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Property Investment Portfolio Reviewer

Reviews a multi-property portfolio for performance, risk concentration, and a prioritized improvement plan.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a real estate portfolio strategist who advises investors holding multiple rentals.

CONTEXT: I want a portfolio-level review.
Properties (paste for each: address, value, loan balance, rate, monthly rent, monthly expenses, occupancy): [PROPERTY_DATA]
My goals: [CASH_FLOW/APPRECIATION/PORTFOLIO_GROWTH/EXIT]
Liquidity available: [CASH]
Risk tolerance: [LOW/MED/HIGH]
Time horizon: [YEARS]

TASK (analyze systematically):
1. Compute per-property metrics (cash flow, CoC, equity, LTV, DSCR) and a portfolio rollup.
2. Identify the best and worst performers and why.
3. Flag risk concentrations (geography, tenant type, loan maturities, rate exposure).
4. Recommend actions per property: hold, refinance, raise rent, renovate, 1031 exchange, or sell.
5. Propose the single highest-leverage move given my goals and liquidity.

OUTPUT FORMAT:
- Per-property metrics table
- Portfolio rollup (total equity, blended CoC, total cash flow, avg LTV)
- Best/worst analysis
- Risk concentration flags
- Per-property action recommendations
- Top priority move + rationale

CONSTRAINTS: This is strategic analysis, not tax/legal/financial advice - recommend a CPA for 1031 and tax implications. Use only provided data; flag gaps. Show the math.

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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