Airbnb® is an American company operating an online marketplace for lodging, primarily for vacation rentals. The purpose of this study is to perform an exploratory data analysis of the two datasets containing Airbnb® listings and across 10 major cities. We aim to use various data visualizations to gain valuable insight on the effects of pricing, covid, and more!

Summary Review data and Listing ID (to facilitate time-based analytics and visualizations linked to a listing). This dataset can be used for NLP usecases, e.g. Exploratory Data Analysis (EDA), Text summarization, sentiment analysis, intent analysis and many more.

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.

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

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.

Comprehensive Airbnb dataset repository offering detailed vacation rental analytics worldwide including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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,...

Comprehensive Airbnb dataset for Austin, United States providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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)

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

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.

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

Use this dataset to analyze Airbnb listings in Paris to determine the impact of recent regulations. Find out the Average listings price, find the average accommodation numbers, find the neighbourhood with expensive and least listings. Explore and find more Insights.

According to a March 2025 analysis, most Airbnb listings in Florence, Italy, featured entire homes or apartments. While the website showed 10,497 such establishments, there were 1,908 private rooms in Florence listed on Airbnb as of that month.

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.

These are the Airbnb statistics on gross revenue by country.

Comprehensive Airbnb dataset for Tokyo, Japan providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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

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.