Real estate bubbles and contagion: Evidence from selected European countries

Bago, Jean-Louis; Rherrad, Imad; Akakpo, Koffi; Ouédraogo, Ernest Article Real estate bubbles and contagion: Evidence from selected European countries Review of Economic Analysis (REA) Provided in Cooperation with: International Centre for Economic Analysis (ICEA), Waterloo, Ontario Suggested Citation: Bago, Jean-Louis; Rherrad, Imad; Akakpo, Koffi; Ouédraogo, Ernest (2021) : Real estate bubbles and contagion: Evidence from selected European countries, Review of Economic Analysis (REA), ISSN 1973-3909, International Centre for Economic Analysis (ICEA), Waterloo (Ontario), Vol. 13, Iss. 4, pp. 389-405, https://doi.org/10.15353/rea.v13i3.1823 This Version is available at: https://hdl.handle.net/10419/328112 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Review of Economic Analysis 13 (2021) 389–405 1973-3909/2021389 Real Estate Bubbles and Contagion: Evidence from Selected European Countries JEAN-LOUIS BAGO*† Department of Economics and CRREP, Laval University, Canada IMAD RHERRAD Department of Finance, Government of Quebec, Canada KOFFI AKAKPO Department of Finance, Insurance and Real estate, Laval University, Canada ERNEST OU ´ EDRAOGO Department of Economics and Management, University Thomas Sankara, Burkina Faso Using quarterly housing price-to-rent ratios from 1970 to 2020, this paper investigated the presence of real estate bubbles at a national level in six selected European countries, namely France, Germany, Italy, Netherlands, Spain, and the United Kingdom. We applied the generalized sup ADF test developed by Phillips et al. (2015) to detect explosive behavior in house prices. Subsequently, we implemented the non-parametric model with time varying coefficients developed by Greenaway-McGrevy and Phillips (2016) to estimate bubbles contagion among these real estate markets. We found evidence of housing prices exuberance in all these markets. Results suggest that Germany, France, Spain, and the Netherlands experienced a bubble during the COVID-19 pandemic period, pushing prices higher, suggesting that speculators anticipated capital gains . In terms of bubbles migration, we find that bubbles migrate between these real estate markets. Keywords: Bubble, Contagion, real estate, Europe JEL Classifications: C12, G12, R31 *The authors would like to thank Greenaway-McGrevy from the University of Auckland for the Matlab code. We are also grateful to Les Oxley from the University of Waikato, Itamar Caspi from the bank of Israel for discussions. We also thank Mardoch´ ee Mokengoy from the Department of Finance of the Governement of Quebec, Landry Kuate Fotue from University of Ottawa and the three anonymous referees for their insightful comments on earlier versions of this paper. †Corresponding author: [email protected] ©2021 Jean-Louis Bago, Imad Rherrad, KoffiAkakpo, and Ernest Ou´ edraogo. Licenced under the Creative Commons Attribution-Noncommercial 4.0 Licence (http://creativecommons.org/licenses/by-nc/4.0/). Available at http://rofea.org. 389 Review of Economic Analysis 13 (2021) 389–405 1 Introduction Real estate bubbles have been of interest to both researchers and policy makers, especially since the Great Recession. While the seminal contribution of Case and Shiller (2003) was a huge step to understand prices dynamics in real estate markets, Phillips et al. (2011, 2015) model allowed to detect and datestamp bubbles, and to assess whether they are contagious (Greenaway-McGrevy and Phillips, 2016). Although an abundant literature since the great recession has relied on Phillips et al. (2011, 2015) to detect the appearance of real estate bubbles, little empirical evidence has focused on the interconnection of real estate markets and their contagiousness (Greenaway-McGrevy and Phillips, 2016). In fact, when it comes to price exuberance in real estate markets, most of the empirical literature is focused on detecting bubbles and stamping their emergence and duration without taking into account market interconnectedness (Phillips et al., 2011, 2015; Engsted and Pedersen, 2015). In the case of Europe, previous papers have investigated the presence of bubbles (Zhou and Sornette, 2003; G¨ urkaynak, 2008; Agnello and Schuknecht, 2011; Kholodilin et al., 2014; Engsted and Pedersen, 2015; Engsted et al., 2016; Chen