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 dataset contains locations and attributes of Hotels, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the DC Taxi Commission (DCTC) and research at various commercial websites identified Hotels and DC GIS staff geo-processed the data.

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

The OTA, booking websites have a ton of information like pricing, promotions, occupancy reviews, etc about hotels. Our data as a service offering helps our customers get this data through web scraping. The data is refreshed every day and delivered to our customers via Amazon S3, The most common use cases are competitive intelligence and marketing spend optimization.

[Metadata] Locations of the Visitor Plant Inventory (VPI) in the State of Hawaii (updated December, 2021). Source: Hawaii Tourism Authority, September 2022. This inventory includes apartment hotels, bed and breakfasts (B&Bs), condominium hotels, hostels, hotels, individual vacation units, timeshares, and other types of visitor accommodations.

The Hotel Area KPIs dataset provides comprehensive insights into hotel performance metrics across global markets.

Sourced directly from hotel reservation systems, this dataset offers a real-time view of key performance indicators such as occupancy rates, average daily rates (ADR), revenue per available room (RevPAR), and booking patterns.

With weekly updates and both historical and forward-looking data, it enables hoteliers, investors, and analysts to track market trends, benchmark performance, and make data-driven decisions.

This dataset is invaluable for understanding seasonal variations, forecasting demand, and optimizing pricing strategies in the dynamic hospitality industry.

This dataset includes quantitative and categorical features from online reviews from 21 hotels located in Las Vegas Strip, extracted from TripAdvisor (http://www.tripadvisor.com). All the 504 reviews were collected between January and August of 2016. The dataset contains 504 records and 20 tuned features (as of “status = included”, from Table 1 of the article mentioned below), 24 per hotel (two per each month, randomly selected), regarding the year of 2015.

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).

Data on the average achieved hotel room rate by hotel category in Hong Kong in the past five years

Hotel Revenue Management System Market Outlook

The global hotel revenue management system market size is projected to experience significant growth over the years, with estimations indicating a climb from USD 2.4 billion in 2023 to approximately USD 5.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.8%. This remarkable growth can be attributed to several factors, including the increasing adoption of technology-driven solutions in the hospitality industry, a growing emphasis on optimizing operational efficiency, and the rising need for data-driven decision-making processes. As the hospitality sector continues to evolve, the role of hotel revenue management systems becomes increasingly critical in ensuring competitive advantage and profitability.

One of the primary growth factors for the hotel revenue management system market is the continuous technological advancements in the hospitality sector. The adoption of artificial intelligence, machine learning algorithms, and big data analytics has revolutionized how hotels manage their revenue streams. These technologies enable hoteliers to predict demand patterns more accurately, optimize pricing strategies, and enhance guest experiences, thereby driving revenue growth. Moreover, the integration of these advanced technologies into revenue management systems allows hotels to tailor their services to meet the ever-changing needs of their guests, ensuring personalized experiences and higher customer satisfaction, which in turn enhances brand loyalty and repeat business.

Another critical driver for the market is the growing competition in the hospitality industry, which necessitates the adoption of sophisticated revenue management systems. With an increasing number of hotels entering the market, hoteliers are under pressure to maximize their revenue and maintain profitability. Revenue management systems provide a strategic approach to revenue optimization by analyzing various factors such as market demand, competitor pricing, and booking patterns. By leveraging these insights, hotels can implement dynamic pricing strategies, optimize room inventory, and improve their sales and marketing efforts. This results in increased occupancy rates, higher average daily rates, and ultimately, enhanced revenue performance.

Furthermore, the rising importance of data-driven decision-making processes in the hospitality sector is also propelling the growth of the hotel revenue management system market. In an era where data is considered the new oil, hotels are increasingly relying on data analytics to gain valuable insights into their operations and customer preferences. Revenue management systems equipped with powerful analytics tools enable hoteliers to make informed decisions based on real-time data, allowing them to identify trends, assess market conditions, and adjust their strategies accordingly. This data-driven approach not only helps hotels stay ahead of the competition but also ensures efficient resource allocation and improved financial performance.

From a regional perspective, North America currently dominates the hotel revenue management system market, owing to the high concentration of luxury and high-end hotel chains in the region. The increasing prevalence of advanced technologies and the presence of key market players contribute to the region's strong market position. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid expansion of the hospitality sector in countries such as China, India, and Southeast Asian nations. This growth is further fueled by the rising disposable incomes of the middle-class population and the increasing demand for travel and tourism in the region.

