Capital Flows, House Prices, and the Macroeconomy:
Evidence from Advanced and Emerging Market Economies∗
Ambrogio Cesa-Bianchi, Luis Felipe Cespedes, and Alessandro Rebucci†
[Preliminary – Comments Welcome – Do not quote without
permission]
First draft: September 13, 2013
Abstract
In this paper we 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 show that house
prices in emerging markets grow slower, are more volatile and less persistent, and are
more correlated with the real effective exchange rate and the current account, than
in advanced economies. We provide evidence that indicates that house price booms
are much larger and are more closely associated with capital inflows, loose global
liquidity conditions, shallow domestic financial systems in emerging economies than
in advanced economies. Finally, we find that while an exogenous change in global
liquidity has a sizable impact on consumption, house prices and the current account
in emerging markets, it has a much smaller and economically not significant impact
on the same variables 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.
Keywords: Capital Flows, House Prices, Emerging markets
JEL codes: C32, E44, F44
∗
The views expressed in this paper are those of the authors, and not necessarily those of the Bank
of England.
†
Bank of England, Universidad Adolfo Ibanez, and Johns Hopkins University, respectively. Correspondence to: arebucci@jhu.edu
1
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 timecoverage 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
2
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 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 capital 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 monetary 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 (potentially harmful) cross-border movements of liquid assets,
3
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 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 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.
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).
4
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 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 performed 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 cov1
This step of the process is not documented in the paper but the material is available on request
from the authors.
2
The data set is avaiable at sites.google.com/site/ambropo/ and is updated occasaionally. The
specific data sources are listed in the data appendix.
5
ered 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 experienced house price declines with the crisis episodes
of the early 2000s. However, in the run up to 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
6
(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 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
3
The 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 consistent estimate of the true cross-section variance.
7
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 macroeconomic 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 others, 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,
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
8
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
considered. We consider output, consumption, labor productivity, equity prices, the real
effective exchange rate, and the current account to GDP ratio.4 As before, the crosscorrelations 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 correlation 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 factors 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.
4
The macroeconomic and financial variables are defined in the data appendix, where we also list the
data sources. 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 meaningful business cycle analysis (such as 10 years rolling statistics or alike).
9
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 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 exchange 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 withdrawal —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 propensity 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
5
Such 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 into AEs which, in turn, were an important reason why long-term interest
rates were lower than expected in the early 2000s. See Bernanke (2005).
10
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 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
strengthens 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.
11
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 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
≶ g ± xσ
3
(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.
6
7
See Gurkaynak (2008) for a recent survey on econometric tests of asset price bubbles.
This procedure was implemented recently by IMF (2009) and Barajas et al. (2008).
12
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 boombust 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 boombust 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 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.
8
The value for x is significantly lower than the original value used by Bordo and Jeanne (2002). This
is due to the fact that our sample period includes the global financial crisis which clearly increased the
average standard deviation of house prices.
13
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 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
14
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.
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 private 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
9
This global liquidity index is defined as the sum of world official reserves (excluding gold) plus US
M0 deflated by the US CPI. See the next section of the paper for a discussion and a comparison with
other measures of global liquidity.
15
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 interesting, 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 (ECI i ) + γZi + δ(ECI i × Zi ),
where ∆Pi corresponds to the increase in real house price in episode i as discussed
previously, ECI i 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 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
16
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 inflows, 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
10
In our analysis we use the 13 different categories constructed by these authors, indexing countries
from 1 to 13 according to the degree of exchange rate flexibility, with higher values indicating more
flexibility.In our analysis we use the 13 different categories constructed by these authors, indexing
countries from 1 to 13 according to the degree of exchange rate flexibility, with higher values indicating
more flexibility.
17
to mitigate the impact of global 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 competitiveness 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.
11
See Landau (2013) for a recent analysis and policy discussion.
18
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 “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
12
Note that under the fed fund targeting procedure that the Fed has followed during our sample
period, money is endogenous.
13
Rey (2013) also focuses on the VIX as a measure of global liquidity and finds that the VIX index
is closely related to a global factor in international flows of risky assets, credit, and leverage. This is
consistent with the view that global liquidity captures global monetary conditions.
19
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, 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 countryspecific 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.
