What drives returns to euro area housing? Evidence from a dynamic dividend–discount model

Hiebert, Paul; Sydow, Matthias

doi:10.1016/j.jue.2011.03.001

Cited by 45 publications

(46 citation statements)

References 17 publications

(14 reference statements)

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“…Only in two countries, Germany and Ireland, is the rent news component the dominating factor. These results are in contrast to the …ndings reported by Hiebert and Sydow (2011) where the rent news component explains the bulk of return volatility in eight European countries. Second, when we decompose returns into the risk-free rate and a risk-premium we …nd that in the majority of those countries -including the US -where return news is the dominating factor, risk-free rate news is either the most important, or equally important as news about future risk-premia.…”

Section: Introductioncontrasting

confidence: 99%

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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Section: Introductioncontrasting

confidence: 99%

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

Engsted

Pedersen

2014

Journal of Macroeconomics

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“…In particular, Gallin (2008), looking into the capability of the rent-toprice ratio to predict real rents and prices, found that this ratio did have some predictive power for real prices over periods of 4 years but failed to predict changes in real rent over periods of the same duration. Additional evidence has also been provided by Goswami and Tan (2012), Piazzesi, Schneider, and Tuzel (2007), Hiebert andSydow (2011), Liu, Miao, andZha (2016), and Lai and Order (2017).…”

Section: Literature Reviewmentioning

confidence: 80%

Homeownership motivation, rationality, and housing prices: Evidence from gloom, boom, and bust‐and‐boom economies

Alexiou

Chan

Vogiazas³

2018

Int. J Fin Econ

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By focusing on three types of homebuyers, we address three questions: (a) Do households drive housing prices? (b) Do households and landlords act rationally in their buying decisions? (c) Is the market conducive to promoting homeownership or is it a speculators' territory? We use system Generalized Method of Moments (GMM) estimations based on 1970–2016 data, for 34 economies clustered as gloom, boom, or bust‐and‐boom, and we provide novel evidence that all three types of homebuyers contribute significantly to the determination of housing prices. Households and landlords seem to act irrationally as their decisions are not greatly affected by housing affordability or shrinking yields.

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“…To motivate our subsequent empirical analysis and the challenges posed by aggregation, we start with a theoretical distinction between non-fundamental behavior (bubbles) in episodes of exuberance and fundamental-based explosiveness in the housing data. We adopt the asset pricing framework (in line with Clayton, 1996;Hiebert and Sydow, 2011;Pavlidis et al 2016) under standard arbitrage conditions for the study of financial contagion and explosive behavior in house prices (whose formal derivation from first principles can be found in Martínez-García and Grossman, 2018). The asset pricing model yields the following difference equation for house prices…”

Section: Appendix Mildly Explosive Behavior Uncertainty and Risk-smentioning

confidence: 99%

Detecting periods of exuberance: A look at the role of aggregation with an application to house prices

2019

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The recently developed SADF and GSADF unit root tests of Phillips and Yu (2011) and Phillips et al. (2015a,b) have become popular in the literature for detecting exuberance in asset prices. In this paper, we examine through simulation experiments the effect of cross-sectional aggregation on the power properties of these tests. The simulation design considered is based on simulated data and actual housing data for both U.S. metropolitan areas and international housing markets and thus allows us to draw conclusions for different levels of aggregation. Our findings suggest that aggregation lowers the power of both the SADF and GSADF tests. The effect, however, is much larger for the SADF test. We also provide evidence that tests based on panel data techniques, namely the panel GSADF test recently proposed by Pavlidis et al. (2016), can perform substantially better than univariate tests applied to aggregated series. Furthermore, we also illustrate the date-stamping procedure under the univariate/panel GSADF procedure uncovering novel evidence on the role of interest rates and policy uncertainty as factors explaining episodes of widespread mildly explosive dynamics in housing markets.

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What drives returns to euro area housing? Evidence from a dynamic dividend–discount model

Cited by 45 publications

References 17 publications

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

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

Homeownership motivation, rationality, and housing prices: Evidence from gloom, boom, and bust‐and‐boom economies

Detecting periods of exuberance: A look at the role of aggregation with an application to house prices

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