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,...

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.

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

These are the Airbnb statistics on gross revenue by country.

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.

This statistic shows the number of Airbnb users in the United States and Europe from 2015 to 2016. It also includes a forecast of the number of Airbnb users from 2017 to 2020. In 2020, 40 million travelers in the United States and Europe are predicted to use Airbnb.

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

This statistic shows the average number of stays per year by Airbnb users in the United States and Europe from 2015 to 2016. It also shows the estimated number of stays from 2017 to 2018. In 2016, Airbnb was used an average of *** times per year by its users.

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.

The number of users is forecast to experience significant growth in all segments in 2029. Especially notable is the remarkably robust growth observed in the Hotels segment as we approach the end of the forecast period. This value, reaching 384.4 thousand users, stands out significantly compared to the average changes, which are estimated at 176.56 thousand users. Find further statistics on other topics such as a comparison of the revenue in Spain and a comparison of the revenue in Germany. The Statista Market Insights cover a broad range of additional markets.

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)

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

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.

In March 2024, over *** million unique global visitors visited Airbnb.com, up from **** million visitors in October 2023. Airbnb is an online marketplace for short-term holiday and travel rentals.

In 2017, the majority of Airbnb users in the United States and Europe were between the ages ** to **. People in older age groups generally made up a smaller share of Airbnb users. Only **** percent of Airbnb users were aged 65 or older - indicating that Airbnb is more popular among younger users. Airbnb popularity The accommodation rental and sharing website Airbnb is gaining popularity all over the world. This can most likely be attributed to the company allowing for cheaper accommodation alternatives and a more personal experience of a location. In 2018, there were forecast be around ***** million Airbnb guest arrivals worldwide – and the average number of guests per listing was **. A survey found that ** percent of European and American 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 not use Airbnb? Despite the large amount of people being satisfied with their Airbnb experience, there still remain people in Europe and the U.S. that do not want to use their service. A survey found that the most common reason for people not to use Airbnb was privacy concerns – with ** percent of the respondents expressing this concern.

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.

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.

Context

This dateset was created for the final project of the Data Science Course I am pursuing.

** Description**

This dataset contains data about Airbnb property listings in nine major states in US.The datasets contains various features about the Airbnb host like the host id,host name,name of the property ,location of the property etc.The dataset was created from the web scrapped data made available in InsideAirbnb.com, a non profit initiative committed to studying Airbnb operations.

** Acknowledgements**

I wouldn't be here without the help of wonderful team behind the InsideAirbnb.com initiative.I would like to express my gratitude to the entire team behind InsideAirbnb.com for their continuous efforts on providing quality data on Airbnb.All copyrights associated with the use of this dataset belong to them. ** Inspiration ** The dataset is useful for different Data Science Projects based on Supervised and Unsupervised ML models,Exploratory Data Analysis .I strongly recommend that my fellow Kaggle users make use of this dataset or the original data provided in Insideairbnb.com.

Dataset

This dataset was created by Tushar Roul

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