哈尔滨市商品住宅价格研究
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摘要
改革开放以来,我国房地产业获得了迅猛的发展,房地产业也逐步成为我国的支柱产业,对于带动国民经济其他产业的发展起到了极其重要的作用。商品住宅市场是房地产市场重要部分,它的变化不仅关系到广大消费者的切身利益,同时也是社会关注的焦点问题。由于商品住宅市场的特殊性,产品的异质性,价格形成的区域性比较强,需要对各个城市商品住宅市场进行研究。正是鉴于此,我们以哈尔滨市商品住宅价格为研究对象对哈尔滨市商品住宅价格进行分析和预测。自1998年住宅分配制度改革以来,哈尔滨商品住宅市场得到迅速发展,商品住宅价格出现持续增长的势头,城市的商品住宅价格出现了大幅上涨,部分板块的住宅价格出现了在短期内成倍上涨的现象,商品住宅价格增长速度大大超出居民收入增长速度,房价成为社会关注和讨论的热点话题。稳定住房价格,将其变动限定在一个合理的区间范围内,已成为社会关注的热点问题,也是哈尔滨市宏观调控的主要任务。商品住宅价格与人民的生活关系密切,无论是促进地区经济发展,还是在满足人民群众生活需要上,商品住宅价格的变动和发展趋势举足轻重。因此,从商品住宅价格入手,研究影响商品住宅价格各因素之间的关系,定量分析影响商品住宅价格的各因素,研究商品住宅价格的变动趋势并进行科学预测,对合理确定商品住宅价格,维护居民的住房权益,指导国家政策调节与管理,促进商品住宅市场健康发展都有着重要的意义。
本论文主要以灰色理论为主要的研究工具,运用定性分析和定量分析相结合的方法对哈尔滨市商品住宅价格进行研究,分析了影响哈尔滨市商品住宅价格的相关因素,运用灰色关联分析的方法确定各个因素对哈尔滨市商品住宅价格影响关联度的大小;在定性分析哈尔滨市商品住宅价格现状的基础上运用灰色理论建立GM(1,1)——马尔科夫模型价格预测模型,根据分析得出以下结论:1)影响哈尔滨市商品住宅的主要因素是家庭财富状况、人均住房使用面积,哈尔滨市商品价格的上涨主要是由需求拉动的2)预测结果显示2010年哈尔滨市市区商品住宅价格呈持续上升的趋势,最后得到2010年哈尔滨市城区商品房均价各季度预测值分别为6012.6、6311.9、6426.7、6757.6元/平方米,房价整体稳定上涨仍然是主流。但是这种上涨不会象2009年一样呈现快速、跳跃的态势,上涨的幅度仍然不小,但是与2009年相比增速将放缓。3)把灰色系统理论运用于房地产价格变动的研究具有一定的可靠性和适用性,灰色—马尔科夫预测模型比GM(1,1)预测模型得出的结果更准确。
Since reform and opening, real estate market in China has gained rapid development and gradually become the pillar industry in China; it played a very important role to promote the development of other industries in the national economy. Commercial housing market is an important part of the real estate market; its changes are not only related to the vital interests of the consumers but also the focus of social concern. As the special nature of the heterogeneity of products, price formation of regional relatively strong of commercial housing market the study for cities’commercial housing market is necessary. For this, we view commercial housing prices in Harbin as the object of study then commodity analysis and forecasting house prices. Since the reform of domestic distribution system from 1998, commercial housing market has developed rapidly. commodity housing prices continued growth, the city's commercial housing prices have soared, some sections of the housing prices doubled rising in the short term phenomenon of commercial housing price growth rate far exceeded income growth, house prices become a social concern and a hot topic, Stable housing prices have become a hot issue and a main task of macroeconomic regulation and control. Commercial housing prices and close to people's lives, whether on the promotion of economic development or on meet to people's needs, the commercial housing price‘s changes and trends are all important potentially Therefore, Therefore, study the relationship between factors which affecting commodity housing price, quantitative analysis the impact of various factors, research the commodity movements in housing prices and make scientific predictions has an important significance to determine the price, guidance regulation and management and promote the healthy development of commodity housing market .
