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

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

These are the Airbnb statistics on gross revenue by country.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2024, ************* earned the largest regional share of Airbnb's revenue at ** percent. Meanwhile, the Europe and the Middle East and Africa (EMEA) region ranked second at ** percent.

Comprehensive Airbnb dataset repository offering detailed vacation rental analytics worldwide including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Leverage our Airbnb dataset to gain comprehensive insights into global short-term rental markets. Track property details, pricing trends, reviews, availability, and amenities to optimize pricing strategies, conduct market research, or enhance travel-related applications. Data points may include listing ID, host ID, property type, price, number of reviews, ratings, availability, and more. The dataset is available as a full dataset or a customized subset tailored to your specific needs.

************* was the region that brought in the highest amount of Airbnb’s worldwide revenue in 2024, at ************ U.S. dollars. As the company is based in the United States, this is not surprising. However, the Europe, Middle East, and Africa (EMEA) region was not too far behind with *********** U.S. dollars in revenue.************** also reported the highest average number of nights booked by region with Airbnb in 2024.

Description

This dataset provides extensive information about Airbnb properties listed in Los Angeles, California. It offers a wealth of details suitable for analyzing short-term rental trends, exploring traveler behavior, and studying pricing dynamics within one of the most vibrant tourism markets in the U.S.

Dataset Context and Purpose

As Airbnb continues to impact urban rental markets, this dataset allows analysts, researchers, and real estate professionals to investigate how the short-term rental market shapes the local economy and influences housing availability. Users can leverage this dataset to perform location-based analysis, identify seasonal occupancy trends, and explore the popularity of amenities and property types.

Content

id: Unique identifier assigned to each property listing.

name: Property listing name as provided by the host.

host_id:Unique identifier assigned to the host of the property.

host_name:Name of the host associated with the property.

host_since:Date on which the host joined Airbnb.

host_response_time: Typical response time of the host to guest inquiries.

host_response_rate:Percentage of guest inquiries that the host responded to.

host_is_superhost: Indicates whether the host is a Superhost (True/False).

neighbourhood_cleansed: Neighborhood name where the property is located.

neighbourhood_group_cleansed: Standardized neighborhood group or district where the property is located.

latitude: Geographic latitude coordinate.

longitude: Geographic longitude coordinate.

property_type: Type of property.

room_type: Type of room offered (e.g., Entire home/apt, Private room, Shared room).

accommodates: Maximum number of guests that the property can accommodate.

bathrooms: Number of bathrooms in the property.

bedrooms: Number of bedrooms in the property.

beds: Number of beds in the property.

price: Total price based on minimum nights required for booking.

minimum_nights: Minimum number of nights required for a booking.

availability_365:Number of days the property is available for booking in the next 365 days.

number_of_reviews: Total number of reviews received for the property.

review_scores_rating: Average rating score based on guest reviews (5 is maximum value).

license: License, if applicable.

instant_bookable: Indicates whether guests can book the property instantly (True/False).

Inspiration

• Host Insights: Analyze host behavior, response times, and Superhost status to understand their impact on guest satisfaction and property performance.
• Property Characteristics: Identify popular property types, room types, and amenities, and how they correlate with pricing and occupancy rates.
• Neighborhood Analysis: Explore neighborhood-level trends in pricing, occupancy, and guest reviews to identify popular areas and potential investment opportunities.
• Pricing Strategies: Analyze factors influencing pricing, such as property type, location, amenities, and seasonality.

Source

This dataset is part of Inside Airbnb, Los Angeles California on September 04, 2024. Link found here

Netherlands Airbnb Listings

Location, Property Types and Reviews

By Amber Ewart [source]

About this dataset

This dataset contains detailed information about the accommodation listings on Airbnb including the characteristics of each listing and the feedback from guests. It includes data on host names, years in service, neighbourhoods, cities and states where the listings are located, zip codes, countries, coordinates(latitude/longitude), property type, room type capacity(accommodates), number of bathrooms and bedrooms/ beds as well as other amenities such as bed types. Furthermore pricing data is also included along with extra people charges, minimum nights per stay and host response time / rate. Additionally number of reviews left by guests is also available along with individual ratings based on accuracy cleanliness check-in communication location value etc.. These metrics provide invaluable insights into properties listed on Airbnb giving potential customers an informed decision platform

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How to use the dataset

This dataset contains information about Airbnb listings, including the host information, location, property type, room type, amenities offered, price points of the listings and review scores. This data can be used to understand Airbnb trends in various cities and uncover areas with potential for higher demand.

Analyzing this data can help Airbnb hosts determine high-demand areas for their rental properties and maximize bookings by understanding which amenities are attracting more customers and exactly how much people are willing to pay for different types of accommodation.

