capital flows, house prices, and the macroeconomy ... · 04.06.2014 · world availability of us...

Capital Flows, House Prices, and the Macroeconomy: Evidence

from Advanced and Emerging Market Economies9

Ambrogio Cesa-Bianchi† Luis Felipe Cespedes‡ Alessandro Rebucci§

March 12, 2014

Abstract

In this paper we build a new comprehensive, quarterly house price data set comprising 57advanced and developing economies —representing more than 95 percent of world GDP—with varying time-coverage from 1970 to 2012. We show that house prices in emergingmarkets grow slower, are more volatile and less persistent, and are more correlated withthe real effective exchange rate and the current account, than in advanced economies. Weprovide evidence that indicates that house price booms are much larger and are more closelyassociated with capital inflows, loose global liquidity conditions, shallow domestic financialsystems in emerging economies than in advanced economies. Finally, we find that whilean exogenous change in global liquidity has a sizable impact on consumption, house pricesand the current account in emerging markets, it has a much smaller and economicallynot significant impact on the same variables in advanced economies. We interpret ourempirical evidence as suggesting that while global imbalances would have played a lesser rolein explaining house price boom in developed economies in the period previous to the GreatRecession, the increase in global liquidity in response to it may be playing an important roleexplaining recent house price dynamics in emerging markets.

Keywords: Capital Flows, House Prices, Emerging marketsJEL codes: C32, E44, F44

9The views expressed in this paper are those of the authors, and not necessarily those of the Bank of England.†Bank of England (part of this work has been done while at IADB).‡Universidad Adolfo Ibanez.§Johns Hopkins University Carey Business School and IADB. Correspondence to: [email protected]

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1 Introduction

Housing is the quintessential non-tradable asset, and the non-tradable sector has often been

at the core of financial crises. For instance, booms in the non-tradable sectors were at the

epicenter of many of the banking and currency crises that emerging market economies (EMEs)

experienced in the 1980s and the 1990s. In those cases, positive external factors, such as

increases in commodity prices and large capital inflows, fueled booms in the non-tradable sector

and credit. When those external factors were reversed, the currency collapsed, the non-tradable

sector suffered a significant contraction (including a sharp drop in house prices), and investment

felt down abruptly. A similar mechanism played an important role in the recent banking and

external crises in some European countries (Figure 1).

At the same time, over the past several years —in Asia and other emerging markets first,

and in the United States and other advanced economies (AEs) more recently— capital has

been abundant and highly mobile, while the set of profitable investment opportunities has

been limited with many developed economies countries recovering from a crisis state. On this

backdrop, policy makers in emerging market economies have worried for sometime now about

the side effects of large and volatile capital flows. Indeed, while some analysts have blamed

(at least in part) the United States financial crisis on the glut of Asian saving that exerted

downward pressure on United States long-term interest rates, emerging markets have blamed

many of their recent policy challenges on the monetary policy stimulus enacted by the United

States in response to the global financial crisis (Figure 2).

In this paper we explore empirically the relation among capital flows, house prices, and

the broader macroeconomy using a new, quarterly data set on house prices for a large group

of advanced and emerging market economies. We first build a new comprehensive, quarterly

house price data set comprising 57 advanced and developing economies —representing more

than 95 percent of world GDP— with varying time-coverage from 1970 to 2012. We then

describe house prices characteristics and their boom and bust behavior, comparing advanced

and emerging economies. Next, we explore the nexus between these boom-bust episodes, capital

flows and a set of macroeconomic and policy variables. Taking advantage of the large cross

section dimension of our data, we also explore how structural characteristics of the economy

(such as financial market depth and the exchange rate regime) affect these episodes. Then,

in the final part of the paper, we identify an external financial shock (i.e., a “global liquidity

shock”) in a panel VAR model and we explore its transmission to house prices, the current

account and the real exchange rate.

The paper relates to several strands of literature. The first set of references belong to

an early strand of literature on housing and macroeconomics focused on the impact of house

prices and borrowing constraints over the business cycle —see for example Iacoviello (2005),

Monacelli (2009), Iacoviello and Neri (2010). Iacoviello (2005) for instance, using VAR analysis,

finds that lower interest rates increase real house prices and GDP in the United States, while

Monacelli (2009) shows that house price shocks can have a significant impact on aggregate

demand and particularly consumption. The theoretical analyses of Iacoviello (2005), Iacoviello

and Neri (2010), and Monacelli (2009) indicate that collateral constraints significantly amplify

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the response of macroeconomic aggregates (such as output and consumption) to house price

changes in a way consistent with the empirical evidence above. Therefore, the introduction of

this type of collateral constraints seems to be useful to explain excess business cycle volatility

in developed economies. Relative to this first group of papers, we introduce an open economy

dimension to the analysis—a dimension that is particularly relevant for economies that are

transiting from emerging market to advanced economy status.

A second strand of the literature has explored the relation between capital flows and house

prices in an attempt to gauge the role played by global external imbalances during the global

financial crisis. Using data for advanced economies, Laibson and Mollerstrom (2010), Favilukis

et al. (2012), Adam et al. (2012) and Ferrero (2012) provide evidence of a robust association

between real house price appreciations and a widening of the current account deficit. Similarly,

using data for both AEs and EMEs in a panel setting, Aizenman and Jinjarak (2009) find that

lagged changes in current account deficits are associated with an appreciation of real house

prices. A similar analysis has been conducted in a VAR setting —and using data for AEs

only— by Gete (2009), Sa and Wieladek (2010) and Sa et al. (2011). In particular, Sa et al.

(2011) show that a capital inflow shock —identified with sign restrictions in a panel VAR— has

a significant and positive effects on house prices in advanced economies.

Relative to these papers not only we compare systematically AEs and EMEs, but we also

explore the cross section dimension of the data in a panel VAR analysis and an event study

analysis. This allows us to investigate the role of financial development and the exchange

rate regime in the transmission of an external financial shock to house prices and the broader

macroeconomy in both advanced and emerging economies. In addition, rather than identifying

a generic capital flow shock, which could be triggered by many different underlying drivers of

capital flows, we focus on the identification of exogenous changes to one particular driver of cap-

ital flows—global liquidity conditions, arguably the most important factor driving international

capital flows—and study their impact on house prices, consumption, current account, and the

real exchange rate.

Finally, because it focuses on global liquidity as a driver of capital flows, this paper also

relates to the ongoing debate on the side-effects of (and prospective exit from) ultra-easy mon-

etary policies in advanced economies in response to the global financial crisis. Landau (2013)

stresses the importance of understanding the consequences of AEs’ monetary policies on (po-

tentially harmful) cross-border movements of liquid assets, which are driven more and more by

global risk appetite and, to a lesser extent, by interest rate differentials. Similarly, Rey (2013)

highlights the impact of unconventional monetary policy in the United States on the nature

and the direction of international capital flows which, in turn, affect credit conditions and asset

price behavior, such as exchange rates, equities and house prices. Relative to these papers, we

use alternative measures of global liquidity (including measures based on assets issued by the

official and private sectors, as well as uncertainty-based measures of global liquidity such as the

VIX index of uncertainty) and investigate the impact of an exogenous change in such measures

on both house prices and the broader macroeconomy.

The analysis yields several novel empirical results. First we report a new set of house price

stylized facts, showing that house prices in emerging markets grow slower and are more volatile

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and less persistent than in advanced economies, and are more correlated with the real effective

exchange rate and the current account than in advanced economies. We then show that house

price booms are much larger in EMEs than in AEs and are more closely associated with loose

global liquidity conditions; also we find that they are larger the shallower the domestic financial

system. Finally we show that while in emerging economies a global liquidity shock has an

economically meaningful impact on consumption, house prices and the current account, its

impact in advanced economies is qualitatively very similar but quantitatively negligible.

The empirical evidence reported in the paper is consistent with the closed economy view

presented above that borrowing constraints, which are certainly tighter and more pervasive in

EMEs than in AEs, amplify the response of macroeconomic aggregates (such as output and

consumption) to shocks triggering house price movements. Given the quantitative differences

uncovered in the response of a typical advanced and emerging economy, we interpret our em-

pirical evidence as suggesting that while global imbalances would have played a lesser role in

explaining house price boom in developed economies in the period previous to the Great Re-

cession, the increase in global liquidity in response to it may be playing an important role

explaining recent house price dynamics in emerging markets.

The rest of the paper is organized as follow. In section two we describe our new house

price data set. In section three we report house price unconditional moments and correlations.

In section four we conduct an event study of boom-bust cycles in advanced and emerging

economies. In section five we explore the causal link between global liquidity, house prices

and the macroeconomy with a relatively simple panel VAR analysis. Section six concludes.

Additional information on the data and the details of the analysis is reported in appendix (not

necessarily for publication).

2 A new global house price data set

An important contribution of the paper is the construction of a new, comprehensive house price

data base comprising 57 advanced and developing economies —covering more than 95 percent

of world GDP— at business cycle frequency.

Our data set merges data from the OECD house price database, the BIS new property

price data set, national central banks, national statistical offices, and academic publications

on housing markets. It is novel because, relative to its main building blocks —i.e., the OECD

house price database and the BIS property price data set— it extends the time coverage of

some series and it includes several additional countries. Relative to the OECD and BIS data

sets, we collected data for the following countries: Argentina, Brazil, Chile, Colombia, Croatia,

India, Peru, Taiwan, Ukraine and Uruguay. And we extended the existing series for Austria,

Chile, Czech Republic, Estonia, Hong Kong, Hungary, Indonesia, Malaysia, Philippines, Poland,

Serbia, Singapore, Slovakia, Slovenia, Thailand, and Uruguay.

The coverage of some existing series was extended with a backward extrapolation procedure.

Specifically, consider a country i for which there exists a (quarterly, nominal) house prices series

that has a longer coverage than the OECD or the BIS data sets. To construct the final house

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price index in the database, we use the quarterly growth rates of this new series to extrapolate

backward the existing OECD or BIS data. To make sure that the two series are comparable we

use any overlapping period to evaluate the quality of the extrapolation.1

In order to use most of the available data, we also interpolate annual data to quarterly

frequency by using the following 3-step procedure. First, we compute the annual growth rates

of the original annual series. Second, by assuming that house prices grow at a constant rate

within the year, we back out the quarterly growth rate that yields the annual price increase

computed in the first step. Finally, we extend the existing data with the backward extrapolation

procedure described above.

All series are seasonally adjusted with Eviews (using the X12 procedure) and then deflated

with a country-specific (seasonally adjusted) CPI measure. The seasonal adjustment is per-

formed on the quarterly growth rate of the nominal house price series using additive option.

The resulting data set is an unbalanced panel of 57 quarterly time series with varying

coverage from 1970.I–2012.IV.2 To get a good sense of the time coverage of the data set, Figure

3 provides a data map. As we can see, most advanced economies are covered from the 1970s,

while the coverage for emerging market economies is much more heterogeneous, with most

countries covered from the mid-1980s onward.

The unbalanced nature of this panel of house price series raises the question of which sample

period to use in the empirical analysis. As we shall see, we will use different sample periods

depending on the specific aspect of the analysis and (when possible) we will conduct robustness

analysis to the choices made.

Figure A.1 and A.2 in appendix display the behavior of all house price series in our data

set (advanced economies and emerging economies, respectively). The charts show the high

degree of heterogeneity of house price behavior, reflecting the importance of country-specific

developments in housing markets. For instance, note that in the 2000s —while many advanced

and emerging economies were experiencing sustained house price growth— house prices were

stagnant in several other countries. For instance, house prices in Germany and Japan fell in

real terms during that period, while house prices in Austria and Switzerland were relatively

flat. In fact, prices in Germany were depressed by the excessive supply of houses following

the construction boom after the German unification, while Japan was still suffering from the

bursting of the housing bubble in the early 1990s.

Heterogeneity is even higher in emerging economies’ housing markets, as Figure A.2 shows.

Quite strikingly, in fact, while some advanced economies started to experience sustained house

price growth since the mid-1990s, most emerging markets house prices did not bottom out from

their earlier slumps until the early 2000s. Indeed, virtually all Asian countries experienced a

prolonged house price decline after the 1997-98 crises, while Latin American countries experi-

enced house price declines with the crisis episodes of the early 2000s. However, in the run up to

1This step of the process is not documented in the paper but the material is available on request from theauthors.

2The data set is avaiable at sites.google.com/site/ambropo/ and is updated occasaionally. The specific datasources are listed in the data appendix.

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https://sites.google.com/site/ambropo

the global financial crisis of 2007-08, in the second half of 2000s, it is possible to see a common

pattern across almost all emerging economies of rapid increases in the price of houses in the

pre-Lehman period, followed by large drops in the most of the countries in the sample.

This is prima facie evidence of the absence of a strong global factor in house prices at least

through the mid-2000s, and much stronger house price co-movement toward the end of the

sample period. Indeed, as we shall see in section 5, capital flows and global liquidity are a

significant source of movements in house prices, possibly explaining pattern of co-movement in

global house prices documented by Hirata et al. (2012) and Rey (2013).

3 House prices in advanced and emerging economies

In this section we compare the main time series features of house prices in the two groups of

countries that we consider, namely advanced (AEs) and emerging market economies (EMEs).

3.1 Trend growth, volatility and persistence

To compare and assess the key properties of house price series across groups of countries, Table

1 reports summary statistics of annual growth rates of real house prices, real GDP, and real

consumption. The group statistics are computed country by country over the period 1985.I–

2012.IV. We then average these country specific statistics across countries within each group.

