The rapid urbanisation of China has received growing attention regarding its urban residential environments. In this article, we model the spatial heterogeneity of housing prices and explore the spatial discrepancy of landscape effects on property values in Shenzhen, a large Chinese city. In contrast to previous studies, this paper integrates the official housing transaction records and housing attributes from open data along with field surveys. Then, the results using the hedonic price model (HPM), geographically weighted regression (GWR) without landscape metrics and GWR with landscape metrics are compared. The results show that GWR with landscape metrics outperforms the other two models. In summary, this research provides new insights into landscape metrics in real estate studies and can guide decision-makers plan and design cities while also providing guidance to regulate and control urban property values based on local conditions.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Economics and Econometrics
- housing prices
- landscape index
- spatial heterogeneity