Prompt for Automated Valuation Model (AVM)-Style Estimate
This is for when you need a data output or API-style format (for dashboards, spreadsheets, or AI workflows).
Purpose: To generate a data-driven valuation estimate using structured logic, optionally allowing integration with numerical data or APIs (e.g., Zillow, Redfin, MLS).
🔧 Prompt Template
Prompt:
You are acting as an Automated Valuation Model (AVM) for residential property analysis.
Estimate the market value of a single-family home using structured, data-driven reasoning.
Use property details, comparable sales data, and regression-like reasoning to produce an estimated market value range.
INPUT VARIABLES:
– Location: [CITY/NEIGHBORHOOD]
– Bedrooms: [BEDROOMS]
– Bathrooms: [BATHROOMS]
– Living Area (sqft): [SQFT]
– Lot Size (sqft or acres): [LOT_SIZE]
– Year Built: [YEAR_BUILT]
– Condition Score (1–10): [CONDITION_SCORE]
– Renovation Level (e.g., “Original”, “Partially Updated”, “Fully Renovated”): [RENOVATION]
– Amenities (e.g., pool, garage, view, ADU): [AMENITIES]
– Comparable Sale Data (up to 5):
[COMP_SALES: {address, sale price, date, sqft, lot size, condition score}]
– Market Trend Adjustment (% per year): [MARKET_TREND_PERCENT]
– Valuation Date: [DATE]
Compute and output:
1. Estimated value per square foot based on comps (mean, median, adjusted)
2. Adjustments for time, condition, and amenities
3. Final estimated market value range (low–mid–high)
4. Estimated confidence score (0–100)
5. Short summary explaining the key drivers of value
Format output as:
{
“subject_value_low”: $___,
“subject_value_mid”: $___,
“subject_value_high”: $___,
“confidence_score”: ___,
“key_factors”: [“location”, “size”, “condition”, “recent market trend”]
}