媒体情绪与房价波动*

本文使用2000—2015年中国112个大中城市媒体报道和房价月度指数研究了媒体异质性对房价波动的影响。研究发现，纸媒情绪对房价波动的影响大于网媒情绪，中央媒体情绪对房价波动的影响大于地方媒体情绪，明星微博情绪对房价波动的影响大于普通微博情绪，媒体语气确定性和报道篇幅对房价波动有正影响，明星城市媒体情绪较非明星城市更易引起房价波动。据此，为防范非理性房价波动，应加强对不同媒体报道的监管。     

媒体报道、投资者情绪与股价波动

媒体报道是有倾向、 有选择的,而投资者决策是有偏且关注是有限的.因此,媒体报道的倾向性成为影响投资者情绪的一个指标,进而影响股票市场的整体走势.基于此,本文通过构建媒体报道综合指标,运用中介效应分析方法,基于投资者情绪视角研究媒体报道对股价波动的影响.研究结果表明:由于大多数投资者通过媒体报道的新闻事件获取和构建自己的信息框架,因此,媒体报道的议程设置这一特点使得投资者情绪发生积极或消极的变化,而投资者情绪对投资者的决策产生直接影响,进而表现为媒体报道倾向性引发股价波动.     

媒体报道与股市波动:一个理论综述

传统资产定价模型假设,投资者之间仅通过市场价格机制相互联系和影响,而忽视了媒体因素的作用。现实中,投资者将新闻媒体作为获取信息的主要途径,媒体报道是影响股价波动的重要因素之一。因此,近些年不少国外学者关注媒体因素对股票市场的影响;而在国内,相关研究仍较少。本文主要基于行为金融视角,从上市公司、金融中介、投资者、媒体机构、记者等角度剖析了媒体偏差产生的原因,以及媒体偏差对股票市场的影响,并探讨了其未来研究可能的发展方向。     

宏观共同因子、特质因子以及货币政策对中国各线城市房价的影响——基于FAVAR模型

运用FAVAR模型,将中国各线城市房价分离出宏观共同因子和特质因子部分,研究了各因子及货币政策对房价的影响。研究发现:大城市的房价更易受宏观共同因子和地区特质因子的影响,且变动的持续性更大;共同因子可在很大程度上解释房价变动的持续性和波动性;共同因子对房价的影响较为持久,而地区特质因子只在短期内影响房价;利率和货币供应量可以有效地影响房价;相比利率,货币供应量对一线和二线城市房价水平的影响更大,对各线城市房价波动的影响更为持久;一线城市的房价水平及其波动对货币政策冲击的敏感度较高,二线城市的敏感度居中,三线城市的敏感度较低;未发现货币政策的"价格之谜"现象,表明本文模型设定的合理性较强。     

利率效果区域异质性、收入跨区 影响与房价溢出效应*

本文首先构建了理论分析框架,解释了中国不同城市间房价溢出效应、收入对房价的跨区影响,以及利率调整对不同城市房价的区域异质性影响。本文利用GVAR模型对该框架进行了实证,结果显示北京等一线城市的房价波动对其他城市具有较大的溢出性,而中西部城市的房价溢出性则不明显。一线城市和东部城市的房价波动不仅受本城市人均收入变动的影响,还在很大程度上受其他城市收入变动的影响,而中西部城市的房价则主要受本城市收入变动的影响。利率变动对一线城市和东部城市的房价影响则较大,而对中西部城市的房价影响有限。本文结论具有明确的政策含义,比如政府应通过稳定一线城市房价以达到稳定全国房价的目的,促进公共产品均等化,实行地区差异化的房地产政策等。通过利率调整来调控房价也是一个可行的政策选项。     

城市住房价格波动对居民消费的影响

本文基于我国2009—2014年34个大中城市的面板数据,采用固定效应模型,围绕房价收入比的变化对大中城市进行分类,分析不同类型的城市面对房价波动所产生的财富效应,以此来考察城市住房价格波动对居民消费的影响.研究结果表明,房价上涨对居民消费产生较强的挤出效应,但在不同类型的城市中存在结构性差异.在房价收入比上升的城市不存在明显的挤出效应;在房价收入比下降的城市存在明显的挤出效应.同时,在研究过程中笔者考察城镇化建设、产业结构变动和人口增长对房地产财富效应的影响,并基于此提出相应建议.     