14
Note also that, given the relatively large size of our cross section of countries, individual IRFs will
have a limited impact on the panel estimate that we report below. This is an advantage of the estimation
approach taken.
20
5.2
5.2.1
The panel VAR Model
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
movements 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 (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
15
Theory suggests that house prices affect the broader economy primarily through their wealth effect
on consumption or a Tobin’s Q effect on residential investments. As we do not have data on residential
investments for emerging 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.
16
Note also that IMF WEO (2008) finds that the role of housing supply shocks is marginal in house
price dynamics.
17
Sims et al. (1990) show that if cointegration among the variables exists, the system’s dynamics can
be consistently estimated in a VAR in levels.
21
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-bycountry, 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 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.
18
The 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
performance of this estimator relative to a fixed effect estimator and instrumental variable estimator
for the dimensions of the panel that we have.
22
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
response to the global liquidity shock in a statistically significant manner, but the effect
is relatively 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 account
displays a slight improvement on impact and then deteriorates over time but its response
is statistically not different from zero.
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.
19
As all variables are in logs and the model is linear, we can divide all impulse responses by the size
of the shock to obtain implied elasticities.
23
The response of the typical emerging market economy to a global liquidity shock
is qualitatively 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 percent 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
gradually 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 experiencing 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 period previous to the
Great Recession. Nonetheless, the increase in global liquidity in the years following the
20
The results for the robustness checks conducted are in a separate Appendix not necessarily for
publication.
24
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.
25
7
Figures
Figure 1 Real house prices & Current account to GDP ratio
SPAIN
IRELAND
200
0
180
10
150
−5
147
3
100
−10
113
−3
−15
12
80
160
15
250
10
127
7
183
3
−2
117
−3
−10
86
50
50
00
03
06
09
00
SOUTH AFRICA
77
80
06
09
−10
12
HONG KONG
93
60
03
83
Real House Price Index (left ax.)
89
92
95
98
−10
01
Current Account / GDP (right ax.)
Figure 2 US M0 & Emerging economies reserves
8000
Billion US$
6000
4000
2000
0
2000
2003
2006
US M0
EMEs Reserves
26
2009
2012
Figure 3 Real house prices – Data map
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
ARG AUS AUT BEL BRA BGR CAN CHL CHN COL HRV CZE DNK EST FIN FRA DEU GRC HKG HUN ISL IND IDN IRL ISR ITA JPN KOR
#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
#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
#N/A
38.87
#N/A
38.44
#N/A
#N/A
43.17
#N/A
#N/A
#N/A
#N/A
#N/A
56.91
#N/A
62.99
42.09 132.36 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
24.48
#N/A
40.47 131.97 #N/A
#N/A
43.25
#N/A
41.59
#N/A
#N/A
51.62
#N/A
#N/A
#N/A
#N/A
#N/A
59.30
#N/A
70.58
44.18 132.55 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
24.52
#N/A
41.74 149.35 #N/A
#N/A
42.75
#N/A
40.84
#N/A
#N/A
53.03
#N/A
#N/A
#N/A
#N/A
#N/A
53.53
#N/A
68.42
44.54 131.11 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
25.21
#N/A
62.45 124.46 #N/A
#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
#N/A
40.55
#N/A
48.53
#N/A
#N/A
57.98
1.00
#N/A
#N/A
#N/A
#N/A
58.16
#N/A
56.65
47.52 124.62 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
26.36
#N/A
60.77 105.87 #N/A
#N/A
39.82
#N/A
53.68
#N/A
#N/A
54.72
1.00
#N/A
#N/A
#N/A
#N/A
60.44
#N/A
51.98
48.98 127.43 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
