20M+ properties
190+ countries
15+ years history
95%+ accuracy
Properties Tracked
Countries Covered
Years of History
Revenue Accuracy
API Endpoints
AirROI covers 190+ countries, from major metropolitan markets in North America and Europe to emerging vacation rental hotspots in Southeast Asia and Latin America. Whether you're analyzing a single neighborhood in Manhattan or comparing resort markets across three continents, the API delivers consistent, analytics-enriched data with native currency support for every region.
US (all 50 states), Canada, Mexico, Caribbean
The most mature STR market in the world, with deep coverage across urban centers, resort destinations, and rural retreats. Millions of tracked listings span from Manhattan penthouses to Montana cabins.
UK, France, Spain, Italy, Portugal, Greece, Scandinavia, Eastern Europe
Comprehensive coverage of Western and Eastern Europe, including the Mediterranean coast, Alpine destinations, and Nordic cities. Over 3 million tracked properties across 40+ countries.
Japan, Australia, New Zealand, Southeast Asia, India, China
Rapidly growing STR markets tracked from Tokyo to Bali. Covers established markets like Australia and Japan alongside emerging hotspots in Vietnam, Thailand, and the Philippines.
Brazil, Colombia, Argentina, Costa Rica, Peru, Chile
Expanding coverage of Latin America's booming vacation rental scene, from Rio de Janeiro beachfronts to Medellín digital nomad apartments and Patagonian lodges.
UAE, South Africa, Morocco, Kenya, Egypt, Turkey
Emerging market coverage spanning Dubai luxury villas, Cape Town waterfront apartments, Marrakech riads, and Nairobi serviced accommodations. Native currency support for all local markets.
Maldives, Bali, Fiji, Hawaii, Mauritius, Seychelles
Island and resort destinations where STR revenue can be 3-5x mainland averages. Track occupancy, ADR, and seasonal patterns across the world's most sought-after vacation markets.
While most competitors offer 12–24 months of data, AirROI provides up to 15+ years of historical data for established markets. This depth enables long-term trend analysis, cycle detection, and seasonality modeling that shallow datasets simply cannot support.
Every metrics endpoint supports up to 60 months of monthly time-series data in a single call. Forward-looking endpoints provide up to 365 days of future nightly rates and market-level booking pacing — leading indicators that trailing metrics miss entirely.
Access up to 60 months of monthly time-series data per API call through the market_metrics_all and listing_metrics endpoints. Track occupancy, ADR, RevPAR, revenue, active listings, booking lead time, and length of stay over multi-year windows — enabling long-term trend analysis and cycle detection.
Look up to 365 days into the future with nightly rate data for individual listings and market-level booking pacing. Forward pacing data reveals the rate at which future dates are being booked — a leading demand indicator that trailing occupancy and revenue metrics cannot capture.
While most competitors offer between 12 months and 5 years of history, AirROI delivers 15+ years for established markets. This depth supports seasonality modeling, pandemic-recovery analysis, and investment underwriting that requires long-horizon data.
Listing data and market metrics are refreshed frequently to maintain accuracy. Forward-looking data — future nightly rates and booking pacing — is updated more often to capture real-time market movements as hosts adjust pricing and travelers book.
See the full competitive comparison of historical depth, data freshness, and coverage across providers.
AirROI's ML models are trained on billions of data points from 20M+ properties. Advanced calendar classification algorithms distinguish genuine bookings from owner blocks, achieving 95%+ correlation with actual Airbnb revenue figures — the highest verified accuracy in the industry.
Calendar Booking Classification: Proprietary algorithms analyze calendar patterns to distinguish true guest bookings from owner blocks, last-minute cancellations, and multi-day holds. This eliminates the false-positive bookings that inflate competitor accuracy claims.
Comp-Based Revenue Estimation: The estimate_revenue endpoint returns the actual comparable properties used in every calculation, providing full transparency into the methodology. No other API exposes its comp set this way.
Cross-Validation: Models are validated against reported revenue data for major markets, ensuring predictions remain calibrated as market conditions shift. The training corpus spans 100+ data fields per listing across 20M+ properties.
How AirROI ensures data accuracy — a deep dive into the ML pipeline and validation methodology.
