Realtor.com via Scavio

Search Realtor.com listings for sale, pending, for rent or sold with the full filter set, read one property in full, pull housing market stats for an area, resolve a place name to a location, and find agents and read a full agent profile with every review. All endpoints return structured JSON.

When to trigger

Use this skill when the user asks to:

• Search US homes, land or rentals on Realtor.com by city, county, ZIP, neighborhood, a point and radius, or a listing agent
• Pull sold comps: recent sales in an area filtered by property type, lot size, price band and sold-within days
• Watch new listings whose description mentions a keyword (e.g. workshop, live/work, storefront) across several cities
• Look up one property by Realtor.com URL or property id: price history, tax history with assessed and market values, home value estimates, schools, flood and wildfire risk, the listing agent
• Get median listing, sold and rent prices, days on market and year-over-year change for a city, county or ZIP
• Find real estate agents in a city or ZIP, or read one agent's profile, license, sales stats and reviews
• Build a land, rental or new-construction lead list from Realtor.com

Setup

Get a free API key at scavio.dev (50 free credits to get started, no card required):

export SCAVIO_API_KEY=sk_live_your_key

If you do not have a key yet

An agent running this skill without SCAVIO_API_KEY set will get 401 on every call below. The whole path from nothing to a working key is self-serve:

1. Sign up at dashboard.scavio.dev - email or Google, no card required.
2. A key is created for the account automatically at signup. It is on the dashboard under API Keys, ready to copy.
3. A new account starts with 50 credits. Every endpoint in this skill costs 1 credit.

When the balance runs out the API answers 402 with a JSON body carrying billing_url. Topping up needs no code change - the same key keeps working. The smallest purchase is 2,500 credits for $25, and monthly plans work out cheaper per credit if the usage is steady rather than one-off.

Every request is a POST with a JSON body and:

Authorization: Bearer $SCAVIO_API_KEY

Endpoints

Base URL: https://api.scavio.dev. Every Realtor.com endpoint costs 1 credit per call.

Workflow

1. Search: call /realtor/search with location (e.g. "Maricopa County, AZ", "Austin, TX", a ZIP) plus filters. Each row carries property_id and url.
2. Property: call /realtor/property with a property_id from step 1, or a pasted url.
3. Market: call /realtor/locations with a place name, take a slug_id (e.g. Austin_TX, Travis-County_TX), then call /realtor/market with location. "City, ST" also works directly.
4. Agents: call /realtor/agents with location ("Austin, TX"), postal_code or name, then /realtor/agent with an agent_id. A listing's agent.agent_id also works, and /realtor/search with agent_id returns that agent's listings.

Pagination

• Search: page (1-based) and page_size (default 50, up to 200). The response carries total, count and max_page. page x page_size may not exceed 10,000 - see Guardrails.
• Agents: page (1-based) and page_size (default 20, up to 50). The response carries total.
• Property, locations, market, agent: no paging.

Every page is 1 credit, so state the budget before looping.

Parameters

Search (/search)

Pass one of location, latitude + longitude + radius_miles, or agent_id.

Property (/property)

Pass exactly one of property_id or url.

Locations (/locations)

Market (/market)

Agents (/agents)

Pass exactly one of location, postal_code or name.

Agent (/agent)

Examples

curl -X POST https://api.scavio.dev/api/v1/realtor/search \
  -H "Authorization: Bearer $SCAVIO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"location": "Maricopa County, AZ", "listing_status": "sold", "property_types": ["land"], "sold_within_days": 180, "lot_sqft_min": 5000, "page_size": 200}'

import requests

BASE = "https://api.scavio.dev"
# Your key from https://scavio.dev. Load it from your environment or secret
# store in real code - keep it out of source control.
API_KEY = "sk_your_key_here"
HEADERS = {"Authorization": f"Bearer {API_KEY}"}

def realtor(endpoint, body):
    r = requests.post(f"{BASE}/api/v1/realtor/{endpoint}", headers=HEADERS, json=body)
    r.raise_for_status()
    return r.json()["data"]

