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.

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...

📊 The Dataset

The data used for this project is sourced from a publicly available Airbnb Listings dataset. The dataset contains over 560,000 records across 10 major cities, including Paris. For this project, the data is filtered to focus solely on Paris listings.

Key Fields:

host_since: Date when the host started listing on Airbnb

neighbourhood: The neighborhood where the listing is located

price: The price per night for the listing

accommodates: Number of people the listing can accommodate

The Dataset Column Descriptions

host_since - Date when the host joined the Airbnb platform. neighbourhood - Name of the neighborhood in Paris where the listing is located. city - City name. This dataset is filtered for Paris listings only. accommodates - The maximum number of guests the listing can accommodate. price - Price per night for the listing in USD. room_type - Type of room offered in the listing (e.g., Entire home/apt, Private room, Shared room). availability_365 - Number of days the listing is available for booking throughout the year. number_of_reviews - Total number of reviews the listing has received. review_scores_rating - Average rating score given by guests for the listing (out of 100). minimum_nights - Minimum number of nights required for booking the listing. host_listings_count - Number of listings managed by the host. latitude - Latitude coordinate of the listing. longitude - Longitude coordinate of the listing.

This dataset shows main information about rooms available on AirBnB. Information come from the open data website of Air Bnb wich covered major cities worldwide.For anonymizing data, precision of geo-coordinates point is 300m.

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!

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

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.

Data Set Information

This data set describes the listing activity of Airbnb homestays in New Orleans, Louisiana, as part of the Inside Airbnb initiative. The data set was compiled on November 7, 2021. See the New Orleans Airbnb data visually here.

Some personally identifying information has been removed from the data uploaded here.

Contents

The following Airbnb activity is included in this New Orleans data set:

Listings, including full descriptions and average review score (new_orleans_airbnb_listings.csv) Reviews, including unique id for each reviewer and detailed comments (reviews.csv)

Acknowledgements

Data credit goes to Murray Cox and Inside Airbnb. The original source for this particular New Orleans data can be found here--where you can also find information on the different listing ids and their price and availability for different calendar dates (if you're interested in looking at how Airbnb rental listing price fluctuates over time).

Context

The data set can be used to answer some interesting questions, such as: - Can you predict how much a short-term rental in New Orleans should charge per night based on it's location and amenities? - Can you describe the vibe of each neighborhood in using listing descriptions? - What are the most common amenities to have among short-term rental listings in New Orleans? - What elements contribute to a popular or highly-rated listing? - Is there any noticeable difference in favorability among different NOLA neighborhood/areas and what could be the reason for it?

Furthermore, it's also important to note that Inside Airbnb (provider of dataset) is a mission driven activist project with the objective to provide data that quantifies the impact of short-term rentals on housing and residential communities; and also provides a platform to support advocacy for policies to protect cities from the impacts of short-term rentals.

According to travel guides, New Orleans is one of the top ten most-visited cities in the United States. It was severely affected by Hurricane Katrina in August 2005, which flooded more than 80% of the city, killed more than 1,800 people, and displaced thousands of residents, causing a population decline of over 50%. Since Katrina, major redevelopment efforts have led to a rebound in the city's population. Concerns about gentrification, new residents buying property in formerly closely knit communities, and displacement of longtime residents have all been a major discussion topic.

Bearing the given context in mind, this data set shared by Inside Airbnb also allows you to ask fundamental questions about Airbnb in any neighbourhood, or across the city as a whole, 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 questions (and their 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

(Visit their site for more details.)

Comprehensive Airbnb dataset for Big Bear Lake, 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.

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.

Context

This dataset was made possible by Inside Airbnb Adding data to the debate.

The purpose of the dataset is to help facilitate public discussion on Airbnb's housing practices among major cities across the globe.

Content

Both files offer summary information and metrics for listings in the Seattle area.

The data was last complied from the Airbnb site on July 14, 2021. Offering only the past 12 months from the date of compilation.

Inspiration

How is Airbnb really being used in and affecting your neighborhood ?

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.

This dataset provides a snapshot of Airbnb listings across major Italian cities, offering valuable insights into the short-term rental market in Italy. Whether you're interested in pricing trends,impact of seasonality, superhost classification, 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.
• Comprehensive metrics: Data includes pricing, review scores, host details, and more.
• Seasonal analysis: Data spans different periods, allowing for comparisons across seasons.

Data Dictionary:

• Listings id: Unique identifier for each listing.
• Last year reviews: Number of reviews received in the twelve months before the scraping data.
• Date of scraping.
• Host since: Date the host joined Airbnb.
• Host is superhost: Whether the host is a simple host or a superhost .
• Host number of listings: Total number of listings the host has.
• Neighbourhood: Neighborhood where the listing is located.
• Beds number.
• Bedrooms number.
• Property type: Type of room (e.g., entire home, private room).
• Maximum allowed guests: Number of guests the listing can accommodate.
• Price: Price per night (in Euro).
• Total reviews: Total number of reviews.
• Rating score: Overall rating of the listing.
• Accuracy score: Accuracy rating.
• Cleanliness score: Cleanliness rating.
• Checkin score: Check-in rating.
• Communication score: Communication rating.
• Location score: Location rating.
• Value for money score: Value rating.
• Reviews per month: Number of reviews per month.
• City
• Season: Time period when the data was scraped (e.g., Early Winter).
• Bathrooms number: Number of bathrooms
• Bathrooms type: Type of bathrooms (shared vs private)
• Coordinates: latitude, longitude

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

Disclaimer:

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

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 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.

gradio/NYC-Airbnb-Open-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

The Airbnb dataset is a large dataset containing information about apartments for rent in Chicago. The dataset includes features such as neighborhood, number of beds, amenities, and more.