and Xie, 2017). The issue is central to understand the appearance of bubbles and the exuberance of prices in markets increasingly dependent on each other. Indeed, real estate bubbles are determined by a dysfunctional relationship between prices and macroeconomic fundamentals (Garber, 1990; Flood and Hodrick, 1990; Case and Shiller, 2003), bubbles can migrate from one country to another because of their proximity or their economic connection (International Monetary Fund, 2013). Ignoring the transmission of bubbles across countries can affect the effectiveness of housing policies, especially in the context of an increasing cross-country synchronization of real estate prices (Grjebine, 2014; Katagiri et al., 2018; International Monetary Fund, 2013). A recent paper by Gomez-Gonzalez et al. (2018) suggested that, except for Spain, housing bubbles have only migrated from US housing market to European countries. To extend this analysis, our paper focus on bubbles contagion between European countries. Our methodology use a new model developed by Greenaway-McGrevy and Phillips (2016) to estimate bubbles contagion. This model estimate time-varying coefficients of bubbles contagion based on a non parametric estimation. We also extend the analysis to test for the presence of a COVID-19-related housing bubble in these countries. The aim of the present paper is two-fold. First, we test for housing bubbles within the six countries with the largest share of European Union’s (EU) gross domestic product (GDP), namely Germany, United Kingdom, France, Italy, Spain, and the Netherlands1. We rely on the GSADF test developed by Phillips et al. (2015) to detect the real estate 1The selection of countries included in this study was guided by three factors: the size of the economy, the availability of a long series of house price data and the overheating of the housing market as suggested by the literature. For eexample, in 2017, 75.0% of the EU’s GDP was generated by these countries. For more details, see. https://ec.europa.eu/eurostat/web/products-eurostat-news/-/DDN-20200508-1. 390 BAGO, RHERRAD, AKAKPO, OU ´ EDRAOGO Real Estate Bubbles and Contagion bubbles episodes. The results confirm the existence of at least one housing bubble episode in each country. France, Germany, Spain, Italy, Netherland, and the United Kingdom experienced several real estate price bubbles between 1970Q1 and 2020Q2. Second, the paper investigates market interconnectedness by focusing on the transmission of real estate bubbles within these countries. The existence of historical bubble episodes in all six economies; the integrated nature of the European economies; the excess of capital in the eurozone, due to the low interest rates policy led by the European Central Bank during these last years; and the synchronization in house prices across countries and major cities, as shown by International Monetary Fund (2013), raises our second question of interest, which focuses on real estate price transmissions from one European country to another. We also find evidence that Germany, France, Spain and Netherlands are experienced a bubble during the period of COVID-19 pandemic which begins in the quarter 2019Q4. We use the non-parametric model with time-varying coefficients of Greenaway-McGrevy and Phillips (2016) to investigate housing price transmissions withing the selected European countries. We found strong evidence that most of the real estate markets were connected during several periods in terms of housing prices migration from 1970 to 2020. This paper contribute to the literature which found evidence of bubbles migration in regional housing markets in several countries, including the United States (Xie and Chen, 2015; Phillips and Yu, 2011; Cohen and Zabel, 2018), New Zealand (Greenaway-McGrevy and Phillips, 2016), Israel (Caspi, 2017), Canada (Rherrad et al., 2019, 2020) and Chile (Gil-Alana et al., 2019). Other papers also found evidence of bubble migration between the stock and housing market (Balcilar et al., 2016; Deng et al., 2017; Hu and Oxley, 2018) and bubbles contagion in cryptocurrency markets (Fry and Cheah, 2016; Ferreira and Pereira, 2019). The paper proceed as follows: Section 2 presents the data and descriptive statistics. Section 3 presents the model specification and empirical approach for detecting bubble episodes and investigating bubble migration. Section 4 reports our empirical results, and Section 5 provides the conclusion. 