# 1. Sold land comps in one county, up to 200 rows for 1 credit
comps = realtor("search", {"location": "Maricopa County, AZ", "listing_status": "sold",
                           "property_types": ["land"], "sold_within_days": 180,
                           "lot_sqft_min": 5000, "sort": "sold_newest", "page_size": 200})
print(comps["total"], "sales;", comps["max_page"], "pages at this size")
for row in comps["listings"]:
    a = row["address"]
    print(row["sold_price"], row["sold_date"], row["lot_sqft"], a["line"], a["city"], a["postal_code"])

# 2. Keyword watch: new for-sale listings mentioning "workshop"
fresh = realtor("search", {"location": "Austin, TX", "keywords": ["workshop"],
                           "listed_within_days": 7, "sort": "newest"})
for row in fresh["listings"]:
    print(row["list_price"], row["beds"], row["sqft"], row["url"])

# 3. One property in full
home = realtor("property", {"property_id": "9564057635"})
print(home["list_price"], home["price_per_sqft"], home["hoa_fee"], home["risk"]["flood_severity"])
for event in home["property_history"]:
    print(event["date"], event["event"], event["price"])
for school in home["schools"][:3]:
    print(school["name"], school["rating"], school["distance_miles"])

# 4. Market stats for a city
loc = realtor("locations", {"query": "Austin"})["locations"][0]   # slug_id "Austin_TX"
market = realtor("market", {"location": loc["slug_id"]})
print(market["median_listing_price"], market["median_days_on_market"], market["year_over_year_pct"])

# 5. Agents in a city, then one full profile with every review
agents = realtor("agents", {"location": "Austin, TX", "page_size": 10})
top = agents["agents"][0]
profile = realtor("agent", {"agent_id": top["agent_id"]})
print(profile["name"], profile["license"], profile["sold_total"], len(profile["reviews"]))

Response shapes

Every response uses the envelope { data, response_time, credits_used, credits_remaining }. Key data fields:

• search - listing_status, total, count, page, page_size, max_page, listings[]. A row: property_id, listing_id, url, status, list_date, list_price, list_price_min, list_price_max, price_per_sqft, sold_price, sold_date, last_status_change_at, flags {new_listing, price_reduced, foreclosure, new_construction, pending, contingent, coming_soon}, property_type, sub_type, beds, baths, baths_full, baths_half, sqft, lot_sqft, year_built, garage, stories, address {line, unit, city, state_code, postal_code, county, county_fips, lat, lng}, hoa_fee, photo, photo_count, open_houses[], tags[], mls {id, listing_id}, agent {agent_id, name}, office {office_id, name}, price_reduced_amount, estimate.
• property - the search row fields plus last_price_change_amount, last_price_change_date, days_on_market, description, features, neighborhoods[], details[] {category, parent_category, items[]}, property_history[] {date, event, price, price_per_sqft, source}, tax_history[] {year, tax, assessed {building, land, total}, market {...}}, valuations[] {source, estimate, high, low, date, is_best}, valuation_history[] and valuation_forecast[] {source, points[] {date, estimate}}, schools[] {school_id, name, rating, parent_rating, distance_miles, levels[], grades[], funding, students, district}, risk {flood_factor, flood_severity, fema_zone, fire_factor, fire_severity, noise_score}, popularity[], listing_agent {agent_id, name, email, phone, license, office {name, email, phone}}, buyer_agent, mortgage_estimate {tax_rate, insurance_rate, monthly_payment, loan_amount, down_payment}, monthly_fees[], photos[], virtual_tours[], parcel {apn}, mls_disclaimer.
• locations - query, count, locations[] {text, area_type, slug_id, city, state_code, postal_code, county, property_id, latitude, longitude}.
• market - location, geo_type, state_code, latitude, longitude, parents[] {geo_type, slug_id, name}, median_listing_price, median_sold_price, median_rent_price, median_price_per_sqft, median_days_on_market, median_lot_sqft, listing_count, rental_listing_count, year_over_year_pct {active_listings, days_on_market, listing_price, price_per_sqft, rent}, month_over_month_pct, by_property_type[] {type, median_listing_price, median_sold_price, median_price_per_sqft, median_days_on_market, median_lot_sqft}.
• agents - total, count, page, page_size, agents[] {agent_id, name, is_realtor, broker, office, photo, rating, review_count, recommendation_count, for_sale_count, for_sale_price_min, for_sale_price_max, last_listing_at, sold_last_year, first_year, services {buyer[], seller[]}}.
• agent - the agents row plus bio, phones[] {type, number}, website, license {number, state}, designations[], languages[], specializations[], served_areas[] {name, state_code}, mls_memberships[], sold_total, last_sold_date, office_details {name, website, phones[], address}, social {facebook, instagram, linkedin, x}, reviews[] {review_id, rating, responsiveness, market_expertise, negotiation, professionalism, comment, reviewer, reviewer_type, location, year, date, reply}.

