基于ANN的普通商品住宅价格预警模型研究
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摘要
近年来,住宅业被确立为新的经济增长点和消费热点,发展迅速。而最受人们瞩目的要属其价格,尤其随着近年来全国各地房价不断上涨,房地产价格成为房地产市场的任何主体都讨论的热点。因此,如何科学地运用房价这一无形的指挥棒来调控住宅市场,保障城市住宅市场健康、稳定、有序发展,促进宏观经济的稳定健康运行,已成为社会各界所关注的问题。
本研究在分析国内外经济预警及房地产市场预警研究成果的基础上,提出本论文主要应用人工神经网络预测法进行普通商品住宅价格预警研究。因此论文首先对人工神经网络的理论进行简要分析,随后提出本文研究的主要对象普通商品住宅,在对其概念、价格内涵、价格特征及形成机制进行详细分析的基础上讨论影响普通商品住宅价格的因素,并通过详细论证选择相应因素的指标且进行相关分析对这些指标进行筛选,建立了普通商品住宅价格影响因素指标体系。然后,在此基础上选择预警指标,建立影响合理价格因素的指标体系,并应用人工神经网络对其进行预测,通过与市场价格的比较对普通商品住宅价格波动进行实时监测预警。最后,本文以西安市为例进行普通商品住宅价格预警的实证研究,并根据结论进行西安市普通商品住宅价格预警分析,结果是目前西安市普通商品住宅价格水平合理,与其社会经济发展水平相适应且房价水平将继续稳步攀升,短期内不会有大的波动。
In recent years, the domestic industry was established as a new economic growth point and consumption hot spots, is developing rapidly. And to the attention of most people is their prices, especially in recent years throughout the country as rising house prices, real estate prices into the real estate market of any of the main hot spots are discussed. Therefore, how to use science to the price of this invisible baton to control the housing market, protection of urban housing market healthy, stable and orderly development, and promote the healthy operation of macroeconomic stability, has become a community concern.
In this study the analysis of domestic and international economic early warning and the real estate market on the basis of research results, the main thesis of the present application of artificial neural network forecasting method general warning on house prices of goods. So the first papers on artificial neural network theory brief analysis, followed by this paper, the main target of ordinary commercial housing, its concept, content prices, features and price formation mechanism for detailed discussions on the basis of the analysis of the impact of commodity housing price factor And, through detailed feasibility studies of the factors select the appropriate indicators and related analysis of these indicators of screening, a general merchandise domestic factors affect the price index system. Then, on the basis of this choice of early warning indicators, the impact of the establishment of a reasonable price index system and its application of artificial neural networks to predict, by comparison with the market price of the commodity price fluctuations in residential real-time monitoring of early warning. Finally, to Xi'an as an example for ordinary residential commodity prices early warning of empirical research and conclusions based on domestic commodity prices in Xi'an early warning analysis, Xi'an is the result of ordinary commercial housing price level reasonable, rather than the level of socio-economic development And the level of house prices will continue to steadily rise, the short term there will be no major fluctuations.
本研究在分析国内外经济预警及房地产市场预警研究成果的基础上,提出本论文主要应用人工神经网络预测法进行普通商品住宅价格预警研究。因此论文首先对人工神经网络的理论进行简要分析,随后提出本文研究的主要对象普通商品住宅,在对其概念、价格内涵、价格特征及形成机制进行详细分析的基础上讨论影响普通商品住宅价格的因素,并通过详细论证选择相应因素的指标且进行相关分析对这些指标进行筛选,建立了普通商品住宅价格影响因素指标体系。然后,在此基础上选择预警指标,建立影响合理价格因素的指标体系,并应用人工神经网络对其进行预测,通过与市场价格的比较对普通商品住宅价格波动进行实时监测预警。最后,本文以西安市为例进行普通商品住宅价格预警的实证研究,并根据结论进行西安市普通商品住宅价格预警分析,结果是目前西安市普通商品住宅价格水平合理,与其社会经济发展水平相适应且房价水平将继续稳步攀升,短期内不会有大的波动。
In recent years, the domestic industry was established as a new economic growth point and consumption hot spots, is developing rapidly. And to the attention of most people is their prices, especially in recent years throughout the country as rising house prices, real estate prices into the real estate market of any of the main hot spots are discussed. Therefore, how to use science to the price of this invisible baton to control the housing market, protection of urban housing market healthy, stable and orderly development, and promote the healthy operation of macroeconomic stability, has become a community concern.
