SPATIAL ECONOMETRIC MODELING IN THE MASS APPRAISAL OF REAL ESTATE: A GEOGRAPHICALLY WEIGHTED REGRESSION APPROACH (THE CASE OF TERMEZ CITY)
DOI:
https://doi.org/10.5281/zenodo.21908436Abstract
Although location is a decisive factor in determining real estate values, classical regression
models widely used in mass appraisal are based on the assumption of spatial independence among
observations. This study applies spatial econometric models that account for spatial dependence and
spatial heterogeneity—namely, the spatial autoregressive model (SAR), the spatial error model (SEM), and
geographically weighted regression (GWR)—to the mass appraisal of real estate. The empirical analysis is
based on the 2025 mass appraisal (cadastral) values of 13,116 apartments located in 380 multi-storey buildings
in the city of Termez, geocoded building coordinates, and GIS-based distances to social infrastructure facilities.
Strong spatial autocorrelation was detected in the residuals of the classical regression model (Moran’s I =
0.842; p < 0.001), confirming the relevance of spatial modeling. Among the models examined, the GWR
model demonstrated the best performance across all evaluation criteria: the coefficient of determination
increased from 0.90 to 0.94, MAPE decreased from 9.6% to 7.0%, and the coefficient of dispersion (COD)
declined from 9.6% to 7.1%. The spatial distribution of local coefficients revealed substantial variation in the
effects of building age and location-related factors across different city districts, providing a methodological
basis for the scientifically grounded delineation of price zones in cadastral valuation.
Keywords
mass appraisal, cadastral value, spatial econometrics, spatial autocorrelation, Moran’s I, geographically weighted regression (GWR), spatial autoregressive model (SAR), GIS, price zones, Termez CityReferences
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