Note that the choice of the starting date of the sample over which these statistics are

computed (i.e., 1985.I) reflects a trade-off between the amount of observations used for the

analysis and the degree of comparability across groups. In fact, and as we already mentioned,

only few house price series are available for EMEs during the 1970s. Computing the summary

statistics over the full sample would therefore introduce a bias in the comparison across AEs and

EMEs: we therefore choose —somewhat arbitrarily— to use the sample period 1985.I–2012.IV

(our main findings are robust to alternative sample periods).

Turning to the statistics in Table 1, first note that the long-term trend in real house prices

growth in emerging economies is lower than in advanced economies, especially if compared

to higher trend output and consumption growth in these economies. Real house prices have

grown at an average rate of 2 and 1.2 percent per year in advanced economies and in emerging

economies, respectively. But while in advanced economies the average growth of house prices is

broadly similar to the growth of real GDP and consumption, in emerging economies real GDP

and consumption have grown much faster during the past 25 years.

Real house prices in emerging economies are also twice as volatile as in advanced economies

and significantly less persistent. The standard deviation of real house price yearly returns has

averaged about 6 and 12 percent in advanced economies and emerging economies, respectively;

while persistence has averaged .92 and .86, respectively.

Figure 4 provides a visual characterization of the house price cycle in the two groups of

economies. It plots 5-years rolling average and 5-years rolling standard deviation of real house

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prices annual returns in both advanced and emerging economies over the 1985.I–2012.IV period.

The cross-country average of these moving statistics (solid line) is reported together with the 2

standard deviations confidence bands (shaded area).3

The house price cycle looks very different across these two groups of countries. Advanced

economies experienced an exceptionally long expansion, starting from the mid-1990s and ending

only with the outburst of the global financial crisis in 2007-08. As we shall see below, the

average expansion phase in advanced economies lasts about 5-6 years, whereas this upturn

had the exceptional duration of about 12 years. The expansion lost momentum at the end of

2006, when the United States housing market started to decline, leading to the global housing

collapse that we observed in 2008. Finally note that the very long expansion in house prices

was associated with a period of historically low volatility, as it is shown by panel (c) of Figure

4.

While virtually all advanced economies’ housing markets were booming, emerging economies

witnessed a prolonged downturn, already in motion since the beginning of the 1990s, which

tenured into a collapse by the late 1990s (likely driven by the financial crises of the mid- and

late-1990s). As we shall see, many of these protracted busts were preceded by house price booms

in the 1970s and 1980s. Note also that house price volatility in EMEs has been systematically

higher than in AEs (panel c and d), with sharp time changes in the period under consideration.

From the early 2000s on, however, the two cycles show more similarities in both level and

volatility. Interestingly, the volatility observed during the global financial crisis in both advanced

economies and emerging economies is comparable to the volatility observed in the earlier parts

of the sample, suggesting that house price volatility may recur again in the future.

3.2 Co-movement between house prices and the broader macroeconomy

We now turn to compare the co-movement between real house prices and some selected macroe-

conomic and financial variables. Note that the cyclical behavior of housing variables and their

relation to the macroeconomy has been documented for advanced economies (see, among oth-

ers, IMF (2004), Loungani (2010) and Andre (2010)). Two notable studies, namely Igan and

Loungani (2012) and Claessens et al. (2012), also consider EMEs in their analysis. However, the

empirical regularities between house prices exchange rates and capital flows for both advanced

and emerging economies have not been explored yet, at least in a comprehensive and systematic

way as presented in this section.

We compute the cross-correlation between the yearly growth rates of house prices and other

macroeconomic and financial variables as,

ρi = COR(RHPi,t, xi,t±n) n = 0, 1, ..., 4,

3The variance of the cross-section is calculated by taking the variance across countries and dividing it by(N − 1), where N is the number of countries. As Pesaran et al. (1996) prove, this adjustment yields a consistentestimate of the true cross-section variance.

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where RHPi,t is the annual growth rate of house prices in country i, xi,t is annual growth rate

of the generic macroeconomic or financial variable in country i, and n stands for the lead/lag

of the generic variable xi,t for which the correlation coefficient is computed.

Figure 5 displays the cross-correlation between house prices and all other variables consid-

ered. We consider output, consumption, labor productivity, equity prices, the real effective

exchange rate, and the current account to GDP ratio.4 As before, the cross-correlations are

first computed on a country-specific basis and then averaged across advanced and emerging

economies (dark dots), respectively. The heterogeneity within each group is taken into account

by computing confidence bands (light shaded areas) as in Pesaran et al. (1996). The sample is,

again, 1985.I–2012.IV.

Let’s focus first on advanced economies, whose correlation are plotted in panel (a) of Figure

5. Consistent with the available evidence, house prices appear strongly procyclical. The corre-

lation with GDP is, in fact, significantly positive at all leads and lags considered. The largest

correlation is with GDP in t+ 1 at a value of around 0.5. A similar pattern is observed for the

cross-correlation with consumption for which the cross-country average of the contemporaneous

correlation is even larger than for GDP, with a coefficient of about 0.6.

Movements in house prices are instead loosely connected with past and contemporaneous

movements in consumer price inflation. The cross-country average of the contemporaneous

correlation between year-on-year changes in consumer prices and house prices is, in fact, close to

zero. If anything, house price inflation seems to lead consumer price inflation by approximately

three to four quarters, consistent with the idea that house prices relax borrowing constraints

and support consumption.

The correlation between the annual changes in house prices and labor productivity is slightly

positive, averaging at around 0.2 contemporaneously. This low correlation (relative to GDP and

consumption) is quite surprising, since labor productivity should be one of the fundamental fac-

tors driving house prices: an increase in productivity should lead to higher income expectations,

therefore strengthening the current and future demand for housing.

The co-movement between house price and equity price returns is slightly positive and

significantly different from zero. In theory one would expect that, at least in the long-run,

the stock market and the housing market should be driven by the same fundamentals, namely

income and productivity, although house price changes are much more persistent. The low level

of the average correlation with real equity price may be due to the sample period used, which

includes the stock market crash of 2001. During the burst of the dot-com bubble, in fact, equity

prices collapsed while house prices kept on increasing.

Changes in the short-term interest rate are positively correlated with changes in the price

of housing. In particular, changes in house prices lead changes in the same direction in interest

rates by three-to-four quarters. Of course, given the nature of the analysis, it is not possible

4The macroeconomic and financial variables are defined in the data appendix, where we also list the datasources. Note that in this section we use a subset of our house price data set (including 40 out of 57 countries):this is partly due to the fact that the available sample period for some countries is too short to allow a meaningfulbusiness cycle analysis (such as 10 years rolling statistics or alike).

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to infer any causality between the evolution of house prices and short-term interest rates or,

ultimately, monetary policy. The correlations analyzed above, however, show that house prices

are procyclical, coincident, and lead inflation by a few quarters.

The cross-correlation between changes in the price of housing and in the real effective ex-

change rate shows that surges in the price of housing are followed by a slight appreciation of the

exchange rate. Figure 5 also shows that changes in house prices are negatively correlated with

changes in the current account to GDP ratio. The negative correlation between house prices and

the current account balance in advanced economies—a measure of net capital flows— has been

already pointed out by many empirical studies.5 As we shall see this relation is even stronger

for emerging markets.

The same cross-correlation coefficients, computed across emerging economies are displayed

in panel (b) of Figure 5. The stylized facts identified for emerging economies are broadly in

line with the ones identified for advanced economies, with some interesting quantitative and

qualitative differences.

Like in advanced economies, house prices in emerging economies are strongly procyclical.

However, the cross-country average of the contemporaneous correlation with consumption is

lower than with output, at about 0.4. This is consistent with a lower degree of financial market

development (and in particular housing finance) in emerging economies relative to advanced

economies (see Warnock and Warnock, 2008, for example). For instance, home equity with-

drawal —i.e., the decision of consumers to borrow money against the real value of their houses—

has been found to be strongly positively correlated with the degree of mortgage market depth

(see, for example, Klyuev and Mills, 2007). The financial liberalization and innovation that took

place in AEs made such practice easier. In turn, home equity withdrawal increases the propen-

sity to consume out of housing wealth and, ultimately, increases the co-movement between the

house prices and consumption. Note, finally, that the larger confidence bands in panel (b) of

Figure 5 show that there is a much higher degree of heterogeneity within emerging economies

relative to advanced economies.

A second important difference is the cross-correlation of house prices with labor productivity,

which averages at around 0.4 in EMEs compared to 0.2 in AEs, suggesting a much closer

association between house prices and fundamentals in EMEs. Also, the co-movement with

equity prices and the current account is stronger in EMEs than in AEs, while the co-movement

with inflation, short term interest rates and the exchange rate is weaker. This suggests more

role for capital flows, but different role for monetary policy. In fact emerging markets exposed to

strong capital inflows usually cannot raise interest rates to cool their domestic economy without

attracting more capital.

Did the relation between the price of housing and the macro-financial variables documented

above evolve over time? Figure 6 displays the cross-country average of the contemporaneous

correlation between house prices and the variables considered above with a 10-years moving

5Such negative correlation has been often associated to the global savings glut hypothesis (see Bernanke (2010)for example). According to the global savings glut hypothesis, excessive savings in EMs fuelled capital flows intoAEs which, in turn, were an important reason why long-term interest rates were lower than expected in the early2000s. See Bernanke (2005).

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window (solid line). Consistently with the findings of IMF (2004), the average correlation

between real house prices and output (and consumption) in advanced economies displays a

steady decline since the mid-1990s. The two standard deviation error bands show that, for

some countries, such correlation was close to zero before the onset of the global financial crisis.

This fact, which goes against the typical theoretical consideration, is viewed as an indication

of how atypical was the last house price boom in industrial countries: prices have soared for

many years, while economic activity was weakening. However, the average correlation has

started increasing from 2007 and has spiked after the financial crisis hit, pointing to increased

synchronization of the housing markets. Notice, finally, that at the end of 2009 the average

correlation had reverted to its level of the mid 1990s.

Note here that the documented relation between house prices and labor productivity strength-

ens the point made in the cross-correlation analysis. The average correlation between house

prices and labor productivity remained very small and borderline insignificant till late 2000s

in advanced economies, suggesting that house prices were very loosely connected to economic

fundamentals.

The average correlation with equity prices, short-term interest rates and the real effective

exchange rate has been fairly stable during the period under consideration. In contrast the

average correlation with the current account balance over GDP seems to have decreased over

time.

The same moving correlations are displayed for emerging economies in panel (b) of Figure

6. From the comparison with advanced economies some interesting facts stand out. First,

the disconnect of house prices from changes in output and consumption observed in advanced

economies is not present in emerging economies. The average correlation is, in fact, much

more stable over time, averaging slightly below 0.5 for both GDP and consumption. Also,

the correlation with labor productivity is always positive, significant and averages about 0.4,

a value much higher relative to advanced economies. Second, the average correlation with the

real effective exchange rate is fairly stable, positive and significant.

3.3 Boom-bust cycles in house prices

The definition of asset-price bubbles is somewhat controversial and their identification with

econometric techniques is still an open issue in the literature.6 To overcome these difficulties,

many studies have focused on asset-price booms and busts, defined as periods in which the price

of an asset exceeds a pre-determined threshold. The threshold is commonly defined in terms

of deviations from a trend. Note, however, that the choice of the detrending procedure (first

difference, HP filter, etc.) importantly affects the identification of the boom-bust episodes.

In this section, we use a simple methodology similar to that used by Bordo and Jeanne

(2002).7 An asset-price boom or bust is identified when the three-year moving average of the

asset price annual real return rate falls outside a confidence interval defined by reference to the

6See Gurkaynak (2008) for a recent survey on econometric tests of asset price bubbles.7This procedure was implemented recently by IMF (2009) and Barajas et al. (2008).

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historical first and second moments of the series. Specifically, a boom (bust) occurs in a period

when the three-year moving average of the annual return is larger (smaller) than a threshold,

as specified by the following equation:

gi,t + gi,t−1 + gi,t−2

3≶ g ± xσ (1)

where gi,t is the annual return of the asset price in country i; g is the average of the annual

return across all periods and countries; σ is the average of the standard deviation of the annual

return across all countries; and x is a parameter to be calibrated so as to select the notorious

boom-bust episodes without selecting too many spurious events. Some of the advantages of

using the methodology proposed by Bordo and Jeanne (2002) is that it is objective, easily

reproducible, and, most importantly for this study, can be applied consistently across countries.

This makes possible to analyze separately industrialized and developing economies and assess

their differences.

The procedure described above is applied to 21 advanced and 19 emerging economies from

1970.I to 2012.IV (subject to data availability). However, to avoid identifying as booms or busts

high-frequency movements in the data, we proceed as follows. First, we compute a centered

moving average of our quarterly house price series, with a window of 4 quarters on each side (8

quarters in total). Then, since the procedure proposed by Bordo and Jeanne (2002) is applied

to annual time series, we take the last quarterly value for each year.

Alternatively, to transform the quarterly data set into annual frequency, we could have

simply used the average within the year (or the value of the last quarter for each year). Note

here that, the number of boom-bust episodes and their dating is very robust to these alternative

approaches.

Following Bordo and Jeanne (2002), the parameter x is set to 0.8 so as to match few well

known house price boom-bust episodes.8 Note that when the condition in equation (1) holds,

then the periods t− 2 through t are labeled as a boom (bust). Moreover, a boom-bust episode

is defined as a boom followed by a bust that starts no later than one-year after the end of the

bust.

Across all countries, the procedure identifies 66 booms, 69 busts, and 28 boom-bust cycles.

Figure A.3 and A.4 display the annual house price index, computed as described above (solid

line), together with the identified booms (light shaded area) and the busts (dark shaded area).

