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.

Listings per region on Airbnb declined from 2020 to 2021. Globally in 2021, there were a total of 12.7 million listings.

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

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.

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,...

Airbnb has a total of 6,132 employees that work for the company. 52.5% of Airbnb workers are male and 47.5% are female.

The current average price per night globally on Airbnb is $137 per night.

Airbnb Price Determinants in Europe

Characteristics and Effects

By [source]

About this dataset

This dataset contains Airbnb rental data for European cities, including characteristics and their effects on price. The dataset includes several features such as the total price of the listing, room type, host status (superhost or not), amenities, and location information which can be used to analyze the factors that affect Airbnb prices. This data can help travelers find an accommodation that satisfies their needs without spending more than necessary. It can also provide business owners valuable insights on how to set competitive prices and optimize their listings for increased bookings. Furthermore, this data is useful for property investors who want to understand pricing trends in different cities across Europe and make informed decisions about investing in real estate

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains Airbnb rental data for multiple European cities, including price, room type, host status, amenities and location information. This data can be used to better understand the factors that influence Airbnb rental prices in Europe.

The columns of the dataset include: - realSum (total price of the listing) - room_type (type of room offered such as private/shared/entire home/apt)
- room_shared (whether or not the room is shared) - person_capacity (maximum number of people allowed in the property)
- host_is_superhost(whether or not the host is a superhost) (boolean value so either true or false)
- multi (whether it’s for multiple rooms or not)
- biz(whether it’s for business use or family use ) .
dist(the distance from city center )
metro dist (the distance from nearest metro station ) lng(longitude value ) lat(latitude value ) guest satisfaction overall () Cleanliness rating () Bedrooms () and Real sum -Total Price.

First step would be to select features that are important and relevant to you according to your purpose. You can start by selecting the features like realSum ,room type ,host etc which will give you an understanding on how potential customers best fits your requirements i.e how many people will fit into a particular property when renting out a single bedroom versus renting out an entire home/apartment. After that review associated values; this could help you decide on pricing strategies such as offering discounts or raising prices according to needs and demands in different neighbourhoods depending on demand levels, availability and seasonality etc.. The next step would be to plot distance variables with respect to latitude & longitude which will indicate geographical locations where businesses could benefit from having higher occupancy rates by leveraging neighbourhood proximityi n order tackle seasonal variations . And lastly using correlation matrix between all other variables one can correlating parameters which display strong correlations thereby helping establish relationships across other variables relative towards each other as well as decide what set parameters should come into play when based upon one parameter . This dataset however does not provide dates

Research Ideas

• Price forecasting - Analyzing previous data about Airbnb listings, such as pricing, room type and amenities, could help predict potential rental prices in the future.

• Business or tourist rental hotspots - By looking at each listing’s location in relation to business and tourism centers and correlating this with pricing can help determine areas where Airbnb rentals will be most profitable.

• Customer sentiment analysis - Analyzing customer comments and satisfaction ratings to measure the effectiveness of a specific listing on their overall customer experience could be an useful tool for...

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.

Comprehensive Airbnb dataset for La Paz, Bolivia providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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.

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.

These are the Airbnb statistics on gross revenue by country.

************* was the region that brought in the highest amount of Airbnb’s worldwide revenue in 2024, at ************ U.S. dollars. As the company is based in the United States, this is not surprising. However, the Europe, Middle East, and Africa (EMEA) region was not too far behind with *********** U.S. dollars in revenue.************** also reported the highest average number of nights booked by region with Airbnb in 2024.

