--- 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 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 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...

--- 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...

--- 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...