Research Ideas

• Identifying popular areas to evaluate new listing opportunities in cities with a high demand for Airbnb rentals
• Analyzing competition among existing listings and identifying key factors that could drive success (e.g., price points, amenities, etc.)
• Predicting future user behavior based on reviews and ratings of existing bookings to provide actionable insights for hosts

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: Unit_1_Project_Dataset (1).csv | Column name | Description | |:---------------------------|:-----------------------------------------------------------------| | host_name | Name of the host of the listing. (String) | | host_since_year | Year the host joined AirBnb. (Integer) | | host_since_anniversary | Anniversary of the host joining AirBnb. (Integer) | | neighbourhood_cleansed | The neighbourhood the listing is located in. (String) | | city | The city the listing is located in. (String) | | state | The state the listing is located in. (String) | | zipcode | The zipcode of the listing. (Integer) | | country | The country the listing is located in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | property_type | The type of property the listing is. (String) | | room_type | The type of room the listing is. (String) | | accommodates | The number of people the listing can accommodate. (Integer) | | bathrooms | The number of bathrooms the listing has. (Integer) | | bedrooms | The number of bedrooms the listing has. (Integer) | | beds | The number of beds the listing has. (Integer) | | bed_type | The type of bed the listing has. (String) | | price | The price of the listing. (Float) | | guests_included | The number of guests included in the price. (Integer) | | extra_people | The additional cost for extra people. (Float) | | minimum_nights | The minimum number of nights a guest must stay. (Integer) | | host_response_time | The time it takes for the host to respond ...

What makes your data unique? - We have our proprietary AI to clean outliers and to calculate occupancy rate accurately.

How is the data generally sourced? - Web scraped data from Airbnb. Scraped on a weekly basis.

What are the primary use-cases or verticals of this Data Product? - Tourism & DMO: A one-page CSV will give you a clear picture of the private lodging sector in your entire country. - Property Management: Understand your market to expand your business strategically. - Short-term rental investor: Identify profitable areas.

Do you cover country X or city Y?

We have data coverage from the entire world. Therefore, if you can't find the exact dataset you need, feel free to drop us a message. Our clients have bought datasets like 1) Airbnb data by US zipcode 2) Airbnb data by European cities 3) Airbnb data by African countries.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2024, the North America region had the largest share of Airbnb's gross booking value, with **** billion U.S. dollars.

See the average Airbnb revenue & other vacation rental data in san antonio in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

See the average Airbnb revenue & other vacation rental data in Pittsburgh in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Airbnb® is an American company operating an online marketplace for lodging, primarily for vacation rentals. The purpose of this study is to perform an exploratory data analysis of the two datasets containing Airbnb® listings and across 10 major cities. We aim to use various data visualizations to gain valuable insight on the effects of pricing, covid, and more!

The region with the most nights and experiences booked with Airbnb worldwide in 2024 was Europe, the Middle East, and Africa (or EMEA). That year, the EMEA region reported *** million bookings. Asia Pacific had the lowest number of bookings at ** million. The Asia Pacific region also had the lowest average number of nights per Airbnb booking in 2024.

Context

Since 2008, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world. This dataset describes the listing activity and metrics in Malibu, Jousha Tree, Brighton (UK) in 2023. The data is owned by Airbtics.

Airbtics is a short-term rental data & analytics company monitoring 20 million listings from various short-term rental booking sites.

Content

This data file includes all the needed information to find out the exact performance of Airbnb listings. You can find out how many nights were booked in a specific month, and average daily rate.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website. The data was processed by Airbtics.

Inspiration

What is the occupancy rate of listing X in January 2023? What is the average daily rate of a listing Y in January 2023? How many bookings did a listing Z receive in January 2023?

To find more granular data in other cities, visit Airbtics.com

In 2025, Airbnbs in Perth, Western Australia, had the highest average occupancy rates across the Australian cities and regions represented, with an average occupancy of around ** percent. Airbnbs in the Surfers Paradise, Brisbane, and Gold Coast areas had the next highest occupancy rates that year.

See the average Airbnb revenue & other vacation rental data in San Francisco in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

The total revenue of Airbnb reached **** billion U.S. dollars in 2024. This was an increase over the previous year's total of **** billion. The decrease in revenue in 2020 can be attributed to the coronavirus (COVID-19) pandemic, which caused travel disruption across the globe. When breaking down Airbnb revenue by region, ***************************************, brought in the most revenue in 2024. Where are Airbnb’s biggest markets? Airbnb is a home sharing economy platform that operates in many countries around the world. The company’s biggest market is in ************* where Airbnb’s gross booking value amounted to **** billion U.S. dollars. Meanwhile, Latin American travelers stayed more nights with Airbnb on average than those in the Asia Pacific region. How did COVID-19 impact Airbnb? The COVID-19 pandemic impacted the travel and tourism industry worldwide, with many countries initiating stay at home orders or travel bans to prevent the spread of the virus. In addition to a decrease in revenue in 2020, the company also experienced a reduction in the number of nights and experiences booked with Airbnb. Bookings fell to under *** million in 2020 due to these travel restrictions. In 2024, Airbnb reported over *** million booked nights and experiences, a significant increase over the previous year.

In 2025, Airbnbs in Queenstown had the highest average occupancy rates across the New Zealand cities and regions represented, with an average occupancy of around ** percent. Airbnbs in the Christchurch and Otago areas had the next highest occupancy rates that year.