2 Data We used quarterly housing price-to-rent series from France, Germany, Italy, the Netherlands, Spain, and the United Kingdom. Based on OECD.Stat OECD (2019) methodology, the price to rent data for each of these countries refers to their nominal house price index divided by their housing rent price index. As stated by the OECD Residential Property Prices Indices manual, the nominal house price index covers the sales of newly-built and existing dwellings, while the housing rent price index refers to consumer Price Indices for actual rentals for housing2. The 2According to the OECD guidelines, ”if this indicator is missing for a country, another indicator is chosen. The chosen indicator are usually those corresponding to the CPI aggregate for Housing including Actual rentals for housing, imputed rentals for housing and Maintenance and repair of the dwelling”. Please, for more details see : https ://stats.oecd.org/Index.aspx?DataS etCode =HOUS EPRICES 391 Review of Economic Analysis 13 (2021) 389–405 price to rent ratios are indices with base year 2015. The housing real price and price-to-rent ratios evolution from 1970Q1 to 2020Q2 are presented in Figure (1). For the majority of the countries, real price and price-to-rent ratios displayed a non-monotonous, increasing trend with very sharp rises during certain periods. The statistics summary presented in Table (1) reveals that, on average, Germany (160.1), and Italy (149.3) recorded the highest price-to-rent ratios, while United Kingdom (48.4) recorded the lowest. Table 1: Descriptive statistics Country Min Max Mean Sd Kurtosis Skewness France 57.41 115.89 81.07 18.68 -1.22 0.56 Germany 88.74 160.07 118.38 21.01 -1.29 0.15 Italy 53.75 149.29 107.03 22.29 -0.05 -0.53 Netherlands 59.54 146.02 98.23 27.58 -1.43 0.18 Spain 29.16 162.92 85.96 35.67 -0.85 0.21 United Kingdom 48.39 115.27 74.83 21.20 -1.24 0.47 The stationary analysis (Table (2)) revealed that the price-to-rent ratios were not stationary for all three stationary tests (ADF. KPSS. and PP) for all the countries. In the following of our study, we test if this non stationary is normal or explosive. Table 2: Unit root and stationary test ADF PP KPSS Zi-An stat p-val stat p-val stat p-val stat p-val France -1,14 0,69 -3,39 0,91 2,57 0,01 -4,28 0,1 Germany -1,28 0,63 2,01 0,99 3,16 0,01 -2,22 0,1 Italy -5,25 0,52 -8,95 0,60 0,74 0,01 -5,40 0,01 Netherlands -0,90 0,78 -3,51 0,91 1,71 0,01 -2,99 0,1 Spain -1,61 0,47 -4,90 0,83 3,25 0,01 -4,15 0,1 United Kingdom -1,31 0,62 -6,93 0,71 2,68 0,01 -4,90 0,03 3 Empirical Methodology 3.1 Testing for Explosive Behavior Empirical methods such as the seminal work of Kindleberger and Aliber (2005) and the recursive tests procedure for explosive behavior by Phillips et al. (2011, 2015) have been developed 392 BAGO, RHERRAD, AKAKPO, OU ´ EDRAOGO Real Estate Bubbles and Contagion Figure 1: Evolution of price to rent ratios from 1970Q1 to 2020Q2 (a) France (b) Germany (c) Spain (d) Italy (e) Netherlands (f) United Kingdom 393 Review of Economic Analysis 13 (2021) 389–405 to identify the presence of bubbles in time series. In this paper, we used the generalized sup ADF (GSADF) test developed by Phillips et al. (2015) to examine the explosive behavior of housing prices in European real estate markets. Consider the following equation: ∆yc,t=α+βyc,t−1+ K X i=1 γi∆yc,t−i+c,t(1) where ytis the property price at period t in country c,αis the intercept, Kis the optimal lag order, and c,tis the error term. This procedure consists of testing the hypothesis that implies the series has a normal unit root (β=0), versus the alternative hypothesis of an explosive behavior (β > 0). The GSADF test repeatedly estimates Equation (1) on sub-samples of data in a recursive fashion and is based on global backwards supremum ADF (BSADF) statistics of the form GS ADF(r0)=sup r2∈[r0,1]r1∈[0,r2−r0] ADFr2 r1(2) The BSADF statistic, which is used to determine the origination and collapse of each bubble, was defined by Phillips et al. (2015) as the sup value of the ADF statistic sequence: BS ADFr2(r0)=sup r1∈[0,r2−r0] ADFr2 r1(3) where rw=r2−r1represents the window size of the regression; r0is the minimum window size; r1is the starting point, which varies