房地产预警指标体系构建与实证研究
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摘要
建立房地产市场预警系统,对客观分析房地产运行轨迹,正确评价和判断房地产形势及发展趋势,引导房地产市场向一个良性的方向发展、引导市场理性投资和消费,具有越来越重要的现实意义。
本文在对国内外经济预警和房地产预警研究相关研究的动态评述基础上,总结了建立房地产预警系统理论基础,提出了构建房地产预警系统的方法选择、基本思路、基本步骤。根据指标体系的选取原则、利用定量和定性结合方法选取了房地产预警系统指标。在此基础上以构建上海市房地产预警系统作为实证分析,给出了上海市房地产预警系统指标体系的确定、三大板块预警指数的定量计算、上海综合预警指数的合成、警度的划分、上海市预警结果的分析。最后提出了上海房地产市场排警的政策建议
The real estate industry is an industry which affects the livelihood of the country and pulls other industries. Once the real estate industry falls into a recession, it is inevitable that the economic growth rate may decline, the credit and the ability lifting of commercial bank may be face crisis. What is worse, the whole society may fall into chaos. In the second half of 2006 the U.S. subordinated debt crisis triggered by real estate bubble illustrates the importance of the real estate industry. In August 2007 the crisis of subordinated debt swept the global financial markets. In 2008, it even spreads into the real economy; almost the whole world is dragged into a recessionary economy. All states manage to rescue the market, but still fail to avoid a recession in the global economy.
In order not to repeat the mistakes of the United States, the government should pay close attention to real estate industry, looking forward to judge the trend of the real estate market and taking macro-control means such as the currency policy or administrative means to avoid or reduce the influence of price drop of the adverse ahead of time. An early warning system of real estate market should be established to assure the healthy development of the situation and to issue a warning in time. Thus according to the analysis of data, relevant policies could be derived and the risk be released ahead of time. The stability of real estate market and the security of economic therefore could be ensured which have been reached to a consensus.
Domestic and foreign scholars, as well as the research institutions, manage to build an early warning system of real estate which could forecast the recession and prosperity of the real estate industry in the real estate boom. It is mainly about applying the economic warning theory to the field of real estate and analyzing the real estate cycles as well as other perspectives. At present. the real estate price index methodology all over the world can be divided into the following five: the cost of input method, the median price law, repeat Exchange Act, the characteristics of the price law (also known as the model law and the effectiveness of Hedonic Valuation Law), a mixed model (also known as the Pooled GLS model). In 1997, Hill, Knight and Sirmans improved the Pooled GLS model and put forward the Pooled MLE model based on maximum likelihood estimation (MLE). Decision tree, fuzzy evaluation method, artificial neural networks and other methods all have been studied empirically abroad. These studies for early warning of China's real estate research provide a good draw on ideas.
Research on Chinese economic warning can be traced back to the 1980s which have undergone a changing process from macro-economic early warning to the infiltration Warning enterprises, from qualitative to quantitative and qualitative combined, from the point of warning to the state early warning. There have been principal component analysis, system dynamics, single and comprehensive early-warning indicators of early warning indicators combination, BP neural networks, and emerging with a qualitative and quantitative theory and the experience of the unit}' of the dynamic adjustment of the advantages of large-scale system control "Hierarchical structure" of real estate early-warning systems are useful in this regard to explore,
Summing up the researching achievement at home and abroad, this article attempts to establish an easy-to-use urban real estate early-warning model which is suitable to the Chinese economy and the Chinese real estate development. Chinese market though is a newly emerging one, can compete with the developed western countries for the relatively complete real estate system and mature market system. China has many unique situations: such as, China is still in the historical process of the rapid promotion of current urbanization and industrialization which has great influence on China's Housing Real estate market. The real estate industry, growing in high speed accounting for an increasing proportion of GDP over the years, is a pillar industry of the nation's economy; real estate financial markets are gradually opening up; consumer's demand for housing remains rigid. On the whole it still belongs to a huge-profit industry.
As a result, how to determine the choice of early warning indicators of the police intelligence should be seriously considered in the establishment of the China Real Estate early warning system. This article do not believe that the level of economic development can determine the industry's health and China's real estate climate index does not surely mean the normalness of real estate development. Therefore, this paper aims to take the entire real estate industry as an objective of the study of the early warning system. Many of the study is qualitative analysis, this article attempts to use cluster theory and qualitative analysis combined with the way the selection of targets. Shanghai real estate industry being considered, this article selects 11 indicators to measure the coordination of relations between the three major sections. (The real estate industry with the overall coordination of the national economy: real estate investment / GDP, investment in real estate development / investment in fixed assets, real estate category of loans / bank loans, the average commercial housing price growth rate / wage increase for workers Rate; to coordinate the relationship between supply and demand in the real estate market: sales, sales of commercial housing price index / leasing of commercial housing price index, housing all of the rate of absorption; coordination within the real estate industry: the new land can be built on an area / area of new housing starts, the sale of real estate Area / total amount of social housing, the completion of the residential area / commercial housing completed in all areas, high-grade apartment house completed in the area / completion of all the residential area).
