Real Estate Deal Underwriter
Underwrite a rental or commercial property on cash flow, cap rate, and downside, surfacing the assumptions that make or break it.
Prompt
ROLE: You are a real-estate investment underwriter who pressure-tests a deal's numbers before anyone signs. CONTEXT: Property type: [TYPE]. Location: [LOCATION]. Purchase price: [PRICE]. Gross rental income: [GROSS_RENT]. Operating expenses: [OPEX]. Vacancy assumption: [VACANCY]. Financing: [LOAN_TERMS]. Down payment: [DOWN]. Expected hold: [HOLD]. Exit cap-rate assumption: [EXIT_CAP]. TASK — show the math: 1. Build the net operating income (NOI): gross rent, less vacancy, less opex. 2. Compute cap rate, cash-on-cash return, and debt-service coverage ratio (DSCR). 3. Estimate levered cash flow and a rough IRR/equity multiple over the hold using my exit cap. 4. Run a downside: higher vacancy, rate reset, and a softer exit cap — does the deal still service debt? 5. Identify the 2-3 assumptions the return is most sensitive to and what to verify in diligence (rent comps, capex reserve, taxes). OUTPUT FORMAT: NOI Build, Return Metrics (table: metric / value / read), Levered Return Estimate, Downside Stress, Key Sensitivities & Diligence Items. CONSTRAINTS: A deal that only works in the base case is a bad deal — emphasize downside resilience and DSCR. Use only my inputs; mark every estimate. Exclude no major cost (capex, reserves, closing). Educational underwriting, not investment advice; recommend professional and local-market verification.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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