Testing the Present Value Relation for Housing Prices: Should I Leave My House in San Francisco?

Meese, Richard; Wallace, Nancy

doi:10.1006/juec.1994.1015

Cited by 206 publications

(130 citation statements)

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“…The relevant literature is voluminous focusing on different types of indices including the repeat-sales indices (Case and Shiller, 1987), appraisal indices, such as the National Property Index, the Transaction Based Index by the MIT Centre for Real Estate and stock market based indices, such as the CRSP/Ziman and the FTSE NAREIT US indices. The property type investigated, (residential or commercial real estate) including direct or indirect real estate investment, the econometric method employed in predicting real estate returns including the use of insampling or out-sampling forecasting, as well as the market under examination have an impact on the empirical results (Meese and Wallace, 1994;Capozza and Seguin, 1996;Abraham and Hendershott, 1996). Thus, forecasting real estate prices is a complex task to perform due to the heterogeneous nature of real estate assets, being illiquid, and characterised by high transaction costs, information asymmetry and tax considerations.…”

Section: Literature Reviewmentioning

confidence: 99%

Real estate returns predictability revisited: novel evidence from the US REITs market

et al. 2016

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In this paper we examine the real estate returns predictability employing US Real Estate Investment Trusts (REITs) and a set of possible predictors for the period January 1991 to December 2014. To this end we employ several forecasting models to test for REITs predictability under a flexible framework that captures parameter instability. Apart from the traditional factors examined in relevant studies, we also account for a series of sentiment and uncertainty indicators that may be significant predictors of REITs returns, especially during turbulent times when sentiment determines investment decisions to a greater extent. The empirical results indicate that the good predictors of REITs returns vary over time and over the forecast horizons. Our results suggest that economy-wide indicators, monetary policy instruments and sentiment indicators are among the most powerful predictors of REITs returns. In economic terms an investment strategy that is based on our forecasts outperforms a buy and hold strategy. The issue of the most suitable forecasting method is also discussed in detail. Our results might entail implications for investors and market authorities. JEL Classification: C22; C32; E52; R31Keywords: Real estate investment trusts; return predictability; dynamic model averaging; uncertainty indicator 1 Authors are grateful to two anonymous reviewers and to the editor for their constructive comments that helped improve the paper. 

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Section: Literature Reviewmentioning

confidence: 99%

Real estate returns predictability revisited: novel evidence from the US REITs market

et al. 2016

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“…Our study is also related to an extensive literature analyzing the role of expectations in house price determination, see e.g. Hamilton and Schwab (1985), Meese and Wallace (1994), Geltner and Mei (1995), and Clayton (1996) for early analyses based on present value models, and Gelain and Lansing (2013) for a recent analysis of the in ‡uence of expectations on housing valuation in a model with timevarying risk-aversion and time-varying expected rent growth. Our paper is most directly related to two recent studies (Campbell, Davis, Gallin, and Martin, 2009, and Hiebert and Sydow, 2011) that also conduct VAR based variance decompositions of housing market variables.…”

Section: Introductionmentioning

confidence: 94%

Housing market volatility in the OECD area: Evidence from VAR based return decompositions

Engsted

Pedersen

2014

Journal of Macroeconomics

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Vector-autoregressive models are used to decompose housing returns in 18 OECD countries into cash ‡ow (rent) news and discount rate (return) news. Only for two countries -Germany and Ireland -do changing expectations of future rents play a dominating role in explaining housing return volatility. For the majority of countries news about future returns is the main driver, and both real interest rates and risk premia play an important role in accounting for housing market volatility. Bivariate cross-country correlations and principal components analyses indicate that part of the return movements have a common factor among the majority of countries. However, in a minority of countries (Germany, Japan, and the Netherlands) return movements have been basically unrelated to return movements in other countries.JEL Classi…cation: C32, G12, R31

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“…The cycle in dispersion also shows that there was not an equal shock to house price growth across MSAs; had there been, the entire distribution of house price growth would have shifted rather than expanding. Meese and Wallace (1994) for an early exposition of this present value relationship for housing. The intuition is robust to an even more complete house pricing model, such as Ortalo-Magné and Prat (2010).…”

Section: Annual House Price Growthmentioning

confidence: 99%

House Price Moments in Boom-Bust Cycles

Sinai¹

2013

Housing and the Financial Crisis

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Testing the Present Value Relation for Housing Prices: Should I Leave My House in San Francisco?

Cited by 206 publications

References 0 publications

Real estate returns predictability revisited: novel evidence from the US REITs market

Real estate returns predictability revisited: novel evidence from the US REITs market

Housing market volatility in the OECD area: Evidence from VAR based return decompositions

House Price Moments in Boom-Bust Cycles

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