A Study on Estimation of Land Value Using Spatial Statistics: Focusing on Real Transaction Land Prices in Korea

Abstract

Abstract:The aim of this research is 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.

Land Valuation Sustainable Model of Urban Planning Development: A Case Study in Badajoz, Spain

“…In fact, there are models that value the land through sustainability indicators [19], on explanatory theories of the space formation of land valuation, and depending on accessibility, space qualification or space social hierarchy [20,21]. However, the fact remains that to determine land valuation with urban development, it must be performed using models that quantify economically its real and reasonable value, which will depend on the realization of land use rights [22][23][24][25][26][27].…”

A comparative study of land price estimation and mapping using regression kriging and machine learning algorithms across Fukushima prefecture, Japan

“…Developing an estimation model of rent and land prices Two classified maps of land prices for residential and commercial uses (Kim and Kim, 2016) Seoul (Chica-Olmo, 2007) City of Granada, Spain…”

“…This modeling can be implemented using ordinary least squares (OLS; Crespo and Grêt-Regamey, 2013). It was among the first techniques for evaluating and predicting land or house prices and has long been used by many researchers, including Löchl (2006), Kim and Kim (2016), Bourassa et al (2010), Schernthanner et al (2016), and Hilal et al (2016), among others. These models generally have several limitations because they depend on the availability of a large dataset which has high costs and requires time-consuming field surveys (Kuntz and Helbich, 2014).…”

Precio del suelo dado por la oferta del mercado y el avalúo municipal: Relaciones y distribución espacial en la ciudad de Cuenca

“…En este concepto se basa el modelo hedónico de valuación y también el de costo de reposición. El modelo hedónico es adecuado al incluir un variado número de factores en la estimación del precio (Paterson & Boyle, 2002), sin embargo, tiene limitaciones en el sentido de que no considera los efectos espaciales que ocurren en cada región, las cuales se superan con econometría espacial (Kim & Kim, 2016), y la necesidad de contar con una amplia información de las características específicas de cada predio. Adicionalmente, el valor de un inmueble que se encuentre bajo explotación económica puede ser calculado en función de las expectativas de rentas.…”