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. North America averaged *** nights per Airbnb booking in 2024, more than any other region that year

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.

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

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. The platform also allows consumers to book "experiences" in the regions they visit. In 2024, Airbnb reported over *** million booked nights and experiences. How much revenue does Airbnb make? In 2024, the total revenue of Airbnb worldwide increased by nearly ten percent over the previous year. This continued the upward trend which the company has experienced since recovering from the coronavirus (COVID-19) pandemic. ************* generated the highest share of Airbnb’s worldwide revenue in 2024, at **** billion U.S. dollars. How many people visit the Airbnb website? Airbnb ranked ***** among the most popular travel and tourism websites worldwide based on average monthly visits, behind *******************************. In 2024, airbnb.com saw its highest number of unique global visitors in March, at *** million. Meanwhile, Airbnb ranked fourth among leading travel apps globally, with over ** million downloads 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

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.

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.

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

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

This statistic shows the average number of nights per Airbnb booking in the United States and Europe from 2015 to 2016. It also shows the estimated average number of nights per booking from 2017 to 2018. In 2018, the estimated average number of nights per Airbnb booking is ***.

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

As of December 2024, San Diego recorded the highest average price per night of Airbnb listings among the selected cities in the United States. In this city, accommodation listed on the Airbnb website cost on average *** U.S. dollars per night. Meanwhile, prices in New York City amounted to an average of *** U.S. dollars per night.

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

Airbnb Global Accommodation and Reviews

Exploring Location-Based Listing Data

By Debayan Kar [source]

About this dataset

The Airbnb Global Dataset contains a wealth of information about the locations, availability, reviews and other details related to short-term rentals available around the world. Use this dataset to explore how guests rate their experiences, discover new places in various neighbourhood groups and geographical locations, compare prices of different room types, consider minimum nights required for bookings and more! With this data set you can evaluate factors associated with: host name; neighbourhood group; latitude & longitude; room type; price; minimum nights required for bookings; number of reviews - both in total and over the last 12 months (number_of_reviews_ltm); license (if applicable); last review received; average number of reviews per month (reviews per month) as well as calculated host listing counts which reflect seasonal demand variations. With this information at your fingertips you could travel anywhere your heart desires - so let's turn those dreams into reality!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The following guide will help you get started in your journey to gain insights from this data set.

First, specify the fields that you want to focus on. In order to do this, make sure you take into consideration the columns available within this dataset. By doing so, not only are you able to hone in on specific aspects of Airbnb accommodation and reviews (i.e neighborhood groups, room types or even pricing), but also identify themes or common trends among listings which could prove useful when formulating hypotheses.

Once you have identified which fields will be useful for analysis, it is important that they are converted into appropriate data types if they need any sort of conversion at all (i.e converting strings to integers). Moreover, make sure there are no inconsistencies across your features when exploring the entries in those columns; take care of them before any substantial analysis is done.

You are now ready for some exploratory analysis! Start by creating visualizations such as bar graphs or box plots in order to get an overview of particular aspects related to listings (i.e distribution of prices around a neighbourhood group) - these can be very useful indicators! Then try out correlations between different exponential variable datasets such as availability_365 versus minimum_nightsand explore how they fluctuate with changes in pricing over time - examining how these relationships relate over different locations can yield interesting results like unexpected concentration points which demand research! Another field worth exploring would be reviews associated with each listing by digging down into their components like ratings breakdowns under different criteria such as security/price value ratio etc.. All these evaluations should give an excellent outline on what potential customers might look out for while browsing through options online so as entrepreneurs we can hover upon those trends specially mentioning needs fulfilled during our advertisement campains.... Lastly examine publicly available information about each host such as number_of_reviews or calculated_listings count variation over time , with ability provided here we have ample opportunities predicting customer opinion about newly created businesses offering same services...so many things one could dive deep !

Overall , after gaining ample amount insights taking about current market scenario it’s best suggested procuring feedback from active host & using it devise plans bringing mutual mutually beneficial solutions making both hosts & guests happy . This is where creativity play huge role designing perks forming long lasting trust inducing relationship between service providers &

Research Ideas

• Predicting price points for Airbnb listings based on factors such as room type, neighborhood group, and reviews.
• Identifying areas with a high demand for Airbnb rentals, by looking at the ratio of availability to number of reviews for listings in different neighborhoods.
• Analyzing guest satisfaction levels based on factors such as room type and location, by correlating the reviews_per_month with the number_of_reviews indicator and other variables in the dataset

Acknowledgements

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

License

See the dataset description ...

