarking property performance and availability patterns.
Assess guest satisfaction through sentiment analysis of ratings and reviews.
Evaluate sustainability efforts by examining eco-friendly features and certifications.

Designed for hospitality businesses, travel platforms, AI-powered recommendation engines, and pricing strategists, this dataset enables data-driven decision-making to improve customer experience and business performance.

Use Cases

Booking Hotel Listings in Greece
Gain insights into Greece’s diverse hospitality landscape, from luxury resorts in Santorini to boutique hotels in Athens. Analyze review scores, availability trends, and traveler preferences to refine booking strategies.

Booking Hotel Listings in Croatia
Explore hotel data across Croatia’s coastal and inland destinations, ideal for travel planners targeting visitors to Dubrovnik, Split, and Plitvice Lakes. This dataset includes review scores, pricing, and sustainability features.

Booking Hotel Listings with Review Scores Greater Than 9
A curated selection of high-rated hotels worldwide, ideal for luxury travel planners and market researchers focused on premium accommodations that consistently exceed guest expectations.

Booking Hotel Listings in France with More Than 1000 Reviews
Analyze well-established and highly reviewed hotels across France, ensuring reliable guest feedback for market insights and customer satisfaction benchmarking.

This dataset serves as an indispensable resource for travel analysts, hospitality businesses, and data-driven decision-makers, providing the intelligence needed to stay competitive in the ever-evolving travel industry.

Dataset

This dataset was created by Favour oyinbo

Contents

Explore our extensive Booking Hotel Reviews Large Dataset, featuring over 20.8 million records of detailed customer feedback from hotels worldwide. Whether you're conducting sentiment analysis, market research, or competitive benchmarking, this dataset provides invaluable insights into customer experiences and preferences.

The dataset includes crucial information such as reviews, ratings, comments, and more, all sourced from travellers who booked through Booking.com. It's an ideal resource for businesses aiming to understand guest sentiments, improve service quality, or refine marketing strategies within the hospitality sector.

With this hotel reviews dataset, you can dive deep into trends and patterns that reveal what customers truly value during their stays. Whether you're analyzing reviews for sentiment analysis or studying traveller feedback from specific regions, this dataset delivers the insights you need.

Ready to get started? Download the complete hotel review dataset or connect with the Crawl Feeds team to request records tailored to specific countries or regions. Unlock the power of data and take your hospitality analysis to the next level!

Access 3 million+ US hotel reviews — submit your request today.

Many people enjoy traveling. When comparing the people booking hotels in selected countries worldwide, the highest share can be found in Malaysia, where ** percent of consumers fall into this category. Singapore ranks second with ** percent of respondents being part of this category as well.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

We will create a customized hotel bookings dataset tailored to your specific requirements. Data points may include hotel names, location details, pricing information, amenity lists, guest ratings, occupancy rates, and other relevant metrics.

Utilize our hotels bookings datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations understand guest preferences and market trends within the hospitality industry, allowing for more precise operational adjustments and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: optimizing booking strategies, enhancing guest experience, and competitive benchmarking.

The Online Hotel Booking industry comprises establishments primarily providing hotel booking services via online platforms. These websites are third-party platforms for customers to research and make hotel reservations. Consequently, this industry excludes hotels that offer direct bookings on their websites and brick-and-mortar travel agencies. Over the past five years, more individuals willing to make online reservations have benefited the industry. However, rising inflation and consumer uncertainty since 2022 have collectively reduced travel demand. Therefore, over the five years to 2025, industry revenue is expected to grow at an annualized rate of 19.5% to $55.8 billion, including a 4.7% growth in 2025 alone. The surge in growth rate is due to the low pandemic base year when industry revenue suffered from travel restrictions. Traditionally, travelers could book hotel hotels directly on websites or via travel agencies. However, the introduction of online hotel booking services enables customers to search and browse hotels according to their desired criteria, compare rooms at different hotels, and finally make a reservation from the comfort of their homes. Consequently, the industry has grown due to its added convenience compared with its direct substitutes. The industry has grown strongly due to the consistent rises in the number of trips made by US travelers and inbound trips by non-US residents. Industry revenue will continue to grow over the next five years as the economy improves from the record-high inflation. As consumer confidence recovers, individuals will feel more financially comfortable traveling. However, the industry contends with higher competition from direct hotel websites as some customers still make reservations directly with hotels. Nonetheless, industry revenue is projected to increase at an annualized rate of 2.5% to $63.1 billion over the five years to 2030.

