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.

We will create a customized hotels 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 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 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 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.

The Global Hotel Data dataset is an extensive collection of data providing insights into the hotel industry worldwide. This dataset encompasses diverse information, including hotel profiles, room types, amenities, pricing, occupancy rates, and guest reviews. With a size of 500k lines, the Global Hotel Data offers valuable information for hotel chains, independent hotels, travel agencies, and researchers to understand market trends, optimize pricing strategies, and enhance guest experiences globally.

Features:

1. Hotel Profiles: Data on hotel properties, including location, star ratings, facilities, and contact information, enabling analysis of hotel distribution and market positioning.
2. Room Types: Information about room categories, room sizes, bed configurations, and amenities, allowing travelers to compare room options and make informed booking decisions.
3. Amenities: Details about hotel amenities and services, such as Wi-Fi availability, parking facilities, swimming pools, and restaurants, facilitating guest preferences and satisfaction assessments.
4. Pricing: Metrics such as room rates, seasonal pricing variations, and promotional offers, enabling hoteliers and revenue managers to optimize pricing strategies and maximize revenue.
5. Occupancy Rates: Data on room occupancy rates, demand patterns, and booking lead times, allowing hoteliers to forecast demand, manage inventory, and allocate resources effectively.
6. Guest Reviews: Ratings and feedback from guests regarding their stay experience, including cleanliness, staff friendliness, and overall satisfaction, providing insights into hotel reputation and service quality.
7. Size: The dataset comprises 500 thousand lines of data.

Potential Applications:

1. Competitive Analysis: Hotel chains can analyze competitor profiles, pricing strategies, and guest reviews to benchmark their performance and identify areas for improvement and differentiation.
2. Revenue Optimization: Revenue managers can use the dataset to monitor demand trends, adjust pricing strategies dynamically, and maximize revenue through strategic pricing and distribution decisions.
3. Guest Experience Enhancement: Hoteliers can leverage guest feedback and reviews to identify service gaps, address guest concerns, and enhance overall guest satisfaction and loyalty.
4. Market Research: Travel agencies and researchers can analyze hotel data to understand market trends, traveler preferences, and destination popularity, informing product development and marketing strategies.

Usage Considerations:

1. Data Privacy: Protect guest privacy and personal information in compliance with data protection regulations, ensuring secure handling and storage of sensitive guest data while analyzing and sharing hotel data.
2. Data Quality: Validate the accuracy and reliability of hotel data sources and performance metrics to ensure data integrity and reliability for decision-making and analysis.
3. Ethical Use: Use hotel data ethically and responsibly, respecting guest consent and preferences regarding data collection, usage, and sharing in hotel analytics and research.

Disclaimer:

While the Global Hotel Data dataset provides valuable insights into the hotel industry and guest preferences, users are reminded to use the data responsibly and ethically. Hotel data analytics should be interpreted with caution, considering factors such as data biases, seasonal variations, and market dynamics, and any actions taken based on the dataset should prioritize guest satisfaction, data privacy, and regulatory compliance.

Data includes occupancy rates, average daily rates, and revenue per available room.

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.

Smart Hospitality Management Market Outlook

The global smart hospitality management market size is projected to see significant growth from 2023, valued at approximately $18.5 billion, to an anticipated $54.3 billion by 2032, reflecting a strong compound annual growth rate (CAGR) of 12.8%. The driving force behind this impressive expansion is the growing demand for efficient and personalized guest experiences in the hospitality industry. The integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing is revolutionizing how hospitality services are managed, ensuring streamlined operations and enhanced customer satisfaction. This surge is predominantly fueled by the increasing adoption of smart technologies that cater to the evolving expectations of modern-day travelers who demand seamless and personalized experiences.

One of the most significant growth factors in the smart hospitality management market is the increased emphasis on enhanced guest experiences. Today’s travelers are looking for more than just a place to stay; they want experiences that are seamless, personalized, and convenient. Smart technologies in hospitality facilitate a variety of personalized guest services, such as automated check-ins, customized room settings, and virtual concierge services. These technologies not only enhance guest satisfaction but also empower hotels to manage their resources more efficiently by reducing manual work and minimizing human errors. Furthermore, the ability to analyze customer preferences and behaviors through big data analytics enables hospitality providers to tailor their services and create a more personalized experience, which in turn drives customer loyalty and repeat business.