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

The vacation rental market is experiencing robust growth, driven by increasing disposable incomes, a preference for unique travel experiences, and the rise of the sharing economy. The market's expansion is fueled by several key trends: the increasing popularity of alternative accommodations beyond traditional hotels, the growing adoption of online booking platforms, and the diversification of rental options, ranging from apartments and private homes to unique properties like villas and cabins. Technological advancements, such as improved search functionalities and mobile booking apps, are further enhancing accessibility and convenience for travelers. While factors like fluctuating travel restrictions and economic downturns can pose challenges, the market's inherent resilience and the continued demand for flexible and personalized travel experiences suggest a positive long-term outlook. The segmentation within the market indicates strong performance across various applications, including the travel industry and commercial sectors, with apartment and private home rentals holding significant market share. Major players like Airbnb, Booking Holdings, and Expedia are key contributors to market growth, constantly innovating to attract and retain customers. Geographic data indicates North America and Europe as major revenue generators, although Asia Pacific and other regions are showing significant growth potential. The forecast period (2025-2033) suggests continued expansion, driven by consistent demand and technological enhancements. Competition is fierce but opportunities abound for established players and new entrants alike who can effectively leverage technology and cater to evolving traveler preferences. The competitive landscape is dynamic, with established players like Airbnb and Booking Holdings continuously innovating their platforms and services to remain competitive. New entrants are also emerging, leveraging technological advancements and specialized offerings to carve out niche markets. However, regulatory challenges, particularly concerning licensing and taxation, represent a significant restraint for the industry. Maintaining sustainable practices and addressing environmental concerns are also becoming increasingly important. Despite these challenges, the long-term growth trajectory for the vacation rental market remains optimistic, propelled by persistent demand for unique and personalized travel experiences, and the ongoing evolution of technology within the hospitality sector. Data suggests that this market will experience significant growth across all regions, with some exhibiting faster growth rates than others. Understanding the nuances of regional demand and preferences will be crucial for success in this dynamic market.

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing across multiple OTAs. This report provides performance metrics and ranking insights that help users benchmark their rental properties and key in on performance drivers across all global vacation markets Key Data has to offer.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Identifiers: Unique identifiers for each property with OTA-specific IDs - Historic Performance Metrics: Revenue, ADR, guest occupancy and more over the last 12 months. - Forward Looking Performance Metrics: Revenue, ADR, guest occupancy and more over the next 6 months. - Performance Tiering and Percentile Ranking amongst peer listings within the specified performance ranking groups. --How Listings Are Grouped: Listing Source (e.g., Airbnb vs. Vrbo) Market (identified by uuid) - Market type = vacation areas Property Type (house, apartment, unique stays, etc.) Number of Bedrooms (0, 1, 2, 3, 4, 5, 6, 7, 8+)

--- USE CASES --- Market Research and Competitive Analysis: VR professionals and market analysts can use this dataset to conduct detailed analyses of vacation rental supply across different markets. The data enables identification of property distribution patterns, amenity trends, pricing strategies, and host behaviors. This information provides critical insights for understanding market dynamics, competitive positioning, and emerging trends in the short-term rental sector.

Property Management Optimization: Property managers can leverage this dataset to benchmark their properties against competitors in the same geographic area. By analyzing listing characteristics, amenity offerings and guest reviews of similar properties, managers can identify optimization opportunities for their own portfolio. The dataset helps identify competitive advantages, potential service gaps, and management optimization strategies to improve property performance.

Investment Decision Support: Real estate investors focused on the vacation rental sector can utilize this dataset to identify investment opportunities in specific markets. The property-level data provides insights into high-performing property types, optimal locations, and amenity configurations that drive guest satisfaction and revenue. This information enables data-driven investment decisions based on actual market performance rather than anecdotal evidence.

Academic and Policy Research: Researchers studying the impact of short-term rentals on housing markets, urban development, and tourism trends can use this dataset to conduct quantitative analyses. The comprehensive data supports research on property distribution patterns and the relationship between short-term rentals and housing affordability in different markets.

Travel Industry Analysis: Travel industry analysts can leverage this dataset to understand accommodation trends, property traits, and supply and demand across different destinations. This information provides context for broader tourism analysis and helps identify connections between vacation rental supply and destination popularity.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: monthly | quarterly | annually • Delivery Method: scheduled file loads • File Formats: csv | parquet • Large File Format: partitioned parquet • Delivery Channels: Google Cloud | Amazon S3 | Azure Blob • Data Refreshes: monthly

Dataset Options: • Coverage: Global (most countries) • Historic Data: Last 12 months performance • Future Looking Data: Next 6 months performance • Point-in-Time: N/A

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data collection and processing methodology ensures high-quality data with comprehensive coverage of the vacation rental market. Regular quality assurance processes verify data accuracy, completeness, and consistency.

