Vacation Rental Statistics: Renting a place on vacation is what refreshes our minds. Every year, global tourists focusing on millennials spend around 180 billion dollars on travel every year. Therefore, the market is expected to rise at a CAGR of 5.3% between 2022 to 2030. Today, planning and booking a short or long vacation has become easy, you can simply ask ChatGPT your itinerary for the trip and book on the websites that provide the cheapest price rates for the accommodation. These Vacation Rental Statistics are including the most recent data focusing on global as well as American holiday rental markets. Don’t you think it's already summertime and you should be booking a vacation to the beach? Editor’s Choice Due to the remote working system, the duration of vacations has been increased by 68% resulting in 21 to 30-day stays. As of today, there are 31.3% of privately owned vacation rentals in the United States of America with 600,000 Americans using online platforms to rent out their places. As of 2022, around 138 million nights got booked for rental listing in the United States of America. From a worldwide perspective, revenue in the vacation rentals market is expected to reach $96.85 billion in 2023. The global comparison of Vacation Rental Statistics confirms that in 2023, most of the revenue in the market will be generated from the United States of America. Around the world, 700 million travellers used vacation rentals and more than 60 million Americans preferred to stay in holiday rentals in 2022. As of 2022, the primary booking method for vacation rentals in the United States of America was online methods (76%), and offline methods (24%). The demand for vacation rentals that allow pets have increased by 40%. Furthermore, Vacation Rental Statistics of online booking state that the percentage of the same will rise to 80% by 2026. 43% of the rental hosts manage their property by themselves whereas 25% of the properties are managed by professionals.

A merged dataset of the Hotels, Motels, B&Bs, and Boarding Houses and the Short-Term Rentals datasets.

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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.

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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

Data includes occupancy rates, average daily rates, and revenue per available rental.

Introduction

Vacation Rental Statistics: The way people choose to stay during vacations has changed over the years. Instead of opting for traditional hotel stays, many travelers now prefer to rent separate properties. This allows them to cook their meals and enjoy more privacy. While hotels offer more amenities, privacy is a top priority for most travelers.

Renting out properties can also be very lucrative for owners, especially with the right amenities. Vacation rental trends are expected to be more significant in 2024, as seen in recent vacation rental statistics.

--- DATASET OVERVIEW --- This dataset provides critical insights into market pricing dynamics, availability patterns, and booking trends with AI-enhanced forecasting capabilities for vacation rental properties across global markets. With daily updates and extensive coverage, it provides a detailed view of pricing strategies, demand patterns, and market positioning for properties across different segments and regions.

The data is sourced directly from major OTA platforms using advanced collection methodologies that ensure high accuracy and comprehensive coverage. Our proprietary algorithms enhance the raw data with AI and machine learning driven booking probability predictions, enabling users to anticipate future booking patterns and occupancy levels with increased precision.

--- 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 - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Quoted Rates: Price points for each available date - Minimum Stay Requirements: Minimum night requirements for different booking periods - Availability Status: Available/unavailable including guest stay detection for each calendar date - Key Pricing Patterns: Price variations across different seasons and months as well as event driven and other high-demand periods. - Price Positioning: Relative price positioning compared to similar properties in the same area - Historical Price Trends: Price changes over time for the same property and dates

--- USE CASES --- Revenue Management Optimization: Property managers and revenue specialists can leverage this dataset to develop sophisticated dynamic pricing strategies. By analyzing how similar properties adjust pricing based on seasonality, day of week, and market demand, managers can optimize their own pricing to maximize revenue without sacrificing occupancy. The AI-detected guest bookings provide the best context for expected demand, allowing for more precise rate adjustments during different booking windows.

Demand Forecasting and Trend Analysis: Market analysts and tourism organizations can use this dataset to forecast demand patterns across different destinations. The comprehensive availability data, coupled with AI-detected guest bookings, enables accurate prediction of occupancy trends, booking pace, and seasonal fluctuations. These insights support capacity planning, marketing timing, and resource allocation decisions.

Competitive Benchmarking: Property owners and managers can benchmark their pricing and availability strategies against competitors in the same market. The dataset allows for detailed comparison of pricing strategies, minimum stay requirements, and booking pace across similar properties. This competitive intelligence helps identify opportunities for market positioning adjustments and pricing optimization.

Investment Decision Support Real estate investors focused on the vacation rental sector can analyze pricing and occupancy patterns across different markets to identify investment opportunities. The dataset provides insights into rate potential, seasonal demand variations, and overall market performance, supporting data-driven acquisition and portfolio expansion decisions.