Following the warning methods mentioned in other domestic research literature, this paper takes the integrated approach to give out early warning and to further study the role of early warning indicators to the crisis. Principal component analysis method (linear purpose is to transform the original into a number of linear independent of each other indicators of the few able to fully reflect the overall message of the indicators, which are not lost in the main premise of the information to avoid the A total of inter-linear variable, to facilitate further analysis) and qualitative analysis method combined are used in this paper to determine the weight of indicators and the weight of panels. Thus a comprehensive early-warning index is calculated.
In the division of warning degree, the cycle fluctuations of the real estate are taken into account. Abnormal situation occurs if these cycles are divorced from. Therefore the division of warning degree helps make correct judgments on the real estate market's normal, basic normal and abnormal state. These judgments need to combine the mature quantitative analysis with experience analysis. Based on the error theory, this article puts forward 3sigma method. However, because of the limited years of the real estate economic data, if the 3 times standard deviation is chosen, almost no data will fall on abnormal range: real estate-related data at the same time has a greater volatility: while the choosing of 1 time standard deviation may lead to over strict demand for the data. Therefore the 2 times standard deviation is chosen as a basis for the unusual, with the 1 time to 2 times standard deviation span being chosen as the basic normal range. When the real estate market becomes fully developed, to be more mature, the data fluctuations will tend to be stable and the standard deviation of the scope will gradually converge.
Based on empirical analysis, following conclusions have been drawn:
1. Comprehensive simulation analysis will be helpful for the analysis of the relationship between the qualitative variable and all the affecting factors. All the indicators used in this article play certain early-warning function for the crisis.
2. This article takes the 2sigma principle in the division of warning degree which follows the law of real estate development and reflects the development of the industry. The conciseness of the early warning has been improved effectively.
3. Integrated simulation can be used for the judgment of the real estate development of cities and for the forecasting and warning of the crisis which can help judge the development of the real estate industry basically. If such a method is recognized by the community, it may provide a convincing basis for the evaluation of real estate development which is also an effective tool for the real estate industry's self-awareness.
This article aims to explore the characteristics of China's real estate industry to build early warning models. As China's real estate industry continues to develop and relevant systems continues to improve, people can enter the market indicators carefully using the macro-market, three micro-level crisis early warning indicators; and gradually perfect the multi-level real estate early-warning system so as to establish a more effective early warning systems in real estate.
本文在对国内外经济预警和房地产预警研究相关研究的动态评述基础上,总结了建立房地产预警系统理论基础,提出了构建房地产预警系统的方法选择、基本思路、基本步骤。根据指标体系的选取原则、利用定量和定性结合方法选取了房地产预警系统指标。在此基础上以构建上海市房地产预警系统作为实证分析,给出了上海市房地产预警系统指标体系的确定、三大板块预警指数的定量计算、上海综合预警指数的合成、警度的划分、上海市预警结果的分析。最后提出了上海房地产市场排警的政策建议
The real estate industry is an industry which affects the livelihood of the country and pulls other industries. Once the real estate industry falls into a recession, it is inevitable that the economic growth rate may decline, the credit and the ability lifting of commercial bank may be face crisis. What is worse, the whole society may fall into chaos. In the second half of 2006 the U.S. subordinated debt crisis triggered by real estate bubble illustrates the importance of the real estate industry. In August 2007 the crisis of subordinated debt swept the global financial markets. In 2008, it even spreads into the real economy; almost the whole world is dragged into a recessionary economy. All states manage to rescue the market, but still fail to avoid a recession in the global economy.
In order not to repeat the mistakes of the United States, the government should pay close attention to real estate industry, looking forward to judge the trend of the real estate market and taking macro-control means such as the currency policy or administrative means to avoid or reduce the influence of price drop of the adverse ahead of time. An early warning system of real estate market should be established to assure the healthy development of the situation and to issue a warning in time. Thus according to the analysis of data, relevant policies could be derived and the risk be released ahead of time. The stability of real estate market and the security of economic therefore could be ensured which have been reached to a consensus.
