Detecting Imbalances in House Prices: What Goes Up Must Come Down?*

Published date01 October 2019
DOIhttp://doi.org/10.1111/sjoe.12349
Date01 October 2019
AuthorAndré K. Anundsen
Scand. J. of Economics 121(4), 1587–1619, 2019
DOI: 10.1111/sjoe.12349
Detecting Imbalances in House Prices: What
Goes Up Must Come Down?*
Andr´e K. Anundsen
Oslo Metropolitan University,NO-0166 Oslo, Norway
andre-kallak.anundsen@oslomet.no
Abstract
I suggest a toolkit of four bubble-detection methods that can be used to monitor developments
in house prices. These methods are applied to US, Finnish, and Norwegian data.For the United
States, all measures unanimously suggest a bubble in the earlyto mid-2000s, whereas current US
house prices are found to be aligned with economic fundamentals. One of the measures indicates
imbalances in Finland, while there are no signs of a bubble in Norway. I find that large parts
of the US house price bubble can be explained by the sharp increase in capital inflows and the
extension of loans to the subprime mortgage market.
Keywords: Cointegration; explosive roots; housing bubbles
JEL classification:C22; C32; C51; C52; C53; G01; R21
I. Introduction
Is it true that what goes up must come down? Starting in the late 1990s,
there was an unprecedented international house price boom accompanying
the favorable economic situation in most industrialized countries. The boom
was in many cases succeeded by a significant bust, with real house prices
falling by more than 30 percent in several countries. The consequences
for the real economy following the bust in house prices have been severe,
and it was one of the factors contributing to the deepest downturn in the
world economy since the Great Depression (see Mian etal., 2013; Mian and
Sufi, 2014). The collapse culminated with the meltdown of the US housing
market and financial system in the autumn of 2008 – the epicenter for the
*Large parts of this paper were written while the author was workingat Norges Bank. The views
expressed are those of the author and do not necessarilyreflect those of Norges Bank. I am grateful
for the editorial and reviewer comments that have helped to improve this paper. The paper was
presented at seminars in Norges Bank, and at UC Berkeley,University of Leipzig, and the 10th
International Conference on Computational and Financial Econometrics in Seville, Spain, 9–11
December 2016. I thank the participants for constructive comments. I would also like to thank
Knut Are Aastveit, Farooq Akram, Gunnar B˚ardsen, Øyvind Eitrheim, Francesco Furlanetto,
Frank Hansen, Erling Røed Larsen, Ragnar Nymoen, Thomas Steger, and Bernt Stigum for
helpful comments and feedback.
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The editors of The Scandinavian Journal of Economics 2019.
1588 House price imbalances
ensuing global financial crisis that is still putting a strain on the global
economic recovery. Against this background, I ask whether econometric
methods can be used to detect pending imbalances in the housing market.
Using aggregate house price data for the United States, Finland, and
Norway, I consider four alternative econometric approaches to identifying
imbalances in the housing market. While one would want measures of
housing market imbalances to detect a bubble that is building up, one
would not want the methods to signal a bubble whenever house prices are
increasing. This makes the analysis of these three countries particularly
relevant, as house prices increased rapidly in all three countries from the
beginning of the 1990s and until the global financial crisis. While there
was a major and sustained drop in US house prices in the late 2000s,
Norwegian and Finnish house prices quickly rebounded after the drop,
and reached new historical heights by 2013. This prompts the question
of whether house prices in these countries can be explained by underlying
economic fundamentals. If not, it is imperative from a policy perspective to
detect and quantify such imbalances in real time so that necessary actions
can be taken to prevent further overvaluations – especially given the strong
role that the housing market has in affecting the real economy, both through
consumption wealth effects (Aron et al., 2012; Mian et al., 2013) and
through its interactions with the credit market (Hofmann, 2003; Fitzpatrick
and McQuinn, 2007; Gimeno and Martinez-Carrascal, 2010; Anundsen and
Jansen, 2013). In addition, Leamer (2007, 2015) has shown that large drops
in housing investments are a strong indicator of future recessions in the US
economy – a result that has gained international support in a recent study
by Aastveit et al. (2017).
The first approach I take to evaluate whether house prices were
overvalued in these countries in the 2000s is to calculate a fundamental
house price using information that would have been available in 1999q4.
Then, I investigate how actual house prices developed relative to the model-
implied fundamental prices in the period thereafter.
My second measure of housing market imbalances is based on a
dynamic forecasting exercise. As a forecast is a conditional expectation, one
would not expect an econometric model that includes the relevant house
price fundamentals to produce forecasts that systematically underpredict
house prices if they are close to their equilibrium value. Hence, large
and systematic underpredictions of house prices can be interpreted as an
overheating of house prices.
As a third approach, I apply the bubble-indicator methodology suggested
by Anundsen (2015). This methodology relies on the bubble definition
provided by Stiglitz (1990, p. 13), which states that a bubble exists “if
the reason why the price is high today is only because investors believe
that the selling price will be high tomorrow – when ‘fundamental’ factors
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A. K. Anundsen 1589
do not seem to justify such a price.” This bubble definition is combined
with the modeling assumption that fundamental factors are non-stationary
economic time series. Given this assumption, house prices are determined
by fundamentals if and only if there exists a cointegrating relationship
between house prices and these non-stationary economic variables. This
leads to several possible scenarios. First, if cointegration can be established
over the full sample period as well as for different subsamples, the bubble
hypothesis is clearly rejected. Conversely, if no evidence of cointegration
can be found, we cannot reject a bubble, but it might also indicate
that our information set does not include the relevant fundamentals. If
a cointegrating relationship can be established early in the sample but
is lost subsequently, we might suspect a structural break. This finding
is therefore consistent with the transition from a stable market with
equilibrium correction behavior (no bubble) to a market where there are
no forces in place to correct disequilibrium constellations (a bubble).
The final measure I consider utilizes recent econometric tools developed
by Phillips et al. (2011, 2015a,b) to test for a transition to explosive house
price behavior.
All four approaches provide a possible way of identifying imbalances in
the housing market. The approach based on deviations of actual prices from
fundamental prices and the forecasting exercise are particularly relevant
for making quantitative statements about the magnitude of house price
misalignments. A drawback with these approaches is that it is far from
trivial to translate evidence of overvaluation into bubble behavior. This is
because the overvaluation might be temporary and might be expected to be
adjusted relatively quickly. In addition, both these approaches require that
the researcher or policy-maker takes a position on how large misalignments
must be for there to be a bubble. Thus, using these measures in isolation
to call bubbles in real time might not be feasible. That said, the bubble
indicator and the test for a transition to explosive house prices are
particularly useful for real-time monitoring and dating of the onset of a
housing bubble, as they directly test for the transition to a bubble regime.
Thus, as part of a broader toolkit for monitoring the housing market, these
measures might help signal a bubble and – conditional on bubble detection
– the deviations of actual house prices from fundamental prices and the
forecasting exercise can be applied to quantify the degree of departure
from equilibrium.
My results show that all four measures signal a bubble in the US
housing market starting in the early 2000s. There are, however, no signs
of overheating during the more recent house price boom starting in 2012.
Similarly, I do not find evidence of a bubble in the Norwegian housing
market for a sample ending in 2016, while only one of the measures
suggests that Finnish house prices are overvalued for a sample that ends in
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The editors of The Scandinavian Journal of Economics 2019.

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