Abstract
In this article, we use tests of explosive behavior in real house prices with annual data for the case of Australia for the period 1870–2020. The main contribution of this paper is the use of very long time series. It is important to use longer span data because it offers more powerful econometric results. To detect episodes of potential explosive behavior in house prices over this long period, we use the recursive unit root tests for explosiveness proposed by Phillips et al. (2011), (2015a,b). According to the results, there is a clear speculative bubble behavior in real house prices between 1997 and 2020, speculative process that has not yet been adjusted.
1 Introduction
In this article, we use tests of explosive behavior in real house prices with annual data for the case of Australia for the period 1870–2020. The Australian case can be of interest given that it has experienced strong growth since the mid1990s, leading the ranking of OECD countries, as shown in Figure 1.^{[1]}
Real housing prices in Australia have risen significantly over the past 33 years (total increase of 175.6% and on average of +3.7% per annum), and housing has become the most important type of asset in Australia. According to Bank of International Settlements statistics (BIS, 2021), real housing prices in Australia increased by 31.6% between 2012 and 2017 (on average +4.3% annually). This rapid growth in house prices not only generates a debate about the affordability of housing but also increases unrest over the presence of speculative bubble behaviors and their impact on economic and financial stability.
The changes in house prices can negatively influence the behavior of different macroeconomic variables. First, household consumption can be influenced through the housing wealth channel. Second, Tobin’s Q relationship would explain movements in housing investment (where the investment occurs as long as the expected return is higher than the cost of the investment). Finally, investment by small businesses may be limited by restrictions on access to credit that affects many small firms.^{[2]}
In Australia, housing prices have experienced a significant growth that promoted an intense debate about the existence of a housing bubble. The related literature on testing the determinants of Australian house prices is abundant, see Boldman and Crosby (2004), Costello, Fraser, and Groenewold (2011), Fox and Tulip (2014), Fry, Martin, and Voukelatos (2010), Kholer and van der Merwe (2015), Kulish, Richards, and Gilltzer (2012), Otto (2007), Shi, Valadkhani, Smyth, and Vahid (2016), and Shi, Raman, and Wang (2020), among others.^{[4]}
There is abundant empirical evidence on the different approaches to the analysis of this series and for different countries; for UK house prices, see Brown, Song, and McGillivray (1997), Giussani and Hadjimatheou (1992), Hendry (1984), Levin and Wright (1997), and Nellis, Longbottom, and Nellis (1981); for US house prices, see Clark and Coggin (2011), Kivedal (2013), and Nneji, Brooks, and Ward (2013); for Japan house prices, Ito and Hirono (1993); and for some international house prices data, see Beltratti and Morana (2010), and Engsted, Hviid, and Pedersen (2016), among others.
In our paper, we try to analyze the behavior of real house prices by using a long span series data (151 years), which represents a contribution to the literature in this regard. The use of a longer span of data than usual span of data should allow us to obtain some more robust results than in previous analyses. As far as we know, there are no empirical tests available in the literature regarding the existence of speculative bubbles in the Australian housing market from a longterm perspective for such a long period.
The search and the theoretical and empirical analysis of periods of exuberant or explosive behavior in nonstationary time series has been a main topic of interest in time series econometrics. Perhaps, the starting point has been the modeling of bubble processes arising from departures of the rational valuation of assets (see e.g., the seminal papers by Blanchard & Watson, 1982; Flood & Garber, 1980; Tirole, 1982), with the additional difficulty of the identification of the more relevant variables integrating the set of fundamental factors.
On the one hand, to examine the structural changes in the level or slope of the trend function of the series of real house prices over the full sample, we use the test statistics for structural changes in deterministic components proposed by Perron and Yabu (2009a,b). We also use the test statistics to test jointly for structural changes in mean and variance proposed by Perron, Yamamoto, and Zhou (2020). On the other hand, to detect episodes of potential explosive in house prices dynamic, we use the recursive unit root tests for explosiveness recently proposed by Phillips, Wu, and Yu (2011) and Phillips, Shi, and Yu (2015a,b).
The scheme of the paper is as follows. In Section 2, we introduce the econometric methodology. Section 3 presents and discusses the main empirical results. Section 4 draws the main conclusions.
2 Econometric Methodology
The main hypothesis to solve in our work is the identification of explosive processes that periodically collapse, independently of the potential structural instability in some deterministic component of the series, i.e., the possible timedependence of the parameters in level or variance.
