Empirical comparison of the various spatial prediction models: In spatial econometrics, spatial statistics, and semiparametric statistics

Hajime Seya; Morito Tsutsumi; Yasushi Yoshida; Yuichiro Kawaguchi

doi:10.1016/j.sbspro.2011.07.025

Empirical comparison of the various spatial prediction models: In spatial econometrics, spatial statistics, and semiparametric statistics

Hajime Seya, Morito Tsutsumi, Yasushi Yoshida, Yuichiro Kawaguchi

Graduate School of Business and Finance

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

Abstract

This study compares the performance of various spatial prediction models which consider spatial dependence among the real estate data in spatial econometrics, spatial statistics, and semiparametric statistics. Thus far, surprisingly few researches have been conducted from this perspective. This study employs the dataset of apartment rent in the Tokyo 23 wards for empirical comparison. This study in particular focuses on a geoadditive model which considers both spatial dependence and nonlinearity of a hedonic function, and suggests the predictive performance of this method superior to those of the traditional methods like kriging or EM type algorithm.

Original language	English
Pages (from-to)	120-129
Number of pages	10
Journal	Procedia - Social and Behavioral Sciences
Volume	21
DOIs	https://doi.org/10.1016/j.sbspro.2011.07.025
Publication status	Published - 2011
Event	International Conference on Spatial Thinking and Geographic Information Sciences 2011, STGIS 2011 - Tokyo, Japan Duration: 2011 Sep 14 → 2011 Sep 16

Keywords

Apartment rent
Geoadditive model
Nonlinearity
Spatial hedonic model
Spatial prediction

ASJC Scopus subject areas

Social Sciences(all)
Psychology(all)

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title = "Empirical comparison of the various spatial prediction models: In spatial econometrics, spatial statistics, and semiparametric statistics",

abstract = "This study compares the performance of various spatial prediction models which consider spatial dependence among the real estate data in spatial econometrics, spatial statistics, and semiparametric statistics. Thus far, surprisingly few researches have been conducted from this perspective. This study employs the dataset of apartment rent in the Tokyo 23 wards for empirical comparison. This study in particular focuses on a geoadditive model which considers both spatial dependence and nonlinearity of a hedonic function, and suggests the predictive performance of this method superior to those of the traditional methods like kriging or EM type algorithm.",

keywords = "Apartment rent, Geoadditive model, Nonlinearity, Spatial hedonic model, Spatial prediction",

author = "Hajime Seya and Morito Tsutsumi and Yasushi Yoshida and Yuichiro Kawaguchi",

note = "Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; International Conference on Spatial Thinking and Geographic Information Sciences 2011, STGIS 2011 ; Conference date: 14-09-2011 Through 16-09-2011",

year = "2011",

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language = "English",

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AU - Seya, Hajime

AU - Tsutsumi, Morito

AU - Yoshida, Yasushi

AU - Kawaguchi, Yuichiro

PY - 2011

Y1 - 2011

N2 - This study compares the performance of various spatial prediction models which consider spatial dependence among the real estate data in spatial econometrics, spatial statistics, and semiparametric statistics. Thus far, surprisingly few researches have been conducted from this perspective. This study employs the dataset of apartment rent in the Tokyo 23 wards for empirical comparison. This study in particular focuses on a geoadditive model which considers both spatial dependence and nonlinearity of a hedonic function, and suggests the predictive performance of this method superior to those of the traditional methods like kriging or EM type algorithm.

AB - This study compares the performance of various spatial prediction models which consider spatial dependence among the real estate data in spatial econometrics, spatial statistics, and semiparametric statistics. Thus far, surprisingly few researches have been conducted from this perspective. This study employs the dataset of apartment rent in the Tokyo 23 wards for empirical comparison. This study in particular focuses on a geoadditive model which considers both spatial dependence and nonlinearity of a hedonic function, and suggests the predictive performance of this method superior to those of the traditional methods like kriging or EM type algorithm.

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KW - Geoadditive model

KW - Nonlinearity

KW - Spatial hedonic model

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