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.

Description

This dataset provides extensive information about Airbnb properties listed in Los Angeles, California. It offers a wealth of details suitable for analyzing short-term rental trends, exploring traveler behavior, and studying pricing dynamics within one of the most vibrant tourism markets in the U.S.

Dataset Context and Purpose

As Airbnb continues to impact urban rental markets, this dataset allows analysts, researchers, and real estate professionals to investigate how the short-term rental market shapes the local economy and influences housing availability. Users can leverage this dataset to perform location-based analysis, identify seasonal occupancy trends, and explore the popularity of amenities and property types.

Content

id: Unique identifier assigned to each property listing.

name: Property listing name as provided by the host.

host_id:Unique identifier assigned to the host of the property.

host_name:Name of the host associated with the property.

host_since:Date on which the host joined Airbnb.

host_response_time: Typical response time of the host to guest inquiries.

host_response_rate:Percentage of guest inquiries that the host responded to.

host_is_superhost: Indicates whether the host is a Superhost (True/False).

neighbourhood_cleansed: Neighborhood name where the property is located.

neighbourhood_group_cleansed: Standardized neighborhood group or district where the property is located.

latitude: Geographic latitude coordinate.

longitude: Geographic longitude coordinate.

property_type: Type of property.

room_type: Type of room offered (e.g., Entire home/apt, Private room, Shared room).

accommodates: Maximum number of guests that the property can accommodate.

bathrooms: Number of bathrooms in the property.

bedrooms: Number of bedrooms in the property.

beds: Number of beds in the property.

price: Total price based on minimum nights required for booking.

minimum_nights: Minimum number of nights required for a booking.

availability_365:Number of days the property is available for booking in the next 365 days.

number_of_reviews: Total number of reviews received for the property.

review_scores_rating: Average rating score based on guest reviews (5 is maximum value).

license: License, if applicable.

instant_bookable: Indicates whether guests can book the property instantly (True/False).

Inspiration

• Host Insights: Analyze host behavior, response times, and Superhost status to understand their impact on guest satisfaction and property performance.
• Property Characteristics: Identify popular property types, room types, and amenities, and how they correlate with pricing and occupancy rates.
• Neighborhood Analysis: Explore neighborhood-level trends in pricing, occupancy, and guest reviews to identify popular areas and potential investment opportunities.
• Pricing Strategies: Analyze factors influencing pricing, such as property type, location, amenities, and seasonality.

Source

This dataset is part of Inside Airbnb, Los Angeles California on September 04, 2024. Link found here

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.

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.

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 London UK in 2022.

This public dataset is part of Airbnb, and the original source can be found Here

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 Global Accommodation and Reviews

Exploring Location-Based Listing Data

By Debayan Kar [source]

About this dataset

The Airbnb Global Dataset contains a wealth of information about the locations, availability, reviews and other details related to short-term rentals available around the world. Use this dataset to explore how guests rate their experiences, discover new places in various neighbourhood groups and geographical locations, compare prices of different room types, consider minimum nights required for bookings and more! With this data set you can evaluate factors associated with: host name; neighbourhood group; latitude & longitude; room type; price; minimum nights required for bookings; number of reviews - both in total and over the last 12 months (number_of_reviews_ltm); license (if applicable); last review received; average number of reviews per month (reviews per month) as well as calculated host listing counts which reflect seasonal demand variations. With this information at your fingertips you could travel anywhere your heart desires - so let's turn those dreams into reality!

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How to use the dataset

The following guide will help you get started in your journey to gain insights from this data set.

First, specify the fields that you want to focus on. In order to do this, make sure you take into consideration the columns available within this dataset. By doing so, not only are you able to hone in on specific aspects of Airbnb accommodation and reviews (i.e neighborhood groups, room types or even pricing), but also identify themes or common trends among listings which could prove useful when formulating hypotheses.

Once you have identified which fields will be useful for analysis, it is important that they are converted into appropriate data types if they need any sort of conversion at all (i.e converting strings to integers). Moreover, make sure there are no inconsistencies across your features when exploring the entries in those columns; take care of them before any substantial analysis is done.

You are now ready for some exploratory analysis! Start by creating visualizations such as bar graphs or box plots in order to get an overview of particular aspects related to listings (i.e distribution of prices around a neighbourhood group) - these can be very useful indicators! Then try out correlations between different exponential variable datasets such as availability_365 versus minimum_nightsand explore how they fluctuate with changes in pricing over time - examining how these relationships relate over different locations can yield interesting results like unexpected concentration points which demand research! Another field worth exploring would be reviews associated with each listing by digging down into their components like ratings breakdowns under different criteria such as security/price value ratio etc.. All these evaluations should give an excellent outline on what potential customers might look out for while browsing through options online so as entrepreneurs we can hover upon those trends specially mentioning needs fulfilled during our advertisement campains.... Lastly examine publicly available information about each host such as number_of_reviews or calculated_listings count variation over time , with ability provided here we have ample opportunities predicting customer opinion about newly created businesses offering same services...so many things one could dive deep !

