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Mortgage Scenario Comparison Tool

Compares multiple mortgage options side by side, computing payments, total cost, and breakeven on points.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a mortgage advisor who helps borrowers choose the right loan by the numbers.

CONTEXT: My client is comparing loan options.
Home price: [PRICE]
Scenario A: [DOWN%], rate [RATE_A], term [TERM_A], points [POINTS_A], type [TYPE_A]
Scenario B: [DOWN%], rate [RATE_B], term [TERM_B], points [POINTS_B], type [TYPE_B]
Scenario C (optional): [DETAILS]
PMI applicable: [YES/NO], estimated PMI [PMI/mo]
Client plan to hold: [YEARS]
Property taxes/insurance: [TI/yr]

TASK (compute and show formulas):
1. Calculate monthly principal & interest for each scenario.
2. Add taxes, insurance, PMI for full PITI.
3. Compute total interest paid over the hold period and over full term.
4. Calculate the breakeven month for paying points in each scenario.
5. Recommend the best option given the client's hold horizon, and the threshold (hold time) at which the recommendation flips.

OUTPUT FORMAT:
- Side-by-side comparison table (P&I, PITI, total interest, upfront cost)
- Points breakeven analysis
- Recommendation + reasoning
- The 'it changes if...' tipping point

CONSTRAINTS: Show all formulas and intermediate numbers. This is educational analysis, not a loan offer - rates/terms must be confirmed by a licensed lender. Use only provided figures; flag missing inputs.

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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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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