Global House Price Fluctuations: Synchronization and Determinants
2013; University of Chicago Press; Volume: 9; Issue: 1 Linguagem: Inglês
10.1086/669585
ISSN2150-8372
AutoresHideaki Hirata, M. Ayhan Köse, Christopher Otrok, Marco E. Terrones,
Tópico(s)Economic theories and models
ResumoPrevious articleNext article FreePart II: Global Business CyclesGlobal House Price Fluctuations: Synchronization and DeterminantsHideaki Hirata, M. Ayhan Kose, Christopher Otrok, and Marco E. TerronesHideaki HirataHosei University, Japan Center for Economic Research Search for more articles by this author , M. Ayhan KoseIMF Search for more articles by this author , Christopher OtrokUniversity of Missouri, Federal Reserve Bank of St. Louis Search for more articles by this author , and Marco E. TerronesIMF Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreI. IntroductionHouse prices in many advanced countries have moved in tandem during the past decade. They first increased unusually rapidly prior to the global financial crisis, reaching in some cases levels not previously seen. House prices then collapsed over the period 2006 to 2011 and have recently started to rebound in some of these countries. These highly synchronized fluctuations in housing markets first coincided with a period of high growth, but then were followed by severe financial disruptions and deep recessions. In light of these observations, this paper addresses two specific questions to have a better understanding of fluctuations in global housing markets: First, how synchronized are housing cycles across countries? Second, what are the main shocks driving movements in global house prices?Our interest in house prices is clearly motivated by recent developments, but there are also simpler, and probably more fundamental, reasons to study the dynamics of housing markets because of the key role housing plays in modern societies. First, housing satisfies peoples’ need for shelter. Second, housing-related activities account for an important fraction of GDP and household expenditures. Third, housing is the main asset and mortgage debt is the main liability held by households in many advanced countries. Therefore, large movements in house prices, by affecting households’ net wealth and their capacity to borrow and spend in residential investment, can have serious macroeconomic implications.In theory, interactions between house prices and the real economy can be amplified when financial imperfections are present. This amplification largely occurs through the financial accelerator and related mechanisms operating through firms, households, and countries’ balance sheets. According to these mechanisms, an increase (decrease) in asset prices improves (worsens) an entity’s net worth, enhancing (reducing) its capacity to borrow, invest, and consume. This process, in turn, can lead to further increases (decreases) in house prices and produce general equilibrium effects.1 In other words, disturbances in housing markets can translate into much larger cyclical fluctuations in the real economy.A number of recent theoretical studies have shown how developments in housing markets can magnify and transmit shocks to the real economy using the financial accelerator mechanism in the context of dynamic stochastic general equilibrium (DSGE) models. For example, Iacoviello (2005) constructs a model with firms’ collateral constraints connected to real estate, and finds that collateral effects are critical to replicate the changes in consumption in response to movements in house prices.2 Other studies have focused on how credit constraints affect macroeconomic fluctuations, using a framework where house prices and business investment are linked (Liu, Wang, and Zha 2011).A series of recent empirical studies document strong linkages between developments in housing markets and the real economy. For example, Claessens, Kose, and Terrones (2011, 2012) report that downturns in housing markets are highly synchronized across countries and that the degree of comovement rises, especially during periods of synchronized recessions (figure 1). Their results suggest that recessions accompanied with housing busts tend to be longer and deeper than other recessions, and recoveries associated with housing booms are often shorter and stronger (table 1).3Fig. 1. Coincidence of house price downturns and recessions (in percent) Notes: Solid line shows the share of countries experiencing recessions, dotted line shows the share of countries experiencing house price downturns, and the gray bars represent the years of global recessions (1975, 1982, 1991, and 2009) and the preceding (1974, 1981, 2008) and succeeding (1992) periods. Recessions and house price downturns are identified following Claessens, Kose, and Terrones (2012). At the beginning and end of samples only complete episodes are included. The dates of global recessions are from Kose, Loungani, and Terrones (2009)View Large ImageDownload PowerPointTable 1. Housing Cycles, Recessions, and Recoveries Number of EventsDurationAmplitudeCumulative LossSlopeA. Recessions without House Price Busts733.27−1.72−2.43−0.51Recessions with House Price Busts404.28**−2.35−3.57**−0.52Recessions with Severe House Price Busts244.38*−2.64**−5.23***−0.72B. Recoveries without House Price Booms1024.792.97 0.75Recoveries with House Price Booms132.08***6.25*** 1.45***Recoveries with Strong House Price Booms82.13***7.36*** 1.59***Source: Authors’ calculations.Notes: All statistics, except “Duration,” correspond to sample medians. For “Duration,” sample means are reported. Duration for recessions is the number of quarters between peak and trough. Duration for recoveries is the number of quarters it takes to attain the level of output at the previous peak. The amplitude for recessions is defined as the decline in output from the peak to the trough. The amplitude for recoveries is the one year change in output after the trough. Cumulative loss combines information about the duration and amplitude to measure the overall cost of a recession and is expressed in percent. The slope of a recession is the amplitude from the peak to the trough divided by the duration. The slope of a recovery is the amplitude from the trough to the period where output reached the level at its last peak, divided by the duration. Booms correspond to the observations in the top 25 percent of upturns calculated by the amplitude. Busts correspond to the observations in the worst 25 percent of downturns calculated by the amplitude. Recessions, recoveries, housing busts, and booms are identified following Claessens, Kose, and Terrones (2012). Asterisks denote the significance of difference between recession (recoveries) with house price busts (booms) and those without house price busts (booms).