A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Jiping Liu,Yi Yang,Shenghua Xu,Yong Wang,Yangyang Zhao,Fuhao Zhang

doi:10.3390/e18080303

Jiping Liu, Yi Yang + Show 4 more

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https://doi.org/10.3390/e18080303

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Abstract

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and temporal nonstationarity simultaneously to estimate house prices based on travel time distance metrics. Using house price data collected between 1980 and 2016, the house price response and explanatory variables are then modeled using both the GWR and the GTWR approaches. Comparing the GWR model with Euclidean and travel distance metrics, the GTWR model with travel distance obtains the highest value for the coefficient of determination ( R 2 ) and the lowest values for the Akaike information criterion (AIC). The results show that the GTWR model provides a relatively high goodness of fit and sufficient space-time explanatory power with non-Euclidean distance metrics. The results of this study can be used to formulate more effective policies for real estate management.

Highlights

The first law of geography, proposed by Waldo Tobler, is that “everything is related to everything else, but near things are more related than distant things” [1]
The results showed that non-Euclidean distance metrics are superior to Euclidean distance metrics for the Geographically weighted regression (GWR) model
All methods were tested against the same dataset, and we evaluated their goodness of fit in terms of the R2 and

Summary

Introduction

The first law of geography, proposed by Waldo Tobler, is that “everything is related to everything else, but near things are more related than distant things” [1]. Weighted regression (GWR) is a method used in spatial statistical analysis tools to discover geographical variations in the relationship between a response variable and a set of covariates [2,3,4,5]. In Northern China, heating costs have a significant influence on house prices. The influence of heating costs on house prices is subtle in Southern China. The mixed geographically weighted regression (MGWR) model has been proposed to explore spatially stationary and non-stationary effects [6,7]. MGWR empirical examples show that there is significant spatial variation in some of the estimated parameters, while the global effects provide evidence for policy-based linkages and an economically-connected housing market [6,7]. McMillen demonstrated that nonparametric estimation is feasible for large datasets, using statistical tests of individual covariates and tests of model specifications [10,11]

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