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dc.contributor.advisorNi, Shawn, 1962-eng
dc.contributor.authorKang, Wensheng, 1975-eng
dc.coverage.spatialUnited Stateseng
dc.date.issued2009eng
dc.date.submitted2009 Summereng
dc.descriptionTitle from PDF of title page (University of Missouri--Columbia, viewed on Feb 26, 2010).eng
dc.descriptionThe entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.eng
dc.descriptionDissertation advisor: Dr. Shawn Ni.eng
dc.descriptionVita.eng
dc.descriptionPh.D. University of Missouri--Columbia 2009.eng
dc.description.abstract[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] This dissertation consists of three essays. The first essay estimates the joint effects of spatial diffusion and high-tech industry fluctuations on housing prices. The work finds these effects are significant but generate different housing price dynamics. The spatial diffusion effect is instantaneous but short-lived, whereas the high tech industry effect is persistent. This conclusion is supported by estimates of a dynamic panel model using data of 42 MSAs (Metropolitan Statistics Area) and Vector Autoregressive models using data of each MSA. The second essay examines the gain of housing portfolio efficiency obtainable through a mixed portfolio by combining geographic characteristics and high-tech industry activities across 40 metropolitan areas. A Bayesian stochastic search is conducted to compute the efficient covariance matrix for the high-dimensional posterior distribution of the panel-data model. Quadratic programming of Fortran/IMSL subroutines is applied to simulate the risk-return efficient frontier of various diversification strategies. The evidence shows that the mixed diversification strategy outperforms the geography based strategy. The gain is superior and can reach as high as 50% in relative risk reduction during high-tech cycle growth periods. The third essay examines the transmission mechanism of tech-pole housing prices and investigates the economic forces behind it. For this purpose, I develop a MCMC algorithm to extract the common stochastic trend and cycle of the integrating prices and conduct Bayesian stochastic search for restriction selection of the panel data model. The evidence shows that the transmission magnitude and persistence depend importantly on the degree of IT-industry concentration between two metropolitan areas. While the common stochastic trend behind the price dynamics is primarily determined by normal income, the monetary policy is responsible for the common boom and bust of tech-pole housing cycles. The policy implication for the real asset pricing and risk hedging strategies are also discussed.eng
dc.description.bibrefIncludes bibliographical references.eng
dc.format.extent[v], 70, [1] pageseng
dc.identifier.oclc608211582eng
dc.identifier.urihttps://hdl.handle.net/10355/6967
dc.identifier.urihttps://doi.org/10.32469/10355/6967eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcommunityUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.rightsAccess is limited to the campus of the University of Missouri--Columbia.eng
dc.subject.lcshHousing -- Prices -- Mathematical modelseng
dc.subject.lcshHousing -- Finance -- Government policyeng
dc.subject.lcshHousing -- Regional disparitieseng
dc.titleThree essays of housing price dynamicseng
dc.typeThesiseng
thesis.degree.disciplineEconomics (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelDoctoraleng
thesis.degree.namePh. D.eng


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