On the one hand, for the analysis of structural instability in some deterministic component of the series, the procedures proposed by Perron and Yabu (2009a,b) and Perron et al. (2020) allow estimation of a trend function and testing for structural changes regardless of whether the stochastic component is stationary or contains an autoregressive unit root, but it remains to study their properties under explosiveness, as in the bubble case.
On the other hand, for the analysis of periodically collapsing explosive processes, the recurrent ADFtype test statistics proposed by Phillips et al. (2011, PWY henceforth), and Phillips et al. (2015a,b, PSY henceforth) are implemented without taking into account the possibility of structural breaks in the deterministic components and hence remains unsolved their properties under this situation.
Therefore, it could be of valuable interest and also relevant for the interpretation of the empirical analysis, to discuss whether the test results for explosiveness could be due to some type of structural instability or if, in fact, they correctly identify some type of periodically collapsing explosive mechanism. The very different nature of these two types of behavior patterns would have different possible explanations and implications for the series analyzed.^{[5]}
2.1 Structural Break Tests in the Level or Slope of the Trend Function of the Time Series
A structural break makes reference to an abrupt and permanent change in the magnitude of some parameter at some point in time, so that it is only a particular type, although the most commonly considered, of a more general concept known as structural instability. These changes could involve a change in mean or a change in the other parameters of the process that produces the series such as persistence or explosiveness.^{[6]} Both the statistic and econometric literature contain a vast amount of work on issues related to structural changes in macroeconomic time series with unknown break dates (for an extensive review, see Casini & Perron, 2019; Perron, 2006).
The issue of structural change is of considerable importance in the analysis of macroeconomic time series. Structural change occurs in many time series for various reasons, including economic crises, changes in institutional arrangements, policy changes, and regime shifts. Most importantly, if such structural changes are present in the data generating process, they are not allowed for in the specification of an econometric model, results may be biased toward.
It also implies that any shock – whether demand, supply, or policyinduced – on the variable will have effects on it in the longrun. It is therefore very important to test for the presence of multiple structural breaks in the data so as to more reliably conduct the tests of nonstationarity or tests of explosiveness.
The seminal works of Chow (1960) and Quandt (1992) and the CUSUM test focused on testing for structural change at a single known break date. Over time, the econometric literature has led to the development of methods that allow for estimation and testing of structural changes at unknown break dates. These include the tests proposed by Andrews (1993) and Andrews and Ploberger (1994) for the case of a single structural change, and Andrews, Lee, and Ploberger (1996), Liu, Wu, and Zidek (1997), and Bai and Perron (1998, 2003a,b) for the case of multiple structural changes.
More recently, Perron and Yabu (2009a,b) proposed a test for structural changes in the deterministic components of a univariate time series when it is unknown a priori whether the series is trendstationary or contains an autoregressive unit root. The Perron and Yabu test statistic, called
2.2 Structural Break Tests in the Variance of the Time Series
Recently, both statistic and econometric literature related to structural changes have focused to test changes in the variance of macroeconomic times series (for a review, see Perron et al., 2020). These testing problems are important for practical applications in macroeconomics and finance to detect structural changes in the variability of shocks in time series.
In empirical applications based on linear regression models, structural changes often occur in both the error variance and the regression coefficients, possibly at different dates. McConnell and PerezQuiros (2000) confirmed a break in the volatility of US production, occurring in the early mid1980s. In the same line of research, and with a broader database of macroeconomic series for the United States, Sensier and van Dijk (2004) found that in the vast majority of real series, a change in variance is observed in the early mid1980s; see also Gadea, GómezLoscos, and PérezQuirós (2018), Perron and Yamamoto (2021), and Stock and Watson (2002, 2003a,b).
We have used the test statistics to test jointly for structural changes in mean and variance proposed by Perron et al. (2020). More specifically, these authors presented a new methodology to address this problem in a single equation regression model that involves stationary regressors, allowing the break dates for the two components to be different or overlap.
Perron et al. (2020) consider several types of test statistics for testing structural changes in mean and/or variance: (1) the
2.3 A Model for Recurrent Explosive Behavior in TimeSeries Data
