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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.
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gradio/NYC-Airbnb-Open-Data dataset hosted on Hugging Face and contributed by the HF Datasets community
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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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.
Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
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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.
https://en.wikipedia.org/wiki/Public_domainhttps://en.wikipedia.org/wiki/Public_domain
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.
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
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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 New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.
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!
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.
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.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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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
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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
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!
- 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
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.
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...
Complete database of Airbnb's mergers and acquisitions
Monthly occupancy rate, ADR and seasonality trends for Airbnb listings in Scottsdale, United States. Based on aggregated real-time market data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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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.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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.
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.
How is Airbnb really being used in and affecting your neighborhood ?
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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United States Google Search Trends: Travel & Accommodations: Airbnb data was reported at 8.000 Score in 14 May 2025. This records an increase from the previous number of 7.000 Score for 13 May 2025. United States Google Search Trends: Travel & Accommodations: Airbnb data is updated daily, averaging 3.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 8.000 Score in 14 May 2025 and a record low of 0.000 Score in 18 Feb 2023. United States Google Search Trends: Travel & Accommodations: Airbnb data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
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).
https://brightdata.com/licensehttps://brightdata.com/license
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.