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.     

On the cyclicity of regional house prices: New evidence for U.S. metropolitan statistical areas

This paper is mainly concerned with the analysis of regional house price cycles. We introduce a wavelet transform based metric to study the housing cycle synchronization across the largest U.S. MSAs with a focus on the recent housing bubble. We derive several conclusions: (i) We show that regional housing cycle dissimilarities are significantly and strongly connected to geography. (ii) After the burst of the recent housing bubble there was greater significant co-movement in shorter cycles for a short period of time. (iii) Coastal regions, which have a higher house price volatility compared to other MSAs are also those with a higher degree of house supply regulation. (iv) A mortgage-rate reduction decrease the dissimilarity between the MSAs and the national housing cycle before the end of 2006 at higher frequencies. Hence, monetary policy is partially effective, whereas effectiveness depends on time, frequency, and the considered MSA.     

城市住房价格指数编制方法的拓展设计及实证检验

科学监测城市房价走势,在当前环境下尤为重要。为拓展国际通行方法编制国内单一城市房价指数的适用性,引入样本匹配重复交易法构建房价指数,以提高样本容量与可比性。基于上海数据的实证结果表明,相较于传统重复交易法和特征价格法,样本匹配重复交易法能更准确地反映住房价格变动,结果异常波动性更小,噪声影响程度更低,在克服样本代表性误差和变量缺失误差方面效果更显著,对编制国内城市房价指数具有较好应用价值。     

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.     

住房流动性对房价指数的影响及其定量分析

目前中国城市住房市场不稳定程度较高,这不仅体现为房价的迅速变化,而且反映在交易量的大幅波动上,后者就是住房流动性的变化。有学者研究指出,以实际交易价格为基础的房价指数可能会低估住房市场的波动程度。为了更准确地把握住房市场的运行状态,本文借鉴美国MIT的相关技术,分析了住房流动性（交易活跃程度）对房价指数的影响,并尝试将流动性信息引入房价指数当中。我们发现,住房流动性对于房价指数有较大影响,且符合人们对于市场走势的直观判断,能够较好地反映市场转折点。     

The regulatory tax and house price appreciation in Florida

Much attention was given to the soaring price of housing that took place in different parts of the country in the 1990s and the first half of the current decade. Traditional explanations for the increase include rising land values and costs of construction, but a strand of literature, popularized by Glaeser et al. [Glaeser, Edward L., Gyourko, Joseph, Saks, Raven, 2005a. Why have housing prices gone up? National Bureau of Economic Research Working Paper #11129; Glaeser, Edward L., Gyourko, Joseph, Saks, Raven, 2005b. Why is manhattan so expensive? Regulation and the rise in housing prices. The Journal of Law and Economics 48(2)], has looked at the role of land use regulations and posits that complying with them imposes a regulatory tax on housing consumers. In this paper, we apply and extend Glaeser and Gyourko’s methodology in order to estimate the regulatory tax on an individual house level in a set of Florida metropolitan areas. Our novel data address some of the quality measurement concerns raised about the Glaeser and Gyourko methodology and allow us to look at the evolution of the regulatory tax over a 10-year period. We find that the tax is an important component of sales price and that as a percentage of sales price has increased in a majority of Florida’s MSAs. In addition, we decompose the overall house price increase into land, materials and regulatory components and find that increasing stringency in the regulatory environment within Florida represents a substantial portion of the run-up in house prices in most metropolitan areas.     

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.     

List price and sales prices of residential properties during booms and busts

This paper investigates the relationship between the list and sale price of residential properties over the housing cycle. In down or normal markets the list price generally exceeds the sales price; however, when the housing market is strong, homes sell for more than their list price. This observation is not consistent with the assumptions made in the standard model of home sellers’ search behavior. We consider alternative models. In one, sellers set list prices based on their expectations of future changes in sales prices and the arrival rate of buyers; however, demand shocks occur. This model partially explains our data from the Belfast, U.K. housing market, but it fails to predict the list to sales price ratio during a sustained housing boom. We next describe a model where sellers’ endogenously select their search mechanism depending on the strength of the housing market. We find support for the conjecture that sellers switch to an auction-like model during housing booms. There also is evidence that during a downturn in the market, sellers’ list prices are sticky.     

Oil price shocks and economic growth: The volatility link

This paper shows that oil shocks impact economic growth primarily through the conditional variance of growth. Our comparison of models focuses on density forecasts. Over a range of dynamic models, oil shock measures and data, we find a robust link between oil shocks and the volatility of economic growth. We then develop a new measure of oil shocks and show that it is superior to existing measures; it indicates that the conditional variance of growth increases in response to an indicator of the local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of the growth volatility to oil price changes. The uncertainty about future growth is considerably lower than with a benchmark AR(1) model when no oil shocks are present.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Revealed preference for relative status: Evidence from the housing market

This paper investigates the value individuals place on their relative housing consumption as compared to absolute housing consumption. Using observed housing sales from three Ohio MSAs in 2000, a spatial Durbin hedonic price model provides total marginal willingness-to-pay estimates for both characteristics of housing units and those of its neighbors. Using this revealed-preference approach, we find evidence suggesting individuals do value relative house size, but the absolute effect dominates. For instance, the estimates indicate that if all homes in Columbus were to increase in size by 100 square feet, the net effect of impacts on absolute and relative consumption would be to increase house prices by $605 on average. This stands in contrast to the stated preference literature, which frequently find individuals to be willing to forgo absolute well-being in exchange for relative status gains.     