Another key driver in this market is the operational efficiency offered by smart hospitality solutions. With the integration of IoT, AI, and cloud-based systems, hospitality providers can significantly optimize their operations. These technologies enable real-time monitoring and management of resources, which leads to reduced costs and improved service delivery. For example, smart energy management systems help in reducing energy consumption by automating lighting and climate control based on occupancy, thereby contributing to sustainability initiatives and cost savings. Additionally, predictive maintenance enabled by IoT sensors can foresee potential equipment failures, thus minimizing downtime and extending the lifespan of assets. This operational efficiency not only enhances the bottom line of hospitality businesses but also aligns with the growing trend of sustainable and eco-friendly practices within the industry.

The burgeoning trend of technology adoption in smaller and budget hospitality establishments is also contributing to the market's growth. While luxury hotels were the early adopters of smart technologies, smaller hotels and budget accommodations are increasingly recognizing the value these systems bring. By implementing scaled-down versions of smart solutions, these establishments can improve their competitiveness without incurring the high costs associated with extensive renovations. The democratization of technology, with affordable and customizable solutions, is enabling a wider range of hospitality providers to enhance their operational capabilities and guest experiences. This trend is particularly pronounced in emerging markets where smaller establishments are looking to differentiate themselves and capture a broader customer base through improved service offerings.

Regionally, the Asia Pacific is expected to witness the highest growth rate in the smart hospitality management market. The region’s booming tourism industry, coupled with rapid urbanization and technological advancements, makes it a hotbed for smart hospitality solutions. Additionally, governmental support for smart city projects and increased investment in tourism infrastructure contribute to the robust growth in this region. The North American market, on the other hand, is characterized by early technology adoption and a strong focus on innovation, maintaining a significant share of the global market. In Europe, stringent regulations around sustainability and data privacy drive the adoption of smart solutions that comply with these standards, while Middle East & Africa see a surge in luxury tourism and mega-events, further boosting the demand for advanced hospitality technologies.

Component Analysis

In the smart hospitality management

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

In fiscal year 2024, the occupancy rate of hotels in India was estimated to be 67.5 percent. This was an increase from about 66 percent in the previous year. However, it is slowly bouncing back to pre-pandemic rate. Mumbai – a city that never sleepsMumbai, the capital of Maharashtra, witnessed a slow-paced growth in the hotel industry in recent years mainly due to the changes in real estate policies and exorbitant land prices. Despite this, the financial and entertainment capital of India outpaced all other major markets in the country by achieving the highest occupancy rate and revenue per available room in 2020. With the availability of international convention centers like the Jio World Centre located in the heart of commercial district of Bandra-Kurla Complex, Mumbai was the preferred business location in the country. In 2021, it still had the highest occupancy rate and revenue per available room rate, but at a much lower level. Leading hotel companyIn 2019, the Indian Hotels Company Limited stood out as India’s largest hospitality company by net sales. One of the first hotels opened by this company was the Taj Mahal hotel located in Mumbai. Opened almost a century ago, the Taj Mahal hotel has hosted some of the most illustrious guest from all over the world. Ever since the hotel has held on to the legacy of providing warm hospitality and world-class facilities. Well known for its grandeur, the Taj remains a hallmark of Indian hospitality year after year.

Beijing: Star-Rated Hotel: Room Occupancy Rate: 5 Star data was reported at 61.300 % in Dec 2024. This records a decrease from the previous number of 64.400 % for Nov 2024. Beijing: Star-Rated Hotel: Room Occupancy Rate: 5 Star data is updated monthly, averaging 64.750 % from Jan 2008 (Median) to Dec 2024, with 184 observations. The data reached an all-time high of 80.200 % in Aug 2019 and a record low of 8.300 % in Apr 2020. Beijing: Star-Rated Hotel: Room Occupancy Rate: 5 Star data remains active status in CEIC and is reported by Beijing Municipal Commission of Tourism Development. The data is categorized under China Premium Database’s Hotel Sector – Table CN.QHRA: Star-Rated Hotel: Beijing.

