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.

Listings per region on Airbnb declined from 2020 to 2021. Globally in 2021, there were a total of 12.7 million listings.

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.

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 are the Airbnb statistics on gross revenue by country.

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

As of December, 2024, there were over ** thousand listings for room and apartment rentals in London on the Airbnb website, the highest of any other major European city. Airbnb listings were also high in Paris, Rome and Madrid. Paris accounted for around ** thousand listings, while Rome and Madrid had over ** and ** thousand, respectively. Controversy of Airbnb in Europe Airbnb has become an increasingly popular option for tourists looking for local accommodation. Visitors are attracted to using Airbnb properties instead of hotels and other traditional travel accommodation mainly due to cheaper prices, but also for the location, and to gain an authentic experience. However, the site is facing ongoing legal problems, with some destinations moving to ban or restrict rentals from the site because they worsen housing problems and undermining hotel regulations. Many European cities, including Amsterdam and Paris, have placed limits on the length of rentals, and others such as Barcelona have introduced strict regulations for hosts. The rise of Airbnb Airbnb is one of the most successful companies in the global sharing economy. The company was founded in San Francisco, California in 2008, after being conceived by two entrepreneurs looking for a way to offset their high rental costs. Airbnb was developed as an online platform for hosts to rent out their properties on a short-term basis. It now competes with other online travel booking websites, including Booking.com and Expedia.

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.

In the United Kingdom (UK) Airbnb had over 223 thousand active listings in 2018. From July 2017 to July 2018 such Airbnb hosts welcomed a total of 8.4 million guests into their accommodation. UK users of Airbnb outweighed the number of inbound guests, with 11.1 million guests from the UK renting Airbnb properties in the UK and elsewhere.

What is Airbnb?

Airbnb is an online accommodation portal that allows hosts to list their properties for short term rental. The company was founded in San Francisco in 2008 by two entrepreneurs who came up with the idea when looking for ways to meet their high rental costs. Travelers like using Airbnb as it offers a cheaper alternative to hotels and gives them a more authentic experience. The company was valued at 38 billion U.S dollars in 2018.

Airbnb in London

London is one the most popular cities in the world for Airbnb use: London had the highest number of Airbnb guest arrivals in Europe as of 2017, which is unsurprising considering it is one of the most visited tourist cities in the world. Compared to the rest of the UK, just over a third of active Airbnb listings were in London. The growth of Airbnb in London however was not without its problems. Like other cities, councils and communities raised concerns regarding the impact on the local housing supply and having control over who is living in the rentals. This led the Greater London Council to apply a 90-day limit on listings for entire homes in one year.

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.

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 2023, Airbnb reported that ** percent of their hosts identified as women.

Dataset is from http://tomslee.net/airbnb-data-collection-get-the-data

room_id: A unique number identifying an Airbnb listing. The listing has a URL on the Airbnb web site of http://airbnb.com/rooms/room_id

host_id: A unique number identifying an Airbnb host. The host’s page has a URL on the Airbnb web site of http://airbnb.com/users/show/host_id

room_type: One of “Entire home/apt”, “Private room”, or “Shared room”

borough: A subregion of the city or search area for which the survey is carried out. The borough is taken from a shapefile of the

city that is obtained independently of the Airbnb web site. For some cities, there is no borough information; for others the borough may be a number. If you have better shapefiles for a city of interest, please send them to me.

neighborhood: As with borough: a subregion of the city or search area for which the survey is carried out. For cities that have both, a neighbourhood is smaller than a borough. For some cities there is no neighbourhood information.

reviews: The number of reviews that a listing has received. Airbnb has said that 70% of visits end up with a review, so the number of reviews can be used to estimate the number of visits. Note that such an estimate will not be reliable for an individual listing (especially as reviews occasionally vanish from the site), but over a city as a whole it should be a useful metric of traffic.

overall_satisfaction: The average rating (out of five) that the listing has received from those visitors who left a review.

accommodates: The number of guests a listing can accommodate.

bedrooms: The number of bedrooms a listing offers.

price: The price (in $US) for a night stay. In early surveys, there may be some values that were recorded by month.

minstay: The minimum stay for a visit, as posted by the host.

latitude and longitude: The latitude and longitude of the listing as posted on the Airbnb site: this may be off by a few hundred metres. I do not have a way to track individual listing locations with

last_modified: the date and time that the values were read from the Airbnb web site. The first line of the CSV file holds the column headings.

