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.

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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.

About this Dataset

Contact list of managers/owners of over 1,100 hotels in major markets across the US. Hotel name, address, and website are provided with owner/manager's name and contact information (phone & email). The hotel's star rating is available for many of the listings.

Category

Lead Generation

Keywords

hotels

Row Count

1144

Price

$99.00

The United States Luxury Hotel Market Report is Segmented by Room Type (Standard Luxury Room, Suites, Villas/Bungalows, Penthouses & Presidential Suites), Booking Channel (Direct Booking, Online Travel Agencies, and Other), Service Type (Business Hotels, Airport Hotels, Suite Hotels, Resorts, Other Service Types), and Geography (Northeast, Midwest, South, West). The Market Forecasts are Provided in Terms of Value (USD).

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!

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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.

The accommodation booking value worldwide amounted to nearly 1.1 trillion U.S. dollars in 2023. That year, hotels accounted for the highest figure, generating 792 billion U.S. dollars. As predicted, the global accommodation booking value was forecast to reach an estimated 1.66 trillion U.S. dollars in 2027.

As of April 2025, the market size of the casino hotel industry in the United States totaled **** billion U.S. dollars. Meanwhile, there were around ******* individuals employed in the sector.

When looking at the leading hotel and resort chains by sales, as ranked by Forbes in its 2025 "Global 2000" list of the largest companies worldwide, Marriott International Inc. came out on top. The hotel chain generated around 25.1 billion U.S. dollars in sales in the 12 months prior to April 2025. Meanwhile, second in the ranking was the gambling and resort chain MGM Resorts International with 17.24 billion U.S. dollars in sales.

The occupancy rate of hotels in the United States reached ** percent in October 2024. This shows a slight increase when compared to the previous year. The low occupancy rate during 2020 was due to the impact of the coronavirus (COVID-19) pandemic on the hotel industry.

This dataset provides points for 375 Hotels in Iowa as well as the lowest price for each hotel and their address. This does not necessarily contain all of the hotels in Iowa. Values of -1 represent no data available.

The inspiration behind creating the OYO Review Dataset for sentiment analysis was to explore the sentiment and opinions expressed in hotel reviews on the OYO Hotels platform. Analyzing the sentiment of customer reviews can provide valuable insights into the overall satisfaction of guests, identify areas for improvement, and assist in making data-driven decisions to enhance the hotel experience. By collecting and curating this dataset, Deep Patel, Nikki Patel, and Nimil aimed to contribute to the field of sentiment analysis in the context of the hospitality industry. Sentiment analysis allows us to classify the sentiment expressed in textual data, such as reviews, into positive, negative, or neutral categories. This analysis can help hotel management and stakeholders understand customer sentiments, identify common patterns, and address concerns or issues that may affect the reputation and customer satisfaction of OYO Hotels. The dataset provides a valuable resource for training and evaluating sentiment analysis models specifically tailored to the hospitality domain. Researchers, data scientists, and practitioners can utilize this dataset to develop and test various machine learning and natural language processing techniques for sentiment analysis, such as classification algorithms, sentiment lexicons, or deep learning models. Overall, the goal of creating the OYO Review Dataset for sentiment analysis was to facilitate research and analysis in the area of customer sentiments and opinions in the hotel industry. By understanding the sentiment of hotel reviews, businesses can strive to improve their services, enhance customer satisfaction, and make data-driven decisions to elevate the overall guest experience.

Deep Patel: https://www.linkedin.com/in/deep-patel-55ab48199/ Nikki Patel: https://www.linkedin.com/in/nikipatel9/ Nimil lathiya: https://www.linkedin.com/in/nimil-lathiya-059a281b1/

Macau Average Length of Stay: 2-Star Hotels: North America: USA data was reported at 1.300 Night in May 2018. This records a decrease from the previous number of 1.600 Night for Apr 2018. Macau Average Length of Stay: 2-Star Hotels: North America: USA data is updated monthly, averaging 1.377 Night from Jan 1998 (Median) to May 2018, with 245 observations. The data reached an all-time high of 11.939 Night in May 1999 and a record low of 0.926 Night in Jun 2000. Macau Average Length of Stay: 2-Star Hotels: North America: USA data remains active status in CEIC and is reported by Statistics and Census Service. The data is categorized under Global Database’s Macau – Table MO.Q010: Average Length of Stay.