Local Hotel Occupancy Tax (HOT) data has been compiled by municipalities complying with Tax Code Section 351.009 since 2018. In January 2021, counties began reporting their HOT data in accordance with Tax Code Section 352.009.

If local HOT data related to a specific municipality or county is not available in this dataset, it may be because that entity does not levy such a tax or that the local government failed to submit their information to the Comptroller's office within the specified reporting period.

The data reported through the Comptroller's Local HOT Submission Form and available in this dataset is self-reported by submitting municipalities, counties, or third parties on their behalf and has not been independently verified by the Texas Comptroller of Public Accounts. Specific questions or concerns regarding a local government's HOT rate, revenue, allocations and/or submitted webpage links should be directed to that entity.

General questions regarding this spreadsheet, Tax Code Sections 351.009 or 352.009 may be directed to the Comptroller’s Transparency Team, either by email (transparency@cpa.texas.gov) or by phone (844-519-5676).

Average Occupancy Hotel Rate: City: Coimbatore data was reported at 44.700 % in 2017. This records a decrease from the previous number of 57.900 % for 2016. Average Occupancy Hotel Rate: City: Coimbatore data is updated yearly, averaging 65.500 % from Mar 1999 (Median) to 2017, with 16 observations. The data reached an all-time high of 79.700 % in 2006 and a record low of 44.700 % in 2017. Average Occupancy Hotel Rate: City: Coimbatore data remains active status in CEIC and is reported by The Federation of Hotel & Restaurant Associations of India. The data is categorized under Global Database’s India – Table IN.QHC003: Indian Hotel Industry Survey: Average Hotel Occupancy Rate: by Cities.

China Hotel Room Occupancy Rate: Total data was reported at 50.690 % in 2023. This records an increase from the previous number of 38.350 % for 2022. China Hotel Room Occupancy Rate: Total data is updated yearly, averaging 56.180 % from Dec 1999 (Median) to 2023, with 25 observations. The data reached an all-time high of 61.030 % in 2006 and a record low of 38.350 % in 2022. China Hotel Room Occupancy Rate: Total data remains active status in CEIC and is reported by Ministry of Culture and Tourism. The data is categorized under Global Database’s China – Table CN.QHA: Star-Rated Hotel: Room Occupancy Rate.

Utilize our Trivago dataset for diverse applications to enrich business strategies and market insights. Analyzing this dataset can aid in understanding travel and hospitality industry dynamics and trends, empowering organizations to refine their service offerings and marketing strategies. Access the entire dataset or tailor a subset to fit your requirements.

Popular use cases include pricing optimization, where organizations can define a pricing strategy and create dynamic pricing models by comparing similar accommodations and categories among competitors. Additionally, the dataset helps in identifying gaps in hotel inventory, recognizing increased demand for certain destinations, and spotting travel trends that are gaining popularity with consumers. Furthermore, it supports market strategy optimization by leveraging insights to analyze key travel trends and customer preferences, enhancing overall business decision-making.

This dataset features hotel review data from TripAdvisor, specifically focusing on the La Misión Hotel Boutique located in Asunción, Paraguay. It offers valuable insights into the traveller's decision-making process by including both the numerical rating (bubble score) and the complete review text. The reviews are predominantly in Spanish, with some content also in English. This data is key for understanding customer sentiment and feedback within the hospitality sector.

Columns

• review_body: Contains the full text of the hotel review, primarily in Spanish.
• review_date: Specifies the date on which the review was posted.
• rating: Represents the TripAdvisor bubble score assigned to the review, indicating the hotel's rating.

Distribution

The dataset is typically provided as a data file, commonly in CSV format. The review_date column includes 695 distinct date entries, covering a period from 08 October 2009 to 11 January 2019. The rating column contains 5 distinct values, ranging from 1.00 to 5.00. The distribution of ratings is as follows: 4 reviews between 1.00-1.40, 2 reviews between 1.80-2.20, 12 reviews between 3.00-3.40, 124 reviews between 3.80-4.20, and 553 reviews between 4.60-5.00.

Usage

This dataset is highly suitable for various applications, including: * Text Mining: For extracting meaningful patterns and insights from unstructured review content. * Natural Language Processing (NLP): For developing and training models for sentiment analysis, topic extraction, or language understanding related to hotel reviews. * Market Research: To analyse consumer behaviour and identify trends in customer satisfaction within the hospitality industry. * Hospitality Management: To inform strategic decisions regarding guest experience and service enhancements.