In this study the analysis of domestic and international economic early warning and the real estate market on the basis of research results, the main thesis of the present application of artificial neural network forecasting method general warning on house prices of goods. So the first papers on artificial neural network theory brief analysis, followed by this paper, the main target of ordinary commercial housing, its concept, content prices, features and price formation mechanism for detailed discussions on the basis of the analysis of the impact of commodity housing price factor And, through detailed feasibility studies of the factors select the appropriate indicators and related analysis of these indicators of screening, a general merchandise domestic factors affect the price index system. Then, on the basis of this choice of early warning indicators, the impact of the establishment of a reasonable price index system and its application of artificial neural networks to predict, by comparison with the market price of the commodity price fluctuations in residential real-time monitoring of early warning. Finally, to Xi'an as an example for ordinary residential commodity prices early warning of empirical research and conclusions based on domestic commodity prices in Xi'an early warning analysis, Xi'an is the result of ordinary commercial housing price level reasonable, rather than the level of socio-economic development And the level of house prices will continue to steadily rise, the short term there will be no major fluctuations.
引文
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[3]郭峰.房地产预警系统研究综述[J].贵州大学学报(自然科学版),第22卷第4期,2005.11
[4]Ronald W.Kaiser.the long cycle in real estate[J].Journal of Real Estate Research,1997.11
[5]丁烈云,李斌.房地产市场预警调控系统的构筑技术要点及流程设计[J].系统工程理论与实践,2002.4
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[25]王俊英.基于SD的房地产仿真预测及监测预警模型研究[J].东南大学硕士学位论文,2004.3
[26]余健.南京市房地产市场预警系统模型及其应用研究[J].东南大学硕士学位论文,2004.3
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[31]陈莉.基于BP神经网络的商品房销售量预测研究[J].基建优化,2005.4
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[33]於世为,诸克军.基于主成分BP人工神经网络的人力资本预测[J].系统工程理论方应用,2006.8
[34]顾荣炎.SPSS12.0 for Windows实用教程与操作技巧[M].上海,上海科学技术文献出版社,2005.9
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[36]陆力.上海市房地产预警系统研究[J].电子科技大学硕士学位论文,2005.5
[37]裘建国,袁翠华.南京市商品住宅市场预警实证研究[J].房地经济,2006.4
[38]梅青海.基于人工神经网络的桩基础选型研究[J].武汉理工大学硕士学位论文,2004.10
[39]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京,清华大学出版社,2005
[40]郭涛.西安房地产预警系统研究[J].西安建筑科技大学硕士学位论文,2007.5
[41]CIHAF中国城市房地产投资价值报告[J].2007.6
