Realtor.com

Realtor.com Market Stats API

Housing market statistics for a city, county or ZIP: median listing, sold and rent price, median price per square foot, days on market and lot size, listing and rental counts, year-over-year and month-over-month changes, and a breakdown by property type. Costs 1 credit per request.

POST/api/v1/realtor/market

Authorizations

Authorizationstringheaderrequired

Bearer authentication header of the form Bearer <token>, where <token> is your Scavio API key (e.g. Bearer sk_live_your_key).

Body

application/json

locationstringrequired

A slug_id from Locations (Austin_TX, Travis-County_TX) or "City, ST".

Example: Austin_TX

Notes

Some medians are null. Not every statistic exists at every geography level. In the captured example, Austin has a median listing and rent price but median_sold_price is null at city level, while the by-property-type rows carry sold medians. Texas is a sold-price non-disclosure state, so treat sold figures there with care.

Request

from scavio import ScavioClient

client = ScavioClient(api_key="sk_live_your_key")

res = client.realtor.market("Austin_TX")
m = res["data"]

print(m["median_listing_price"], m["median_rent_price"], m["median_days_on_market"])
print(m["year_over_year_pct"]["listing_price"], m["listing_count"])
for t in m["by_property_type"]:
    print(t["type"], t["median_listing_price"], t["median_sold_price"])

Response

"location": "Austin_TX",

"geo_type": "city",

"state_code": "TX",

"latitude": 30.2986219,

"longitude": -97.7541339,

"median_listing_price": 500000,

"median_sold_price": null,

"median_rent_price": 1949,

"median_price_per_sqft": 310,

"median_days_on_market": 81,

"median_lot_sqft": 7359,

"listing_count": 7072,

"rental_listing_count": 7143,

"response_time": 833

}

from scavio import ScavioClient client = ScavioClient(api_key="sk_live_your_key") res = client.realtor.market("Austin_TX") m = res["data"] print(m["median_listing_price"], m["median_rent_price"], m["median_days_on_market"]) print(m["year_over_year_pct"]["listing_price"], m["listing_count"]) for t in m["by_property_type"]: print(t["type"], t["median_listing_price"], t["median_sold_price"])