What makes your data unique? - We have our proprietary AI to clean outliers and to calculate occupancy rate accurately.

How is the data generally sourced? - Web scraped data from Airbnb. Scraped on a weekly basis.

What are the primary use-cases or verticals of this Data Product? - Tourism & DMO: A one-page CSV will give you a clear picture of the private lodging sector in your entire country. - Property Management: Understand your market to expand your business strategically. - Short-term rental investor: Identify profitable areas.

Do you cover country X or city Y?

We have data coverage from the entire world. Therefore, if you can't find the exact dataset you need, feel free to drop us a message. Our clients have bought datasets like 1) Airbnb data by US zipcode 2) Airbnb data by European cities 3) Airbnb data by African countries.

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

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,...

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.

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

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.

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.

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.

Data was downloaded from: http://insideairbnb.com/get-the-data.html Data was compiled on 31 August, 2019

Files description: - listings_detailed.csv - Detailed Listings data for Stockholm - reviews_detailed.csv - Detailed Review Data for listings in Stockholm - listings.csv - Summary information and metrics for listings in Stockholm (good for visualisations). - reviews.csv - Summary Review data and Listing ID (to facilitate time based analytics and visualisations linked to a listing).

Point data representing Airbnb listing for 44 cities across the world recorded between year 2015 - 2017. These listings are downloaded from Inside Airbnb (URL: http://insideairbnb.com/get-the-data.html), which is an independent, non-commercial set of tools and data that allow user to explore how Airbnb is being used in cities around the world.

The New York City Airbnb 2019 Open Data is a dataset containing varius details about a listed unit, when the goal is to predict the rental price of a unit.

This dataset contains the details for units listed in NYC during 2019, was adapted from the following open kaggle dataset: https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data. This, in turn was downloaded from the Airbnb data repository http://insideairbnb.com/get-the-data.

This dataset is licensed under the CC0 1.0 Universal License (https://creativecommons.org/publicdomain/zero/1.0/).

The typical ML task in this dataset is to build a model that predicts the average rental price of a unit.

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.

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.

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?

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.

Context

Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.

Content

The following Airbnb activity is included in this Seattle 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 Seattle neighborhood using listing descriptions?
• What are the busiest times of the year to visit Seattle? By how much do prices spike?
• Is there a general upward trend of both new Airbnb listings and total Airbnb visitors to Seattle?

For more ideas, visualizations of all Seattle datasets can be found here.

Acknowledgement

This dataset is part of Airbnb Inside, and the original source can be found here.

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

Airbnb Price Determinants in Europe

Characteristics and Effects

By [source]

About this dataset

This dataset contains Airbnb rental data for European cities, including characteristics and their effects on price. The dataset includes several features such as the total price of the listing, room type, host status (superhost or not), amenities, and location information which can be used to analyze the factors that affect Airbnb prices. This data can help travelers find an accommodation that satisfies their needs without spending more than necessary. It can also provide business owners valuable insights on how to set competitive prices and optimize their listings for increased bookings. Furthermore, this data is useful for property investors who want to understand pricing trends in different cities across Europe and make informed decisions about investing in real estate

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How to use the dataset

This dataset contains Airbnb rental data for multiple European cities, including price, room type, host status, amenities and location information. This data can be used to better understand the factors that influence Airbnb rental prices in Europe.

The columns of the dataset include: - realSum (total price of the listing) - room_type (type of room offered such as private/shared/entire home/apt)
- room_shared (whether or not the room is shared) - person_capacity (maximum number of people allowed in the property)
- host_is_superhost(whether or not the host is a superhost) (boolean value so either true or false)
- multi (whether it’s for multiple rooms or not)
- biz(whether it’s for business use or family use ) .
dist(the distance from city center )
metro dist (the distance from nearest metro station ) lng(longitude value ) lat(latitude value ) guest satisfaction overall () Cleanliness rating () Bedrooms () and Real sum -Total Price.

First step would be to select features that are important and relevant to you according to your purpose. You can start by selecting the features like realSum ,room type ,host etc which will give you an understanding on how potential customers best fits your requirements i.e how many people will fit into a particular property when renting out a single bedroom versus renting out an entire home/apartment. After that review associated values; this could help you decide on pricing strategies such as offering discounts or raising prices according to needs and demands in different neighbourhoods depending on demand levels, availability and seasonality etc.. The next step would be to plot distance variables with respect to latitude & longitude which will indicate geographical locations where businesses could benefit from having higher occupancy rates by leveraging neighbourhood proximityi n order tackle seasonal variations . And lastly using correlation matrix between all other variables one can correlating parameters which display strong correlations thereby helping establish relationships across other variables relative towards each other as well as decide what set parameters should come into play when based upon one parameter . This dataset however does not provide dates

Research Ideas

• Price forecasting - Analyzing previous data about Airbnb listings, such as pricing, room type and amenities, could help predict potential rental prices in the future.

• Business or tourist rental hotspots - By looking at each listing’s location in relation to business and tourism centers and correlating this with pricing can help determine areas where Airbnb rentals will be most profitable.

• Customer sentiment analysis - Analyzing customer comments and satisfaction ratings to measure the effectiveness of a specific listing on their overall customer experience could be an useful tool for...