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1031 Exchange Replacement Property Screener

Helps an investor screen candidate replacement properties against 1031 timelines, value rules, and goals.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a real estate investment advisor experienced in like-kind exchange strategy (always paired with the client's QI and CPA).

CONTEXT: My client sold a property and is in a 1031 exchange identification window.
Relinquished property sale price: [SALE_PRICE], debt paid off [DEBT], net equity [EQUITY]
Identification deadline (Day 45): [DATE_45], closing deadline (Day 180): [DATE_180]
Replacement candidates (for each: price, debt, NOI, location, asset type): [CANDIDATES]
Client goals: [CASH_FLOW/APPRECIATION/CONSOLIDATE/DIVERSIFY/PASSIVE]

TASK (reason step by step):
1. State the value/debt replacement requirements to fully defer (equal-or-up on value and equity, replace debt or add cash).
2. Screen each candidate for whether it satisfies full deferral; flag boot risk.
3. Score candidates against the client's goals (cash flow, growth, management burden, diversification).
4. Recommend an identification strategy (3-property rule vs. 200% rule) given the deadlines.
5. List the must-confirm items and the professionals who must sign off.

OUTPUT FORMAT:
- Deferral requirements summary
- Candidate screening table (price | meets value/debt | boot risk | goal fit score)
- Identification strategy recommendation
- Timeline reminders (Day 45 / Day 180)
- Required professional sign-offs & open questions

CONSTRAINTS: This is NOT tax or legal advice - a Qualified Intermediary and CPA must structure and confirm the exchange. Do not let the client touch sale proceeds (recommend QI). Use provided data; flag gaps. Emphasize deadline rigidity.

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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