--- 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...

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.

--- 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...