Evans (1991) argued that standard righttailed unit root tests, when applied to the full sample, have little power to detect periodically collapsing bubbles (the explosive behavior is only temporary) and demonstrated this effect in simulations. The low power of standard unit root tests is due to the fact that periodically collapsing bubble processes behave rather like an
To overcome the problem identified in Evans (1991), PWY and PSY developed a new recursive econometric methodology for realtime bubble detection that proved to have a good power against mildly explosive alternatives. The interest in the testing algorithm is whether a particular set or group of consecutive observations comes from an explosive process (
On the one hand, the martingale null is specified as,
with constant
The hypothesis that the parameter
On the other hand, the alternative is a mildly explosive process, namely,
where
In addition to the classic reference of Evans (1991) and Charemza and Deadman (1995), the above analysis is extended to the case of multiplicative processes with a stochastic explosive root encompassing nonnegative processes used in the analysis of exuberant time series. The formulation of equation (1), as a restrictive representation of the generating process under the null hypothesis, includes a particular, not standard, representation for the drift term. Given that the recursive representation can be written as follows:
where
2.4 Recursive Unit Root Test for Explosiveness
The methodology developed in PWY and PSY can be applied to test the unit root hypothesis in the standard model described in (1) against an alternative of multiple subperiods of explosive behavior
The testing procedure is developed from a regression model of the form:
where
First, PWY proposed a
The SADF test is then a
Second, PSY developed a doublerecursive algorithm that enables bubble detection and consistent estimation of the origination (and termination) dates of bubble expansion and crisis episodes while allowing for the presence of multiple structural breaks within the sample period. They showed that when the sample includes multiple episodes of exuberance and collapse, the PWY procedures may suffer from reduced power and can be inconsistent, thereby failing to reveal the existence of bubbles. This weakness is a particular drawback in analyzing long time series or rapidly changing of data where more than one episode of explosive behavior is suspected.
To overcome this weakness and deal with multiple breaks of exuberance and collapse, PSY proposed the backward
where the endpoint of each subsample is fixed at
PSY also proposed a generalized version of the
The statistic (7) is used to test the null of a unit root against the alternative of recurrent explosive behavior, as the statistic (5). It is important to note, and it must be clearly stated, that the fact that the two sequential versions of the
The origination date
where
The termination date
where
3 Empirical Results
3.1 Data
We consider a long historical time series in which many cycles in Australian real houses prices are known to have occurred. The length of this database makes it particularly suitable for the econometric approach adopted in this paper (1870–2020, 151 years).
The data and sources are as follows: 1870–2017: (a) nominal house prices,
Figure 2 plots the data of the Australian real house price series,
The longrun history of data allows some observations on the two boom cycles in Australian real house prices. The first historical cycle in house prices took place between 1950 and 1974. Such boom occurred after the lifting of World War II price controls introduced in 1943 which, because they kept during a period of high inflation from 1943 to 1949, caused real house prices to be artificially reduced. These house prices controls, in conjunction with low construction activity and ceilings on house rents during the Wartime, aggravated a postWorld War II shortage of housing, which triggered the later increase in house prices. In this period, house prices in Australia increased on average by 7% per annum in real terms.
The second historical cycle in house prices spanned from 1997 to 2017. In this period, house prices in Australia increased on average by 5% per annum in real terms. There are several important determinants such as population and interest rates. First, this boom cycle in houses prices is mainly due to the inflexibility of the supply side of the housing market in response to large shifts in population growth. Since the mid2000s, Australia has experienced much higher net immigration, and thus, population growth has increased at a significantly higher rate; see Kholer and van der Merwe (2015), among others. Second, Otto (2007) finds that the level of the mortgage interest rate was an important explanatory factor for the growth dwelling of prices in the Australian capital city during the period 1986:2–2005:2. Most recently, Kholer and van der Merwe (2015) suggested that the reduction in real mortgage rates since 2011 has been associated with stronger growth in both house prices and dwelling construction.
3.2 Structural Changes of the Time Series
The first step in our analysis is to examine the structural changes in the level or slope of the trend function of the series of real house prices over the full sample. We have used the test statistics for structural changes in deterministic components proposed by Perron and Yabu (2009a,b). The results of the
Annual Growth Rate  

