Risk shocks and housing supply: A quantitative analysis

This paper analyzes the role of stochastic uncertainty in a multi-sector housing model with financial frictions. We include time varying uncertainty (i.e. risk shocks) in the technology shocks that affect housing production and provide estimates of the time-series properties of risk shocks by using firm level productivity data. The analysis demonstrates that risk shocks to the housing production sector are a quantitatively important impulse mechanism for understanding housing price movements. Specifically, the model can match the volatility of housing prices observed in the data. It is also demonstrated that adjustment costs are important in replicating the contemporaneous correlation of housing prices with GDP and residential investment. Critically, bankruptcy costs act as an endogenous markup factor in housing prices and are an important determinant of house price volatility. However, in comparison to housing demand shocks, risk shocks have low explanatory power for real quantities.     

What factors drive the price–rent ratio for the housing market? A modified present-value analysis

We consider which factors determined the price–rent ratio for the housing market in 18 U.S. metropolitan statistical areas (MSAs) and at the national level over the period of 1975–2014. Based on a present-value framework, our proposed empirical model separates the price–rent ratio for a given market into unobserved components related to the expected real rent growth and the expected housing return, but is modified from standard present-value analysis by also including a residual component that captures non-stationary deviations of the price–rent ratio from its present-value level. Estimates for the modified present-value model suggest that the present-value residual (PVR) component is always important and sometimes very large at the national and MSA levels, especially for MSAs that have experienced frequent booms and busts in the housing market. In further analysis, we find that house prices in MSAs that have larger PVR components are more sensitive to mortgage rate changes. These are also the MSAs with less elastic housing supply. Also, comparing our results with a recent statistical test for periodically-collapsing bubbles, we find that MSAs with large estimated PVR components are the same MSAs that test positively for explosive sub-periods in their price–rent ratios, especially during the 2005–2007 subsample. Our approach allows us to estimate the correlation between shocks to expected rent growth, the expected housing return, and the PVR component. We find that the expected housing return and movements in the PVR component are highly positively correlated implying an impact of the expected housing return on house prices that is amplified from what a standard present-value model would imply. Our results also show that most of the variation in the present-value component of the price–rent ratio arises due to the variation in the expected housing return.     

The shale revolution and shifting crude dynamics

Oil price fluctuates in response to both demand and supply shocks. This paper proposes a new methodology that allows for timely identification of the shifting contribution from the two types of shock through a joint analysis of crude futures options and stock index options. Historical analysis shows that crude oil price movements are dominated by supply shocks from 2004 to 2008, but demand shocks have become much more dominant since then. The large demand shock following the 2008 financial crisis contributes to the start of this dynamics shift, whereas the subsequent shale revolution has fundamentally altered the crude supply behavior.     

Supply constraints and housing market dynamics

Although the volatility of house prices is often ascribed to demand-side factors, constraints on housing supply have important and little-studied implications for housing dynamics. I illustrate the strong relationship between the volatility of house prices and the regulation of new housing supply. I then employ a dynamic structural model of housing investment to investigate the mechanisms underlying this relationship. I find that supply constraints increase volatility through two channels: First, regulation lowers the elasticity of new housing supply by increasing lags in the permit process and adding to the cost of supplying new houses on the margin. Second, geographic limitations on the area available for building houses, such as steep slopes and water bodies, lead to less investment on average relative to the size of the existing housing stock, leaving less scope for the supply response to attenuate the effects of a demand shock. My estimates and simulations confirm that regulation and geographic constraints play critical and complementary roles in decreasing the responsiveness of investment to demand shocks, which in turn amplifies house price volatility.     

DISENTANGLING DEMAND AND SUPPLY SHOCKS IN THE CRUDE OIL MARKET: HOW TO CHECK SIGN RESTRICTIONS IN STRUCTURAL VARS

Sign restrictions have become increasingly popular for identifying shocks in structural vector autoregressive (SVAR) models. So far there are no techniques for validating the shocks identified via such restrictions. Although in an ideal setting the sign restrictions specify shocks of interest, sign restrictions may be invalidated by measurement errors, data adjustments or omitted variables. We model changes in the volatility of the shocks via a Markov switching (MS) mechanism and use this device to give the data a chance to object to sign restrictions. The approach is illustrated by considering a small model for the market of crude oil. Earlier findings that oil supply shocks explain only a very small fraction of movements in the price of oil are confirmed and it is found that the importance of aggregate demand shocks for oil price movements has declined since the mid 1980s. Copyright © 2013 John Wiley & Sons, Ltd.     

Empirical evidence on the reaction speeds of housing prices and sales to demand shocks

This article studies the co-movement and dynamics between price movements and transactions in the housing market using data for the period 1988–2008 from Finland. While the previous related literature examines the reactions of sales and prices to an interest rate shock only, this study investigates the responses to income and debt shocks as well. The empirical estimations show that the response of prices to demand shocks is substantially slower than that of sales. The estimated reactions of sales substantially differ from those reported in the earlier literature. The reaction patterns can create the kind of strong positive co-movement between price movements and sales volume and the kind of negative correlation between price level and sales that have been found in several housing markets.     

