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.

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.

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

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 May 2024, nearly ****** nights were booked in Airbnbs located in Hong Kong. The number of overnight bookings in Hong Kong reached its peak in August 2023, with more than ****** Airbnb nights sold out.

Airbnb In NYC

Room Prices, Reviews, and Availability

By Huggingface Hub [source]

About this dataset

This dataset offers a unique and comprehensive look into the expansive Airbnb industry in New York City. We capture 20,000+ Airbnbs with its associated data such as descriptions, rates, reviews and availability. Professionals researching this industry will find it an invaluable resource in providing insight to the ever popular Airbnb market that can be used for their advantage.

This dataset showcases some of the most important attributes for each listing: host name, neighborhood group, location (latitude/longitude coordinates), room type, price per night, minimum nights required to book a stay at this listing , total number of reviews and ratings received by guests over time (including reviews per month and last review date), calculated host listing count (indicates how many listings are offered by each host) along with 365 days worth of availability score. With all these parameters one can understand dynamics of demand & supply & further utilize them accordingly to maximize returns or occupancy greeting never before seen transparency into NYC’s Airbnb scene

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How to use the dataset

This dataset can be used to gain a comprehensive understanding of the Airbnb market in New York City. The data offers descriptions, rates, reviews and availability for over 20,000 Airbnbs in NYC.

Here are few tips on how to use this dataset: - Use the latitude and longitude coordinates to visualize the variety of Airbnbs located across all five boroughs of New York City using mapping programs like Google Maps or ArcGIS. - Determine the versatile price ranges offered by Airbnb listings by looking at the “price” column available for each listing . - Analyze reviews scored by guests who have used an Airbnb in order to better understand customer experience with different services through columns such as “number_of_reviews” and “last_review.
4 Understand how often properties are made available for booking based on their popularity through columns like “availability_365 and reviews_per_month. . 5 Investigate listing host data by looking into their description (host name) as well as number of listings they have booked (calculated host listing count)

Research Ideas

• Determining the listings with the highest satisfaction ratings for potential customers to book.
• Analyzing neighborhood trends in prices, availability, and reviews to identify hot areas of competition within the Airbnb market.
• Predicting future prices throughput examining properties such as review scores and availability rate to provide forecast information to AirBnB owners

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

File: train.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group | The neighbourhood group the Airbnb listing is located in. (String) | | latitude | The latitude coordinate of the Airbnb listing. (Float) | | longitude | The longitude coordinate of the Airbnb listing. (Float) | | room_type | The type of room offered by the Airbnb listing. (String) | | price | The price per night of the Airbnb listing. (Integer) | | minimum_nights | The minimum number of nights required for booking the Airbnb listing. (Integer) | | number_of_reviews | T...

In the first week of 2020, Airbnb overnight bookings in Washington D.C. grew by ***** percent over the previous year. From week ** onwards, this year-on-year growth dropped under 100 percent and began to decrease steadily as a result of the coronavirus (COVID-19) pandemic. The lowest week for overnight bookings was week **, reporting *** percent YoY growth. By week **, YoY growth of Airbnb overnight bookings was up to **** percent.

Description: The dataset contains information on property listings from Airbnb, an online marketplace connecting hosts offering accommodations with guests seeking lodging in various locales. Specifically, it includes data on the number of property images associated with each listing and the corresponding number of bookings it attracts. Additionally, the dataset highlights a significant trend that Airbnb has witnessed indicating an intriguing trend that suggests a correlation between the number of property images associated with a listing and the number of bookings it attracts. It also addresses the issue of redundant listings lacking associated images, which fail to attract bookings.

Variables in Listing dataset: Here's a data dictionary for the given dataset:

1. id

   • Description: Unique identifier for each listing.
   • Data Type: String or Integer

2. host_days

   • Description: Number of days since the host joined the platform.
   • Data Type: Integer

3. host_response_time

   • Description: Time taken by the host to respond to inquiries.
   • Data Type: Categorical (e.g., within an hour, within a few hours, within a day, not available)

4. host_response_rate

   • Description: Percentage of inquiries to which the host responds.
   • Data Type: Numeric (Percentage)

5. host_acceptance_rate

   • Description: Percentage of booking requests accepted by the host.
   • Data Type: Numeric (Percentage)

6. host_is_superhost

   • Description: Indicates whether the host is a superhost or not.
   • Data Type: Boolean (True/False)

7. host_listings_count

   • Description: Number of listings managed by the host.
   • Data Type: Integer

8. host_identity_verified

   • Description: Indicates whether the host's identity is verified.
   • Data Type: Boolean (True/False)

9. neighbourhood_cleansed

   • Description: Name of the neighborhood where the listing is located (cleaned version).
   • Data Type: String

10. city

    • Description: Name of the city where the listing is located.
    • Data Type: String

11. property_type

    • Description: Type of property (e.g., apartment, house, villa).
    • Data Type: String

12. room_type

    • Description: Type of room available (e.g., entire home/apt, private room, shared room).
    • Data Type: String

13. accommodates

    • Description: Maximum number of guests accommodated.
    • Data Type: Integer

14. bathrooms

    • Description: Number of bathrooms in the listing.
    • Data Type: Numeric

15. bedrooms

    • Description: Number of bedrooms in the listing.
    • Data Type: Numeric

16. beds

    • Description: Number of beds in the listing.
    • Data Type: Numeric

17. bed_type

    • Description: Type of bed (e.g., real bed, sofa bed, futon).
    • Data Type: String

18. price

    • Description: Price per night for the listing.
    • Data Type: Numeric

19. security_deposit

    • Description: Amount of security deposit required for the booking.
    • Data Type: Numeric

