PRACTICAL TOOLS FOR POLICY ANALYSIS IN DSGE MODELS WITH MISSING SHOCKS

In this paper we analyze the propagation of shocks originating in sectors that are not present in a baseline dynamic stochastic general equilibrium (DSGE) model. Specifically, we proxy the missing sector through a small set of factors that feed into the structural shocks of the DSGE model to create correlated disturbances. We estimate the factor structure by either matching impulse responses of the augmented DSGE model to those generated by an auxiliary model or by using Bayesian techniques. We apply this methodology to track the effects of oil shocks and housing demand shocks in models without energy or housing sectors. Copyright © 2014 John Wiley & Sons, Ltd.     

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.     

A model of rental and owner-occupied housing

A complete model of owner-occupied and rental housing is developed. The model allows for the endogenous determination of rents, the user cost of owner-occupied housing and housing tenure choice by individuals. In the short run, structure prices are endogenous, while in the long run the size of the housing stock adjusts to equate structure prices to exogenous construction costs. Comparative static results emphasize the importance of marginal tax rates and distinguishing between the short and long run for a complete understanding of the impacts of inflation on housing markets.     

Racial prejudice in a search model of the urban housing market

A simple model of buyer search in an urban housing market is employed to demonstrate that if some whites are unwilling to sell housing to blacks competitive equilibria in which blacks pay more for housing than whites are sustainable. The model is also used to consider a number of issues in the literature on housing discrimination. Most important, it is shown that in equilibrium the housing market will be racially segmented under a wide variety of conditions.     

基于信息熵的多属性决策方法在商品住宅价格中的研究

在市场经济条件下,房地产价格在房地产经济发展和房地产经济运行中有着重要的功能和作用,因此,对房价变动的预测以及如何能够合理制定房价,显得尤为重要。价格的预测,究其本质,是一种体现在数值上的决策活动,本文结合了多属性综合决策模型与回归分析对房价进行了预测研究。通过选取影响房价的部分宏观因素,建立基于熵的多属性综合决策模型,得到各因素与房价之间的关系,并通过回归分析,对房价进行预测。     

Topographic amenities,building height,and the supply of urban housing

This article develops a housing supply model that treats explicitly the effects of location-specific amenities. The model employs a production function in which housing services are produced not only by structures, but also by access to and views of location-specific amenities. Housing supply is measured by building height. The model is tested with data from the city of Chicago. Access to and view of Lake Michigan are found to have a significant effect on the height of buildings.     

Public housing units vs. housing vouchers: Accessibility,local public goods,and welfare

We develop a general equilibrium model of residential choice and study the effects of two housing aid policies, public housing units and housing vouchers. Land is differentiated by both residential accessibility and local public goods, and the provision levels of local public goods are determined by property tax revenues and neighborhood compositions. Households differ in their incomes and preferences for local public goods. Housing aid policies are financed by general income taxes. We discuss how the location of public housing units is a fundamental policy variable, in addition to the numbers and sizes of units, and argue that vouchers not only cause less distortion for social welfare compared to public housing, but may also improve overall welfare.     

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。     

保障性住房中各主体之间的博弈分析

在我国的住房市场中,保障性住房是很重要的组成成分,其福利性的特点使其与一般的商品房在开发与运作上有明显的不同。然而长期以来,由于中央政府、地方政府、开发商之间种种制约因素和局部利益冲突,使得保障性住房并没能够发挥住房保障的作用。文章通过对中央政府与地方政府,地方政府与开发商分别建立"智猪博弈"模型、委托-代理关系模型,剖析问题根源所在,寻求解决保障性住房问题的途径,并提出相对应的对策与建议。     

On the welfare cost of rare housing disasters

This paper examines the welfare cost of rare housing disasters characterized by large drops in house prices. I construct an OLG general equilibrium model with recursive preferences and housing disaster shocks. The likelihood and magnitude of housing disasters are inferred from historical housing market experiences in the OECD. The model shows that despite the rarity of housing disasters, Canadian households would willingly give up 6 percent of their non-housing consumption each year to eliminate the housing disaster risk. The welfare evaluation of this risk, however, varies considerably across age groups. The risk translates into a welfare loss of as much as 16 percent of annual non-housing consumption for the old, but a welfare gain of 2 percent for the young. This asymmetry stems from the fact that, compared to the old, younger households suffer less from house price declines in disaster periods, due to smaller holdings of housing assets, and benefit from lower house prices in normal periods, due to the negative price effect of disaster risk.     

Can a stochastic cusp catastrophe model explain housing market crashes?

