【作者】 刘丹；

【导师】 杨德礼；

【作者基本信息】 大连理工大学 ， 管理科学与工程， 2007， 博士

【摘要】 随着全球经济一体化，金融危机的频繁发生，一国的宏观经济稳定不但关乎本国的国计民生，对于世界任何一个经济体也至关重要。而房地产市场具有与信贷和国民经济的强相关性使得房地产市场价格波动直接影响金融系统稳定，房地产市场恶性泡沫是导致金融危机的重要因素。鉴于此，本文站在宏观风险管理的角度，本着风险产生——风险度量——风险处置的思路，对房地产市场尤其是住房市场进行研究，在综述当前研究进展基础上，主要开展了住房市场系统研究，住房市场复杂网络模型研究，房价波动风险度量方法和房价风险管理研究工作：(1)通过对住房市场进行系统分析，将住房市场系统抽象为银行、房地产开发商和购房消费者三部分主体，处在宏观经济环境中，它们之间相互影响共同决定房地产市场价格，同时受房地产市场价格的反馈影响。通过选取体现系统中宏观经济环境和组成要素的5个主要变量进行协整关系检验和线性Granger因果检验，建立住房系统长期均衡函数和短期动态调整函数，揭示了住房市场各因素之间因果关系，从宏观上揭示中国房地产市场发展的客观规律。(2)通过扩展“金融加速器”模型到包含银行、房地产开发商和购房消费者以及宏观经济环境在内的住房市场系统，揭示了信贷对住房市场的投资和消费的放大作用。通过实证研究得出目前中国房地产开发商投资的边际预期收益率为2．82％，如果预期收益率大于此临界收益率，则会考虑投资；确定了影响此边际预期收益率的参数是：贷款利率、外部融资比率和抵押贷款率，宏观政策制定者可以通过调整这些参数提高边际预期收益率抑制投资冲动。研究改变了目前由于数据有限性带来的对于中国住房市场缺乏系统性定量研究的现状。(3)通过建立代表银行、房地产开发企业和购房消费者资产规模及相互交易关系的静态复杂网络拓扑结构模型和反映购房消费者对房地产开发企业择优选择规律及企业退出机制的动态演化模型，揭示了住房市场系统的结构性特征和动态演化规律。通过上海房地产市场交易变动情况的动态仿真表明模型可以很好的解释大企业的规模效应和在政策调整下小企业更容易被淘汰出局的客观事实，体现出房地产企业与客户群之间的动态演化特性和政策变动对房地产开发企业的影响规律。研究提出的住房市场复杂网络模型为住房市场研究提供了新的研究方法，克服了以往研究模型由于数据有限性导致的房地产市场的波动的相关统计实证研究无法开展而使得复杂系统过度线性化近似以及动态性研究不足的问题，并且在此基础上开展实证研究具有重要现实意义。(4)通过房价波动相对于GDP缺口和CPI波动之间的对比关系合理地刻画了房价波动风险，反映出房价超出实体经济支撑程度和资源局部配置失衡程度，揭示了由于房价增长超出实体而虚长的财富最终会缩水而与实体经济发展相一致的运行规律。通过对美国、英国、香港、日本的房地产市场的实证检验，揭示了历史上出现的典型的房地产泡沫及产生原因，证明了工具的有效性。最后通过上海房地产市场的实证研究表明，2005年房地产市场泡沫由资源局部配置失衡导致，应该通过积极拓展投、融资渠道解决。此研究为房地产市场运行提供了风险度量工具，对于政策当局准确判断房地产市场发展状态，分类别的有针对性的采取有效措施控制风险有确定性的指导意义，同时对经济周期理论、均衡理论的研究提供了新的研究启示。(5)处置风险的一般办法有消化、释放、吸收、分散、转移、对冲、控制和抑制风险。建议通过货币政策的积极响应对房地产市场引发的宏观经济风险和金融系统风险进行控制和抑制，通过拓展房地产投融资渠道对房地产市场引发的风险进行风险转移和分散，提出改进的房地产价格波动的货币政策响应模型和对房地产投融资渠道进行概念性梳理。更多还原

【Abstract】 Financial crisis happened more frequently with the globalization of economy, the macro economy stability is not only important for the domain development but also for any economy in the worldwide. Credit market plays an important role in real estate market. The asset-price volatility to be of concern to policy-makers is that booms and busts in asset markets have important effects on the real economy. Considering about all these, we do some research on the real estate market under the baseline: the risks forming- the risks measurement- the risks management, after overview the main research the main work is carrying out the research on residential housing markets system model, residential housing market complex networks model, house price risk valuation method and house price risk management:(1) In a bank-dominated country, the residential housing market system abstractly includes banks, house producers, and house buyers; each interacts with the others through transactions. Through credit arrangements, banks affect both the supply and demand sides of the residential housing market. In addition, select the main 5 variables to make cointegration test and Granger causality tests, we build up the long term equilibrium function and the short term dynamic adjustment function and uncover the laws of the housing market in China.(2) Extend "Financial Accelerator" model to residential housing market sector. Considering the credit support for both supply and demand sides and its amplificatory effect on the market activities, a theoretical model contain house buyers, house producers and banks is built up. The critical expected return for investment is 2.82% according to the empirical analysis in China, if the expect return is larger than it, they can invest; the three factors affect the investment decision are: the interest rate of loans, external finance rate and mortgage rate. Policy makers can adjust these factors to control the impulse of investment. The "financial accelerator" based housing real estate market system model change the status quo of the lack of quantitatively analysis on housing markets system due to the limited data in China.(3) Build up a theoretical complex networks model contain a topology of banks, house producers, house buyers and their interactions and a dynamics that house buyers select house producers according to a preferential rule, uncover the laws of housing real estate markets instruction and dynamics. An evolving network in the Shanghai residential market is also simulated. It brings a new method to analyze the housing markets to meet the challenges of the complexity of residential real estate markets and a dearth of large databases, and it is very instructive to carry out related research on empirical analysis.(4) The asset price fluctuation relative to the GDP gap and CPI volatility indicates the house price risk, describe the parts beyond the economy could bolster and the imbalance extent of the resource allocation. Uncover the laws that the real estate markets must be consisted with the real economy, and the bubbles will shrink at last. After quantitative assessment in US, UK, Hong Kong and Japan, the historic bubbles are tested and the framework is proved to be effective. It provides a new method and instrument for risk managers, regulators and monetary policy makers. At last, a further empirical analysis is made in Shanghai real estate markets. The results indicate that bubbles in 2005 caused by imbalance extent of the resource allocation, and should be comforted by increasing fmancing channels. The research put forward a method to assess house price risk, give some advices to policy makers and give some new clues for business cycles and equilibrium theory.(5) How to process the risks include to assimilate, release, absorb, disperse, transfer, hedge, control, and restrain risks. The monetary policy responds to house price fluctuation is to control and restrain risks to macro economy and financial system; real estate finance products is to hedge and transfer the risks. So the Taylor model is improved and a muti-goal monetary policy respond model is brought out. It provides a summary on real estate finance at last.更多还原