Context

This dataset contains 119390 observations for a City Hotel and a Resort Hotel. Each observation represents a hotel booking between the 1st of July 2015 and 31st of August 2017, including booking that effectively arrived and booking that were canceled.

Content

Acknowledgements

The dataset, sourced from Mujtaba's Kaggle profile, provides information on hotel reservations. It has been tailored for analysis by excluding features deemed irrelevant. Additionally, preprocessing steps have been applied to enhance data quality, including the handling of missing values and removal of outliers.

Hotel Booking Market size was valued at USD 14.63 Billion in 2024 and is projected to reach USD 526.40 Billion by 2032, growing at a CAGR of 15.25% from 2026 to 2032. Global Hotel Booking Market DriversIncreased Travel and Tourism: The demand for unique travel experiences, growing disposable incomes, and the growth of low-cost airlines have all contributed to an increase in both domestic and international travel, which has greatly increased hotel reservations across the globe.Growth of Online Travel Agencies (OTAs): Customers can now compare prices, read reviews, and book hotels online more easily thanks to the rise of OTAs like Booking.com, Expedia, and Airbnb. This ease of use is a key factor in the market's expansion.Mobile and Digital Adoption: The way passengers look for and reserve hotels has changed as a result of the growing usage of smartphones and mobile apps. Market expansion is being driven by consumers' increasing preference for mobile bookings, which is bolstered by user-friendly interfaces and safe payment channels.Personalization and AI Technology: By combining machine learning and artificial intelligence with hotel booking platforms, users can receive tailored recommendations based on their prior actions and preferences, which improves user experience and increases bookings.Growing Business Travel: Major cities and business centers see a large increase in hotel reservations due to the growth of multinational corporations and globalization, which has led to a rise in business travel.Expansion of Hospitality Networks: More reservations have been made due to the availability of more hotel rooms as a result of hotel networks' global expansion and the construction of new facilities in developing nations.Middle-Class Growth and Emerging Markets: As the middle class grows in emerging markets, especially in Asia-Pacific, there is a corresponding surge in travel and hotel reservations.

Hotel Booking Engine Market Outlook

The global hotel booking engine market size was valued at approximately USD 3.5 billion in 2023 and is expected to grow to USD 7.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.2%. The market's growth is driven by the increasing adoption of digital platforms for travel bookings and the growing preference for online reservations among consumers. The ease of access and the convenience provided by hotel booking engines are key factors contributing to this rapid expansion.

One of the primary growth factors for the hotel booking engine market is the proliferation of internet usage and the widespread adoption of smartphones. As more people gain access to high-speed internet and increasingly rely on their mobile devices for various daily activities, the trend towards online booking has surged. This has prompted hotels and travel agencies to invest in advanced booking engines to streamline their operations and enhance customer experiences. Furthermore, the convenience offered by these platforms, such as instant booking confirmations and secure payment options, has significantly bolstered their popularity.

Another significant driver is the growing emphasis on customer experience and personalization in the hospitality industry. Modern consumers expect a seamless and customized booking experience, which has led to the integration of artificial intelligence (AI) and machine learning (ML) technologies into booking engines. These technologies analyze user behavior and preferences to provide personalized recommendations, thereby improving customer satisfaction and loyalty. Additionally, the incorporation of features like virtual tours and real-time room availability updates further enhances the user experience, driving market growth.

The increasing competition among hotels and the need for a competitive edge have also fueled the adoption of advanced hotel booking engines. Hotels are leveraging these platforms to offer exclusive deals and personalized packages to attract and retain customers. The ability to manage bookings efficiently, optimize pricing strategies, and access valuable customer data for targeted marketing campaigns has made booking engines an indispensable tool for hoteliers. Moreover, the rising trend of direct bookings, which eliminates the need for intermediaries and reduces commission costs, further propels the market's expansion.

