The hospitality industry is booming in the last 10 years of India. It is due to growing business opportunities and IT presence in the cities of India, especially metro cities. Here the data is scrapped from MakeMyTrip booking site, which includes price and other information of hotels in different cities of the country. Data was scrapped on 19th August 2023. Only nearly 100 hotels have been added for each city. Other cities will be updated soon.

Available cities🏙️: - Bangalore - Chennai - Hyderabad - Mumbai - Delhi - Kolkata

Data Source: MakeMyTrip🔗

Data Scraping code: GitHub🔗

Columns in dataset: - Hotel Name - Rating - Rating Description - Reviews - Star rating - Location - Nearest Landmark - Distance to the Landmark - Price - Tax

Please Note: 1. Price given here is for one night (base room). 2. Tax given here is slapped on top of the price payable. Therefore, total amount = Price + Tax

In 2024, the Hotel Price Index (HPI) in Spain reached a peak in May at over *** points, which was the highest HPI recorded since at least January 2020 and was above by *** percent versus the same month of the previous year.

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.

Graph and download economic data for Producer Price Index by Industry: Hotels and Motels, Except Casino Hotels (PCU721110721110) from Dec 2003 to Sep 2025 about casino, hotel, PPI, industry, inflation, price index, indexes, price, and USA.

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.

Dataset

This dataset was created by ShreyasBagwe1015

Released under Apache 2.0

Contents

As a result of the coronavirus (COVID-19) pandemic the hotel industry has taken a hit in 2020. In May 2020, the average daily rate (ADR) of hotels in Europe was ***** U.S. dollars. Daily hotel prices were lowest in the Asia Pacific region during the same month.

Hotel rate changes worldwide

In each region, corporate average daily hotel rates are forecast to increase by 2020. Asia’s rates are predicted to be higher than the global average, increasing by about ***** percent. Latin America should see a smaller rise of about *** percent, due to the more modest growth in demand within this region. However, these rates were forecast prior to the coronavirus (COVID-19) pandemic therefore will be subject to change.

Hotel occupancy rate

Average daily rates in the hotel industry tend to change throughout the year as they are closely linked to hotel occupancy rates. Specific regions are visited more frequently during certain times of year. For instance, hotel rooms in the Americas were rented more frequently during the summer months, compared to the colder winter months in 2019.

Expedia Group, Inc. is an American online travel shopping company for consumer and small business travel.

👉🏻 Expedia is the world’s largest online travel agency (OTA) and powers search results for millions of travel shoppers every day. In this competitive market matching users to hotel inventory is very important since users easily jump from website to website. As such, having the best ranking of hotels (“sort”) for specific users with the best integration of price competitiveness gives an OTA the best chance of winning the sale.

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👉🏻 Expedia has provided a dataset that includes shopping and purchase data and information on price competitiveness. The data are organized around a set of “search result impressions”, or the ordered list of hotels that the user sees after they search for a hotel on the Expedia website. In addition to impressions from the existing algorithm, the data contain impressions where the hotels were randomly sorted, to avoid the position bias of the existing algorithm. The user response is provided as a click on a hotel or/and a purchase of a hotel room.

Appended to impressions are the following: 1) Hotel characteristics 2) Location attractiveness of hotels 3) User’s aggregate purchase history 4) Competitive OTA information

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Hotel Price Index: Hotel Price Index (HPI): Coefficient of variation of the national overall index. Monthly. National.

Dataset is about easily finding an ideal hotel and comparing prices from different websites. Hence deciding the best hotel search comparing accommodation prices. Also refining search results, simply filter by price, distance (e.g. from the beach), star category, facilities, and more. It shows the average rating and extensive reviews from other booking sites, e.g. Hotels.com, Expedia, Agoda, leading hotels, etc. The dataset includes hotel budgets from luxury suites to the heavenly paradise resorts. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8355503%2F892a4d89a1795973d64403c1fc9beace%2FHotels%20rev.PNG?generation=1698592504477953&alt=media" alt=""> It includes a large variety of rooms and locations across different popular cities and holiday destinations in the USA.

According to a travel budgeting website, Iceland had the highest price of hotels among Nordic countries as of December 2024. At that time, the average hotel price in the Northernmost European country was at around 450 U.S. dollars for three nights.