Domestic and foreign scholars, as well as the research institutions, manage to build an early warning system of real estate which could forecast the recession and prosperity of the real estate industry in the real estate boom. It is mainly about applying the economic warning theory to the field of real estate and analyzing the real estate cycles as well as other perspectives. At present. the real estate price index methodology all over the world can be divided into the following five: the cost of input method, the median price law, repeat Exchange Act, the characteristics of the price law (also known as the model law and the effectiveness of Hedonic Valuation Law), a mixed model (also known as the Pooled GLS model). In 1997, Hill, Knight and Sirmans improved the Pooled GLS model and put forward the Pooled MLE model based on maximum likelihood estimation (MLE). Decision tree, fuzzy evaluation method, artificial neural networks and other methods all have been studied empirically abroad. These studies for early warning of China's real estate research provide a good draw on ideas.
Research on Chinese economic warning can be traced back to the 1980s which have undergone a changing process from macro-economic early warning to the infiltration Warning enterprises, from qualitative to quantitative and qualitative combined, from the point of warning to the state early warning. There have been principal component analysis, system dynamics, single and comprehensive early-warning indicators of early warning indicators combination, BP neural networks, and emerging with a qualitative and quantitative theory and the experience of the unit}' of the dynamic adjustment of the advantages of large-scale system control "Hierarchical structure" of real estate early-warning systems are useful in this regard to explore,
Summing up the researching achievement at home and abroad, this article attempts to establish an easy-to-use urban real estate early-warning model which is suitable to the Chinese economy and the Chinese real estate development. Chinese market though is a newly emerging one, can compete with the developed western countries for the relatively complete real estate system and mature market system. China has many unique situations: such as, China is still in the historical process of the rapid promotion of current urbanization and industrialization which has great influence on China's Housing Real estate market. The real estate industry, growing in high speed accounting for an increasing proportion of GDP over the years, is a pillar industry of the nation's economy; real estate financial markets are gradually opening up; consumer's demand for housing remains rigid. On the whole it still belongs to a huge-profit industry.
As a result, how to determine the choice of early warning indicators of the police intelligence should be seriously considered in the establishment of the China Real Estate early warning system. This article do not believe that the level of economic development can determine the industry's health and China's real estate climate index does not surely mean the normalness of real estate development. Therefore, this paper aims to take the entire real estate industry as an objective of the study of the early warning system. Many of the study is qualitative analysis, this article attempts to use cluster theory and qualitative analysis combined with the way the selection of targets. Shanghai real estate industry being considered, this article selects 11 indicators to measure the coordination of relations between the three major sections. (The real estate industry with the overall coordination of the national economy: real estate investment / GDP, investment in real estate development / investment in fixed assets, real estate category of loans / bank loans, the average commercial housing price growth rate / wage increase for workers Rate; to coordinate the relationship between supply and demand in the real estate market: sales, sales of commercial housing price index / leasing of commercial housing price index, housing all of the rate of absorption; coordination within the real estate industry: the new land can be built on an area / area of new housing starts, the sale of real estate Area / total amount of social housing, the completion of the residential area / commercial housing completed in all areas, high-grade apartment house completed in the area / completion of all the residential area).
Following the warning methods mentioned in other domestic research literature, this paper takes the integrated approach to give out early warning and to further study the role of early warning indicators to the crisis. Principal component analysis method (linear purpose is to transform the original into a number of linear independent of each other indicators of the few able to fully reflect the overall message of the indicators, which are not lost in the main premise of the information to avoid the A total of inter-linear variable, to facilitate further analysis) and qualitative analysis method combined are used in this paper to determine the weight of indicators and the weight of panels. Thus a comprehensive early-warning index is calculated.
In the division of warning degree, the cycle fluctuations of the real estate are taken into account. Abnormal situation occurs if these cycles are divorced from. Therefore the division of warning degree helps make correct judgments on the real estate market's normal, basic normal and abnormal state. These judgments need to combine the mature quantitative analysis with experience analysis. Based on the error theory, this article puts forward 3sigma method. However, because of the limited years of the real estate economic data, if the 3 times standard deviation is chosen, almost no data will fall on abnormal range: real estate-related data at the same time has a greater volatility: while the choosing of 1 time standard deviation may lead to over strict demand for the data. Therefore the 2 times standard deviation is chosen as a basis for the unusual, with the 1 time to 2 times standard deviation span being chosen as the basic normal range. When the real estate market becomes fully developed, to be more mature, the data fluctuations will tend to be stable and the standard deviation of the scope will gradually converge.
Based on empirical analysis, following conclusions have been drawn:
1. Comprehensive simulation analysis will be helpful for the analysis of the relationship between the qualitative variable and all the affecting factors. All the indicators used in this article play certain early-warning function for the crisis.
2. This article takes the 2sigma principle in the division of warning degree which follows the law of real estate development and reflects the development of the industry. The conciseness of the early warning has been improved effectively.