Grey system theory is major research tool of this paper. Qualitative analysis and quantitative analysis using the method of combining commercial housing prices in Harbin study analyzed the impact of Harbin City, the price of commercial housing-related factors, the use of gray correlation analysis method to determine the various factors on the price of commercial housing in Harbin Correlation size; the qualitative analysis of commercial housing prices in Harbin on the basis of the status of the gray theory GM (1,1) - Markov model price forecasting model, based on an analysis the following conclusions: 1) The impact of Harbin, a major commercial residential household wealth status factors, the per capita housing floor area, Harbin commodity prices are mainly driven by the needs of 2) Forecasting results show that products of Harbin City in 2010 showed housing prices continued to rise, end up in Harbin City in 2010 Average price of the quarter predictive value were 6012.6,6311.9,6426.7,6757.6 yuan / square meter, house prices rose overall stability is still the mainstream. But this increase will not show as fast as in 2009, jumping the situation, the rising rate is still small, but compared with 2009, growth will slow. 3) The gray system theory applied to study changes in house prices has a certain reliability and applicability of gray - Markov model is better than GM (1,1) forecasting model results more accurate.
本论文主要以灰色理论为主要的研究工具,运用定性分析和定量分析相结合的方法对哈尔滨市商品住宅价格进行研究,分析了影响哈尔滨市商品住宅价格的相关因素,运用灰色关联分析的方法确定各个因素对哈尔滨市商品住宅价格影响关联度的大小;在定性分析哈尔滨市商品住宅价格现状的基础上运用灰色理论建立GM(1,1)——马尔科夫模型价格预测模型,根据分析得出以下结论:1)影响哈尔滨市商品住宅的主要因素是家庭财富状况、人均住房使用面积,哈尔滨市商品价格的上涨主要是由需求拉动的2)预测结果显示2010年哈尔滨市市区商品住宅价格呈持续上升的趋势,最后得到2010年哈尔滨市城区商品房均价各季度预测值分别为6012.6、6311.9、6426.7、6757.6元/平方米,房价整体稳定上涨仍然是主流。但是这种上涨不会象2009年一样呈现快速、跳跃的态势,上涨的幅度仍然不小,但是与2009年相比增速将放缓。3)把灰色系统理论运用于房地产价格变动的研究具有一定的可靠性和适用性,灰色—马尔科夫预测模型比GM(1,1)预测模型得出的结果更准确。
Since reform and opening, real estate market in China has gained rapid development and gradually become the pillar industry in China; it played a very important role to promote the development of other industries in the national economy. Commercial housing market is an important part of the real estate market; its changes are not only related to the vital interests of the consumers but also the focus of social concern. As the special nature of the heterogeneity of products, price formation of regional relatively strong of commercial housing market the study for cities’commercial housing market is necessary. For this, we view commercial housing prices in Harbin as the object of study then commodity analysis and forecasting house prices. Since the reform of domestic distribution system from 1998, commercial housing market has developed rapidly. commodity housing prices continued growth, the city's commercial housing prices have soared, some sections of the housing prices doubled rising in the short term phenomenon of commercial housing price growth rate far exceeded income growth, house prices become a social concern and a hot topic, Stable housing prices have become a hot issue and a main task of macroeconomic regulation and control. Commercial housing prices and close to people's lives, whether on the promotion of economic development or on meet to people's needs, the commercial housing price‘s changes and trends are all important potentially Therefore, Therefore, study the relationship between factors which affecting commodity housing price, quantitative analysis the impact of various factors, research the commodity movements in housing prices and make scientific predictions has an important significance to determine the price, guidance regulation and management and promote the healthy development of commodity housing market .
Grey system theory is major research tool of this paper. Qualitative analysis and quantitative analysis using the method of combining commercial housing prices in Harbin study analyzed the impact of Harbin City, the price of commercial housing-related factors, the use of gray correlation analysis method to determine the various factors on the price of commercial housing in Harbin Correlation size; the qualitative analysis of commercial housing prices in Harbin on the basis of the status of the gray theory GM (1,1) - Markov model price forecasting model, based on an analysis the following conclusions: 1) The impact of Harbin, a major commercial residential household wealth status factors, the per capita housing floor area, Harbin commodity prices are mainly driven by the needs of 2) Forecasting results show that products of Harbin City in 2010 showed housing prices continued to rise, end up in Harbin City in 2010 Average price of the quarter predictive value were 6012.6,6311.9,6426.7,6757.6 yuan / square meter, house prices rose overall stability is still the mainstream. But this increase will not show as fast as in 2009, jumping the situation, the rising rate is still small, but compared with 2009, growth will slow. 3) The gray system theory applied to study changes in house prices has a certain reliability and applicability of gray - Markov model is better than GM (1,1) forecasting model results more accurate.
