Description:
Retrieve all lookup data used for filters and metadata.
This endpoint provides lists of valid KPI types, property types, date range types, markets, amenities, and hierarchical market structure.
It is typically used to populate dropdowns, selectors, and reference data within dashboards or integration tools.
This endpoint does not require parameters.
\nCommonly used to initialize filter dropdowns and dashboard options.
\nResponse data changes infrequently — caching is recommended.
\nUse market_hierarchy for rendering nested market or region structures.
Description:
Return session-scoped metadata for the authenticated tenant. Use this endpoint to confirm the tenant identity and the upstream reservation data provider associated with the provided API key.
Useful for environment checks (e.g., verifying which tenant an API key maps to).
\nThe reservation_data_provider_id can inform conditional logic for provider-specific behaviors.
Description:
Retrieve a list of property records with optional filters. This endpoint supports pagination and advanced filtering to fetch all properties or a subset by criteria such as property IDs, market IDs, number of bedrooms, property types, and activity status.
data[])Filter by property ID(s)
\nFilter by market ID(s)
\nFilter by bedrooms or property type
\nFilter by is_active status
Filter by last_updated_on timestamp
Use Properties - All example to fetch all properties with defaults.
\nUse Properties - By ID to fetch a single property.
\nReference Properties By Filters Example for more patterns.
\nEnsure public_api_key and url are set in your environment before requests.
Pagination controlled via page and limit fields.
Description:
Retrieve reservations within a specified date window, with rich filtering and pagination. Use this endpoint to list upcoming, past, or in-window stays and related guest and revenue details.
\n\nPrimary range rule: Provide either
\nprimary_range.commonseasontypeor the pairprimary_range.startandprimary_range.end.
Filter semantics
\nOmit a filter or pass an empty array to disable that filter.
\nWhen both include and exclude are supplied for the same dimension, exclude removes matches from the included set.
data[])Date range: Use either primary_range.commonseasontype (preferred presets from Lookups) or explicit primary_range.start/end dates.
Pagination: Combine page and limit (max 1000). Use pagination.has_next_page to iterate.
Include/Exclude: Empty/missing lists disable that filter; when both are present, exclude wins.
\nTimescales: Valid values align with Lookups (Day, Week, Month, Quarter, Year).
Performance tips: Narrow by markets, property IDs, or last update to reduce payload size on large accounts.
\nDescription:
Retrieve nightly calendar availability, minimum stay, and pricing for specific properties within a date range. Use this endpoint to power availability widgets, pricing calendars, or operational planning.
\n\nRange rule: Provide either
\nrange.commonseasontypeor the pairrange.startandrange.end.
Filter semantics
\nSupplying property_ids restricts results to those properties; omit to fetch none (this field is required).
When using explicit dates, range.start must be ≤ range.end.
data[])data[].calendar[])Date range: Use either a preset range.commonseasontype from Lookups or explicit range.start/range.end.
Pricing as-of: Provide rate_pricing_date to view historical pricing snapshots; omit or set null to use the latest rates.
Availability vs. pricing: status reflects bookability; rate/adr reflect pricing and may be present even when nights are not open.
Minimum stay logic: Enforce min_stay when constructing search results or quoting stays that include this night.
Description:
\nRetrieve property-level KPI metrics with optional filtering by property attributes, amenities, and capacity. Use this endpoint to analyze individual property performance, compare properties within your portfolio, and generate property-specific reports with time series breakdowns.
\nRange semantics
\nProvide either commonseasontype or explicit start/end dates (or dates.start/dates.end for compare ranges).
When using explicit dates, start must be ≤ end.
Comparison ranges are optional; omit compare_ranges to retrieve only primary period data.
Filter semantics
\nFilters are combined with AND logic: properties must match all specified filter criteria.
\nbedrooms and sleeps use OR logic within each filter: \"bedrooms\": [2, 3] matches properties with 2 OR 3 bedrooms.
amenities uses AND logic: \"amenities\": [\"Pool\", \"HotTub\"] matches only properties with both amenities.
Omit a filter to apply no restriction on that attribute.