Like stock market prices, housing prices often exhibit temporary booms and busts. A possible explanation for the observed abrupt changes is offered by the stochastic catastrophe model. This paper addresses the question whether the catastrophe model can describe and predict the dynamics of housing markets. We fit a stochastic cusp catastrophe model to empirical housing market data for six OECD countries, US, JP, UK, NL, SE and BE. Two different estimation approaches are considered – Cobb׳s method and Euler discretization. The analysis shows that while Cobb׳s approach describes the long-run stationary density better, Euler discretization is more tailored for time series, as it provides better one-step-ahead predictions. Proceeding using the Euler discretization method we discuss the dynamics of housing markets in terms of the multiple equilibria cusp catastrophe model. By considering the long-term interest rate as an exogenous variable we obtain new insights into the policy implications of interest rate levels, in particular concerning the stability of housing markets.     

The baby boom,the baby bust,and the housing market

This paper explores the impact of demographic changes on the housing market in the US, 1st by reviewing the facts about the Baby Boom, 2nd by linking age and housing demand using census data for 1970 and 1980, 3rd by computing the effect of demand on price of housing and on the quantity of residential capital, and last by constructing a theoretical model to plot the predictability of the jump in demand caused by the Baby Boom. The Baby Boom in the U.S. lasted from 1946-1964, with a peak in 1957 when 4.3 million babies were born. In 1980 19.7% of the population were aged 20-30, compared to 13.3% in 1960. Demand for housing was modeled for a given household from census data, resulting in the finding that demand rises sharply at age 20-30, then declines after age 40 by 1% per year. Thus between 1970 and 1980 the real value of housing for an adult at any given age jumped 50%, while the real disposable personal income per capita rose 22%. The structure of demand is such that the swelling in the rate of growth in housing demand peaked in 1980, with a rate of 1.66% per year. Housing demand and real price of housing were highly correlated and inelastic. If this relationship holds in the future, the real price of housing should fall about 3% per year, or 47% by 2007. The theoretical model, a variation of the Poterba model, ignoring inflation and taxation, suggests that fluctuations in prices caused by changes in demand are not foreseen by the market, even though they are predictable in principle 20 years in advance. As the effects of falling housing prices become apparent, there may be a potential for economic instability, but people may be induced to save more because their homes will no longer provide the funds for retirement.     

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.     

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.     

Measuring the diffusion of housing prices across space and over time: Replication and further evidence

Brady (Journal of Applied Econometrics, 2011, 26(2), 213–231) studies how fast and how long a change in housing prices in one region affects its neighbors by estimating the impulse response functions using a spatial autoregressive model (SAR). This paper replicates Brady's empirical results, but reports different SAR test statistics. Additional robustness checks are conducted by analyzing three different housing price indexes covering a more extensive period. Analysis shows that the model specifications and model estimates vary with the housing price indexes.     

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.     

Evolution of urban housing stocks: A model applied to Baltimore and Houston

Historical simulations of urban residential growth in Baltimore and Houston based on a model of the growth process which has two distinct components are presented. The vintage component utilizes the growth of income and population, and an assumption that housing is putty-clay, to predict the age distribution of the housing stock in each period. The spatial component of the model determines where this housing construction will take place according to (1) housing is built on vacant land and (2) the pattern of construction obeys the rules of the standard monocentric models. Housing is demolished when economically obsolete. The putty-clay (vintage) aspect of the model produces fairly accurate city-wide vintage distributions, but there is much more mixing of vintages and income (in Baltimore) by location than predicted, even under monocentric assumptions most favorable to mixing.     

A commodity hierarchy model of the rental housing market

This paper presents and analyzes a theoretical model of the rental housing market which addresses the durable-good nature and the heterogeneous nature of housing, while focusing on the quality distribution of housing units. Units of different qualities are viewed as distinct substitute commodities. New construction rates, deterioration rates, rental prices, and stocks are considered to be endogenous. Equilibrium concepts are introduced; both the long-run and short-run equilibria are shown to exist and to be unique. Comparative static results are established. The model's utility is demonstrated by its application to the analysis of several housing market programs.     

基于社会、经济可持续发展的城镇廉租住房租金定价及补贴模型研究——以广州市为例

利用可持续发展理论,从社会、经济可持续发展视角对廉租住房租金与补贴方案的制定进行理论分析。从廉租户的房租负担能力、政府财政负担能力、建造运营成本、区域因素与个别因素等方面分析影响廉租住房租金和补贴的因素。结合广州现行的廉租住房租金政策,逐步建立符合可持续、可负担原则和具备动态调整性的廉租住房实物配租租金定价模型与货币配租补贴模型,并利用案例对其检验,最后得出结论。