A sold land row, trimmed (captured, Maricopa County, AZ, listing_status: "sold", property_types: ["land"]):

{
  "property_id": "1795946305",
  "url": "https://www.realtor.com/realestateandhomes-detail/9155-W-Garfield-St_Tolleson_AZ_85353_M17959-46305",
  "status": "sold",
  "sold_price": 135000,
  "sold_date": "2025-06-13",
  "property_type": "land",
  "lot_sqft": 6591,
  "address": {
    "line": "9155 W Garfield St",
    "city": "Tolleson",
    "state_code": "AZ",
    "postal_code": "85353",
    "county": "Maricopa",
    "county_fips": "04013",
    "lat": 33.455652,
    "lng": -112.256709
  }
}

A property, trimmed (captured, 9564057635, Austin, TX):

{
  "data": {
    "property_id": "9564057635",
    "status": "for_sale",
    "list_price": 535900,
    "price_per_sqft": 301,
    "beds": 3,
    "baths": 3,
    "sqft": 1778,
    "lot_sqft": 5066,
    "year_built": 2027,
    "hoa_fee": 75,
    "flags": { "new_listing": true, "new_construction": true },
    "property_history": [
      { "date": "2026-10-10", "event": "Listed", "price": 535900, "source": "UnlockMLS" }
    ],
    "schools": [
      { "name": "Newton Collins Elementary School", "rating": 7, "distance_miles": 1, "funding": "public" }
    ],
    "risk": { "flood_factor": 1, "flood_severity": "minimal", "fema_zone": "X (unshaded)", "fire_factor": 6, "fire_severity": "Major" },
    "listing_agent": { "agent_id": "100431241", "name": "Lee Jones", "phone": "(713) 948-6666", "license": "0439466" },
    "mortgage_estimate": { "monthly_payment": 4218, "loan_amount": 428720, "down_payment": 107180 }
  }
}

Market stats, trimmed (captured, Austin_TX):

{
  "location": "Austin_TX",
  "geo_type": "city",
  "median_listing_price": 500000,
  "median_sold_price": null,
  "median_rent_price": 1949,
  "median_price_per_sqft": 310,
  "median_days_on_market": 81,
  "listing_count": 7072,
  "rental_listing_count": 7143,
  "year_over_year_pct": { "active_listings": -3.46, "listing_price": -9.09, "rent": -2.31 },
  "by_property_type": [
    { "type": "home", "median_listing_price": 525000, "median_sold_price": 484375, "median_days_on_market": 78 }
  ]
}

An agent review (captured, agent 1918615):