引文
程松林.2008.基于灰色理论的武汉商品房价格预侧和分析[D].华中师范大学
邓卫,宋杨.2008.住宅经济学[M].北京:清华大学出版社,36-38
邓聚龙.1990.灰色系统理论教程[M].武汉:华中理工大学出版社
董晨辉,彭雪峰等.2009.MATLAB 2008全程指南[M ].北京:电子工业大学出版社
冯套柱,孟亚宁.2005.基于灰色理论的房地产市场需求分析[J].西安石油大学学报.14(3):43-47
郭章林,肖美丹,张豪.2005.房地产工程项目投资风险的多层次灰色评价[J].建筑技术开发32(6):153-155
赫飞.2006.马尔科夫预测法在股市预测中的应用[J].科学之友11(2):28-30
侯继松.2008基于灰色理论分析成都市商品住宅价格变动[D].四川:四川经贸大学46-50
黄贤金,周寅康等.2008.城市土地供应与房地产市场运行研究[M].科学出版社
李菲,孙文彬.2004.灰色理论在商品住宅价格预测中的应用[J].辽宁工程技术大学学报271
李国柱.2004.中国房地产市场价格波动数量研究—基于资产定价视角的考察[J].西南财经大学博士学位论文
李启宇,张文秀.2006.四川省粮食单产影响因素的灰色关联分析[J].安徽农业科学34(15):3585-3586
李学全.1995.灰色关联度量化模型的进一步研究[J].系统工程’(13):33-35
李木祥.2007.中国房地产泡沫研究[M].北京:中国金融出版社.210-220
厉以宁.1999.中国住宅市场的发展与政策分析[M].北京:中国物价出版社
廉庆.2007.基于灰色理论的商品住宅价格研究[D].哈尔滨工业大学.(4):9-13
刘琳,刘洪玉.2003.地价和房价关系的经济学分析[J].数量经济技术经济研究(4):12
刘峰.2006.基于灰色模型GM(1,1)的我国货币需求量进行预测[J].金融经济.(4):30-32
罗文春.2008.咸阳市房地产市场发展研究[D].西北农林科技大学
马家斌.2006.基于灰色系统的浙江主要城市商品房价格相关因素分析[J].技术经济与管理研究.(4):36
彭志行,夏乐天.2004.马尔可夫链及其在股市分析中的应用[J].应用数学.(17):159-163
全威.2007.我国商品住宅价格影响因素实证分析[J].中南财经政法大学研究生学报.(4):39
宋喜民,周书敬.2004.基于灰色关联的房地产市场有效需求分析研究[J].唐山学院学报.17(2):19-21
宋巧娜,唐德善.2007.基于灰色马尔可夫模型的农业用水量进行预测[J].安徽农业科.(6):15-17
孙红湘,沈思.2003.我国房地产开发的灰色预测和分析[J].西安科技大学学报.(9):45-47
汤斌超.2009三大因素制约房地产价格的继续上涨[J].中国房地产金融. 11(167):21-23
王靖,田澎.2005.小波神经网络在房地产价格指数预测中的应用[J].计算机仿真.(7):19
王燕,杨斌.2006.四川省工业结构现状的灰色关联度分析[J].区域经济.(12):159
武秀丽,张锋.2007.时间序列分析法在房价预测中的应用—以广州市的数据为例[J].科学技术与工程,(2l):37-41
谢经荣,曲波,朱勇等.2004.地产泡沫与金融危机.北京:经济管理出版社.83-284
薛文碧.2006.西安市经济适用住房价格分析[J].西安邮电学院学报.11(2):10-13
徐雷.2006.山东省产业结构灰色预测分析[J].东岳论丛.(11):73-75
杨东朗,王战宏,张婷.2007.基于少数据的城市住宅价格预测分析[J].当代经济科学,9(5):99-102
杨贵中,邓学芬.2007.成都市房价影响因素的回归分析与事后模拟[J].价值工程,(4):46-50.
