🤖 Real Estate MCP Server — AI-Native Property Data for Claude, ChatGPT & LLM Agents

Connect AI agents to live U.S. real-estate listings, comps, and market stats through the Model Context Protocol. A drop-in replacement for HouseCanary, CoreLogic, and ATTOM Data — without the enterprise contracts.

Why This MCP Server Beats Zillow API, HouseCanary, CoreLogic & ATTOM Data

Feature	NexGenData Real Estate MCP	Zillow / Redfin direct API	HouseCanary	CoreLogic	ATTOM Data
Cost	$0.01 per result, pay-per-event	Restricted / partner-only	$$$$ enterprise contract	$$$$ enterprise contract	$$$ per-record pricing
AI agent integration	Native MCP — Claude Desktop, Cursor, n8n	None — REST only	None	None	None
Time-to-first-call	<60 seconds	Weeks of partner approval	Sales cycle	Sales cycle	Onboarding required
Auth	Apify API token	OAuth + partner status	License key	License key	API key + quota
Coverage	Property listings, comps, sold history, market stats	Limited (deprecated public API)	Valuations	Property records	Property + transactions
Output format	Structured JSON streamed to LLM tools	JSON	XML / JSON	JSON	JSON

Most teams pick this MCP server because it is the only way to give Claude or ChatGPT direct, structured access to real-estate data without writing custom scrapers or signing six-figure enterprise contracts.

What You Get

• Property search by city, ZIP, address, or filters (beds/baths/price/sqft)
• Full listing detail: price history, days on market, photos, description, agent
• Sold comparables for valuation modeling
• Neighborhood market stats (median price, inventory, price-per-sqft trends)
• Tax history and assessment data where available
• Structured JSON responses optimized for LLM function-calling

Use Cases

• AI buyer agents — Claude or GPT-4 negotiates listings, pulls comps, recommends offers
• Real-estate co-pilots — chat-first investor tools that answer "what's the average DOM in 90210?"
• Underwriting automation — feed comparable sales into LLM-driven valuation models
• Lead-qualifying chatbots — answer prospects' property questions with live MLS-grade data
• Investor research agents — multi-step reasoning chains pulling listings → comps → cap rates
• n8n / Make.com agent workflows — trigger an MCP tool from any low-code automation

Quick Start

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("nexgendata/real-estate-mcp-server").call(run_input={
    "tool": "search_properties",
    "params": {"location": "Austin, TX", "max_price": 750000}
})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

Or connect it as a tool server in Claude Desktop's mcp_servers config — Claude will auto-discover every exposed tool.

Pricing

Pay-per-event — you only pay for results that hit your dataset.

• Actor Start: $0.00005 per GB-instance
• Result: $0.01 per dataset item

A typical agent reasoning chain costs $0.05–$0.30 — orders of magnitude cheaper than HouseCanary or CoreLogic enterprise contracts.

Related NexGenData Actors

Use case	Actor
Redfin listings, sold history, comps in flat JSON	Redfin Real Estate Scraper
UK property listings from Rightmove	Rightmove UK Real Estate
Singapore HDB resale transactions	SG HDB Resale Prices
Singapore URA private-property transactions	SG URA Property Transactions
Denmark housing market via Boliga	Boliga Denmark Real Estate
AI-native financial data for LLM agents	Finance MCP Server
Local-business + map data MCP for agents	Google Maps MCP Server
Developer / SaaS tooling MCP for agents	Developer Tools MCP Server

Input Example

Most agents call the MCP server with no parameters — the LLM picks tools at reasoning time. For Apify console runs:

{
  "debug": false,
  "maxToolCallSeconds": 90
}

When the LLM selects a tool (e.g. search_properties), it passes arguments like:

{
  "tool": "search_properties",
  "params": {
    "location": "Austin, TX",
    "max_price": 750000,
    "min_beds": 3,
    "property_type": "single_family"
  }
}

Output Example

Each tool returns a structured JSON payload. Example response from search_properties:

{
  "tool": "search_properties",
  "items": [
    {
      "address": "1234 Oak Ridge Dr, Austin, TX 78745",
      "price": 695000,
      "beds": 4,
      "baths": 3,
      "sqft": 2410,
      "year_built": 2008,
      "lot_size_sqft": 8500,
      "days_on_market": 12,
      "listing_url": "https://www.redfin.com/TX/Austin/1234-Oak-Ridge-Dr-78745/home/12345678",
      "agent": {"name": "Jane Doe", "brokerage": "Compass"},
      "photos": ["https://ssl.cdn-redfin.com/photo/.../IMG_0.jpg"],
      "price_history": [
        {"date": "2025-03-12", "event": "Listed", "price": 715000},
        {"date": "2025-03-28", "event": "Price reduced", "price": 695000}
      ]
    }
  ],
  "next_page_token": "eyJvZmZzZXQiOjI1fQ"
}

Each items[] entry lands in the Apify dataset and is billed at $0.01 per item.

