Measuring House Price Bubbles
Florida Atlantic University
University of Geneva – Graduate School of Business (HEC-Geneva); University of Aberdeen – Business School; Swiss Finance Institute
University of Turku, Department of Economics
January 6, 2016
Using data for six metropolitan housing markets in three countries, this paper provides a comparison of methods used to measure house price bubbles. We use an asset pricing approach to identify bubble periods retrospectively and then compare those results with results produced by six other methods. We also apply the various methods recursively to assess their ability to identify bubbles as they form. In view of the complexity of the asset pricing approach, we conclude that a simple price-rent ratio measure is a reliable method both ex post and in real time. Our results have important policy implications because a reliable signal that a bubble is forming could be used to avoid further house price increases.
Measuring House Price Bubbles – Introduction
Several countries experienced soaring house prices in the early 2000s, with severe price declines during the latter part of the decade. Rapid price increases also occurred in the late 1980s in some other countries. When prices rise rapidly, commentators often refer to this as a housing bubble, which is then hypothesized to have collapsed when house prices drop. A bubble refers to house price levels that depart markedly from “fundamental” values (Stiglitz 1990). There is, however, a lack of consensus in the literature as to what method should be used to measure fundamental house values and hence any discrepancy between such values and actual house prices. This issue applies with respect to both ex post and real time measurement of bubbles. It has been suggested that problems in specifying house price fundamentals imply that bubbles should instead be defined as dramatic increases in prices followed by rapid falls in prices (Lind 2009). Of course, this definition begs the question of how much and how rapidly nominal or real prices must rise and fall to constitute a bubble.
In spite of the woolly nature of the concept of a bubble, there seems to be some consensus about when they have occurred in the past and the negative effects they have had on the economy. For example, there is widespread agreement that there was a bubble in the U.S. (or, at least, parts of the U.S.) in the 2000s. Moreover, the U.S. bubble has generally been seen as a major factor contributing to the recent financial and economic crises (Shiller 2008, Brunnermeier 2009, Martin 2010). In addition to contributing to a general decline in the economy, this has also caused substantial harm to households and neighborhoods in many parts of the U.S. House price volatility has had similar effects in other countries, most notably in Spain and Ireland. In hindsight, it appears that efforts to avoid the formation of bubbles would be sound public policy (Crowe et al. 2013).
The methods that have been used in the literature to identify housing bubbles may be categorized as: (1) analyses of various ratios that typically compare house prices to either rents or incomes (Himmelberg, Mayer and Sinai 2005); (2) regression analyses of various sorts, including models based on either housing supply and demand theory or asset pricing (present value) concepts as well as cointegration and unit root tests (Abraham and Hendershott 1996, Black, Fraser and Hoesli 2006, Oikarinen 2009b, Yiu, Yu and Jin 2013); and (3) a method drawn from physics that focuses on the rate of growth in prices (Zhou and Sornette 2006). The first two of these categories are usually consistent with the “Stiglitz” bubble definition as in most cases the aim is to compare house prices with fundamentals, such as income, rent, and other variables. The third category seems consistent with the “Lind” bubble definition in that the focus is solely on how fast prices grow.
This paper is the first to provide a comparison of a broad subset of these methods. We apply the methods to a set of metropolitan areas representing a range of circumstances, including variations in the occurrence and timing of bubbles as well as contextual differences across locations both within and across countries. We use an asset pricing model to retrospectively identify bubble periods given that the value of an asset is the sum of the present values of future earnings. Although other methods are less firmly based on theory, they are generally simpler to implement. Initially, we compare findings from retrospective or “ex post” analyses of each location. Then we apply the methods recursively to determine which is the most effective in capturing the formation and dissolution of bubbles in “real” time. We also undertake a number of variations on our analysis to check the robustness of our conclusions.
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