Real Estate Negotiation Counteroffer Drafter
Drafts a strategic counteroffer with justification and a professional cover note that keeps the deal alive.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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