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dc.contributor.advisorRichard de Neufville.en_US
dc.contributor.authorZhang, Xin, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.date.accessioned2018-05-23T16:34:11Z
dc.date.available2018-05-23T16:34:11Z
dc.date.copyright2017en_US
dc.date.issued2018en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/115773
dc.descriptionThesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, February 2018.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 269-287).en_US
dc.description.abstractThis thesis analyzes the potential housing bubble in the Chinese urban housing market. Using an operational model built on system dynamics (SD), it explores the unique housing market structures, information flows, key agents and their decision-making processes, and the system constraints that may contribute to the bubble forming phenomenon. Its SD models differ from intensive data-driven economic models. They are structural, operational, and focus on causal relationships. They can more easily accommodate non-market features and unique institutional components, where long-range historical data are not readily available. As the Chinese government heavily controls its housing markets, housing prices greatly depend on political decisions. The models incorporate the incentives and decisions of key agents in the market and have predictive power through simulation and scenario analysis. They can thus help decision-makers transform decisions, actively manage risks and opportunities, timely design and implement policies, and consequently change the system from within. This thesis specifically discusses three Chinese-specific features: 1. Rising housing price and the cap rate change; 2. High vacancy rates caused by speculators purchasing multiple housing units as money-storage investments they are unwilling to rent; 3. Land financing schemes where local governments rely on income from land sales to support their budgets which leads them to use their monopolistic position to short supply the land. The research design starts from the DiPasquale-Wheaton model (D-W) which Western urban economics theory has validated for analyzing housing cycles. It operationalizes this by: 1. Converting the D-W to a basic SD model; 2. Augmenting this with additional generic features; 3. Incorporating unique Chinese market features to create China-specific models; and then 4. Creating an integrated overarching model for conducting a case study using historical data of Nanjing, China. The result is one of the first operational models for the Chinese housing market that has the explanatory mechanisms and somehow overcomes the data availability issues. It provides an intuitive and transparent structure that we can easily modify to address complex issues. This thesis analyzes the potential housing bubble in the Chinese urban housing market. Using an operational model built on system dynamics (SD), it explores the unique housing market structures, information flows, key agents and their decision-making processes, and the system constraints that may contribute to the bubble forming phenomenon. Its SD models differ from intensive data-driven economic models. They are structural, operational, and focus on causal relationships. They can more easily accommodate non-market features and unique institutional components, where long-range historical data are not readily available. As the Chinese government heavily controls its housing markets, housing prices greatly depend on political decisions. The models incorporate the incentives and decisions of key agents in the market and have predictive power through simulation and scenario analysis. They can thus help decision-makers transform decisions, actively manage risks and opportunities, timely design and implement policies, and consequently change the system from within. This thesis specifically discusses three Chinese-specific features: 1. Rising housing price and the cap rate change; 2. High vacancy rates caused by speculators purchasing multiple housing units as money-storage investments they are unwilling to rent; 3. Land financing schemes where local governments rely on income from land sales to support their budgets which leads them to use their monopolistic position to shortsupply the land. The research design starts from the DiPasquale-Wheaton model (D-W) which Western urban economics theory has validated for analyzing housing cycles. It operationalizes this by: 1. Converting the D-W to a basic SD model; 2. Augmenting this with additional generic features; 3. Incorporating unique Chinese market features to create China-specific models; and then 4. Creating an integrated overarching model for conducting a case study using historical data of Nanjing, China. The result is one of the first operational models for the Chinese housing market that has the explanatory mechanisms and somehow overcomes the data availability issues. It provides an intuitive and transparent structure that we can easily modify to address complex issues.en_US
dc.description.statementofresponsibilityby Xin Zhang.en_US
dc.format.extent287 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectInstitute for Data, Systems, and Society.en_US
dc.subjectEngineering Systems Division.en_US
dc.titlePotential housing bubble with Chinese characteristics : analysis and policy design through an operational modelen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Engineering Systemsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Engineering Systems Division.en_US
dc.identifier.oclc1036987600en_US


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