CRE Intelligence
Live CRE analysis: Federal Reserve rates, Census 1/3/5-mile demographics, DCF models, IC memos.
사용해야 할까요
품질 및 안전성
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"cre-intelligence-mcp": {
"url": "https://cre-intelligence-mcp.onrender.com/mcp"
}
}
}원격 엔드포인트
https://cre-intelligence-mcp.onrender.com/mcpstreamable-http할 수 있는 일
도구 목록
도구 (13)
🟢get_current_rates
Get live interest rates from the Federal Reserve (FRED). Returns SOFR, 10-year Treasury, 5-year Treasury, Fed Funds Rate, and 30-day SOFR average. Also calculates implied cap rate ranges based on current treasury spreads. Use this BEFORE any DCF model or loan underwriting. These are real-time numbers Claude cannot access on its own.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢get_market_demographics(address)
Get Census Bureau demographics for any US property address. Returns median income, population, employment rate, housing vacancy, median rents, and education levels for the census tract. This is address-specific data from the actual Census tract — not estimates. Claude cannot access this without the MCP. For 1/3/5-mile trade-area rings, use get_radius_demographics instead.
입력 스키마
{
"type": "object",
"properties": {
"address": {
"type": "string",
"description": "Full US property address (e.g. \"1234 Main St, Charlotte, NC 28202\")"
}
},
"required": [
"address"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢get_radius_demographics(address, radii_miles)
Get aggregated Census demographics for radius rings around a US property address — the standard 1/3/5-mile trade-area format used in CRE site analysis. Aggregates every census tract whose centroid falls within each radius: population, household-weighted median income, employment rate, college attainment, housing vacancy, renter share, and median rent. Use this for trade-area / site analysis. Use get_market_demographics for the single census tract immediately around the address.
입력 스키마
{
"type": "object",
"properties": {
"address": {
"type": "string",
"description": "Full US property address (e.g. \"1234 Main St, Charlotte, NC 28202\")"
},
"radii_miles": {
"default": "1,3,5",
"type": "string",
"description": "Comma-separated radii in miles (default \"1,3,5\", each capped at 15)"
}
},
"required": [
"address"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}⚪screen_land_market(state, county)
Screen a US county as a LAND-INVESTING market (raw-land flip / Podolsky style). Grades the county on the signals that matter for buying cheap rural land and reselling on terms: population growth, demographics, owner share, and affordability. IMPORTANT: This screens on FREE Census data only (growth + demographics + a home-value affordability proxy). It does NOT include actual land sale prices or comps — those require county records or a paid service, and must be verified per-parcel before buying. Use this to rank/shortlist markets, not to buy.
입력 스키마
{
"type": "object",
"properties": {
"state": {
"type": "string",
"description": "2-letter state abbreviation (e.g. \"AZ\") or 2-digit state FIPS"
},
"county": {
"type": "string",
"description": "County name (e.g. \"Mohave\" or \"Mohave County\")"
}
},
"required": [
"state",
"county"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢screen_parcel_dd(lat, lng)
Pre-screen a land parcel's location for the AUTOMATABLE due-diligence red flags: FEMA flood zone and federal wetlands. Pulls live from FEMA's National Flood Hazard Layer and the US Fish & Wildlife National Wetlands Inventory. Use this to kill obviously-bad parcels (flood zone, wetlands) at scale BEFORE spending time on manual due diligence. IMPORTANT: Checks flood + wetlands only. It does NOT check legal ACCESS (landlocked — the #1 land deal-killer), title/liens, or zoning — those stay MANUAL, per-parcel checks via county records. A clean screen here is necessary, NOT sufficient.
입력 스키마
{
"type": "object",
"properties": {
"lat": {
"type": "number",
"description": "Parcel latitude (decimal degrees)"
},
"lng": {
"type": "number",
"description": "Parcel longitude (decimal degrees)"
}
},
"required": [
"lat",
"lng"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢get_inflation_data
Get current CPI and rent inflation data from the Federal Reserve. Returns overall inflation, shelter inflation, and rent-specific CPI with YoY changes. Use this to calibrate rent growth assumptions in your DCF model — don't guess.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢get_cre_market_data
Get Commercial Real Estate price index and broader market data from the Federal Reserve. Returns CRE price trends, office/retail/industrial vacancy proxies, and credit spreads. Provides macro context for deal underwriting and cap rate analysis.
입력 스키마
{
"type": "object",
"properties": {},
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢analyze_rent_roll(text, property_name)
Extract structured tenant and lease data from a rent roll document. Paste the text content of your rent roll PDF here (copy-paste from PDF reader). Returns tenant list, suite/SF, lease dates, monthly rent, escalations, and options.
