US House Prices Research Articles

Personal Experiences and Expectations About Aggregate Outcomes

We use novel survey data to estimate how personal experiences affect household expectations about aggregate economic outcomes in housing and labor markets. We exploit variation in locally experienced house prices to show that individuals systematically extrapolate from recent locally experienced home prices when asked for their expectations about US house price changes over the next year. In addition, higher volatility of locally experienced house prices causes respondents to report a wider distribution over expected future national house price movements. We find similar results for labor market expectations, where we exploit within-individual variation in labor market status to estimate the effect of own experience on national labor market expectations. Personally experiencing unemployment leads respondents to be significantly more pessimistic about future nationwide unemployment. The extent of extrapolation is unrelated to proxies for how informative personal experiences are, and is more pronounced for less sophisticated individuals.

The Market Structure of Securitisation and the US Housing Bubble

Housing finance and, specifically, the subprime private label securitisation market in the US, was at the epicentre of the global financial crisis. Excessive debt expansion in the run-up to the crisis resulted in credit risk, under-identified and mispriced ex ante, and in systemic risk. This paper considers the role of financial innovation in debt markets and the changing market structure of securitisation in the evolution of the US housing price bubble. New financing vehicles contributed to growing risk, but the more salient factor was the change in the structure of securitisation, which led to unsustainable levels of debt.

US House Price Bubbles and Busts

We use data for a large panel of US Metropolitan Statistical Areas (US MSAs) in order to assess the ex ante likelihood of house price bubbles. We employ a vector error correction–based model in order to project house prices under different scenarios. Projected house price (and employment) distributions are generated for each MSA. The model’s predictions are compared to actual outcomes—focusing primarily on the house price bubble and bust of the Great Recession. In applying these results to local governments, we document a substantial rise in property tax rates during the recent crisis. Results from this exercise suggest that interactions between the property tax and house price fluctuations may have exacerbated the bubble and bust. More generally, our analysis underscores the complexity of forecasting bubbles and cautions against attempting to do so using the notion of a national housing market. Even in recent years, housing markets are heavily influenced by local supply and demand conditions.

Regional US House Price Formation: One Model Fits All?

Does a "one model fits all" approach apply to the econometric modeling of regional house price determination? To answer this question, we utilize a panel of 100 US Metropolitan Statistical Areas over the period 1980q1-2010q2. For each area we estimate a separate cointegrated VAR model, focusing on differences in the effect of subprime lending and lagged house price appreciation. Our results demonstrate substantial differences in the importance of subprime lending for house price determination across regional housing markets. Specifically, we find a greater impact of subprime lending in areas with a high degree of physical and regulatory restrictions on land supply. Likewise, lagged house price appreciation - interpreted as capturing an adaptive expectation channel - is found to be more important in areas where the supply of dwellings is more constrained, in areas located in a state with non-recourse lending and in more populous areas. Our results also suggest that disequilibrium constellations are restored more slowly in areas located in a state with non-recourse lending.

Forecasting House Prices in the United States with Multiple Structural Breaks

The boom-bust cycle in U.S. house prices has been a fundamental determinant of the recent financial crisis leading up to the Great Recession. The risky financial innovations in the housing market prior to the recent crisis fueled the speculative housing boom. In this backdrop, the main objectives of this empirical study are to i) detect the possibility of multiple structural breaks in the US house price data during 1995-2010, exhibiting very sharp upturns and downturns; ii) endogenously determine the break points and iii) conduct house price forecasting exercises to see how models with structural breaks fare with competing time series models linear and nonlinear. Using a very general methodology (Bai-Perron, 1998, 2003), we found four break points in the trend in the S&P/Case-Shiller 10 city aggregate house-price index series. Next, we compared the forecasting performance of the model with structural breaks to four competing models namely, Random Acceleration (RA), Autoregressive Moving Average (ARMA), Self- Exciting Threshold Autoregressive (SETAR), and Smooth Transition Autoregressive (STAR). Our findings suggest that house price series not only has undergone structural changes but also regime shifts. Hence, forecasting models that assume constant coefficients such as ARMA may not accurately capture house price dynamics.

The Market Structure of Securitisation and the US Housing Bubble

Housing finance, and, specifically, the subprime private label securitisation market in the US, was at the epicentre of the global financial crisis. Excessive debt expansion in the run-up to the crisis resulted in credit risk, under-identified and mispriced ex ante, and in systemic risk. This paper considers the role of financial innovation in debt markets and the changing market structure of securitisation in the evolution of the US housing price bubble. New financing vehicles contributed to growing risk, but the more salient factor was the change in the structure of securitisation, which led to unsustainable levels of debt.

Convergence in US house prices

This paper analyses the convergence of US house prices. Our results confirm the existence of some degree of segmentation in the US housing market. We also provide robust evidence that the bursting of the housing price bubble has altered this market, observing different results when the sample includes information posterior to 2010. However, we appreciate different effects depending on the geographical level of disaggregation that is employed.