The procedure identifies some major well-known boom-bust episodes —such as the housing

crash in Sweden and Finland in the late 1980s and in Spain and Ireland in recent years— but

does not pick up many spurious events. Consistently with the notion that boom-bust episodes

are rare events, the frequency of a boom-bust episode per year is of about 2 percent in advanced

economies and emerging economies alike.

Three features of this exercise are worth noting. First, many countries experienced a boom

in real house prices, but even though in some cases booms are followed by protracted busts, this

8The value for x is significantly lower than the original value used by Bordo and Jeanne (2002). This is due tothe fact that our sample period includes the global financial crisis which clearly increased the average standarddeviation of house prices.

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is not always true. In the sample considered, only 28 booms out of the 66 identified episodes

are followed by a bust, implying that only 40 percent of booms ends up in a bust. Second

the average duration of a boom, bust, and boom-bust episode is around 5.6, 4.5 and 9.5 years,

respectively.

Table 2 displays some summary statistics of the identified boom-bust episodes for house

prices. As the sample period used for advanced economies is different from the sample period

used for emerging economies, in this part of the analysis we shall not analyze the number of

boom-bust episodes, but rather the frequency of occurrence of such episodes. The frequency

probability of a boom converting into a bust is computed as the number of booms over the

number of boom-bust cycles for each country.

Consistently with the findings of Bordo and Jeanne (2002) and Burnside et al. (2011), not all

booms convert into a bust. Historically, across all asset prices and all groups, only 40 percent of

the booms were followed by a bust. Also, consistently with the literature, boom-bust episodes

are rare events. The frequency probability of observing a boom-bust cycle in a given year

is 0.021 and the probability is comparable across advanced and emerging economies. Similar

results are obtained for the probability of observing a boom in a given year, which is equal to

about 0.05 with no significant differences across groups. Finally, concerning the duration, we

observe that house price boom-bust episodes last on average 10 years.

4 Capital flows and house prices: an event study

So far we looked at unconditional associations. In this section, we condition the analysis on a

particular phase of the cycle, the boom episodes identified above. In the next section, we will

condition on a particular shock to a typical capital flow driver: a global liquidity shock.

Specifically, we use the 66 boom episodes of real house price boom identified in the previous

section to study systematically the behavior of relevant macroeconomic variables and country

characteristics in this particular phase of the cycle. We concentrate on boom episodes because

some of the bust episodes we have identified are still ongoing as of the time of writing. We first

compare episodes in advanced and emerging economies, and then we investigate the role played

by country characteristics.

4.1 House prices boom episodes

Figure 7, Panel (a) reports the average real house price increase during booms in advanced

(AEs) and emerging economies (EMEs). For each episode, we take the average level of the real

house price index during the boom phase and compare it with the average level of the index in

the two years prior to the beginning of the boom. The chart shows that house prices booms

in EMEs are twice as large as in AEs, with house prices increasing 70 percent relative to the

average before the beginning of the boom in EMEs as compared to 35 percent in AEs.

Figure 7, Panel (b) to (h), shows the behavior of relevant external and domestic macro

12

variables during the typical boom episode. Panel (b) shows the behavior of the (ex post) US

real federal funds interest rate during the boom episodes. In particular, the figure plots the

average (ex post) US real federal funds rate during the real house price boom minus the average

(ex post) real federal funds rate in the two years prior to the beginning of the boom. In AEs,

during a boom, this measure of the US real interest rate is almost one full percentage point lower

than in the two years prior the boom. In contrast, in EMEs, there is almost no difference in this

measure of the real US interest rate compared to the period right before the boom. Inspection of

individual episodes, however, reveals that in many cases in which there is no significant change

between this interest rate relative to the previous period, the (ex post) US real federal funds

rate was already in an expansionary position at the beginning of the boom phase. The fact that

there is no significant difference between the US real interest rate during the boom compared to

the previous period does not mean that international financial conditions were not expansionary

during those episodes.

The behavior of global liquidity provide direct evidence regarding this fact. Panel (c) plots

a global liquidity index increase during the boom episode relative to the average value of this

index in the two years prior to the beginning of the boom.9 Interestingly, the behavior of this

index indicates that, in both AEs and EMEs, global liquidity conditions faced by the countries

that experienced a real house price boom in our sample were significantly higher than in the

period preceding the boom. And the global liquidity conditions are much looser in the case of

the EMEs booms relative to the AEs booms. Interestingly, the results also indicate that during

the real house price boom episodes in EMEs, uncertainty —as measured by the VIX index—

was also lower than in the period proceeding the beginning of the episode. In contrast, the

behavior of the VIX index in the case of AEs does not exhibit a significant difference to the

period prior to the boom.

Consistently with this broader picture, in the context of relatively lower US interest rates,

higher global liquidity and lower uncertainty, capital inflows are larger in EMEs during episodes

of house price booms. Capital inflows are constructed as the sum of the change in international

reserves as a percentage of GDP minus the current account balance. Indeed, the average capital

inflows as a percentage of GDP during the boom episode minus the average capital inflows in

the two years prior to the beginning of the boom is significantly higher in the case of emerging

market economies and only slightly positive in the case of developed economies during the boom

episodes (Panel (d)).

We now turn to the behavior of domestic macro variables during the real house price booms.

Figure 12, Panel (e) shows the behavior of the output gap, constructed using the HP filter,

during the boom episodes in developed and emerging market economies. We display the average

output gap during the boom episodes for developed and emerging market economies minus the

average output gap over the 2 years prior to the episode. The results are consistent with the

correlation analysis above, in the sense that real house prices are positively correlated with

economic activity. During the boom episodes, economic activity increases above trend. This

increase is significantly larger in emerging market economies.

9This global liquidity index is defined as the sum of world official reserves (excluding gold) plus US M0 deflatedby the US CPI. See the next section of the paper for a discussion and a comparison with other measures of globalliquidity.

13

Panel (f) shows that also private consumption increases more strongly in emerging market

economies during the real house price booms compared to more developed economies. The pri-

vate consumption increase corresponds to the percentage change of average private consumption

during the boom episode with respect to average private consumption in the two years prior to

the beginning of the boom.

The difference in the behavior of the current account during the boom episodes in emerging

market economies compared to developed economies is quite significant as panel (g) shows. The

current account increase corresponds to the average current account balance as a percentage

of GDP during the boom episode minus the average current account balance in the two years

prior to the beginning of the boom. In the case of emerging market economies, the current

account deteriorates more than 3.5 percent during the real house price booms while in developed

economies the deterioration in the current account balance is less than 1 percent of GDP on

average.

Finally, the evolution of the real exchange rate during the house price boom episodes is

presented in Panel (h). The evidence, consistent with the preliminary evidence presented in the

correlation analysis, indicates that during the real house price booms the real exchange rate

appreciates more in emerging market than in developed economies . This evidence is consistent

with previous literature that documents the behavior of the real exchange rate in the context

of capital inflows.

While suggestive, what reported thus far are simple associations. Also, averages are inter-

esting, but they do hide substantial heterogeneity in individual experiences. Individual episodes

have to be conditioned on the actual change in real house prices and potential determinants of

house price booms needs to be assessed in the same framework, which is what we are going to

do in the next subsection.

4.2 The determinants of real house prices during boom episodes

In order to explain the cross-section variation of real house prices during our boom episodes,

we estimate different versions of the following equation:

∆Pi = α+ β (ECIi) + γZi + δ(ECIi × Zi),

where ∆Pi corresponds to the increase in real house price in episode i as discussed previously,

ECIi corresponds to an external condition index of choice (namely, either the change in the

real federal funds interest rate during the episode, the global liquidity index change during the

episode, capital inflows during the episode and our proxy for risk premium, the VIX index).

The vector Zi contains two potentially key variables in the amplification of external financial

shocks in real house prices: financial market depth and the exchange rate regime. The vector

Zi also includes a dummy variable that that takes value 1 if the episode corresponds to a

developed economy and 0 if corresponds to an emerging market economy. We allow for the

interaction between the external condition index and the vector Z in order to capture the

potential amplification of these shocks through channels associated to financial constraints and

14

the exchange rate flexibility as discussed previously.

Regarding the external condition index we use two variables to capture this effect, the change

in capital inflows during the episode and the global liquidity index change during the episode

defined before. Using capital inflows is more problematic because it is a country specific variable

and is endogenous to the domestic factors pulling capital in. So we report these results, but then

also use an arguably more exogenous determinant of country-specific capital inflows, namely

our global liquidity measure (and discussed in details in the next section of the paper). The

exchange rate regime variable comes from Ilzetzki, Reinhart and Rogoff (2008)’s exchange rate

system classification.10 In order to measure financial development, we use the variable credit

to the private sector (as percentage of GDP) in the year prior to the beginning of the episode.

Results are reported in Table 3. The results indicate that there is a significant impact of the

capital inflows on the real house price dynamics during the boom episode. For the whole sample,

an increase of 3 percent of GDP in capital inflows is associated with an increase of almost 7

percent on average in real house prices during the boom. This effect differs significantly once we

control for the classification of the country. In particular, the increase in real house prices during

a boom episode almost double in the case of emerging market economies, while for developed

economies the impact of capital inflows on real house prices is close to zero.

Our results also indicate that the impact of capital inflows on real house prices during

our episodes is larger the less developed financial markets are and the less flexible exchange

rate regimes are (the interaction effects between capital inflows and these variables are also

significant).

Acknowledging the endogeneity problem that arises in this estimation by using capital in-

flows, we use our global liquidity measure to study the impacts of capital inflows in real house

prices. The results (also reported in Table 3) indicate that the change in the global liquidity

index is a significant determinant of real house prices dynamics. A global liquidity increase of

25 percent (as the one observed on average in the real house prices boom occurred in emerging

market economies) is associated with an average 23 percent increase in real house prices in the

estimations we present. The impact of the same increase in global liquidity is reduced to 6

percent increase in the case of developed economies.

Also, the interaction of financial market depth and global liquidity index is significant and

negative, which indicates that the impact of global liquidity of real house prices is lower in the

cases of more developed financial markets. The interaction between the exchange rate flexibility

and the global liquidity variable is not found to be significant in these estimations.

In sum, these results show that the impact of capital inflows, and less controversially of our

global liquidity index, is positively related with real house prices during episodes of house price

boom in emerging markets, but much less during the same episodes in advanced economies.

Moreover, we show that more financial developed markets tend to mitigate the impact of global

10In our analysis we use the 13 different categories constructed by these authors, indexing countries from 1to 13 according to the degree of exchange rate flexibility, with higher values indicating more flexibility.In ouranalysis we use the 13 different categories constructed by these authors, indexing countries from 1 to 13 accordingto the degree of exchange rate flexibility, with higher values indicating more flexibility.

15

liquidity conditions on the economy, with more mixed evidence in the case of insulation property

of exchange rate flexibility.

5 Global liquidity and house prices: a panel VAR analysis

Empirical models of international capital flows typically include “push” (i.e., global) and “pull”

(i.e., local) drivers such as interest rates differentials, growth differentials, as well as competi-

tiveness measures reflecting differences in productivity and production costs between the home

economy and the he rest of the world (e.g., Calvo et al. (1996)). To investigate the causal link

from capital flows to house prices and the broader macroeconomic dynamics, in this section, we

specify and estimate a panel vector autoregression (VAR) model that embeds both typical “pull”

and “push” factors, although we shall identify only exogenous changes to one particular push

factor: global liquidity, broadly defined as the total world availability of US dollar-denominated

liquid assets and measured in alternative ways as we discuss below. Hence, in the rest of this

section, we first discuss issues related to global liquidity and the identification of an exogenous

change in this variable. We then present the panel VAR model we use and finally we report

and discuss the empirical results that we find.

5.1 Global liquidity

We focus on global liquidity for several reasons.11 Global liquidity, broadly defined as the total,

world availability of US dollar-denominated liquid assets is a proxy for the monetary policy

stance in whole world economy, as opposed to any individual economy pulling in capital flows

or the rest of the world economy pushing them to a particular country Like in a closed economy,

when global monetary conditions are looser, global credit expands and financing conditions

loosen, and capital flows across borders to individual countries (like different sectors of a closed

economy) with varying intensities and in the form of different instruments (such as loans, bonds,

equities, or direct investments). Indeed, as Figure 2 suggests, the recent synchronized boom-

bust cycle in house prices described in section 2 is associated with an unprecedented increase in

global liquidity. We also find that global liquidity is an important determinant of house price

booms in our event study. Last but not least, it is relatively simple to identify an exogenous

change to global liquidity in our panel VAR model.

5.1.1 Alternative measures of global liquidity

We measure global liquidity in three different ways (plotted in Figure 8). The first measure that

we use is the sum of world international reserves (excluding gold) evaluated in current US dollar

plus the US M0, deflated by the US CPI. We will call this measure “official global liquidity” and

Figure 2 plots its two key components: US M0 and total official reserves. It is meant to capture

“the funding that is unconditionally available to settle claims through the monetary authorities”.

Thus, this measure of global liquidity captures official reserve accumulation associated with the

11See Landau (2013) for a recent analysis and policy discussion.

16

“global trade imbalances”, such as for instance the high and growing saving rate of emerging

Asian economies and oil exporters. But also encompasses both interest rate policy as well

as non-conventional monetary policy in the US.12 This is the measure most often used by

practitioners—see for example The Economist magazine (2005) and Deutsche Bank (2007).

The two alternatives that we consider are a measure of “private” global liquidity suggested by

the BIS (2011) and the VIX index of uncertainty as suggested by Matsumoto (2011). BIS (2011)

notes that liquid assets are issued and held by both private intermediaries and official entities.