Model 

Break dates  Prebreak  Postbreak 
III  18.12^{3}  1986  1.8%  3.5% 
The second step in our analysis is to examine the structural changes in the variance of the real house price series for the full sample. We have used the test statistics to test jointly for structural changes in mean and variance proposed by Perron et al. (2020). We investigate structural changes in the conditional mean and in the error variance. We use
(a) Tests for structural changes in mean and/or variance  










0.80  1.93  1.73  1.93 

0.93  1.38  1.50 
(b) Tests for structural changes in mean  












Break dates  

4.94  4.98  3.88  4.98  4.22  4.22  4.22  — 

3.91  3.43  2.38  3.91  4.45  3.69  3.72  — 

1.88  0.50  2.53  2.53  3.69  3.69  3.72  — 
(c) Tests for structural changes in variance  










Break dates  

6.05 


6.65  6.87  — 

24.44^{3} 


4.66  5.40  1949 




7.08  7.08  1949 




6.53  6.58  1949 
3.3 Explosive Dynamics of the Time Series
The third step in our analysis is to examine the explosive behavior in over the full sample. The methodology developed in PWY and PSY was originally proposed to test for recurrent explosive behavior for U.S. stock market. In this paper, we use this methodology to examine whether the Australian real house prices have speculative bubble behavior at any point time for the period 1870–2020. The method of Phillips et al. (2015a,b) has also been applied in the housing market for other countries; see Pan (2019), Rherrad, Mokengoy, and Fotue (2019), Rherrad, Mokengoy, and Bago (2021), Shi (2017), and the references therein.
As far as we know, part of this methodology has only been used to test the explosive behavior of house prices for the case of Australian in two previous papers. First, Shi et al. (2016) use the method of Phillips et al. (2015a,b) for the house price to rent ratio in Australian capital cities using monthly data for the period 1995–2016. Their results pointed to a sustained, yet varying, degree of speculative behavior in all capital cities in the 2000s before the international financial crisis of 2008. Second, Shi et al. (2020) investigate the presence of housing bubbles for the house price to rent ratio in Australia at the national, capital city, and local government area levels. They control for housing market demand and supply fundamentals using the approach of Shi (2017), and employ the recursive evolving method proposed by Phillips et al. (2015a,b) for the detection of explosive bubbles. While the nationallevel analysis suggests a shortlived bubble episode (2017Q3) throughout the sample period from 1999 to 2017, the results at the capital city level show notable differences between cities, with transitory and isolated bubbles in Sydney and Melbourne in the period of acceleration in house prices between 2013 and 2017.
For our empirical application, the lag order
Table 3 reports the SADF and
Unit root tests  Estimated value  Finite critical value  

1%  5%  10%  
SADF 

1.984  1.361  1.057 
GSADF 

2.686  2.023  1.770 
Note: Superscripts 1, 2, 3 indicate significance at the 10, 5, and 1% levels, respectively.
As can be seen in Table 3, we reject the unit root null hypothesis in favor of the explosive alternative at the 1% significance level for the SADF test and the 1% significance level for
Next, we conduct a realtime bubble monitoring exercise for the Australian real house prices using the PSY strategy. The PSY procedure also has the capability of identifying market downturns, in our case, potential house prices adjustments. To locate the origin and conclusion of the explosive real house prices behavior and the adjustments episodes, Figure 3 plots the profile of the GSADF statistic for the Australian real house prices. We compared the GSADF statistic with the 95%
Next, we also conduct a realtime bubble monitoring exercise for Australian real house prices using the PWY strategy. Figure 4 plots the SADF test against the corresponding 95% critical value sequence. According to Figures 3 and 4, there is a clear speculative bubble behavior in real house prices in 1997–2020.
These results of the recurrent ADFtype test statistics (the speculative bubble behavior starts in 1997) are clearly different from the results obtained in the analysis of structural instability in some deterministic component of the series (a single break date in the trend function estimated in 1986, and a single break date in the variance estimated in 1949). It implies that the results of test for explosiveness could not be due to some type of structural instability, and they correctly identify some type of periodically collapsing explosive mechanism.
In relation to these results, there is some evidence on the possible spurious effect of a bubble or explosive component on the measurable persistence and properties of the stochastic component of a time series (see, e.g., Evans, 1991 and, more recently, Yoon, 2012), but it seems not to be a clear connexion, at least explained in some detail, with the identifiable structure of the deterministic component of the series. At most, it can be argued that many existing testing procedures can confuse a structural break in some deterministic component with a change in the persistence of the stochastic component, in the sense of Kim (2000).
Finally, Figure 2 shows the slight price adjustments in the 2018–2020 period. Since 2018, real prices have fallen just by 4.6 per cent (on average by
4 Concluding Remarks
In this article, we use tests of explosive behavior in real house prices for the case of Australian for the period 1870–2020. The main contribution of this paper is the use of long time series for testing the explosive behavior. It is important to use longer span data because it provides more powerful econometric results.
First to examine the structural changes in the level or slope of the trend function of the series of real house prices over the full sample, we use the test statistics for structural changes in deterministic components proposed by Perron and Yabu (2009a,b). We also use the test statistics to test jointly for structural changes in mean and variance proposed by Perron et al. (2020). According to the results, the breaking point is estimated at 1986 and the changes in the growth rates of the real houses price series are very large, from 1.8 to 3.5% in each subperiod. In addition, we obtain a structural change in the error variance estimated in 1951 and no change in the conditional mean.
Second to detect episodes of potential explosive in house prices over this long period, we use the recursive unit root tests for explosiveness proposed by Phillips et al. (2011) and Phillips et al. (2015a,b). According to the results, there is a clear speculative bubble behavior in real house prices between 1997 and 2020, speculative process that has not yet been adjusted.

Funding information: Vicente Esteve acknowledges the financial support from the Spanish Ministry of Science and Innovation through the projects PID2020114646RBC42 and PID2020115183RBC22, and the GV (Project GCPROMETEO 2018/102). María A. Prats acknowledges the financial support from the Spanish Ministry of Economy, Industry and Competitiveness through the project ECO201565826P.

Conflict of interest: None.

Ethical statements: None.
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