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.

Dataset

This dataset was created by sajin

Contents

Dataset

This dataset was created by surya

Released under Apache 2.0

Contents

Dataset

This dataset was created by nickwu7

Contents

In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

These Airbnb statistics detail how fast Airbnb is currently growing and where it’s going in the future.

Here are the numbers on the countries with the most nights booked on Airbnb in 2020 and 2021.

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.

Content

The following Airbnb activity is included in this Seattle dataset: * Listings, including full descriptions and average review score * Reviews, including unique id for each reviewer and detailed comments * Calendar, including listing id and the price and availability for that day

Inspiration

• Can you describe the vibe of each Seattle neighborhood using listing descriptions?
• What are the busiest times of the year to visit Seattle? By how much do prices spike?
• Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?

For more ideas, visualizations of all Seattle datasets can be found here.

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here.

Airbnb has a total of 6,132 employees that work for the company. 52.5% of Airbnb workers are male and 47.5% are female.

The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

The current average price per night globally on Airbnb is $137 per night.

These are the Airbnb statistics on gross revenue by country.

Dataset

This dataset was created by Michaela Solomon

Contents

The dataset presents the outcomes of a PhD study investigating how municipalities manage the ethical dilemmas arising from the competing interests of multiple stakeholders in governing the shared accommodation industry. Platform enterprises operating in SA have altered how people think about paying for a place to stay, whether for social housing, business or leisure purposes. Some of these changes have had mixed results, leaving municipalities to deal with ethical dilemmas from a management and governance perspective. The inquiry was conducted through a qualitative multiple case study method using the cities of Cape Town and eThekwini municipalities as units of analysis. Semi-structured interviews and observations were the primary techniques for collecting the data from 20 research participants drawn from both municipalities, as well as from external private and public sector and community organisations. The study used the purposeful, snowballing and opportunistic sampling techniques to maximize the opportunity to get more insights from the multiple research participants. Thematic analysis of the qualitative data from semi-structured interviews was used. Following Collis and Hussey (2021), the analysis of data commenced immediately during the transcription process of the interviews. Upon completion of the interviews, the qualitative data underwent content analysis, employing Otter.ai for transcription and identifying response patterns. The first transcriptions of the interviews were then cross-checked with memos and observation notes made by the researcher during the interview phases. Following the feedback, the transcribed interview data was coded and concepts were produced. These concepts were then merged to form categories. The categories and the interpretations of the interviews were triangulated using memos, observation notes, and documents obtained from the two municipalities and organisations such as Airbnb and Tourism Grading Council of South Africa. The researcher adopted the common ways of coding recommended by other qualitative researchers (Myers, 2019; Rashid et al., 2019; Yin, 2018). The adopted procedure involves following a four-step approach for interpreting the research material, viz: preparation, exploration, specification, and integration. The four-step technique provided a more organised and systematic method of interpretation, which proved useful in the presentation of the research data. Once the individual interviews were transcribed with rigorous analysis, the responses to both sets of research questions were extracted and organised to produce into two data summary tables. One data summary table recorded the research participants’ key responses to the primary research questions, separating the responses of the internal research participants (municipal employees) from the external research participants (stakeholders including businesses and community organisations). In the same manner, the second data summary table recorded the research participants’ key responses to the secondary research questions. These data summary tables included the research participants’ recommendations for improved governance for both municipalities. A separate consolidated data summary table was developed to capture the data of the research participants with a national footprint including their recommendations. The dataset include the customised "Interview questionnaire" that were used in interviewing the two categories of research participants in each municipaity; and a third “Interview Questionnaire” for the research participants with a national footprint.

These are the cities that had the most demand. London is the most popular city.

De gemeente Groningen wil het huidige Ruimtelijke Beleidskader Hotelsector Groningen uit 2004 actualiseren. Gezien het toenemende aantal aanvragen voor hotelontwikkelingen, de ontwikkeling van serviced apartments en de invloed van particuliere verhuur (zoals Airbnb), is het daarvoor van belang om inzicht te krijgen in hoe de overnachtingenmarkt zich heeft ontwikkeld en wat de verwachtingen zijn voor de toekomst.

The most Airbnb listings are in the US, with an average of 2.25 million active listings throughout 2021.

London is the city with the most Airbnbs listings in the world at 156,511.

The average host on Airbnb earns $13,800 annually. The fastest-growing host demographic is seniors.

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.