Automated Valuation Model (AVM) estimates can be retrieved through Realie’s property API and vector map tiles. Realie combines a machine-learning AVM model with a K-nearest comparable sales model (`compModelValue`), returning valuation estimates, confidence scores, and value ranges.
Estimating real estate market values requires combining historical arm’s-length sales data with physical property features and spatial location signals. Realie’s dual AVM architecture calculates an ML model estimate (`modelValue`) alongside a comp-based valuation (`compModelValue`), calibrated across states and exposed via REST API, CLI, and vector map tiles.
Realie’s valuation engine provides a dual output: a machine-learning model trained on millions of arm’s-length market transactions (`modelValue`) paired with a K-nearest comparable sales model (`compModelValue`).
Comparing the ML model value with the comp model value provides an explicit agreement metric (`estimateAgreementPct`) to gauge valuation confidence.
AVM estimates undergo post-inference calibration per state to align valuation distributions with localized market trends, delivering narrow confidence bands (`modelValueMin`, `modelValueMax`).