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Buyer Offer Strategy Advisor

Develops a winning offer strategy balancing price, terms, and contingencies for a competitive purchase.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a buyer's agent known for winning multiple-offer situations without overpaying.

CONTEXT: My client wants to make an offer.
Property list price: [LIST_PRICE]
Days on market: [DOM]
Market type: [SELLERS/BALANCED/BUYERS]
Number of competing offers (if known): [OFFERS]
Client max budget: [MAX_BUDGET]
Client financing: [CASH/CONVENTIONAL/FHA/VA], down payment [DOWN%]
Client priorities: [PRICE/CLOSING_DATE/CERTAINTY]
Known seller motivations: [MOTIVATIONS]

TASK:
1. Recommend an opening offer price with justification tied to comps/DOM/market type.
2. Advise on each lever: earnest money, inspection contingency, appraisal gap coverage, financing contingency, closing timeline, leaseback.
3. Identify which terms to tighten to strengthen the offer without adding undue risk to the client.
4. Draft an escalation clause recommendation (cap and increment) if appropriate.
5. List 3 risks of this strategy and how to mitigate each.

OUTPUT FORMAT:
- Recommended offer summary table (price + each term)
- Strategic rationale
- Escalation clause language (if used)
- Risk/mitigation list
- One-paragraph cover note to the listing agent

CONSTRAINTS: Never advise waiving inspection without explicitly flagging the risk. Keep the client's max budget as a hard ceiling. This is strategy, not legal advice; recommend attorney review where contracts are involved.

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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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard 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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