입력 스키마
{
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Raw text copied from a rent roll PDF"
},
"property_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional property name for context"
}
},
"required": [
"text"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢abstract_lease(text)
Extract all key terms from a commercial lease document. Returns term, base rent schedule, escalations, TI allowance, CAM structure, renewal options, termination rights, exclusivity, co-tenancy, and red flags.
입력 스키마
{
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Raw text copied from a commercial lease PDF"
}
},
"required": [
"text"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}🟢flag_lease_risks(rent_roll_json)
Analyze a parsed rent roll for investment risks. Feed the output from analyze_rent_roll directly into this tool. Returns: rollover risk, tenant concentration, credit risk, and actionable recommendations.
입력 스키마
{
"type": "object",
"properties": {
"rent_roll_json": {
"type": "string",
"description": "JSON string from the analyze_rent_roll tool output"
}
},
"required": [
"rent_roll_json"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}⚪build_dcf_model(noi_year1, purchase_price, hold_years, noi_growth_rate, exit_cap_rate, ...)
Build a levered DCF model using live Federal Reserve rates. Automatically fetches current SOFR to derive the loan rate if not provided. Returns: annual cash flows, IRR, equity multiple, cash-on-cash, DSCR, and exit analysis.
입력 스키마
{
"type": "object",
"properties": {
"noi_year1": {
"type": "number",
"description": "Year 1 Net Operating Income ($)"
},
"purchase_price": {
"type": "number",
"description": "Acquisition price ($)"
},
"hold_years": {
"default": 10,
"type": "integer",
"description": "Hold period in years (default 10)"
},
"noi_growth_rate": {
"default": 3,
"type": "number",
"description": "Annual NOI growth rate % (default 3.0)"
},
"exit_cap_rate": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Exit cap rate % — if None, uses entry cap + 25bps (conservative)"
},
"equity_pct": {
"default": 35,
"type": "number",
"description": "Equity as % of purchase price (default 35%)"
},
"loan_rate": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Loan interest rate % — if None, fetches live SOFR + 175bps"
},
"amortization_years": {
"default": 30,
"type": "integer",
"description": "Loan amortization period (default 30 years)"
}
},
"required": [
"noi_year1",
"purchase_price"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}⚪generate_deal_memo(property_address, property_type, noi, asking_price, rent_roll_summary, ...)
Generate a formatted CRE acquisition memo / Investment Committee memo. Automatically pulls live rates from FRED and demographics from Census Bureau to provide real market context — not guesses.
입력 스키마
{
"type": "object",
"properties": {
"property_address": {
"type": "string",
"description": "Full property address"
},
"property_type": {
"type": "string",
"description": "Multifamily / Office / Retail / Industrial / Mixed-Use"
},
"noi": {
"type": "number",
"description": "Net Operating Income ($)"
},
"asking_price": {
"type": "number",
"description": "Asking price ($)"
},
"rent_roll_summary": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional: paste output from analyze_rent_roll or flag_lease_risks"
},
"additional_context": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Any additional deal notes, seller info, market color"
}
},
"required": [
"property_address",
"property_type",
"noi",
"asking_price"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"properties": {
"result": {
"type": "string"
}
},
"required": [
"result"
],
"x-fastmcp-wrap-result": true
}🟢export_dcf_excel(noi_year1, purchase_price, address, property_name, hold_years, ...)
Generate a downloadable Excel (.xlsx) underwriting model with LIVE formulas — editable assumptions, PMT/FV amortization, IRR, equity multiple, a sensitivity grid, live Fed rates, and (if an address is given) Census trade-area demographics. Returns a download link valid for 60 minutes.
입력 스키마
{
"type": "object",
"properties": {
"noi_year1": {
"type": "number",
"description": "Year 1 Net Operating Income ($)"
},
"purchase_price": {
"type": "number",
"description": "Acquisition price ($)"
},
"address": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional property address — adds a demographics sheet"
},
"property_name": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"description": "Optional label for the model header"
},
"hold_years": {
"default": 10,
"type": "integer",
"description": "Hold period (default 10)"
},
"noi_growth_rate": {
"default": 3,
"type": "number",
"description": "Annual NOI growth % (default 3.0)"
},
"exit_cap_rate": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Exit cap % — default entry cap + 25bps"
},
"equity_pct": {
"default": 35,
"type": "number",
"description": "Equity as % of price (default 35)"
},
"loan_rate": {
"anyOf": [
{
"type": "number"
},
{
"type": "null"
}
],
"default": null,
"description": "Loan rate % — default live SOFR + 175bps"
},
"amortization_years": {
"default": 30,
"type": "integer",
"description": "Amortization (default 30)"
}
},
"required": [
"noi_year1",
"purchase_price"
],
"additionalProperties": false
}출력 스키마
{
"type": "object",
"additionalProperties": true
}커뮤니티
증거