Modeling US housing prices by spatial dynamic structural equation models

This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity “regions” that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using State level unemployment and per capita personal income.

The Spatial Dimension of US House Price Developments

Spatial heterogeneity and spatial dependence are two well established aspects of house price developments. However, the analysis of differences in spatial dependence across time and space has not gained much attention yet. In this paper we jointly analyze these three aspects of spatial data. We apply a panel smooth transition regression model that allows for heterogeneity across time and space in spatial house price spillovers and for heterogeneity in the effect of the fundamentals on house price dynamics. We find evidence for heterogeneity in spatial spillovers of house price developments across space and time: house price developments in neighboring regions spill over stronger in times of increasing neighboring house prices compared to declining neighboring house prices. This is interpreted as evidence for the disposition effect. Moreover, heterogeneity in the effect of the fundamentals on house price dynamics could not be detected for all variables; real per capita disposable income and the unemployment rate have a homogeneous effect across time and space.

Wall Street’s Bail-out Bet: Market Reactions to House Price Releases in the Presence of Bail-out Expectations

The rise and subsequent collapse of US house prices was one of the factors underlying the recent financial crisis. One could expect that the crisis brought increased attention to the housing market and thus led to stronger market reactions to house price news. We find that reactions indeed change, but with a peculiar twist: from September 2008 on, good news from the housing market are associated with falling US stock prices, and vice versa. The likely explanation, for which we provide cross-sectional evidence, is that falling house prices increased the market’s trust in a government bail-out, thereby increasing market valuations of firms that were expected to benefit from government rescue measures.

Wall Street’s bailout bet: Market reactions to house price releases in the presence of bailout expectations

The rise and subsequent collapse of US house prices was one of the factors underlying the recent financial crisis. One could expect that the crisis brought increased attention to the housing market and thus led to stronger market reactions to house price news. We find that reactions indeed change, but with a peculiar twist: from September 2008 on, good news from the housing market are associated with falling US stock prices, and vice versa. The likely explanation, for which we provide cross-sectional evidence, is that falling house prices increased the market’s trust in a government bailout, thereby increasing market valuations of firms that were expected to benefit from government rescue measures.

House price convergence: Evidence from US state and metropolitan area panels

We study house price convergence in panels of US states and metropolitan areas. Our analysis is centered on three issues. First, we test whether the US house prices are converging over time using log t convergence test by Phillips and Sul (2007). The test results suggest that there is little evidence of overall convergence. The second issue is to investigate the possibility of a convergence club where the cross-sectional dispersion of house prices of the club members decreases over time. We utilize a clustering algorithm and the results support that there is strong evidence of multiple convergence clubs. Finally, we examine the general characteristics of the various convergence and divergence subgroups as well as some important driving forces of convergence clubs. We find that housing supply regulation together with climate are important determinants of convergence club membership.

Eyes Wide Shut? The U.S. House Market Bubble Through the Lense of Statistical Process Control

While most economists agree that the recent worldwide financial crises evolved as a consequence of the US house price bubble, the related literature yet failed to deliver a consensus on the question when exactly the bubble started developing. The estimates in the literature range in between 1997 and 2002, while applications of market-based-procedures deliver even later dates. In this paper we employ the methods of statistical process control (SPC) to date the likely beginning of the bubble. The results support the view that the bubble on the US house market already emerged as early as 1996. We also show that SPC in general might be a useful tool in constructing early warning systems for asset price bubbles.

Residential land use regulation and the US housing price cycle between 2000 and 2009

In a sample covering more than 300 cities in the US from January 2000 to July 2009, we find that more restrictive residential land use regulations and geographic land constraints are linked to larger booms and busts in housing prices. The natural and man-made constraints also amplify price responses to the subprime mortgage credit expansion during the decade, leading to greater price increases in the boom and subsequently bigger losses. Contrary to prior literature, our findings indicate a significant link between supply inelasticity and price declines during the bust, whereas Glaeser et al. (2008) found little evidence of such a relationship from an earlier downturn from 1989 to 1996.

House Price Booms and the Current Account

A simple open economy asset pricing model can account for the house price and current account dynamics in the G7 over the years 2001-2008. The model features rational households, but assumes that households entertain subjective beliefs about price behavior and update these using Bayes’ rule. The resulting beliefs dynamics considerably propagate economic shocks and crucially contribute to replicating the empirical evidence. Belief dynamics can temporarily delink house prices from fundamentals, so that low interest rates can fuel a house price boom. House price booms, however, are not necessarily synchronized across countries and the model is consistent with the heterogeneous response of house prices across the G7 following the reduction in real interest rates at the beginning of the millennium. The response to interest rates depends sensitively on agents’ beliefs at the time of the interest rate reduction, which in turn are a function of the country specific history prior to the year 2000. According to the model, the US house price boom could have been largely avoided, if real interest rates had decreased by less after the year 2000.

