Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

ABSTRACT

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai, as well as structure and location characteristics. Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018. Six neighborhood features, structural and location characteristics, are selected according to their statistical significance and multicollinearity test results from an OLS model. Regression analysis is performed by OLS, GWR, and MGWR to compare their performance in housing price research at community level. The comparison of the three models also demonstrates that the GWR (66%) and MGWR (68%) models perform much better than OLS model (52%). MGWR is not significantly different from GWR in areas with few sample points, and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area. The regression parameter indicates that building age is the most important factor among all influencing factors. Proximity to schools and factories have positive and negative significant effects on housing resale prices, respectively. The spatial pattern of neighborhood features is also detected at town level. GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market, which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present. The results provide references for local planning departments, helping to reveal the complicated relationship and spatial patterns between housing price and determinants over space.

KEYWORDS:

• Housing resale price
• neighborhood features
• ordinary least squares
• multi-scale geographically weighted regression

Acknowledgment

The author would like to express appreciations to Dr. Yongze Song, Yufeng He, Xiang Ye, Peng Luo and all the colleagues from Z_GIS, University of Salzburg and Department of Geoinformatics, Palacký University Olomouc. Special acknowledgement is given to the Erasmus+< program, offering a full scholarship to the first author for completing his joint master degree in Europe.

Disclosure statement

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

Data availability statement

The Zhuhai housing price dataset that supports this study is openly available at:

https://drive.google.com/drive/folders/1WyYHlA_-yjMNQfS4a1_E-rVKcStlM_uU?usp=sharing

Additional information

Funding

This work was supported by the European Union under the Erasmus+ EACEA Grant Agreement 2018-1478 Programme 599182-EPP-1-2018-1-AT-EPPKA1-JMD-MOB

Notes on contributors

Nianhua Liu

Nianhua Liu is a master student from Z_GIS, University of Salzburg and Geoinformatics, Palacký University Olomouc. He is pursing Erasmus Mundus Joint Master Degree, majoring in Copernicus Master in Digital Earth program. He gets full scholarship from European Union under the Erasmus+ Programme. He holds Bachelor degree in Geographical Information Science from Sun Yat-Sen University.

Josef Strobl

Prof Josef Strobl is Professor at the University of Salzburg, leading the Interfaculty Department of Geoinformatics. He holds degrees in Geography from Vienna University and has been teaching GIScience and related subjects at universities worldwide. He is a full member of the Austrian Academy of Sciences and head of its Commission for Geographic Information Science. Serving as a board member for international organizations like GSDI, ISDE and GISIG and on the editorial boards of leading journals in Geoinformatics and GIScience, he is promoting and spearheading various SDI-related initiatives.

Josef Strobl works with researchers focusing on distance education, spatial analysis and distributed architectures for SDI. Current interests aim at real time and mobile services, terrain analysis and dynamic process modeling, fostering applications in planning, public participation and resource management. Prof. Strobl has been instrumental in developing and implementing the worldwide UNIGIS postgraduate distance learning programme, and is working with a global network of partner institutions towards education, curriculum development, basic and applied research in Geographic Information Science.