Oil structural shocks,bank-level characteristics,and systemic risk: Evidence from dual banking systems

By performing a structural VAR analysis on oil price shocks, we provide an evidence on how the origins of oil price shocks impact the risk level of banks in oil-exporting countries and whether bank-level characteristics can influence the sensitivity of risk to oil shocks. When conducting panel regression analysis, we document the following findings. First, not all shocks have the same effect on bank risk. Due to oil supply shocks, the increase in oil price raises bank risk, whereas the similar increase in price due to economic expansion or oil-market specific demand reduces that risk. Second, the business model (whether the bank is Islamic or conventional), size, income diversification, profitability, and financial leverage influence the bank risk exposure to oil shocks differently. Third, the two major recent crises (global financial crises and COVID-19 pandemic) magnified bank risk exposure to oil supply shocks and speculative oil demand shocks. Overall, the structural oil shocks explain a large fraction of the variation in financial stability in GCC countries.     

Gasoline prices,transport costs,and the U.S. business cycles

The effects of gasoline prices on the U.S. business cycles are investigated. In order to distinguish between gasoline supply and gasoline demand shocks, the price of gasoline is endogenously determined through a transportation sector that uses gasoline as an input of production. The model is estimated for the U.S. economy using five macroeconomic time series, including data on transport costs and gasoline prices. The results show that although standard shocks in the literature (e.g., technology shocks, monetary policy shocks) have significant effects on the U.S. business cycles in the long run, gasoline supply and demand shocks play an important role in the short run.     

Macroeconomic Uncertainty and Oil Price Volatility

This paper shows that higher macroeconomic uncertainty causes higher oil price volatility. Regimes of low and high uncertainty are identified in a threshold VAR model in which the effects of structural oil demand and supply shocks are estimated. The results show that higher macroeconomic uncertainty, as measured by global industrial production volatility, significantly increases the sensitivity of oil prices to shocks in oil demand and supply. This occurs as uncertainty lowers the price elasticity of oil demand and supply. The difference in the estimated oil price elasticities is economically meaningful as the price impact of a similar change in oil production might double when it hits the economy in uncertain times. As such, varying uncertainty can explain why oil price volatility is typically higher during periods such as financial crises and recessions, and why oil price volatility changes over time more generally.     

Self-reinforcing effects between housing prices and credit

The financial crisis has brought the interaction between housing prices and household borrowing into the limelight of the economic policy debate. This paper examines the nexus of housing prices and credit in Norway within a structural vector equilibrium correction model (SVECM) over the period 1986q2–2008q4. The results establish a two way interaction in the long-run, so that higher housing prices lead to a credit expansion, which in turn puts an upward pressure on prices. Interest rates influence housing prices indirectly through the credit channel. Furthermore, households’ expectations about the future development of their own income as well as in the Norwegian economy have a significant impact on housing price growth. Dynamic simulations show how shocks are propagated and amplified. When we augment the model to include the supply side of the housing market, these effects are dampened.     

我国普通商品住房供求的影响因素及其稳定性研究

本文利用1997年到2009年全国以及各地区的房地产面板数据,借鉴蛛网模型的相关理论,构建供给与需求的联立方程,选择固定效应IV估计法拟合面板联立方程模型,对普通商品住房供求的影响因素及其稳定性进行了实证研究。结果表明,我国的普通商品住房市场处于不稳定状态,普通商品住房当期及滞后期的价格、城镇人均可支配收入、城镇就业人口、土地购置面积、经济适用房、别墅的供求状况等因素都对普通商品住房的供求变化产生较为显著的影响。     

Commodity house prices

This paper studies how commodity price movements have affected the local house prices in commodity-dependent economies, Australia and New Zealand. We build a geographically hierarchical empirical model and find that the commodity prices influence local house prices directly and also indirectly through macroeconomic variables. The impacts of commodity price changes are analogous to “income shocks” rather than “cost shocks”. Regional heterogeneity is also observed in terms of differential dynamic responses of local house prices to energy versus non-energy commodity price movements. The results are robust to alternative approaches. Directions for future research are also discussed.     

Oil Prices and Personal Consumption Expenditures: Does the Source of the Shock Matter?

This paper studies the effect of structural oil shocks on personal consumption expenditures (PCE). First, we estimate a nonlinear simultaneous equation model, compute impulse responses by Monte Carlo integration, and conduct a test of the symmetry of the impulse response functions. We find that aggregate PCE responds asymmetrically to positive and negative oil‐specific demand shocks. Second, we find that aggregate PCE responds negatively to positive oil demand shocks, while adverse oil supply shocks are of limited effect. Third, we find important heterogeneity in the magnitude, sign and timing of the disaggregate PCE responses to structural shocks in the crude oil market. Our results clearly indicate that the response of PCE to an unexpected oil price increase depends on the source of the oil price shock. Our findings are robust to different nonlinear transformations for the real price of oil.     

