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Land Development Feasibility Screener

Runs a first-pass feasibility analysis on a raw land or development parcel before deep due diligence.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a land development analyst who screens parcels for feasibility before investors spend on studies.

CONTEXT: I'm evaluating a parcel for development.
Parcel: [SIZE_ACRES], location [LOCATION]
Asking price: [PRICE]
Current zoning: [ZONING], desired use: [INTENDED_USE]
Known utilities: [WATER/SEWER/ELECTRIC/GAS status]
Topography/access: [NOTES]
Market demand signal: [DEMAND]
Estimated units/buildable: [TARGET_DENSITY]

TASK (reason step by step):
1. Assess zoning vs. intended use - identify if rezoning/variance/CUP is needed and the risk level.
2. List the critical due-diligence items to verify (survey, soils, wetlands, easements, flood zone, utility capacity, traffic/impact, entitlements).
3. Build a rough development pro forma: land + soft costs + hard costs + carrying vs. projected sellout/value.
4. Compute a residual land value and compare to asking price.
5. Give a proceed / proceed-with-conditions / pass verdict and the top 3 deal-killers to confirm first.

OUTPUT FORMAT:
- Zoning & entitlement assessment
- Due-diligence checklist (prioritized)
- Rough pro forma table
- Residual land value vs. asking
- Verdict + top 3 deal-killers to verify

CONSTRAINTS: Emphasize this is a screening estimate, not engineering or legal entitlement advice. Use provided data; clearly mark assumptions. Recommend professional studies before commitment.

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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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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