房地产的宏观风险

中国的房地产市场看起来已经到达拐点,但是我们认为房价崩溃的可能性仍然有限,原因在于基本面支持和存在政策调整空间。     

从新房价收入比看中国房价

房地产的快速发展致使国内房价连连走高,房屋居住功能被人们所看淡,更多将购房看做是一种致富与投资的捷径.当一个物体被抛向空中划出的抛物线总有一个顶端,过后便是下落的曲线,我们从当前的房价收入比可以大致的看出这个抛物线的轨迹,本文将传统的房价收入比赋予权重,使得新定义后的房价收入比更具有现实意义,更直观的考察当前的房地产泡沫的存在性.     

从新房价收入比看中国房价

房地产的快速发展致使国内房价连连走高,房屋居住功能被人们所看淡,更多将购房看做是一种致富与投资的捷径。当一个物体被抛向空中划出的抛物线总有一个顶端,过后便是下落的曲线,我们从当前的房价收入比可以大致的看出这个抛物线的轨迹,本文将传统的房价收入比赋予权重,使得新定义后的房价收入比更具有现实意义,更直观的考察当前的房地产泡沫的存在性。     

房价波动、抵押资产价值与企业风险承担

本文将房价波动作为外生事件,探讨了由房价波动所带来的抵押资产价值变化对企业风险承担水平的影响。房价上涨所带来的抵押资产价值上升会增强企业的融资能力,此时,理论上,一方面,管理层为了规避风险,会将抵押资产价值增加所带来的资源配置到那些风险较小但短期内可以获得高额利润的行业(如:房地产行业),从而会降低企业的风险承担水平;另一方面,因为企业融资能力的增强,管理层将有更充裕的资金投入到那些高风险且收益为正的项目,从而会提高企业的风险承担水平。实证结论支持第一种理论机制。进一步的实证检验支持非房地产企业大量进行房地产投资是上述结论形成的主要路径。在克服内生性问题和度量误差的可能影响之后,本文结论依然存在。     

风险网络视角下中国城市间房价波动溢出效应研究

本文从风险网络视角出发,以我国69个大中城市的房价波动率为研究对象,运用LASSO-VAR模型构建城市间房价波动溢出网络,在此基础上考察房价波动总体溢出和方向性溢出特征,分析不同等级、不同区域城市间房价波动溢出效应,并探讨城市间强房价波动溢出结构。研究发现:第一,我国城市间房价波动溢出效应显著存在,并且各城市房价波动溢出水平的变化范围大于溢入水平。同时,房价波动溢出水平较高的城市,其溢入水平也普遍较高;溢出水平较低的城市,其溢入水平差异较大。第二,一二三线城市的房价波动溢出水平依次降低,而且一线对二三线、二线对三线城市的溢出水平均高于反方向溢出,不同等级城市的房价关联具有显著的非对称性。第三,东部城市的房价波动溢出水平最高、溢入水平最低,西部城市正好相反。此外,同区域城市间房价波动溢出强度不一定大于跨区域溢出强度。第四,我国城市间强房价波动溢出网络具有显著的"无标度特性"和时变特征,东部城市、二线城市具有较强的强房价波动溢出能力,而且随着时间推移,二线城市强房价波动溢出能力不断增强。     

全球房价飙升

Crude oil prices remain a key determinant of global economic prospects. Higher prices affect the global economy through a variety of channels.In the case of advanced countries,this supply-side effect, which was sizable during the 1970s, has fallen in the …     

房价将进入下行通道

关于如何应对房地产泡沫,吴敬琏在接受采访时,讲了一段很有意思的话：“我现在担心的不是它降不下来,我是担心它崩盘。日本当年崩盘以后,到现在还没有起来。所以我想,宏观当局也不会采取措施。主动把这个泡沫捅破。泡沫这个东西最好不要形成,形成以后,就是要想办法让它慢慢菱缩。”     

房贷、房价与银行风险规避对策

20世纪90年代以来的很长一段时期,我国对于房地产市场一直施行从松的经济政策,目的在于快速推进房地产市场运行,长时间的政策惯性助长了房产投机活动的高回报预期,致使房价一涨再涨,诸多情况加大了银行资产的潜在风险：借贷双方信息不对称;假按揭铸成银行不良资产;同业竞争使银行变相从松放款条件。银行需要完善对策体系保障其资金安全：滚动更新客户分级分类系统;尝试金融产品创新;开发房贷保险分散抵押贷款风险;完善以政府为龙头的担保体系。     

读懂中国房价

正近年来,中国的房价增长速度过快,远远超过人均国民的年平均收入增速,不仅极大影响到城市居民的生活质量,也是整个国民经济持续平稳发展的不稳定因素,房价问题已经成为引起广泛关注的重要经济和社会问题。影响房价的因素,总体可以概括为:资金、供求、利率、政策、投机性、预期和人为操控。     

