北京市商品住宅价格研究
|
摘要
房地产业是国民经济的先导性、支柱性和基础性产业,它的发展在很大程度上带动和制约着城市和其他产业的发展,甚至导致经济的周期性波动。从一些发达国家和地区房价变化历史经验来看(如日本地产泡沫和美国次贷危机),房价在短期内上升过快容易滋生房地产泡沫,破坏金融系统,可能会带来长时间的经济萧条。房价持续快速上涨另一方面还会影响到居民的购买力,居住问题不能妥善解决会对社会稳定造成很大的负面影响。如何更好地把握房地产价格的影响因素和运行规律,对于运用房地产调控手段熨平房地产价格波动,促进房地产市场稳定、协调、可持续的发展具有重要意义。
本文首先从理论上分析了影响北京房价的供给和需求因素。需求分析包括购房群体的消费特征、消费水平、影响消费的主要因素等;供给分析包括市场供给特征、供给规模、供给影响因素等。其次,在理论分析的基础上,笔者针对北京住宅价格供求影响因素设计了计量模型,并进行了实证检验。根据国内外学者的研究成果,提取了影响商品住宅价格的主要因素,需求因素包括人口数量、在职职工年平均工资额、年成交面积、住宅价格上涨预期等4方面,供给因素包括主要成本、住宅竣工面积和房地产开发投资等3方面,环境条件因素包括年人均GDP、人均GDP增长率和三次产业比例等3方面。在价格形成机制分析的基础上,分别设计需求、供给和综合因素影响下的商品住宅价格决定模型,并提出模型的估计和检验方法。实证检验结果表明,需求因素影响下,住宅价格上涨预期每增加1%,住宅价格增长124.584元/m~2,对房价影响非常明显,其他因素对房价影响相对较小。供给影响下的商品住宅价格模型检验结果表明,竣工面积每增长1万m~2时,房价将下降2.149元/ m~2;房地产投资每增长1亿元,房价将下降1.056元/ m~2,成本每增1元/m~2,对应住宅价格增长1.097元/ m~2。从实证结果来看,成本因素对房价影响较小,说明开发商不是根据成本来定价的。因而作者提出目前房价与成本的“脱钩论”,实际上,开发商是根据市场需求程度来决定住宅售价的,开发商的定价策略就是尽量榨取消费者剩余,与住宅成本关系不大。综合因素影响下的商品住宅价格模型检验结果表明,常住人口数量(万人)、在职职工年平均工资(百元)、住宅价格上涨预期(%)、主要成本(建安成本、土地价格)(元/m~2)、房地产开发投资(亿元)、人均GDP增长率(%)等要素,每增加一单位所对应的商品住宅价格(元/m~2)的变化分别是13.932、0.421、105.320、0.640、-1.203、0.454单位。从结果来看,外来人口涌入是影响北京住宅价格上涨的主要因素之一,外地具有超强购买力的投资客(如山西煤老板、温州炒房团),以“扫楼”的形式大肆抢购楼盘,供不应求的假象造成房价节节攀高。研究结果还表明,住宅价格上涨预期对住宅价格的影响很大,北京房价存在明显的“预期主导”效应。房地产市场既有实业投资的性质,也有金融投机的性质,北京住宅市场的“预期主导”效应表明,金融投机已经居于住宅市场的主导地位,而房价上涨预期正是金融投机盛行的幕后“推手”。北京房价持续上涨不是供给绝对不足造成的,而是大量的投机需求导致供给相对不足。这一点也可以从北京很多已售楼盘较高的空置率得到印证。
由于我国在1998年才真正取消实物分房,房地产市场发展的历史相对较短,市场化程度比较低,因此,对于房地产价格分布规律的研究起步很晚。目前,国内该领域的理论研究还不够系统,有关房地产价格分布规律的测度方法更是凤毛麟角。本文在国外关于房地产价格分布规律研究成果的基础上,建立了北京市房地产市场实际价格的线性回归模型,并在此基础上对一年间北京房地产市场价格分布进行了量化分析和计算。笔者的研究结果发现,北京房地产市场的价格并未如想象的那样存在严重“偏离”问题,这一结论和某些学者认为的观点不一致。原因在于,仅从名义房价来看,北京的房地产市场价格似乎很高,但考虑到北京日渐增值的城市价值,北京住宅市场的价格并不算高。北京的城市价值目前在全国35个大城市中列第三位,2008年奥运概念进一步拉升北京的城市价值,房价亦水涨船高。所以,笔者认为近几年北京房价并不是毫无理由地疯涨,房价总体水平上涨的部分原因是因为城市价值拉升所致。
在上述研究基础上,作者按四象限和扇区划分对北京住宅楼盘进行了市场调研,并根据调研情况对北京未来房地产市场走势进行了分析。调研结果发现,在房价分布上,环线效应明显,从城中心城区沿环线房价逐次递减,体现出“地租效应”;就整体而言,北京房价区域分布不均衡源自于地价分布不均衡,存在着“北高南低、东高西低”的现象;具体到各区域,房价却与板块特征关系紧密,学区房现象尤为明显,投资性需求主要分布在热点板块,而自住性需求一般位于周边地区;此外,与国际大都市相比,北京房价绝对值较低,但住宅压力指数偏高。
虽然北京的房地产市场价格偏离问题还不太严重,但日本房地产泡沫破裂和美国次贷危机为我们敲响了警钟。2010年4月份国家集中出台了一系列的打压一线城市高房价的政策,其中纲领性的文件是4月17日国务院下发的《关于坚决遏制部分城市房价过快上涨的通知》(“国十条”)。在历经数年GDP增长率“保八”之后,中央政府已基本具备了与国际资本博弈的实力。中央调控的一方面是防止人民币升值导致国际热钱涌入房地产领域,使中国重蹈日本80年代的覆辙;另一方面,也是引导资金进入贸易领域,保证中国在国际贸易市场的竞争力。所以,此次新政有“舍弃房地产,转战国际”的战略考虑,所以,本轮政策不同于以往,来势凶猛,且有不达目的誓不罢休之势,我们应当严肃认真地看待本轮政策波。最后,笔者结合此次新政,提出了保持房价稳定的若干政策,以其对决策层有所参考。
本文研究结果对促进北京市房地产业与社会经济协调发展,保持北京市房地产市场持续健康发展有重要意义,对其它大中城市也有一定的借鉴作用。
Real estate industry acts a leading, supporting and basic role in national economy; its development has great affections on the development of cities and other industries, even leads to cyclical movements of economy. Over-investments in real estate and rapid increases in the price of realty during the last two years have aroused the attention of the whole society.The histric experience of developed countries had proved that,for example Japanese real estate bubbles froth and American loan crisis, excessively rises of real estate price in a short time would multiply the real estate froth, destroy financial system, even possibly bring economic depression for a long time.Continious rise of real estate also affects resident's purchasing power,if the housing question cannot be solved properly ,it would have extremely tremendous negative influence to the social stability.Grasp the influencing factor and the operating law of real estate price better, will help us control real estate price fluctuations by using macroeconomic means to accelerate the real estate market’s stable, coordinated, the sustainable development.
