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Housing price, districts, and transportation infrastructure : a study of price spillover in Shanghai by GVAR method

Author: Changqing Mu; Siyan Wang; University of Delaware,; University of Delaware. Department of Economics.
Publisher: 2017. ©2017
Dissertation: Ph. D. University of Delaware 2017
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
This dissertation provides an empirical study of housing price spillover in metropolitans Shanghai. In this study, Shanghai is divided into nineteen districts based on geographic locations and official administrative districts. Given the close connection among the districts, the spillovers in housing prices across districts are expected to be particularly strong. This study focuses on the spillover of housing prices  Read more...
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Changqing Mu; Siyan Wang; University of Delaware,; University of Delaware. Department of Economics.
ISBN: 9780355260533 0355260530
OCLC Number: 1020171544
Notes: Principal faculty advisor: Siyan Wang, Department of Economics.
Degree concentration and department: Economics.
Description: 1 online resource (xvii, 266 pages) : illustrations (some color)
Other Titles: Study of price spillover in Shanghai by GVAR method
Responsibility: Changqing Mu.

Abstract:

This dissertation provides an empirical study of housing price spillover in metropolitans Shanghai. In this study, Shanghai is divided into nineteen districts based on geographic locations and official administrative districts. Given the close connection among the districts, the spillovers in housing prices across districts are expected to be particularly strong. This study focuses on the spillover of housing prices at the district level, in particular, how a housing price shock in one district spreads over to other districts. A global vector autoregressive (GVAR) model is estimated with district-specific variables, weighted foreign variables, and common variables. The novelty of this study lies in the construction of a time-varying weight matrix used in the GVAR model. Previous GVAR studies on housing prices have used physical distance or neighbor indicators to construct the weight matrix, which is guaranteed to be a constant. This study instead uses commute time to construct the weight matrix, which is time-varying since several new lines of public transportation have been constructed during the sample period. In addition, using simulation study and counter-factual analysis, this study also estimates to what extent the newly-constructed public transportation affects the spillover effects in the Shanghai housing markets as well as the effect of money supply on housing prices. The conclusions from this dissertation could have significant policy implications for urban planning and public finance.

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