ScavioScavio
FeaturesPricingDocs
Sign InGet Started
  1. Home
  2. Tutorials
  3. How to Build a Real Estate Search Tool with Google APIs
Tutorial

How to Build a Real Estate Search Tool with Google APIs

Aggregate real estate listings and neighborhood data from Google search using the Scavio API. Build a property research tool without a dedicated real estate API.

Get Free API KeyAPI Docs

Real estate search tools typically rely on expensive proprietary data APIs like Zillow, Redfin, or MLS feeds. For many use cases — neighborhood research, market trend analysis, investment opportunity scanning — publicly available Google search data is sufficient. The Scavio Google SERP API returns organic results, shopping results, and knowledge graph data that can surface real estate listings and neighborhood insights. This tutorial builds a property research tool that aggregates data from Google search results.

Prerequisites

  • Python 3.8 or higher
  • requests library installed
  • A Scavio API key
  • Understanding of real estate terminology

Walkthrough

Step 1: Search for properties by location and type

Construct targeted queries that surface real estate listings from Zillow, Realtor.com, and similar sites in Google results.

Python
def search_properties(location: str, property_type: str = "homes for sale") -> dict:
    query = f"{property_type} in {location}"
    r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY},
                      json={"query": query, "country_code": "us"})
    r.raise_for_status()
    return r.json()

Step 2: Extract listing results

Filter organic results for real estate listing sites by checking for known domains in the URL.

Python
LISTING_SITES = ["zillow.com", "realtor.com", "redfin.com", "trulia.com"]

def extract_listings(data: dict) -> list[dict]:
    results = []
    for r in data.get("organic_results", []):
        if any(site in r.get("link", "") for site in LISTING_SITES):
            results.append({"title": r["title"], "snippet": r.get("snippet"), "link": r["link"]})
    return results

Step 3: Get neighborhood knowledge graph

Fetch the knowledge graph for a neighborhood or city to get population, median income, and other demographic data.

Python
def get_neighborhood_info(location: str) -> dict | None:
    r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY},
                      json={"query": location, "country_code": "us"})
    r.raise_for_status()
    return r.json().get("knowledge_graph")

Step 4: Build a property research summary

Combine listing results and neighborhood knowledge graph into a structured property research report.

Python
def research(location: str) -> dict:
    prop_data = search_properties(location)
    listings = extract_listings(prop_data)
    kg = get_neighborhood_info(location)
    return {
        "location": location,
        "listing_count": len(listings),
        "top_listings": listings[:3],
        "neighborhood": kg.get("title") if kg else None,
    }

Python Example

Python
import os
import requests

API_KEY = os.environ.get("SCAVIO_API_KEY", "your_scavio_api_key")
ENDPOINT = "https://api.scavio.dev/api/v1/search"
LISTING_SITES = ["zillow.com", "realtor.com", "redfin.com"]

def search(q: str) -> dict:
    r = requests.post(ENDPOINT, headers={"x-api-key": API_KEY},
                      json={"query": q, "country_code": "us"})
    r.raise_for_status()
    return r.json()

def research(location: str, prop_type: str = "homes for sale") -> None:
    data = search(f"{prop_type} in {location}")
    listings = [r for r in data.get("organic_results", []) if any(s in r.get("link", "") for s in LISTING_SITES)]
    kg = data.get("knowledge_graph")
    print(f"Location: {location}")
    print(f"Listing results found: {len(listings)}")
    if kg:
        print(f"Knowledge panel: {kg.get('title')} — {kg.get('description', '')[:100]}")
    for listing in listings[:3]:
        print(f"  {listing['title'][:60]}")

if __name__ == "__main__":
    research("Austin, Texas")

JavaScript Example

JavaScript
const API_KEY = process.env.SCAVIO_API_KEY || "your_scavio_api_key";
const ENDPOINT = "https://api.scavio.dev/api/v1/search";
const LISTING_SITES = ["zillow.com", "realtor.com", "redfin.com"];

async function search(q) {
  const res = await fetch(ENDPOINT, {
    method: "POST",
    headers: { "x-api-key": API_KEY, "Content-Type": "application/json" },
    body: JSON.stringify({ query: q, country_code: "us" })
  });
  return res.json();
}

async function research(location, propType = "homes for sale") {
  const data = await search(`${propType} in ${location}`);
  const listings = (data.organic_results || []).filter(r => LISTING_SITES.some(s => r.link.includes(s)));
  console.log(`Location: ${location}`);
  console.log(`Listings found: ${listings.length}`);
  listings.slice(0, 3).forEach(l => console.log(`  ${l.title.slice(0, 60)}`));
}

research("Austin, Texas").catch(console.error);

Expected Output

JSON
Location: Austin, Texas
Listing results found: 4
Knowledge panel: Austin — State capital of Texas, known for its technology industry...
  Zillow: 2,847 Homes For Sale in Austin, TX
  Realtor.com: Austin, TX Real Estate and Homes for Sale
  Redfin: Austin TX Homes for Sale and Real Estate

Related Tutorials

  • How to Extract Google Knowledge Graph Data via API
  • How to Fetch Google Shopping Results via API

Frequently Asked Questions

Most developers complete this tutorial in 15 to 30 minutes. You will need a Scavio API key (free tier works) and a working Python or JavaScript environment.

Python 3.8 or higher. requests library installed. A Scavio API key. Understanding of real estate terminology. A Scavio API key gives you 250 free credits per month.

Yes. The free tier includes 250 credits per month, which is more than enough to complete this tutorial and prototype a working solution.

Scavio has a native LangChain package (langchain-scavio), an MCP server, and a plain REST API that works with any HTTP client. This tutorial uses the raw REST API, but you can adapt to your framework of choice.

Related Resources

Best Of

Best Search API for Deep Research Agents in 2026

Read more
Best Of

Best Search APIs After Google I/O 2026 AI Mode Changes

Read more
Use Case

Real Estate Search

Read more
Use Case

Real Estate Aggregation

Read more
Glossary

Search API Provider Landscape (2026)

Read more
Comparison

Brave Search API vs Scavio

Read more

Start Building

Aggregate real estate listings and neighborhood data from Google search using the Scavio API. Build a property research tool without a dedicated real estate API.

Get Free API KeyRead the Docs
ScavioScavio

Real-time search API for AI agents. Search every platform, not just Google.

Product

  • Features
  • Pricing
  • Dashboard
  • Affiliates

Developers

  • Documentation
  • API Reference
  • Quickstart
  • MCP Integration
  • Python SDK

Alternatives

  • Tavily Alternative
  • SerpAPI Alternative
  • Firecrawl Alternative
  • Exa Alternative

Tools

  • JSON Formatter
  • cURL to Code
  • Token Counter
  • All Tools

© 2026 Scavio. All rights reserved.

Featured on TAAFT
Terms of ServicePrivacy Policy