This article first theoretically analyzed the supplies and the demand factor of Beijing house price. The demand analysis the primary factor which including the purchase characteristics, the consumption level and so on; Supplies analysis including market supplies characteristic, supplies scale, supplies influencing factor and so on. Next, the author has designed the measurement model in view of Beijing housing price supply and demand influencing factor in theoretical analysis's foundation, and has carried on the real diagnosis examination.According to the domestic and foreign scholar's research results, the author have withdrawn primary factors influence housing price, the demand factor include the population quantity, the active staff annual mean amount of wages, the year deal area, the supply actor include the main cost, the housing completed residential areas and the development investment,the environmental condition factor include year average per person GDP, the average per person GDP rate of increment and three industrial proportions.On the foundation of price mechanism analysis, the author separately designs commodity housing price determine model based on demand, the supplies and under both of the synthesis factor, and proposes the model estimate and the inspection procedure.The real diagnosis result indicated that under the demand factor influence, the staff yearly average wages increases 100 Yuan, the commodity housing price will correspond grow 10.867 Yuan/m2; Population quantity increases 10,000 people every time, the commodity housing price grows 4.557 Yuan /m~2; The commodity housing year deal area increased 10,000 m~2 , the commodity housing price will correspond drop 5.045 Yuan /m~2. Obviously, although the commodity housing year deal area grew quickly,it still could not catch up with the rise of the rate of Beijing population, which caused the commodity housing price rose quicker, makes people feel that the wages increase scope always could not catch up with the rise scope of commodity housing price. The positive result of commodity housing price model under supplies influence indicated that the main cost increases 1 Yuan /m~2 cause the commodity housing price grow 1.147 Yuan/m~2; housing completed residential areas grows 10,000 m~2 cause the commodity housing price drop 4.629 Yuan/m~2; The investment in real estates grow 1 hundred million Yuan cause the commodity housing price drop 1.376 Yuan/m~2. The increase of completed residential areas and investment in real estates has suppressed the rise of commodity housing price. Because when supply and demand balanced, the completed residential areas increased (and becomes deal area) generally brought the receding of housing price. The positive rusult of synthesis factor model indicated that resident population quantity (ten thousand people), the active staff annual mean wages (hundred Yuan), the main cost (building cost, land price) (Yuan /m~2), the property development investment (hundred million Yuan), the average per person GDP rate of increment (%) and so on, cause the change of the commodity housing price a unit corresponds (Yuan /m~2) respectively as 8.932, 7.329, 10.951, - 9.353, 1.954 units. The result indicated that the demand’s power of drawing to house price is bigger by far than the supplies’suppression to house price.
As our country cancelled the material object house allocation in 1998, the real estate market development's history was relatively short, the marketability degree was quite low, therefore, the research regarding the real estate price distribution rule was very late.At present, fundamental research of this field is insufficient, the measure method related real estate price distribution rule even extremely rare.This article based on the research results of real estate price distribution rule overseas, established linear regression model of Beijing real estate market actual price, and carried on the quantification analysis and the computation regarding Beijing real estate market price distribution during one year. The findings discovered that Beijing real estate market's price has not been serious deviate, which is inconsistent with some scholars’s viewpoint.The reason lies the urban value. Beijing's urban value is 0.510 in 2006, the third in the national 35 big cities row, the Olympic Games concept further pulls the rises of Beijing's urban value in 2007 and 2008, with the house price also rise. Therefore, the author thought that in recent years Beijing house price insanely rises with lots of reason, urban value is the main cause.On the base of above researches, the author has carried on the market investigation according to four quadrants and the sector divisions of Beijing housing estate, and analysis the real estate market trend. The investigation and study result discovered that in the house price distribution prespect, the loop line effection is obvious, house price decreases progressively gradually from the city center city along the loop line, manifests“the land rent effect”;As for the whole, the imbalanced Beijing house price regional distribution comes from the imbalanced of the soil-rent value distribution,prescribed as“north and east higher,south and east lower”; Makes concrete to various regions, the house price is actually close with the tectonic plate characteristic,the school district phenomenon is especially obvious, the investment demand mainly distributes in the hot spot tectonic plate, but live demand generally to be located at the peripheral locality; In addition, compares with the cosmopolis, Beijing house price absolute value is low, but the housing pressure index is high.
Although the deviation of price in Beijing's real estate is not very serious, but the American Subprime mortgage crisis has make the alarm for us. Therefore this article finally proposed the methods and policy measures how to guard against the housing price serious deviation, pointed out that the government should synthesize using finance, tax revenue methods and so on to regulates Beijing housing market to prevent to the deviation of price. This article findings have important meaning to promote Beijing real estate industry coordinated with social economy development, and maintain the healthy development of Beijing real estate market, also has some profits to other big or middle-sized cities.