曾又.2008.基于BP神经网络的西安市房地预警系统研究[D].西安建筑科技大学,28-30
张红.2005.房地产经济学北京[M].清华大学出版社.(1)421-423
Birch John W.2003. Sunder man Mark A. Estimating price paths for residential real Estate[J].Journal of Real Estate Research., 25(3):277-300 [26]
Cook R D ,Weisberg S.1992Residual and Influence in Regression [M].New York 56-58.
Court. 1999.Hedonic Price Indexes with Automobile Examples [M]. Journal of Real Estate Research,8(2):123-141
Deng Ju long.1999.The law of grey and white efficient GM(l,l)[J].The Journal of Grey System(3):224
Donald R Haurin, David M Brasington.1996.The impact of school quality of real House prices: Inter jurisdictional effects [J].Journal of Housing Economics, 12(4):351-368
Earl D Benson,Julia L Hansen,1999. et al. Canadian/U.S. exchange rates and non-resident investors: their influence on residential property values [J].Journal of Real Estate Research, 18(3):433-461
MarkAndrew.1998.ModellingRejionalHousePriees:AReviewoftheliterature,by(ISBN0704913054).The Center for Spatial and Real Estate Economies,Department of Economies,The University of Reading
Hite,Diane.1998.Information and bargaining in markets for environmental quality [J]. Land Economics, 74(3):303-316
Kathy.J Hayes,Lori L Taylor.1996.Neighborhood school characteristics: what signals quality to homebuyers [J].Federal Reserve Bank of Dallas Economic Review,(3):2-9.
MalPezzi.1998A simple correction model of housing prices [J]of Housing Economics, (8):27-62
Seko.2003.M. Housing Prices and economic cycles [J].Paper Presented in the "Housing Market and the Economy: the nexus conference,Hong Kong,China.24-26
Stephen MalPezzi.A.1987.Microeconomic estimates of housing depreciating [J].Land Economics, 63(4):372-385
Tamara,Zhang De Ping, Umeda.N.and Sakishima.1992.Land Forecasting Using Grey Model[J].The Journal of Grey System(4):49-58
Walden,MichaeL.1990.Magnet schools and the differential impact of school quality on residential property values [J].Journal of Real-estate Research, 5(2):221-230
Wallace,Nancy E.1996. Hedonic-based price indexes of housing: Theory, estimation, and index construction [J].Economic Review-federal Reserve Bank of San Francisco, (3):34-49
邓卫,宋杨.2008.住宅经济学[M].北京:清华大学出版社,36-38
邓聚龙.1990.灰色系统理论教程[M].武汉:华中理工大学出版社
董晨辉,彭雪峰等.2009.MATLAB 2008全程指南[M ].北京:电子工业大学出版社
冯套柱,孟亚宁.2005.基于灰色理论的房地产市场需求分析[J].西安石油大学学报.14(3):43-47
郭章林,肖美丹,张豪.2005.房地产工程项目投资风险的多层次灰色评价[J].建筑技术开发32(6):153-155
赫飞.2006.马尔科夫预测法在股市预测中的应用[J].科学之友11(2):28-30
侯继松.2008基于灰色理论分析成都市商品住宅价格变动[D].四川:四川经贸大学46-50
黄贤金,周寅康等.2008.城市土地供应与房地产市场运行研究[M].科学出版社
李菲,孙文彬.2004.灰色理论在商品住宅价格预测中的应用[J].辽宁工程技术大学学报271
李国柱.2004.中国房地产市场价格波动数量研究—基于资产定价视角的考察[J].西南财经大学博士学位论文
李启宇,张文秀.2006.四川省粮食单产影响因素的灰色关联分析[J].安徽农业科学34(15):3585-3586
李学全.1995.灰色关联度量化模型的进一步研究[J].系统工程’(13):33-35
李木祥.2007.中国房地产泡沫研究[M].北京:中国金融出版社.210-220
厉以宁.1999.中国住宅市场的发展与政策分析[M].北京:中国物价出版社
廉庆.2007.基于灰色理论的商品住宅价格研究[D].哈尔滨工业大学.(4):9-13
刘琳,刘洪玉.2003.地价和房价关系的经济学分析[J].数量经济技术经济研究(4):12
刘峰.2006.基于灰色模型GM(1,1)的我国货币需求量进行预测[J].金融经济.(4):30-32