The spillover effects of infill developments on local housing prices

This paper examines the spillover effects of infill developments, which involve developing vacant or under-used parcels within existing urban areas that are largely developed, on local housing prices. Employing a difference-in-difference specification on a sample of 275 new developments and 55,887 sale transactions of houses in Singapore, we find that infill developments have a positive and persistent impact on local housing prices. The contagion effect is larger for infill developments that are built on teardown sites. The spillover effect can also be traced to the overpricing of new homes by developers. Overall, the evidence indicates that developers act as price leaders and contribute significantly to price discovery in the local housing market.     

House price risk and the hedging benefits of home ownership

Using a repeat-sales methodology, this paper finds that estimates of house price risk based on aggregate house price indices substantially underestimate the true size of house price risk. This is the result of the fact that aggregate house price indices average away the idiosyncratic volatility in house prices. Additional results show that the idiosyncratic risk exceeds the hedging benefits of home ownership. These results imply that for many home owners, owning a house may well add more price risk than it hedges away. These findings are based on a detailed dataset of individual housing transactions in the Netherlands.     

Housing and the Korean economy

This paper explores the nexus between housing and the Korean economy. It starts with an overview of the size, growth, and volatility of residential investment in conjunction with long-term resource allocation and short-term macroeconomic fluctuations. Then, the evolution of housing finance and its implications for recent house price run-up are discussed. The relationships among housing price, consumer spending, and inflation are also investigated. Particular attention is paid to the debate over house price bubbles, housing wealth effects on consumption, and the causality between house price and inflation. The paper concludes with a brief assessment of government intervention to stabilize house prices.     

Property taxes and home prices: A tale of two cities

We explore the influence of property taxes on home prices, taking advantage of a policy experiment of property taxation in Shanghai and in Chongqing starting from January 2011. Using the approach suggested by Hsiao, Ching and Wan (2012) we estimate hypothetical home prices in the absence of property taxation for Shanghai and Chongqing using home prices in other cities and provinces. We show that the OLS generates consistent estimators when the price series are non-stationary I(1) processes. We apply the model to a panel of average home prices of 31 cities and provinces in China, and find the property-tax experiment lowered the Shanghai average home price by 11%–15% but raised the Chongqing average home prices by 10%–12%. An examination of the policy details and data on prices by home types suggests the post-treatment price increase in Chongqing can be driven by a spillover effect from high-end to low-end properties.     

Housing prices and the business cycle: An empirical application to Hong Kong

This paper develops a two-agent, two-sector, open-economy DSGE model with a housing-market sector and a borrowing constraint. Contrary to standard conventions, domestic households are allowed to invest in foreign housing and vice versa. Using Bayesian methods, the model is applied to data for Hong Kong. We identify strong and robust housing wealth effects, and show that property prices are mainly driven by intratemporal preference perturbations rather than by disturbances in financial frictions or price mark up shocks. These disturbances also explain a non-negligible part of the volatility of consumption, GDP and employment.     

School Quality and Real House Prices: Inter- and Intrametropolitan Effects

This study focuses on explaining variations in real constant-quality house prices in jurisdictions located in multiple MSAs. Using a hedonic house price framework, we test competing theories of house price determination. Using two variants of the random coefficients model, we find that public school quality has a very large impact on real constant-quality house prices. Our results suggest that capitalization of school quality differences occurs on a per lot basis rather than per square foot of land. Also important to the explanation of variations in house prices are variables derived from urban theory, such as distance to the CBD, and from the amenity literature, such as a community's crime rate, arts, and recreational opportunities     

Impacts of Second Home Development on Housing Prices in the Southern Appalachian Highlands

This study estimates the value of socioeconomic, spatial and environmental attributes on housing prices of both urban and rural communities in the primary and second home areas of the Southern Appalachian Highlands, using the hedonic property price model. Distance and environmental attributes are valued more heavily in the rural communities of the second home area than in the urban communities of the primary home area. The effect of second homes on housing prices is mainly evident in the rural communities. Second home development impacts a home's value by US$2,378, or 4.2% of $56,245, the average value of a rural home.     

Estimation of variance of housing prices using spatial conditional heteroskedasticity (SARCH) model with an application to Boston housing price data

In this paper we investigate housing price volatility within a spatial econometrics setting. We propose an extended spatial regression model of the real estate market that includes the effects of both conditional heteroskedasticity and spatial autocorrelation. Our suggested model has features similar to those of autoregressive conditional heteroskedasticity (ARCH) in the time-series context. We utilize the spatial ARCH (SARCH) model to analyze Boston housing price data used by Harrison and Rubinfeld (1978) and Gilley and Pace (1996). We show that measuring the variability of housing prices is an important issue and our SARCH model captures the conditional spatial variability of Boston housing prices. We argue that there is a different source of spatial variation, which is independent of traditional housing and neighborhood characteristics, and is captured by the SARCH model.     

区域城市间住宅价格波动溢出效应的内涵分析

为了明确住宅价格波动溢出效应所包含的深层内涵,并为以后的空间计量经济建模数据分析提供理论阐释,通过对波纹效应和波动溢出效应的概念归纳得出住宅价格波动溢出效应的定义,从基础理论支撑、主体关系、客体影响因素和存在环境四个方面构建其理论框架。最后,从城市层级、人口迁移、城市经济基本面和心理预期四个类别进行多层次的内在因果关系分析,得出其完整的形成机理。