本文首先从理论上分析了影响北京房价的供给和需求因素。需求分析包括购房群体的消费特征、消费水平、影响消费的主要因素等;供给分析包括市场供给特征、供给规模、供给影响因素等。其次,在理论分析的基础上,笔者针对北京住宅价格供求影响因素设计了计量模型,并进行了实证检验。根据国内外学者的研究成果,提取了影响商品住宅价格的主要因素,需求因素包括人口数量、在职职工年平均工资额、年成交面积、住宅价格上涨预期等4方面,供给因素包括主要成本、住宅竣工面积和房地产开发投资等3方面,环境条件因素包括年人均GDP、人均GDP增长率和三次产业比例等3方面。在价格形成机制分析的基础上,分别设计需求、供给和综合因素影响下的商品住宅价格决定模型,并提出模型的估计和检验方法。实证检验结果表明,需求因素影响下,住宅价格上涨预期每增加1%,住宅价格增长124.584元/m~2,对房价影响非常明显,其他因素对房价影响相对较小。供给影响下的商品住宅价格模型检验结果表明,竣工面积每增长1万m~2时,房价将下降2.149元/ m~2;房地产投资每增长1亿元,房价将下降1.056元/ m~2,成本每增1元/m~2,对应住宅价格增长1.097元/ m~2。从实证结果来看,成本因素对房价影响较小,说明开发商不是根据成本来定价的。因而作者提出目前房价与成本的“脱钩论”,实际上,开发商是根据市场需求程度来决定住宅售价的,开发商的定价策略就是尽量榨取消费者剩余,与住宅成本关系不大。综合因素影响下的商品住宅价格模型检验结果表明,常住人口数量(万人)、在职职工年平均工资(百元)、住宅价格上涨预期(%)、主要成本(建安成本、土地价格)(元/m~2)、房地产开发投资(亿元)、人均GDP增长率(%)等要素,每增加一单位所对应的商品住宅价格(元/m~2)的变化分别是13.932、0.421、105.320、0.640、-1.203、0.454单位。从结果来看,外来人口涌入是影响北京住宅价格上涨的主要因素之一,外地具有超强购买力的投资客(如山西煤老板、温州炒房团),以“扫楼”的形式大肆抢购楼盘,供不应求的假象造成房价节节攀高。研究结果还表明,住宅价格上涨预期对住宅价格的影响很大,北京房价存在明显的“预期主导”效应。房地产市场既有实业投资的性质,也有金融投机的性质,北京住宅市场的“预期主导”效应表明,金融投机已经居于住宅市场的主导地位,而房价上涨预期正是金融投机盛行的幕后“推手”。北京房价持续上涨不是供给绝对不足造成的,而是大量的投机需求导致供给相对不足。这一点也可以从北京很多已售楼盘较高的空置率得到印证。
由于我国在1998年才真正取消实物分房,房地产市场发展的历史相对较短,市场化程度比较低,因此,对于房地产价格分布规律的研究起步很晚。目前,国内该领域的理论研究还不够系统,有关房地产价格分布规律的测度方法更是凤毛麟角。本文在国外关于房地产价格分布规律研究成果的基础上,建立了北京市房地产市场实际价格的线性回归模型,并在此基础上对一年间北京房地产市场价格分布进行了量化分析和计算。笔者的研究结果发现,北京房地产市场的价格并未如想象的那样存在严重“偏离”问题,这一结论和某些学者认为的观点不一致。原因在于,仅从名义房价来看,北京的房地产市场价格似乎很高,但考虑到北京日渐增值的城市价值,北京住宅市场的价格并不算高。北京的城市价值目前在全国35个大城市中列第三位,2008年奥运概念进一步拉升北京的城市价值,房价亦水涨船高。所以,笔者认为近几年北京房价并不是毫无理由地疯涨,房价总体水平上涨的部分原因是因为城市价值拉升所致。
在上述研究基础上,作者按四象限和扇区划分对北京住宅楼盘进行了市场调研,并根据调研情况对北京未来房地产市场走势进行了分析。调研结果发现,在房价分布上,环线效应明显,从城中心城区沿环线房价逐次递减,体现出“地租效应”;就整体而言,北京房价区域分布不均衡源自于地价分布不均衡,存在着“北高南低、东高西低”的现象;具体到各区域,房价却与板块特征关系紧密,学区房现象尤为明显,投资性需求主要分布在热点板块,而自住性需求一般位于周边地区;此外,与国际大都市相比,北京房价绝对值较低,但住宅压力指数偏高。
虽然北京的房地产市场价格偏离问题还不太严重,但日本房地产泡沫破裂和美国次贷危机为我们敲响了警钟。2010年4月份国家集中出台了一系列的打压一线城市高房价的政策,其中纲领性的文件是4月17日国务院下发的《关于坚决遏制部分城市房价过快上涨的通知》(“国十条”)。在历经数年GDP增长率“保八”之后,中央政府已基本具备了与国际资本博弈的实力。中央调控的一方面是防止人民币升值导致国际热钱涌入房地产领域,使中国重蹈日本80年代的覆辙;另一方面,也是引导资金进入贸易领域,保证中国在国际贸易市场的竞争力。所以,此次新政有“舍弃房地产,转战国际”的战略考虑,所以,本轮政策不同于以往,来势凶猛,且有不达目的誓不罢休之势,我们应当严肃认真地看待本轮政策波。最后,笔者结合此次新政,提出了保持房价稳定的若干政策,以其对决策层有所参考。
本文研究结果对促进北京市房地产业与社会经济协调发展,保持北京市房地产市场持续健康发展有重要意义,对其它大中城市也有一定的借鉴作用。
Real estate industry acts a leading, supporting and basic role in national economy; its development has great affections on the development of cities and other industries, even leads to cyclical movements of economy. Over-investments in real estate and rapid increases in the price of realty during the last two years have aroused the attention of the whole society.The histric experience of developed countries had proved that,for example Japanese real estate bubbles froth and American loan crisis, excessively rises of real estate price in a short time would multiply the real estate froth, destroy financial system, even possibly bring economic depression for a long time.Continious rise of real estate also affects resident's purchasing power,if the housing question cannot be solved properly ,it would have extremely tremendous negative influence to the social stability.Grasp the influencing factor and the operating law of real estate price better, will help us control real estate price fluctuations by using macroeconomic means to accelerate the real estate market’s stable, coordinated, the sustainable development.