\nresult_ranges[])properties[])kpis_rollup[] and data[].kpis[])data[])Property filtering: Combine property_ids with attribute filters to narrow results. When property_ids is provided, attribute filters further restrict within that set.
Amenity filtering: Amenity names must match exactly as returned by the Lookups endpoint (e.g., Fireplace, GameRoom, Pool). Amenity filtering uses AND logic—properties must have all specified amenities.
Empty comparison results: When comparison periods return empty properties arrays, this indicates no matching data exists for the comparison criteria (e.g., property wasn't active during the comparison period).
Aggregation levels: The response provides KPIs at two levels: portfolio-wide rollups in result_ranges[].kpis_rollup and individual property rollups in properties[].kpis_rollup.
Zero values: A KPI value of 0 typically indicates no bookings or activity during that period, not missing data.
Time series alignment: For Month timescale, dates represent the first day of each month. Partial months at range boundaries include only days within the specified range.
Year-over-year comparison: Use TODAYMINUSONEYEAR as-of type for historical comparisons to ensure booking pace metrics reflect equivalent points in time.
Description:
\nRetrieve aggregated portfolio-level KPI metrics with optional filtering by booking source and specific properties. Use this endpoint to analyze overall portfolio performance, compare booking channel contributions, and generate aggregate reports with time series breakdowns across multiple comparison periods.
\nRange semantics
\nProvide either commonseasontype or explicit dates.start/dates.end for each range object.
When using explicit dates, start must be ≤ end.
Multiple comparison ranges are supported; each appears as a separate Compare period in the response with its own compare_index.
Filter semantics
\nproperty_ids restricts calculations to the specified properties.
booking_source_types filters reservations by channel/source before calculating KPIs.
Filters are combined with AND logic: properties must match property_ids AND reservations must match booking_source_types.
result_ranges[])kpis_rollup[] and data[].kpis[])data[])Booking source filtering: Use booking_source_types to analyze channel-specific performance. Source type IDs correspond to booking channels (e.g., Airbnb, Direct, Expedia). Retrieve valid IDs from the Lookups endpoint.
Multiple comparisons: This endpoint supports multiple compare_ranges for analyzing trends across several historical periods simultaneously. Each comparison period is returned with a unique compare_index for correlation.
As-of type variations: Use TODAYMINUSONEYEAR for pace comparisons that factor in booking lead time. Use NONEMINUSONEYEAR (or NONE) for final/actual performance without as-of date restrictions.
Portfolio aggregation: Unlike property-level endpoints, this endpoint returns only aggregate KPIs across all matching properties. For per-property breakdowns, use the Property KPIs endpoint.
\nTime series alignment: For Month timescale, dates represent the first day of each month. The response includes all periods within the specified range.
Revenue metrics: UnitRevenue represents net accommodation revenue while RevPAR factors in all available nights (occupied and vacant) for yield analysis.
Occupancy calculations: PaidOccupancy excludes owner stays, holds, and blocked nights from both numerator and denominator for accurate rental performance measurement.
Description:
\nRetrieve aggregated market-level KPI metrics for a specified date range with optional year-over-year or custom comparison periods. Use this endpoint to power market performance dashboards, trend analysis, and benchmarking reports.
\nRange semantics
\nProvide either commonseasontype or the pair start and end for each range object.
When using explicit dates, start must be ≤ end.
Comparison ranges are optional; omit compare_ranges to retrieve only primary period data.
result_ranges[])kpis_rollup[] and data[].kpis[])data[])Timescale alignment: Time series dates align to period boundaries. For Week, dates represent the week's start (typically Sunday or Monday based on account settings).
Year-over-year comparison: Use PRIMARYRANGEMINUSONEYEAR with TODAYMINUSONEYEARSAMEDAYOFWEEK as-of type for accurate pace comparisons that account for day-of-week alignment.
Rollup vs. time series: Set include_time_series: false when only summary totals are needed to reduce response size.
Multi-market aggregation: Provide multiple market_ids to aggregate KPIs across markets. Results represent combined totals, not per-market breakdowns.