{
  "review_id": "9f45762e-d0b0-4d9e-a28f-1376ff4a4011",
  "rating": 5,
  "responsiveness": 5,
  "market_expertise": 5,
  "negotiation": 5,
  "professionalism": 5,
  "reviewer": "Nick",
  "reviewer_type": "BUYER",
  "location": "Hutto, TX",
  "date": "2026-09-11T03:03:43.157Z",
  "reply": null
}

Guardrails

• 10,000 results per search. page x page_size may not exceed 10,000; a request past that is a 400. To cover a large area, slice it into separate searches by price band, property type, ZIP or radius, and de-duplicate by property_id. Never claim a search returned "every listing" when total is above 10,000.
• Sold prices in non-disclosure states. sold_price is null in US sold-price non-disclosure states (Texas, Utah, Wyoming, Alaska, Idaho, Kansas, Louisiana, Mississippi, Montana, New Mexico, North Dakota and parts of Missouri); sold_date still comes back. Say so when comps come back unpriced, and never estimate a missing sold price.
• valuations, schools and risk can be null or empty for land and new construction. Some market medians are null at some geography levels (e.g. median_sold_price for a city).
• keywords matches the listing text, not a structured field; confirm a hit in description via /property when it matters.
• Public listing data only: no owner names, buyer or seller identities, or mortgage records.
• Results usually take 1-5 seconds per call; batch sequentially rather than firing many calls at once.
• Never fabricate prices, estimates, school ratings or reviews. Only return what the API returned, and surface the listing url so the user can verify.

Failure handling

• 400 means an invalid or missing parameter (no location, point or agent on search; both or neither of property_id and url; more than one of location, postal_code and name on agents; page x page_size above 10,000) - fix the body and retry.
• 401 means the API key is invalid or missing. Check SCAVIO_API_KEY.
• 402 means the balance is out of credits. The body carries billing_url.
• 404 means the property, market or agent does not exist on Realtor.com; the error names what was not found, and the call is billed (1 credit).
• 429 means rate or usage limit exceeded. Wait before retrying. See rate limits.
• 502 / 503 mean Realtor.com data is temporarily unavailable - wait a few seconds and retry, up to a few times. These are not billed.
• If a location string returns nothing, resolve it with /realtor/locations first and retry with the suggestion's text or slug_id.
• If SCAVIO_API_KEY is not set, prompt the user to export it before continuing.

SDKs

pip install scavio==0.20.0

from scavio import ScavioClient

client = ScavioClient()  # reads SCAVIO_API_KEY

comps = client.realtor.search(location="Maricopa County, AZ", listing_status="sold",
                              property_types=["land"], sold_within_days=180, page_size=200)
home = client.realtor.property(property_id="9564057635")
places = client.realtor.locations("Austin")
market = client.realtor.market("Austin_TX")
agents = client.realtor.agents(location="Austin, TX", page_size=10)
profile = client.realtor.agent("1918615")

npm install scavio@0.20.0

import { Scavio } from "scavio";

const scavio = new Scavio(); // reads SCAVIO_API_KEY
const comps = await scavio.realtor.search({ location: "Maricopa County, AZ", listing_status: "sold", property_types: ["land"], page_size: 200 });
const home = await scavio.realtor.property({ property_id: "9564057635" });
const market = await scavio.realtor.market({ location: "Austin_TX" });

MCP: the Scavio MCP server exposes all 6 endpoints as tools (search_realtor, get_realtor_property, get_realtor_locations, get_realtor_market, search_realtor_agents, get_realtor_agent). Realtor.com is opt-in: add realtor to SCAVIO_PLATFORMS.

{
  "mcpServers": {
    "scavio": {
      "command": "npx",
      "args": ["-y", "@scavio/mcp-server@0.18.1"],
      "env": {
        "SCAVIO_API_KEY": "sk_live_your_key",
        "SCAVIO_PLATFORMS": "default,realtor"
      }
    }
  }
}

Docs

Full reference per endpoint: Realtor.com search, property, locations, market, agents, agent. Overview: Realtor.com API.