This article first theoretically analyzed the supplies and the demand factor of Beijing house price. The demand analysis the primary factor which including the purchase characteristics, the consumption level and so on; Supplies analysis including market supplies characteristic, supplies scale, supplies influencing factor and so on. Next, the author has designed the measurement model in view of Beijing housing price supply and demand influencing factor in theoretical analysis's foundation, and has carried on the real diagnosis examination.According to the domestic and foreign scholar's research results, the author have withdrawn primary factors influence housing price, the demand factor include the population quantity, the active staff annual mean amount of wages, the year deal area, the supply actor include the main cost, the housing completed residential areas and the development investment,the environmental condition factor include year average per person GDP, the average per person GDP rate of increment and three industrial proportions.On the foundation of price mechanism analysis, the author separately designs commodity housing price determine model based on demand, the supplies and under both of the synthesis factor, and proposes the model estimate and the inspection procedure.The real diagnosis result indicated that under the demand factor influence, the staff yearly average wages increases 100 Yuan, the commodity housing price will correspond grow 10.867 Yuan/m2; Population quantity increases 10,000 people every time, the commodity housing price grows 4.557 Yuan /m~2; The commodity housing year deal area increased 10,000 m~2 , the commodity housing price will correspond drop 5.045 Yuan /m~2. Obviously, although the commodity housing year deal area grew quickly,it still could not catch up with the rise of the rate of Beijing population, which caused the commodity housing price rose quicker, makes people feel that the wages increase scope always could not catch up with the rise scope of commodity housing price. The positive result of commodity housing price model under supplies influence indicated that the main cost increases 1 Yuan /m~2 cause the commodity housing price grow 1.147 Yuan/m~2; housing completed residential areas grows 10,000 m~2 cause the commodity housing price drop 4.629 Yuan/m~2; The investment in real estates grow 1 hundred million Yuan cause the commodity housing price drop 1.376 Yuan/m~2. The increase of completed residential areas and investment in real estates has suppressed the rise of commodity housing price. Because when supply and demand balanced, the completed residential areas increased (and becomes deal area) generally brought the receding of housing price. The positive rusult of synthesis factor model indicated that resident population quantity (ten thousand people), the active staff annual mean wages (hundred Yuan), the main cost (building cost, land price) (Yuan /m~2), the property development investment (hundred million Yuan), the average per person GDP rate of increment (%) and so on, cause the change of the commodity housing price a unit corresponds (Yuan /m~2) respectively as 8.932, 7.329, 10.951, - 9.353, 1.954 units. The result indicated that the demand’s power of drawing to house price is bigger by far than the supplies’suppression to house price.
As our country cancelled the material object house allocation in 1998, the real estate market development's history was relatively short, the marketability degree was quite low, therefore, the research regarding the real estate price distribution rule was very late.At present, fundamental research of this field is insufficient, the measure method related real estate price distribution rule even extremely rare.This article based on the research results of real estate price distribution rule overseas, established linear regression model of Beijing real estate market actual price, and carried on the quantification analysis and the computation regarding Beijing real estate market price distribution during one year. The findings discovered that Beijing real estate market's price has not been serious deviate, which is inconsistent with some scholars’s viewpoint.The reason lies the urban value. Beijing's urban value is 0.510 in 2006, the third in the national 35 big cities row, the Olympic Games concept further pulls the rises of Beijing's urban value in 2007 and 2008, with the house price also rise. Therefore, the author thought that in recent years Beijing house price insanely rises with lots of reason, urban value is the main cause.On the base of above researches, the author has carried on the market investigation according to four quadrants and the sector divisions of Beijing housing estate, and analysis the real estate market trend. The investigation and study result discovered that in the house price distribution prespect, the loop line effection is obvious, house price decreases progressively gradually from the city center city along the loop line, manifests“the land rent effect”;As for the whole, the imbalanced Beijing house price regional distribution comes from the imbalanced of the soil-rent value distribution,prescribed as“north and east higher,south and east lower”; Makes concrete to various regions, the house price is actually close with the tectonic plate characteristic,the school district phenomenon is especially obvious, the investment demand mainly distributes in the hot spot tectonic plate, but live demand generally to be located at the peripheral locality; In addition, compares with the cosmopolis, Beijing house price absolute value is low, but the housing pressure index is high.
Although the deviation of price in Beijing's real estate is not very serious, but the American Subprime mortgage crisis has make the alarm for us. Therefore this article finally proposed the methods and policy measures how to guard against the housing price serious deviation, pointed out that the government should synthesize using finance, tax revenue methods and so on to regulates Beijing housing market to prevent to the deviation of price. This article findings have important meaning to promote Beijing real estate industry coordinated with social economy development, and maintain the healthy development of Beijing real estate market, also has some profits to other big or middle-sized cities.
引文
陈博.2010.抑制房价的根本:流动性控制与结构性改革[J].价格理论与实践,(02).
陈英凤.2010.遏制房价过快上涨关键要落实地方政府责任[J].中国房地产金融,(02).
董智勇.2006.深圳住宅价格影响因素实证研究,浙江大学硕士论文.
葛红玲.2008.货币政策对北京房价的影响分析,中央财经大学学报,7.
葛红玲.2008.地价与房价关系研究——基于北京数据的检验,中国物价,(9)
何帆.2010.是沙堆总会崩塌是泡沫总会破灭[J].观察与思考,(01).
韩丽鹏,谢秀娥,郭晓杰.2010我国房地产价格的财富效应研究——基于35个大中城市面板数据的分析[J].价格理论与实践,(01)12-15.