The average price of accommodation in hotels and similar lodging establishments in the Brazilian City of Rio de Janeiro in amounted to 117 U.S. dollars in February 2024. The average price of hotel rooms reached the highest peak of the previous year in January, at 115 U.S. dollars.

Hotel Price Index: 2001=100: Weekend data was reported at 110.200 2001=100 in Dec 2007. This records a decrease from the previous number of 111.800 2001=100 for Nov 2007. Hotel Price Index: 2001=100: Weekend data is updated monthly, averaging 107.900 2001=100 from Jan 2000 (Median) to Dec 2007, with 96 observations. The data reached an all-time high of 121.000 2001=100 in Aug 2001 and a record low of 90.170 2001=100 in Apr 2000. Hotel Price Index: 2001=100: Weekend data remains active status in CEIC and is reported by National Statistics Institute. The data is categorized under Global Database’s Spain – Table ES.Q026: Hotel Price Index.

Hotel Price Parity Monitoring Market Outlook

According to our latest research, the global hotel price parity monitoring market size is valued at USD 1.12 billion in 2024 and is expected to reach USD 3.17 billion by 2033, expanding at a robust CAGR of 12.3% over the forecast period. This growth trajectory is primarily driven by the escalating demand for real-time pricing intelligence, the proliferation of online travel agencies (OTAs), and the increasing focus by hotels on optimizing revenue management strategies. The market’s expansion is further fueled by the digital transformation of the hospitality sector and the growing complexity of distribution channels worldwide.

One of the central growth factors for the hotel price parity monitoring market is the intensifying competition among hotels and OTAs. As digital bookings become the norm, maintaining consistent pricing across various distribution platforms is critical for brand reputation and customer trust. Hotels are increasingly investing in advanced software and analytics tools to monitor and enforce price parity, preventing undercutting by third-party sellers. This trend is further accelerated by the rise of meta search engines and aggregators, which make price discrepancies instantly visible to consumers. As a result, the need for comprehensive price parity monitoring solutions has never been more pronounced, driving market growth across all regions.

Another significant driver is the adoption of cloud-based solutions, which offer scalability, real-time data processing, and seamless integration with existing property management systems (PMS) and revenue management systems (RMS). Cloud deployment not only reduces infrastructure costs but also enables hotels to access critical pricing data from any location, thereby enhancing operational efficiency. Furthermore, the integration of artificial intelligence and machine learning into price parity monitoring platforms is enabling predictive analytics and dynamic pricing strategies, allowing hoteliers to respond swiftly to market changes and optimize their revenue streams. These technological advancements are expected to further propel the market in the coming years.

The increasing globalization of the hospitality industry is also contributing to market expansion. As hotel chains and independent properties strive to attract international travelers, the complexity of managing pricing across multiple currencies, languages, and regional regulations becomes a formidable challenge. Price parity monitoring tools help hotels navigate these complexities by providing centralized dashboards, automated alerts, and comprehensive reporting features. This, in turn, ensures compliance with contractual agreements with OTAs and maintains a level playing field in diverse markets. The growing awareness among hoteliers regarding the financial implications of price disparities is expected to sustain the demand for these solutions throughout the forecast period.

Regionally, North America leads the hotel price parity monitoring market, driven by the high penetration of digital booking platforms and the presence of major hotel chains. Europe follows closely, benefiting from a mature hospitality sector and stringent regulatory frameworks governing online pricing. The Asia Pacific region, meanwhile, is witnessing the fastest growth, fueled by rapid urbanization, increasing internet penetration, and the burgeoning travel and tourism industry. Latin America and the Middle East & Africa are also emerging as promising markets, supported by rising investments in hospitality infrastructure and the growing adoption of technology-driven revenue management practices.