罗文春.2008.咸阳市房地产市场发展研究[D].西北农林科技大学
马家斌.2006.基于灰色系统的浙江主要城市商品房价格相关因素分析[J].技术经济与管理研究.(4):36
彭志行,夏乐天.2004.马尔可夫链及其在股市分析中的应用[J].应用数学.(17):159-163
全威.2007.我国商品住宅价格影响因素实证分析[J].中南财经政法大学研究生学报.(4):39
宋喜民,周书敬.2004.基于灰色关联的房地产市场有效需求分析研究[J].唐山学院学报.17(2):19-21
宋巧娜,唐德善.2007.基于灰色马尔可夫模型的农业用水量进行预测[J].安徽农业科.(6):15-17
孙红湘,沈思.2003.我国房地产开发的灰色预测和分析[J].西安科技大学学报.(9):45-47
汤斌超.2009三大因素制约房地产价格的继续上涨[J].中国房地产金融. 11(167):21-23
王靖,田澎.2005.小波神经网络在房地产价格指数预测中的应用[J].计算机仿真.(7):19
王燕,杨斌.2006.四川省工业结构现状的灰色关联度分析[J].区域经济.(12):159
武秀丽,张锋.2007.时间序列分析法在房价预测中的应用—以广州市的数据为例[J].科学技术与工程,(2l):37-41
谢经荣,曲波,朱勇等.2004.地产泡沫与金融危机.北京:经济管理出版社.83-284
薛文碧.2006.西安市经济适用住房价格分析[J].西安邮电学院学报.11(2):10-13
徐雷.2006.山东省产业结构灰色预测分析[J].东岳论丛.(11):73-75
杨东朗,王战宏,张婷.2007.基于少数据的城市住宅价格预测分析[J].当代经济科学,9(5):99-102
杨贵中,邓学芬.2007.成都市房价影响因素的回归分析与事后模拟[J].价值工程,(4):46-50.
曾又.2008.基于BP神经网络的西安市房地预警系统研究[D].西安建筑科技大学,28-30
张红.2005.房地产经济学北京[M].清华大学出版社.(1)421-423
Birch John W.2003. Sunder man Mark A. Estimating price paths for residential real Estate[J].Journal of Real Estate Research., 25(3):277-300 [26]
Cook R D ,Weisberg S.1992Residual and Influence in Regression [M].New York 56-58.
Court. 1999.Hedonic Price Indexes with Automobile Examples [M]. Journal of Real Estate Research,8(2):123-141
Deng Ju long.1999.The law of grey and white efficient GM(l,l)[J].The Journal of Grey System(3):224
Donald R Haurin, David M Brasington.1996.The impact of school quality of real House prices: Inter jurisdictional effects [J].Journal of Housing Economics, 12(4):351-368
Earl D Benson,Julia L Hansen,1999. et al. Canadian/U.S. exchange rates and non-resident investors: their influence on residential property values [J].Journal of Real Estate Research, 18(3):433-461
MarkAndrew.1998.ModellingRejionalHousePriees:AReviewoftheliterature,by(ISBN0704913054).The Center for Spatial and Real Estate Economies,Department of Economies,The University of Reading
Hite,Diane.1998.Information and bargaining in markets for environmental quality [J]. Land Economics, 74(3):303-316
Kathy.J Hayes,Lori L Taylor.1996.Neighborhood school characteristics: what signals quality to homebuyers [J].Federal Reserve Bank of Dallas Economic Review,(3):2-9.
MalPezzi.1998A simple correction model of housing prices [J]of Housing Economics, (8):27-62
Seko.2003.M. Housing Prices and economic cycles [J].Paper Presented in the "Housing Market and the Economy: the nexus conference,Hong Kong,China.24-26
Stephen MalPezzi.A.1987.Microeconomic estimates of housing depreciating [J].Land Economics, 63(4):372-385
Tamara,Zhang De Ping, Umeda.N.and Sakishima.1992.Land Forecasting Using Grey Model[J].The Journal of Grey System(4):49-58
Walden,MichaeL.1990.Magnet schools and the differential impact of school quality on residential property values [J].Journal of Real-estate Research, 5(2):221-230
Wallace,Nancy E.1996. Hedonic-based price indexes of housing: Theory, estimation, and index construction [J].Economic Review-federal Reserve Bank of San Francisco, (3):34-49