AbstractThis thesis investigates the modelling of house prices in China. The first empirical chapter(Chapter 4) scrutinises the determinants of property prices in seven districts of Beijing, China.While the house prices of the panel model, noted in recent literature (Huang et al., 2017), are confirmed in the case of flat-related factors. Chapter 4 also reveals several new flat-related factors, such as directions of house facing (orientation) and house floor level, influencing house prices that have not been presented in previous studies (e.g., Hyuna and Milchevab,2018 and Yang et al., 2019). However, as well as these flat-related factors, this investigation also incorporates macroeconomic factors, such as GDP, inflation, income, unemployment rates, mortgage rates and factors of fiscal policy. The application of panel analysis extends the current literature by taking into account endogeneity in the GMM framework with instrumental variables.
The second empirical chapter (Chapter 5) investigates the spatial statistics of house prices in Beijing. This chapter examines whether house prices in one region are affected by house prices in neighbouring regions. This investigation also analyses how house prices in one region are affected by unknown characteristics of the neighbouring regions. It explores whether the explanatory factors of house prices in one region are affected by explanatory factors of house prices in neighbouring regions. In addition, this chapter investigates the spillover effects of explanatory factors on house prices. This investigation also examines the partitioning of direct effect and indirect effect from the impacts of the neighbouring factorson house prices. Chapter 5 overcomes the shortcomings of the previous studies ((Mussa et al.,2017) by extending the range of examining spatial models, providing reasonable spatial model selection procedures, and employing improved spatial weights to analyse spill-overeffects of explanatory factors.
Finally, the thesis investigates real options with the spatial analysis in the Chinese real estate markets (Chapter 6). This investigation extends the real options method with the spatial Durbin model (SDM), making this the first study in which real option forecast have been assessed in a spatial case. This method improves the accuracy of predicting house prices by considering neighbouring house prices. Chapter 6 measures the degree of price uncertainty by a generalised autoregressive conditional heteroskedasticity (GARCH) model. The Black-Scholes’ (1973) pricing model is employed to explore the option premium of land value. Evidence is found in this chapter provides there are real options in China’s real estate markets. Uncertainty about future house prices of neighbouring regions drives up land prices in China.The results suggest that uncertainty about future house prices in neighbouring regions decreases investment activity in the current period; and uncertainty about future house prices in neighbouring regions increases land prices. Market house prices in neighbouring regions reflect a premium for optimal development. The likelihood of developing the land is lower interms of the increase of one-standard-deviation.
|Date of Award||2019|
|Supervisor||Konstantinos Vergos (Supervisor)|