Component Analysis

The component segment of the hotel price parity monitoring market is bifurcated into software and services. The software segment dominates the market, accounting for a significant share in 2024, owing to the widespread adoption of automated solutions that streamline the process of tracking, analyzing, and enforcing price parity across multiple distribution channels. These software tools are equipped with advanced features such as real-time rate comparison, customizable reporting, and integration with existing hotel management systems. The increasing complexity of online distribution networks has made manual monitoring impractical, further driving the demand for robust software solutions. As hotels and OTAs continue to expand their digital presence, the need

Hotel Price Intelligence Market Outlook

According to our latest research, the global Hotel Price Intelligence market size reached USD 1.84 billion in 2024, reflecting robust industry growth supported by the increasing adoption of dynamic pricing strategies and advanced analytics within the hospitality sector. The market is projected to grow at a CAGR of 10.7% from 2025 to 2033, ultimately reaching a forecasted value of USD 4.16 billion by 2033. This remarkable expansion is primarily driven by the escalating demand for data-driven decision-making tools, the proliferation of online travel agencies, and the intensifying competition among hotels to optimize occupancy and maximize revenue.

A significant growth factor for the Hotel Price Intelligence market is the surging reliance on sophisticated pricing analytics to enhance revenue management strategies. As hotels face increasing pressure to remain competitive in a rapidly evolving digital landscape, the integration of real-time price intelligence solutions enables them to monitor competitor pricing, analyze market trends, and dynamically adjust their own rates. The growing complexity of distribution channels, coupled with fluctuating demand patterns, has made it essential for hoteliers to leverage advanced software and services that provide actionable insights. This trend is further amplified by the proliferation of online booking platforms and meta-search engines, which have heightened price transparency and intensified the need for competitive benchmarking.

Another key driver propelling the Hotel Price Intelligence market is the widespread adoption of cloud-based deployment models. Cloud technology offers unparalleled scalability, flexibility, and cost-effectiveness, enabling hotels of all sizes to access powerful price intelligence tools without significant upfront investment in IT infrastructure. The shift towards cloud-based solutions has democratized access to advanced analytics, allowing even small and boutique hotels to implement sophisticated revenue management strategies. Moreover, cloud platforms facilitate seamless integration with property management systems, channel managers, and other hospitality software, streamlining operations and enhancing the accuracy of pricing decisions.

Additionally, the growing focus on personalized guest experiences and the need for granular demand forecasting have spurred innovation within the Hotel Price Intelligence market. Hoteliers are increasingly utilizing artificial intelligence (AI) and machine learning algorithms to anticipate booking patterns, segment customer profiles, and optimize rates in real time. These technologies enable hotels to respond swiftly to market shifts, capture incremental revenue opportunities, and build more resilient pricing strategies. The emergence of new data sources, such as social media sentiment and online reviews, is further enriching the analytics landscape, empowering hotels to make more informed pricing decisions and deliver greater value to guests.

From a regional perspective, North America currently dominates the Hotel Price Intelligence market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region's leadership can be attributed to the high concentration of international hotel chains, advanced technological infrastructure, and a mature online travel ecosystem. However, Asia Pacific is expected to witness the highest growth rate over the forecast period, driven by rapid urbanization, expanding tourism sectors, and increasing digitalization across emerging economies. The competitive landscape is also evolving, with local players and global technology providers vying for market share by offering innovative, region-specific solutions tailored to diverse hotel segments.

Component Analysis

The Component segment of the Hotel Price Intelligence market is bifurcated into Software and Services, each playing a pivotal role in shaping the industry’s growth trajectory. The software segment encompasses a wide arr

The average price of accommodation in hotels and similar lodging establishments in Mexico City in amounted to 110 U.S. dollars in February 2024. The average price of hotel rooms reached the highest peak of the previous year in October, at 119 U.S. dollars.

Hotel Price Intelligence Market Outlook

According to our latest research, the global Hotel Price Intelligence market size reached USD 1.42 billion in 2024, with a robust CAGR of 12.8% projected from 2025 to 2033. This growth trajectory is expected to drive the market to USD 4.22 billion by 2033. The primary growth factor for the hotel price intelligence market is the increasing demand for dynamic pricing strategies and real-time data analytics to optimize room rates and maximize revenues in an intensely competitive hospitality sector worldwide.

