房地产市场泡沫的金融支持以及演化风险分析

本文从房地产行业价格形成的交易模型入手,分别分析了没有金融支持和有金融支持的市场均衡过程,研究了金融支持对房地产市场泡沫形成的推动作用。进而探讨了房地产市场泡沫化带来的金融风险,并在此基础上提出了遏制房地产市场泡沫的政策建议。     

风险预警模型在房地产企业资金管理中的应用探讨

房地产企业资金管理普遍存在负债比例高、资金沉淀严重、投资行为盲目、融资形式单一、资金管理不善等问题。风险预警模型有预知风险、控制风险、防范风险等功能,运用风险预警系统对房地产行业进行监测、预测并判断其景气状态十分必要。选择了灰色风险预警模型和营运资金周转率作为房地产企业资金管理预警模型的核心指标,设计了房地产资金管理风险预警模型的具体算法和资金管理风险预警模型在房地产企业的具体应用。     

我国房地产业金融租赁市场风险因素分析

本文通过对我国房地产业金融租赁市场风险因素的结构层次分析的研究,有利于建立适用于我国房地产公司内对金融租赁市场风险评价和监控的方法,为金融租赁市场风险的预防和管理提供依据。     

抵押房地产的公开市场价值、抵押价值及变现价值

在金融机构发放抵押贷款时,抵押房地产的价值是确定房地产抵押贷款的贷款额的一个重要参数。我国《房地产抵押估价指导意见》规定:房地产抵押估价采用公开市场价值标准,房地产抵押价值等于假定未设立法定优先受偿权利下的市场价值减去房地产估价师知悉的法定优先受偿款。但是,金融机构通过变卖或拍卖方式行使处置权和收益权得到变现净值或清算净值,因而金融机构房地产抵押所追求的是“变现净值”或“清算净值”标准,不是公开市场价值标准。所以,金融机构要防范金融风险,科学确定房地产抵押贷款的贷款额,就必须准确界定抵押房地产的市场价值、抵押价值和变现价值。     

房地产上市公司Logistic预警模型研究

目前,从整个房地产市场来看,随着房贷新政的作用延续,开发商的融资渠道压缩。投资者纷纷立场,购房者购房趋于理性。开发商受资金链压力,建立预警管理体系可提升企业的反应能力。因此,基于我国房地产行业背景,建立针对房地产企业的财务预警系统,非常具有现实意义。论文以我国61家上市房地产公司为研究样本,其中主要包含A股企业以及少部分的B股企业。并根据是否为ST公司分为正常和危机公司两类,基于主成分分析法和Logistic回归分析,利用SPSS经济统计软件,对各上市公司的财务指标数据进行分析来构造财务预警模型,最后再对构建的预警模型进行回判和最优度拟合检验,估计模型预警效果。     

调控政策下的房地产企业财务预警分析

随着2011年下半年国家密集的房地产调控政策的出台,我国房地产行业出现了一系列的财务问题。本文以我国沪深上市房地产公司为研究对象,并通过各种财务比率建立房地产行业的财务预警模型,以解释财务风险出现的原因,最后根据目前房地产行业的财务风险现状提出企业应对的措施。     

基于Web和数据仓库的房地产市场预警预报系统研究

介绍了基于Web和数据仓库的房地产市场预警预报系统的体系结构，探索设计了房地产市场预警预报系统的技术与方法，并提出了基于Web和数据仓库的房地产市场预警预报系统的完整解决方案。     

后金融危机时代房地产金融创新与风险防范

由美国次贷危机引发的全球金融危机深刻展现了房地产金融创新的现实风险,住房贷款供需两旺的繁荣背后,商品房价的高位运行足以引起我们对房地产市场风险的重视,以加快金融创新,增强防范意识和能力。     

信息不对称下我国房地产金融风险研究

近年来,我国房地产市场发展迅速,为商业银行拓展房地产金融业务提供了巨大的商机。但是由于我国房地产属于典型的不完全竞争市场,市场化程度还不够高,房地产市场机制还不够完善。信息不对称的广泛存在,给商业银行信贷业务带来了巨大的潜在的金融风险。从信息经济学理论出发,研究了信息不对称下我国房地产金融风险的具体表现形式,并为弱化信息不对称的影响提供了政策建议。     

房地产企业财务危机防范策略研究

房地产企业存在的财务危机隐患已成为很多房地产企业生存发展迫切需要解决的现实问题。本文具体分析了房地产企业财务危机产生的主要原因,提出防范房地产企业财务危机应从资金管理模式、财务风险组合、融资方式、把握项目不同生命周期中财务危机特点、建立企业财务危机预警系统几个方面加以解决,促进我国房地产企业获得长期经济效益和得到稳定健康发展。     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.     

