Estimation of Land Value using Spatial Statistics: An approach towards Focusing on Real Transaction Land Prices in Korea

Abstract

to The present work is aimed to compare OLS (Ordinary Least Squares) and spatial regression models which are methods of calculating the traditional value of land—using data on the practical transaction price of land—and to enhance the applicability of estimation of official land assessment prices set by the Korean government while deducing policy implications for effective implementation. That is, as a way to overcome the limitations of the traditional regression model, we compare various Generalized Regression Models such as SLM (Spatial Lag Model), SEM (Spatial Error Model) with OLS. Consequently, an in-depth diagnosis is conducted to generate a proper estimation model for land pricing, and, also, the analysis focuses on vertical and horizontal equity using COD (Coefficient of Dispersion), COV (Coefficient of Variation) and PRD (Price-Related Differential). The results indicate that SEM is more appropriate than AIC (Akaike info criterion) and SC (Schwarz criterion) in terms of measuring log-likelihood, demonstrating that the spatial autocorrelation model is superior to the traditional regression model. It shows that the SEM is also the best among the tested models with regard to measuring horizontal equity. The spatial econometric model, therefore, is strongly recommended for estimating the prices of land and houses.