Market Entry Analysis Companies considering entering new vacation rental markets can utilize this dataset to understand pricing dynamics, seasonality impacts, and demand patterns before committing resources. The comprehensive pricing and availability data reduces market entry risk by providing clear visibility into potential revenue opportunities and competitive positioning requirements.

Economic Impact Studies Researchers and economic development organizations can leverage this dataset to analyze the economic impact of vacation rentals on local communities. By tracking pricing trends, occupancy patterns, and overall inventory utilization, researchers can quantify the contribution of the vacation rental sector to local economies and tax bases.

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

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Not Available • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by o...

Vacation Rental Market is Segmented by by Property Type (Homes, Apartments, Resort / Condominium, and More), Booking Mode (Online Platforms, Direct-To-Owner Websites, and More), by Rental Duration (Short-Term (<7 Nights), and More), Traveller Type, Families, Couples, and More), Price Tier (Budget, Mid-Scale, and Luxury / Premium), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Snapshot img

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.

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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

--- DATASET OVERVIEW --- This dataset captures detailed information about each vacation rental property listing, providing insights that help users understand property distribution, characteristics, management styles, and guest preferences across different regions. With extensive global coverage and regular weekly updates, this dataset offers in-depth snapshots of vacation rental supply traits at scale.

The data is sourced directly from major OTA platforms using advanced data collection methodologies that ensure high accuracy and reliability. Each property listing is tracked over time, enabling users to observe changes in supply, amenity offerings, and host practices.

--- 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 - Geographic Information: Location data including neighborhood, city, region, and country - Listing Characteristics: Property type, bedroom count, bathroom count, in-service dates. - Amenity Inventory: Comprehensive list of available amenities, including essential facilities, luxury features, and safety equipment. - Host Information: Host details, host types, superhost status, and portfolio size - Guest Reviews: Review counts, average ratings, detailed category ratings (cleanliness, communication, etc.), and review timestamps - Property Rules: House rules, minimum stay requirements, cancellation policies, and check-in/check-out procedures

--- 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: weekly | 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: weekly

Dataset Options: • Coverage: Global (most countries) • Historic Data: N/A • Future Looking Data: N/A • Point-in-Time: N/A • Aggregation and Filtering Options: • Area/Market • Time Scales (weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

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 preprocess...

--- 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.

--- DATASET OVERVIEW --- This dataset captures detailed performance data for individual vacation rental properties, providing a complete picture of operational success metrics across different timeframes and market conditions. With weekly updates and four years of historical data, it enables both point-in-time analysis and long-term trend identification for property-level performance.

The data is derived from OTA platforms using advanced methodologies that capture listing, calendar and quote details. Our algorithms process this raw information to produce standardized and enriched performance metrics that facilitate accurate comparison across different property types, locations, and time periods. By leveraging our other datasets and machine learning models, we are able to accurately detect guest bookings, revenue generation, and occupancy patterns.

--- 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 - Geographic Information: Location data including neighborhood, city, region, and country - Property Characteristics: Property type, bedroom count, bathroom count, and capacity - Occupancy Metrics: Daily, weekly, and monthly occupancy rates based on actual bookings - Revenue Generation: Total revenue, average daily rate (ADR), and revenue per available day (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Seasonality Indicators: Performance variations across seasons, months, and days of week - Competitive Positioning: Performance relative to similar properties in the same market - Historical and Forward Looking Trends: Year-over-year and month-over-month performance changes

--- USE CASES --- Property Performance Optimization: Property managers can leverage this dataset to evaluate the performance of individual listings against market benchmarks. By identifying properties that underperform relative to similar listings in the same area, managers can implement targeted improvements to pricing strategies, property amenities, or marketing approaches. The granular performance data enables precise identification of specific improvement opportunities at the individual property level.

Competitive Benchmarking: Property owners and managers can benchmark their listings against competitors with similar characteristics in the same market. The property-level performance metrics enable detailed comparison of occupancy rates, ADR, and revenue generation across comparable properties. This competitive intelligence helps identify realistic performance targets and market positioning opportunities.

Portfolio Optimization: Vacation rental portfolio managers can analyze performance variations across different property types and locations to optimize investment and management decisions. The dataset supports identification of high-performing property configurations and locations, enabling strategic portfolio development based on actual performance data rather than assumptions.