Overall , after gaining ample amount insights taking about current market scenario it’s best suggested procuring feedback from active host & using it devise plans bringing mutual mutually beneficial solutions making both hosts & guests happy . This is where creativity play huge role designing perks forming long lasting trust inducing relationship between service providers &

Research Ideas

• Predicting price points for Airbnb listings based on factors such as room type, neighborhood group, and reviews.
• Identifying areas with a high demand for Airbnb rentals, by looking at the ratio of availability to number of reviews for listings in different neighborhoods.
• Analyzing guest satisfaction levels based on factors such as room type and location, by correlating the reviews_per_month with the number_of_reviews indicator and other variables in the dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description ...

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 Airbnb Reviews dataset provides structured, multilingual guest feedback from Airbnb listings worldwide. Each entry includes the full review text, star rating, reviewer profile, location, timestamp, and language. Ideal for sentiment analysis, reputation monitoring, travel market research, and AI/ML training, this dataset allows country-level filtering for the US, EU, and Australia. Continuously updated and scalable to millions of reviews, the data is exportable in CSV, JSON, or JSONL formats. It is ready for analytics pipelines, NLP applications, recommendation engines, and travel trend analysis.

You can request the large dataset at: Airbnb Reviews
To get a custom data quote, visit: Get quote

Key Features:

• Full review text with star ratings and verified stay flags.

• Metadata including reviewer name, profile, property type, location, timestamp, and language.

• Multilingual coverage for global analysis.

• Country-specific filtering for US, EU, and AU markets.

• Continuous updates to include new reviews and listings.

• Export formats: CSV, JSON, JSONL.

• Scalable for millions of reviews.

Use Cases:

• Train AI models for sentiment analysis and review classification.

• Monitor property and host reputation across regions.

• Build semantic search engines for Airbnb reviews.

• Conduct global or regional travel market research.

• Feed review summarizers and QA models with structured review data.

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

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 Boston, MA.

Content

This data file includes all needed information to about the listing details, the host, geographical availability, and necessary metrics to make predictions and draw conclusions. Basic data cleaning has been done, such as dropping redundant features (ex: city) and converting amenities into a dictionary. The data includes both numerical and categorical data, as well as natural language descriptions.

Acknowledgements

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

Inspiration

• Listing visualization
• What features drive the price of a listing up?
• 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?

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 source of this dataset in AirBnb open data. I have given insight to data by following : Treating Null , Blank, 0 values. Proper Data Types Random figured data Changing Column name to describe it proper usage Create Indexing Handle text data for Extra spaces, its cases ,inconsistent values Removing unwanted data

Context This dataset describes the listing activity and metrics in NYC, NY from 2008-2015.

Content This data file includes all the 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?

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

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

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).

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.

According to 2023 estimates, Booking Holdings' global revenue was evenly split between mobile and desktop bookings. As estimated, the online travel agency (OTA) generated revenue of roughly 10.8 billion U.S. dollars through mobile devices and 10.5 billion U.S. dollars via desktop bookings. In contrast, it was estimated that most of the Expedia Group and Airbnb's revenue came from desktop users that year. What are the most visited travel and tourism websites? In January 2024, booking.com topped the ranking of the most visited travel and tourism websites worldwide, ahead of tripadvisor.com and airbnb.com. When breaking down the visits to booking.com by country that month, the United States emerged as the leading market, followed by the United Kingdom and Germany. What are the most popular online travel agency apps worldwide? In 2024, Airbnb, Booking.com, and Expedia were among the most downloaded online travel agency apps worldwide. Booking.com is one of the leading brands of Booking Holdings, along with Priceline, Agoda, and Kayak. Meanwhile, Expedia is among the most popular brands of the Expedia Group, together with Vrbo, Hotels.com, and Trivago.

Boston Airbnb Reviews

Insights into Customer Satisfaction

By Marcos Dias [source]

About this dataset

This dataset from Boston Air BnB provides you with an in-depth look into the experiences of past customers and insightful reviews about their stay. It includes detailed information such as the date of the review, reviewer name, their specific comments, and more! Allowing you to better understand each customer's personal opinion on a property, this dataset is perfect for those who wish to gain a comprehensive understanding of what makes AirBNB experiences so special. Not only do real estate investors have access to important data points like price and location, but they can now gain insider information on what made past customers truly happy with their stay - that could mean all the difference in building a successful business! Dive into this remarkable collection of reviews now and get ready to experience it yourself soon

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How to use the dataset

This dataset of Boston AirBNB Reviews provides detailed insights into the guest experience with a wide range of AirBNBs in Boston. It provides key information that can be used to identify areas of strength and improvement when developing hospitality strategies in this area.

Research Ideas

• Using the reviewer name and comments to create a sentiment analysis tool that rate hosts and listings based on customers' experience, helping potential guests make informed choices when booking.
• Combining data from the reviews with other data such as prices, availability, and amenities for Airbnb users to enable customers to compare cost efficiency with specific needs in one platform.
• Analyzing the comments made by reviewers over time in order to track trends of customer concerns or feedback allowing Airbnb host to adjust their services accordingly

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: reviews.csv | Column name | Description | |:------------------|:---------------------------------------------------------------| | date | Date of the review submission. (Date) | | reviewer_name | Name of the reviewer. (String) | | comments | Comments made by the reviewer about their experience. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Marcos Dias.