Here are the cities, the survey dates, and a link to download each zip file.

Aarhus Survey dates: 2016-10-28 (2258 listings), 2016-11-26 (1900 listings), 2017-01-21 (2167 listings), 2017-02-21 (2295 listings), 2017-03-30 (2323 listings), 2017-04-18 (2398 listings), 2017-04-28 (2360 listings), 2017-05-15 (2437 listings), 2017-06-19 (2802 listings), 2017-07-28 (3142 listings)

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 NYC, NY for 2019.

Content

This data file includes all needed information to find out more about hosts, geographical availability, necessary metrics to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website.

Inspiration

• 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?

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.

This dataset describes Airbnb homestay listing activity in New Orleans, Louisiana. Compiled on 7 November 2021, it is part of the Inside Airbnb initiative, which aims to quantify the impact of short-term rentals on housing and residential communities. The data includes listing details and reviews, with personally identifying information removed.

It offers insights into the New Orleans short-term rental market, a city significantly impacted by Hurricane Katrina and subsequent redevelopment efforts, which have raised concerns about gentrification and resident displacement. The dataset allows users to explore fundamental questions about Airbnb's presence, such as the number of listings in a neighbourhood, how many properties are rented to tourists versus long-term residents, host earnings, and the prevalence of hosts operating multiple listings. It can also inform discussions around city and state legislation concerning residential housing, short-term rentals, and zoning.

Columns

• id: Airbnb's unique identifier for each listing.
• name: The name given to the listing.
• description: A detailed account of the listing.
• neighborhood_overview: The host's description of the local area.
• host_id: Airbnb's unique identifier for the host or user.
• host_since: The date the host or user account was created. For hosts who also use Airbnb as guests, this may be their guest registration date.
• host_location: The self-reported location of the host.
• host_response_time: The average duration it takes for a host to reply to a message on the Airbnb platform.
• host_response_rate: The percentage of messages a host responds to on the Airbnb platform.
• host_acceptance_rate: The rate at which a host accepts booking requests.

Distribution

The dataset is provided in CSV format, including new_orleans_airbnb_listings.csv and reviews.csv. Specific total row or record counts are not available within the provided information.

However, details on value distribution for certain columns are present: * host_id: 5,752 unique values. * host_location: 5,487 unique values, with 68% reporting 'New Orleans, Louisiana, United States', 12% from 'US', and 20% from 'Other'. * host_response_time: 61% of hosts respond 'within an hour', with 26% being null. * host_response_rate: 58% of hosts have a '100%' response rate, with 26% being null. * host_acceptance_rate: 28% of hosts have a '100%' acceptance rate, with 24% being null. * host_since dates range from 13 December 2008 to 20 October 2021.

Usage

This dataset is ideal for: * Predicting short-term rental charges in New Orleans based on location and amenities. * Describing the 'vibe' of each neighbourhood using listing descriptions, suitable for Natural Language Processing (NLP) tasks. * Identifying the most common amenities offered in short-term rental listings. * Determining factors that contribute to popular or highly-rated listings. * Analysing differences in favourability among different New Orleans neighbourhoods. * Exploratory Data Analysis (EDA) and Regression modelling. * Researching the impact of short-term rentals on housing affordability and community dynamics.

Coverage

The dataset focuses on New Orleans, Louisiana, United States. It covers a time range for host activity from 13 December 2008 to 20 October 2021, with the data compilation date being 7 November 2021. While not directly demographic, the context addresses concerns about gentrification and the displacement of longtime residents in the city.

License

CC-BY

Who Can Use It

• Data Scientists and Analysts: For data science projects, statistical analysis, machine learning model building, and deriving insights from listing and review data.
• Urban Planners and Policy Makers: To understand the spread of short-term rentals, their impact on local housing markets, and to inform regulations and zoning decisions.
• Researchers and Activists: Studying the socio-economic effects of tourism and short-term rentals on urban communities, particularly concerning housing and gentrification.
• Real Estate Professionals: To gain market intelligence on short-term rental trends, pricing, and amenities in New Orleans.
• Hospitality Industry Stakeholders: To analyse competition and market demand in the New Orleans accommodation sector.