房价7个月暴涨94％呼唤楼市价格监管

继2006～2007年的那、一轮房地产泡沫之后,当前国内一些大城市的房地产市场,又以令人瞠目结舌的房价新高,制造了更为可观的资本泡沫。如今,深圳又成为新一轮泡沫的领跑者。今年9月,深圳新房成交均价达到创纪录的20940元／平方米,较深圳官方公布的2月份10770元／平方米的低点,上涨94％,7个月内房价几近翻番。上海、南宁、长春等一些大中城市不甘落后,房价同样创出新高。     

Index Revision,House Price Risk,and the Market for House Price Derivatives

It is widely recognized that options and futures markets for housing can reduce and manage the risks inherent in consumers’ large investments in housing equity. The integrity of such markets depends, however, upon the use of transparent and replicable benchmarks for house prices and settlement values. In the USA, a series of state and metropolitan indexes have been produced by a government agency (the US Office of Housing Enterprise Oversight, OFHEO), and they have been widely disseminated for over a decade. By construction, the entire historical path of each of these indexes is, in principle, subject to revision quarterly, that is, every time the index is recalculated and data are published. This paper provides the first analysis of the magnitude and bias of these revisions, and it analyzes their systematic effects on the settlement prices in housing options markets. The paper considers the implications of these magnitudes for the development of risk-reducing futures markets.

货币供应量对居民消费价格指数与房屋销售价格指数的影响——基于1978-2009年中国经验数据的分析

本文利用结构VAR模型分析了我国货币供应量对居民消费价格指数和房屋销售价格指数的影响,并通过1978年至2009年的经验检验,发现我国货币供应量对居民消费价格指数和房屋销售价格指数的影响较为显著,居民消费价格指数与房屋销售价格指数会此消彼长,并且,货币供应量对居民消费价格指数的影响存在半年到一年的滞后期。基于此,我们认为,在当前货币供应量持续处于高位、房地产价格被严格控制的背景下,要加倍警惕2010年下半年至2011年上半年居民消费价格指数上涨,从而产生通货膨胀局面的形成。     

收入差距与房地产价格泡沫：基于门槛效应研究

基于不同收入居民对住房消费品和投资品双重属性的偏好差异,结合居民的住房偏好特性,考量收入差距对房价收入比的影响。结果显示：收入差距对房价收入比的影响存在门槛效应：低于门槛值时,影响为抑制作用;高于门槛值时,影响转为促进作用,且随着收入差距进一步的扩大效应更为明显。鉴此,应聚焦缩小收入差距,合理引导居民住房消费与投资,以有效控制我国房地产价格泡沫趋势。     

Trading Frictions and House Price Dynamics

We model liquidity in housing markets. The model provides a simple characterization for the joint process of prices, sales, and inventory. We compare the implications of the model to certain properties of housing markets. The model can generate the large price changes and the positive correlation between prices and sales that we see in the data. Unlike the data, prices are negatively autocorrelated and high inventory predicts price appreciation. We investigate several amendments to the model. Informational frictions show promise.     

House Price Markups and Mortgage Defaults

The transaction price of identical housing units can vary widely due to heterogeneity in buyer and seller preferences, matching, and search costs, generating what we term “markups” above or below the average market price. We measure markups for 3.4 million purchase-money mortgages and show that they can predict mortgage defaults and credit losses conditional on default even after accounting for collateral coverage (loan-to-value ratio) and a comprehensive set of other covariates. The findings suggest that standard collateral coverage estimation may be inaccurate, with implications for both individual and portfolio-level credit risk assessment.     

The Life-Cycle Effects of House Price Changes

We develop a life-cycle model that explicitly incorporates the dual feature of housing as both a consumption good and an investment asset. Our analysis indicates that the consumption and welfare consequences of house price changes on individual households vary significantly. In particular, the non-housing consumption of young and old homeowners is much more sensitive to house price changes than that of middle-aged homeowners. More importantly, while house price appreciation increases the net worth and consumption of all homeowners, it only improves the welfare of old homeowners. Renters and young homeowners are worse off due to higher lifetime housing consumption costs.     

Controlling for Quality in House Price Indices

Given the importance of house prices it is not surprising that house price indices are used for many purposes. One of the factors that differentiates these indices is the house price determinants (such as structural characteristics and neighborhood quality) that are accounted for—that is, held constant. Indices are usually generated from house price regressions. It is shown that, regardless of the desired level of accounting, it is necessary to control for all significant determinants of house prices in these regressions to obtain unbiased estimates of the growth in house prices. An empirical example shows that not controlling for neighborhood quality can lead to substantial biases in estimates of house price appreciation rates even if the index does not account for this factor.     

Spatial Dependence and House Price Index Construction

Accurate estimation of prevailing metropolitan housing prices is important for both business and research investigations of housing and mortgage markets. This is typically done by constructing quality-adjusted house price indices from hedonic price regressions for given metropolitan areas. A major limitation of currently available indices is their insensitivity to the geographic location of dwellings within the metropolitan area. Indices are constructed based on models that do not incorporate the underlying spatial structure in housing data sets. In this article, we argue that spatial structure, especially spatial dependence latent in housing data sets, will affect the precision and accuracy of resulting price estimates. We illustrate the importance of spatial dependence in both the specification and estimation of hedonic price models. Assessments are made on the importance of spatial dependence both on parameter estimates and on the accuracy of resulting indices.