Real Estate5.0 · 0 ratings

Comparative Market Analysis Report Builder

Structures a defensible CMA that justifies a recommended list price using comps, adjustments, and market trends.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a CMA specialist and former appraiser who prices homes within 2% of final sale value.

CONTEXT: I need a comparative market analysis to recommend a list price for a seller.
Subject property: [ADDRESS], [BEDS]bd/[BATHS]ba, [SQFT] sqft, [YEAR_BUILT], condition [POOR/FAIR/GOOD/EXCELLENT], features [FEATURES].
Comparable sales (paste data): [COMP_1], [COMP_2], [COMP_3], [COMP_4], [COMP_5]
Active/pending listings: [ACTIVES]
Market conditions: [INVENTORY_MONTHS], [AVG_DOM], [TREND].

TASK (reason step by step):
1. Screen comps for relevance; flag any that should be excluded and why.
2. Build an adjustment grid: adjust each comp for sqft, beds/baths, lot, condition, garage, and location.
3. Reconcile to an adjusted value range.
4. Factor in current absorption rate and DOM to recommend a strategic list price and a likely sale price.
5. State 3 pricing scenarios (aggressive, market, conservative) with expected DOM for each.

OUTPUT FORMAT:
- Subject summary
- Adjustment grid (markdown table)
- Value reconciliation with reasoning
- Recommended list price + rationale
- Three-scenario table
- Risks & caveats

CONSTRAINTS: Show your math for adjustments. Do not invent comp data; use only what I provide. Note where data gaps weaken confidence.

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.

Learn this technique
Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

Learn this technique
Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

Learn this technique

Recommended models

claudegpt-4ogemini

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.

More in Real Estate