Risk and Structural Instability in US House Prices

This paper employs a Component GARCH in Mean model to show that house prices across a number of major US cities between 1987 and 2009 have displayed asset market properties in terms of both risk-return relationships and asymmetric adjustment to shocks. In addition, tests for structural breaks in the mean and variance indicate structural instability across the data range. Multiple breaks are identified across all cities, particularly for the early 1990s and during the post-2007 financial crisis as housing has become an increasingly risky asset. Estimating the models over the individual sub-samples suggests that over the last 20 years the financial sector has increasingly failed to account for the levels of risk associated with real estate markets. This result has possible implications for the way in which financial institutions should be regulated in the future.

The Great Recession: US dynamics and spillovers to the world economy

This paper provides an empirical investigation of both the within-US and international channels of transmission of macroeconomic and financial shocks by means of a 50-country macroeconometric model (estimated over the 1980–2009 period), including measures of excess liquidity and financial fragility, specifically designed in order to evaluate the relevance of the boom-bust credit cycle view put forward as an interpretation of the recent “Great Recession” episode. We find that such a view is consistent with the empirical evidence. Moreover, concerning the real effects of financial shocks within the US, we detect stronger evidence of an asset prices channel, rather than a liquidity channel. Concerning the spillovers to the world economy, we find that while financial disturbances are transmitted to foreign countries through US house and stock price dynamics, as well as excess liquidity creation, the trade channel is the key trasmission mechanism of real shocks.

International Capital Flows and the Return to Safe Assets in the United States, 2003-2007

A broad array of domestic institutional factors ‐including problems with the originate-to-distribute model for mortgage loans, deteriorating lending standards, defi ciencies in risk management, confl icting incentives for the government-sponsored enterprises (GSEs), and shortcomings of supervision and regulation‐ were the primary sources of the US housing boom and bust and the associated fi nancial crisis. In addition, the extended rise in US house prices was likely also supported by long-term interest rates (including mortgage rates) that were surprisingly low, given the level of short-term rates and other macro fundamentals ‐a development that Greenspan (2005) dubbed a “conundrum.” The “global saving glut” (GSG) hypothesis (Bernanke, 2005 and 2007) argues that increased capital infl ows to the United States from countries in which desired saving greatly exceeded desired investment ‐including Asian emerging markets and commodity exporters‐ were an important reason that US longer-term interest rates during this period were lower than expected. This essay investigates further the effects of capital infl ows to the United States on US longer-term interest rates; however, we look beyond the overall size of the infl ows emphasised by the GSG hypothesis to examine the implications for US yields of the portfolio preferences of foreign creditors. We present evidence that, in the spirit of Caballero and Krishnamurthy (2009), foreign investors during this period tended to prefer US assets perceived to be safe. In particular, foreign investors ‐especially the GSG countries‐ acquired a substantial share of the new issues of US Treasuries, Agency debt, and Agency-sponsored mortgage-backed securities. The downward pressure on yields exerted by infl ows from the GSG countries was reinforced by the portfolio preferences of other foreign investors. We focus particularly on the case of Europe: although Europe did not run a large current account surplus as did the GSG countries, we show that it leveraged up its international balance sheet, issuing external liabilities to fi nance substantial purchases of apparently safe US “private label” mortgage-backed securities and other fi xed-income products. The strong demand for apparently safe assets by both domestic and foreign investors not only served to reduce yields on these assets but also provided additional incentives for the US fiservices industry to develop structured investment products that “transformed” risky loans into highly-rated securities. Our fi ndings do not challenge the view that domestic factors, including those listed above, were the primary sources of the housing boom and bust in the United States. However, examining how changes in the pattern of international capital fl ows affected yields on US assets helps provide a deeper understanding of the origins and dynamics of the crisis.

House Prices and Credit Constraints: Making Sense of the US Experience

Most US house price models break down in the mid-2000s, due to the omission of exogenous changes in mortgage credit supply (associated with the sub-prime mortgage boom) from house price-to-rent ratio and inverted housing demand models. Previous models lack data on credit constraints facing first-time home-buyers. Incorporating a measure of credit conditions – the cyclically adjusted loan-to-value ratio for first-time buyers – into house price-to-rent ratio models yields stable long-run relationships, more precisely estimated effects, reasonable speeds of adjustment and improved model fits.

Housing Risk and Return: Evidence from a Housing Asset-Pricing Model

This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a housing asset pricing model (H-CAPM), whereby expected returns of metropolitan-specific housing markets are equated to the market return, as represented by aggregate US house price time-series. We augment the model by examining the impact of additional risk factors including aggregate stock market returns, idiosyncratic risk, momentum, and Metropolitan Statistical Area (MSA) size effects. Further, we test the robustness of H-CAPM results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. Consistent with the traditional CAPM, we find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show thatmarket betas have varied substantially over time. Also, we find the basic housing CAPM results are robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of the validity of the model using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller behavioral research indicating the importance of speculative forces in the determination of U.S. housing returns.