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.

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.

Bengaluru, the southern Indian city had the highest occupancy rate of over ** percent among Airbnb listings in 2023. New Delhi followed closely with average occupancy rate of nearly ** percent.

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.

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.

Airbnb & Short term rental statistics.

Available columns - average occupancy rate - average daily rate - revenue - active listing

Timeframe - 2024 TTM (2023 June - 2024 May) - 2023 TTM, 2022 TTM

This data covers following cities:

Brasilia Buenos Aires Mexico City El Calafate Tamarindo São Paulo Panama City Santa Teresa Medellin Cancun Santiago Playa del Carmen Manaus Cartagena Bariloche Bogota Antigua and Barbuda Rio De Janeiro Florianopolis Lima Havana Punta Arenas Salvador de Bahia Cusco Cali Tijuana Oxahaca Punta del Este Jericoacoara Quito Foz do Iguacu Bonito

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

The top 100 Airbnb markets in 2025 are: 1. London - Lenient regulations, 51,638 listings, 73% occupancy rate, $190 daily rate. See other 99 places.

Airbnb In NYC

Room Prices, Reviews, and Availability

By Huggingface Hub [source]

About this dataset

This dataset offers a unique and comprehensive look into the expansive Airbnb industry in New York City. We capture 20,000+ Airbnbs with its associated data such as descriptions, rates, reviews and availability. Professionals researching this industry will find it an invaluable resource in providing insight to the ever popular Airbnb market that can be used for their advantage.

This dataset showcases some of the most important attributes for each listing: host name, neighborhood group, location (latitude/longitude coordinates), room type, price per night, minimum nights required to book a stay at this listing , total number of reviews and ratings received by guests over time (including reviews per month and last review date), calculated host listing count (indicates how many listings are offered by each host) along with 365 days worth of availability score. With all these parameters one can understand dynamics of demand & supply & further utilize them accordingly to maximize returns or occupancy greeting never before seen transparency into NYC’s Airbnb scene

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How to use the dataset

This dataset can be used to gain a comprehensive understanding of the Airbnb market in New York City. The data offers descriptions, rates, reviews and availability for over 20,000 Airbnbs in NYC.

Here are few tips on how to use this dataset: - Use the latitude and longitude coordinates to visualize the variety of Airbnbs located across all five boroughs of New York City using mapping programs like Google Maps or ArcGIS. - Determine the versatile price ranges offered by Airbnb listings by looking at the “price” column available for each listing . - Analyze reviews scored by guests who have used an Airbnb in order to better understand customer experience with different services through columns such as “number_of_reviews” and “last_review.
4 Understand how often properties are made available for booking based on their popularity through columns like “availability_365 and reviews_per_month. . 5 Investigate listing host data by looking into their description (host name) as well as number of listings they have booked (calculated host listing count)

Research Ideas

• Determining the listings with the highest satisfaction ratings for potential customers to book.
• Analyzing neighborhood trends in prices, availability, and reviews to identify hot areas of competition within the Airbnb market.
• Predicting future prices throughput examining properties such as review scores and availability rate to provide forecast information to AirBnB owners

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: train.csv | Column name | Description | |:-----------------------------------|:------------------------------------------------------------------------------------| | name | The name of the Airbnb listing. (String) | | host_name | The name of the host of the Airbnb listing. (String) | | neighbourhood_group | The neighbourhood group the Airbnb listing is located in. (String) | | latitude | The latitude coordinate of the Airbnb listing. (Float) | | longitude | The longitude coordinate of the Airbnb listing. (Float) | | room_type | The type of room offered by the Airbnb listing. (String) | | price | The price per night of the Airbnb listing. (Integer) | | minimum_nights | The minimum number of nights required for booking the Airbnb listing. (Integer) | | number_of_reviews | T...

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

In 2025, ****** reported the highest number of Airbnb listings per 1,000 inhabitants among the selected European destinations. Overall, ********************** had roughly ** Airbnb listings per 1,000 inhabitants that year. ****************** followed on the list, with around ** and ** Airbnb listings per 1,000 inhabitants in 2025, respectively.

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.

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.

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

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

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!

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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...

Unlock the full potential of the short-term rental market with our comprehensive Airbnb Listing Data. This dataset provides a granular, 360-degree view of listing performance, property characteristics, and market dynamics across key global geographies. Designed for Real Estate Investors, Property Managers, Hedge Funds, and Travel Analysts, our data serves as the backbone for data-driven decision-making in the hospitality sector.

