CRE Intelligence

CRE Intelligence MCP

Live market data for commercial real estate analysis — inside Claude.

Connect this MCP to Claude Desktop and instantly access Federal Reserve interest rates, Census Bureau demographics, DCF modeling, rent roll parsing, lease abstraction, and IC memo generation — all from a single prompt.

*"Analyze this deal: 2201 South Blvd, Charlotte NC. NOI $400k, asking $6M, retail strip."*

→ Pulls live SOFR from the Fed. Pulls Census demographics for that exact block. Builds a full levered 10-year DCF. Writes an institutional-quality IC memo. 30 seconds.

---

Why this exists

The #1 problem with AI in CRE: 66% of professionals use it daily, but only 5% trust it for actual deal decisions.

The reason? AI guesses at rates and demographics. A DCF built on a hallucinated SOFR rate is worthless.

This MCP fixes that. Every number comes from a verified public source:

• Interest rates → Federal Reserve (FRED API)
• Demographics → US Census Bureau ACS
• Document analysis → Claude AI with structured output

Zero data licensing fees. Zero hallucinations on financial inputs.

---

Tools

---

Example output

Prompt: *"Get me current interest rates"*

SOFR:           3.63%   (June 8, 2026)

SOFR 30-day: 3.59% 10yr Treasury: 4.55% 5yr Treasury: 4.29% Fed Funds: 3.63%

Implied cap rates (spread over 10yr T): Core Multifamily: 5.30% – 6.05% Core Industrial: 5.55% – 6.30% Core Office: 6.05% – 7.05% Value-Add: 6.05% – 7.05%

Loan rate guidance: Floating: SOFR + 150–250bps = ~5.38%–5.88% Fixed: 10yr T + 150–200bps = ~6.05%–6.55%

Prompt: *"Analyze this deal: 2201 South Blvd Charlotte NC, NOI $400k, asking $6M, retail strip"*

The MCP automatically chains get_current_rates + get_market_demographics + build_dcf_model + generate_deal_memo and returns a full IC memo including:

Entry Cap Rate:   6.67%  (+212bps over 10yr Treasury)

Loan Rate: 5.38% (derived from live SOFR 3.63% + 175bps) Year 1 DSCR: 1.53x IRR: 14.6% Equity Multiple: 3.11x

Census Tract demographics (2023 ACS): Median HHI: $141,419 Employment rate: 97.2% College educated: 64.9% Vacancy rate: 8.6%

Recommendation: GO — subject to rent roll review and comp analysis.

---

Setup

Option A — Hosted (fastest, no API keys)

Add this to your claude_desktop_config.json and restart Claude Desktop:

{

"mcpServers": { "cre-intelligence": { "type": "streamable-http", "url": "https://cre-intelligence-mcp.onrender.com/mcp" } } }

If your MCP client only supports stdio servers, use the mcp-remote bridge instead:

{

"mcpServers": { "cre-intelligence": { "command": "npx", "args": ["-y", "mcp-remote", "https://cre-intelligence-mcp.onrender.com/mcp"] } } }

Free during beta. All data fetching runs server-side.

Option B — Self-hosted

Prerequisites

• Claude Desktop
• Python 3.11+
• Free API keys (takes ~2 minutes total):
• FRED: fred.stlouisfed.org/docs/api/api_key.html
• Census: api.census.gov/data/key_signup.html
• Anthropic: console.anthropic.com

Install

git clone https://github.com/Zwondra/cre-intelligence-mcp

cd cre-intelligence-mcp python3.11 -m venv venv venv/bin/pip install -r requirements.txt cp .env.example .env

Add your API keys to .env

Connect to Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{

"mcpServers": { "cre-intelligence": { "command": "/path/to/cre-intelligence-mcp/venv/bin/python3", "args": ["/path/to/cre-intelligence-mcp/server.py"], "env": { "ANTHROPIC_API_KEY": "sk-ant-...", "FRED_API_KEY": "your_fred_key", "CENSUS_API_KEY": "your_census_key" } } } }

Restart Claude Desktop. The tools will appear automatically.

Test it

Open Claude Desktop and say:

*"Get me current interest rates"*

You should see it call get_current_rates and return live Federal Reserve data.

---

Document analysis

The document tools (analyze_rent_roll, abstract_lease) work by pasting PDF text directly into the prompt. In Claude Desktop:

1. Open your rent roll or lease PDF
2. Copy all the text
3. Say: *"Analyze this rent roll: [paste text]"*

The tool extracts every tenant, suite, SF, rent, lease dates, and expiration into structured JSON — then flag_lease_risks can immediately analyze it for rollover and concentration risk.

---

Data sources

---

Roadmap

• Comparable sales search (CREXI public listings)
• Multi-property portfolio analysis
• Sensitivity table generation (cap rate / NOI / LTV scenarios)
• Export to Excel / Word
• Deal history / comparison across sessions

---

License

MIT