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Chicago Booth BUSF 35150 - Journal of Urban Economics

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What moves housing markets: A variance decomposition of the rent–price ratioIntroductionThe Campbell–Shiller decompositionImplementing the dynamic Gordon growth modelDataHouse prices and rentsReal interest ratesHousing returns and premiaMacroeconomic conditionsSpecification and estimates of the VARDecomposing the variability of rent–price ratiosSources of rent–price ratio variability: 1975–1996Sources of rent–price ratio variability: 1997–2007Sources of relative rent–price ratio variation: 1975–2007ConclusionSupplementary dataReferencesWhat moves housing markets: A variance decomposition of the rent–price ratioqSean D. Campbella, Morris A. Davisb,*, Joshua Gallina, Robert F. MartinaaBoard of Governors of the Federal Reserve System, Washington, DC, USAbWisconsin School of Business, University of Wisconsin-Madison, WI, USAarticle infoArticle history:Received 4 March 2009Revised 19 June 2009Available online 26 June 2009JEL classification:E31G12R31Keywords:Rent–price ratioHouse pricesHousing rentsInterest ratesabstractWe apply the dynamic Gordon growth model to the housing market in 23 US metropolitan areas, the fourCensus regions, and the nation from 1975 to 2007. The model allows the rent–price ratio at each date tobe split into the expected present discounted values of rent growth, real interest rates, and a housing pre-mium over real rates. We show that housing premia are variable and forecastable and account for a sig-nificant fraction of rent–price ratio volatility at the national and local levels, and that covariances amongthe three components damp fluctuations in rent–price ratios. Thus, explanations of house-price dynamicsthat focus only on interest rate movements and ignore these covariances can be misleading. These resultsare similar to those found for stocks and bonds.Ó 2009 Elsevier Inc. All rights reserved.1. IntroductionThe boom and bust to house prices and housing returns over thepast 12 years is likely unprecedented in the United States. Accord-ing to data from the Bureau of Economic Analysis and MacroMar-kets LLC, real house prices in the United States increased by about6-1/2% per year over the 1997–2006 period. To put this growth incontext, over the decade spanning 1987–1996, the same datasources suggest that real house prices in the United States didnot increase at all; and, the available evidence suggests that realhouse prices in the United States increased by less than 2% per yearin real terms over the 1950–1996 period (Davis and Heathcote,2007; Shiller, 2005).From year-end 2006 through the first quarter of 2009, realhouse prices have fallen by 34%, and many expect house pricesto continue to fall over the next few quarters. Extraordinary eventsin the financial sector and the macroeconomy as a whole haveaccompanied this decline of house prices. The fall in house pricestriggered a wave of mortgage defaults and home foreclosures,perhaps because some borrowers did not fully understand theterms of their mortgage contract (Bucks and Pence, 2008) or per-haps because a significant portion of homeowners chose to strate-gically default once their mortgage was sufficiently under water(Haughwout et al., 2008; Foote et al., 2008). The increase in defaultrates on mortgages lead to a collapse in the price of mortgage-backed securities, which likely contributed to a run on the‘‘shadow” banking system (Gorton, 2009) and sharp devaluationof stock prices. According to data from the Flow of Funds Accountsof the United States, the decline in house prices and stock prices re-duced household net worth by 20% in nominal terms ($13 trillion)from mid-2007 through year-end 2008. The loss of wealth wasassociated with a sharp decline in consumer spending via standard‘‘wealth-effect” arguments (Davis and Palumbo, 2001) leading tothe contraction of real GDP and the current recession.With this background in mind, the goal of this paper is to exam-ine time-series fluctuations in house prices and the returns tohousing using tools that have proved successful in characterizingthe nature of returns in the stock and bond markets. Specifically,we start with the definition of the one-period return to housing.It can be shown that this definition implies that the ratio of hous-ing rents to house prices, the ‘‘rent–price ratio,” must be equal tothe present discounted value of expected future housing serviceflows and the expected future returns to housing assets. Theexpected future returns to housing assets can further be split intothe sum of expected future risk-free rates of interest and expected0094-1190/$ - see front matter Ó 2009 Elsevier Inc. All rights reserved.doi:10.1016/j.jue.2009.06.002qWe thank Mike Gibson, Michael Palumbo, David Reifschneider, Tom Tallarini,two anonymous referees and the editor. The views expressed in this paper are thoseof the authors and should not be attributed to the Board of Governors of the FederalReserve System or other members of its staff.* Corresponding author. Address: Wisconsin School of Business, Department ofReal Estate and Urban Land Economics, 5261 Grainger Hall, 975 University Avenue,Madison, WI 53706, USA. Fax: +1 608 262 8775.E-mail address: [email protected] (M.A. Davis).Journal of Urban Economics 66 (2009) 90–102Contents lists available at ScienceDirectJournal of Urban Economicswww.elsevier.com/locate/juefuture premia paid to housing over the real risk-free rate. Thismodel is known in the finance literature as the dynamic versionof the Gordon growth model (Campbell and Shiller, 1988a,b). Theapproach is equivalent to assuming that house prices are the dis-counted sum of housing rents, where the growth rate of housingrents and required return to housing can vary over time. It is pre-cisely this variation over time in expected required returns and ex-pected growth rate of housing rents that yields changes in relativehouse prices, enabling us to study the factors responsible for time-series changes to housing valuations.To put the dynamic Gordon growth model to practice, at eachpoint in time we need to measure expectations of the expectedpresent value of risk-free interest rates, housing premia, and rentgrowth. Our strategy, which is common in the finance literature,is to specify that households form expectations using a VAR withfixed coefficients. We use the VAR to directly compute expected fu-ture real risk-free rates and expected housing risk premia and then,given the accounting


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