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.     

What drives housing dynamics in China? A sign restrictions VAR approach

We study housing dynamics in China using vector autoregressions identified with theory-consistent sign restrictions. We study seven potential drivers: (1) population increases; (2) a relaxation of credit standards, for example, due to the shadow banking system; (3) increasing preferences towards housing, for example, due to a housing bubble, or to housing being a status asset in the marriage market; (4) an increase in the savings rate; (5) expected productivity progress; (6) changes in land supply; and (7) tax policy, a proxy for policy stimulus. Our results show that, even if all shocks play relevant roles, productivity, savings glut, and policy stimulus have been the dominant drivers. When the sample is closer to 2014, housing preferences and credit shocks increase their importance to explain house prices and volume, while population shocks explain a larger share of the dynamics of residential investment. The results show some differences if we use house price indices constructed by the government or by private sources. The official indices show smaller increases in house prices and assign a smaller role to credit and preference shocks.     

A national or local phenomenon? A comparison of the US and the Chinese housing markets

This article investigates the common movements of house prices across cities as well as the macroeconomic underpinnings of the comovements in the US and China. Our empirical results indicate more differences than similarities between the US and the Chinese housing markets. The results from a Bayesian dynamic latent factor model indicate that the fluctuations of house prices across cities in the US are more a national phenomenon, while the dynamics of house prices across cities in China are mainly driven by the city-specific component. We further use VAR models to compare the roles of the underlying determinants in these two housing markets. The results show that the roles of monetary policy shocks and aggregate fluctuations in driving the common movements of house prices across cities differ substantially between the US and China at both short and long horizons.     

油价冲击、货币政策调整与产出波动——基于中国的经验证据

本文基于我国2001-2010年宏观经济月度数据,采用SVAR模型分析了国际油价波动时,央行货币政策在排除回应油价干扰与未排除干扰下的反应差异及油价波动对产出的影响。研究发现,在排除货币政策回应油价波动干扰后,通过脉冲响应函数反映的油价波动对产出的短期负面影响消失。方差分解结果显示,长期内产出波动由油价冲击和货币政策解释的比例分别为5716%和32480%,比排除干扰前分别下降了2569%和4560%。这说明我国油价冲击带来的经济衰退主要是因为货币政策及其回应油价冲击紧缩所致。此外,面对油价的短期冲击,CPI指数并未随着生产者购进价格指数上升而上升,产出也未发生明显的衰减;但在较长时间内,油价上升会因为相对价格的改变,而影响CPI水平和货币政策,从而对产出产生显著的负面影响。     

Expectations and the housing market: A model of house price dynamics

We examine the characteristics of housing markets under adaptive and heterogeneous expectations. Model agents have finite horizons, and their borrowings are constrained by the collateral value of housing stock. Our model shows that expectation-driven housing price dynamics constantly change the direction of movement. The steady-state process of housing prices follows an endogenous oscillation process, and the magnitude of the cycles can be amplified by external shocks. Our quantitative results imply that (i) short-term positive and long-term negative serial correlations in housing price changes are inherent, (ii) house prices and expected house price movements are positively correlated, and (iii) fluctuations in housing prices are not fully explained by fundamentals.     

Joint Adjustment of House Prices, Stock Prices and Output Towards Short‐run Equilibrium

A dynamic IS‐LM model including houses and stocks as additional assets will be analysed in this paper. Providing also housing services, a major consumption item for most households, houses create an additional link between the monetary and the real sector, distinct from the traditional wealth effect channel. We analyse the adjustment path of output, house prices and stock prices after policy shocks within a rational expectation setup. Depending crucially on the elasticity of housing services demand, different reaction patterns of asset prices will emerge. The results are contrasted with relevant empirical findings, particularly Lastrapes (Journal of Housing Economics, 11 (2002), pp. 40–74), leading to the identification of plausible elasticity ranges. The analysis sheds new light on the ongoing discussion about demand effects from changing real estate wealth and about determinants of house price fluctuations.     

House price dynamics: Evidence from Beijing

To study the house price dynamics in China, this paper extends the traditional life-cycle model by incorporating land supply, regime shifts and government regulation factors. The models are estimated with an error correction framework using quarterly data from 2000 to 2007 in Beijing. The conclusions are as follows. (1) There exits a stable co-integration relationship between house price and fundamentals; land supply and financial regimes are also important determinants of long-run equilibrium house prices. (2) Short-run dynamics depend on changes of fundamentals and the adjustment process of housing market. Land supply has a significant impact on house price fluctuations while demand factors such as user costs, income and residential mortgage loan have greater influences. The adjustment speed of real house prices to the long-run equilibrium has been reduced significantly since 2005 which means exogenous shocks can cause prolonged deviation of real house prices from the equilibrium level.     

Housing,consumption and monetary policy: how different are the first-, second- and third-tier cities in China?

