Realtor.com

Realtor.com Property API

One Realtor.com property in full, by property_id or URL: description and detail groups, price history, tax history with assessed and market values, three home value estimates with ranges plus their history and forecast, nearby schools with ratings, flood, wildfire and noise risk, listing popularity, the listing agent and office with contact details, a mortgage estimate, photos and the MLS disclaimer. Costs 1 credit per request.

POST/api/v1/realtor/property

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

property_idstring

Numeric property id from Search, or from an address in Locations. Pass this or url.

Example: 9564057635

urlstring

A realtor.com property or rental URL, ending in _M<digits>-<digits>. Alternative to property_id.

Example: https://www.realtor.com/realestateandhomes-detail/7202-Eagle-Rose-Rd_Austin_TX_78744_M95640-57635

Notes

Some blocks can be empty. valuations, valuation_history, valuation_forecast, tax_history, schools and risk can be empty or null for land and new construction. The captured example is a new-construction home listed the same day, so it has schools and risk but no estimates or tax history yet.

Sold prices in non-disclosure states. In Texas, Utah, Wyoming and the other US non-disclosure states, sold_price and the price on "Sold" history events are null: unknown, not zero. Dates are still returned.

Unknown ids are billed. A property_id that Realtor.com does not know returns a 404 naming the id, and costs 1 credit. Temporary failures (502/503) are not billed.

Request

from scavio import ScavioClient

client = ScavioClient(api_key="sk_live_your_key")

res = client.realtor.property(property_id="9564057635")
p = res["data"]

print(p["list_price"], p["beds"], p["baths"], p["sqft"], p["lot_sqft"])
for e in p["property_history"]:
    print(e["date"], e["event"], e["price"])
for v in p["valuations"]:
    print(v["source"], v["estimate"], v["low"], v["high"])
print(p["risk"]["flood_severity"], p["listing_agent"]["phone"])

Response

"property_id": "9564057635",

"listing_id": "3001735896",

"url": "https://www.realtor.com/realestateandhomes-detail/7202-Eagle-Rose-Rd_Austin_TX_78744_M95640-57635",

"status": "for_sale",

"list_date": "2026-10-10T16:06:03Z",

"list_price": 535900,

"list_price_min": null,

"list_price_max": null,

"price_per_sqft": 301,

"sold_price": null,

"sold_date": null,

"last_status_change_at": "2026-10-10T16:06:03Z",

"property_type": "single_family",

"sub_type": null,

"beds": 3,

"baths": 3,

"baths_full": 2,

"baths_half": 1,

"sqft": 1778,

"lot_sqft": 5066,

"year_built": 2027,

"garage": 2,

"stories": 1,

"hoa_fee": 75,

"photo": "https://ap.rdcpix.com/2dc62746e451529377ebc2c5abe97f7dl-m1343891438s.jpg",

"photo_count": 6,

"open_houses": [],

"last_price_change_amount": null,

"last_price_change_date": null,

"days_on_market": null,

"description": "Front porch opens into an inviting entryway with a rotunda ceiling. The kitchen space features an island with built-in seating, a 5-burner gas cooktop, and a corner walk-in pantry. A private home office with French doors is adjacent to the dining area. The family room lets in ample natural light from the wall of windows. The primary bedroom is at the rear of the home. The primary bathroom features dual vanities, a glass enclosed shower, a linen closet, and a walk-in closet. The secondary bedrooms come complete with ample storage and a shared bathroom. The utility room, mud room, and additional storage are located off of the two-car garage.",

"tax_history": [],

"valuations": [],

"valuation_history": [],

"valuation_forecast": [],

"popularity": [],

"buyer_agent": null,

"monthly_fees": [],

"virtual_tours": [],

"response_time": 2138

}

from scavio import ScavioClient client = ScavioClient(api_key="sk_live_your_key") res = client.realtor.property(property_id="9564057635") p = res["data"] print(p["list_price"], p["beds"], p["baths"], p["sqft"], p["lot_sqft"]) for e in p["property_history"]: print(e["date"], e["event"], e["price"]) for v in p["valuations"]: print(v["source"], v["estimate"], v["low"], v["high"]) print(p["risk"]["flood_severity"], p["listing_agent"]["phone"])