洪开荣,房地产泡沫.2001.形成、吸收与转化,《中国房地产金融》,8.
金京玉.2010.遏制房价:征收差别物业税与利率改革[J].中国物价,(02)48-50.
刘洪玉等.2003.房地产业所包含经济活动分类体系和增加值估算[J].统计研究,8.
李宇嘉.2010.国际房价变动对我国影响的传导途径研究[J].中国物价,(02)53-54.
刘洪玉,郑思齐.2003.中国房地产市场中的“泡沫”与“过热”问题分析[J].建筑经济,(2)36-40.
鲁春阳,杨庆媛,文枫,龙拥军.2010城市用地结构与产业结构关联的实证研究——以重庆市为例[J].城市发展研究,(01).
刘玉录.2002.产权:中国房地产业发展的一个关键因素[J].中国房地产金融,(3)6-11.
刘福垣.2003.我国房地产业结构失衡的根源及对策[J].计划与市场探索,(6)1-4.
罗伯特·J·希勒.2008.《非理性繁荣》[M],北京,中国人民大学出版社.
林英彦.2004.《土地经济学通论》,台北.文笔限书局. 《马克思恩格斯全集》[M],第二十三卷
马歇尔:《经济学原理》[M],北京商务印书馆.
聂辰席.耿洁.王秀玲.2006.《宏观经济运行与调控》,社会科学文献出版.
欧绍华.2010.中国房价研究的新视角——评龙均云《中国房价博弈论》[J].湖南工业大学学报(社会科学版),(01)45-47.
乔志敏.2002.《房地产价格研究》,经济管理出版社.
沈悦,刘洪玉.2003.我国房地产业的增长空间分析[J].建筑经济,(7)44-49.
萨缪尔森.2007.《经济学》,中国发展出版社.
斯蒂格利茨.2006.《经济学》[M],中国人民大学出版社(125). 《统计年鉴》1992-2009年.
王全民等.2002.房地产经济学[M].东北财经大学出版社,12(1)16-17.
骆飞群.2010.对2010年中国房地产宏观调控政策的探讨[J].浙江金融,9(02)24-26.
王岳龙.2010.房价与地价关系的再审视——基于土地招拍挂制度的一个博弈论解释,学习与实践,5.
王岳龙,张瑜.2010基于中国省级面板数据的房价与地价关系研究[J].山西财经大学学报,(01).
王林.2009.房价与地价的因果检验——以重庆市为例,建筑经济,12.
王菁娜.2009.我国房价上涨的特殊原因及对策建议,经济纵横,(12).
王克忠.2006.《房地产经济学教程》,上海,复旦大学出版.
王菲.2006.论房地产宏观调控与房地产价格,华东师范大学硕士学位论文.
谢经荣等. 2002地产泡沫与金融危机[M].经济管理出版社.
徐策.2010.当前我国房地产市场运行的特征、问题及政策建议[J].中国物价,11.
许由.2010.土地闸门能调控下游房价吗?[J].国土资源导刊,(01).
肖元真,张天,戴骏冬.2010.2010年房地产业发展的政策指引与战略抉择[J].广东经济,(03).
徐滇庆等.2000.《泡沫经济与经济危机》[M].,中国人民大学出版社.
尹德洪.2010房价波动与地方政府的诺斯悖论行为分析[J].统计与决策,(04).
杨文武.2003.中国房地产业指标体系建立的理论分析与实证研究[D].四川大学博士论文.
杨瑾.2010.完善我国房地产价格形成机制的思考[J].学术论坛,1.
项勇.2009.基于特征价格的城市交通对房价影响分析,技术经济与管理研究,06.
郑志来.2009.基于利益相关者的房价波动因素分析,现代经济探讨,(12). 《中国经济景气月报》1998-2009年.
Berkovec.J. 1997.Consumption and Investment Motives and the Portfolio Choices of Home owners, Journal of Real Estate Finance and Economics,14(2):159-180.
BrownG .R.Duration and Risk.2000..Journal of Real Estate Research[J],20(3):337-356.
Bertaud A.and B.Renaud,1992.Cities without land markets(M ),World Bank Discussion Paper N0 .227, (Washington D .C:World Bank).
Bion Howard.2006.G reen Building Primer.5 th.Edition1996-2000 Building Enviromental Science and Technology.
Blandchard & Fisher.1994.Lecture on Macroeconomics , MIT.
Born, W. L. and S. A. Pyhrr.1994.Real Estate Valuation:The Effect of Market and Property Cycles, Journal of Real Estate Research, 9:((4) 455–85.
Boykin, J. H. and M. T. Gray.1994.The Relevance and Application of the Gross Income Multiplier, The Appraisal Journal,52:203–8.
Card & Fisher.1989.Lecture on Macroeconomics , MIT.
Clayton, J., Market Fundamentals, Risk and the Canadian Property Cycle: Implications for
Case.K. E. and Shiller.R.J.1998.The Behavior of Home Buyers in Boom and Post Boom Markets, New England Economic Review,11:29-46.
Clapp, J. M. and C. Giacotto.1992.Estimating Price Indices for Residential Property: A Comparison of Repeat Sales and Assessed Value Methods, Journal of the American Statistical Association,87:418
Clayton, J.2009.Market Fundamentals, Risk and the Canadian Property Cycle: Implications for Property Valuation and Investment Decision, Journal of Real Estate Research,9:(12)347–67. Case.K. E. and Shiller.R.J.2008.The Behavior of Home Buyers in Boom and Post Boom Markets, New England Economic Review,11:29-46.
Dokko, Y.1999.R.H.Edelsein.Real Estate Income and Value Cycle : A Model of Market Dynamics[J].Journal of Real Estate Research,18(1):69-95.
Dowall D.E..1993.Establishing Urban Land Market in the People' s Republic of China [J].Journal of the A merican Planning Association(Spring),12:182-192.
Dixit, A. K. and R. S. Pindyck.1994.Investment under Uncertainty, Princeton, NJ: Princeton University Press.