Dataset Name Suggestions

• New Orleans Airbnb Listings and Reviews
• New Orleans Airbnb Host and Listing Data
• NOLA Airbnb Activity Dataset
• Inside Airbnb New Orleans
• New Orleans Short-Term Rental Analysis Data

Attributes

Original Data Source: New Orleans Airbnb Listings and Reviews

Detailed categorization of Airbnb datasets including listings information, host profiles, guest reviews, pricing analysis, and availability calendars - providing comprehensive rental market data for researchers, investors, and short-term rental operators.

Context

The data was taken from http://tomslee.net/airbnb-data-collection-get-the-data. The data was collected from the public Airbnb web site and the code was used is available on https://github.com/tomslee/airbnb-data-collection.

Content

room_id: A unique number identifying an Airbnb listing. The listing has a URL on the Airbnb web site of http://airbnb.com/rooms/room_id
host_id: A unique number identifying an Airbnb host. The host’s page has a URL on the Airbnb web site of http://airbnb.com/users/show/host_id
room_type: One of “Entire home/apt”, “Private room”, or “Shared room”
borough: A subregion of the city or search area for which the survey is carried out. The borough is taken from a shapefile of the city that is obtained independently of the Airbnb web site. For some cities, there is no borough information; for others the borough may be a number. If you have better shapefiles for a city of interest, please send them to me.
neighborhood: As with borough: a subregion of the city or search area for which the survey is carried out. For cities that have both, a neighbourhood is smaller than a borough. For some cities there is no neighbourhood information.
reviews: The number of reviews that a listing has received. Airbnb has said that 70% of visits end up with a review, so the number of reviews can be used to estimate the number of visits. Note that such an estimate will not be reliable for an individual listing (especially as reviews occasionally vanish from the site), but over a city as a whole it should be a useful metric of traffic.
overall_satisfaction: The average rating (out of five) that the listing has received from those visitors who left a review.
accommodates: The number of guests a listing can accommodate.
bedrooms: The number of bedrooms a listing offers.
price: The price (in $US) for a night stay. In early surveys, there may be some values that were recorded by month.
minstay: The minimum stay for a visit, as posted by the host.
latitude and longitude: The latitude and longitude of the listing as posted on the Airbnb site: this may be off by a few hundred metres. I do not have a way to track individual listing locations with
last_modified: the date and time that the values were read from the Airbnb web site.

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.

This dataset provides a snapshot of Airbnb listings across major Italian cities and regions, offering valuable insights into the short-term rental market in Italy. Whether you're interested in pricing trends, regional variations, or the impact of seasonality, this dataset has something for you.

Data refer to a period between September 2023 and September 2024

Key Features:

• City-level data: Explore listings in popular cities like Florence, Milan, Naples, Rome, and Venice.
• Regional insights: Analyze trends across broader regions including Puglia, Sicily, and Trentino.
• Comprehensive metrics: Data includes pricing, review scores, host details, and more.
• Seasonal analysis: Data spans different periods, allowing for comparisons across seasons.

Data Dictionary:

• id: Unique identifier for each listing.
• number_of_reviews_ltm: Number of reviews received in the last twelve months.
• date_of_scraping: Date the data was scraped.
• host_since: Date the host joined Airbnb.
• host_is_superhost: Whether the host is a superhost (t/f).
• host_total_listings_count: Total number of listings the host has.
• neighbourhood: Neighborhood where the listing is located.
• latitude: Latitude coordinate of the listing.
• longitude: Longitude coordinate of the listing.
• room_type: Type of room (e.g., entire home/apt, private room).
• accommodates: Number of guests the listing can accommodate.
• price: Price per night (in local currency).
• number_of_reviews: Total number of reviews.
• review_scores_rating: Overall rating of the listing.
• review_scores_accuracy: Accuracy rating.
• review_scores_cleanliness: Cleanliness rating.
• review_scores_checkin: Check-in rating.
• review_scores_communication: Communication rating.
• review_scores_location: Location rating.
• review_scores_value: Value rating.
• reviews_per_month: Number of reviews per month.
• place: City or region where the listing is located.
• period: Time period when the data was scraped (e.g., Early Winter).

For visualization reason it is also provide a csv with all city neighbourhoods and the relative geojson.

I also added datasets that group listings according to period and neighbourhood/cities, quantitative features were been aggregate according to median and MAD, qualitative according to mode and Shannon's entropy.

Disclaimer:

This dataset is intended for informational and research purposes only. It is not affiliated with Airbnb or any other organization.