This article presents an empirical analysis of the relationship between house prices and the real economy in China’s first-, second- and third-tier cities. A Structural Vector Autoregression model is applied to study the impacts of monetary policy shocks and housing demand shocks on various housing markets across China. We also investigate the role of house prices in the transmission mechanism of monetary policy. The results reveal that in first-tier cities, raising interest rates has a stronger negative effect on house prices. Also, as house prices decrease in first-tier cities, private consumption declines sharply. There is a stronger role of housing markets in the transmission of monetary policy shocks in these cities. Our findings indicate that interest rate adjustment could effectively curb spikes in housing prices in the first-tier cities, but the impact of such adjustments on household consumption must also be considered.     

Effects of house-sale restrictions in China: a difference-in-difference approach

Chinese housing prices have increased rapidly in recent years. Concerned about the potential for a burst bubble, the Chinese government has been intervening in the market to rein in the rising price trend since 2010. In March 2017, a new housing policy, called house-sale restriction, was developed to curtail speculation in the Chinese housing market. This study uses a difference-in-difference model to examine the policy effect of this new restriction. By using house-sale and price data of 43 cities across 30 provinces in China, we find that house-sale restrictions could effectively reduce speculation in the market as well as decrease housing prices in the short term. This study analyses only the short-term effect of this new housing policy; the long-term effect requires further examination.     

房地产价格、泰勒规则与宏观经济调控——基于2000-2010年我国宏观经济数据的检验

央行是否应该针对房地产价格制定货币政策进行调控,一直是学者们关注的焦点,但研究结果仍存在分歧。基于2000-2010年我国季度经济数据,对比检验了融入房地产价格的泰勒规则与标准泰勒规则对我国制定货币政策调控宏观经济的适用性。实证结果表明,依据标准泰勒规则所制定的利率政策,可以降低央行损失函数值,提高利率政策有效性。这一结果意味着,在房地产价格波动不影响物价稳定和经济增长的情况下,央行不应针对房地产价格进行调控。也就是说,央行需考虑房地产价格波动与通货膨胀和产出之间的相关关系,判断其对政策目标的潜在影响,制定利率政策对宏观经济进行调控。     

Housing price fluctuations and financial risk transmission: a spatial economic model

Financial risk derived from housing price fluctuations in China garnered much public concern recently. Based on the theoretical analyses of the transmission of financial risk from housing price fluctuations, this paper establishes panel spatial Durbin models to empirically analyse housing price fluctuations and financial risks transmission from a spatial economic perspective. Employing the panel provincial data from 1999–2015, we conduct an analysis on the 30 provinces in China as well as a comparison among the Eastern, Middle and Western regions of China. The results indicate that: (1) The soaring housing prices driven by bank credit, real estate developers’ heavy investment, local governments’ land revenue and individuals and households demands leads to financial risk in various sectors; (2) due to the ‘substitution effect’, the capital agglomeration in metropolis from bank credits, real estate developers, and individuals and households furthers the amassment of financial risks; (3) housing prices have a significant spatial contagion effect throughout the country, and financial risk could directly transmit across provinces through housing price fluctuations; (4) financial risks could indirectly transmit across provinces via the ‘imitative behaviour’ or ‘driving effect’ of different sectors for different regions of China.     

房价对城市全要素生产率的影响机制研究——基于Tobit模型实证分析

基于2008—2016年全国35个大中型城市的面板数据,利用Tobit模型和投入导向型的数据包络分析(DEA)方法探究城市房屋销售价格对城市全要素生产率的影响。研究发现：在整体维度城市房价与城市生产率呈现先增加后减少的倒U型曲线,且城市的房价水平超过了“房住不炒”的最高临界值,房价上升的抑制效应显著。房价对于不同人口规模城市的生产率影响具有异质性,在人口规模达到特大城市时,房价与城市生产率呈现倒U型特征,人口规模达到超大城市后,房价与城市生产率倒U型特征消失。不同类型城市房价与城市生产率之间的倒U型关系和城市的异质性特征表明,我国大中型城市应进行房价的“一城一策”精细化调节,“房住不炒”的政策定位具有坚实理论依据和现实必然。     

What are the effects of fiscal policy on asset markets?

We investigate the link between fiscal policy shocks and asset markets. Our results show that spending shocks have: a positive and persistent effect on GDP in the U.S. and in the U.K., while for Germany and Italy, such impact is temporary; a positive and persistent effect on housing prices; a negative effect on stock prices; and mixed effects on the price level. A VAR counter-factual exercise suggests that fiscal shocks play a minor role in the asset markets of the U.S. and Germany, and substantially increase the variability of housing and stock prices in the U.K., while government revenue shocks have increased volatility in Italy.     

