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.

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,...

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

These are the Airbnb statistics on gross revenue by country.

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

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

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.

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, ** million travelers in the United States and Europe are predicted to use Airbnb.

Introduction

Airbnb Statistics:Â Airbnb is one of the best booking websites on the internet, and presently, there are almost 150 million users of this website. Moreover, the COVID-19 pandemic had impacted Airbnb's valuation, which had decreased its value from USD 35 billion to USD 18 billion in 2022. Since the company was launched in 2007, they have gone from one rental to almost 5.6 million active listings and nearly 4 million hosts.

Short-term rentals have changed the way people think about traveling, and this trend has continued to develop despite major benders like accommodation restrictions and travel restrictions. Let's shed more light on Airbnb Statistics through this article.

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.

Welcome to New York City, one of the most-visited cities in the world. There are many Airbnb listings in New York City to meet the high demand for temporary lodging for travelers, which can be anywhere between a few nights to many months. In this project, we will take a closer look at the New York Airbnb market by combining data from multiple file types like .csv, .tsv, and .xlsx.

Recall that CSV, TSV, and Excel files are three common formats for storing data. Three files containing data on 2019 Airbnb listings are available to you:

data/airbnb_price.csv This is a CSV file containing data on Airbnb listing prices and locations.

listing_id: unique identifier of listing price: nightly listing price in USD nbhood_full: name of borough and neighborhood where listing is located data/airbnb_room_type.xlsx This is an Excel file containing data on Airbnb listing descriptions and room types.

listing_id: unique identifier of listing description: listing description room_type: Airbnb has three types of rooms: shared rooms, private rooms, and entire homes/apartments data/airbnb_last_review.tsv This is a TSV file containing data on Airbnb host names and review dates.

listing_id: unique identifier of listing host_name: name of listing host last_review: date when the listing was last reviewed

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!

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.

This statistic shows the the level of Airbnb customer satisfaction in the United States and Europe from 2015 to 2017. In 2017, ** percent of Airbnb users were 'very satisfied' with their experience.

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.

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.

London's Airbnb

Airbnb listings in London

About this dataset

This dataset provides information on Airbnbs in London. Each row represents one listing, and there are a variety of columns with information on the listing, such as the name, host, price, etc.

This dataset could be used to study patterns in Airbnb pricing, to understand how Airbnbs are being used in London, or to compare different neighborhoods in London

How to use the dataset

If you're looking for information on Airbnbs in London, this dataset is a great place to start. It provides information on the listings and reviews for Airbnb in the city of London.

Airbnb is a popular vacation rental platform that allows travelers to find and book accommodations around the world. With over 3 million listings in more than 65,000 cities, Airbnb has something for everyone.

London is one of the most popular tourist destinations in the world, and Airbnb offers a unique way to experience the city. With so many different neighborhoods to choose from, there's an Airbnb listing for everyone.

This dataset includes information on the listing price, minimum nights required, number of reviews, and more. With this data, you can begin to understand how people are using Airbnb in London and what factors affect pricing. So whether you're looking for a place to stay during your next trip or just curious about how Airbnb is being used in different cities, this dataset is for you!

Research Ideas

• If there's a relationship between the price per listing and how long it is available on Airbnb, this could be used to recommend lower prices for listings that are unlikely to stay booked for very long periods of time.
• There might be a relationship between the number of reviews per month and the calculated host listings count. If there is, this information could be used to help improve customer satisfaction by either recommending that hosts with lots of listings receive more reviews or that they stagger their listing availabilities so that they can provide better service.
• The neighbourhood data could be used to cluster listings into areas with similar characteristics, which would then allow customers to easily find similar listings in different areas of the city based on their preferences

Acknowledgements

This dataset is brought to you by Kelly Garrett. If you use it in your research, please cite her 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: listings.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------| | name | The name of the listing. (String) | | host_name | The name of the host. (String) | | neighbourhood_group | The neighbourhood group the listing is in. (String) | | latitude | The latitude of the listing. (Float) | | longitude | The longitude of the listing. (Float) | | room_type | The type of room. (String) | | price | The price of the listing. (Integer) | | minimum_nights | The minimum number of nights required to stay at the listing. (Integer) | | number_of_reviews | The number of reviews for the listing. (Integer) | | last_review | The date of the last review. (Date) | | reviews_per_month | The number of reviews per month. (Float) | | calculated_host_listings_count | The number of listings the host has. (Integer) | | availability_365 | The number of days the listing is available in a year. (Integer) |

File: reviews.csv | Column name | Description | |:----------------|:--------------------------------------| | last_review | The date of the last review. (String) |

File: neighbourhoods.csv | Column...

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.