As of December 2024, San Diego recorded the highest average price per night of Airbnb listings among the selected cities in the United States. In this city, accommodation listed on the Airbnb website cost on average *** U.S. dollars per night. Meanwhile, prices in New York City amounted to an average of *** U.S. dollars per night.

As of December 2024, the average price per night of Airbnb listings in Edinburgh was *** British pounds. Meanwhile, the average price per night of Airbnb listings in London stood at *** British pounds, which was around ** British pounds less than in Greater Manchester.

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

According to a June 2025 analysis, Florence reported the highest average price per night of Airbnb listings among the selected Italian cities, at *** euros. Meanwhile, Airbnb listings in Venice and Rome cost an average of *** and *** euros per night, respectively.

These are the Airbnb statistics on gross revenue by country.

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 Malibu, Jousha Tree, Brighton (UK) in 2023. The data is owned by Airbtics.

Airbtics is a short-term rental data & analytics company monitoring 20 million listings from various short-term rental booking sites.

Content

This data file includes all the needed information to find out the exact performance of Airbnb listings. You can find out how many nights were booked in a specific month, and average daily rate.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website. The data was processed by Airbtics.

Inspiration

What is the occupancy rate of listing X in January 2023? What is the average daily rate of a listing Y in January 2023? How many bookings did a listing Z receive in January 2023?

To find more granular data in other cities, visit Airbtics.com

See the average Airbnb revenue & other vacation rental data in Sydney in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

See the average Airbnb revenue & other vacation rental data in New York in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

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

Context

Welcome to New York City (NYC), one of the most-visited cities in the world. As a result, Since 2008 to 2019 there are many Airbnb listings to meet the high demand for temporary lodging for anywhere between a few nights to many months, guests and hosts have used Airbnb to expand on traveling possibilities and present more unique, personalized way of experiencing the world.

Content

This data file includes all needed information from guests name, id, date, neighborhood name and it's listing price to rooms and its type, using dataset you can perform and apply various data cleaning techniques and also to make predictions

You can find complete tutorial about this dataset in this notebook: https://www.kaggle.com/code/ebrahimelgazar/exploring-nyc-airbnb-market

You can find full notebook documentation on this GitHub link: https://github.com/EbGazar/Exploring-NYC-Airbnb-Market

Inspiration

• What is the average price, per night, of an Airbnb listing in NYC?
• How does the average price of an Airbnb listing, per month, compare to the private rental market?
• How many adverts are for private rooms?
• How do Airbnb listing prices compare across the five NYC boroughs?

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

See the average Airbnb revenue & other vacation rental data in San Francisco in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Airbnb Price & Room Analysis in Boston Using Tableau 📊

I recently worked on an Airbnb Boston dataset to analyze pricing trends and room details using Tableau. This project focused on understanding Airbnb pricing patterns, room availability, and geographic price distribution across different zip codes in Boston.

🔹 Key Steps & Techniques: ✔ Data Cleaning & Preparation:

Used Data Interpreter to clean the raw Excel dataset. Removed duplicates and handled missing values for accurate insights. ✔ Data Joining:

Joined listings and calendar tables using a common key (ID) to combine pricing information with room details. Ensured correct relationship to avoid duplication and incorrect aggregations. ✔ Dashboard Insights: 📈 Revenue Trends Over Time – Visualized how Airbnb prices fluctuated over a year in Boston. 🏠 Price Per Zipcode & Bedroom Count – Mapped average prices across Boston zip codes to highlight expensive and affordable areas. 📊 Distinct Listings by Room Type – Explored how many 1, 2, 3, 4, and 5-bedroom listings are available in Boston.

🔥 Key Takeaways from the Boston Airbnb Analysis: 📌 Larger Listings Are More Expensive – As expected, the average price increases with the number of bedrooms, with 1-bedroom listings averaging $144 and 5-bedroom listings reaching $445. 📌 Certain Boston Zip Codes Are More Expensive – Prices vary significantly, with some areas averaging over $200 per night, while others remain below $50. 📌 Seasonality Impacts Pricing – The revenue trend shows fluctuations over time, suggesting that Airbnb prices increase during peak seasons and drop during low-demand periods in Boston.

🛠 Tools Used: ✅ Tableau for visualization & dashboard creation. ✅ Microsoft Excel for raw data handling.

In Marseille, as of 2017, renting a one-bedroom apartment via Airbnb cost on average 63 euros.