***Significant at the 1% level.**Significant at the 5% level.*Significant at the 10% level.View Table ImageDespite the apparent consensus on the importance of housing market movements for the real economy, our understanding of the sources of synchronization in housing markets is rather limited. As we summarize in the next section, a number of studies analyze the sources of house price movements, but they report mixed findings. Moreover, the nature and identification of shocks vary significantly across studies, making the interpretation of their findings difficult. For instance, some studies emphasize the importance of country-specific house price shocks in the transmission and synchronization of house prices. Others argue that interest rate shocks play a key role in driving movements in house prices. There are also other studies highlighting the importance of demand and supply shocks in housing markets and country-specific structural characteristics.Our study contributes to the large body of research by focusing on the extent and sources of the synchronization in global housing cycles. Specifically, we extend the literature in four dimensions. First, we study different measures of the synchronization of house prices and analyze how the features of house price cycles compare with cycles in output and other financial variables. Second, we identify shocks driving house prices using various approaches commonly employed in the literature, including a standard recursive method and one based on sign restrictions. In the former, we consider how shocks to output, house prices, equity prices, credit, and interest rates affect movements in house prices. In the latter, we formally identify and study the importance of a sequence of structural shocks, including monetary, credit, productivity, and uncertainty shocks.Third, we employ a series of FAVAR (factor augmented vector autoregression) models to analyze the importance of different types of shocks in explaining movements in global house prices. It is critical to study how house prices react to worldwide shocks to get a better understanding of the synchronization of global housing cycles. Finally, we consider the impact of shocks on housing cycles in different groups of advanced countries over a long period.The remainder of this paper is organized as follows. In Section II, we briefly summarize recent research analyzing the roles of different types of shocks in explaining house price movements. In Section III, we introduce our database and methodology. In Section IV, we present the main features of housing cycles and analyze the synchronization of housing cycles. In Section V, we analyze the importance of a variety of shocks in driving house prices. Section VI concludes.II. What Do We Know About Synchronization of House Prices? A Brief ReviewThere is a growing literature that analyzes the importance of various shocks in driving national and global house prices. We present a brief review of this literature, considering three types of studies according to the methods they employed. As the review shows, the literature paints a rather blurry picture about the relative importance of different types of shocks.Studies employing VAR models. The first group of studies examines the roles played by shocks to interest rates or monetary policy in explaining national house prices using VAR models. Some of these studies use a recursive scheme to identify shocks (Assenmacher-Wesche and Gerlach 2010; Calza, Monacelli, and Stracca 2009; Goodhart and Hoffmann 2008; Cardarelli et al. 2008; Gupta et al. 2012). In these studies, shocks to interest rates are often interpreted as monetary policy shocks. Others employ sign restrictions to identify monetary policy shocks (Carstensen, Hulsewig, and Wollmershauser 2009; Del Negro and Otrok 2007; Jaro-cinski and Smets 2008). Some recent studies also consider the importance of the housing sector in the transmission of monetary policy (see Feroli et al. 2012). Sa, Towbin, and Wieladek (2011) find that house prices respond more to monetary policy shocks in countries with more developed mortgage markets using a VAR model. In his survey of this growing literature, Kuttner (2012, p. 2) concludes that the evidence suggests “the impact of interest rates on house prices appears to be quite modest.” In particular, he notes that the estimated impact of interest rates shocks on house prices reported in these studies are consistently smaller than the predictions of the standard user cost theory of house prices.Studies employing global VAR (GVAR) models. Studies in the second group mostly use GVAR models to analyze the transmission and synchronization of house prices across countries. Ambrogio Cesa-Bianchi (2011), for instance, report that house price shocks originating in the United States play a significant role in driving global house prices. In contrast, Hiebert and Vansteenkiste (2009) conclude that house price shocks play a relatively minor role in explaining house price spillovers in the euro area. Vansteenkiste (2007) consider the same approach in the context of the US states and find that house price shocks in California appear to be an important factor driving prices in other states. The GVAR methodology does not structurally identify shocks, implying that there is no economic interpretation of the housing shocks in these studies. In addition, since the methodology characterizes cross-border linkages by averaging variables into a global aggregate, it is difficult to understand how country weights