后金融危机条件下我国房地产证券化研究\n——基于美国房地产证券化实践的启示

我国房地产市场发展过程中现存问题的背后是房地产金融市场风险.美国的房地产资产证券化历程从正反两方面都给中国寻找有效控制和分散风险的方法及中国房地产金融创新等提供了很好的借鉴和启示.我国房地产金融市场建设总体上应遵循"长短结合,疏堵并举"的原则.短期有"堵"有"疏";长期以"疏"为主.     

政府调控房价政策下的银行房地产信贷研究

依据国家统计局、中国人民银行发布的房地产市场以及房地产金融方面的统计资料,对近几年政府调控房价政策下我国房地产开发贷款、个人住房贷款余额变化以及在银行业贷款总额的占比等进行了分析,并对金融机构房地产信贷风险进行了评估。     

基于多级模糊综合评价法的房地产企业财务预警研究

随着国内经济形势的不断变化和国家房地产相关政策的出台,房地产企业在经营中各种不确定因素不断增加,财务风险也与日俱增。房地产企业的财务风险具有模糊性和复杂性,它受到财务和非财务等多种因素的影响。以财务指标为变量建立的预警模型无法全面预测企业的财务风险。为提高财务预警系统的有效性,本文以房地产企业这一微观群体为视角,在以财务指标为主体的财务预警系统基础上,结合房地产企业的特征选取非财务指标,对影响房地产企业财务预警的非财务指标进行分层研究,运用多级模糊综合评价法构建房地产企业的财务预警模型。这将对房地产企业构建财务预警体系大有裨益。     

基于BP神经网络的房地产预警研究——以沈阳为例

本文利用BP神经网络对房地产市场进行预警研究,选取了沈阳2005年²011年的预警指标数据,作为训练样本和检测样本进行仿真训练,其训练效果有着较好的可信度,该模型能够较好反应出警兆指标和房地产市场警情之间的关系,可以用于市场预警。     

基于扩散指数模型的房地产市场景气循环研究——以北京市为例

基于扩散指数模型构建房地产市场景气循环指标体系，采用北京市住宅市场与经济基本面的历史数据，计算不同时期北京市房地产市场的扩散指数，绘制北京市房地产市场的景气循环曲线，并根据景气循环曲线与历史数据对北京市未来房地产市场的景气情况进行预测。最后，提出在上述市场预测情况下的市场调控策略。     

国房地产市场金融加速器效应的实证研究

中国房地产市场近年来一直处于非理性繁荣状态,因此,中央政府陆续出台一系列房地产调控政策旨在挤出房地产泡沫,平抑其投资过热现象,从而实现宏观经济在金融危机后的软着陆。此外,央行不断上调存款准备金率和基准利率更是加重了仍处在调控政策消化阶段的房地产市场的下行预期。根据金融加速器理论,初始的外部冲击通过信贷市场的放大作用,最终将引发大幅度的市场波动,破坏实体经济的正常运行。房地产企业的高负债率和开发模式决定了房地产市场中金融加速器效应十分显著,尤其是在政策变动和楼市供求关系发生逆转的情况下,表现更为明显。最后提出了相应的政策建议．     

Are the stock and real estate markets integrated in China?

This paper examines the dynamic short-run and long-run co-movement between the real estate and stock markets in China by employing a continuous wavelet method. We use gross domestic product and M2 (broad money supply) as control variables to eliminate the common factors of the two markets and to identify the real nexus between them. The empirical results show that the co-movement between real estate and stock prices is weak in the short run, except during the financial crisis period. Since the stock market is highly volatile, while real estate prices are relatively stable, the two markets are less correlated in the short run. The results also show that real estate prices affect stock prices in the long run, which supports the existence of a credit-price effect in China. Real estate prices remained very high in most time periods. Enterprises and individuals can obtain funds from bank loans to invest in the stock market, thus raising stock prices. These findings indicate that the two markets are generally segmented in the short run but are integrated in the long run. The stabilization of the real estate market is critical for stability in the stock market, but not vice versa. Additionally, investments in the two markets may not provide a high level of risk dispersion in the long run in China.

     

我国房地产泡沫形成机制实证研究——基于五级市场结构视角的分析

本文从土地市场、新房市场、旧房市场、租赁市场和金融市场等五个方面分析房地产泡沫形成机制，构建了五级市场结构的房地产泡沫分析框架，利用1998-2010年季度数据进行实证研究。研究结果表明，我国房地产泡沫兼有“成本推动型”、“投机推动型”和“信贷推动型”三重特征。因此，根治“土地财政依赖症”、打击投机炒房行为、完善住房金融市场将有助于抑制房价的过快上涨。     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.