Currency conversion: Specify currency to receive monetary KPIs in a consistent currency. Conversion uses rates as of the as-of date.
KPI selection: Request only needed KPIs to optimize response time. All requested KPIs appear in both kpis_rollup and each data[] entry.
Description:
Retrieves weekly time-series Key Performance Indicator (KPI) data for a specific listing over a specified date range. This endpoint provides detailed performance metrics including revenue, occupancy rates, ADR, guest statistics, and booking patterns aggregated by week. Commonly used for short-term performance tracking, weekly trend analysis, and operational decision-making.
\n\nNote: The
\nend_datemust be after thestart_date. Recommended maximum date range is 104 weeks (2 years) for optimal performance.
date field represents the start date of each week. The endpoint returns one KPI object per week within the specified date range.guest_occupancy = guest_nights / available_nights (actual guest usage)calendar_occupancy = (property_nights - available_nights) / property_nights (calendar blocking)adr = revenue_rent / guest_nights (average rate per occupied night)revpar = revenue_rent / available_nights (revenue efficiency)revpar_adj = adjusted revenue per available room (includes unbooked potential)avg_booking_window indicates how far in advance guests book. Lower values suggest last-minute bookings. Weekly data helps identify booking pattern changes.avg_stay_length shows typical guest stay duration. Weekly tracking helps optimize minimum stay requirements.kpis array is returned with a 200 status.updated_on field indicates when the KPI data was last calculated. KPI data is typically updated daily.This endpoint allows users to retrieve Key Performance Indicators (KPIs) for a specific listing over a defined date range. The KPIs provide insights into various metrics related to the listing's performance, such as average daily rate (ADR), occupancy rates, and revenue figures.
\nMethod: POST
\nURL: {{url}}/api/v1/ota/listing/kpis/month
The request body must be in JSON format and include the following parameters:
\nExample Request Body:
\n{\n \"listing_id\": \"airbnb_17024610\",\n \"start_date\": \"2025-01-01\",\n \"end_date\": \"2025-12-31\",\n \"currency\": \"USD\"\n}\n\nOn a successful request, the API returns a response with a status code of 200 and the following JSON structure:
\nlisting_id: The ID of the listing.
\ntime_scale: The time scale of the data.
\ndate: The specific date for the KPI data.
\nadr: Average Daily Rate.
\nguest_occupancy: Percentage of occupied guest nights.
\nrevpar: Revenue per available room.
\nrevenue_rent: Total revenue from rent.
\nguest_nights: Total guest nights.
\nguest_checkins: Total number of guest check-ins.
\nguest_checkouts: Total number of guest check-outs.
\nadr_unbooked: ADR for unbooked nights.
\nrevpar_adj: Adjusted revenue per available room.
\navg_stay_length: Average length of stay.
\navg_booking_window: Average booking window.
\navg_stay_value: Average value of stays.
\ncalendar_occupancy: Occupancy rate on the calendar.
\nproperty_nights: Total nights the property was available.
\nhold_nights: Total nights held for reservations.
\navailable_nights: Total nights available for booking.
\nopen_nights: Total nights open for reservations.
\ntotal_stay_length: Total length of all stays.
\ntotal_booking_window: Total booking window across all reservations.
\nupdated_on: Timestamp of the last update.
\nExample Response:
\n{\n \"data\": {\n \"kpis\": [\n {\n \"listing_id\": \"\",\n \"time_scale\": \"\",\n \"date\": \"\",\n \"adr\": 0,\n \"guest_occupancy\": 0,\n \"revpar\": 0,\n \"revenue_rent\": 0,\n \"guest_nights\": 0,\n \"guest_checkins\": 0,\n \"guest_checkouts\": 0,\n \"adr_unbooked\": 0,\n \"revpar_adj\": 0,\n \"avg_stay_length\": 0,\n \"avg_booking_window\": 0,\n \"avg_stay_value\": 0,\n \"calendar_occupancy\": 0,\n \"property_nights\": 0,\n \"hold_nights\": 0,\n \"available_nights\": 0,\n \"open_nights\": 0,\n \"total_stay_length\": 0,\n \"total_booking_window\": 0,\n \"updated_on\": \"\"\n }\n ]\n },\n \"errors\": null\n}\n\nDescription:
Retrieve detailed listing data for a specific market with comprehensive filtering, sorting, and pagination capabilities. This endpoint returns structured listing information including property details, geographic data, amenities, host information, reviews, and historical/forward-looking performance metrics. Use this to power market analysis, competitive intelligence, portfolio comparisons, and property discovery features.