BIS (2011) suggests to measure global liquidity with the sum of the net cross-border lending

positions in foreign currencies to residents of all BIS reporting banks (BIS (2011)). Matsumoto

(2011) instead notes that, in addition to the availability of funds for settling transactions in safe

assets measured by the official or private liquidity measures above, there is another aspect of

global liquidity related to the availability of funds for settling trade in risky assets, measured

by its corresponding price—the market price of risk or risk premium. Following this author, we

use the VIX index as a proxy for the global market price of risk.13

These three measures are plotted in Figure 8, in both levels and first differences (with

official and private liquidity deflated by the US CPI). As Table 4 shows, private and official

liquidity co-move very well in first differences throughout the sample period, except for the

very short period 2008.III–2009.I at the peak of the global financial crisis. After 2009.I, official

liquidity grows much faster than private liquidity. As a result, the level correlation between these

two variables breaks down after the peak of the crisis, but the correlations in first differences

continues to be positive and sizable. The VIX index co-moves well in both levels and first

differences before 2008.II with both official and private liquidity. After the peak of the crisis,

however, the correlation between the VIX index and the other two measures in first differences

changes sign. This suggests that the VIX index is not a good measure of liquidity after the

peak of the crisis. Also, while reserve accumulation after the peak of the crisis continued at a

comparable rate with the pre crisis period, there is a clear structural break in US M0 (Figure

2) due to the adoption of non conventional measures. We will therefore use private liquidity in

our baseline VAR specification, and we conduct extensive robustness checks to the choice of the

sample period as well as the other two measures of global liquidity discussed.

5.1.2 Identification of an exogenous global liquidity shock

The identification of an exogenous change to global liquidity (i.e. a global liquidity shock) is

relatively simple and naturally follows from the global nature of this variable. As this variable

is global in nature, we can reasonably assume that no individual country is large enough to

affect it significantly within a given quarter. It follows that it is possible to assume that this

variables does not respond to innovations to any other country specific variable in the VAR

system within a given quarter. For instance, as Figure 2 shows, in the case of official liquidity,

12Note that under the fed fund targeting procedure that the Fed has followed during our sample period, moneyis endogenous.

13Rey (2013) also focuses on the VIX as a measure of global liquidity and finds that the VIX index is closelyrelated to a global factor in international flows of risky assets, credit, and leverage. This is consistent with theview that global liquidity captures global monetary conditions.

17

even for a country like the United States, US M0 averaged only 28 percent of the measure

we constructed over the period 1985.I–2006.IV, declining to only 17 percent by 2006.IV and

climbing back up to 23 percent by only by 2012.IV.14

A similar argument hold for the private global liquidity measure that we use, while assuming

that the VIX index is exogenous relative to other macroeconomic variable at quarterly frequency

is a standard assumption in the growing literature on uncertainty and macroeconomic dynamics.

Note finally that, given the relatively large size of our cross section of countries, any individual

impulse response will have a limited impact on the panel estimate that we report below. So if

the identification assumption made were not to be satisfied in any particular country like the

US or China, the panel bias on the panel estimate would of the order 1/N where N is the cross

section size.

A global liquidity shock therefore is easily identified in our model by assuming that, within a

quarter, it is not affected by any other variable in the country-specific VAR systems. Given this

assumption, a global liquidity shock and associated impulse responses of all other variables in

the system can be obtained from the Cholesky decomposition of the variance covariance matrix

of the estimated reduced form residuals of each country-specific VAR, with global liquidity

ordered first in the system. In fact, for the purpose of identifying the effects of the global

liquidity shock, the order of the other endogenous variables in the VAR system does not matte.

5.2 The panel VAR Model

5.2.1 Specification

In addition to a measure of global liquidity, the panel VAR model that we specify includes

two external variables and three domestic variables. The external variables are the real effective

exchange rate and the current account to GDP. The domestic variables are a short term interest

rate, real private consumption, and real house prices.

The real effective exchange rate is a measure of relative competitiveness that reflects move-

ments in trade partners inflation rates and costs as well as bilateral nominal exchange rates. The

current account is a measure of net capital flows identically equal to the gap between domestic

saving and investment. Both domestic and global shocks therefore (i.e., pull and push factors

in empirical models of capital flows) typically drive these variables.

The domestic variables that we include in our VAR model are the level of domestic economic

activity (represented by consumption rather than GDP),15 a short term interest rate, and real

house prices. These three domestic variables can be seen as the reduced form of a typical New

Keynesian monetary model, with an IS curve, a house price pricing equation and a policy rule

14Note also that, given the relatively large size of our cross section of countries, individual IRFs will have alimited impact on the panel estimate that we report below. This is an advantage of the estimation approachtaken.

15Theory suggests that house prices affect the broader economy primarily through their wealth effect on con-sumption or a Tobin’s Q effect on residential investments. As we do not have data on residential investments foremerging markets, an alternative specification would include both consumption and GDP (or labor productivity).To keep the size of the VAR model as small as possible, we prefer to include only one variable at the time.

18

(see Iacoviello (2005) and Iacoviello and Neri (2010), for example). To keep the system small,

we do not include CPI inflation and we include instead the real house price, which is the relative

price of a durable good in the DSGE models with housing of Iacoviello (2005) and Iacoviello

and Neri (2010). Indeed, house prices are the amplification mechanism that we want to study.

Ideally one would want to include both a price and a quantity variable to describe the domestic

housing sector —real house prices and real residential investment, respectively, for instance. As

we noted however there are no data for EMEs.16 We therefore include real house prices as a

proxy for housing demand as an amplification mechanism of shocks to global liquidity.

All variables considered enter the VAR in (log) levels, except the current account to GDP as

they are either real variables, relative prices or nominal variables at constant prices. Following

Sims et al. (1990), we estimate the systems in levels, without explicitly modeling any possible

cointegration relationships.17 The specification is balanced, in the sense that all series have the

same expected order of integration. In addition to a constant, a linear and a quadratic time

trends are also included to capture the gradual decline of inflation rates and nominal policy

interest rates over time as well as any secular trend in consumption, real house prices, and the

real pace of reserve accumulation that has accelerated sharply in the second part of the sample

period.

The VAR specification is the same for all countries to avoid introducing differences in country

responses due to different model specifications, and because it would be practically difficult to

search for a data-congruent specification for each considered country. In particular, somewhat

arbitrarily, we include one lag of each variable in every system (determined by using standard

specification tests on the VAR for the United States).

5.2.2 Estimation

To estimate the model above, we use the mean group estimator of Pesaran and Smith (1995)

and Pesaran et al. (1996). This is because fixed effect, random effect, and instrumental variable

estimators are inconsistent in a dynamic panel data model under slope heterogeneity. This

is a standard technique for the estimation of dynamic panel data models with heterogenous

slope coefficients (i.e., slope coefficients that vary across countries).18 The technique involves

estimating the VAR model above country-by-country, with ordinary least squares, and then

taking averages of the estimates (e.g., impulse responses or variance decompositions) across

countries. We use arithmetic averages, but one could also compute weighted averages, weighting

by the inverse of the standard error of the individual estimate or the size of the unit in the cross

section, usually yielding similar results. The variance of the mean group estimator can be

calculated by taking the variance across individual units (i.e., across countries for each time

16Note also that IMF WEO (2008) finds that the role of housing supply shocks is marginal in house pricedynamics.

17Sims et al. (1990) show that if cointegration among the variables exists, the system’s dynamics can beconsistently estimated in a VAR in levels.

18The literature that extends this estimation approach to PVAR models is surveyed by Coakley et al. (2006)and Canova and Ciccarelli (2013). Rebucci (2010) provides Monte Carlo evidence on the superior performanceof this estimator relative to a fixed effect estimator and instrumental variable estimator for the dimensions of thepanel that we have.

19

horizon in the case of the impulse responses and the variance decompositions) and dividing it

by (N − 1). As Pesaran et al. (1996) prove, this adjustment yields a consistent estimate of the

true cross-section variance of the mean group response. Note that if an estimated VAR has

unstable roots or less than 40 observations, the VAR is not included in the impulse response.

In our application, this VAR systems is estimated separately at the country level for 21 AEs

and 19 EMEs, using quarterly data for the period 1985.I to 2012.IV, with choice of the starting

date consistent with the considerations made in sections 2 and 3. For robustness, we will also

cut the sample period in 2006.IV to avoid contaminating our global liquidity variable with the

policy response to the crisis as some of the correlation reported above between the three global

liquidity variables we use break down after 2008.II.

5.3 Empirical results

To make sure that the estimation results are not inconsistent with the identification assumptions

made, we first look at the variance decomposition of global liquidity itself. The average forecast

variance decomposition in Figure 10 shows that global liquidity is largely explained by itself

within the first year after the shock, thus confirming the validity of the identification assumptions

made. Over a longer forecast horizon, however, Figure 10 shows that it is also affected also by

other factors. This is the case in both advanced and emerging economies, and even in the case

of the United States. Our identification assumptions, therefore, do not seem inconsistent with

these results.

Next we compare the impulse responses of all variables in the model to our global liquidity

shock. Figure 9 reports the impulse responses to a global liquidity shock of 3 percent on

impact in the typical (i.e., average) advanced and emerging economy—panel (a) and panel (b),

respectively.19 As we noted earlier, error bands for the mean group estimator are computed

following Pesaran et al. (1996): specifically, the dark and light shaded areas represent the 1 and

2 standard deviations confidence intervals. These results are obtained with the private liquidity

measure over the full sample, namely 1985.I–2012.IV.

In the typical advanced economy, both real consumption and house prices increase in re-

sponse to the global liquidity shock in a statistically significant manner, but the effect is rela-

tively short-lived relative to the high persistence of the shock, losing statistical significance after

about 2 years. Specifically, both real consumption and real house prices increase by 0.1 percent

on impact and stay significantly different from 0 for about 8 quarters. The short-term interest

rate also increases on impact, and keeps raising for about 8 quarters: this is consistent with a

monetary policy authority reacting countercyclically to the acceleration of economic activity.

Finally, the real effective exchange rate jumps slightly on impact, possibly driven by changes in

the nominal exchange rate and then reverts to its equilibrium level over time. The current ac-

count displays a slight improvement on impact and then deteriorates over time but its response

is statistically not different from zero.

19As all variables are in logs and the model is linear, we can divide all impulse responses by the size of theshock to obtain implied elasticities.

20

Albeit statistically significant, the impact of the shock on consumption and house prices is

economically very small: for a 1 percent increase in global liquidity, the peak increase in house

prices is less than 7 basis points. This means that global liquidity has to more than triple (a

300 percent increase) to generate a peak increase in house prices of about 10 percent from their

long-term equilibrium. These impulse responses also imply that house prices have to increase

about 30 percent to generate a 1 percent increase in real consumption.

The response of the typical emerging market economy to a global liquidity shock is quali-

tatively similar to the typical advanced economy except for the exchange rate. Quantitatively,

however, there are large differences. Both consumption and real house prices increase on impact

and display a significant response for about 8 quarters. The consumption response is twice as

large as in AEs while the house price response is 4-5 times as strong: so it would take a 60 per-

cent increase in global liquidity to generate a 10 percent increase in house prices in the typical

emerging market economy. Despite the stronger response of consumption and house prices, the

short-term interest rate increases by about the same in emerging markets in AEs for about 8-10

quarters. The current account deficit widens twice as much in EMEs. The average response of

the current account, however, musks a lot of heterogeneity as the error bands for this response

are quite large.

Interestingly, in emerging markets, the real effective exchange rate appreciates only gradu-

ally over time, without responding on impact. This may reflect EMEs’ fear of exchange rate

flexibility and gradually higher domestic inflation in response to a relatively weaker domestic

monetary policy response to the global shock (for fear of attracting more capital and experienc-

ing even larger external imbalances). In quantitative terms, the peak effect on the real exchange

rate in emerging markets is comparable to the impact effect in advanced economies. Given a

comparable monetary policy response, this suggests that the stronger effect of a global liquidity

shock on consumption and house prices in emerging markets, may be coming from a different

exchange rate policy. The even study in the previous section, however, did not provide support

for this conjecture.

We obtain similar results if we cut the sample through 2006.IV. If instead we use official

rather than private liquidity, we obtain exactly the same results if we cut the sample in 2006.IV,

but slightly less clear cut results if we use the full sample. Results with the VIX index are similar,

but less clear cut in both the full and the shorter sample.20

Overall, these results are consistent with the idea that house prices are an amplifier of

external financial shocks in advanced and emerging economies alike. In advanced economies,

however, a global liquidity shocks has effects that are quantitatively much smaller than in

emerging markets. This evidence is consistent with the closed economy models of housing and

the macroeconomy discussed in the introduction. This evidence is also consistent with the fact

that credit constraints, which in those models amplify the response of macroeconomic aggregates

(such as output and consumption) to changes in house prices, are certainly tighter and more

pervasive in EMEs than in AEs. This evidence is consistent with the view that global imbalances

would have played a lesser role in explaining house price boom in developed economies in the

20The results for the robustness checks conducted are in a separate Appendix not necessarily for publication.

21

period previous to the Great Recession. Nonetheless, the increase in global liquidity in the years

following the beginning of the Great Recession may be playing an important role explaining

recent house price dynamics in emerging markets.

6 Conclusions

In this paper we analyze the relation between house prices and capital flows with a new house

price data set for 57 advanced and emerging economies. First, we provide a comprehensive

empirical characterization of house prices at business cycle frequency over the 1970.I–2012.IV

period for 57 countries. We describe house price characteristics, systematically comparing

advanced and emerging economies, their relation with the broader macroeconomy, and their

boom and bust behavior. Second, we explore the nexus between house prices, capital flows

and a set of macroeconomic variables in an event study on 66 identified episodes of house

price booms. Third, and finally, we explore the transmission of a global liquidity shock to

consumption, house prices, the current account and the real exchange rate in a panel VAR

model.