affect the influence of individual country variables in the transmission of shocks across borders.Studies considering a wider range of shocks and methods. The third group of studies includes research that employs various other methodologies, including dynamic factor and FAVAR models. These also provide mixed results about the importance of different types of shocks in explaining housing cycles. For example, Case, Goetzmann, and Rouwenhorst (1999), who study the dynamics of international commercial real estate markets from 1987 to 1997 using global factors, report that the comovement among commercial real estate markets is through output linkages. Terrones and Otrok (2004) examine the synchronization of housing prices in a sample of 14 advanced countries using a FAVAR model from 1970 to 2004. They find evidence of a global housing cycle, which moves closely with global GDP, but they do not identify the sources of the changes in house prices. In a related study, de Bandt, Barhoumi, and Bruneau (2010) find that house prices in the United States lead movements in house prices in other Organization for Economic Cooperation and Development (OECD) countries using a FAVAR model. Beltratti and Morana (2010) also consider a FAVAR model using the G7 countries. They identify shocks using a recursive decomposition and consider demand, supply, house price, stock price, and oil price shocks. They report that both house price and supply shocks are important in explaining global house price movements.4III. Database and MethodologyA. DatabaseOur main data set includes quarterly series of GDP, house prices, equity prices, credit, and the short- and long-term interest rates of 18 advanced OECD countries for the period 1971:1 to 2011:3. We concentrate on this sample because it provides a broad perspective of fluctuations in global housing markets and it is a common denominator of the cross-country data we analyze. Our sample provides a good representation of developments in global housing markets as it accounts for slightly more than 60 percent of global GDP over the 1971 to 2011 period (in PPP, or Purchasing Power Parity, exchange rates).We provide a systematic examination of the synchronization of house prices and the sources of this synchronization over two different subperiods. The first subperiod, 1971:1 to 1984:4, witnessed a set of common shocks associated with sharp fluctuations in the price of oil and contractionary monetary policy in major industrial economies. We call this period the “preglobalization period.” The second period, 1985:1 to 2011:3, represents the “globalization period” in which there were dramatic increases in the volume of cross-border trade in both goods and assets. This period also covers a substantial portion of the so-called “Great Moderation” era, as well as the latest global financial crisis, and coincides with a rapid increase in trade and financial linkages among the advanced countries and a broader converge of their business cycles (see Kose, Otrok, and Whiteman 2008). This demarcation is helpful for differentiating the impact of common shocks from that of globalization on the degree of comovement of housing cycles.House prices correspond to various measures of indices of house or land prices depending on the source country.5 Equity prices are share price indices weighted with market value of outstanding shares. Our measure of credit is aggregate claims on the private sector by deposit money banks. This measure is also used in earlier cross-country studies on credit dynamics (Mendoza and Terrones 2008; and Claessens, Kose, and Terrones 2011). We track aggregate business cycles with real GDP measured by chained volume series. The short-term interest rates correspond to nominal short-term government bill rates, generally the Treasury Bill Rates, and the long-term interest rates typically are those of the long-term government bonds.We also use measures of uncertainty, reserves, credit spreads, and default rates. Following Bloom (2009), uncertainty is constructed using the volatility of daily equity prices of the G7 countries.6 Reserves series correspond to total reserves. Unlike other variables, credit spread and default rates series are available for only the United States. In order to measure credit spreads, we use corporate bond spreads, which are the yield differences between Moody’s Seasoned Aaa and Baa corporate bonds for the United States. The Aaa bonds are “judged to be the highest quality with minimal credit” risk while the Baa bonds are “subject to moderate credit risk and possess certain speculative characteristics.”7 The default rate series corresponds to the monthly rates for Moody’s rated US speculative-grade corporate bonds. As in the case of credit spreads, we take the observation of the last month of each quarter as our quarterly default rates.Before constructing our factors and estimating the VAR models, we make appropriate transformations in each data series. Whenever necessary, we deflate the series using the Consumer Price Index (CPI) to obtain real variables. We take four-quarter growth rates of house prices, credit, equity prices, and GDP. All variables are seasonally adjusted and expressed in percentages. We provide a detailed list of the data series and their sources in the appendix.B. MethodologyBecause our objective is to analyze the extent and sources of synchronization of house price fluctuations, we undertake our exercise in three steps. First, we study the main features of house price movements by paying special attention to the extent of their synchronization. For this purpose, we use a range of approaches, including basic correlations and concordance statistics. Second, we estimate the common component (global factor) in each variable. Third, we use a set of FAVAR models to analyze the importance of various shocks that could explain fluctuations in house prices. We briefly explain next the estimation of global factors and FAVAR models.Estimation of global factors. To estimate the global factors, we extract the first principal component of each variable in our database. There are, of course, alternative approaches to construct