北京市房地产价格影响因素分析及限购政策绩效评估

随着经济的高速发展,城市的房价也呈现出急剧攀升的态势,脱离了正常的价格波动轨道,大大增加了城市居民的生活成本。以首都北京为例,房价的上升幅度远远超过预期。在经济理论的基础上,运用近几年北京房地产行业的统计数据建立回归模型,可以对影响北京房价的因素进行分析,并且可以对北京市政府2011年2月出台的＂京八条＂限购政策进行绩效评估。     

上市公司、资本虹吸与房价的地区差异

鉴于上市公司与房价地区差异的研究鲜见,文章首次从地区层面寻找上市公司影响房价的微观传导路径.研究发现,上市公司从一级市场上融资、内部人在二级市场上减持均会"虹吸"全国资金,增加当地货币资本,进而正向影响房价,即高融资或高减持地区成为"虹吸方";融资虹吸与减持虹吸对房价的影响存在差异,减持虹吸引起财富集聚,对房价的影响更多地由富裕阶层的购房需求所推动,而融资虹吸带来的财富影响比较分散.地区股票市值与房价呈现螺旋增长关系,在股市上涨期间替代效应占主导地位,两者的增长率负相关;而在下跌期间财富效应占主导地位,两者的增长率正相关.文章从企业层面阐释了房价地区差异的金融成因及路径,并试图厘清股票市场与房价的增长结构,为地区经济的协调发展提供了参考.     

人民币汇率波动与货币政策调控难度

文章采用动态随机一般均衡（DSGE）模型研究了中国货币政策实施时不能忽略的人民币汇率波动特征。文章构建了人民币汇率波动与中国货币政策及其宏观经济系统影响机制的理论模型,并在模型参数校准的基础上进行了政策模拟。研究结果表明,较大的人民币汇率波动会在一定程度上减弱中国货币政策的调控效果,但是对每个变量冲击响应的影响程度有所不同。较大的人民币汇率波动将显著干扰货币政策对宏观经济需求的调控,人民币汇率升值波动幅度较大时,货币政策对需求变量的调控作用会减弱,但不会影响相关需求变量在不同时点的冲击响应走势特征。较大的汇率波动会减弱利率上行对出口的负面影响,有利于缓解货币政策对出口的负面冲击,但会导致贸易条件（出口价格和进口价格的比值）进一步恶化。     

中国改革历程中的产出和价格波动:货币所扮演的角色

中国经济正在其改革历程中经历着经济增长和通货膨胀的周期性波动.关于货币在这些波动中所起的作用,经济学家有着不同的观点.该文运用基于交易方程式的结构化 VEC 模型,对这些观点进行了探讨.我们发现,在长期,货币对产出和价格的变化做出适应性调整,而并非这些变化的原因.而在短期,价格变动要归因于那些对货币和价格有持久影响而对真实产出没有持久影响的冲击.这些冲击对多数的货币波动负责,并且强烈地影响产出.     

房价波动、调控政策立场估计及其影响效应研究--基于1998-2014年数据的实证分析

调控政策对房价的反应、立场和影响效应是政府政策设计的重要依据,2007年底金融危机爆发后,调控政策和房价波动都发生了显著变化。文章以2008年为界,构建了三个房价和政策变量的月度时间序列样本：1998-2007年、2008-2014年和1998-2014年,在此基础上,对2008年前后调控政策的反应、立场和影响效应进行了实证分析。结果表明：(1)实际贷款利率和货币供应量在2008年前后对目标变量的反应是交错变化的,货币供应量对房价的反应与假定相反,货币政策并未对房价做出充分反应,而财政和土地政策表现出了较好的反应结果。(2)货币供应量对房价波动的影响最大,是房价持续上涨的重要原因,2008年以来,固定资产投资的影响非常显著,脉冲响应分析发现利率只能在短期内对房价产生负向影响,除土地政策外,货币和财政政策皆会在长期内对房价产生稳定影响。文章的研究为推进房价政策调控和房地产市场健康稳定发展提供了可靠的经验支持。     

中国改革历程中的产出和价格波动：货币所扮演的角色

中国经济正在其改革历程中经历着经济增长和通货膨胀的周期性波动。关于货币在这些波动中所起的作用，经济学家有着不同的观点。本文运用基于交易方程式的结构化VEC模型，对这些观点进行了探讨。我们发现，在长期，货币对产出和价格的变化做出适应性调整，而并非这些变化的原因。而在短期，价格变动要93因于那些对货币和价格有持久影响而对真实产出没有持久影响的冲击。这些冲击对多数的货币波动负责，并且强烈地影响产出。     

The asymmetric impacts of monetary policy on housing prices: A viewpoint of housing price rigidity

Previous studies have discovered the defensive characteristics of housing prices, which is also known as downward price rigidity. This paper discusses whether this feature would result in an asymmetric relationship between housing prices and monetary policy. This paper first uses the loss aversion behavior of traders to assess the viability of housing price rigidity in the housing market and to deduce further that if downward housing price rigidity actually existed, then the impact of monetary policy on housing prices should be asymmetric. For empirical tests, this paper uses data from the UK housing market and then uses the money supply as the proxy variable of monetary policy. The relationships between these two variables are observed. This paper performs estimation using both traditional and threshold error correction models by comparing the coefficients of both models. The results indicate that housing price is indeed asymmetrically adjusted to money supply. When housing prices increase to reflect a loose monetary policy, a modification behavior is evident. Conversely, housing prices cannot easily reflect a tight monetary policy. This result indicates that housing prices tend to overreact in upturn and underreact in downturn. The results imply that when implementing relevant policies for the housing market, the government should consider the asymmetry of housing price changes. Otherwise, the situation can easily result in the creation of a bubble or the collapse of the housing market because of incorrect policies.