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.

These are the Airbnb statistics on gross revenue by country.

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.

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.

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 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

Overview

This dataset provides detailed information about various Airbnb listings from different regions. It includes essential features such as property details, host information, location, pricing, amenities, and guest reviews. This dataset can be utilized for a variety of data analysis and machine learning tasks, including price prediction, sentiment analysis, and geographic trends in short-term rentals.

Dataset Contents

•  id: Unique identifier for each listing
•  name: Name of the Airbnb listing
•  rating: Average rating of the listing
•  reviews: Number of reviews received
•  host_name: Name of the host
•  host_id: Unique identifier for the host
•  address: Location of the listing (city, region, country)
•  features: Summary of features (number of guests, bedrooms, beds, bathrooms)
•  amenities: List of amenities provided
•  price: Price per night in the local currency
•  country: Country where the listing is located
•  bathrooms: Number of bathrooms
•  beds: Number of beds
•  guests: Number of guests the listing can accommodate
•  toilets: Number of toilets
•  bedrooms: Number of bedrooms
•  studios: Number of studio units
•  checkin: Check-in time
•  checkout: Check-out time

Usage Examples

•  Price Prediction: Build machine learning models to predict the price of an Airbnb listing based on its features and location.
•  Sentiment Analysis: Analyze guest reviews to determine the sentiment and identify factors contributing to positive or negative experiences.
•  Geographic Trends: Study the distribution and popularity of Airbnb listings across different regions and countries.

How to Use

1. Exploratory Data Analysis (EDA): Perform EDA to understand the distribution of data, detect missing values, and identify patterns.
2. Feature Engineering: Create new features from the existing columns to enhance the predictive power of machine learning models.
3. Model Building: Use regression models for price prediction or classification models to predict the likelihood of receiving high ratings.

Data Source

The data was collected from publicly available Airbnb listings and includes various property types and locations, providing a comprehensive view of the short-term rental market.

Acknowledgments

This dataset is intended for educational and research purposes. Please ensure to comply with Airbnb’s terms of service and data privacy regulations when using this data.

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?

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 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.

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...

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

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 ...

As of 2018, the average price for an Airbnb in the center of Amsterdam amounted to approximately *** euros for a whole house, ****** euros for a private room and *** euros for a shared room per rent. The cheapest district of Amsterdam for a whole house and private room was Southeast costing *** and ***** euros, respectively. When observing the total average price of Airbnb accommodations in Amsterdam, it increased steadily between 2016 and 2018. In 2016, people paid on average *** euros, whereas by 2018 this amounted to roughly *** euros.

Annual increase of price Airbnb

Although the average price of Airbnb’s listings grew annually in Amsterdam, the number of overnight stays decreased from 2017 to 2018. In total, *** million nights were spent at Airbnb accommodations in the capital city of the Netherlands, whereas in 2018 this figure decreased slightly, reaching approximately **** million registered overnight stays. Other major cities in the Netherlands, such as Rotterdam, The Hague and Utrecht, had an increase in overnight stays of Airbnb accommodations, even though the number of nights spent is significantly lower compared to Amsterdam.

Number of hotel nights increased annually in Amsterdam

Looking at Airbnb’s competitors, the volume of hotel nights in Amsterdam increased annually between 2008 and 2018. In 2008, hotels registered **** million overnight stays whereas by 2018 this figure more than doubled with approximately ***** million nights that were spent in hotels in Amsterdam.

As of July 3, 2020, properties with **** or more bedrooms in New York had the highest average daily rate (ADR) on Airbnb, at *** U.S. dollars. Shared rooms in New York had the lowest ADR, with around ** U.S. dollars. Average daily rates for Airbnb are calculated based on the median (** percent) average rental price of that type of property. For example the Airbnb supply shows there are ***** listings for a studio in New York - the data therefore indicates the average rate of the ***** cheapest property.

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

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