[42]中国房地产市场年度发展报告[M].北京,国家信息中心,2007.4
[43]西安房地产市场透视与分析[M].西安,西安房地产信息网信息中心,2006.8
[2]Tae Yoon Kim,Kyong Joo Oh,Insuk Sohn,Changha Hwang.Usefulness of artificialne ural networks for early warning system of economic crisis[J].Expert Systems with Applications,2004.4
[3]郭峰.房地产预警系统研究综述[J].贵州大学学报(自然科学版),第22卷第4期,2005.11
[4]Ronald W.Kaiser.the long cycle in real estate[J].Journal of Real Estate Research,1997.11
[5]丁烈云,李斌.房地产市场预警调控系统的构筑技术要点及流程设计[J].系统工程理论与实践,2002.4
[6]赵黎明,贾永飞.房地产预警系统研究[J].天津大学学报(社会科学版),1999.4
[7]顾海兵.经济预警新论[J].数量经济技术经济研究,1994.1
[8]陈述云.建立经济监测预警系统的方法初探[J].科学管理研究,1993.2
[9]黄继鸿,雷战波,凌越.经济预警法研究综述[J].系统工程,2003-3
[10]顾海兵.宏观经济预警研究[J].经济理论与经济管理,1997.5
[11]丁烈云,徐泽清.城市房地产预警系统的设计与开发[J].基建优化,2000.4
[12]王锋,苏良生.深圳房地产预警体系研究[J].建设科技,2003.12
[13]朱大奇,史慧.人工神经网络原理及应用[M].北京,科学出版社,2006.3
[14]高隽.人工神经网络原理及仿真实例[M].北京,机械工业出版社,2003.7
[15]王宏远,史国栋.人工神经网络技术及其应用[M].北京,中国石化出版社,2002.5
[16]注册咨询工程师(投资)考试教材编写委员会.现代咨询方法与实物[M].北京,中国计划出版社,2003.6
[17]中国房地产估价师与房地产经纪人学会.房地产估价理论与方法[M].北京,中国建筑工业出版社,2007.4
[18]中国房地产估价师与房地产经纪人学会.房地产估价相关知识[M].北京,中国建筑工业出版社,2007.4
[19]中国房地产估价师与房地产经纪人学会.房地产基本制度与政策[M].北京,中国建筑工业出版社,2007.4
[20]张红.房地产经济学[M].北京,清华大学出版社,2005.8
[21]苏娅.城市普通商品住宅价格影响因素分析[J].北京师范大学硕士学位论文,2004.5
[22]Witold Witkiwicz.The Use of the HP-filterin Constructing Real Estate Cycle Indicators[J].Journal of Real Estate Research,2002.5
[23]John okunev,Patrick Wilson and Ralf zurbruegg.Relationship between Australian Real Estate and Stock Market Prices-a Case of Market Inefficiency[J].Journal of Forecasting.2002.12
[24]阳辉.城市商品住宅价格模型研究[J].大连理工大学硕士学位论文,2005.11
[25]王俊英.基于SD的房地产仿真预测及监测预警模型研究[J].东南大学硕士学位论文,2004.3
[26]余健.南京市房地产市场预警系统模型及其应用研究[J].东南大学硕士学位论文,2004.3
[27]赵莉萍.模糊神经网络在综合评判中的应用研究[J].华东船舶工业学院学报,1999.6
[28]葛新权,王斌.应用统计[M].北京,社会科学文献出版社,2006.6
[29]苏金明.统计软件SPSS12.0 for Windows应用及开发指南[M].北京,电子工业出版社,2004.9
[30]邓新忠.城市地价监测预警研究[J].湖南师范大学硕士学位论文,2005.6
[31]陈莉.基于BP神经网络的商品房销售量预测研究[J].基建优化,2005.4
[32]李洁明,祁新娥.统计学原理[M].上海,复旦大学出版社,1999.12
[33]於世为,诸克军.基于主成分BP人工神经网络的人力资本预测[J].系统工程理论方应用,2006.8
[34]顾荣炎.SPSS12.0 for Windows实用教程与操作技巧[M].上海,上海科学技术文献出版社,2005.9
[35]Tae Yoon Kim,Kyong Joo Oh,Insuk Sohn,Changha Hwang.Usefulness of artificialne ural networks for early warning system of economic crisis[J].Expert Systems with Applications,2004.6
[36]陆力.上海市房地产预警系统研究[J].电子科技大学硕士学位论文,2005.5
[37]裘建国,袁翠华.南京市商品住宅市场预警实证研究[J].房地经济,2006.4
[38]梅青海.基于人工神经网络的桩基础选型研究[J].武汉理工大学硕士学位论文,2004.10
[39]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京,清华大学出版社,2005
[40]郭涛.西安房地产预警系统研究[J].西安建筑科技大学硕士学位论文,2007.5
[41]CIHAF中国城市房地产投资价值报告[J].2007.6
[42]中国房地产市场年度发展报告[M].北京,国家信息中心,2007.4
[43]西安房地产市场透视与分析[M].西安,西安房地产信息网信息中心,2006.8