All data is pre-calculated and analytics-enriched — not raw data requiring secondary processing. Each listing includes 100+ fields, and market endpoints deliver aggregate metrics with native currency support for non-USD markets.
| Per-Listing Data | Per-Market Data |
|---|---|
Property attributes (bedrooms, bathrooms, accommodates, property type) | Aggregate metrics (median revenue, average occupancy, ADR, RevPAR) |
Location data (coordinates, city, neighborhood, country) | Supply metrics (active listing count, growth rate, new listing velocity) |
Host information (superhost status, listing count, response rate) | Demand signals (booking lead time, length of stay, future pacing) |
Amenities (pool, hot tub, kitchen, Wi-Fi, parking, 50+ categories) | Time-series up to 60 months (monthly occupancy, ADR, RevPAR, revenue) |
Pricing details (nightly rate, cleaning fee, security deposit) | Forward rates (up to 365 days of market-level nightly rate projections) |
Review scores (overall, accuracy, cleanliness, communication, location, value) | Native currency support (all metrics in local currency for non-USD markets) |
Performance metrics (occupancy rate, ADR, RevPAR, annual revenue) | Property type breakdown (entire home, private room, shared room splits) |
Booking patterns (lead time, length of stay, cancellation policy) | Bedroom mix distribution (studio through 5+ bedroom composition) |
Beyond scale and accuracy, AirROI provides four capabilities that no competing STR data API offers. These features exist because they solve real problems that developers and analysts encounter when building production applications.
Define any geographic boundary as coordinate points. Analyze school districts, zoning areas, HOA boundaries, or custom neighborhoods that don't align with standard market definitions. No other STR data API offers arbitrary polygon queries.
Export every listing in a market through AirROI's MCP Server — ideal for bulk analysis and data warehousing. For REST API users, the search_by_market endpoint with pagination covers market-wide listing retrieval with consistent filtering and sorting.
Market-level booking velocity shows how fast future dates are filling up. This leading demand indicator lets investors and revenue managers spot trends weeks before they appear in trailing occupancy data.
Query STR data through natural language via the Model Context Protocol. Connect Claude, ChatGPT, Cursor, or any MCP-compatible AI client to 20M+ listings. Ask questions in plain English and receive structured data responses instantly.
Learn about the MCP Server — connect your AI to 20M+ properties with a single command.
Over 20 million active Airbnb and vacation rental properties worldwide — the largest STR dataset available via API. Coverage spans 190+ countries, from major metropolitan markets like New York and London to rural destinations and emerging vacation rental hotspots. This is approximately 2x the coverage of AirDNA's API (10M+ listings).
Up to 15+ years for established markets, with up to 60 months of monthly time-series data available through the metrics endpoints in a single API call. This depth enables long-term trend analysis, seasonality modeling, and cycle detection that competitors with 12–24 months of data cannot provide.
Data is refreshed frequently to maintain accuracy. Market metrics and listing data are updated on a regular cadence. Forward-looking data — future nightly rates and booking pacing — is updated more often to capture real-time market movements as hosts adjust pricing and travelers book.
AirROI's ML models achieve 95%+ correlation with actual Airbnb revenue figures. The models are trained on billions of data points from 20M+ properties, using advanced calendar classification algorithms to distinguish genuine bookings from owner blocks. The estimate_revenue endpoint also returns the comparable properties used, providing full transparency into the methodology.
Yes. AirROI covers 190+ countries — the most comprehensive global coverage of any STR data API. This includes major markets (US, UK, France, Spain, Australia, Japan) and emerging markets (Southeast Asia, Latin America, Africa, Middle East). All market endpoints support native currency, so you can query metrics in EUR, GBP, JPY, BRL, or any local currency.
AirROI tracks 20M+ properties — the largest short-term rental dataset available through an API. By comparison, AirDNA covers approximately 10M listings, Mashvisor focuses primarily on US markets, and Inside Airbnb provides limited data for roughly 50 cities updated quarterly. AirROI's coverage advantage is independently verified through third-party data marketplaces.
Everything you need to integrate short-term rental data into your application — from endpoint docs to competitive benchmarks.
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