The dataset undergoes continuous enhancement through advanced data enrichment techniques, including property categorization, geographic normalization, and time series alignment. This processing ensures that users receive clean, structured data ready for immediate analysis without extensive preprocessing requirements.

As of October 2020, there were over ** thousand active Airbnb listings in New York. This figure has grown significantly since January 2010, where there were only *** Airbnb listings in the city. In 2020, Airbnb earned a total revenue of **** billion U.S. dollars, showing a decline when compared to the previous year. This was most likely due to the impact of the coronavirus (COVID-19) pandemic.

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.

Snapshot img

Vacation Rental Market Size 2025-2029

The vacation rental market size is forecast to increase by USD 22 billion, at a CAGR of 4.1% between 2024 and 2029. The market is experiencing significant growth, fueled by the expanding tourism industry and the increasing preference for short-term stays.

Major Market Trends & Insights

Europe dominated the market and accounted for a 32% share in 2023.
The market is expected to grow significantly in North America region as well over the forecast period.
Based on the Management, the managed by owners segment led the market and was valued at USD 61.00 billion of the global revenue in 2023.
Based on the Method, the offline segment accounted for the largest market revenue share in 2023.

Market Size & Forecast

Market Opportunities: USD 98.00 Billion
Future Opportunities: USD 22 Billion
CAGR (2024-2029): 4.1%
Europe: Largest market in 2023

Marketing automation tools, rental income tracking, guest experience metrics, calendar synchronization, and host communication platforms facilitate effective marketing and guest engagement. Legal compliance standards, cleaning service scheduling, digital marketing strategies, online reputation management, booking platform integration, customer relationship management, multi-property management, and revenue management software are indispensable for managing a large and diverse rental portfolio. Prices for vacation rentals are expected to grow by 5% annually, driven by the increasing popularity of short-term rentals and the adoption of advanced technologies. The market is witnessing a shift towards automation and integration, with automated check-in/out, keyless entry systems, and data analytics dashboards becoming standard offerings.

What will be the Size of the Vacation Rental Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, with innovative technologies and strategies shaping the industry landscape. Dynamic pricing algorithms are increasingly being adopted to optimize revenue based on real-time market demand and supply dynamics. For instance, a leading player in the market reported a 15% increase in average daily rate through dynamic pricing. Maintenance request systems, tax compliance software, and smart home integration are essential tools for property managers, ensuring efficient operations and regulatory compliance. Moreover, rental agreement templates, payment gateway security, and security camera monitoring enhance the guest experience and property protection. Insurance policy coverage, occupancy rate optimization, and channel management strategies are crucial components of a successful rental business. The professionally managed segment is the second largest segment of the management and was valued at USD 33.50 billion in 2023.

In conclusion, the market is characterized by continuous innovation and adaptation to meet the evolving needs of property managers and guests. By leveraging technologies such as dynamic pricing algorithms, maintenance request systems, tax compliance software, smart home integration, and more, rental businesses can optimize operations, enhance guest experiences, and grow their revenue.

The convenience of instant booking features has made vacation rentals an attractive alternative to traditional hotels, particularly for travelers seeking more personalized and affordable accommodations. However, this market is not without challenges. The rise of fraudulent vacation rental properties poses a significant risk to both renters and property owners. Malicious actors create fake listings or misrepresent existing properties, leading to dissatisfied customers and potential financial losses.

Companies operating in this market must prioritize security measures to mitigate these risks and maintain customer trust. By addressing these challenges and capitalizing on the growing demand for vacation rentals, businesses can effectively position themselves to thrive in this dynamic and evolving market.

How is this Vacation Rental Industry segmented?

The vacation rental industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Management

  Managed by owners
  Professionally managed


Method

  Offline
  Online


Type

  Home
  Apartments
  Resort/Condominium
  Others
  Home
  Apartments
  Resort/Condominium
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Management Insights

The managed by owners segment is estimated