Multiscale geographically and temporally weighted regression with a unilateral temporal weighting scheme and its application in the analysis of spatiotemporal characteristics of house prices in Beijing

ABSTRACT

Geographically and temporally weighted regression (GTWR) has been demonstrated as an effective tool for exploring spatiotemporal data under spatial and temporal heterogeneity. Exploiting the advantages of the two most popular GTWR methods, we propose an alternative GTWR with a good balance between complexity and interpretability via a unilateral temporal weighting scheme called unilateral GTWR (UGTWR). When compared to the other two popular GTWR methods, the simulation experiment shows that UGTWR has comparable estimation accuracy and model fit, but it is more efficient. Furthermore, we propose its multiscale extension, coined multiscale UGTWR (MUGTWR), to characterize the spatiotemporal dynamic regression relationships at multiple scales. The proposed MUGTWR was applied to the analysis of house prices in the period of 2014–2018 in Beijing as a case study. Our analysis reveals that MUGTWR can effectively capture different levels of spatiotemporal heterogeneity in selected factors affecting house prices at different scales. Therefore, this study is useful for the formulation of housing policy in which the spatiotemporal dynamics of house prices with respect to specific factors can be considered.

KEYWORDS:

• House prices
• spatiotemporal heterogeneity
• geographically and temporally weighted regression
• multiscale geographically and temporally weighted regression

Acknowledgments

The authors thank the editor and reviewers for their useful comments and suggestions regarding the improvement of the manuscript.

Data and codes availability statement

The codes that support the findings of this study are available in figshare.com with the identifier: http://doi.org/10.6084/m9.figshare.12057834. The raw data that support the findings of this study belong to Home Link, a private real estate agency company. Hence, the raw data are not publicly available unless authorized by Home Link. Simulated data with a description are shared at the link to demonstrate how the codes work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the earmarked grant of the Hong Kong Research Grants Council [CUHK14653316] and the National Natural Science Foundation of China [11871056].

Notes on contributors

Zhi Zhang

Zhi Zhang is currently a Ph.D. candidate in the Department of Statistics, School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China. Her research interests include spatiotemporal analysis and statistic modeling.

Jing Li

Jing Li is an Assistant Professor at Department of Geography and Resource Management, and Deputy Director for Center of Land Resource and Housing Policy, Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, China. His areas of expertise include real estate economics, housing economics and urban economics.

Tung Fung

Tung Fung is Professor and Chairperson of the Department of Geography and Resource Management, The Chinese University of Hong Kong (CUHK), Hong Kong, China. He serves concurrently as Director of the Institute of Future Cities (IOFC) and Associate Director of the Institute of Environment, Energy and Sustainability (IEES) at CUHK. His researches include the integration of geospatial data for environmental quality assessment, wetland monitoring, mangrove species mapping and leaf area index modeling. He had also developed techniques in hyperspectral data analysis, object-oriented image analysis and change detection.

Huayi Yu

Huayi Yu is Associate Professor at School of Public Administration and Policy, Renmin University of China, Beijing, China. His research interest is in urban economics, real estate economics, and applied spatial econometrics.

Changlin Mei

Changlin Mei is a Professor at School of Science, Xi’an Polytechnic University, Xi’an, PR China. His main research interests include spatial data analysis and non-parametric regression.

Yee Leung

Yee Leung is Emeritus Professor in the Department of Geography and Resource Management and Senior Research Fellow in the Institute of Future Cities at The Chinese University of Hong Kong, Hong Kong, China. His research focuses on the statistical approach to uncertainty analysis and propagation in GIS, fuzzy set approach to geographical analysis and planning, intelligent spatial decision support systems, artificial intelligence, spatial data mining and knowledge discovery, and remote sensing.

Yu Zhou

Yu Zhou is a Research Assistant Professor in the Institute of Future Cities at The Chinese University of Hong Kong, Hong Kong, China. His research interests include applied mathematics, such as nonlinear dynamics, especially fractals, and time series analysis, and geography, including spatial analysis, quantitative methods, and geocomputation.