The market and neighborhood data endpoints give you external context beyond your own listings — what's happening in the broader area, how local occupancy and rates are trending, and how your listings compare to the market. Use this data to build competitive benchmarking tools, surface demand signals, or help hosts make better pricing decisions.
Plan requirement
Market report endpoints require at least one Pro tier listing in the requested market. Neighborhood data endpoints require an active listing with location data.
Market reports
Market data is accessed by market ID, not by listing ID. The typical flow is: look up available markets for a country, then query a specific market by its ID.
Step 1 — List markets for a country
curl "https://api.usewheelhouse.com/ss_api/v1/market_report?country_code=US" \
-H "X-Integration-Api-Key: your_api_key_here"
Required query parameter: country_code — ISO 3166-1 alpha-2 (e.g. US, GB).
Returns an array of market objects:
[
{
"market_id": 0,
"market_name": "string",
"geometry": { ... },
"postal_codes": [ ... ]
}
]
Use the market_id from this response in the endpoints below.
Step 2 — Get time-series market metrics
Returns daily time-series data for one or more market-level metrics over a date range.
curl "https://api.usewheelhouse.com/ss_api/v1/market_report/42/time_series?metric=occupancy&metric=asking_rate_v_fees&start_date=2026-01-01&end_date=2026-06-30" \
-H "X-Integration-Api-Key: your_api_key_here"
Path parameter: market_id — integer from GET /market_report.
Query parameters:
| Parameter | Type | Description |
| ------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| metric | string (repeatable) | Metrics to include. Omit to get all. Available: asking_rate_v_fees, occupancy, occupancy_adjusted, adr_v_fees, lead_time, revpar_adjusted_v_fees, revpar_v_fees, revenue_v_fees, nights_bookable |
| start_date | string (date) | YYYY-MM-DD. Defaults to today. Must be within 3 years past, 1 year future. |
| end_date | string (date) | YYYY-MM-DD. Defaults to 1 year from today. |
| performance | string | Filter to a performance tier: low, average, high. Omit for all. |
| bedrooms | string | Filter by bedroom count: 0, 1, 2, 3, 4+. Omit for all. |
| property_type | string | Filter by property type (e.g. apartment, house, cabin). Omit for all. |
Response:
{
"currency": "USD",
"updated_at": "2026-07-01T00:00:00Z",
"data": [
{ ... }
]
}
Get metric distributions for a month
Returns histogram distributions for one or more market metrics for a given calendar month.
curl "https://api.usewheelhouse.com/ss_api/v1/market_report/42/distribution?metric=occupancy&month=2026-06-01" \
-H "X-Integration-Api-Key: your_api_key_here"
Available metrics for distributions: occupancy, occupancy_adjusted, asking_rate_v_fees, adr_v_fees, lead_time, revpar_adjusted_v_fees, revpar_v_fees, length_of_stay.
Response:
{
"currency": "USD",
"month": "2026-06-01",
"updated_at": "2026-07-01T00:00:00Z",
"data": { ... }
}
Neighborhood data
Neighborhood data is scoped to a listing's local cluster — a geographic group of comparable properties with the same bedroom count near the listing. This is the same data shown in the Wheelhouse pricing chart.
Both endpoints require listing_id (path) and channel (query, required).
Daily neighborhood pricing
Returns the median nightly price, p25 (low_price), and p75 (high_price) price percentiles, plus the number of listings in the cluster for each day.
curl "https://api.usewheelhouse.com/ss_api/v1/listings/12345678/neighborhood/pricing?channel=airbnb" \
-H "X-Integration-Api-Key: your_api_key_here"
Response:
json
{
"data": [
{ ... }
],
"currency": "USD"
}
Possible errors: 404 if the listing has no neighborhood cluster or no location data, 406 if the listing is inactive, 422 if the listing is not covered by a Wheelhouse market.
Daily neighborhood occupancy
Returns daily occupancy and booking model data for the listing's local cluster.
bash
curl "https://api.usewheelhouse.com/ss_api/v1/listings/12345678/neighborhood/occupancy?channel=airbnb" \
-H "X-Integration-Api-Key: your_api_key_here"
Fields in each data entry:
occupancy— raw occupancy rate (booked nights / calendar nights)adjusted_occupancy— occupancy computed against bookable (unblocked) nights onlyexpected_bookings— model-predicted booking count for the stay dateexpected_bookings_sd— standard deviation of the expected bookings predictionobserved_bookings— actual booking count observed for that daycalendar_nights— total calendar nights in the cluster for that day
Response:
json
{
"data": [
{ ... }
]
}
Possible errors: 404 if no neighborhood cluster or occupancy data found, 406 if the listing is inactive, 422 if not covered by a Wheelhouse market.
Next steps
Error codes explained