There are there sets of results. First, the descriptive analysis shows that real house prices

in emerging economies grew slower and are more volatile and less persistent than in advanced

economies over the past twenty years or so, even though the two cycles became more homogenous

in the run up to (and in the aftermath of) the recent global financial crisis. In both advanced

and emerging economies, house prices are subject to boom-bust dynamics with a probability of

observing a boom-bust episode of about 2 percent per year in both groups of countries. Second,

the event study uncovers a strong association between capital inflows (or our measure of official

liquidity) and episodes of real house prices booms in emerging markets, without finding a similar

connection in advanced economies. We also find that more financially developed markets tend

to mitigate the impact of global liquidity conditions on the economy, with more mixed evidence

in the case of more exchange rate flexibility. And third, the VAR analysis of an exogenous

change in global liquidity confirms that while the transmission mechanism is qualitatively similar

in advanced and emerging economies, the effects of a global liquidity shock are economically

significant only in the case of emerging markets.

This empirical evidence is consistent with the idea that the nexus between house price

and capital flows is tighter in emerging markets, possibly because borrowing and collateral

constraints that are relaxed by capital inflows are much more pervasive in emerging economies

than in advanced economies. We interpret our empirical evidence as suggesting that while

global imbalances would have played a lesser role in explaining house price boom in developed

economies in the period previous to the Great Recession, the increase in global liquidity in

response to it may be playing an important role explaining recent house price dynamics in

emerging markets.

22

7 Figures

Figure 1 Real house prices & Current account to GDP ratio

−15

−10

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0

SPAIN

00 03 06 09 1250

100

150

200

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3

10

IRELAND

00 03 06 09 1280

113

147

180

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7

15

SOUTH AFRICA

77 80 83 8660

93

127

160

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3

10

HONG KONG

89 92 95 98 0150

117

183

250

Real House Price Index (left ax.) Current Account / GDP (right ax.)

Figure 2 US M0 & Emerging economies reserves

2000 2003 2006 2009 20120

2000

4000

6000

8000

Bil

lio

n U

S$

US M0 EMEs Reserves

23

Figure 3 Real house prices – Data map

ARG AUS AUT BEL BRA BGR CAN CHL CHN COL HRV CZE DNK EST FIN FRA DEU GRC HKGHUN ISL IND IDN IRL ISR ITA JPN KOR1970 #N/A 34.43 #N/A 37.70 #N/A #N/A 40.52 #N/A #N/A #N/A #N/A #N/A 46.72 #N/A 60.29 39.92 128.98 #N/A #N/A #N/A #N/A #N/A #N/A 23.96 #N/A 41.70 103.30 #N/A

1971 #N/A 36.06 #N/A 36.67 #N/A #N/A 41.61 #N/A #N/A #N/A #N/A #N/A 52.53 #N/A 58.28 39.86 132.42 #N/A #N/A #N/A #N/A #N/A #N/A 24.49 #N/A 42.26 112.57 #N/A




1975 #N/A 42.20 #N/A 43.24 #N/A #N/A 56.09 1.00 #N/A #N/A #N/A #N/A 59.47 #N/A 60.48 45.19 125.58 #N/A #N/A #N/A #N/A #N/A #N/A 26.11 #N/A 55.73 112.32 #N/A





1980 #N/A 42.77 #N/A 50.22 #N/A #N/A 56.96 1.00 #N/A 134.35 #N/A #N/A 53.49 #N/A 50.53 54.28 143.54 #N/A 66.91 #N/A #N/A #N/A #N/A 35.54 #N/A 71.79 119.26 #N/A






1986 #N/A 41.20 59.82 37.06 #N/A #N/A 55.58 1.00 #N/A 95.57 #N/A #N/A 61.23 #N/A 64.15 48.34 123.92 #N/A 44.42 #N/A #N/A #N/A #N/A 27.82 #N/A 52.20 140.60 114.52




1990 #N/A 50.25 106.28 48.35 #N/A #N/A 64.50 #N/A #N/A 104.26 #N/A #N/A 44.30 #N/A 88.51 58.90 129.46 #N/A 65.01 #N/A #N/A #N/A #N/A 32.82 #N/A 81.78 184.44 146.05

1991 119.31 51.67 120.23 49.26 #N/A #N/A 64.56 #N/A #N/A 98.60 #N/A #N/A 44.97 #N/A 72.73 57.86 128.38 #N/A 91.84 #N/A #N/A #N/A #N/A 33.45 #N/A 84.10 178.93 136.40

1992 120.31 51.78 126.88 50.50 #N/A #N/A 66.17 #N/A #N/A 94.30 #N/A #N/A 41.99 #N/A 59.12 55.51 132.80 #N/A 98.61 #N/A #N/A #N/A #N/A 32.61 #N/A 83.62 168.87 122.06

1993 113.95 52.23 125.02 53.02 #N/A 79.88 65.13 #N/A #N/A 100.80 #N/A #N/A 44.85 #N/A 59.04 54.17 133.83 53.46 106.95 #N/A #N/A #N/A #N/A 33.56 #N/A 78.32 161.19 111.71

1994 117.15 52.65 117.56 54.16 #N/A 63.89 66.75 #N/A #N/A 110.66 #N/A #N/A 46.46 #N/A 57.69 52.61 134.51 52.39 107.23 #N/A #N/A #N/A 237.62 32.72 115.84 74.21 157.02 105.19

1995 116.02 50.26 113.25 56.14 #N/A 62.23 62.81 #N/A #N/A 115.50 #N/A #N/A 50.98 #N/A 56.63 49.52 132.84 53.54 93.69 #N/A #N/A #N/A 229.62 36.23 123.37 70.42 155.20 100.52

1996 113.83 50.36 111.25 56.09 #N/A 89.47 62.12 #N/A #N/A 107.11 76.67 #N/A 55.61 #N/A 63.16 49.18 129.22 52.13 114.02 #N/A #N/A #N/A 222.48 37.95 124.85 64.79 151.77 97.05

1997 115.18 53.57 113.50 57.20 #N/A 43.05 62.56 #N/A #N/A 103.25 76.17 #N/A 59.26 27.25 70.55 48.46 124.01 57.54 124.23 #N/A #N/A #N/A 210.22 44.25 123.74 62.48 146.55 94.70

1998 114.56 56.05 105.24 60.21 #N/A 44.59 61.65 #N/A #N/A 92.38 74.76 #N/A 63.76 33.09 75.51 49.58 122.68 62.05 84.28 58.48 #N/A #N/A 125.53 55.97 123.52 63.25 142.55 78.02

1999 117.64 60.23 103.83 62.76 #N/A 42.67 62.72 #N/A 77.08 85.01 87.41 45.25 65.23 36.84 79.98 53.18 123.66 65.90 80.38 74.46 #N/A #N/A 131.17 64.45 120.77 66.29 138.91 79.89

2000 118.26 60.60 101.91 64.85 #N/A 37.68 63.64 #N/A 77.41 76.86 76.59 47.17 68.48 34.19 76.29 56.55 121.39 70.88 70.37 92.06 65.57 #N/A 135.77 73.18 112.76 70.12 134.49 78.52

2001 114.21 67.79 99.47 66.08 69.46 36.17 66.37 #N/A 79.04 77.92 72.39 55.00 69.57 56.14 75.55 60.29 118.20 78.47 65.00 96.73 62.55 51.64 133.89 73.39 109.01 74.32 130.04 82.39

2002 42.87 77.94 99.24 70.58 64.57 36.46 69.70 #N/A 82.47 74.28 73.22 64.49 70.69 60.34 80.56 64.94 113.94 85.51 59.01 107.20 65.79 57.51 129.45 79.20 106.46 79.37 124.46 93.29

2003 59.02 90.84 99.87 74.39 65.87 42.12 74.04 #N/A 84.52 71.38 70.27 72.23 72.14 63.97 85.40 71.65 111.58 85.46 61.04 122.75 70.13 63.34 129.75 88.06 102.49 85.68 117.28 96.00

2004 73.13 88.64 96.85 79.44 68.98 63.26 77.97 #N/A 90.74 78.09 86.51 68.19 79.54 75.90 90.73 81.40 106.95 85.81 77.54 122.67 83.47 69.06 126.42 93.20 100.47 91.66 110.02 90.77

2005 76.48 88.24 98.73 87.35 71.04 71.95 82.76 #N/A 95.22 76.14 82.36 68.43 95.59 127.14 99.04 91.85 103.83 93.53 82.86 117.73 104.63 78.60 112.94 100.56 101.82 95.97 106.06 91.68

2006 88.84 93.81 99.75 95.41 78.48 79.44 92.58 #N/A 98.33 85.29 85.08 76.87 108.05 161.95 102.32 99.69 103.05 102.76 84.59 117.00 102.41 92.17 110.02 110.52 97.94 99.91 103.49 98.07

2007 92.96 103.99 100.81 100.09 90.76 94.79 100.04 #N/A 102.13 95.71 94.73 96.12 106.87 144.36 103.58 102.75 100.78 101.70 102.84 105.23 111.61 104.60 105.93 108.29 96.85 102.10 102.26 99.96

2008 102.75 96.26 102.03 99.86 105.87 96.73 98.64 #N/A 99.30 99.75 108.34 100.97 92.81 78.44 95.85 97.05 99.84 99.14 89.36 100.52 92.74 94.64 97.21 93.99 102.48 98.15 98.57 99.41

2009 97.59 107.54 103.82 101.27 126.54 71.16 100.35 #N/A 106.30 108.82 94.50 93.65 86.98 51.61 104.56 92.74 101.00 93.25 113.22 81.42 75.02 105.68 96.85 80.74 116.88 94.20 96.47 97.76

2010 94.48 109.68 109.24 104.13 146.81 64.44 102.77 #N/A 108.00 111.14 90.90 91.67 87.18 52.10 107.30 98.22 102.55 82.67 133.61 84.97 72.99 110.78 93.77 71.14 130.47 92.70 93.39 96.31

2011 109.30 101.76 111.04 102.45 159.61 58.71 107.11 #N/A 104.00 114.00 92.30 88.70 79.24 42.18 104.88 99.44 107.29 75.21 140.55 79.40 76.14 116.50 94.60 58.29 134.05 90.08 90.80 99.16

2012 109.32 101.97 120.42 101.39 166.57 55.63 109.71 #N/A 103.45 128.20 86.83 85.68 78.08 38.44 105.24 96.09 109.04 64.77 170.38 68.97 77.24 118.26 96.87 54.11 141.17 83.85 88.77 97.81

LVA LTU LUX MYSMLTMEXMARNLD NZL NOR PER PHL POL PRT RUS SRB SGP SVK SVN ZAF ESP SWE CHETWNTHA UKR GBR USA URY1970 #N/A #N/A #N/A #N/A #N/A #N/A #N/A 32.22 34.12 41.75 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 65.12 #N/A 52.88 91.70 #N/A #N/A #N/A 21.89 65.58 #N/A

1971 #N/A #N/A #N/A #N/A #N/A #N/A #N/A 34.05 34.85 40.58 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 64.46 23.84 51.89 97.88 #N/A #N/A #N/A 23.35 69.29 #N/A



1974 #N/A #N/A 25.44 #N/A #N/A #N/A #N/A 35.69 54.20 43.67 #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 62.31 30.10 53.23 102.74 #N/A #N/A #N/A 31.22 71.62 #N/A

1975 #N/A #N/A 21.04 #N/A #N/A #N/A #N/A 38.44 49.85 42.60 #N/A #N/A #N/A #N/A #N/A #N/A 15.23 #N/A #N/A 61.76 27.97 57.20 89.85 #N/A #N/A #N/A 26.71 71.09 #N/A





1980 #N/A #N/A 32.56 #N/A 37.10 #N/A #N/A 44.55 34.57 41.44 #N/A #N/A #N/A #N/A #N/A #N/A 32.69 #N/A #N/A 60.63 27.24 58.81 98.40 #N/A #N/A #N/A 30.21 77.24 #N/A








1988 #N/A #N/A 36.00 61.41 34.20 #N/A #N/A 36.08 47.34 54.85 #N/A #N/A #N/A 90.55 #N/A #N/A 38.91 #N/A #N/A 45.74 43.18 55.43 144.07 #N/A #N/A #N/A 51.60 81.03 #N/A



1991 #N/A #N/A 47.88 76.09 45.18 #N/A #N/A 37.63 44.86 39.41 #N/A #N/A #N/A 103.89 #N/A #N/A 50.34 #N/A #N/A 44.48 55.79 57.69 125.64 #N/A 135.61 #N/A 44.11 77.03 95.22



1994 #N/A #N/A 48.76 85.62 58.34 #N/A #N/A 45.56 52.73 42.33 #N/A 112.26 #N/A 96.59 #N/A #N/A 103.82 #N/A #N/A 39.81 47.65 44.94 105.93 102.37 120.90 #N/A 40.30 77.00 89.42

1995 #N/A #N/A 48.20 98.10 60.52 #N/A #N/A 48.29 56.10 44.65 #N/A 165.13 #N/A 93.52 #N/A #N/A 113.40 #N/A 73.77 39.14 47.28 43.54 99.10 89.84 114.40 #N/A 39.04 76.97 90.04

1996 #N/A #N/A 50.67 107.11 63.95 #N/A #N/A 53.00 59.07 48.57 #N/A 168.07 #N/A 92.59 #N/A 84.45 117.13 #N/A 74.43 37.18 46.99 45.26 93.37 86.03 109.87 20.83 40.95 76.70 88.88

