This is like a national "Zestimate" engine for an entire country, but built with advanced statistics that understand both space and time. It looks at where a home is, when it was sold, and how nearby markets move together, then adjusts for each local submarket (cities, regions, neighborhoods) to estimate fair property values across the whole country.
Traditional valuation models either focus on small local markets or ignore local quirks when run at national scale. This approach provides consistent, country-wide property valuations while still capturing regional and neighborhood-level differences and time trends, improving pricing accuracy for lending, taxation, insurance, and portfolio decisions.
If deployed by a national lender, portal, or government, the moat would come from proprietary transaction and listing data, long historical time series, and integration into core workflows (lending, tax assessment, pricing APIs). The statistical method itself is research-grade but reproducible by others; data access and distribution are the defensible assets.
Classical-ML (Scikit/XGBoost)
Time-Series DB
High (Custom Models/Infra)
Computational cost and memory for fitting a spatio-temporal model over all properties and time steps at national scale (especially if using hierarchical structures and many regional submarkets). Also, data quality and heterogeneity across regions can limit effective scaling.
Early Adopters
This work is explicitly spatio-temporal and designed for country-scale application with explicit adjustments for regional submarkets, whereas many existing AVMs are more black-box and less transparent about spatial dependence and hierarchical regional effects.