Property Price Prediction

Three-Layer AI-Powered Price Estimation

Our property price prediction engine uses a three-layer approach that combines statistical comparable sales analysis, feature-based value adjustments, and Claude AI qualitative assessment to produce blended price estimates with confidence scoring across every state and territory in Australia.

Key Highlights

Comparable Sales Analysis

Identifies similar recently sold properties within a 12-month lookback period. Each comparable is weighted by similarity across bedrooms, bathrooms, land size, property type, and distance. Recency weighting uses exponential decay with a 180-day half-life to prioritise recent market evidence.

Feature Engineering

Calculates value adjustments based on property-specific features: pool (+3%), renovation quality (+5%), views (+4%), flood zone (-8%), heritage overlay impact, and more. Also incorporates suburb growth trends, RBA cash rate, auction clearance rates, seasonal factors, and supply/demand ratios.

Claude AI Assessment

Anthropic's Claude AI analyses the property description, comparable data, feature adjustments, and suburb context to provide a predicted price with detailed reasoning, market sentiment assessment, value drivers, and risk factors.

Blended Confidence Scoring

The three layers are combined using calibrated weights (50% comparable, 20% features, 30% AI) to produce a final estimate. Confidence is calibrated and capped at 85% for houses and 80% for units.

How It Works

1

Property Identification

Enter a property address or select from current listings. The system retrieves property details including bedrooms, bathrooms, land size, and listing description from public sources.

2

Comparable Matching

The engine searches for similar properties sold within 12 months and 2km radius, scoring each by similarity and recency. Minimum 3 comparables required for a prediction.

3

Feature Adjustment

Property features are extracted from the listing description and public records. Each feature applies a calibrated percentage adjustment to the comparable-based estimate.

4

AI Analysis

Claude AI reviews all available data and produces a qualitative assessment with predicted price, confidence level, and detailed reasoning explaining the estimate.

5

Blended Output

All three layers are combined into a single prediction with midpoint estimate, confidence percentage, and price range. Historical accuracy metrics are provided for context.

Data Sources

  • Domain.com.au API — Recent sales and listing data
  • PropTrack API — Historical transaction records
  • ABS Census — Demographics, income, population
  • Local Council Open Data — Planning overlays and zoning
  • Reserve Bank of Australia — Cash rate and economic indicators
  • Planning Alerts — Development application activity

Benefits

  • Transparent three-layer methodology with visible reasoning
  • Confidence scoring that honestly reflects prediction uncertainty
  • All data sources listed — no hidden or proprietary data
  • Accuracy tracked against actual sales and published publicly
  • Works across every state and territory in Australia — 17,000+ suburbs covered
  • Free to use — no account or personal data required

Disclaimer: Property price predictions are estimates based on publicly available data and AI analysis. They are not formal valuations, financial advice, or guarantees of market value. Always obtain a professional valuation and consult qualified financial advisors before making property decisions.

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