global equivalents of these variables. For example, we could employ a full-fledged dynamic factor model, as in Kose, Otrok, and Whiteman (2003). Their method is particularly useful to simultaneously estimate different common factors, such as the global, regional, and country-specific factors. However, the global factor obtained with a dynamic factor model is quite similar to the first principal component. We use the simpler approach here since we are only interested in the global component of each variable.Figure 2 presents some of the estimated global factors. The estimated factors are broadly consistent with a number of well-known cyclical episodes in the global economy. For instance, the downturns in the estimated global house price factor take place around the global recessions. The downturn during the latest episode is particularly striking because of its highly synchronous nature and its depth. The increase in the global housing factor in the mid-1980s was larger than that prior to the recent financial crisis because a larger number of countries experienced greater growth in house prices over a short time period in the former episode. In contrast, global house prices grew gradually over a long period before the financial crisis. The global factor has recently started picking up because of the growth of house prices in some countries, including Australia, Canada, Switzerland, and some Nordic nations. The global output factor is able to capture the growth dynamics around global recessions and recoveries. The estimated factors of other financial variables also reveal interesting patterns as they register significant declines prior to or during the periods of global recessions. The global credit factor features contractions during periods of downturns in housing markets, illustrating the strong interactions between credit and housing markets.Fig. 2. Global factors of financial variables and output (in percent, growth rates) Notes: The graphs show the global factors estimated using the first principal components of the growth rates of respective variables. The gray bars represent the global recessions and preceding and succeeding periods. See figure 1 for additional informationView Large ImageDownload PowerPointFAVAR models. The FAVAR models we estimate can be represented by: where yt is an m × 1 vector of variables at date t, A1 is an m × m coefficient matrix for each lag of the variable vector with a(0) being the constant term, and m is the number of variables in the model. The vector of one-step ahead prediction error is ut. We consider two types of FAVAR models, which differ only in terms of the set of variables in the yt vector. The first type contains only the estimated global factors. The second type mostly includes a mix of the estimated global factors and some country-specific variables, such as default rate, and spreads.8 In our estimation, the lag length, l, is kept at four. When we use sign restrictions to identify the shocks, we employ Bayesian methods to estimate the models. We use a symmetric (across variables) prior with a harmonic decay.9 We present a discussion of the identification of shocks and the use of these models in Section V.As it is often the case in the VAR literature, we need to make challenging decisions with respect to our modeling choices. Ideally, we would use the same set of variables in each model. However, this would require a grand model to nest all the different specifications we have because identification of each shock with sign restrictions requires different data series. One approach to address this, for instance, could be to estimate a large model and then be aggressive on using priors as shrinkage (as is done in the forecasting literature). However, this could lead to problems in identifying some of the shocks, if one pushes the coefficients on some of the variables toward zero that may be needed for the identification of one shock but not another. Instead, we include as many of the same variables as possible across our models, but we note that each model contains a few time series not present in the other models. We do not consider a formal lag length test in each model, but again given that we have a limited number of observations in some of our models, our selection of four lags provides a reasonable benchmark and is consistent with other studies in the literature (Peersman and Straub 2009).IV. House Price Fluctuations: Basic FactsWe start this section with a brief discussion of the main features of fluctuations in house prices, equity prices, credit, interest rates, and output. We then study the degree of comovement between house prices, output, and other financial variables within countries using simple correlations. We conclude with a study of the synchronization of house prices and other variables across countries using different approaches.A. Growth, Volatility, and ComovementHouse prices in advanced economies grew almost at the same pace as economic activity, but the growth rate of house prices has accelerated over recent decades (table 2). Over the past four decades, real house prices have grown at an average rate of 2 1/4 percent per year, slightly slower than the growth of output. The growth of house prices differs significantly across countries (ranging from less than 1/2 percent per year in Germany, Japan, and Switzerland to over 3 percent per year in Spain and the United Kingdom) and over time.Table 2. Summary Statistics MeanVolatilityCoefficient of VariationMaximumMinimumOutput Full Sample2.512.530.999.34−6.39Preglobalization2.822.74*1.038.90−3.29Globalization2.352.301.026.80−5.64House Prices Full Sample2.207.480.2926.40−14.63Preglobalization1.23**8.050.1522.09−12.94Globalization2.706.750.4021.04−12.27Equity Prices Full Sample4.6424.150.1979.50−49.49Preglobalization−0.02***23.220.0062.13−41.74Globalization7.0823.950.3074.74−46.56Credit Full Sample5.306.660.8026.46−10.01Preglobalization4.806.450.7420.25−7.36Globalization5.566.320.8822.56−8.02Short-Term Interest Rate Sample Full−0.152.29−0.078.38−8.02Preglobalization0.21***2.76***0.087.08−6.13Globalizati
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