1997 #N/A #N/A 51.69 106.29 66.65 #N/A #N/A 57.59 61.43 52.85 #N/A 158.08 #N/A 94.85 #N/A 93.84 100.29 #N/A 74.15 40.59 47.12 47.97 91.15 88.60 118.14 20.31 43.37 77.82 88.82

1998 #N/A 26.16 53.35 91.43 68.71 #N/A #N/A 63.15 59.94 55.40 107.99 118.09 #N/A 96.88 #N/A 96.56 67.17 #N/A 71.82 39.43 50.06 53.80 90.07 74.17 111.72 21.20 47.95 80.95 90.64

1999 #N/A 22.21 55.72 91.69 68.58 #N/A #N/A 73.53 60.52 64.04 117.28 103.17 #N/A 103.77 #N/A 118.46 89.70 #N/A 71.92 42.28 53.20 57.83 89.49 73.74 95.56 25.27 53.89 83.75 91.35

2000 #N/A 21.63 57.70 94.32 73.70 #N/A #N/A 82.83 57.97 68.31 105.96 99.13 48.54 108.48 #N/A 123.51 86.96 #N/A 71.79 46.84 54.43 63.90 89.47 66.95 105.23 21.36 60.58 86.61 117.83

2001 #N/A 29.76 64.43 94.02 72.47 #N/A #N/A 86.66 59.21 72.12 98.44 88.51 47.26 107.14 35.58 88.95 76.78 #N/A 69.99 51.00 58.95 65.23 91.08 67.20 102.11 21.97 62.64 90.67 91.05

2002 #N/A 30.13 68.34 96.97 80.88 #N/A #N/A 88.44 65.65 72.55 95.15 85.28 47.82 103.13 39.31 81.72 75.14 46.41 63.67 52.98 67.08 69.56 95.08 69.06 99.20 28.83 77.33 95.51 68.59

2003 #N/A 33.44 74.50 99.71 89.36 #N/A #N/A 90.30 80.12 74.60 83.22 83.27 47.10 102.08 42.18 103.52 73.10 59.24 69.66 65.72 79.15 72.69 96.19 73.88 114.51 38.37 83.89 101.13 87.26

2004 #N/A 43.57 82.99 99.81 101.84 #N/A #N/A 92.81 87.67 81.58 82.01 82.64 47.39 100.13 48.55 103.99 72.50 64.39 73.87 84.91 89.32 79.55 97.43 77.11 120.99 46.91 92.95 107.73 94.40

2005 #N/A 69.54 89.98 99.32 107.99 95.87 #N/A 94.79 97.20 86.79 85.59 86.29 56.52 101.06 53.93 106.19 74.36 65.55 80.97 95.75 98.22 87.13 97.64 85.38 122.00 65.44 93.05 114.46 96.27

2006 126.50 87.61 98.07 101.01 107.93 97.88 101.39 98.14 103.63 98.77 79.27 90.56 94.24 99.46 81.74 99.01 81.26 74.14 95.88 104.34 103.67 94.91 98.89 96.66 120.27 95.51 98.95 115.69 98.21

2007 138.85 101.44 100.74 101.65 104.88 100.08 102.67 100.38 108.07 104.97 89.36 100.99 102.31 98.41 94.52 92.73 102.14 95.19 100.98 106.69 103.58 102.49 99.11 92.20 107.82 97.70 106.45 108.57 95.35

2008 82.93 92.72 100.23 98.34 95.39 99.57 99.08 99.62 95.17 94.25 113.17 99.39 97.12 101.45 99.41 106.54 92.24 96.64 97.63 95.93 97.06 98.07 101.14 95.08 104.61 111.36 93.55 96.48 105.00

2009 62.38 59.78 96.87 103.86 94.36 99.27 97.13 93.79 98.37 103.54 113.51 93.63 88.81 101.53 84.92 114.13 94.13 84.30 88.91 95.67 90.54 105.88 107.52 112.48 103.73 84.98 91.92 93.04 103.99

2010 68.37 55.37 99.22 110.06 90.22 99.58 96.38 91.31 93.04 107.83 128.32 93.22 82.10 100.82 88.70 116.68 106.45 81.54 87.50 93.96 85.49 109.51 111.48 123.34 101.49 68.96 92.30 88.02 104.39

2011 80.13 54.81 102.22 117.08 93.45 100.88 97.90 86.01 93.87 115.07 145.51 95.14 74.85 96.14 66.53 99.44 106.79 75.87 86.93 89.15 77.31 103.57 116.10 112.42 101.99 69.65 87.76 83.18 107.45

2012 79.52 50.18 103.56 129.58 88.79 100.02 96.26 77.56 98.95 121.27 167.96 98.73 68.78 91.77 72.79 #N/A 105.66 73.97 77.41 90.57 67.16 105.84 120.59 110.88 100.06 71.39 87.46 86.06 #N/A

24

Figure 4 Evolution of the average and the standard deviation of real house price annual returns

95 00 05 10−10

−5

0

5

10

(a) Moving Average − AEsP

erce

nt

95 00 05 100

5

10

15

(c) Moving Std. Deviation − AEs

Per

cen

t

95 00 05 10−10

−5

0

5

10

(b) Moving Average − EMEs

Per

cen

t95 00 05 10

0

5

10

15

(d) Moving Std. Deviation − EMEs

Per

cen

t

Note. The moving averages and moving standard deviations are computed with a rolling window of5 years over the sample 1985.I–2012.IV. The solid lines graph the cross-country average of the movingaverage and moving standard deviation of house price annual returns. The shaded areas graph thecross-country two standard deviations confidence bands.

25

Figure 5 Cross-correlations of real house price annual returns

Real GDP

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real Consumption

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

CPI

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Labor Productivity

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real Equity Price

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Short−term Int. Rate

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real Eff. Exch. Rate

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

(a) Advanced Economies

Current Account / GDP

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real GDP

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real Consumption

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

CPI

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Labor Productivity

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Real Equity Price

−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75


−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75


−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

(b) Emerging Economies


−4−3−2−1 0 +1+2+3+4−0.5

−0.25

0

0.25

0.5

0.75

Note. The correlation coefficients are computed over the sample 1985.I–2012.IV. Each dot graphsthe cross-country average of the correlation coefficient. The shaded areas graph the cross-country twostandard deviations confidence bands.

26

Figure 6 Moving correlations of real house price annual returns

99 03 07 11−0.2

0

0.2

0.4

0.6

0.8

Real GDP

99 03 07 110

0.2

0.4

0.6

0.8

Real Consumption

99 03 07 11−0.6

−0.4

−0.2

0

0.2

CPI

99 03 07 11−0.2

0

0.2

0.4

0.6

Labor Productivity

99 03 07 11−0.2

0

0.2

0.4

0.6

Real Equity Price

99 03 07 11−0.2

0

0.2

0.4

0.6


99 03 07 11−0.2

−0.1

0

0.1

0.2

0.3



99 03 07 11−0.5

−0.4

−0.3

−0.2

−0.1

0


99 03 07 110

0.2

0.4

0.6

0.8

Real GDP

99 03 07 110

0.2

0.4

0.6

0.8

Real Consumption

99 03 07 11−0.6

−0.4

−0.2

0

0.2

0.4

CPI

99 03 07 110

0.2

0.4

0.6

0.8

Labor Productivity

99 03 07 11−0.2

0

0.2

0.4

0.6

Real Equity Price

99 03 07 11−0.2

0

0.2

0.4

0.6


99 03 07 11−0.2

0

0.2

0.4

0.6



99 03 07 11−0.6

−0.4

−0.2

0

0.2


Note. The moving correlations are computed with a rolling window of 10 years over the sample1985.I–2012.IV. The solid lines graph the cross-country average of the moving correlation of houseprice annual returns. The shaded areas graph the cross-country two standard deviations confidencebands.

27

Figure 7 Real house prices boom-bust episodes – Event study

0.0

20.0

40.0

60.0

80.0

Advanced Economies Emerging Market Economies

(a) Real House Prices (Average increase during episodes, percentage)

0.0

1.5

3.0

4.5

6.0


(b) Output Gap (Average increase during episodes, percentage)

-4.0

-3.0

-2.0

-1.0

0.0


(c) Current Account (Average increase during episodes, percentage)

0.0

4.0

8.0

12.0

16.0


(d) Real Exchange Rate (Average increase during episodes, percentage)

0.0

1.0

2.0

3.0

4.0


(e) Capital Inflows (Average increase during episodes, percentage)

0.0

7.5

15.0

22.5

30.0


(f) Global Liquidity (Average increase during episodes, percentage)

-4.0

-3.0

-2.0

-1.0

0.0

1.0


(g) VIX Index (Average increase during episodes, percentage)

-2.0

-1.5

-1.0

-0.5

0.0

0.5


(h) US Real Interest Rate (Average increase during episodes, percentage)

Note. Note here.

28

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endefl

ate

dby

the

US

CP

I.

29

Figure 9 Impulse response function – Global liquidity shock

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5

3

3.5

PRIV. LIQ.

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.2

−0.1

0

0.1

0.2

0.3

CONS

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.8

−0.6

−0.4

−0.2

0

0.2

0.4

0.6

RHP

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−1

0

1

2

3

4

5

6

7

IRS

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

REER

Pe

rce

nt

De

via

tio

n

Quarters


0 10 20 30 40−15

−10

−5

0

5

10

15

CA

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.5

0

0.5

1

1.5

2

2.5

3

3.5

PRIV. LIQ.

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.4

−0.2

0

0.2

0.4

0.6

CONS

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.5

0

0.5

1

1.5

2

RHP

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−4

−2

0

2

4

6

IRS

Pe

rce

nt

De

via

tio

n

Quarters

0 10 20 30 40−0.4

−0.2

0

0.2

0.4

0.6

REER

Pe

rce

nt

De

via

tio

n

Quarters


0 10 20 30 40−40

−30

−20

−10

0

10

20

CA

Pe

rce

nt

De

via

tio

n

Quarters

30

Figure 10 Forecast error variance decmoposition – Global liquidity shock

0 5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

90

100

(a) AEs − PRIV. LIQ

Perc

ent E

xpla

ined

Quarters

0 5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

90

100

(b) EMEs − PRIV. LIQ

Perc

ent E

xpla

ined

Quarters

Note. Mean group estimator of the FEVD of PRIV. LIQ. to a PRIV. LIQ. shock in advanced (AE)and emerging (EME) economies (panel (a) and (b), respectively). The dotted line in panel (a) displaysthe FEVD of PRIV. LIQ. to a PRIV. LIQ. shock in the US model.

31

8 Tables

Table 1 Summary statistics of the annual growth rates of real house prices, real GDP, and real con-sumption in advanced economies and emerging economies

Real Real RealHouse Price GDP Consumption

Group AEs EMEs AEs EMEs AEs EMEs

Mean 2.0% 1.2% 2.2% 3.8% 2.3% 4.0%Median 2.1% 1.5% 2.5% 5.0% 2.4% 4.7%Max 18.3% 27.5% 7.0% 13.3% 7.5% 16.7%Min -12.5% -34.5% -5.8% -13.3% -3.9% -16.4%St. Dev. 6.4% 12.5% 2.3% 5.1% 2.2% 5.9%Auto Corr. 0.92 0.86 0.83 0.87 0.81 0.79Skew. 0.10 -0.44 -1.00 -1.12 -0.31 -0.67Kurt. 3.20 4.34 4.91 6.15 3.88 6.12

Note. The country-specific summary statistics are averaged across each group, namely advanced economies(AEs) and emerging economies (EMEs) and are computed across the common sample 1985.I–2012.IV.

Table 2 Boom-bust cycles – Summary statistics

Probability Probability of Probability Duration of Durationof Boom Boom-Bust of Boom Boom-Bust of Boom

Group into Bust per year per year (years) (years)

AEs 0.442 0.022 0.051 10.1 5.5EMEs 0.450 0.020 0.044 8.9 5.6ALL 0.444 0.021 0.048 9.5 5.6

Note. The probability and the duration are computed as averages across countries. AEs are advancedeconomies, EMEs are emerging economies, and ALL refers to all countries in the data set. The boom-bustcycles are computed for each country using real house prices, equity prices and real effective exchange rates onthe largest available sample.

32

Table

3R

eal

hou

sep

rice

det

erm

inan

tsin

boom

epis

od

es

Depen

den

tvariable:

change

inrealhouse

price

duringboom

Explanatory

variable

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Capit

al

inflow

s2.2

64.2

34.0

54.5

9(2

.35)∗∗

(2.4

7)∗∗

(1.9

1)∗

(2.5

3)∗∗

Glo

bal

liquid

ity

0.5

80.8

81.2

11.0

1(3

.04)∗∗∗

(2.8

7)∗∗∗

(3.7

7)∗∗∗

(2.1

3)∗∗

Dum

my

AE

s

Fin

anci

al

mark

etdep

th-0

.09

0.0

2(-

0.7

3)

-0.1

4

Exch

ange

rate

flex

ibilit

y-0

.55

0.6

6(-

0.5

1)

-0.4

4

Dum

my

AE

s×

-5.2

7C

apit

al

inflow

s(-

2.7

3)∗∗∗

Fin

anci

al

mark

etdep

th×

-0.0

5C

apit

al

inflow

s(-

1.7

3)∗

Exch

ange

rate

flex

ibilit

y×

-0.3

8C

apit

al

inflow

s(-

1.9

0)∗

Dum

my

AE

s×

-0.7

0G

lobal

liquid

ity

(-2.2

1)∗∗

Fin

anci

al

mark

etdep

th×

-0.0

1G

lobal

liquid

ity

(-1.9

5)∗

Exch

ange

rate

flex

ibilit

y×

-0.0

6G

lobal

liquid

ity

(-1.1

0)

R2

0.0

60.1

60.1

90.1

10.1

40.2

20.2

80.1

6N

um

ber

of

obse

rvati

ons

60.0

060.0

058.0

060.0

066.0

066.0

062.0

066.0

0F

test

5.5

1∗∗

3.7

3∗∗

1.6

62.3

0∗

9.2

5∗∗∗

4.1

7∗∗

5.8

9∗∗∗

3.1

1∗∗

Note

.A

llre

gre

ssio

ns

are

esti

mate

dusi

ng

aco

nst

ant,

t-te

stin

pare

nth

esis

.Sig

nifi

cance

level

sat

1%

,5%

,and

10%

isden

ote

dby

()∗∗∗,(

)∗∗,(

)∗,

resp

ecti

vel

y.