Macau Average Length of Stay: 3-Star Hotels: North America: USA data was reported at 1.900 Night in May 2018. This records an increase from the previous number of 1.600 Night for Apr 2018. Macau Average Length of Stay: 3-Star Hotels: North America: USA data is updated monthly, averaging 1.940 Night from Jan 1998 (Median) to May 2018, with 245 observations. The data reached an all-time high of 5.600 Night in Jun 2014 and a record low of 1.087 Night in Jun 1998. Macau Average Length of Stay: 3-Star Hotels: North America: USA data remains active status in CEIC and is reported by Statistics and Census Service. The data is categorized under Global Database’s Macau – Table MO.Q010: Average Length of Stay.

As of September 2023, U.S.-based global hotel operator Marriott International, Inc. was the hotel company with the highest market cap worldwide. As of that month, Marriott's market cap amounted to nearly 60.6 billion U.S. dollars. Fellow U.S.-based company Hilton Worldwide Holdings Inc. followed in the ranking with a market cap of 40.4 billion U.S. dollars. Meanwhile, TUI AG was the tour operator company with the highest market cap worldwide.

The revenue per available room (RevPAR) of the United States hotel industry was ***** U.S. dollars in 2022. This figure reflects an increase over the RevPAR of the previous two years which were impacted by the coronavirus (COVID-19) pandemic. What is RevPAR? Revenue per available room (RevPAR) is a key metric in the hospitality industry. RevPAR is calculated by multiplying the average daily rate (ADR) and the occupancy rate of a hotel. Calculating RevPAR can help hotels with things such as comparing their performance to their competitors and measuring their revenue generating performance to accurately price rooms. Impact of COVID-19 on the tourism industry The coronavirus (COVID-19) pandemic massively impacted in the tourism industry across the globe. Governments imposed travel restrictions, including border closures, in an attempt to reduce the spread of the virus. As a result, the projected number of domestic leisure trips that Americans took in 2020 reflected a **** percent decrease compared to the previous year. Furthermore, domestic business trips also plummeted in that year.

Online Hotel Booking Software Market Outlook

The global online hotel booking software market has witnessed significant growth, with the market size valued at USD 3.46 billion in 2023 and projected to reach USD 8.82 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.1% during the forecast period. This growth is driven by the increasing reliance on digital platforms for hotel reservation services owing to the shift in consumer habits towards more convenience and efficiency in travel planning. The advent of advanced technologies such as artificial intelligence and cloud computing has further augmented the market growth by enhancing the user experience and operational efficiency for businesses.

One of the primary growth drivers for the online hotel booking software market is the proliferation of smartphones and the increased penetration of the internet worldwide. As more consumers gain access to these technologies, there is a heightened demand for seamless, user-friendly hotel booking experiences, especially through mobile applications. The convenience of booking, modifying, or canceling reservations at any time and from any location has been a significant factor in the adoption of online booking platforms. Moreover, as consumers become more tech-savvy, their expectations for hotel booking experiences are evolving, leading to increased demand for cutting-edge software solutions that offer personalization, quick processing, and comprehensive customer support.

Another critical factor contributing to the market's growth is the increasing trend of digitalization in the travel and tourism industry. Hotels and travel agencies are increasingly adopting digital strategies to enhance customer engagement and loyalty. This shift is largely driven by the need to remain competitive in a market where consumers have numerous options at their fingertips. Digital tools, including online hotel booking software, are being leveraged to streamline operations, reduce overhead costs, and provide valuable data analytics for better decision-making. Furthermore, the integration of AI and machine learning into these platforms offers enhanced capabilities such as predictive analytics, which can significantly improve customer targeting and personalization.

The rise in global tourism and business travel also serves as a substantial growth factor for the market. As economies recover and international borders open, there has been a resurgence in travel activities, both for leisure and corporate purposes. This resurgence has led to a spike in demand for online hotel booking systems that can efficiently handle an increased volume of bookings while offering features like dynamic pricing, inventory management, and multi-channel distribution. Furthermore, the emphasis on enhancing customer experience through improved software functionalities is expected to sustain market growth over the coming years.