Seasonal Strategy Development: The historical performance data across different seasons enables development of targeted seasonal strategies. Property managers can analyze how different property types perform during specific seasons or events, informing marketing focus, pricing adjustments, and operational planning throughout the year.

Performance Forecasting: Historical performance patterns can be leveraged to develop accurate forecasts for future periods. By analyzing year-over-year trends and seasonal patterns, property managers can anticipate performance expectations and set realistic targets for occupancy and revenue generation.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | 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: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market • Time Scales (daily, weekly, monthly) • Listing Source • Property Characteristics (property types, bedroom counts, amenities, etc.) • Management Practices (professionally managed, by owner)

Contact us to learn about all options.

--- DATA QUALITY AND PROCESSING --- Our data processing methodology ensures high-quality, reliable performance metrics that accurately represent actual property performance. The raw booking and revenue data undergoes extensive validation and normalization processes to address inconsistencies, identify anomalies, and ensure comparability across different pro...

--- DATASET OVERVIEW --- Our Vacation Rental Area KPIs from Direct PM Reservation Data Integrations provides comprehensive market performance metrics for professionally managed vacation rentals sourced directly from property management systems. This dataset delivers authoritative insights into market performance based on actual reservation data rather than listing information, offering an accurate view of booking patterns, revenue generation, and operational metrics across different markets.

The data is sourced directly from property management system integrations, capturing actual reservation details rather than OTA listing information. This direct access to booking data ensures that the performance metrics reflect true market activity rather than just advertised availability or pricing. Our coverage is particularly strong in North America, Europe and Australia, with growing global representation.

--- KEY DATA ELEMENTS --- Our dataset includes the following market-level performance indicators for professionally managed vacation rentals: - Geographic Identifiers: Multiple geographic levels (vacation area, vacation region, county, etc) - Temporal Dimensions: Daily, weekly, monthly, and quarterly performance metrics - Occupancy Metrics: Actual occupancy rates based on confirmed reservations - Revenue Metrics: Total revenue, average daily rate (ADR), and revenue per available rental night (RevPAR) - Booking Patterns: Lead time distribution, length of stay patterns, and booking frequency - Reservation Channel Mix: Distribution of bookings across different reservation channels - Seasonality Indicators: Performance variations across seasons, months, and days of week - Performance Segmentation: Metrics broken down by property type, size, and price tier - Historical Pacing: Snapshots into how stay date ranges developed for tracking pacing trends - Forward Looking Trends: Area KPIs 180-365 days into the future

--- USE CASES --- Performance Benchmarking for Professional Managers: Property management companies can benchmark their portfolio performance against market-wide metrics for professionally managed properties. By comparing company-specific occupancy rates, ADR, and RevPAR against market averages for similar property types, managers can assess relative performance and identify areas for improvement. These benchmarks provide crucial context for performance evaluation and goal setting specific to professional management operations.

Operational Strategy Development: Property management operators can leverage this dataset to develop operational strategies based on industry benchmarks. The reservation patterns, lead time distributions, and cancellation metrics provide insights into optimal staffing levels, maintenance scheduling, and operational workflows. This information supports the development of efficient operational practices aligned with actual booking patterns.

Revenue Management Optimization: Revenue managers can use this dataset to develop sophisticated revenue optimization strategies based on actual booking patterns to benchmark broader, inferred information from OTAs. The detailed revenue metrics and booking patterns provide insights into rate elasticity, optimal minimum stay requirements, and the revenue impact of different pricing approaches. This information supports the development of data-driven revenue management strategies tailored to specific markets and property types.

Distribution Channel Strategy: Property managers can analyze reservation channel performance across different markets to optimize their distribution strategy. By understanding which channels deliver the highest value bookings in specific markets, managers can focus their efforts and investment on the most productive channels for their target areas and property types.

Investment Decision Support: Real estate investors focused on professionally managed vacation rentals can analyze market performance across different regions to identify investment opportunities. The dataset provides insights into revenue potential, seasonality impacts, and overall market health based on actual booking data, supporting data-driven acquisition and portfolio expansion decisions.

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

Dataset Options: • Coverage: North America + Top Global Tourism Markets with Strong Coverage in Europe and Australia • Historic Data: Available (2019 for most areas) • Future Looking Data: Available (Current date + 180 days+) • Point-in-Time: Available (with weekly as of dates) • Aggregation and Filtering Options: • Area/Market (required) • Time Scales (daily, weekly, monthly) • Property Characteris...

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.