33

Table 4 Correlation between global liquidity measures

Off. Liquidity (level) Priv. Liquidity (level) Off. Liquidity (level)Priv. Liquidity (level) VIX index (level) VIX index (level)

Full Sample 0.92 -0.05 0.01Pre-Crisis 0.99 -0.30 -0.28Post-Crisis -0.12 0.00 -0.41

Off. Liquidity (log diff.) Priv. Liquidity (log diff.) Off. Liquidity (log diff.)Priv. Liquidity (log diff.) VIX index (level) VIX index (level)

Full Sample 0.29 -0.18 -0.06Pre-Crisis 0.38 -0.13 -0.23Post-Crisis 0.43 0.12 0.32

Note. Note here.

34

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36

A Appendix. Data Sources

A.1 House Prices

The OECD Nominal House Price (Subject: HP.Index. Measure: Index) was collected for thefollowing countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland,France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, NewZealand, Norway, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom,and United States.

The BIS property price database was used to collect data fro the following countries: Bul-garia, China, Estonia, Hong Kong, Hungary, Iceland, Indonesia, Latvia, Lithuania, Malaysia,Mexico, Morocco, Poland, Russia, South Africa, and Thailand.

Data provided by national central banks or national statistical offices was used for Brazil,Croatia, Colombia, Malta, Peru, Serbia, Singapore, and Uruguay.

For the remaining countries we used alternative sources: Argentina (ARKLEMS), Ukraine(Blagovest), Philippines (Colliers International), India (National Housing Bank), Chile (Morandeand Soto (1992)), Taiwan (Synyi).

All house price indices, together with their sources, are displayed in Table A.1. Seasonaladjustment was performed using Eviews applying the National Bureau’s X12 program on thechange of the logged house price series, using the additive option. The nominal seasonallyadjusted indices were then deflated with country-specific CPI indices (described below).

A.2 Other variables

To construct the database we relied on three main sources: the OECD Analytical Database, theIMF International Financial Statistics (IFS) Database and Bloomberg. The variables used arereal gross domestic product (RGDP ), consumer price index (CPI), real consumption (CONS),labor productivity of employed persons (LPROD), real equity prices (EQ), short-term interestrates (IRS), real effective exchange rates (REER), and current account balance as a percent ofGDP (CA). Data were collected in June2013 to cover the period from 1970.I to 2012.IV, unlessotherwise specified. In what follows, we shall refer to the data set as the “2012 Vintage”.

Real GDP

For those countries for which OECD data is available, the OECD Real GDP (Subject:B1 GE: Gross domestic product - expenditure approach. Measure: VPVOBARSA. Millionsof US dollars, volume estimates, fixed PPPs, OECD reference year, annual levels, seasonallyadjusted) is used. These countries are: Australia, Austria, Belgium, Canada, Czech Republic,Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Korea,Netherlands, New Zealand, Norway, Portugal, Slovenia, Spain, Sweden, Switzerland, UnitedKingdom, United States.

The IFS Real GDP (Subject: 99BVRzf. Measure: Index GDP VOL., 2005 = 100, seasonallyadjusted) was collected for South Africa. The IFS Real GDP (Subject: 99BVPzf. Measure: In-dex GDP VOL., 2005 = 100, not seasonally adjusted) was collected for the remaining countries,namely Argentina, Bulgaria, Colombia, Croatia, Hong Kong, Indonesia, Lithuania, Malaysia,Peru, Singapore, Thailand.

For China, a quarterly seasonal adjusted real GDP index was constructed with data fromthe National Bureau of Statistics (NBS) of China. As no institution publishes a quarterly real

37

GDP Index for China, it has to be derived by a nominal GDP series. The National Bureauof Statistics (NBS) of China releases a quarterly nominal GDP not seasonally adjusted. weconstructed a quarterly real GDP index as follows. First, we seasonally adjusted (with theprocedure described above) the nominal GDP from NBS. Then, we used the following formula

log(RGDP1) = log(GDP1CPI1

)for t = 1

log(RGDPt) = log(RGDPt−1) + log(

GDPtGDPt−1

)− log

(CPIt

CPIt−1

)for t ≥ 2

where CPI is defined in the next subsection. The series displays noisy features in the first part ofthe sample and starts to behave better from 1994.I, providing a natural cut-off date. Therefore,we used the new quarterly series from 1994.I to 2012.IV.

For Philippines, a quarterly seasonal adjusted real GDP index was obtained from Bloomberg(Ticker: PHNAGDPS Index).

Where the inception date of the quarterly real GDP series above was posterior to HousePrice Index inception date, quarterly series were interpolated linearly from the correspondingIFS annual series (the interpolation procedure is the same used for house prices). Interpolateddata were used in the following periods: Bulgaria from 1990.I to 2001.IV, Colombia from 1988.Ito 1993.IV, Indonesia from 1994.I to 1996.IV, Singapore from 1975.I to 1984.II, Thailand from1991.I to 1992.IV.

Seasonal adjustment was performed using Eviews applying the National Bureau’s X12 pro-gram on the change of the log(RGDP ) using the additive option.

Consumer Price Index

The OECD Consumer Price Index (Subject: Consumer prices - all items. Measure: Index,2005=100, not seasonally adjusted) was collected for the following countries: Australia, Aus-tria, Belgium, Canada, China, Czech Republic, Denmark, Estonia, Finland, France, Germany,Greece, Hungary, Indonesia, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway,Portugal, Slovenia, South Africa, Spain, Sweden, Switzerland, UK, and US. Note that Hungaryand Ireland were extended backward using the rates of change of IFS Consumer Price Index(Subject: 64zf series. Measure: Index, 2005=100, not seasonally adjusted), which has a longercoverage.

The IFS Consumer Price Index was used for the remaining countries namely, Argentina,Bulgaria, Colombia, Croatia, Hong Kong, Lithuania, Malaysia, Peru, Philippines, Singapore,and Thailand.

Seasonal adjustment was performed using Eviews applying the National Bureau’s X12 pro-gram on the change of the log(CPI) using the additive option.

Real Private Consumption

In order to create the 2012 Real Private Consumption Vintage we relied on OECD AnalyticalDatabase, IFS Database and Bloomberg.

For those countries for which OECD data is available, the OECD Real Private Consump-tion (Subject: P31S14 S15: Private final consumption expenditure. Measure: Millions of U.S.dollars, volume estimates, fixed PPPs, OECD reference year, annual levels, seasonally adjusted)was collected, namely for Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Es-tonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Korea, Netherlands,New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and UnitedStates. Seasonal adjustment was performed using Eviews applying the National Bureau’s X12program on the change of the log(CONS) using the additive option.

38

For the remaining countries, Argentina, Bulgaria, China, Colombia, Croatia, Hong Kong,Indonesia, Lithuania, Malaysia, Peru, Philippines, South Africa, Thailand, as no real indexwas available, IFS Nominal Private Consumption (Subject: code 96F..zf. Measure: Billions ofNational Currency, not seasonally adjusted) was used. After performing seasonal adjustment,the IFS Nominal Private Consumption was deflated by Consumer Price Index (described above)in order to obtain the 2012 Real Private Consumption Vintage.

For Singapore, for which neither OECD nor IFS data is available at quarterly frequency, weuse Bloomberg Real Private Consumption (Subject: SIFCPTE Index. Measure: GDP PrivateConsumption Expenditure, Constant 2000 Prices, Millions of national currency, not seasonallyadjusted) after adjusting the series for seasonality.

Labor Productivity Of Employed Persons

The Labor Productivity Index is constructed with data from HAVER and form the Confer-ence Board Total Economy Database from Groningen University.21

The HAVER quarterly Labor Productivity Index (Subject: Productivity: Output per Em-ployed Person (SA, 2000=100). Measure: Index) is collected for the following countries: Aus-tralia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy,Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, UK,US.

For the remaining countries, annual values for Total Persons Employed (in thousands) arecollected from the Conference Board Total Economy Database from 1970 to 2012. Quarterlyseries for Employed Persons then were interpolated linearly with the procedure used above.Finally, the Labor Productivity Index is computed as real output divided by number of personsemployed, using the real output described above.

Equity Price Index

The OECD Equity Price Index (Subject: Share Prices. Measure: Index 2005 = 100) wascollected for the following countries: Australia, Austria, Belgium, Canada, Czech Republic, Den-mark, Estonia, Finland, France, Germany, Greece, Hungary, Indonesia, Ireland, Italy, Japan,Korea, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United King-dom, United States.

As IFS data has a longer coverage, Belgium, Denmark, Indonesia, Korea, Norway, and Spainhave been extended backward with IFS Equity Price Index (Subject: 62zf, Share Price Index.Measure: Index 2005 = 100.). Moreover, as quarterly data for Korea were not available from1973.I to 1977.IV, a quarterly series was interpolated linearly from the corresponding IFS annualseries.

IFS Equity Price Index has been used as well for Hong Kong, Colombia, Malaysia, Philip-pines, Singapore, and Thailand. The series for Malaysia, Singapore, and Thailand have beenextended backward with Dees et al. (2007) data set, which has a longer coverage for thesecountries.

Short-term Interest Rate

The OECD Short-term interest rate (Subject: Short-term interest rates. Measure: Per centper annum) was collected for the following countries: Australia, Austria, Belgium, Canada,China, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy,Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland,United Kingdom, and United States.

21See the Conference Board Total Economy Database here.

39

http://www.conference-board.org/data/economydatabase/

Where the OECD does not have complete coverage, the changes in short-term interest ratesfrom IFS were used to extend the series backward. The following IFS rates (Measure: Per centper annum) were used from 1979.I to the date in brackets: Discount Rate (Subject: IFS 60zfseries) for New Zealand, Norway, Spain, and Switzerland; Money Market Rate (Subject: IFS60Bzf series) for Italy; Deposit Rate (Subject: IFS 60Lzf series) for Ireland, and Korea; andTreasury Bill Rate (Subject: IFS 60Czf series) for Greece.

For the countries for which OECD data was not available, the following IFS short-terminterest rates were used: Discount Rate for Colombia, Peru, Croatia, and Hong Kong; MoneyMarket Rate for Estonia, Indonesia, Singapore, and Thailand; Treasury Bill Rate for Malaysia,Philippines, and South Africa; Deposit Rate for Argentina, Bulgaria, Lithuania, and Slovenia.

Real Effective Exchange Rate

The IFS real effective exchange rate (Subject: RECZF. Measure: Index) is used for thefollowing countries: Australia, Austria, Belgium, Bulgaria, Canada, China, Colombia, Croatia,Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan,Malaysia, Netherlands, New Zealand, Norway, Philippines, Portugal, Singapore, South Africa,Spain, Sweden, Switzerland, Ukraine, United Kingdom, and United States. Note that the IFSreal effective exchange rate is derived from the nominal effective exchange rate index, adjustedfor relative changes in consumer prices.22

We used the indices provided by national central banks for Bulgaria (Subject: Deflator -Consumer Price Index - Quarterly Data. Measure: Index), Estonia (Subject: Real effectiveexchange rate of the Kroon. Measure: Percentage change on the previous quarter) and forLithuania (Subject: Real Effective Exchange Rate Indices of the Litas. Measure: Index).

Finally, we relied on Bloomberg for the remaining countries and, when possible, to ex-tend backward the previous series with the JP Morgan Real Effective Exchange Rate: Ar-gentina (Ticker: JBXRARS Index), Bulgaria (extended to 1994.I. Ticker: JBXRBGN Index),Hong Kong (Ticker: JBXRHKD Index), Indonesia (Ticker: JBXRIDR Index), Korea (Ticker:JBXRKRW Index), Peru (Ticker: JBXRPEN Index), and Slovenia (Ticker: JBXRSIT Index).

Current Account to GDP Ratio

In order to create the 2012 Current Account to GDP Ratio Vintage we relied on OECDAnalytical Database, IFS Database and Bloomberg.

The OECD Current Account Balance to GDP ratio (Subject: Current Account Balance,as a percentage of GDP. Measure: Percentage) is used for the following countries: Australia,Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany,Hungary, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal, Spain,Sweden, Switzerland, United Kingdom, United States.

For all other countries we use IFS data, with the following procedure. First, we down-load the IFS Current Account balance (Subject: ALDZF. Measure: Millions of USD) and IFSnominal GDP in local currency (Subject: 99BZF. Measure: Millions of National Currency).After performing seasonal adjustment, we download the IFS Exchange Rate (Subject: AE.ZF.Measure: units of foreign currency per 1 USD) and we transform seasonally adjusted GDP incurrent USD. Finally, we compute the ratio between the Current Account balance and GDP incurrent USD.

22Note that the real effective exchange rate is constructed such that a decline of the index is a depreciation (ora gain in competitiveness).