All filter fields are optional. When multiple filters are provided, they are combined with AND logic.
\nEach listing in the listings array contains the following structure:
geo)amenity)Conditionally included based on data_select.exclude settings.
host)Conditionally included based on data_select.exclude settings.
reviews)Conditionally included based on data_select.exclude settings.
performance)Conditionally included based on data_select.exclude settings. Contains historical and forecasted performance metrics.
performance.last_12_months)Historical performance metrics for the trailing 12-month period.
\nperformance.next_6_months)Forecasted performance metrics for the next 6-month period.
\nMarket Selection: Use the Lookups endpoint to retrieve valid market_uuid values and hierarchical market structures.
Pagination: For large result sets, use offset to paginate through results. Calculate next page as offset + limit.
Sorting: Default sort is by last_12mo_revenue (descending). Use listing_id for stable pagination or review_count for reputation-based sorting.
Performance Optimization: Use data_select.exclude to remove unnecessary data sections and reduce response payload size. Common exclusions include amenity_list for simple property searches or performance for basic listing discovery.
Filter Combinations: All filters use AND logic. For example, filtering by both property_type and bedrooms returns only properties matching both criteria.
Percentile Rankings: All percentile ranks are calculated relative to the specified market and property type cohort. Higher percentiles indicate better performance.
\nOccupancy Calculations: Guest occupancy = guest_nights / available_nights. Available nights = total_nights - hold_nights.
\nForward-Looking Metrics: The next_6_months performance data includes already-booked reservations plus forecasted bookings based on historical patterns and current booking pace.
Currency Handling: All monetary values are returned in the specified currency. Exchange rates are applied at time of data processing.
\nDescription:
Retrieves daily availability and pricing data for a specific listing over a specified date range. This endpoint provides day-by-day calendar information including availability status, nightly rates, booking status, and guest activity. Essential for calendar management, pricing analysis, and occupancy tracking.
\n\nNote: The
\nend_datemust be after thestart_date. Recommended maximum date range is 365 days to ensure optimal performance.
quoted_nightly_rate is converted to the requested currency using current exchange rates. Default is USD.is_available = true: Night is open and can be bookedis_available = false: Night is unavailable (booked, held, or blocked)is_guest_night, is_guest_checkin, is_guest_checkout) indicate actual or predicted guest activity based on bookings.is_hold_night = true indicates the host has manually blocked the date (not available for booking but not booked by a guest).rate_type field categorizes pricing (e.g., standard weekday rates vs. weekend or holiday premiums).availability array is returned with a 200 status.quoted_on and rate_updated_on timestamps indicate data freshness.is_available_for_checkin to identify these cases.booked_on) relative to check-in dates to understand booking windows.Description:
Retrieves comprehensive details for a specific short-term rental listing by its listing ID. This endpoint returns detailed information including property characteristics, geographic data, amenities, host information, reviews, and basic performance metrics. Supports optional field exclusion to reduce response size and currency conversion for monetary values. Commonly used for detailed property analysis, competitive intelligence, and portfolio management.