Barcelona Airbnb Listings

Understanding Rental Prices and Trends in the City

By Kelly Garrett [source]

About this dataset

This dataset contains detailed information about Airbnb listings for the city of Barcelona, Spain, including reviews from guests and hosts, ratings, neighborhoods and more. With over 16000 observations collected from nearly 5000 unique listings, it offers great insight into the demand and popularity of different types of accommodation in Barcelona. It also provides detailed insights into the quality of each listing such as its exact location, number of bedrooms and Cleanliness Rating. Additionally, this dataset gives an opportunity to explore what kind of amenities each listing has to offer (such as parking or internet) and how they affect price range. Ultimately this data allows users to analyze different types of accommodations in Barcelona in order to discover key trends within the rental market - which locations are most popular amongst visitors? Which kinds amenities are associated with higher-priced rentals? How do ratings compare across neighborhoods?

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset is a great way to gain insight into what Barcelona has to offer in terms of Airbnb listings. It provides information on over 19,000 listings throughout the city with details such as availability and pricing, as well as listings that include reviews and amenities offered. Through exploring this dataset you will be able to identify trends in Airbnb's presence in Barcelona and make better informed decisions when booking your stay.

To begin using this dataset: - Start by getting an overview of the data by considering the columns present in the dataset such as 'neighbourhood_group',‘room_type’, ‘price’, 'number_of_reviews' etc., and determining how each of these features influence your analysis or search for certain key properties that interest you. - Gain further insight about individual properties through exploring related columns such as 'amenities' or 'host_name'.
- Identify geographic areas that have higher concentrations of Airbnb's using visualizations or clustering techniques to better understand which neighbourhoods have more activity or data points associated with them making for a potentially more enjoyable stay based on customer ratings & reviews etc,.
- Use summary statistics and rankings (such as describing how far you are from main attractions) to examine overall prices across different neighbourhood components within Barcelona during different times of year taking into consideration factors like peak seasonality vs low seasonality before entering any booking agreement via online travel sites etc,.

By following these steps when utilizing this datasets potential it will allow users get a detailed overview of potential options prior to making any final decisions concerning their prized Airbnb stay!

Research Ideas

• Analyzing the correlation between rental prices in different areas and various socioeconomic factors such as median household income, population density, and types of business establishments in those areas.
• Examining differences in amenities offered at different price points to determine how much more a traveler would be willing to pay for certain amenities (ie luxury sheets, spa-like shower setup).
• Analyze the changes in Airbnb listings over time - including number of new/cancelled listings, average nightly price increases or decreases - that can help inform decision making by tourists or local government on investment into the future of tourism in Barcelona

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Kelly Garrett.

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.

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

The data provided by Inside Airbnb for the city of Lisbon contains detailed information about the accommodation listings available on the Airbnb platform in that region. Below is an explanation of the main columns included in the data:

• id: The unique identifier of the Airbnb listing.
• host_id: The unique identifier of the host responsible for the listing.
• host_name: The name of the host.
• host_since: The date the host registered on the Airbnb platform.
• host_location: The location of the host.
• host_total_listings_count: The total number of listings the host has.
• neighborhood: The neighborhood where the property is located.
• neighborhood_group: The neighborhood group to which the neighborhood belongs.
• latitude: The latitude of the property's location.
• longitude: The longitude of the property's location.
• room_type: The type of room offered in the listing (e.g., entire house/apartment, private room, shared room).
• bathrooms_text: The description of the bathrooms available on the property.
• bedrooms: The number of bedrooms in the property.
• price: The price per night for the listing.
• minimum_nights: The minimum number of nights required to book the property.
• maximum_nights: The maximum number of nights allowed to book the property.
• has_availability: Indicates whether the property is available.
• availability_365: The number of days the property can book over a year.
• number_of_reviews_ltm: The number of reviews received in the last 12 months.
• review_scores_rating: The average score of reviews received by the property.
• license: The license or registration of the property, if applicable.
• calculated_host_listings_count: The number of listings the host has in total.
• calculated_host_listings_count_entire_homes: The host owns the number of entire listings (entire house/apartment).
• reviews_per_month: The average number of reviews the property receives per month.

These columns provide a comprehensive view of the characteristics of available accommodation listings in Lisbon during the period in which the data was collected. This is a valuable data source for exploratory analysis, predictive modeling, and research related to the shared hosting industry in Lisbon. It provides a detailed look at listing features, including information about hosts, property locations, room type, pricing, availability, and reviews received.

************* 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.