3. Integrated simulation can be used for the judgment of the real estate development of cities and for the forecasting and warning of the crisis which can help judge the development of the real estate industry basically. If such a method is recognized by the community, it may provide a convincing basis for the evaluation of real estate development which is also an effective tool for the real estate industry's self-awareness.
This article aims to explore the characteristics of China's real estate industry to build early warning models. As China's real estate industry continues to develop and relevant systems continues to improve, people can enter the market indicators carefully using the macro-market, three micro-level crisis early warning indicators; and gradually perfect the multi-level real estate early-warning system so as to establish a more effective early warning systems in real estate.
引文
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[2]师迎春.长沙市住宅市场泡沫及预警研究[D].湖南:湖南大学,2007
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[4]刘传哲,高静华.房地产市场风险预警研究方法综述[J].中国矿业大学学报(社会科学).2006年3月
[5]Lori Mardockk.Predicting housing abandonment in central:creating an earlywaming System.Central Neighborhood Improvement Association,1998.3
[6]Myott,Eric.1999."Early warning system feasibility in the Hamline Midwayarea.Available:www.npcr.org/reports/npcr1121/riper1121.html[September 2002].Minneapolis,MN:Neighborhood Planning for Community Revitalization
[7]Witold W.The use of the HP-filter in constructing real estate cycleindicators.Journal of Real Estate Research,2002,(23):65-88
[8]凌鑫.西安市房地产业预警系统设计与实证研究[D],西北大学,2007
[9]塞纳:“国内外房地产价格指数的编制方法比较”,http://www.valuer.org.cn
[10]黄继鸿、雷战波、凌越,经济预警方法研究综述[J].系统工程,2003,21(03)
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[12]刘思峰,杨岭.区域经济评估·预警·调控[J].郑州:河南人民出版社,1994
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[14]彭翊.城市房地产预警系统设计.中国房地产[J],2002,(6):50-52
[15]李斌.房地产市场预报预警系统建立过程中的误区及应注意的要点.中 国房地产[J],2004,(2):32-35
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[20]韩立达.我国房地产预警系统的基本特征及方法.燕山大学学报(哲学社会科学版)[N],2004,(8):35-39
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[2]师迎春.长沙市住宅市场泡沫及预警研究[D].湖南:湖南大学,2007
[3]Ronald W.Kaiser.The long cycle in real eatate.Journal of Real Estate Research.1997,(14):233-258
[4]刘传哲,高静华.房地产市场风险预警研究方法综述[J].中国矿业大学学报(社会科学).2006年3月
[5]Lori Mardockk.Predicting housing abandonment in central:creating an earlywaming System.Central Neighborhood Improvement Association,1998.3
[6]Myott,Eric.1999."Early warning system feasibility in the Hamline Midwayarea.Available:www.npcr.org/reports/npcr1121/riper1121.html[September 2002].Minneapolis,MN:Neighborhood Planning for Community Revitalization
[7]Witold W.The use of the HP-filter in constructing real estate cycleindicators.Journal of Real Estate Research,2002,(23):65-88
[8]凌鑫.西安市房地产业预警系统设计与实证研究[D],西北大学,2007
[9]塞纳:“国内外房地产价格指数的编制方法比较”,http://www.valuer.org.cn
[10]黄继鸿、雷战波、凌越,经济预警方法研究综述[J].系统工程,2003,21(03)
[11]朱军,王长胜.经济景气分析预警系统的理论方法[J].北京:中国计划出版社,1993,112-118
[12]刘思峰,杨岭.区域经济评估·预警·调控[J].郑州:河南人民出版社,1994
[13]梁运斌.我国房地产业景气指标设置与预警预报系统建设的基本构想.北京房地产杂志[J],1995,(11):18-21.
[14]彭翊.城市房地产预警系统设计.中国房地产[J],2002,(6):50-52
[15]李斌.房地产市场预报预警系统建立过程中的误区及应注意的要点.中 国房地产[J],2004,(2):32-35
[16]叶剑平.中国房地产监测预警系统指标体系设计.中外房地产导报[J].2000,(19):16-19
[17]叶艳兵,丁烈云.房地产预警指标体系设计研究.基建优化[J].2001,(3):1-3
[18]郭磊,王锋,刘长滨.深圳市房地产预警系统研究.数量经济技术经济研究[J],2003,(7):22-26
[19]李崇明.房地产预警的误区及对策的分析方法.武汉理工大学学报(社会科学版)[N],2003,(3):262-265
[20]韩立达.我国房地产预警系统的基本特征及方法.燕山大学学报(哲学社会科学版)[N],2004,(8):35-39
[21]顾海兵.宏观经济预警:理论、方法、历史.经济理论与经济管理[J].1997,(4):1-7
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