机构投资者会主动管理媒体报道吗？

文章实证检验了机构投资者是否存在通过管理媒体报道来获取超额收益的行为。研究发现：（1）媒体报道对股票价格的影响符合“注意力驱动效应”,即媒体报道数量与股票超额收益显著正相关;（2）机构投资者会利用媒体报道的这一效应来获取股票超额收益,即机构投资者存在利用媒体来制造信息噪音以引导市场热点的短期炒作行为,进而验证了“主动媒体管理”假说;（3）机构投资者的媒体管理行为会提高其所持有股票未来大幅下跌的可能性,即带来更大的股价崩盘风险。文章将机构投资者行为和媒体报道进行了有机结合,拓展了相关领域的研究文献,而且研究结论为进一步规范机构投资者行为和媒体报道提供了经验证据。     

房价、房价偏离与溢出效应：来自珠江三角洲的经验证据

利用国外较新的PMG估计法,探讨影响珠江三角洲商品房均衡价值的因素及房价偏离,并采用脉冲响应函数分析房价偏离在不同城市间的溢出效应。随着收入和人口的增加,房价加速上涨;经济越发达的城市,房价收入比越大,房价的增长速度更快、波动更为剧烈。人均可支配收入是推动房价上涨最重要的长期因素,通货膨胀次之,贷款最小;但贷款、通货膨胀是主要的短期推动力。虽然珠江三角洲地区房价总体上没有明显的泡沫,但波动很剧烈,一旦发生调整,其幅度将很大。除江门外,其他城市只对深圳房价偏离的冲击做出响应,而深圳对其他城市房价偏离的冲击没有响应;江门房价更多的受到来自中山和珠海的影响,深圳是珠江三角洲房价波动的源头。     

长三角城市群房价泡沫测度及空间传染效应

本文选取长三角13个城市2007~2015年间的面板数据,运用静态面板模型对房地产价格泡沫进行测度和研究,并使用GMM估计法对房价泡沫的空间传染性进行研究。结果表明,长三角各城市的房地产价格均不同程度出现了泡沫,并且各年间泡沫比例波动较大;各城市的房地产价格泡沫存在空间上的相关性,某个城市的房价变动,会对其他城市的房价造成影响。抑制房价泡沫要推动房地产行业供给侧结构性改革、建立抑制房价上涨的长效机制以及因地制宜地采取措施,防止房价泡沫扩散。     

媒体报道影响中小板公司IPO抑价吗?

以2009年至2012年在深圳中小板上市的403家公司为研究样本,从媒体关注、媒体监督和媒体负面舆论三个维度考察了媒体报道与IPO抑价之间的关系。实证研究发现:媒体报道越多,媒体关注度越强,公司IPO抑价率越高;由于媒体负面报道次数偏少,媒体监督与IPO抑价虽然具有负相关关系,但并不显著;而以媒体负面报道次数占总报道次数之比作为媒体负面舆论的代理变量,发现媒体负面舆论可以显著降低IPO抑价率,有效抑制IPO高抑价。     

Determinants of housing price volatility in Canada: a dynamic analysis

This article tries to identify the determinants of housing price volatility and to examine the dynamic effects of these determinants on volatility using quarterly data for Canada. The Generalized Autoregressive Conditional Heteroskedastic (GARCH) and the Vector Autoregressive (VAR) models have been employed to analyse possible time variation of the housing price volatility and the interactions between the volatility and the key macroeconomic variables. We find the evidence of time varying housing price volatility for Canada. Our VAR, Granger causality and variance decomposition (VDC) analyses demonstrate that housing price volatility is affected significantly by gross domestic product (GDP) growth rate, housing price appreciation rate and inflation. On the other hand, volatility affects GDP growth rate, housing price appreciation and volatility itself. The impulse response analysis reveals the asymmetric of the positive and negative shocks. The findings of this article have important implications, particularly for those seeking to develop derivatives for housing market prices.     

Housing booms and media coverage

This article analyses news media coverage of the housing market. Building on theories of media influence where word of mouth is the final mechanism of opinion change but media initiate discourse, I examine the relationship between news media and the recent UK house price boom. Over 30 000 articles on the UK housing market from the period 1993 to 2008 are analysed, and it is found that media Granger-caused real house price changes, suggesting the media may have influenced opinions on the housing market. However, media sentiment on the housing market did not change with the secular increase in house prices in the 2000s, suggesting that the media did not contribute to the UK’s housing boom and may have helped constrain it.     

