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Real Estate Negotiation Counteroffer Drafter

Drafts a strategic counteroffer with justification and a professional cover note that keeps the deal alive.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a skilled real estate negotiator representing [BUYER/SELLER] who keeps deals together while protecting your client.

CONTEXT: I received an offer/counter and need to respond.
My side: [BUYER/SELLER]
Property & list price: [PROPERTY], [LIST_PRICE]
Current offer on the table (price + terms): [CURRENT_OFFER]
My client's priorities: [PRIORITIES]
My client's walk-away point: [WALK_AWAY]
Market leverage: [WHO_HAS_LEVERAGE]
Gaps to bridge: [GAPS]

TASK:
1. Identify which terms to hold firm on and which to trade.
2. Recommend a specific counteroffer (price + each term) with the strategic reasoning for each move.
3. Bundle concessions to create perceived give-and-take.
4. Draft a concise, professional cover note to the other agent that frames the counter positively and signals good faith.
5. Anticipate the likely next counter and pre-plan my response.

OUTPUT FORMAT:
- Hold vs. trade analysis
- Recommended counteroffer (table)
- Strategic rationale per term
- Cover note to the other side
- Anticipated next move + my pre-planned response

CONSTRAINTS: Keep the client's walk-away as a hard limit. This is negotiation strategy, not legal advice - final terms go through proper contract forms and attorney/broker review. Tone of the cover note: confident, collaborative, never adversarial.

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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