listing: null and error details in the errors field.revenue, adr) are converted to the requested currency using current exchange rates. Default is USD.include_raw_amenities_list=true to include it.exclude query parameter to omit large nested objects. Valid values: geo, amenity, host, reviews, performance, amenity_list. Multiple values can be specified.best_match_ota and best_match_confidence fields indicate if this listing has been matched to a listing on another OTA platform.markets_list array contains all markets associated with this listing.a data selector to specify which data or objects you wish to exclude from your response payload. possible values: host, amenity_list, reviews, performance
\n","type":"text/plain"},"key":"exclude","value":null},{"disabled":true,"description":{"content":"the monetary currency desired. default is USD
\n","type":"text/plain"},"key":"currency","value":null}],"variable":[]}},"response":[{"id":"c943479a-88d0-45ed-bb71-da4f22ecb045","name":"Valid","originalRequest":{"method":"GET","header":[],"url":{"raw":"{{url}}/api/v1/ota/listing/{{listing_id}}","host":["{{url}}"],"path":["api","v1","ota","listing","{{listing_id}}"],"query":[{"key":"currency","value":"","description":"a data selector to specify which data or objects you wish to exclude from your response payload. possible values: host, amenity_list, reviews, performance","type":"text","disabled":true}]}},"status":"OK","code":200,"_postman_previewlanguage":"json","header":[{"key":"Date","value":"Tue, 05 Aug 2025 23:04:07 GMT"},{"key":"Content-Type","value":"application/json","description":"","type":"text"},{"key":"Content-Length","value":"1486"},{"key":"Connection","value":"keep-alive"},{"key":"Content-Encoding","value":"gzip"},{"key":"Vary","value":"Accept-Encoding"},{"key":"x-azure-ref","value":"20250805T230406Z-16dc8544dbd9ztkphC1CO1vyng00000003u000000000ek4p"},{"key":"X-Cache","value":"CONFIG_NOCACHE"},{"key":"Accept-Ranges","value":"bytes"}],"cookie":[],"responseTime":null,"body":"{\n \"listing\": {\n \"id\": \"vrbo_1447478\",\n \"ota\": \"vrbo\",\n \"title\": \"Luxury Penthouse in Vail | Shared Hot Tub\",\n \"url\": \"https://www.vrbo.com/1447478\",\n \"listing_space\": \"entire\",\n \"license\": \"\",\n \"main_image_url\": \"https://images.trvl-media.com/lodging/31000000/30440000/30435300/30435239/d6691e46_b.jpg\",\n \"photo_count\": 42,\n \"is_instant_book_enabled\": true,\n \"max_guests\": 18,\n \"min_stay\": 3,\n \"check_in_time\": \"16:00\",\n \"check_out_time\": \"11:00\",\n \"currency_native\": \"USD\",\n \"best_match_ota\": null,\n \"best_match_confidence\": null,\n \"property_type\": \"apartment\",\n \"type_detail\": \"apartment\",\n \"bedrooms\": 5,\n \"beds\": 5,\n \"bathrooms\": 5,\n \"updated_on\": \"2025-11-10\",\n \"calendar_updated_on\": \"2024-04-21\",\n \"listing_status\": \"listed\",\n \"listed_on\": \"2020-03-01\",\n \"last_booking_on\": \"2025-11-09\",\n \"markets_list\": [\n {\n \"id\": 7252,\n \"uuid\": \"a0a7b93f-f370-4af6-adf8-72bf7b7fbe42\",\n \"name\": \"North America\",\n \"type\": \"Combined Area\"\n },\n {\n \"id\": 469,\n \"uuid\": \"793d23a7-1e63-4e58-9b8e-c6c6c09f0ed1\",\n \"name\": \"Rocky Mountain States\",\n \"type\": \"Vacation Region\"\n },\n {\n \"id\": 24709,\n \"uuid\": \"516f5ac4-14ce-4486-aea6-93ee08d6f8aa\",\n \"name\": \"Mountain Market C\",\n \"type\": \"Vacation Area\"\n },\n {\n \"id\": 470,\n \"uuid\": \"e6a0aa6e-cdc4-4735-8d66-ca29fc53b2ee\",\n \"name\": \"Colorado Western Slope\",\n \"type\": \"Vacation Region\"\n },\n {\n \"id\": 471,\n \"uuid\": \"6105aacc-e510-4dd2-8ce0-0c290e09ad70\",\n \"name\": \"Northwest Colorado\",\n \"type\": \"Vacation Region\"\n },\n {\n \"id\": 13759,\n \"uuid\": \"60be20c0-3a91-41ca-bf38-819ab6dc9188\",\n \"name\": \"Lionshead\",\n \"type\": \"Vacation Area\"\n },\n {\n \"id\": 24551,\n \"uuid\": \"be092204-e27a-4fcb-b95c-e9d7ef01b7f5\",\n \"name\": \"Mountain Market B\",\n \"type\": \"Vacation Area\"\n },\n {\n \"id\": 1,\n \"uuid\": \"a261d510-d4be-4934-a8b9-967b28579f05\",\n \"name\": \"United States\",\n \"type\": \"Country\"\n },\n {\n \"id\": 154,\n \"uuid\": \"f54346d7-a71c-4570-bf59-84e978836ce0\",\n \"name\": \"Colorado\",\n \"type\": \"State\"\n },\n {\n \"id\": 13752,\n \"uuid\": \"d65d761c-d93d-4520-946c-a675c310990b\",\n \"name\": \"Greater Western States\",\n \"type\": \"Vacation Region\"\n },\n {\n \"id\": 4817,\n \"uuid\": \"d10cf6fb-d8a0-45a0-b917-4e53b5f42b11\",\n \"name\": \"Summit and Eagle Ski Areas\",\n \"type\": \"Vacation Area\"\n },\n {\n \"id\": 480,\n \"uuid\": \"34c09c2e-76f6-4bc7-9334-5c10148ecdf8\",\n \"name\": \"Vail\",\n \"type\": \"Vacation Area\"\n },\n {\n \"id\": 472,\n \"uuid\": \"f8ca1883-b21c-4e75-8b32-eb2c54dc9e34\",\n \"name\": \"Eagle - CO\",\n \"type\": \"County\"\n }\n ],\n \"geo\": {\n \"latitude\": 39.642371,\n \"longitude\": -106.393124,\n \"latlong_exact\": true,\n \"city\": \"Vail\",\n \"state\": \"Colorado\",\n \"postal_code\": \"81657\",\n \"locality\": \"Vail\",\n \"neighborhood\": \"\",\n \"country\": \"United States\",\n \"country_code\": \"US\",\n \"admin_level_1\": \"Colorado\",\n \"admin_level_2\": \"Eagle County\",\n \"admin_level_3\": \"Colorado\",\n \"admin_level_4\": \"\"\n },\n \"amenity\": {\n \"golf\": null,\n \"hot_tub\": \"unknown\",\n \"mountain\": \"view\",\n \"parking\": \"unknown\",\n \"parking_cost\": \"paid\",\n \"pool\": \"communal\",\n \"ski\": null,\n \"water\": null,\n \"list\": null\n },\n \"host\": {\n \"host_id\": \"1429873\",\n \"host_name\": \"Leavetown Vacations\",\n \"host_review_count\": null,\n \"is_professionally_managed\": true,\n \"is_airbnb_superhost\": null,\n \"airbnb_response_rate\": null,\n \"airbnb_response_time_minutes\": null,\n \"host_since\": \"2017-01-01\",\n \"native_language\": \"English\",\n \"property_manager_domain\": \"vailresortrentals.com\",\n \"property_manager_company\": \"leavetown vacations\"\n },\n \"reviews\": {\n \"review_count\": 6,\n \"review_accuracy\": null,\n \"review_check_in\": null,\n \"review_cleanliness\": null,\n \"review_communication\": null,\n \"review_location\": null,\n \"review_value\": null,\n \"review_overall\": 10\n },\n \"performance\": {\n \"performance_updated_on\": \"2025-11-01\",\n \"last_12_months\": {\n \"revenue\": 490683,\n \"guest_occupancy\": 0.3,\n \"adr\": 4421\n },\n \"next_6_months\": {\n \"revenue\": 413164,\n \"guest_occupancy\": 0.34,\n \"adr\": 6773\n }\n }\n },\n \"errors\": null\n}"},{"id":"a7deab07-429d-488f-a87f-d6ed5c8d3ef8","name":"Not Found","originalRequest":{"method":"GET","header":[],"url":{"raw":"{{url}}/api/v1/ota/listing/{{listing_id}}","host":["{{url}}"],"path":["api","v1","ota","listing","{{listing_id}}"],"query":[{"key":"currency","value":null,"description":"the monetary currency desired. default is USD","disabled":true}]}},"status":"Not Found","code":404,"_postman_previewlanguage":"json","header":[{"key":"Date","value":"Tue, 05 Aug 2025 23:04:47 GMT"},{"key":"Content-Type","value":"application/json","description":"","type":"text"},{"key":"Content-Length","value":"29"},{"key":"Connection","value":"keep-alive"},{"key":"x-azure-ref","value":"20250805T230447Z-16dc8544dbd9ztkphC1CO1vyng00000003u000000000eqvr"},{"key":"X-Cache","value":"CONFIG_NOCACHE"}],"cookie":[],"responseTime":null,"body":"{\n \"error\": \"Listing not found\"\n}"}],"_postman_id":"0fb5482a-5b14-4ae8-b4b4-269f18083f25"}],"id":"2df3ec7b-d412-4f6b-83ad-d89b02869642","_postman_id":"2df3ec7b-d412-4f6b-83ad-d89b02869642","description":"","auth":{"type":"apikey","apikey":{"value":"{{public_api_key}}","key":"Description:
Retrieves weekly aggregated Key Performance Indicator (KPI) data for a specific market over a specified date range. This endpoint provides market-level performance metrics including average daily rates, occupancy rates, revenue, and booking patterns across all active listings in the market. Supports optional filtering by OTA source. Commonly used for market analysis, competitive benchmarking, and trend identification.
\n\nNote: The
\nend_datemust be after thestart_date. Recommended maximum date range is 104 weeks (2 years) for optimal performance.
date field represents the start date of each week.filters.ota to get KPIs for a specific OTA source. Omit this filter to get aggregated data across all OTA sources in the market.listing_count field shows how many listings were active and contributing to the market KPIs during each week.guest_occupancy = guest_nights / available_nights (actual guest usage)calendar_occupancy = (property_nights - available_nights) / property_nights (calendar blocking)adr = revenue_rent / guest_nights (average rate per occupied night)revpar = revenue_rent / available_nights (revenue efficiency)revpar_adj = adjusted revenue per available room (includes unbooked potential)avg_booking_window indicates how far in advance guests book in this market. Lower values suggest last-minute booking patterns.avg_stay_length shows typical guest stay duration in the market. Useful for understanding market dynamics.kpis array is returned with a 200 status.updated_on field indicates when the KPI data was last calculated. Market KPI data is typically updated daily.listing_count over time to understand market supply dynamics.Description:
Retrieves monthly aggregated Key Performance Indicator (KPI) data for a specific market over a specified date range. This endpoint provides market-level performance metrics including average daily rates, occupancy rates, revenue, and booking patterns across all active listings in the market. Supports optional filtering by OTA source. Commonly used for long-term market analysis, year-over-year comparisons, and strategic planning.
\n\nNote: The
\nend_datemust be after thestart_date. Recommended maximum date range is 36 months (3 years) for optimal performance.
date field represents the first day of each month.filters.ota to get KPIs for a specific OTA source. Omit this filter to get aggregated data across all OTA sources in the market.listing_count field shows how many listings were active and contributing to the market KPIs during each month.guest_occupancy = guest_nights / available_nights (actual guest usage)calendar_occupancy = (property_nights - available_nights) / property_nights (calendar blocking)adr = revenue_rent / guest_nights (average rate per occupied night)revpar = revenue_rent / available_nights (revenue efficiency)revpar_adj = adjusted revenue per available room (includes unbooked potential)avg_booking_window indicates how far in advance guests book in this market. Lower values suggest last-minute booking patterns.avg_stay_length shows typical guest stay duration in the market. Useful for understanding market dynamics.kpis array is returned with a 200 status.updated_on field indicates when the KPI data was last calculated. Market KPI data is typically updated daily.Analyze vacation rental performance metrics grouped by guest arrival date.
