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