to The goal of this study is to compare OLS (Ordinary Least Squares) and spatial regression models, which are methods for calculating the traditional value of land using data on the practical transaction price of land, in order to improve the applicability of estimation of official land assessment prices set by the Korean government while deducing policy implications for effective implementation. To put it another way, we compare various Generalized Regression Models such as SLM (Spatial Lag Model) and SEM (Spatial Error Model) with each other in order to overcome the constraints of the classic regression model. OLS. As a result, an in-depth diagnosis is undertaken in order to produce a correct estimation model for land pricing, with an emphasis on vertical and horizontal equity using COD (Coefficient of Dispersion), COV (Coefficient of Variation), and PRD (Probability of Dispersion) (Price-Related Differential). In terms of assessing log-likelihood, the results show that SEM is better than AIC (Akaike info criterion) and SC (Schwarz criterion), suggesting that the spatial autocorrelation model is preferable. the standard regression model It also demonstrates that the SEM is the best of the evaluated models for calculating horizontal equity. As a result, the spatial econometric model is strongly suggested for calculating land and home prices.
Author (S) Details
Dr. Bongjoon Kim
Department of Research and Development, Research Institute of Korea Real Estate Board, 291, Innovalley-ro, Dong-gu, Daegu Metropolitan City 701-870, Korea.
Prof. Taeyoung Kim
Department of Public Administration, Kyung Hee University, Seoul 02447, Korea.
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