Realtor Property Data Scraper

The Ultimate Tool for Bulk Real Estate Data

Realtor Property Scraper allows you to extract detailed listing data from Realtor.com at scale. Monitor markets, track pricing trends, and build comprehensive property databases without the manual hassle.

Whether you are an investor looking for the next deal or an analyst tracking inventory, this actor delivers structured, real-time data across any US market.

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🏗️ Core Features

• 🌍 Multi-City Support: Scrape listings from dozens of cities in a single run.
• 🏡 Granular Filtering: Target specific property types (Single Family, Condos, Land, etc.).
• 📊 Smart Sorting: Rank results by price, newness, lot size, or open house availability.
• 👥 Agent Details: Automatically captures the listing agent's name, phone, and email with every property.
• ⚡ High Performance: optimized for speed and reliability, handling large pagination depths easily.

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⚙️ Configuration

Control exactly what data you get with flexible input parameters.

Input Parameters

Parameter	Type	Required	Description
cities	Array	Yes	List of target cities (e.g., ["Austin", "Dallas"]).
status	String	No	Filter by for_sale (default), sold, or all.
property_type	String	No	Filter by type (e.g., single_family, condo, ANY).
sort_option	String	No	Sort order (e.g., newest, lowest price).
page_count	Integer	No	Number of pages to scrape per city (default: 1).

Example Input

Investment Search:

{
  "cities": ["Atlanta", "Charlotte"],
  "page_count": 5,
  "property_type": "multi_family",
  "sort_option": "newest",
  "status": "for_sale"
}

Sold Comps Analysis:

{
  "cities": ["Seattle"],
  "page_count": 10,
  "property_type": "single_family",
  "status": "sold",
  "sort_option": "newest"
}

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📦 Output Data

Data is delivered in a clean, flattened JSON format for easy analysis.

[
  {
    "city": "Atlanta",
    "data": {
      "results": [
        {
          "property_id": "92817452",
          "address": "123 Peachtree St, Atlanta, GA 30303",
          "list_price": 450000,
          "status": "for_sale",
          "beds": 3,
          "baths": "2.5",
          "sqft": 2100,
          "type": "single_family",
          "agent_name": "Sarah Smith",
          "agent_phone": "404-555-0123",
          "agent_email": "sarah.smith@examplerealty.com",
          "photo": "https://p.rdcpix.com/v01/listing_photo.jpg",
          "url": "https://www.realtor.com/realestateandhomes-detail/..."
        }
      ]
    }
  }
]

Key Fields

• Identities: property_id, fulfillment_id (Agent ID)
• Specs: beds, baths, sqft, type, list_date
• Financials: list_price, sold_price, sold_date
• Location: address, city, state, zip, lat, lon
• Contact: agent_name, agent_phone, agent_email, office_name

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🚀 Use Cases

• Market Research: Track inventory levels and days on market in key cities.
• Lead Generation: Identify listing agents for partnership opportunities.
• Alerting: Get notified of new listings matching your specific criteria.
• Valuation Models: Ingest sold data to build accurate pricing models.

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🛡️ Best Practices

• Batching: Group nearby cities to maximize efficiency.
• Sorting: Use newest for monitoring and relevant for general scraping.
• Pagination: Use higher page_count (10+) for deep market scrapes.

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