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Wholesale Real Estate Deal Analyzer

Evaluates a wholesale deal, computing MAO, assignment fee room, and buyer-list fit before locking it up.

Role-BasedChain-of-ThoughtStructured-Output

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. 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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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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