Regionally, North America currently holds a significant share of the market, driven by the presence of leading market players and widespread adoption of advanced technologies. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. Factors such as increasing internet penetration, rising disposable incomes, and a burgeoning middle class are contributing to this rapid growth. Additionally, the region's vibrant tourism sector, with countries like China, India, and Japan emerging as key travel destinations, further fuels the demand for efficient and robust hotel booking solutions. Europe and Latin America also present promising growth prospects, supported by digital transformation initiatives and a growing emphasis on enhancing the tourism infrastructure.

Deployment Type Analysis

In terms of deployment type, the online hotel booking software market is segmented into cloud-based and on-premises solutions. Cloud-based deployment is gaining traction due to its scalability, cost-effectiveness, and ease of access. Hotels and travel agencies are increasingly favoring cloud solutions as they offer real-time data access, seamless updates, and integration with other systems, which is essential for maintaining a competitive edge in the fast-paced hospitality industry. The ability to manage operations remotely and the advantage of reduced IT infrastructure costs are compelling reasons for the shift towards cloud-based solutions.

Cloud-based solutions also provide an added layer of security with regular updates and backups, which are crucial for protecting sensitive customer data. This deployment type supports scalability, allowing business

Graph and download economic data for All Employees, Leisure and Hospitality (USLAH) from Jan 1939 to Aug 2025 about leisure, hospitality, establishment survey, employment, and USA.

Across the world, there were over 1.1 million hotel rooms under construction as of the first quarter of 2024. Meanwhile, 528,251 hotel rooms were planned to start construction in the next 12 months worldwide.

United States CPI U: Housing: Shelter: LAH: Others incl Hotels & Motels data was reported at 352.657 1982-1984=100 in Jun 2018. This records a decrease from the previous number of 359.393 1982-1984=100 for May 2018. United States CPI U: Housing: Shelter: LAH: Others incl Hotels & Motels data is updated monthly, averaging 183.750 1982-1984=100 from Jan 1967 (Median) to Jun 2018, with 618 observations. The data reached an all-time high of 359.393 1982-1984=100 in May 2018 and a record low of 26.200 1982-1984=100 in Jan 1967. United States CPI U: Housing: Shelter: LAH: Others incl Hotels & Motels data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I002: Consumer Price Index: Urban.

For information on economic census geographies, including changes for 2012, see the economic census Help Center..Includes only establishments of firms with payroll. SeeTable Notes for more information. Data based on the 2012 Economic Census. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see Methodology..Table NameAccommodation and Food Services: Subject Series - Misc Subjects: Guestroom Size of Establishments for the U.S.: 2012ReleaseScheduleThe data in this file are scheduled for release in the first quarter of 2016.Key TableInformationSee Methodology for information on data limitations.UniverseThe universe of this file is selected establishments of firms with payroll in business at any time during 2012 and classified in Accommodation and Food Services (Sector 72).GeographyCoverageThe data are shown at the United States level only.IndustryCoverageThe data are shown for 2012 NAICS codes 7211, Traveler accommodation; 72111, Hotels (except casino hotels) and motels; 72112, Casino hotels; 72119, Other traveler accommodation; and 7213, Rooming and boarding houses.Data ItemsandOtherIdentifyingRecordsThis file contains data on:Establishments by guestroom size rangeSales by guestroom size rangeNumber of guestrooms as of December 31 by guestroom size rangeEach record includes a ROOMSIZE code which represents a guestroom size range.FTP DownloadDownload the entire table athttps://www2.census.gov/econ2012/EC/sector72/EC1272SXSB09.zipContactInformation. U.S. Census Bureau, Economy Wide Statistics Division. Data User Outreach and Education Staff. Washington, DC 20233-6900. Tel: (800) 242-2184. Tel: (301) 763-5154. ewd.outreach@census.gov. . .Symbols:D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableFor a complete list of all economic programs symbols, see the Symbols Glossary.Source: U.S. Census Bureau, 2012 Economic Census.Note: The data in this file are based on the 2012 Economic Census. To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only. For the full technical documentation, see Methodology link in above headnote.