Penalised Quantile Regression Analysis of the Land Price in Japan by Using GIS Data

Yuya Katafuchi and Augusto Ricardo Delgado Narro ()
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Augusto Ricardo Delgado Narro: Kyushu University

A chapter in Eurasian Economic Perspectives, 2020, pp 87-105 from Springer

Abstract: Abstract Land price analysis remains one of the active research fields where new methods, in order to quantify the effect of economic and noneconomic characteristics, continually push knowledge frontiers up. Nevertheless, so far, most of the research focus on measuring the causal effect to the mean value of land price by ordinary least squares (OLS) method, despite the possibility that covariates might affect the land price differently at each quantile, that is, causal effects might depend on the quantile of the land price distribution. Furthermore, most of the literature highlights the effect of a few accessibilities, building characteristics, and amenities over the land price by using limited survey data even though the development of geographic information systems (GIS) improves accessibility information to various facilities by positioning properties on the map in terms of their geographic coordinates and provides larger dataset. To identify the heterogeneous causal effects on the land price, the chapter applies the Quantile Regression (QR) method to the land prices function, using GIS data in Japan including micro-level characteristics in 2017. As the number of covariates is large, penalized QR method by regularization helps us to obtain more accurate results in variable selection. We find that QR with GIS data is crucial to obtain detailed relationships between micro-level covariates and land price since GIS data explains that non-macroeconomic variables cause the land price heterogeneously at each quantile. For example, the distance from a medical facility causes a negative effect on the land price; furthermore, this effect is magnified for upper quantiles.

Keywords: GIS; Quantile regression; Land price; Japan (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-53536-0_7

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