20. cleaning_fee

    • Description: Cleaning fee charged for the booking.
    • Data Type: Numeric

21. guests_included

    • Description: Number of guests included in the base price.
    • Data Type: Integer

22. extra_people

    • Description: Additional fee for extra guests beyond the included number.
    • Data Type: Numeric

23. minimum_nights

    • Description: Minimum number of nights required for booking.
    • Data Type: Integer

24. review_scores_rating

    • Description: Overall rating score for the listing.
    • Data Type: Numeric

25. review_scores_accuracy

    • Description: Rating score for accuracy of listing description.
    • Data Type: Numeric

26. review_scores_cleanliness

    • Description: Rating score for cleanliness of the listing.
    • Data Type: Numeric

27. review_scores_checkin

    • Description: Rating score for check-in process.
    • Data Type: Numeric

28. review_scores_communication

    • Description: Rating score for host communication.
    • Data Type: Numeric

29. review_scores_location

    • Description: Rating score for listing location.
    • Data Type: Numeric

30. review_scores_value

    • Description: Rating score for value provided by the listing.
    • Data Type: Numeric

31. instant_bookable

    • Description: Indicates whether instant booking is available for the listing.
    • Data Type: Boolean (True/False)

32. cancellation_policy

    • Description: Policy governing cancellation of bookings.
    • Data Type: Categorical (e.g., flexible, moderate, strict)

33. reviews_per_month

    • Description: Average number of reviews received per month.
    • Data Type: Numeric

In the first week of 2020, Airbnb overnight bookings in Nevada grew by ***** percent over the previous year. From week ** onwards, this year-on-year growth dropped under 100 percent and began to decrease steadily as a result of the coronavirus (COVID-19) pandemic. The lowest week for overnight bookings was week **, reporting **** percent YoY growth. By week **, YoY growth of Airbnb overnight bookings was up to **** percent.

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

In the first week of 2020, Airbnb overnight bookings in California grew by ***** percent over the previous year. From week ** onwards, this year-on-year growth dropped under 100 percent and began to decrease steadily as a result of the coronavirus (COVID-19) pandemic. The lowest week for overnight bookings was week **, reporting **** percent YoY growth. By week **, YoY growth of Airbnb overnight bookings was above 100 percent again at ***** percent.

The Airbnb Accommodation Booking Data Warehouse (2020-2024) is a dataset for business intelligence, and it has a dimensional model comprising four dimension tables and one fact table.

The Dim_Date table provides detailed date information from 2020 to 2024, including day, month, quarter, and weekday details for time-based analysis. The Dim_Host table captures information about property hosts, such as superhost status, total listings, and response times. Dim_Property contains details of accommodations, including location, property type, room type, number of rooms, and pricing. Dim_Customer includes customer demographics such as age group, gender, nationality, and customer segment.

The central Fact_Bookings table records booking transactions, including revenue, nights booked, guests, and fees. Each booking links to specific hosts, customers, properties, and dates through foreign keys.

The dataset supports multi-year analysis of booking trends, revenue performance, customer behaviour, and host activity. It enables insights into seasonal patterns, location performance, and customer segmentation, allowing for strategic decisions in pricing, marketing, and operational planning.

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.

In the first week of 2020, Airbnb overnight bookings in New York grew by ***** percent over the previous year. From week ** onwards, this year-on-year growth dropped under 100 percent and began to decrease steadily as a result of the coronavirus (COVID-19) pandemic. The lowest week for overnight bookings was week **, reporting **** percent YoY growth. By week **, YoY growth of Airbnb overnight bookings was up to ***** percent, the highest weekly growth in New York since the start of 2020.

The dataset used in this project is a publicly available dataset containing Airbnb listing data for New York City. It provides comprehensive details about various aspects of Airbnb listings, such as neighborhood, room types, prices, availability, and host information.

Key Features of the Dataset Here are some of the main columns included in the dataset and what they represent:

id: A unique identifier for each listing. name: The name or title of the Airbnb listing. host_id: The unique ID of the host. host_name: The name of the host. neighbourhood_group: The borough where the listing is located (e.g., Manhattan, Brooklyn, Queens, Bronx, Staten Island). neighbourhood: The specific neighborhood within the borough. latitude and longitude: The geographic coordinates of the listing. room_type: The type of room being offered: Entire home/apt Private room Shared room price: The price per night to stay at the listing. minimum_nights: The minimum number of nights required for booking. number_of_reviews: The total number of reviews received by the listing. reviews_per_month: The average number of reviews the listing receives each month. availability_365: The number of days the listing is available for booking in a year. calculated_host_listings_count: The total number of listings managed by a host. Dataset Characteristics Timeframe: The dataset represents a snapshot of listings and reviews within a specific time period (usually the latest available at the time of collection). Geography: Includes all five boroughs of New York City: Manhattan Brooklyn Queens Bronx Staten Island Diversity: The dataset captures a diverse set of listings, from luxury apartments in Manhattan to budget-friendly shared rooms in the Bronx. Why This Dataset? This dataset is ideal for analysis because:

It allows us to explore trends in NYC's Airbnb market, such as pricing patterns, popular room types, and host activity. It offers valuable insights into neighborhood preferences and pricing strategies for hosts. It helps identify potential areas of improvement, such as boosting listings in underrepresented neighborhoods like Staten Island and Queens. Dataset Source This dataset is commonly hosted on platforms like Kaggle or Inside Airbnb, a project that compiles publicly available information on Airbnb listings. It is designed to provide transparency and insight into Airbnb activity across cities.

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.

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.