Investigation of how land use/cover change impact on housing price at Megaregion

Xiao Tang- PhD student profile
Xiao Tang
PhD student
Housing and real estate, Planning, people and place
Xiao Tang is a current PhD student, Investigating how land use/cover change impact on housing price at Megaregion: Evidence from Guangdong-Hong Kong-Macao Greater Bay Area.

I completed my undergraduate studies in 2016, earning a BSc in Traffic Planning from Shenzhen University, China. In 2021, I was awarded a distinction degree of MSc in Real Estate Asset Management from the University of Manchester.

Following my master's degree, I gained valuable research experience as a research assistant at Shenzhen University and had the privilege of being a visiting scholar at The University of Cambridge.

Currently, I am pursuing a Ph.D. focusing on land use and housing issues in Megaregions. My research, generously funded by the China Scholarship Council (CSC), Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, and an international education company, aims to address the complexities and challenges associated with sustainable urban development.

In addition to my academic pursuits, I have a strong passion for business and philanthropy. I have successfully founded two companies in China, and I co-founded a non-profit organization, to make a positive impact in the community.


The region spatial evolution theory states that urban development adheres to a specific spatiotemporal trajectory. The emergence of the megaregion, as a new urban form characterized by the integration of multiple urban centres and their surrounding areas.The study of land use change in megaregion currently faces several challenges, including unclear the mechanisms underlying the coupling of human activities, land, and natural systems, the lack of consistent and high-precision data, and the absence of robust process simulation and prediction models. Additionally, the spatial distribution and changes in land value within urban housing in mega-city clusters are highly complex. Special geographical and environmental elements such as water bodies and green spaces are considered crucial factors that can lead to sudden changes in regional land value. However, identifying the precise extent of their effects under varying spatial and temporal conditions remains a difficult task.

To address these challenges, this study adopts an innovative approach by applying the theory and methodology of land use change to analyse the patterns of residential price distribution (Doe & Brown, 2019). By integrating the coupled human-land-nature framework (Wang et al., 2018), we develop a predictive land-use simulation model specifically designed for large-scale urban clusters. This model enables us to explore the dynamics of land-use alterations and their underlying mechanisms on residential prices. The findings from this research will contribute significantly to the field of land use planning and provide valuable insights for assessing the real estate market at megaregion.


Supervised by Jacob Macdonald and Stephen Hincks.

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