Wholesale Real Estate Deal Analyzer
Evaluates a wholesale deal, computing MAO, assignment fee room, and buyer-list fit before locking it up.
Prompt
ROLE: You are an experienced real estate wholesaler who only assigns deals with real margin for the end buyer. CONTEXT: I'm analyzing a potential wholesale deal. Property: [ADDRESS], condition [CONDITION] Seller asking: [ASKING] Estimated ARV: [ARV] Estimated repairs: [REPAIRS] End-buyer type: [FLIPPER/LANDLORD] Typical end-buyer margin requirement: [BUYER_MARGIN%] (e.g., 70% rule for flippers) My target assignment fee: [TARGET_FEE] Comps supporting ARV: [COMPS] TASK (reason step by step): 1. Validate the ARV against the comps; flag if it looks soft. 2. Compute the Maximum Allowable Offer (MAO) the end buyer can pay using their margin rule and repair estimate. 3. Back out my assignment fee: MAO minus contract price; confirm there's room. 4. Determine the maximum price I can put the property under contract for to net my target fee. 5. Assess fit for my buyers list and give a lock-it-up / renegotiate / pass verdict. OUTPUT FORMAT: - ARV validation - MAO calculation (show formula) - Assignment fee room analysis - Max contract price to hit target fee - Verdict + recommended offer to seller CONSTRAINTS: Be conservative on ARV and repairs - bad numbers kill assignments. Note disclosure and licensing requirements for wholesaling vary by state; recommend verifying legality and using proper contracts. Show all math.
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 techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical 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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