Englund, P. and Y. M. 1997.Ioannides, Hosing Price Dynamics: An International Empirical Perspective, Journal of Housing Economics,6:119–36.
Eerger, C. P. 1978, Manias, Panics, and Crashes: A history of Financial Crisis, New York, Basic Books .
Flood, R. P. and R. J. Hodrick.1990.On Testing for Speculative Bubbles, Journal of Economic Perspectives, 4:2, 85–101.
Gordon J.,Y.M osbaugh.1996.T.Canter.Integrating Regional Economic Indictors with the Real Estate Cycle. Journal of Real Estate Research, 12(3):469-501.
Grenadier, S. R.. 1995.The Persistence of Real Estate Cycles, Journal of Real Estate Finance and Economics, 10:2, 95–119.
Garber, P. M..1990 .Famous First Bubbles, Journal of Economic Perspectives, , 4:2, 35–54. Guntermann, K. L. and S. C. Norrbin.1991.Empirical Test of Real Estate Market Efficiency, Journal of Real Estate Finance and Economics,6: 297–313.
HartzellD.,J.S .Hekman.1987.M.E. Miles.Real Estate Returns and Inflation[J]. Journal of the American Real Estate and Urban Economics Association,Spring(12):617-637.
Hinze,J·Applate.2009.Cose of Construction Injuries.Journal of Construction Engineering and Manahement ASCE Sept, (8):537-550.
Hamilton,James D.1986.“On Testing for Self - fulfilling Speculative Price Bubbles”, International Economics,Rev.27:545-552.
Hekman, J. S..1985.Rental Price Adjustment and Investment in the Office Market, Journal of the American Real Estate and Urban Economics Association,13:1, 32–47.
HartzellD,J.S .Hekman,.2009.M.E. Miles.Real Estate Returns and Inflation[J]. Journal of the American Real Estate and Urban Economics Association,Spri ng:617-637.
John M.Quigey.1999.Real Estate Prices and Economic Cycles [J].International Real Estate Rev iew, Vo1.2 No.l:1-20.
Jaffee, D. M.1994.The Swedish Real Estate Crisis, Working Paper 94-224, The Fischer Center for Real Estate and Urban Economics, University of California–Berkeley.
Kumerow.1999.M .A System Dynamics Model of Cyclical Office Over supply[J].Journal of Real Estate Research, , 18(1):233-255.
Kim, K.-H. and S. H. Suh.1993.Speculation and Price Bubbles in the Korean and Japanese Real Estate Markets, Journal of Real Estate Finance and Economics,6:73–87.
Lin Justin Yifu.2009.Rueal reforms and agricultural growth in china.American Economic Review, ,82(1):84-98.
Mueller, G.R..1999.Real Estate Rental Growth Rates at Different Points in the Physical Market Cycle[J].Journal of Real Estate Research, 18(1):131-150
Mueller,G .R ,S.1996.Laposa.Rent Distributions Under Alternative Market Cycles,Paper presented at the Twelfth Annual Meeting of the American Real Estate Society[M].South Lake Tahoe,CA, March,(28).
McNulty, J. E.1995.Overbuilding, Real Estate Lending Decisions. and the Regional Economic Base, Journal of Real Estate Finance and Economics,11:37–53.
Meese, R. and N. Wallace.1994.Testing the Present Value Relation for Housing Prices: Should I Leave My House in San Francisco?, Journal of Urban Economics, 35:245–66.
Merrill Lynch.2009.Japanese Refixable Convertibles,Global Secarities Research﹠ Economics Group/Global Convertibles Research.
Newell, G. and D. Higgins.1996.Impact of Leading Economic Indicators on Commercial Property Performance, The Valuer & Land Economist,May, 138–44.
Occuptional Safety and Health Adminastration.U.S,department of Labor,2009-Revsed..
Property Valuation and Investment Decision.1996.Journal of Real Estate Research,12: 347–67.
Renaud, B.1997.The 1985 to 1994 Global Real Estate Cycle: An Overview, Journal of Real Estate Literature, , 5, 13–44.
Richard Stanton,Nancy Wallace.Motgage choice.1997.what’s the point?.Real Estate Economics,16:1-37.
Stephen E.Roulac.1996.The Strategic Real Estate Framework Process Linkages Decisions[J].The Journal of Real Estate Research, (3):323-346.
Smith.L. B. and H o.M. H. C.1996.The Relative Price Differential between Higher and Lower PricedHomes, Journalo f Housing Economics, ,5:1-17.
Su, J. and J. Kelly.1995.Property Cycles in European Office Markets, London: Jones Lang Wootton Research..
Shilton, L. G. and J. K. Tandy.1993.The Information Precision of CBD Office Vacancy Rates, Journal of Real Estate Research,8: 421–44.
Stiglitz, J. E.1990.Symposium on Bubbles, Journal of Economic Perspectives,4(2) 13–18.
Ton,James D.1986.“On Testing for Self - fulfilling Speculative Price Bubbles”, International Economics , Rev,27:545-552.
Toshikazu Kimura and toshio Shinohara,.2009.Monte Carlo analysis of conertible bonds with rest clauses,Euepoean Journal of Operationl Research,52(8)301-310.
Wheaton, W. C.1987.The Cyclical Behavior of the National Office Market, Journal of the American Real Estate and Urban Economics Association,15:4,281–99.
Wheaton, W. C. and L. Rossoff.1998.The Cyclic Behavior of the U.S. Lodging Industry, Real Estate Economics,26:167–82.
West,K.D,1987.A.Specification.Tes.tfor.Speculative.Bubbles’Quarterly.Journal.of .Economics, 553-580.
陈英凤.2010.遏制房价过快上涨关键要落实地方政府责任[J].中国房地产金融,(02).
董智勇.2006.深圳住宅价格影响因素实证研究,浙江大学硕士论文.
葛红玲.2008.货币政策对北京房价的影响分析,中央财经大学学报,7.