from 0 to r2−r0; and r2is the ending point, which varies from r0to 1. Minimum window size r0is determined according to the formula 0.01 +1.8 √T proposed by Phillips et al. (2015). Phillips et al. (2015)’s procedure consists of estimating the equation (1) and then repeatedly calculating the ADF statistics on a sequence of backward expanding sub-samples. As in Rherrad et al. (2020), we compute the critical values using the wild bootstrap method proposed by Harvey et al. (2016), with 10000 replications. Harvey et al. (2016) demonstrates that the wild boostraap procedure is consistent to date stamps bubbles in presence of time varying volatility of prices and helps to avoid spurious identification of a bubble. The maximum value of the ADF statistics (BSADF) is compared to the critical value to determine the presence of a bubble in each sub-period. 3.2 Bubble Contagion We used the non-parametric regression with time varying coefficient developed by GreenawayMcGrevy and Phillips (2016) to analyze the bubbles contagions between pairs of real estate markets. This model uses rolling windows coupled with local kernel regressions to detect the contagion dynamic between the market. Let us consider two markets: A and B. The non394 BAGO, RHERRAD, AKAKPO, OU ´ EDRAOGO Real Estate Bubbles and Contagion parametric regression specified by Greenaway-McGrevy and Phillips (2016) is as follows 3: e βB,t=δt,Te βA,t−d+t(4) where e βk,t=ˆ βk,t−1 T−w+1PT t=wˆ βk,t. The time varying coefficient δis estimated by local kernel regression, such that ˆ δ(r;h,d)=PT j=w+dKh j(r)e βB,je βA,j−d PT j=w+dKh j(r)e β2 A,j−d (5) where Kh j(r)=1 hK(j/T−r h), K(.)=(2π)−1/2e− 1 2(.)2 is a Gaussian kernel, his the bandwidth, r is the fraction date, and dis a non-negative delay parameter that captures the lag in market contagion from the market A to the market B. If ˆ δ(r;h,d)>0, the two real estate markets are connected, and there could be bubble contagion between theses markets. Otherwise, the markets are not connected and there is no bubble migration between these markets. 4 Empirical Results 4.1 Detecting Bubble Episodes The bubble detection results are presented in Table (3) and Figure (2). Overall, the GSADF statistics in Table (3) are significant at 1% threshold for all the countries. This result support the bubble hypothesis that these six selected European real estate markets have been exuberant during the 1970–2020 period. This result is consistent Engsted et al. (2016) and Gomez-Gonzalez et al. (2018) who also found evidence of explosive behaviour in price to rent ratios in the theses countries. To detect date-stamp bubble episodes for each country, the BSADF statistics Table 3: GSADF test for bubble detection Country Period Optimal lags GSADF Interpretation France 1970Q1-2020Q2 3 2,562*** Presence of bubble Germany 1970Q1-2020Q2 3 2,812*** Presence of bubble Italy 1970Q1-2020Q2 2 2,178*** Presence of bubble Netherlands 1970Q1-2020Q2 4 2,947*** Presence of bubble Spain 1971Q1-2020Q2 1 3,468*** Presence of bubble United Kingdom 1970Q1-2020Q2 1 2,161*** Presence of bubble are presented in Figure (2). France’s real estate market (Figure 2a) experienced six bubble 3Deng et al. (2017) notation. 395 Review of Economic Analysis 13 (2021) 389–405 episodes for the periods 1980Q2 - 1981Q1, 1990Q1 - 1990Q4, 1994Q3 - 1997Q3, 2001Q2 - 2009Q1, 2010Q32012Q2 and since 2019Q4. The longest and most intense bubble is from 2001Q2-2009Q1 with a peak at 2005Q4. In Germany, the results in Figure 2b also indicate the presence of seven bubbles during the period 1980Q3 - 1981Q3, 1985Q1 - 1986Q1, 1986Q4 - 1987Q2, 1996Q4 - 2000Q2, 2001Q4-2003Q1, 2004Q2 - 2004Q4 and and since 2015Q4. For Spain (Figure 2c), real estate bubbles took place during the period 1977Q4 - 1978Q4, 1986Q4 - 1992Q1, 2002Q1 - 2008Q3, 2011Q4 - 2014 Q1 and since 2018Q4. Italy (Figure 2d), has experienced bubble episodes in the period 1990Q2 - 1991Q3, 1996Q2 - 1998Q3, 2003Q1 - 2008Q2 and 2012Q4-2016Q1. We also found four bubble episodes in the Netherlands (Figure 2e) from 1976Q3 - 1978Q2, 1996Q2 - 2009Q1, 2012Q1 - 2015Q3 and since 2019Q2. Finally, the United Kingdom (Figure 2f), which is the only country outside the euro area in our study, experienced three majors bubbles in 1988Q2 - 1989Q3, 1999Q3 - 2008Q2 and 2016Q3 - 2019Q1. 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