In 2023, Expedia led the vacation rentals market in the United States, achieving an aided brand awareness of 54.93 percent among consumers. AirBnB followed with 48.81 percent awareness. Booking.com and Kayak reported 44.73 percent and 42.78 percent, respectively.

--- DATASET OVERVIEW --- This dataset delivers critical market intelligence including occupancy rates, average daily rates, revenue per available rental, booking pace, and seasonal demand patterns across different geographic areas. With daily updates, AI-driven forward projections, and four years of historical data, it offers property managers, investors, and market analysts the essential benchmarks needed to understand market performance, identify emerging trends, and develop data-driven strategies in the rapidly evolving vacation rental sector.

The data is sourced from major OTA platforms and processed through advanced aggregation methodologies that ensure statistical validity while preserving crucial market signals. Our proprietary algorithms enhance the raw data with sophisticated trend analysis and forward-looking projections, enabling users to anticipate future market conditions with increased confidence.

--- KEY DATA ELEMENTS --- Our dataset includes the following core performance metrics for each property: - Property Groups: Group by property type, bedroom counts, key amenities groups - Geographic Identifiers: Multiple geographic levels (vacation area, vacation region, county, etc) - Temporal Dimensions: Daily, weekly, monthly, and quarterly performance metrics - Occupancy Metrics: Market-wide occupancy rates and booking pace indicators - Pricing Metrics: Average daily rates (ADR), revenue per available rental night (RevPAR), and price trends - Booking Pattern Indicators: Average lead time, length of stay, and booking frequency - Seasonality Metrics: Seasonal demand patterns and year-over-year comparisons - Demand Forecasts: Forward-looking projections for occupancy and pricing trends - Historical Pacing: Snapshots into how stay date ranges developed for tracking pacing trends - Forward Looking Trends: Area KPIs 180-365 days into the future

--- USE CASES --- Market Performance Benchmarking: Property managers and owners can benchmark their individual property or portfolio performance against market-wide metrics. By comparing property-specific occupancy rates, ADR, and RevPAR against market averages for similar property types, managers can assess relative performance and identify areas for improvement. These benchmarks provide crucial context for performance evaluation and goal setting.

Investment Decision Support: Real estate investors and portfolio managers can use market-level performance data to identify attractive investment opportunities across different geographic areas. The comprehensive market metrics reveal high-performing areas, emerging markets, and potential investment risks based on actual performance data rather than anecdotal evidence. This information supports data-driven acquisition strategies and portfolio diversification decisions.

Demand Forecasting and Planning: Revenue managers and property operators can leverage the historical performance patterns and forward-looking projections to anticipate demand fluctuations and plan accordingly. The seasonal patterns, booking pace indicators, and AI-enhanced forecasts enable proactive rate adjustments, marketing timing, and operational planning to maximize revenue opportunities during high-demand periods.

Market Entry Analysis: Companies considering entering new vacation rental markets can utilize this dataset to understand market dynamics, competitive intensity, and performance expectations before committing resources. The comprehensive market metrics reduce market entry risk by providing clear visibility into potential revenue opportunities, seasonal patterns, and overall market health.

Performance Attribution Analysis: Market analysts can use this dataset to understand the drivers behind performance variations across different markets and time periods. By analyzing how market composition, seasonality, and external factors influence overall performance, analysts can identify the underlying causes of performance trends and develop more accurate forecasting models.

Economic Impact Assessment: Economic development organizations and tourism authorities can leverage this dataset to quantify the economic contribution of the vacation rental sector. The market-wide revenue metrics, occupancy patterns, and supply growth indicators provide valuable inputs for economic impact studies and policy development related to the short-term rental industry.

--- ADDITIONAL DATASET INFORMATION --- Delivery Details: • Delivery Frequency: daily | weekly | 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: daily

Dataset Options: • Coverage: Global (most countries) • Historic Data: Available (2021 for most areas) • Future Looking Data: Available (Current date + 180-365 days) • Point-in-Time: Available (with weekly as of dates) • Aggreg...

This statistic shows the value of the vacation rental market worldwide in 2016 and 2021. In 2021, the value of the global vacation rental market was estimated to reach ****** billion U.S. dollars.

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

Comprehensive Airbnb dataset for George, South Africa 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.

This statistic shows the average rental income for vacation rental properties in selected markets in the United States in 2019. The average rental income from a vacation rental in Nashville, Tennessee amounted to ****** U.S. dollars in 2019.

Comprehensive Airbnb dataset for Winter Park, United States 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.