\nPOST /benchmarking/arrival
currencyarrival_range cannot exceed 1 yearcurrency must be a valid 3-letter uppercase ISO 4217 code (e.g., USD, EUR, GBP)errorsguest_occupancy, calendar_occupancy, percent_canceled_nights) are rounded to 2 decimal places{\n \"market_ids\": [\"e9b635b2-02e1-4037-977c-21b1968f691e\"],\n \"arrival_range\": {\n \"start\": \"2024-01-01\",\n \"end\": \"2024-01-31\"\n },\n \"as_of\": {\n \"date\": \"2024-01-15\",\n \"include_canceled\": false\n },\n \"time_scale\": \"week\",\n \"currency\": \"USD\"\n}\n{\n \"success\": true,\n \"data\": {\n \"breakdown\": [\n {\n \"start_date\": \"2024-01-06\",\n \"end_date\": \"2024-01-12\",\n \"area_id\": null,\n \"group_value\": null,\n \"total_units\": 1250,\n \"reserved_units\": 487,\n \"nights_in_range\": 8750,\n \"nights_available\": 6891,\n \"open_nights\": 3479,\n \"unit_revenue\": 425680,\n \"recognized_unit_revenue\": 312450,\n \"other_revenue\": 42568,\n \"total_revenue\": 468248,\n \"guest_nights\": 3412,\n \"owner_nights\": 1124,\n \"hold_nights\": 735,\n \"canceled_nights\": 156,\n \"guest_checkins\": 412,\n \"guest_checkouts\": 389,\n \"guest_canceled_checkins\": 18,\n \"owner_checkins\": 87,\n \"guest_and_owner_checkins\": 499,\n \"length_of_stays\": 3296,\n \"guest_checkins_for_length_of_stay\": 412,\n \"booking_windows\": 28960,\n \"guest_checkins_for_booking_window\": 398,\n \"adr\": 125,\n \"adr_total\": 137,\n \"revpar\": 49,\n \"revpar_adj\": 62,\n \"trevpar\": 54,\n \"trevpar_adj\": 68,\n \"guest_occupancy\": 0.39,\n \"calendar_occupancy\": 0.6,\n \"avg_stay_length\": 8,\n \"avg_booking_window\": 73,\n \"avg_stay_value\": 1033,\n \"avg_stay_total_revenue\": 1136,\n \"percent_canceled_nights\": 0.04\n }\n ],\n \"rollup\": {\n \"start_date\": \"2024-01-01\",\n \"end_date\": \"2024-01-31\",\n \"area_id\": null,\n \"group_value\": null,\n \"total_units\": 1250,\n \"reserved_units\": null,\n \"nights_in_range\": 38750,\n \"nights_available\": 30520,\n \"open_nights\": 15396,\n \"unit_revenue\": 1875420,\n \"recognized_unit_revenue\": 1375200,\n \"other_revenue\": 187542,\n \"total_revenue\": 2062962,\n \"guest_nights\": 15124,\n \"owner_nights\": 4960,\n \"hold_nights\": 3270,\n \"canceled_nights\": 624,\n \"guest_checkins\": 1812,\n \"guest_checkouts\": 1756,\n \"guest_canceled_checkins\": 72,\n \"owner_checkins\": 348,\n \"guest_and_owner_checkins\": 2160,\n \"length_of_stays\": 14496,\n \"guest_checkins_for_length_of_stay\": 1812,\n \"booking_windows\": 127440,\n \"guest_checkins_for_booking_window\": 1752,\n \"adr\": 124,\n \"adr_total\": 136,\n \"revpar\": 48,\n \"revpar_adj\": 61,\n \"trevpar\": 53,\n \"trevpar_adj\": 68,\n \"guest_occupancy\": 0.39,\n \"calendar_occupancy\": 0.6,\n \"avg_stay_length\": 8,\n \"avg_booking_window\": 73,\n \"avg_stay_value\": 1035,\n \"avg_stay_total_revenue\": 1138,\n \"percent_canceled_nights\": 0.04\n },\n \"count\": 4\n },\n \"errors\": null\n}\n