How This Compares vs Zillow API, HouseCanary & ATTOM (Alternatives)

Alternative	Strength	Why teams pick this MCP instead
Zillow direct API	Brand recognition	Public Zillow API is deprecated; partner-only access; no AI agent integration
HouseCanary	Valuation models	$$$$ enterprise contracts, weeks of sales cycle, no MCP-native interface
CoreLogic	Property records breadth	Six-figure annual commitments; no AI/LLM-native output
ATTOM Data	Transactions + tax	Per-record fees stack fast; no agentic tool surface
Custom Redfin scraper	Free-ish	You maintain proxies, parsing, retries — and still have no MCP layer for Claude/Cursor

This MCP server is the only pay-per-result alternative that gives Claude, GPT-4, or any MCP-compliant agent direct, tool-call access to live U.S. property data — no enterprise contract, no partner approval.

FAQ

Q: Is this compatible with Claude Desktop and ChatGPT custom GPTs? Yes — any MCP-compliant client (Claude Desktop, Cursor, custom LangChain/Anthropic SDK agents, n8n) can call its tools.

Q: Where does the data come from? Publicly available real-estate listing portals. Listings, sold history, and market statistics are scraped fresh per call — no stale cache.

Q: What's the difference between this and your Redfin actor? The Redfin actor is a classic scraper that returns a dataset. This MCP server exposes the same data as tools the LLM picks at reasoning time. For agentic workflows, you want the MCP server.

Q: Are rate limits handled? Yes — proxy rotation, retry, and back-off are managed by Apify infrastructure. You don't tune anything.

Q: How fresh is the data? Pulled live each call. No 24-hour caches like HouseCanary's bulk feeds.

Q: Output formats? JSON streamed back to the LLM, plus the underlying dataset is available in CSV / Excel / JSONL.

Q: Can I use it commercially? Yes — you own the output. Verify the target portal's ToS for downstream use of the listing content itself.

About NexGenData

NexGenData publishes 260+ buyer-intent actors covering SEC filings, YC alumni, real-estate, lead generation, competitive intelligence, stock fundamentals across 30+ exchanges, and a growing fleet of MCP servers for AI agents. All pay-per-result. Browse the full catalog at https://apify.com/nexgendata?fpr=2ayu9b

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How NexGenData Pricing Works

Every NexGenData actor uses pay-per-event pricing — you only pay for results that actually land in your dataset. No monthly minimum, no seat fees, no surprise overage bills.

• Actor Start: a single-event charge each time you spin the actor up (scaled to memory size)
• Result: charged per item written to the default dataset
• No charge for retries, internal proxy rotation, or failed sub-requests — those are absorbed by the platform

If you only need the data once a quarter, you only pay once a quarter. If you scale to millions of records, the unit cost stays the same.

Apify Platform Bonus

New to Apify? Sign up with the NexGenData referral link — you get free platform credits on signup (enough for several thousand free results) and you help fund the maintenance of this actor fleet.

Integration Surface

Every actor in the NexGenData catalog can be triggered from:

• Apify console — point-and-click run
• Apify API — REST + webhooks
• Apify Python / JS SDKs — programmatic batch
• Zapier, Make.com, n8n — official integrations
• MCP — many actors are exposed as MCP tools for Claude / ChatGPT / Cursor agents
• Schedules — built-in cron for daily / weekly / monthly runs
• Webhooks — POST results to any HTTPS endpoint on dataset write

Support

NexGenData maintains 260+ Apify actors and ships updates regularly. Bug reports via the Apify console issues tab get a response within 24 hours. Roadmap requests are welcome — high-demand features ship in the next version.

🏠 Home: thenextgennexus.com 📦 Full catalog: apify.com/nexgendata