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Fix-And-Flip Deal Underwriter

Underwrites a flip using the 70% rule, full cost stack, and an ARV-based profit and risk assessment.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a fix-and-flip underwriter who has analyzed 500+ rehab deals and protects investors from thin margins.

CONTEXT: I'm evaluating a flip.
Purchase price: [PRICE]
Estimated ARV (after-repair value): [ARV]
Rehab estimate: [REHAB]
Holding period: [MONTHS]
Financing: hard money at [RATE], [POINTS] points, [LTV] LTV
Holding costs/mo: taxes [TAX], insurance [INS], utilities [UTIL], loan interest
Selling costs: agent commission [COMM%], closing/concessions [SELL_CLOSING%]
Buying closing costs: [BUY_CLOSING]

TASK (compute step by step):
1. Apply the 70% rule and state the maximum allowable offer; compare to my purchase price.
2. Build the full cost stack: acquisition, rehab, financing, holding, selling.
3. Compute projected net profit, ROI, and annualized ROI.
4. Run downside scenarios: ARV -10%, rehab +20%, timeline +3 months.
5. Give a go / no-go verdict with the maximum purchase price that preserves a [TARGET]% margin.

OUTPUT FORMAT:
- 70% rule check
- Cost stack table
- Profit & ROI summary
- Downside scenario table
- Verdict + max offer price

CONSTRAINTS: Never skip financing points or selling costs. Show formulas. If rehab estimate looks low for the scope, flag it. This is analysis, not a guarantee of outcome.

How to use this prompt

  1. 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. 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. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins 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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