From a regional perspective, North America dominates the hotel booking engine market due to its well-established hospitality sector and high internet penetration rates. The presence of major market players and the rapid adoption of advanced technologies in this region also contribute to its leading position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The burgeoning middle class, increasing disposable incomes, and the rapid growth of the tourism industry in countries like China and India are key factors driving the market in this region.

Deployment Type Analysis

The hotel booking engine market can be segmented by deployment type into cloud-based and on-premises solutions. Cloud-based booking engines have gained significant traction in recent years due to their flexibility, scalability, and cost-effectiveness. These solutions allow hotels to access their booking systems from anywhere with an internet connection, making it easier to manage reservations and update availability in real-time. Additionally, cloud-based systems often come with lower upfront costs and require less maintenance, which is particularly beneficial for small to medium-sized hotels with limited IT resources.

On the other hand, on-premises booking engines are still preferred by some larger hotel chains and establishments with specific security and customization requirements. These systems are installed directly on the hotel's servers, providing greater control over data and system configurations. While on-premises solutions typically involve higher initial investments and ongoing maintenance costs, they offer enhanced data security and the ability to tailor the system to the hotel's unique needs. This segment continues to hold a significant share of the market, particularly among luxury and high-end hotels that prioritize data privacy and bespoke functionality.

The growing preference for cloud-based solutions is also driven by the increasing adoption of Software-as-a-Service (SaaS) models in the hospitality industry. SaaS-based booking engines offer a subscription-based pricing struct

This statistic shows the distribution of hotel room nights booked in the United States by channel in the third quarter of 2016. In this period, **** percent of hotel bookings were made through an online travel agent or an OTA.

The Booking.com Reviews Dataset is a comprehensive collection of user-generated reviews for hotels, hostels, bed & breakfasts, and other accommodations listed on Booking.com. This dataset provides detailed information on customer reviews, including ratings, review text, review dates, customer demographics, and more. It is a valuable resource for analyzing customer sentiment, service quality, and overall guest experiences across different types of accommodations worldwide.

Key Features:

• Review Data: Includes detailed customer reviews with both positive and negative feedback, providing insights into customer experiences and satisfaction levels.
• Ratings: Features individual ratings for various aspects of the accommodations, such as cleanliness, location, service, value for money, and overall satisfaction.
• Review Dates: Provides the dates of each review, enabling trend analysis over time.
• Accommodation Details: Includes information about the accommodations being reviewed, such as name and location.
• Language Support: Reviews are available in multiple languages, reflecting the diverse user base of Booking.com.

Use Cases:

• Sentiment Analysis: Ideal for businesses and researchers conducting sentiment analysis to understand customer opinions and trends in the hospitality industry.
• Market Research: Useful for market research and competitive analysis, identifying strengths and weaknesses of different accommodation types and regions.
• Machine Learning: Beneficial for developing machine learning models for natural language processing, sentiment classification, and recommendation systems.
• Customer Experience Improvement: Helps hotel managers and owners understand customer feedback to improve services and guest experiences.
• Academic Research: Suitable for academic research in hospitality management, consumer behavior, data science, and artificial intelligence.

Dataset Format:

The dataset is available in CSV format making it easy to use for data analysis, machine learning, and application development.

Access 3 million+ US hotel reviews — submit your request today.

This statistic presents the market shares of hotel booking distribution channels in Germany in 2021. Online booking agencies (such as Booking.com and Expedia) represented **** percent of overnight bookings in Germany that year. However, collectively (whether via email, phone, in person or through the hotels own website), direct bookings represented over half of hotel bookings made in Germany in 2021.

In a survey conducted in January 2021 in Japan, ** percent of respondents who were businessmen stated that they personally issued hotel reservations on the internet. According to the survey, this was the most common method of booking hotels, followed by reservations through direct calls at the establishments.

In the second half of 2019, more than ** percent of the online hotel bookings were made through Meituan in China. The Chinese online travel agency Trip.com held more than ********** of the online hotel booking market based on number of bookings.

This statistic shows the share of gross hotel booking revenue coming from bookings made through online travel agencies (OTAs) in Europe between 2012 and 2016. In 2014 online travel agencies accounted for almost ** percent of gross hotel bookings.