Whether you are looking to optimize pricing strategies, identify high-yield investment neighborhoods, or analyze amenity trends, this dataset delivers the raw intelligence required to stay ahead of the competition. We capture high-fidelity signals on listings, availability, pricing, and reviews, allowing you to model supply and demand with precision.

Key Questions This Data Answers Our data is structured to answer the most pressing commercial questions in the short-term rental industry. By leveraging our granular fields, analysts can immediately address:

Market Composition: What is the exact distribution of property types (Entire Home vs. Private Room vs. Shared) in a specific market? Understand supply saturation instantly.

Amenity ROI: Which amenities are most common in top-performing listings? Correlate features (e.g., Pools, Hot Tubs, Wi-Fi speeds) with Occupancy Rates and ADR (Average Daily Rate) to determine the ROI of renovations.

Pricing Intelligence: How does nightly price vary by neighborhood, seasonality, and property type? Visualize price elasticity and identify arbitrage opportunities between sub-markets.

Geospatial Density: What is the density of listings in different geographical areas? Pinpoint "hot zones" for tourism and identify underserved areas ripe for new inventory.

Performance Benchmarking: How do my listings compare to the top 10% of competitors in the same zip code?

Comprehensive Use Cases 1. Market Analysis & Competitive Positioning Gain a competitive edge by understanding the landscape of any target city.

Competitor Mapping: Track the growth of listing supply in real-time. Identify which property managers control the market share.

Saturation Analysis: Avoid over-supplied markets. Use density metrics to find neighborhoods with high demand but low inventory.

Trend Forecasting: Analyze historical data to predict future supply shifts and market saturation points before they occur.

1. Pricing Strategy & Revenue Management Move beyond static pricing. Our data enables dynamic pricing models based on real-world market conditions.

Attribute-Based Pricing: Quantify exactly how much a "Sea View" or "King Bed" adds to the nightly rate.

Seasonality Adjustments: Optimize calendars by analyzing historical price surges during holidays, events, and peak seasons.

RevPAR Optimization: Balance Occupancy and ADR to maximize Revenue Per Available Room (RevPAR).

1. Real Estate Investment & Valuation For investors and funds, this data acts as a fundamental layer for asset valuation.

Cap Rate Calculation: Combine our revenue data with property values to estimate potential yields and Cap Rates for prospective acquisitions.

Investment Scouting: Filter entire regions by "High Occupancy / Low Price" to find undervalued assets.

Due Diligence: Validate seller claims regarding income potential with independent, third-party data history.

1. Property Type & Amenity Distribution Analysis Understand what guests actually want.

Amenity Gap Analysis: Identify amenities that are in high demand (high search volume) but low supply in specific neighborhoods.

Renovation Planning: Data-driven insights on whether installing A/C or allowing pets will significantly increase booking conversion.

Data Dictionary & Key Attributes Our schema is designed for financial modeling and granular analysis. We provide over 50 distinct fields per listing, including calculated financial metrics for Trailing Twelve Months (TTM) and Last 90 Days (L90D).

Listing Identity & Characteristics:

listing_id: Unique identifier for the listing

listing_name & cover_photo_url: Title and main visual

listing_type & room_type: Property classification (e.g., villa, entire home)

amenities: Comprehensive list of offered features

min_nights & cancellation_policy: Booking rules and restrictions

instant_book & professional_management: Operational indicators

Property Specs & Capacity:

guests, bedrooms, beds, baths: Full capacity details

latitude, longitude, city, state, country: Precise geospatial coordinates

photos_count: Quantity of listing images

Host Intelligence:

host_id & host_name: Primary operator details

cohost_ids & cohost_names: Extended management team details

superhost: Quality badge status

Financial Performance (TTM - Trailing 12 Months):

ttm_revenue & ttm_revenue_native: Total gross revenue generated

ttm_avg_rate (ADR): Average Daily Rate achieved

ttm_occupancy & ttm_adjusted_occupancy: Raw vs. Adjusted (excluding owner blocks) occupancy

ttm_revpar & ttm_adjusted_revpar: Revenue Per ...

New York City accounted for ****** Airbnb listings in late 2024. Meanwhile, Los Angeles had ****** listings, making it the city with the most Airbnb listings in the ranking.

In 2025, Airbnbs in Perth, Western Australia, had the highest average occupancy rates across the Australian cities and regions represented, with an average occupancy of around ** percent. Airbnbs in the Surfers Paradise, Brisbane, and Gold Coast areas had the next highest occupancy rates that year.