Airbnb Prices in European Cities

Determinants of Price by Room Type, Location, Cleanliness Rating, and More

By [source]

About this dataset

This dataset provides a comprehensive look at Airbnb prices in some of the most popular European cities. Each listing is evaluated for various attributes such as room types, cleanliness and satisfaction ratings, bedrooms, distance from the city centre, and more to capture an in-depth understanding of Airbnb prices on both weekdays and weekends. Using spatial econometric methods, we analyse and identify the determinants of Airbnb prices across these cities. Our dataset includes information such as realSum (the total price of the listing), room_type (private/shared/entire home/apt), host_is_superhost (boolean value indicating if host is a superhost or not), multi (indicator whether listing is for multiple rooms or not), biz (business indicator) , guest_satisfaction_overall (overall rating from guests camparing all listings offered by host ), bedrooms, dist (distance from city center) , lng & lat coordinates for location identification etc. We hope that this data set offers insight into how global markets are affected by social dynamics and geographical factors which in turn determine pricing strategies for optimal profitability!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset can be used by individuals and companies to gain insight on the cost of Airbnb listings in some of the most popular European cities. It contains information on a variety of attributes such as room type, cleanliness rating, guest satisfaction, distance from the city centre, and more. In addition to exploring general trends in prices across Europe, this dataset can be used for deeper spatial econometric analysis.

To begin using this dataset for your own research or analysis project: - Download the files which contain both weekday and weekend listings data for European cities. - Familiarize yourself with the columns included in each file; these provide descriptions of various attributes associated with each listing.
- Calculate any desired summary statistics - average price per night per city or room type etc. - using statistical software (e.g Excel).
- Perform spatial econometric analysis if desired; use specialized packages such as spdep or spatialreg in R to identify determinants of Airbnb price levels across Europe (e.g., metro distance). - Visualize your results with GIS software if necessary to more easily understand patterns between variables like proximity/location and price level (e.g., QGIS).

By leveraging both descriptive and inferential methods while taking advantage of geographic information systems (GIS), users can apply this dataset to many interesting questions related to rental prices on Airbnb in Europe!

Research Ideas

• Analyzing spatial trends in Airbnb prices across Europe and finding the most favorable cities for hosting.
• Comparing differences between weekday vs weekend booking patterns to project rental rates for vacationers and business travelers in European cities.
• Using spatial econometrics methods to find important determinants of Airbnb prices in order to provide insights into areas of opportunity for improvement, or assess the effectiveness of existing policy changes concerning vacation rentals

Acknowledgements

If you use this dataset in your research, please credit the original authors. 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: vienna_weekdays.csv | Column name | Description | |:-------------------------------|:---------------------------------------------------------------------------| | realSum | The total price of the Airbnb listing. (Numeric) | | room_type | The type of room being offered (e.g. private, shared, etc.). (Categorical) | | room_shared | Whether the room is shared or not. (Boolean) | | room_private | Whether the room is private or not. (Boolean) | | **per...

Checkout my new Dashboard which is based on Airbnb dataset for listings and reviews from 5 major cities in New York and spanning from 2008 to 2012.

The data was collected from Data.world website. Power BI platform was used to perform analysis, data manipulation for measures, and developing appropriate visualizations.

Dataset: Data.world website BI Tool: Power BI

Principal findings from this dataset:

1- Brooklyn City has the greatest rating, with a 92.36 average rating score, while Staten Island has the lowest rating. 2- Based on the number of beds available in each type of room, Manhattan City is the best city, while Staten Island is the worst. 3- Lighthouse has the lowest average price and Other Property Type has the highest average price. 4- Staten Island had the lowest price contribution while Manhattan Neighborhood had the highest.

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!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 ...

See the average Airbnb revenue & other vacation rental data in Atlanta in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

See the average Airbnb revenue & other vacation rental data in Osaka in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

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 London, Dubai, San Francisco, Tokyo, Sydney, Miami, and Toronto in 2023. The data is owned by Airbtics.

Airbtics is a short-term rental data & analytics company monitoring 20 million listings from various short-term rental booking sites.

Content

This data file includes all needed information to find out more about listings, hosts, geographical availability, necessary metrics, such as last twelve months occupancy rate, daily rate and revenue, to make predictions and draw conclusions.

Acknowledgements

This public dataset is part of Airbnb, and the original source can be found on this website. The data was processed by Airbtics.

Inspiration

How much does a typical 2-bedroom Airbnb listing make compared to a 3-bedroom in London? What is the average occupancy rate of Airbnb listings in London?

To find more granular data in other cities, visit Airbtics.