The hotel price intelligence market is witnessing significant expansion, propelled by the rapid adoption of advanced technologies such as artificial intelligence, machine learning, and big data analytics. These technologies empower hoteliers to analyze vast volumes of market data, competitor pricing, and consumer behavior in real-time, enabling them to make informed pricing decisions. As the hospitality industry becomes more digitized, the need for automated solutions that can efficiently monitor and adjust prices based on market trends and demand fluctuations becomes critical. This digital transformation is further fueled by the proliferation of online travel agencies (OTAs) and meta-search engines, which have intensified price transparency and competition, making price intelligence tools indispensable for hotels aiming to maintain profitability and market share.

Another key growth driver is the shift in consumer booking behavior, with travelers increasingly relying on online platforms to compare hotel prices and seek the best deals. This heightened price sensitivity among consumers compels hotels to adopt sophisticated price intelligence solutions that provide actionable insights for competitive benchmarking and price optimization. The integration of hotel price intelligence systems with property management and revenue management systems streamlines decision-making processes, enhances operational efficiency, and ensures that hotels can respond swiftly to market changes. Furthermore, the rise of personalized guest experiences and loyalty programs necessitates granular pricing strategies, which are made possible through advanced price intelligence platforms.

The ongoing recovery of the global tourism and travel sector post-pandemic is another influential factor driving the hotel price intelligence market forward. As travel restrictions ease and international mobility resumes, hotels are experiencing fluctuating demand patterns that require agile pricing strategies. Price intelligence solutions help hoteliers navigate these uncertainties by providing accurate demand forecasting and competitor analysis, allowing them to capitalize on peak periods and mitigate losses during low seasons. Additionally, the increasing penetration of cloud-based price intelligence platforms has lowered the barrier to entry for small and medium-sized hotels, democratizing access to cutting-edge pricing technologies and expanding the market’s user base.

Regionally, North America and Europe remain at the forefront of market adoption, thanks to their mature hospitality sectors and strong presence of global hotel chains. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid urbanization, growing tourism, and increasing investments in hospitality infrastructure. The Middle East & Africa and Latin America are also showing promising growth, supported by rising international arrivals and expanding hotel portfolios. The competitive landscape is marked by the presence of both established technology vendors and innovative startups, each contributing to the market’s dynamic evolution through continuous product development and strategic partnerships.

Component Analysis

The component segment of the hotel price intelligence market is broadly categorized into software and services. The software segment dominates the market, driven primarily by the growing adoption of advanced analytics platforms that offer real-time pricing insights, competitor benchmarking, and automated pricing recommendations. These software solutions are increasingly leveraging artificial intelligence and machine learning algorithms to deliver predictive analytics, enabling hoteliers to anticipate market trends and optimize their pricing strategies accordingly. The high degree of customization and integration capabilities offered by these platforms further enha

How much does a city trip with your sweetheart cost in Valentine's Day(2025)? Which popular tourist destinations are more budget friendly in Europe? I gathered a dataset which consists of search results from more than 15 cities and 600 hotels in Europe.

I searched booking.com, filtered the results for 2 adults, 5-star hotel, private bathroom, more than 7.0 points in reviews. I preferred to present the prices in USD for enabling future comparisons.

Happy Valentines.

This Dataset contains information about hotels in Yerevan, you need to predict the price of a hotel per day for 2 adults,data taken from booking.com website.

Here about the description of the columns:

Hotel Names: The names of various hotels or accommodations.

Star Rating: The rating system typically used to indicate the quality or level of luxury of a hotel, often ranging from 1 to 5 stars.

Rating: The overall customer rating or satisfaction score of the hotel, often based on reviews or surveys.

Free Parking: Indicates whether the hotel provides complimentary parking facilities for guests.

Fitness Centre: Specifies whether the hotel has a gym or fitness center available for guests.

Spa and Wellness Centre: Indicates if the hotel offers spa services and wellness facilities.

Airport Shuttle: Specifies whether the hotel provides transportation to and from the airport for guests.

Staff: Refers to the quality and friendliness of the hotel staff.

Facilities: Describes the range and quality of amenities available at the hotel.

Location: Refers to the convenience and desirability of the hotel's location.

Comfort: Reflects the overall comfort level of the rooms and bedding.

Cleanliness: Indicates the cleanliness and hygiene standards of the hotel.

Price Per Day ($): The cost of staying at the hotel (2 adults) per day (in dollars in this case).

Average achieved hotel room rate in Hong Kong in the past five years