Coverage

The geographic focus of this dataset is Asunción, Paraguay, pertaining specifically to reviews of the La Misión Hotel Boutique. The reviews were collected on 15 January 2019, with the review dates themselves spanning from 08 October 2009 to 11 January 2019. While geographically specific, its relevance extends globally for wider analytical purposes.

License

CC BY-SA

Who Can Use It

This dataset is appropriate for: * AI and Machine Learning Developers: Especially those creating models for text analysis or sentiment detection. * Data Scientists and Analysts: Interested in social network data, text mining, and ratings analysis within the travel domain. * Academic Researchers: Studying consumer behaviour, hotel industry trends, or language analysis in a real-world context. * Businesses in Hospitality: Looking to gain a deeper understanding of customer feedback and improve their services.

Dataset Name Suggestions

• TripAdvisor Asunción Hotel Reviews
• La Misión Hotel Boutique Guest Ratings
• Spanish Hotel Review Sentiment Data
• Paraguay Hotel Traveller Feedback
• Asunción Hotel TripAdvisor Insights

Attributes

Original Data Source: TripAdvisor-LaMisionHotelBoutique-Asunción-PYG-es

We'll customize an Expedia dataset to align with your unique requirements, incorporating data on hotel types, room rates, customer reviews, booking trends, demographic insights, and other relevant metrics.

Leverage our Expedia datasets for various applications to strengthen strategic planning and market analysis. Examining these datasets enables organizations to understand consumer preferences and travel trends, facilitating refined service offerings and optimized booking strategies. Tailor your access to the complete dataset or specific subsets according to your business needs.

Popular use cases include optimizing travel package offerings based on consumer insights, refining marketing strategies through targeted customer segmentation, and identifying and predicting trends to maintain a competitive edge in the travel industry.

India IHIS: Average Hotel Occupancy Rate: Monthly: Five-Star Deluxe: May data was reported at 59.500 % in 2018. This records a decrease from the previous number of 70.100 % for 2017. India IHIS: Average Hotel Occupancy Rate: Monthly: Five-Star Deluxe: May data is updated yearly, averaging 60.000 % from Mar 2000 (Median) to 2018, with 19 observations. The data reached an all-time high of 71.600 % in 2007 and a record low of 45.300 % in 2000. India IHIS: Average Hotel Occupancy Rate: Monthly: Five-Star Deluxe: May data remains active status in CEIC and is reported by Federation of Hotel & Restaurant Associations of India. The data is categorized under India Premium Database’s Hotel Sector – Table IN.QHC004: Indian Hotel Industry Survey: Average Hotel Occupancy Rate: Monthly.

Egypt Hotel Room Occupancy Rate: Average data was reported at 38.700 % in 2020. This records an increase from the previous number of 38.600 % for 2019. Egypt Hotel Room Occupancy Rate: Average data is updated yearly, averaging 53.500 % from Dec 1991 (Median) to 2020, with 30 observations. The data reached an all-time high of 73.000 % in 2000 and a record low of 30.000 % in 2016. Egypt Hotel Room Occupancy Rate: Average data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Egypt – Table EG.Q018: Hotel Room Occupancy Rate.

In the early 1970s, the Texas Legislature authorized certain local governments to begin collecting a hotel occupancy tax (HOT). Almost two decades later, the Legislature offered hotel occupancy taxing authority as one of several revenue options to support sports and community venues. The tax may be levied by a city, county or a partnership between the two. Throughout the years, the Texas Legislature has passed laws that increased local government transparency while also allowing the public to better understand the state’s patchwork of municipal and county HOTs. During the 88th Legislative Session, House Bill 3727 and Senate Bill 1420 were passed to require municipalities and counties to report the amount and percentage of HOT revenue allocated by the local government. While the Comptroller’s office is not required to post submitted local HOT information on its website, this office nonetheless intends to make available all municipal and county data provided to it during the reporting period. The data will be available shortly after the reporting period closes. Questions about the spreadsheet can be sent to the Transparency and Local Government Teams at 844-519-5676 or sent to Transparency@cpa.texas.gov.