Oil price shocks and credit spread: Structural effect and dynamic spillover

This study analyzes the heterogeneous response of U.S. credit spread to global oil price shocks by building an extended structural vector autoregressive model (SVAR), which can distinguish among the U.S. and non-US oil supply shocks, aggregated demand shocks and oil market-specific demand shocks behind the real oil prices. Meanwhile, a spillover index model developed by Diebold and Yilmaz (2012) (hereafter D.Y. (2012)) is used to estimate the link between oil price shocks and the U.S. credit spread over time. The results show that (i) the credit spread does not respond to global oil supply shocks and non-US oil supply shocks, but has a negative reaction to the U.S. oil supply shocks, aggregate demand shocks, and oil-market-specific demand shocks. (ii) There exists a close connectedness between oil price shocks and the U.S. credit spread, and the link fluctuates cyclically and relates to the economic cycle and the U.S. shale oil revolution. (iii) The spillover from different oil price shocks to the U.S. credit spread shows significant heterogeneity over time. Our findings suggest that policymakers and investors can better track the U.S. credit spread changes using oil price information.     

The nonlinear effect of oil price shocks on financial stress: Evidence from China

This paper examines the nonlinear effects of different types of oil price shocks on China’s financial stress index (FSI). For this purpose, we use newly proposed framework by Ready (2018) to decompose oil prices into supply, demand and risk shocks. Then, we use a Markov regime-switching (MRS) model to investigate the nonlinear effects of these oil price shocks on China’s FSI. The empirical results show that the effects of three oil price shocks are nonlinear under different regimes. In particular, oil supply shocks mainly have a significantly positive effect on China’s FSI in the low-volatility state; demand shocks have negative effects on China’s FSI in different regimes, but this effect is larger in the low-volatility state; the effect of risk shocks on China’s FSI is the opposite, and it is positive in the high-volatility state but negative in the low-volatility state.     

Are housing benefit an effective way to redistribute income? Evidence from a natural experiment in France

In this paper, I show that in-kind benefit such as a housing benefit program may have a significant impact on the price of the subsidized good. I use a French housing benefit reform to evaluate the impact of the subsidy on the level of rents. The results indicate that one additional euro of housing benefit leads to an increase of 78 cents in the rent paid by new benefit claimants, leaving only 22 cents available to reduce their net rent and increase their consumption. This large impact of housing benefit on rents appears to be the result of a very low housing supply elasticity. I show that the housing benefit reform induced additional demand, not only from low income households but also from students who used the benefit to become independent. Unfortunately, this increase in demand was unmatched by increasing housing supply in the short and middle term. The only possible effect of the reform is a small increase in housing quality. These results raise questions about the use of such in-kind transfers when the supply of the subsidized good is almost inelastic. It is therefore very important to estimate the incidence of the subsidy when assessing the efficiency of such welfare programs.     

城市发展与住房意愿支付价格

以城市在岗职工的平均劳动收入水平和反映城市生活质量的各类宜居性指标建立了中国35个主要城市的城市发展与住房需求关系的模型,并以此估计了城市住房意愿支付价格。实证结果表明,城市在岗职工的平均劳动收入水平和以各类宜居性指标反映的城市生活质量可以解释70%左右住房价格的城市间差异。虽然城市劳动收入对住房意愿支付价格的影响仍然很大,但随着社会经济的发展,中国主要城市的生活质量对住房意愿支付价格增长的贡献有逐步增大的趋势。     

我国新建住宅长期供给价格弹性估算——基于35个大中城市的实证研究

大多数住宅模型和政策分析,都直接或间接依赖于住宅供给价格弹性的估计值：为了应对市场需求冲击,是多供给住房还是提高住宅价格？基于Mayo（1981）构建的模型,估算了我国35个主要大中型城市的新建住宅供给价格弹性。根据流量模型,2000-2007年我国的新建住宅价格弹性系数在4-11之间,2008到2013年的价格弹性在5-13之间。而存量调整模型得到了截然不同的估算结果：2008-2013年我国的新建住宅供给价格弹性在1-6之间,更精确的估算出了我国新建住宅供给市场的价格弹性。     

限价商品住房管理模式思考

近年来,随着我国房地产市场的持续升温,各地的房价涨幅居高不下,已经对城市中低收入居民的基本住房需求造成很大影响.为了缓解供需矛盾,稳定住房价格,中央提出了"双限双竞"的宏观调控措施,各地也纷纷依据精神推出了限价商品住房供应政策.文章主要从限价商品住房管理中存在的主要问题入手,结合国内几个城市的相关政策经验及存在问题,提出对限价商品住房管理模式的几点思考.     

The price responsiveness of housing supply in OECD countries

The responsiveness of housing supply to changes in prices bears important implications for the evolution of housing prices and the speed of adjustment of housing markets. Based on a stock-flow model of the housing market estimated within an error correction framework, this paper estimates the long-run price elasticity of new housing supply in 21 OECD countries. Estimates suggest that the responsiveness of housing supply to price changes varies substantially across countries. It is relatively more flexible in North America and some Nordic countries, while it is more rigid in continental European countries and in the United Kingdom. The responsiveness of housing supply depends not only on national geographical and urban characteristics but also on policies, such as land use and planning regulations.