L’impatto della cultura tradizionale cinese sulla domanda e sul prezzo di mercato degli immobili a destinazione residenziale

The research components of the thesis include three pillars. Firstly, this research proposes research opportunities and specific research questions based on an extensive literature review of the impact of traditional Chinese preferences on the housing market, the determinants of housing prices, the impact of FS on the housing demand side, and the impact of number preferences on the housing market. Secondly, logical analyses (1) of the impact of FS features on housing prices and (2) of the impact of number preferences in the housing market and (3) empirical research methods including the hedonic price method, sub-sample analysis, quantile regression, logit regression analysis, and theoretical background analysis of the contingent valuation method (CVM) form the theoretical foundation for an in-depth study of the impact of traditional Chinese preferences on housing prices. Finally, research focuses on empirical analysis. Using a sample of second-hand housing transactions in Guangzhou, China, as data sets, and a group of Chinese immigrants in Italy as the subject of the study, the thesis employs the hedonic price method (HPM), sub-sample analysis method, quantile regression method, spatial analysis method, logit regression method, and CVM to empirically analyse the impact of Chinese traditional preferences on Chinese housing purchasing behaviour and housing prices. This research leads to several key findings by considering the characteristics of the data and data collection requirements. 1) The impact of FS features, such as proximity to cemeteries, funeral parlours, hospitals, slaughterhouses, and highways, on housing prices is investigated by measuring proximity. The results from the traditional HPM show that proximities to the above FS features have a significant negative effect on housing prices. The regression results of the housing purchase motivation sub-sample demonstrate both that improvement and self-living purchasers (i.e. first-time purchasers of a home for living purposes) significantly favour FS features and that home purchasers with improvement intentions are more sensitive to FS features. 2) Number preference significantly affects housing prices in mainland China. The first study verified the differences in the effects of lucky and unlucky floor numbers, address numbers, and unit numbers on housing prices in mainland China. Among other things, the study reveals the robustness of the effect of floor numbers on housing prices by controlling for the linear correlation of floor effects on housing prices via control of floor level and comparison of neighbouring floors. Furthermore, based on relevant theories in behavioural economics, this research reveals that women display more preferences in their housing purchase behaviour than men. The sub-sample analysis reveals that women are sensitive to housing involving the unlucky number 4 when the households include elderly members. 3) Number preferences influence the pricing of second-hand housing. Validation of the distribution of secondary housing prices through the 10,000th percentile reveal a pricing tail clustering phenomenon in the Chinese secondary housing market, which is consistent with the findings found in the stock market and general goods market. The sub-sample and logit regression fitting results indicate that buyers who tend to purchase homes on lucky floor prefer to accept lucky pricing of secondary housing prices. 4) I apply CVM to investigate the FS and number preferences effects on Chinese immigrants’ housing purchase behaviour in the Italian context. Simple descriptive statistics show that Chinese immigrants prefer FS features, lucky numbers, and unlucky numbers. Ordinary least squares (OLS) and logit regression results reveal that Chinese immigrants’ religious, attitude towards FS, and gender influence their preferences on FS and number preferences.