葛红玲.2008.地价与房价关系研究——基于北京数据的检验,中国物价,(9)
何帆.2010.是沙堆总会崩塌是泡沫总会破灭[J].观察与思考,(01).
韩丽鹏,谢秀娥,郭晓杰.2010我国房地产价格的财富效应研究——基于35个大中城市面板数据的分析[J].价格理论与实践,(01)12-15.
洪开荣,房地产泡沫.2001.形成、吸收与转化,《中国房地产金融》,8.
金京玉.2010.遏制房价:征收差别物业税与利率改革[J].中国物价,(02)48-50.
刘洪玉等.2003.房地产业所包含经济活动分类体系和增加值估算[J].统计研究,8.
李宇嘉.2010.国际房价变动对我国影响的传导途径研究[J].中国物价,(02)53-54.
刘洪玉,郑思齐.2003.中国房地产市场中的“泡沫”与“过热”问题分析[J].建筑经济,(2)36-40.
鲁春阳,杨庆媛,文枫,龙拥军.2010城市用地结构与产业结构关联的实证研究——以重庆市为例[J].城市发展研究,(01).
刘玉录.2002.产权:中国房地产业发展的一个关键因素[J].中国房地产金融,(3)6-11.
刘福垣.2003.我国房地产业结构失衡的根源及对策[J].计划与市场探索,(6)1-4.
罗伯特·J·希勒.2008.《非理性繁荣》[M],北京,中国人民大学出版社.
林英彦.2004.《土地经济学通论》,台北.文笔限书局. 《马克思恩格斯全集》[M],第二十三卷
马歇尔:《经济学原理》[M],北京商务印书馆.
聂辰席.耿洁.王秀玲.2006.《宏观经济运行与调控》,社会科学文献出版.
欧绍华.2010.中国房价研究的新视角——评龙均云《中国房价博弈论》[J].湖南工业大学学报(社会科学版),(01)45-47.
乔志敏.2002.《房地产价格研究》,经济管理出版社.
沈悦,刘洪玉.2003.我国房地产业的增长空间分析[J].建筑经济,(7)44-49.
萨缪尔森.2007.《经济学》,中国发展出版社.
斯蒂格利茨.2006.《经济学》[M],中国人民大学出版社(125). 《统计年鉴》1992-2009年.
王全民等.2002.房地产经济学[M].东北财经大学出版社,12(1)16-17.
骆飞群.2010.对2010年中国房地产宏观调控政策的探讨[J].浙江金融,9(02)24-26.
王岳龙.2010.房价与地价关系的再审视——基于土地招拍挂制度的一个博弈论解释,学习与实践,5.
王岳龙,张瑜.2010基于中国省级面板数据的房价与地价关系研究[J].山西财经大学学报,(01).
王林.2009.房价与地价的因果检验——以重庆市为例,建筑经济,12.
王菁娜.2009.我国房价上涨的特殊原因及对策建议,经济纵横,(12).
王克忠.2006.《房地产经济学教程》,上海,复旦大学出版.
王菲.2006.论房地产宏观调控与房地产价格,华东师范大学硕士学位论文.
谢经荣等. 2002地产泡沫与金融危机[M].经济管理出版社.
徐策.2010.当前我国房地产市场运行的特征、问题及政策建议[J].中国物价,11.
许由.2010.土地闸门能调控下游房价吗?[J].国土资源导刊,(01).
肖元真,张天,戴骏冬.2010.2010年房地产业发展的政策指引与战略抉择[J].广东经济,(03).
徐滇庆等.2000.《泡沫经济与经济危机》[M].,中国人民大学出版社.
尹德洪.2010房价波动与地方政府的诺斯悖论行为分析[J].统计与决策,(04).
杨文武.2003.中国房地产业指标体系建立的理论分析与实证研究[D].四川大学博士论文.
杨瑾.2010.完善我国房地产价格形成机制的思考[J].学术论坛,1.
项勇.2009.基于特征价格的城市交通对房价影响分析,技术经济与管理研究,06.
郑志来.2009.基于利益相关者的房价波动因素分析,现代经济探讨,(12). 《中国经济景气月报》1998-2009年.
Berkovec.J. 1997.Consumption and Investment Motives and the Portfolio Choices of Home owners, Journal of Real Estate Finance and Economics,14(2):159-180.
BrownG .R.Duration and Risk.2000..Journal of Real Estate Research[J],20(3):337-356.
Bertaud A.and B.Renaud,1992.Cities without land markets(M ),World Bank Discussion Paper N0 .227, (Washington D .C:World Bank).
Bion Howard.2006.G reen Building Primer.5 th.Edition1996-2000 Building Enviromental Science and Technology.
Blandchard & Fisher.1994.Lecture on Macroeconomics , MIT.
Born, W. L. and S. A. Pyhrr.1994.Real Estate Valuation:The Effect of Market and Property Cycles, Journal of Real Estate Research, 9:((4) 455–85.
Boykin, J. H. and M. T. Gray.1994.The Relevance and Application of the Gross Income Multiplier, The Appraisal Journal,52:203–8.
Card & Fisher.1989.Lecture on Macroeconomics , MIT.
Clayton, J., Market Fundamentals, Risk and the Canadian Property Cycle: Implications for
Case.K. E. and Shiller.R.J.1998.The Behavior of Home Buyers in Boom and Post Boom Markets, New England Economic Review,11:29-46.
Clapp, J. M. and C. Giacotto.1992.Estimating Price Indices for Residential Property: A Comparison of Repeat Sales and Assessed Value Methods, Journal of the American Statistical Association,87:418
Clayton, J.2009.Market Fundamentals, Risk and the Canadian Property Cycle: Implications for Property Valuation and Investment Decision, Journal of Real Estate Research,9:(12)347–67. Case.K. E. and Shiller.R.J.2008.The Behavior of Home Buyers in Boom and Post Boom Markets, New England Economic Review,11:29-46.
Dokko, Y.1999.R.H.Edelsein.Real Estate Income and Value Cycle : A Model of Market Dynamics[J].Journal of Real Estate Research,18(1):69-95.