40

Figure A.1 Real house price indices - Advanced economies

70 79 88 97 06 15−20

0

20

40

Australia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Austria

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Belgium

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−3

13

30

Canada

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Denmark

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−40

−13

13

40

Finland

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

−2

7

15

France

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

−3

3

10

Germany

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−15

−5

5

15

Greece

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−30

−10

10

30

Iceland

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

−17

17

50

Ireland

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−40

−7

27

60

Italy

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−5

10

25

40

Japan

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−20

−7

7

20

Luxembourg

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

−17

17

50

Netherlands

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−3

13

30

New Zealand

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Norway

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

−3

3

10

Portugal

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Spain

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−40

−20

0

20

Sweden

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Switzerland

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

United Kingdom

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−10

0

10

United States

70 79 88 97 06 150

50

100

150

Index (left axis) Yearly Growth Rate (right axis)

41

Figure A.2 Real house price indices - Emerging economies

70 79 88 97 06 15−100

−50

0

50

Argentina

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

−3

5

13

20

Brazil

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−100

−50

0

50

Bulgaria

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−100

−50

0

50

100

Chile

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−10

−3

3

10

China

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Colombia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Croatia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

3

17

30

Czech Republic

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−100

−50

0

50

100

Estonia

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−60

−35

−10

15

40

Hong Kong

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−50

−17

17

50

Hungary

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

India

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−60

−40

−20

0

20

Indonesia

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−20

−7

7

20

Israel

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Korea

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−60

−20

20

60

Latvia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

−17

17

50

Lithuania

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−10

0

10

20

Malaysia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−3

13

30

Malta

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−5

0

5

10

Mexico

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−4

−1

1

4

Morocco

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−30

−15

0

15

30

Peru

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−50

−25

0

25

50

Philippines

70 79 88 97 06 150

50

100

150

200

70 79 88 97 06 15−20

7

33

60

Poland

70 79 88 97 06 150

50

100

150

42

Figure A.2 Real House Price Indices - Emerging Economies (Cont’d)

70 79 88 97 06 15−40

−7

27

60

Russia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−60

−27

7

40

Serbia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

0

50

100

Singapore

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

0

20

40

Slovakia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Slovenia

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

−17

17

50

South Africa

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Taiwan

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−20

−7

7

20

Thailand

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−50

0

50

100

Ukraine

70 79 88 97 06 150

50

100

150

70 79 88 97 06 15−30

−10

10

30

Uruguay

70 79 88 97 06 150

50

100

150

Index (left axis) Yearly Growth Rate (right axis)

43

Figure A.3 Boom-bust in real house prices – Advanced economies

1970 1980 1991 2001 20120

50

100

150

Australia

1970 1980 1991 2001 20120

50

100

150

Austria

1970 1980 1991 2001 20120

50

100

150

Belgium

1970 1980 1991 2001 20120

50

100

150

Canada

1970 1980 1991 2001 20120

50

100

150

Denmark

1970 1980 1991 2001 20120

50

100

150

Finland

1970 1980 1991 2001 20120

50

100

150

France

1970 1980 1991 2001 20120

50

100

150

Germany

1970 1980 1991 2001 20120

50

100

150

Greece

1970 1980 1991 2001 20120

50

100

150

Iceland

1970 1980 1991 2001 20120

50

100

150

Ireland

1970 1980 1991 2001 20120

50

100

150

Italy

1970 1980 1991 2001 20120

50

100

150

200

Japan

1970 1980 1991 2001 20120

50

100

150

Luxembourg

1970 1980 1991 2001 20120

20

40

60

80

100

Netherlands

1970 1980 1991 2001 20120

50

100

150

New Zealand

1970 1980 1991 2001 20120

50

100

150

Norway

1970 1980 1991 2001 20120

50

100

150

Portugal

1970 1980 1991 2001 20120

50

100

150

Spain

1970 1980 1991 2001 20120

50

100

150

Sweden

1970 1980 1991 2001 20120

50

100

150

Switzerland

1970 1980 1991 2001 20120

50

100

150

United Kingdom

1970 1980 1991 2001 20120

50

100

150

United States

44

Figure A.4 Boom-bust in real house prices – Emerging economies

1970 1980 1991 2001 20120

50

100

150

Argentina

1970 1980 1991 2001 20120

50

100

150

200

Brazil

1970 1980 1991 2001 20120

20

40

60

80

100

Bulgaria

1970 1980 1991 2001 20120

50

100

150

200

250

Chile

1970 1980 1991 2001 20120

50

100

150

China

1970 1980 1991 2001 20120

50

100

150

Colombia

1970 1980 1991 2001 20120

20

40

60

80

100

Croatia

1970 1980 1991 2001 20120

20

40

60

80

100

Czech Republic

1970 1980 1991 2001 20120

50

100

150

200

Estonia

1970 1980 1991 2001 20120

50

100

150

Hong Kong

1970 1980 1991 2001 20120

50

100

150

Hungary

1970 1980 1991 2001 20120

50

100

150

India

1970 1980 1991 2001 20120

50

100

150

200

250

Indonesia

1970 1980 1991 2001 20120

50

100

150

Israel

1970 1980 1991 2001 20120

50

100

150

Korea

1970 1980 1991 2001 20120

50

100

150

Latvia

1970 1980 1991 2001 20120

20

40

60

80

100

Lithuania

1970 1980 1991 2001 20120

50

100

150

Malaysia

1970 1980 1991 2001 20120

50

100

150

Malta

1970 1980 1991 2001 20120

50

100

150

Mexico

1970 1980 1991 2001 20120

50

100

150

Morocco

1970 1980 1991 2001 20120

50

100

150

200

Peru

1970 1980 1991 2001 20120

50

100

150

200

Philippines

1970 1980 1991 2001 20120

50

100

150

Poland

45

Figure A.4 Boom-bust in real house prices – Emerging economies (Cont’d)

1970 1980 1991 2001 20120

20

40

60

80

100

Russia

1970 1980 1991 2001 20120

50

100

150

Serbia

1970 1980 1991 2001 20120

50

100

150

Singapore

1970 1980 1991 2001 20120

20

40

60

80

100

Slovakia

1970 1980 1991 2001 20120

20

40

60

80

100

Slovenia

1970 1980 1991 2001 20120

50

100

150

South Africa

1970 1980 1991 2001 20120

50

100

150

Taiwan

1970 1980 1991 2001 20120

50

100

150

Thailand

1970 1980 1991 2001 20120

50

100

150

Ukraine

1970 1980 1991 2001 20120

50

100

150

Uruguay

46

Table

A.1

Hou

sep

rice

sd

escr

ipti

on

Cou

ntr

yD

escr

.S

ou

rce

Arg

enti

na

Apart

men

tsin

Bu

enos

Air

esC

ity

-av

erage

pri

ceA

RK

LE

MS

Au

stra

lia

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Au

stri

aR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Bel

giu

mR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Bra

zil

21340

-R

esid

enti

al

Rea

lE

state

Coll

ate

ral

Valu

eIn

dex

CB

BB

ulg

aria

RE

SID

EN

TIA

LP

RO

PE

RT

YP

R.,

EX

IST

.FL

AT

S(B

IGC

ITIE

S),

PE

RS

Q..

M,Q

-AL

LN

SA

BIS

Can

ada

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Ch

ile

Mora

nd

ean

dSoto

,1992

Pap

erC

hin

aR

ES

ID.

PR

OP

ER

TY

PR

ICE

S,

(BE

IJIN

G),

PE

RS

Q.M

,M

-AL

LN

SA

(Ave

rage

exis

tin

gan

dn

ew)

BIS

Col

omb

iaH

PI

-F

or

thre

em

ajo

rco

lom

bia

nci

ties

-Q

uart

erly

data

NC

BC

roat

iaA

VE

RA

GE

PR

ICE

SO

FN

EW

DW

EL

LIN

GS

SO

LD

PE

R1

m2

CB

SC

zech

Rep

ub

lic

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Den

mar

kR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Est

onia

RE

SID

.P

RO

PE

RT

YP

RIC

ES

,A

LL

DW

EL

LIN

GS

,P

ER

SQ

.M.,

Q-A

LL

BIS

Fin

lan

dR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Fra

nce

Res

iden

tial

pro

per

typ

rice

s,new

an

dex

isti

ng

dw

elli

ngs

OE

CD

Ger

man

yR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Gre

ece

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Hon

gK

ong

RE

SID

EN

TIA

LP

RO

PE

RT

YP

R.,

AL

LD

WE

LL

ING

S,

PE

RS

QU

AR

EM

.,M

-AL

LN

SA

BIS

Hu

nga

ryR

ES

ID.

PR

OP

ER

TY

PR

ICE

S,

AL

LD

WE

LL

ING

S,

PE

RS

Q.M

.,Q

-AL

LN

SA

BIS

Icel

and

RE

SID

EN

TIA

LP

RO

P.P

R.,

AL

LD

WE

LL

ING

S(G

R.

RE

YK

JA

VK

),P

ER

SQ

.M,M

-AL

LN

SA

BIS

Ind

iaR

ES

IDE

X-

Med

ian

resi

den

tial

pro

per

typ

rice

acr

oss

20

citi

esN

HB

Ind

ones

iaR

ES

IDE

NT

IAL

PR

OP

ER

TY

PR

ICE

S,

NE

WH

OU

SE

S(B

IGC

ITIE

S),

PE

RD

WE

LL

ING

,NS

AB

ISIr

elan

dR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Isra

elR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Ital

yR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Jap

anR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Kor

eaR

esid

enti

al

pro

per

typ

rice

s,new

an

dex

isti

ng

dw

elli

ngs

OE

CD

Lat

via

RE

SID

EN

TIA

LP

RO

PE

RT

YP

RIC

ES

,E

XIS

TIN

GF

LA

TS

,P

ER

SQ

UA

RE

M.,

Q-A

LL

,N

SA

BIS

Note

.N

ote

her

e.

47

Table

A.1

Hou

sep

rice

sd

escr

ipti

on(C

ont’

d)

Cou

ntr

yD

escr

.S

ou

rce

Lit

hu

ania

RE

SID

EN

TIA

LP

RO

PE

RT

YP

R.,

AL

LD

WE

LL

ING

S,

PE

RS

QU

AR

EM

.,Q

-AL

L,

NS

AB

ISL

uxem

bou

rgR

ES

IDE

NT

IAL

PR

OP

ER

TY

PR

ICE

S,

AL

LD

WE

LL

ING

S,

Y-A

LL

&R

es.

Pro

p.

Pri

ces

OE

CD

Mal

aysi

aR

ES

IDE

NT

IAL

PR

OP

.P

RIC

ES

,A

LL

DW

EL

LIN

GS

,P

ER

SQ

.M

.,Q

-AL

LY

-AL

LN

SA

BIS

Mal

taP

RO

PE

RT

YP

RIC

ES

IND

EX

(BA

SE

DO

NA

DV

ER

TIS

ED

PR

ICE

S)

NC

BM

exic

oR

ES

IDE

NT

IAL

PR

OP

ER

TY

PR

ICE

S,

AL

LD

WE

LL

ING

S,

PE

RD

WE

LL

ING

,Q

-AL

LN

SA

BIS

Mor

occ

oR

ES

IDE

NT

IAL

PR

OP

ER

TY

PR

ICE

S,

EX

IST

.DW

EL

LIN

GS

,P

ER

SQ

UA

RE

M.,

Q-A

LL

NS

AB

ISN

eth

erla

nd

sR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

New

Zea

lan

dR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Nor

way

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Per

uL

aM

oli

na,

Mir

afl

ore

s,S

an

Borj

a,

San

Isid

roy

Su

rco

NC

BP

hil

ipp

ines

Pri

me

3B

RC

on

dom

iniu

mP

rice

-M

aka

tiC

BD

Coll

iers

Pol

and

RE

SID

EN

TIA

LP

RO

PE

RT

YP

RIC

ES

,(B

IGC

ITIE

S),

PE

RS

Q.M

.,Q

-AL

LN

SA

BIS

Por

tuga

lR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Ru

ssia

RE

SID

EN

TIA

LP

RO

PE

RT

YP

RIC

ES

,EX

IST

ING

DW

EL

LIN

GS

,PE

RS

QU

AR

EM

,Q-A

LL

NS

AB

ISS

erb

iaP

rice

sof

dw

elli

ngs

of

new

con

stru

ctio

nN

SO

Sin

gap

ore

pri

cein

dex

,p

riva

tere

sid

enti

al,

Sin

gap

ore

NS

OS

lova

kia

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Slo

ven

iaR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Sou

thA

fric

aR

ES

IDE

NT

IAL

PR

OP

ER

TY

PR

.,A

LL

MID

DL

E-S

EG

ME

NT

HO

US

ES

,PE

RD

WE

LL

.,M

-AL

LS

AB

ISS

pai

nR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Sw

eden

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Sw

itze

rlan

dR

esid

enti

al

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Tai

wan

Sin

yi

Hou

seP

rice

Ind

ex,

Nati

on

al

Synyi

Th

aila

nd

RE

SID

.P

RO

PE

RT

YP

R.,

AL

LD

ET

AC

HE

DH

OU

SE

S(G

R.B

AN

GK

OK

),P

ER

SQ

.M,M

-AL

LS

AB

ISU

kra

ine

Ave

rage

Pri

ceof

Apart

men

ts,

Kie

v(U

S$

per

sq.

m.)

Bla

gov

est

Un

ited

Kin

gdom

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Un

ited

Sta

tes

Res

iden

tial

pro

per

typ

rice

s,n

ewan

dex

isti

ng

dw

elli

ngs

OE

CD

Uru

guay

Ind

ice

Gra

l.d

eP

reci

os

m2

de

Lasp

eyre

sIN

E

Note

.N

ote

her

e.

48

capital flows, house prices, and the macroeconomy ... · 04.06.2014 · world availability of us...

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