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.

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.

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Vacation Rental Market Size 2025-2029

The vacation rental market size is estimated to increase by USD 22 billion, growing at a CAGR of 4.1% between 2024 and 2029. The industry's expansion and the rising popularity of short-term vacation rentals are driving substantial market growth. The vacation rental market is experiencing significant growth, driven by the expanding tourism industry and the increasing preference for short-term stays in vacation rental properties. This trend is further fueled by the convenience of instant booking features, which allow travelers to secure their accommodations with ease. However, the market also faces challenges, including the risks associated with fraudulent vacation rental listings. These risks can lead to financial losses and safety concerns for travelers, making it crucial for market participants to prioritize security measures and transparency. Overall, the vacation rental market is poised for continued growth, with opportunities for innovation and improvement in areas such as customer experience, safety, and technology integration. The market's future looks promising, with opportunities for innovation in cultural tourism and enhancements in areas like customer experience, safety, and technology integration.

What will be the size of Market during the Forecast Period?

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Market Segmentation

The market 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


Geography

  Europe

    UK
    France
    Italy


  North America

    Canada
    US


  APAC

    China
    India
    Japan


  Middle East and Africa

    South Africa


  South America

    Brazil

Which is the largest segment driving market growth?

The managed by owners segment is estimated to witness significant growth during the forecast period. Vacation rentals have emerged as a significant segment in the tourism industry, with B2C enterprises facilitating bookings through various sales channels. According to industry associations and third-party studies, vacation rentals account for a substantial portion of consumer spending on accommodation and features such as spas, with tourism spending projected to increase due to rising internet and device penetration. Forecasting techniques, such as time series forecasts and stationarity of data analysis, are used to estimate short-term trends in the vacation rental market.

Get a glance at the market share of various regions. Download the PDF Sample

The managed by owners segment accounted for USD 48.5 billion in 2019 and showed a gradual increase during the forecast period. These estimates consider factors like rental homes in the accommodation segment, resorts segment, and booking modes, including offline and online. Market players invest in acquisitions and mergers to expand their offerings, with trends favoring short-term rentals and eco-friendly vacation rentals. Statistical offices and trade associations provide price indices to help owners set rental rates based on local market conditions, ensuring flexibility and competitiveness. Consumer preferences for privacy, space, and flexibility continue to drive demand for vacation rentals in the travel industry.

The vacation rental market has grown significantly with the rise of short-term rentals and vacation homes, supported by online booking platforms and property management solutions. Luxury vacation rentals cater to high-end travelers seeking unique travel experiences. HomeAway and Airbnb alternatives have expanded options for tourists, while local tourism benefits from the convenience of digital travel solutions. These trends are shaping the future of the vacation rental market, driving growth and innovation.

Which region is leading the market?

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Europe is estimated to contribute 32% to the growth of the global market during the market forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

The European vacation rental market is experiencing significant growth due to the rising travel trend and the preference for unique experiences over traditional accommodations. Travelers seek more personalized and cost-effective options, leading to the increasing popularity of vacation rentals such as hostels and camping sites. Ancient ruins and historical sites add to Europe's allure, making vacation rentals an attractive choice for tourists. However, the availability of properties and restrictions on ren

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,...

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.

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

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Short Term Vacation Rental Market Size 2025-2029

The short term vacation rental market size is forecast to increase by USD 114.1 billion, at a CAGR of 13.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the expanding tourism industry and the increasing popularity of alternative accommodation options. Travelers seek flexibility, convenience, and unique experiences, making short term rentals an attractive choice over traditional and boutique hotels. Technological advancements further enhance the market's appeal, with digital platforms simplifying the booking process and offering personalized recommendations based on traveler preferences. However, the market faces challenges in ensuring consistent quality across vacation rental properties. The lack of standardization and regulation can lead to inconsistencies in the guest experience, potentially impacting customer satisfaction and brand reputation.
Addressing this challenge requires a commitment to quality assurance, from property maintenance and cleanliness to guest communication and support. Companies that prioritize these aspects and leverage technology to streamline operations will capitalize on the market's opportunities while navigating challenges effectively.

What will be the Size of the Short Term 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.
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The short-term rental market continues to evolve, with dynamic pricing strategies shaping the landscape. Property managers employ guest management systems to optimize operations, while digital marketing and channel management tools expand reach. Email marketing and social media platforms engage guests, driving direct bookings. Property valuation relies on data analysis, including occupancy rates and revenue management. Seasonal demand influences pricing, with peak seasons offering higher yields. Energy efficiency and green initiatives attract eco-conscious travelers, while luxury rentals cater to affluent guests.

