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

In 2017, the number of adults using Airbnb in the United States amounted to **** million, up from ** million the previous year. This figure is forecast to reach **** million by 2022. Why do people use Airbnb? The privately owned accommodation, rental and sharing website Airbnb has gained popularity all over the world. This is due to multiple factors including cheaper lodging alternatives, a more authentic experience, uniqueness of accommodation and more. A survey found that ** percent of U.S.-based & European Airbnb users were ‘very satisfied’ with their experience. On the other hand,**** percent stated that they were ‘somewhat dissatisfied’ or ‘not at all satisfied’ with using the accommodation sharing platform. Why don't people use Airbnb? Despite the large number of people who are satisfied with their Airbnb experience, there still remain those in Europe and the U.S. that do not want to use the company's services. The most common reason for people not to use Airbnb is privacy concerns, according to a 2017 survey – with ** percent of respondents expressing this fear.

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,...

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

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 492 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. North America generated the highest share of Airbnb’s worldwide revenue in 2024, at five billion U.S. dollars. How many people visit the Airbnb website? Airbnb ranked third among the most popular travel and tourism websites worldwide based on average monthly visits, behind booking.com and tripadvisor.com. In 2024, airbnb.com saw its highest number of unique global visitors in March, at 101 million. Meanwhile, Airbnb ranked fourth among leading travel apps globally, with over 75 million downloads in 2024.

The total revenue of Airbnb reached 11.1 billion U.S. dollars in 2024. This was an increase over the previous year's total of 9.92 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, North America, where Airbnb was founded, 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 North America where Airbnb’s gross booking value amounted to 37.8 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 200 million in 2020 due to these travel restrictions. In 2024, Airbnb reported over 492 million booked nights and experiences, a significant increase over the previous 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 201 million bookings. Asia Pacific had the lowest number of bookings at 61 million. The Asia Pacific region also had the lowest average number of nights per Airbnb booking in 2024.

North America was the region that brought in the highest amount of Airbnb’s worldwide revenue in 2024, at five billion 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 4.1 billion U.S. dollars in revenue. North America also reported the highest average number of nights booked by region with Airbnb in 2024.

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 37.8 billion U.S. dollars.

As of December 2024, Airbnb's global market capitalization was 83.3 billion U.S. dollars, down from around 87.3 billion U.S. dollars the previous year. The company's market capitalization peaked in 2021 at over 100 billion U.S. dollars.

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 4.1 nights per Airbnb booking in 2024, more than any other region that year

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

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.

Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.

By analyzing publicly available information about a city's Airbnb's listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.

With Inside Airbnb, you can ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole. Questions such as:

• "How many listings are in my neighbourhood and where are they?"
• "How many houses and apartments are being rented out frequently to tourists and not to long-term residents?"
• "How much are hosts making from renting to tourists (compare that to long-term rentals)?"
• "Which hosts are running a business with multiple listings and where they?"

The tools are presented simply, and can also be used to answer more complicated questions, such as:

• "Show me all the highly available listings in Bedford-Stuyvesant in Brooklyn, New York City, which are for the 'entire home or apartment' that have a review in the last 6 months AND booked frequently AND where the host has other listings."

These questions (and the answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live.

In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including:

• how many nights a dwelling is rented per year
• minimum nights stay
• whether the host is present
• how many rooms are being rented in a building
• the number of occupants allowed in a rental
• whether the listing is licensed

The Inside Airbnb tool or data can be used to answer some of these questions.

The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.

The data has been analyzed, cleansed and aggregated where appropriate to faciliate public discussion. Read more disclaimers here.

https://raw.githubusercontent.com/betanyc/getDataButton/master/png/120x60.png" style="box-sizing: border-box; vertical-align: middle;" vspace="5" width="120">If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.