Dowall D.E..1993.Establishing Urban Land Market in the People' s Republic of China [J].Journal of the A merican Planning Association(Spring),12:182-192.
Dixit, A. K. and R. S. Pindyck.1994.Investment under Uncertainty, Princeton, NJ: Princeton University Press.
Englund, P. and Y. M. 1997.Ioannides, Hosing Price Dynamics: An International Empirical Perspective, Journal of Housing Economics,6:119–36.
Eerger, C. P. 1978, Manias, Panics, and Crashes: A history of Financial Crisis, New York, Basic Books .
Flood, R. P. and R. J. Hodrick.1990.On Testing for Speculative Bubbles, Journal of Economic Perspectives, 4:2, 85–101.
Gordon J.,Y.M osbaugh.1996.T.Canter.Integrating Regional Economic Indictors with the Real Estate Cycle. Journal of Real Estate Research, 12(3):469-501.
Grenadier, S. R.. 1995.The Persistence of Real Estate Cycles, Journal of Real Estate Finance and Economics, 10:2, 95–119.
Garber, P. M..1990 .Famous First Bubbles, Journal of Economic Perspectives, , 4:2, 35–54. Guntermann, K. L. and S. C. Norrbin.1991.Empirical Test of Real Estate Market Efficiency, Journal of Real Estate Finance and Economics,6: 297–313.
HartzellD.,J.S .Hekman.1987.M.E. Miles.Real Estate Returns and Inflation[J]. Journal of the American Real Estate and Urban Economics Association,Spring(12):617-637.
Hinze,J·Applate.2009.Cose of Construction Injuries.Journal of Construction Engineering and Manahement ASCE Sept, (8):537-550.
Hamilton,James D.1986.“On Testing for Self - fulfilling Speculative Price Bubbles”, International Economics,Rev.27:545-552.
Hekman, J. S..1985.Rental Price Adjustment and Investment in the Office Market, Journal of the American Real Estate and Urban Economics Association,13:1, 32–47.
HartzellD,J.S .Hekman,.2009.M.E. Miles.Real Estate Returns and Inflation[J]. Journal of the American Real Estate and Urban Economics Association,Spri ng:617-637.
John M.Quigey.1999.Real Estate Prices and Economic Cycles [J].International Real Estate Rev iew, Vo1.2 No.l:1-20.
Jaffee, D. M.1994.The Swedish Real Estate Crisis, Working Paper 94-224, The Fischer Center for Real Estate and Urban Economics, University of California–Berkeley.
Kumerow.1999.M .A System Dynamics Model of Cyclical Office Over supply[J].Journal of Real Estate Research, , 18(1):233-255.
Kim, K.-H. and S. H. Suh.1993.Speculation and Price Bubbles in the Korean and Japanese Real Estate Markets, Journal of Real Estate Finance and Economics,6:73–87.
Lin Justin Yifu.2009.Rueal reforms and agricultural growth in china.American Economic Review, ,82(1):84-98.
Mueller, G.R..1999.Real Estate Rental Growth Rates at Different Points in the Physical Market Cycle[J].Journal of Real Estate Research, 18(1):131-150
Mueller,G .R ,S.1996.Laposa.Rent Distributions Under Alternative Market Cycles,Paper presented at the Twelfth Annual Meeting of the American Real Estate Society[M].South Lake Tahoe,CA, March,(28).
McNulty, J. E.1995.Overbuilding, Real Estate Lending Decisions. and the Regional Economic Base, Journal of Real Estate Finance and Economics,11:37–53.
Meese, R. and N. Wallace.1994.Testing the Present Value Relation for Housing Prices: Should I Leave My House in San Francisco?, Journal of Urban Economics, 35:245–66.
Merrill Lynch.2009.Japanese Refixable Convertibles,Global Secarities Research﹠ Economics Group/Global Convertibles Research.
Newell, G. and D. Higgins.1996.Impact of Leading Economic Indicators on Commercial Property Performance, The Valuer & Land Economist,May, 138–44.
Occuptional Safety and Health Adminastration.U.S,department of Labor,2009-Revsed..
Property Valuation and Investment Decision.1996.Journal of Real Estate Research,12: 347–67.
Renaud, B.1997.The 1985 to 1994 Global Real Estate Cycle: An Overview, Journal of Real Estate Literature, , 5, 13–44.
Richard Stanton,Nancy Wallace.Motgage choice.1997.what’s the point?.Real Estate Economics,16:1-37.
Stephen E.Roulac.1996.The Strategic Real Estate Framework Process Linkages Decisions[J].The Journal of Real Estate Research, (3):323-346.
Smith.L. B. and H o.M. H. C.1996.The Relative Price Differential between Higher and Lower PricedHomes, Journalo f Housing Economics, ,5:1-17.
Su, J. and J. Kelly.1995.Property Cycles in European Office Markets, London: Jones Lang Wootton Research..
Shilton, L. G. and J. K. Tandy.1993.The Information Precision of CBD Office Vacancy Rates, Journal of Real Estate Research,8: 421–44.
Stiglitz, J. E.1990.Symposium on Bubbles, Journal of Economic Perspectives,4(2) 13–18.
Ton,James D.1986.“On Testing for Self - fulfilling Speculative Price Bubbles”, International Economics , Rev,27:545-552.
Toshikazu Kimura and toshio Shinohara,.2009.Monte Carlo analysis of conertible bonds with rest clauses,Euepoean Journal of Operationl Research,52(8)301-310.
Wheaton, W. C.1987.The Cyclical Behavior of the National Office Market, Journal of the American Real Estate and Urban Economics Association,15:4,281–99.
Wheaton, W. C. and L. Rossoff.1998.The Cyclic Behavior of the U.S. Lodging Industry, Real Estate Economics,26:167–82.
West,K.D,1987.A.Specification.Tes.tfor.Speculative.Bubbles’Quarterly.Journal.of .Economics, 553-580.