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基于系统动力学的房地产市场预警研究

Research on Real Estate Early Warning Based on System Dynamics

【作者】 杜辉

【导师】 西宝;

【作者基本信息】 哈尔滨工业大学 , 管理科学与工程, 2008, 硕士

【摘要】 近年房地产市场持续高速发展,如何根据房地产市场内外部影响因素之间的运行规律进行科学预警和合理调控成为人们关心的热点问题之一。本论文综合分析了传统预警方法的不足,对系统动力学用于房地产预警研究的现状进行了详细评述,并从系统动力学模型结构、参数的选取及数量关系的确定方面进行了优化与创新。为研究的方便,本文将研究对象界定为商品住宅市场。首先从影响商品住宅市场供需的主要因素出发,进行系统分析,建立了系统动力学仿真预警模型,模型主要包括两大核心系统:住宅供给系统和住宅需求系统,以及四个辅助系统,分别为:城市人口子系统、城市经济子系统、土地市场子系统和经济政策子系统。然后,根据北京商品住宅市场的历史数据以数学公式拟合各参数之间的数量关系,并使用Powersim Studio 7.0系统动力学软件,对北京市1996年到2012年16年间的住宅市场发展状况进行仿真模拟。最后,根据仿真结果,从宏观经济、住宅供需、商品住宅价格等方面分析北京市场景气状况;并分别进行了贷款利率、预售政策、土地价格、住宅投资比例的政策调控实验,根据模拟的结果提出宏观调控建议,为房地产业的健康发展提供参考建议。从模型结构来看,本模型将城市商品住宅市场作为一个完整的经济系统分析,较为全面的考虑了城市商品住宅市场内部运行规律及经济、人口、土地供应、金融等各类因素对商品住宅市场发展的影响,本模型包括市场供给和需求两大核心系统,在模型构建过程中进行了参数的简化,具有很好的移植性,在该项研究领域中具有一定的创新性和领先性。从仿真输出结果及其分析来看,本模型具有较强的真实性和实用性,模型仿真数据和历史数据吻合的较好,说明模型建立的有效性和实用性。

【Abstract】 With the sustained and rapid development of the real estate market in recent years, how to scientifically forecast and rationally supervise the market becomes a hotspot of attention.This paper analyzed the traditional methods for early warning, reviewed the system dynamics method for real estate early warning and carried out further optimization and innovation. To facilitate the study, this study will be defined as commodity housing market. By analyzing the influence factors of the housing market, the paper established an early warning model based on System Dynamics method. The model included two core systems and four auxiliary systems. Through the analysis of the historical data of Beijing market, the interrelations of these factors are simulated with mathematics formula. The System dynamics model was set up. Then,simulate the development trend of Beijing’s housing market from 1996 to 2012 with Powersim studio 7.0 , which is a software for system dynamics simulation. Conclude the development trend of Beijing’s housing market through analyzing the main export indexes. Based on the early warning results, the paper carried on the simulation experiments of the adjustments of single index. Lastly, according to the result, the paper proposed macro suggestions about adjustments and controls, in order to offer advices for sound development of the real estate industry.As the model structure, this model regards the urban housing market as a complete economic system, makes a comprehensive consideration of the urban housing market and the internal operation of the economy, population, land supply, finance, and other factors affecting the market. In the study, the paper has great innovation. At the same time, models included two core systems, with a good transplant; as the output from the simulation results, this model has strong practicality.

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