Component Analysis

The hotel revenue management system market is primarily segmented by component into software and services. Within this segment, the software component is anticipated to hold a significant share, driven by the increasing demand for sophisticated and efficient management solutions that can optimize hotel operations and boost revenue. Advanced software solutions offer features such as dynamic pricing, demand forecasting, and real-time analytics, which are essential for the effective management of hotel revenues. These software solutions are continuously being enhanced with new capabilities, such as AI-driven insights and automation features, which are attracting more hotel operators to invest in such systems.

On the other hand, the services component within the hotel revenue management system market also plays a crucial role in supporting

Monthly hotel occupancy rate (%) by district in the past five years

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.

Unlock comprehensive Hospitality, Travel Tourism Data, including Hotel Rates & Pricing, Flight, and Restaurant Data. Dive into insights from Online Travel Agencies (OTAs) and Short-Term Rentals. Explore tourist attractions with our Tourism Data. Take advantage of APISCRAPY Data today!

The hotel industry is essential for tourism. With the rapid expansion of the internet, consumers only search for their desired keywords on the website when they trying to find a hotel to stay, causing the relevant hotel information would appear. To quickly respond to the changing market and consumer habits, each hotel must focus on its website information and information quality. This study proposes a novel methodology that uses rough set theory (RST), principal component analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE), and attribute performance visualization to explore the relationship between hotel star ratings and hotel website information quality. The collected data are based on the star-rated hotels of the Taiwanstay website, and the checklists of hotel website services are used to obtain the relevant attributes data. The results show that there are significant differences in information quality between hotels below two stars and those above four stars. The information quality provided by the higher star hotels was more detailed than that offered by low-star hotels. Based on the attribute performance matrix, the one-star and two-star hotels have advantage attributes in their landscape, reply time, restaurant information, social media, and compensation. Furthermore, the three-five star hotels have advantage attributes in their operational support, compensation, restaurant information, traffic information, and room information. These results could be provided to the stakeholders as a reference.

No. of Hotel Room: Tourist Center: Culiacan data was reported at 12,033.000 Unit in 31 Mar 2024. This stayed constant from the previous number of 12,033.000 Unit for 24 Mar 2024. No. of Hotel Room: Tourist Center: Culiacan data is updated weekly, averaging 16,709.000 Unit from Jan 2006 (Median) to 31 Mar 2024, with 952 observations. The data reached an all-time high of 19,915.000 Unit in 28 Nov 2021 and a record low of 12,033.000 Unit in 31 Mar 2024. No. of Hotel Room: Tourist Center: Culiacan data remains active status in CEIC and is reported by Secretary of Tourism. The data is categorized under Global Database’s Mexico – Table MX.Q019: Number of Hotel Room: by Tourist Center. [COVID-19-IMPACT]

Metrics that can be unearthed will be ones contained in the email booking invoice such as Hotel name, type of room, dates, check in and check out times, price paid, duration of stay. We can go back to 5 years of history.

We also have cancellation emails.

Any hotel vendor can be requested too. We will conduct a search in our database to see if it justifies a parser build to extract the data.

Please contact michelle@measurable.ai for a demo or more data samples.

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.

According to an analysis published in July 2024, there were *** companies operating in the hotel and holiday accommodation industry in Malta as of that month. Such businesses, which employed ***** people overall, were expected to generate nearly *** million euros in revenue.

No. of Hotel Room: Tourist Center: Tuxtla Gutiérrez data was reported at 29,317.000 Unit in 31 Mar 2024. This records an increase from the previous number of 29,301.000 Unit for 24 Mar 2024. No. of Hotel Room: Tourist Center: Tuxtla Gutiérrez data is updated weekly, averaging 24,465.000 Unit from May 2003 (Median) to 31 Mar 2024, with 1076 observations. The data reached an all-time high of 31,346.000 Unit in 31 May 2020 and a record low of 15,126.000 Unit in 06 Jul 2003. No. of Hotel Room: Tourist Center: Tuxtla Gutiérrez data remains active status in CEIC and is reported by Secretary of Tourism. The data is categorized under Global Database’s Mexico – Table MX.Q019: Number of Hotel Room: by Tourist Center. [COVID-19-IMPACT]