CAPITALIZATION EFFECTS OF RIVERS IN URBAN HOUSING SUBMARKETS – A CASE STUDY OF THE YANGTZE RIVER-Reference-Cited by-同舟云学术

CAPITALIZATION EFFECTS OF RIVERS IN URBAN HOUSING SUBMARKETS – A CASE STUDY OF THE YANGTZE RIVER

Published:2024-04-04 Issue:2 Volume:28 Page:76-92
ISSN:1648-715X
Container-title:International Journal of Strategic Property Management
language:
Short-container-title:

Author:

Ke Xiaoling¹,Yang Chang²,Zheng Moujun²,Amal Mougharbel³,Zeng Yanshan²

Affiliation:

1. School of Economics and Management, China University of Geosciences, Wuhan, China; Center for Energy Environmental Management and Decision-Making, China University of Geosciences, Wuhan, China; Laboratory for Natural Disaster Risk Prevention and Emergency Management, China University of Geosciences, Wuhan, China

2. School of Economics and Management, China University of Geosciences, Wuhan, China

3. Business Administration Department, International Technological University, San Jose, USA

Abstract

The study aims to investigate the heterogeneity of the Yangtze River’s impact on housing prices, using the data of 12,325 residential transactions within 8 kilometers of the Yangtze River in Wuhan, based on submarkets divided according to geographical location and buyer groups. The kernel density plots reveal that properties near the Yangtze River have the highest price and the lowest density, while properties further away from the river exhibit the opposite trend. Then the Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model show the following results, respectively: (1) The Yangtze River has an influence range of roughly 5 kilometers on adjacent dwellings, with an average impact of 0.035%. However, within the chosen geographical interval, the impact rises from 1.582% to 2.072%. (2) The Yangtze River has the greatest impact on middle-priced houses, followed by high-priced houses, and the least impact on low-priced houses. (3) The Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model have been proven to be effective at capturing spatial and temporal impacts on data. In conclusion, this article advises that the government should pay more attention to non-central locations with limited natural resources.

Publisher

Vilnius Gediminas Technical University

Reference47 articles.

1. Alas, B. (2020). A multilevel analysis of housing submarkets defined by the municipal boundaries and by the street connections in the metropolitan area: Istanbul. Journal of Housing and the Built Environment, 35(4), 1201-1217. https://doi.org/10.1007/s10901-020-09735-7

2. Bangura, M., & Lee, C. L. (2023). The determinants of homeownership affordability in Greater Sydney: Evidence from a submarket analysis. Housing Studies, 38(2), 206-232. https://doi.org/10.1080/02673037.2021.1879995

3. Bohman, H. (2021). Same, same but different? Neighbourhood effects of accessibility on housing prices. Transport Policy, 107, 52-60. https://doi.org/10.1016/j.tranpol.2021.04.016

4. Bonetti, F., Corsi, S., Orsi, L., & De Noni, I. (2016). Canals vs. streams: To what extent do water quality and proximity affect real estate values? A hedonic approach analysis. Water, 8(12), Article 12. https://doi.org/10.3390/w8120577

5. Chen, G., Zhu, D., Su, Y., & Zhang, L. (2015). The effects of large-scale urban park green spaces on residential prices exemplified by Olympic Forest Park in Beijing. Resources Science, 37(11), 2202-2210.