The Impact of Monetary Policy and Housing-Purchase Restrictions on Housing Prices in China

This paper examines the level and volatility effect of monetary policy on housing prices in China utilizing a novel set of housing price indices constructed by (Fang, H., QuanlinGu, W. X., & Zhou, L.-A. (2015). Demystifying the Chinese housing boom. NBER Macroeconomics Annual 2015, Volume 30. University of Chicago Press.). We find that in the long-run, average housing prices react positively to inflation, money supply and bank lending growth, and negatively to the reserve requirement ratio and benchmark lending rate. Housing prices in Tier 1 cities respond more sensitively to monetary shocks relative to Tier 2 and 3 cities, possibly due to surging demand and limited supply under housing-purchase restrictions (HPR). We further study the volatility effect of monetary shocks using the GARCH model and find that the benchmark lending rate, reserve requirement ratio and money supply growth have strong negative impact on the volatility of housing price growth. Our benchmark results remain robust after incorporating the HPR policy variable in the estimation, with a significant negative effect of HPR on housing price growth in Tier 1 and Tier 2 cities. Lastly, we conclude with recommendations on future monetary policy design and implementation, with a specific focus on the heterogeneous characteristics of China’s housing market.     

房地产税、市场结构与房价

本文在住房流量模型的基础上,构建了一个购房者和开发商的住房市场局部均衡模型,考察了完全垄断和完全竞争情形下房地产税与房价之间的关系。结果表明,无论何种市场结构,提高房地产税均导致房价下降;住房市场垄断性越强,房价越高,房地产税对房价影响越大。笔者对1996-2008年中国33个大中城市数据的检验发现,市场结构对房价影响大于房地产税。房地产税增长率每增加1%,房价增长率将减少0.03%;勒纳指数每增加1%,房价增长率将增加0.16%。房地产税与市场结构相互作用将使房价上涨,但影响微不足道。因此,对住宅开征房地产税,将对房价上涨有一定限制作用,但不能有效抑制房价上涨,而增强住宅市场竞争性、降低开发商垄断具有显著效果。     

Housing prices and population dynamics in urban China

In this study, a dynamic stochastic general equilibrium (DSGE) model is used to investigate the influence of population on housing price dynamics in urban China. The contribution of population growth to the housing price trend and the contribution of population shocks to housing price fluctuations are quantitatively measured. The empirical results indicate that the ongoing policy to control the population size in large cities in China can decrease the growth rate of housing prices by 0.49% every year, which accounts for 1/10 of the total housing price trend. Furthermore, this policy cannot stabilize housing prices because population shocks have negligible effects on the housing price cycle. Housing technology shocks and housing preference shocks are responsible for most of the housing price fluctuations.     

China’s house price: Affected by economic fundamentals or real estate policy?

Many theory and empirical literature conclude that house price can reflect economic fundamentals in the long-term. However, by using China’s panel data of 35 main cities stretching from 1998 to 2007, we find that there is no stable relationship between house price and economic fundamentals. House price has deviated upward from the economic fundamentals since government started macro-control of the real estate market. We consider that the mechanism between the house price and economic fundamentals is distorted by China’s real estate policy, especially its land policy. Meanwhile the policy itself is an important factor in explaining the changes of China’s house price. Then we estimate the dynamic panel data model on house price and the variables which are controlled by real estate policy. The result shows: land supply has negative effects on house price; financial mortgages for real estate have positive effects on house price; and the area of housing sold and the area of vacant housing, which reflects the supply and demand of the housing market, has negative effects on house price. We also find some differences in house price influence factor between eastern and mid-western cities. Finally, we propose policy suggestions according to the empirical results.     

Expectation,volatility and liquidity in the housing market

Measuring housing price volatility is fundamental to understanding the dynamics of housing price risk. This article aims to explore whether a liquidity factor plays a role in explaining the second moment (i.e. the volatility) of housing prices. Housing price volatility is measured as the conditional variance of a Generalized Auto Regressive Conditional Heteroscedasticity (GARCH) model under the Adaptive Expectations framework. The empirical evidence reveals that volatility transmits from smaller housing units to larger housing units, which indirectly supports the trade-up effect discussed in the literature. In addition, less liquid housing classes are more sensitive to unexpected liquidity shocks, and the starter housing class is extraordinarily sensitive to negative liquidity shocks. Consistent with friction search theory, pricing errors are alleviated as the trading volume increases, because the valuation price tends to be more accurate as more information is available.