The Google Maps mobile app reported the highest number of downloads worldwide among the selected travel apps in 2024. That year, this app recorded nearly 127 million aggregated downloads on iOS and Google Play. The Uber app was the second most downloaded app in the ranking, with almost 120 million downloads. The online travel agency app market Focusing on the online travel agency (OTA) market, Airbnb topped the ranking of the OTA apps with the highest number of downloads worldwide in 2024, ahead of Booking.com. When looking at the number of downloads of leading OTA apps in the U.S. that year, Airbnb recorded again the highest figure, while Expedia ranked second in that case. How big is the travel app market? In 2023, the travel app market's global revenue reached nearly 1.3 billion U.S. dollars and was forecast to increase steadily over the following years. When breaking down the global travel app market's revenue by country, the United States and China ranked by far as the biggest players that year, generating around 540 million and 380 million U.S. dollars, respectively.

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!

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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 ...

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. This dataset describes the listing activity of homestays in Copenhagen, Denmark.

Content

The following Airbnb activity is included in the 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 neighborhood using listing descriptions? What are the busiest times of the year to visit Copenhagen? By how much do prices spike? Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Copenhagen?

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here. The data is available and can be downloaded from Here.

Columns name:

  ['id', 'name', 'host_id', 'host_name', 'neighbourhood_group',
  'neighbourhood', 'latitude', 'longitude', 'room_type', 'price',
  'minimum_nights', 'number_of_reviews', 'last_review',
  'reviews_per_month', 'calculated_host_listings_count',
  'availability_365', 'number_of_reviews_ltm', 'license']

Number of rows: 13815

Disclaimers:

• The site http://insideairbnb.com/explore is not associated with or endorsed by Airbnb or any of Airbnb's competitors.
• The data utilizes public information compiled from the Airbnb web-site including the availabiity calendar for 365 days in the future, and the reviews for each listing. Data is verified, cleansed, analyzed and aggregated.
• No "private" information is being used. Names, photographs, listings and review details are all publicly displayed on the Airbnb site.
• This site claims "fair use" of any information compiled in producing a non-commercial derivation to allow public analysis, discussion and community benefit.

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 Boston, MA.

Content

This data file includes all needed information to about the listing details, the host, geographical availability, and necessary metrics to make predictions and draw conclusions. Basic data cleaning has been done, such as dropping redundant features (ex: city) and converting amenities into a dictionary. The data includes both numerical and categorical data, as well as natural language descriptions.

Acknowledgements

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

Inspiration

• Listing visualization
• What features drive the price of a listing up?
• What can we learn about different hosts and areas?
• What can we learn from predictions? (ex: locations, prices, reviews, etc)
• Which hosts are the busiest and why?
• Is there any noticeable difference of traffic among different areas and what could be the reason for it?

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.

This dataset is used in the introductory course Explore and Search for data in ODS Academy, Opendatasoft's training portal.Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world.By analyzing publicly available information about a city's Airbnb's listings, Inside Airbnb provides filters and key metrics so you can see how Airbnb is being used to compete with the residential housing market.With Inside Airbnb, you can ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole. Questions such as: "How many listings are in my neighbourhood and where are they?""How many houses and apartments are being rented out frequently to tourists and not to long-term residents?""How much are hosts making from renting to tourists (compare that to long-term rentals)?""Which hosts are running a business with multiple listings and where they?"The tools are presented simply, and can also be used to answer more complicated questions, such as: "Show me all the highly available listings in Bedford-Stuyvesant in Brooklyn, New York City, which are for the 'entire home or apartment' that have a review in the last 6 months AND booked frequently AND where the host has other listings."These questions (and the answers) get to the core of the debate for many cities around the world, with Airbnb claiming that their hosts only occasionally rent the homes in which they live.In addition, many city or state legislation or ordinances that address residential housing, short term or vacation rentals, and zoning usually make reference to allowed use, including: how many nights a dwelling is rented per yearminimum nights staywhether the host is presenthow many rooms are being rented in a buildingthe number of occupants allowed in a rentalwhether the listing is licensedThe Inside Airbnb tool or data can be used to answer some of these questions.The data behind the Inside Airbnb site is sourced from publicly available information from the Airbnb site.The data has been analyzed, cleansed and aggregated where appropriate to faciliate public discussion. Read more disclaimers here.If you would like to do further analysis or produce alternate visualisations of the data, it is available below under a Creative Commons CC0 1.0 Universal (CC0 1.0) "Public Domain Dedication" license.