published_or_final_version / Housing Management / Master / Master of Housing Management

Compared with local shoppers, tourist shoppers have very different shopping behaviors and preferences, which in turn influence the location selection decisions of retailers and the pricing of retail spaces. The implementation of "Individual Visit Scheme" (IVS) policy since July, 2003 which allows Mainland Chinese tourists to visit Hong Kong on an individual basis has significantly increased the proportion of tourist shoppers. This policy provides us with an invaluable opportunity to examine how changes in tourist-local shopper mix affect the pricing of retail spaces in Hong Kong. There have been very few empirical studies on the pricing of street level retail shops (or "shops") due to lack of transaction data and difficulties in identifying and measuring price-influencing location attributes of shops. Shops in Hong Kong have been more actively transacted compared to most markets around the world, which helps to solve the data availability problem. In this study, transactions of the shops in Causeway Bay, a major shopping area for both local and tourist shoppers in Hong Kong, during the period 1993 to 2011 has been used for empirical analysis. The difficulty of modeling the unknown location attributes of shops is tackled with use of spatial auto-regressive modeling techniques. The significances of this study are threefold. First, we find that the shopping behaviors of tourists are influenced by their hometown shopping experiences. This is evidenced by the reversal of the impact of building age on shop’s transaction price after the implementation of IVS policy. Before this policy, when local shoppers were the majority, the effect of age on shop prices was positive as it captures some of the preferential location attributes of shops that were well known to local shoppers. When the proportion of Mainland Chinese tourist shoppers increased after the implementation of IVS, the impact of age on shop prices becomes negative as their hometown experiences suggest that new shops provide better shopping environment and are more preferred than old ones. The second contribution is to show how tourist shopper’s shopping behaviors are affected by the cost of stay. Compared with local shoppers, tourist shoppers have a much higher cost of stay. Shop location attributes that can reduce the shopper’s idle time (e.g. travelling and shopping around time) would be more valuable to tourist shoppers than local shoppers. This implies that the proximity to Mass Transit Railway (MTR) station and the accommodation facilities around the retail units are preferred by tourist shoppers. Empirical evidence shows that the value of proximity to MTR station has increased significantly after the implementation of IVS, which is consistent with the prediction from tourist shoppers’ high cost of stay. Finally, the third contribution of this study is improvement of empirical analysis method. A major problem in analyzing shop prices is the difficulty in measuring and modeling the specific price-influencing location factors, which give rise to misspecification of the hedonic price model. This study resolves this problem by adopting spatial econometric models. Our empirical results also show strong evidence of spatial auto-correlation in shop prices. / published_or_final_version / Real Estate and Construction / Doctoral / Doctor of Philosophy

published_or_final_version / Housing Management / Master / Master of Housing Management

published_or_final_version / Housing Management / Master / Master of Housing Management

published_or_final_version / Housing Management / Master / Master of Housing Management

published_or_final_version / Real Estate and Construction / Master / Master of Science in Real Estate and Construction

Ding Jiajia. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 112-115). / Abstracts in English and Chinese. / Abstract --- p.i-ii / Acknowledgements --- p.iii / Table of Contents --- p.iv / List of Tables --- p.vi / List of Figures --- p.ix / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.5 / Chapter 2.1 --- Review of Literature on Rental Adjustment Models --- p.5 / Chapter 2.2 --- Review of Literatures on Hedonic Models --- p.10 / Chapter 2.3 --- Review of Literature on Real Estate Dynamics --- p.14 / Chapter 2.4 --- Review of Literature on Markov Chain Models --- p.20 / Chapter Chapter 3 --- Data Description and Econometric Method --- p.23 / Chapter 3.1 --- Data Source and Classification --- p.23 / Chapter 3.2 --- Measurement of Variables --- p.25 / Chapter 3.3 --- Grouping Method and Econometric Methodology --- p.28 / Chapter Chapter 4 --- Empirical Results --- p.32 / Chapter 4.1 --- Correlation of Volume and ROR --- p.32 / Chapter 4.2 --- Granger Causality Tests --- p.33 / Chapter 4.3 --- Estimation of Markov Chain Model --- p.36 / Chapter Chapter 5 --- Concluding Remarks --- p.40 / Appendix I Comparison of Previous Literatures --- p.43 / Appendix II Definition of Districts and Map --- p.49 / Appendix III Tables --- p.54 / Appendix IV Figures --- p.96 / Appendix V Granger Causality Test --- p.99 / Appendix VI Markov Chain Model --- p.101 / Appendix VII Transition Matrix of Markov Chains of commercial property from 1993-2004 --- p.103 / Appendix VIII Transition Matrix of Markov Chains of Residential Estates from 1992-2004 --- p.107 / Bibliography --- p.112

Feng Dandan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 96-101). / Abstracts in English and Chinese. / Abstract --- p.i-ii / Acknowledgements --- p.iii-iv / Table of Contents --- p.v-vi / List of Tables --- p.vii / List of Figures --- p.viii / List of Computer Programs --- p.ix / Chapter Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Background of the Present Study --- p.1 / Chapter 1.2 --- Purpose of the Study --- p.4 / Chapter 1.3 --- Significance of the Research --- p.6 / Chapter 1.4 --- Organization of the Thesis --- p.7 / Chapter Chapter 2. --- Literature Review --- p.8 / Chapter 2.1 --- Review of Literatures on Price-Volume Relation in the Housing Market --- p.8 / Chapter 2.2 --- Review of Literatures on Price-Volume Relation in the Stock Market --- p.15 / Chapter 2.3 --- Review of Literatures on Commercial Real Estate Market --- p.16 / Chapter 2.4 --- Review of Literatures on Rent-Price Ratio --- p.22 / Chapter 2.5 --- Chapter Summary --- p.24 / Chapter Chapter 3. --- Data Description --- p.27 / Chapter 3.1 --- Economic Property Research Center Dataset --- p.27 / Chapter 3.2 --- Sample Selection and Classification --- p.27 / Chapter 3.3 --- Summary of Statistics --- p.28 / Chapter 3.4 --- Chapter Summary --- p.31 / Chapter Chapter 4. --- Methodology --- p.33 / Chapter 4.1 --- Measurement --- p.33 / Chapter 4.2 --- Augmented Dickey-Fuller Test --- p.36 / Chapter 4.3 --- Partial Autocorrelation Function --- p.39 / Chapter 4.4 --- Bootstrap Method --- p.41 / Chapter 4.5 --- Granger Causality Test --- p.43 / Chapter 4.6 --- Chapter Summary --- p.46 / Chapter Chapter 5. --- Empirical Results --- p.47 / Chapter 5.1 --- Sale Market --- p.48 / Chapter 5.2 --- Rental Market --- p.51 / Chapter 5.3 --- Overlapping Sample --- p.56 / Chapter 5.4 --- Aggregate Market --- p.58 / Chapter 5.5 --- Chapter Summary --- p.62 / Chapter Chapter 6. --- Concluding Remarks --- p.64 / Appendix 1. Comparison of Previous Literatures --- p.67 / Appendix 2. Tables --- p.70 / Appendix 3. Figures --- p.77 / Appendix 4. Computer Programs --- p.87 / Bibliography --- p.96

published_or_final_version / Urban Design / Master / Master of Urban Design

published_or_final_version / Urban Design / Master / Master of Urban Design