Amenities, from smart home technology to concierge services, enhance the guest experience. Calendar synchronization ensures seamless booking and maintenance services maintain property condition. Legal compliance remains crucial, with security systems and yield management tools addressing safety and revenue optimization. Budget rentals and cabin rentals cater to diverse markets, expanding the market's reach. Overall, the short-term rental market's continuous evolution reflects the industry's adaptability and innovation.

How is this Short Term Vacation Rental Industry segmented?

The short term 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.

Mode Of Booking

  Offline
  Online


Management

  Managed by owners
  Professionally managed


Type

  Apartments and condominiums
  Villas and luxury homes
  Cottages and cabins
  Resorts and bungalows
  Others


Location

  Urban
  Rural
  Coastal
  Mountain


Traveler Type

  Leisure Travelers
  Business Travelers
  Families


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    The Netherlands
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Mode Of Booking Insights

The offline segment is estimated to witness significant growth during the forecast period.

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The Offline segment was valued at USD 87.10 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

Europe is estimated to contribute 32% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

Request Free Sample

The European the market is experiencing growth due to the rising demand for travel and unique experiences. Travelers seek more personalized accommodations, leading to the popularity of short term rentals over traditional hotels. Weekend getaways and city breaks align with the trend of experiential travel, further fueling market growth. Short term rentals offer flexible options and can be cost-effective for families or groups. Pricing strategies, such as dynamic pricing and seasonal demand, influence rental income. Guest management systems, email marketing, and channel management help optimize bookings. Operating expenses include cleaning services, maintenance, and property management software. Energy efficiency and green initiatives are essential property amenities.

Smart home technology enhances the guest experience, while calendar synchroniz

This dataset provides information about airbnb listings in Pakistan. The data was taken from airbnb's public websites, for listings in Karachi, Lahore, Islamabad and Rawalpindi. The data is about 3000+ listings and was taken for listings available in the month of February 2023. There are 3 files, for cities Lahore, Karachi and Islamabad/Rawalpindi. And there is a 4th file 'combined', which has the merged data for all of the cities. It also contains spreadsheets for clean data and sheet for listings which are rated.

I created this dataset for a project that I did! Feel free to use it but I would really appreciate if you give credit and cite me :)

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

--- 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.

The Riyadh Airbnb dataset provides a comprehensive overview of Airbnb listings in Riyadh, Saudi Arabia, for October, November and December of 2024. This dataset is essential for understanding the short-term rental market and enables basic analysis of various parameters such as pricing, amenities, and host characteristics. It includes a diverse range of properties, from apartments to houses, each with detailed attributes that inform potential guests and hosts about the available options in the region. Note that it is ideal for individuals learning data analytics.

List of available amenity ids:

2 - Kitchen 4 - Wifi 5 - Air conditioning 7 - Pool 8 - Kitchen 9 - Free parking on premises 11 - Smoking allowed 12 - Pets allowed 15 - Gym 16 - Breakfast 21 - Elevator 25 - Hot tub 27 - Indoor fireplace 30 - Heating 33 - Washer 34 - Dryer 35 - Smoke alarm 36 - Carbon monoxide alarm 41 - Shampoo 44 - Hangers 45 - Hair dryer 46 - Iron 47 - Laptop-friendly workspace 51 - Self check-in 58 - TV 64 - High chair 78 - Private bathroom 109 - Wide hallways 110 - No stairs or steps to enter 111 - Wide entrance for guests 112 - Step-free path to entrance 113 - Well-lit path to entrance 114 - Disabled parking spot 115 - No stairs or steps to enter 116 - Wide entrance 117 - Extra space around bed 118 - Accessible-height bed 120 - No stairs or steps to enter 121 - Wide doorway to guest bathroom 123 - Bathtub with bath chair 125 - Accessible-height toilet 127 - No stairs or steps to enter 128 - Wide entryway 136 - Handheld shower head 286 - Crib 288 - Electric profiling bed 289 - Mobile hoist 290 - Pool with pool hoist 291 - Ceiling hoist 294 - Fixed grab bars for shower 295 - Fixed grab bars for toilet 296 - Step-free shower 297 - Shower chair 347 - Piano 608 - Extra space around toilet 609 - Extra space around shower

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb (ABNB) Stock: A Travel Revolution in the Making

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: Is It a Buy, Sell, or Hold for the Next 3 Months?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

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

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Airbnb Stock: A Hold for the Next 6 Months

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data