27.94
#N/A
57.40 104.15 #N/A
#N/A
38.44
#N/A
55.73
#N/A
#N/A
53.81
1.00
#N/A
#N/A
#N/A
#N/A
62.70
#N/A
50.12
49.51 134.82 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
33.68
#N/A
#N/A
40.80
#N/A
56.25
#N/A
#N/A
53.96
1.00
#N/A
#N/A
#N/A
#N/A
61.07
#N/A
50.11
51.92 141.32 #N/A
#N/A
#N/A
#N/A
#N/A
#N/A
36.35
#N/A
60.84 113.66 #N/A
#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
#N/A
43.51
#N/A
44.94
#N/A
#N/A
55.24
1.00
#N/A 138.56 #N/A
#N/A
44.73
#N/A
52.90
52.06 140.51 #N/A
64.63
#N/A
#N/A
#N/A
#N/A
32.82
#N/A
71.92 125.93 #N/A
#N/A
39.05
#N/A
40.50
#N/A
#N/A
46.56
1.00
#N/A 126.09 #N/A
#N/A
40.82
#N/A
58.75
49.44 134.79 #N/A
47.83
#N/A
#N/A
#N/A
#N/A
31.02
#N/A
72.83 131.11 #N/A
#N/A
39.62
#N/A
36.97
#N/A
#N/A
47.33
1.00
#N/A 112.72 #N/A
#N/A
49.28
#N/A
63.96
46.17 130.13 #N/A
37.49
#N/A
#N/A
#N/A
#N/A
31.86
#N/A
64.31 134.13 #N/A
#N/A
42.30
#N/A
35.88
#N/A
#N/A
45.71
1.00
#N/A 110.30 #N/A
#N/A
53.11
#N/A
64.62
44.52 124.90 #N/A
35.50
#N/A
#N/A
#N/A
#N/A
28.73
#N/A
57.80 134.86 #N/A
#N/A
42.99
#N/A
35.41
#N/A
#N/A
48.12
1.00
#N/A
99.37
#N/A
#N/A
61.07
#N/A
63.36
45.46 123.50 #N/A
41.33
#N/A
#N/A
#N/A
#N/A
28.41
#N/A
#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
#N/A
42.02
67.15
38.73
#N/A
#N/A
60.13
1.00
#N/A 101.19 #N/A
#N/A
53.91
#N/A
71.38
51.57 121.88 #N/A
49.95
#N/A
#N/A
#N/A
#N/A
27.89
#N/A
54.63 151.90 115.23
#N/A
51.89
75.36
41.87
#N/A
#N/A
69.62
#N/A 102.06 #N/A
#N/A
53.46
#N/A
96.68
54.79 122.38 #N/A
59.41
#N/A
#N/A
#N/A
#N/A
29.34
#N/A
57.96 157.63 122.94
#N/A
53.21
88.06
46.89
#N/A
#N/A
72.37
1.00
#N/A 108.81 #N/A
#N/A
48.83
#N/A
97.93
57.31 125.00 #N/A
63.34
#N/A
#N/A
#N/A
#N/A
31.79
#N/A
72.77 169.76 132.16
#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
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
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
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
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
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
#N/A
#N/A
#N/A 229.62 36.23 123.37 70.42 155.20 100.52
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
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
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
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
118.26 60.60 101.91 64.85
#N/A
37.68
63.64
1.00
70.37
92.06
65.57
#N/A 135.77 73.18 112.76 70.12 134.49 78.52
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
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
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
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
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
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
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
92.96 103.99 100.81 100.09 90.76
98.64
#N/A
77.41
99.30
76.86
76.59
47.17
68.48
99.75 108.34 100.97 92.81
34.19
78.44
76.29
95.85
56.55 121.39 70.88
52.44 135.62 #N/A
114.21 67.79
102.75 96.26 102.03 99.86 105.87 96.73
#N/A
93.69
62.91 107.02 #N/A
97.05
99.84
99.14
89.36 100.52 92.74
94.64
99.91 103.49 98.07
97.21
93.99 102.48 98.15
98.57
99.41
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
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
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
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 MYS MLT MEX MAR NLD NZL NOR PER PHL POL PRT RUS SRB SGP SVK SVN ZAF ESP SWE CHETWNTHA UKR GBR USA URY
#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
#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
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
34.19
38.63
39.67
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
64.05
24.30
52.87 113.44 #N/A
#N/A
#N/A
31.09
72.11
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
36.13
47.30
41.38
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
69.10
27.78
52.29 114.00 #N/A
#N/A
#N/A
35.55
74.62
#N/A
#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
#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
#N/A
#N/A
25.23
#N/A
#N/A
#N/A
#N/A
48.53
46.17
43.85
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
16.13
#N/A
#N/A
57.47
29.19
60.27
87.57
#N/A
#N/A
#N/A
25.29
73.42
#N/A
#N/A
#N/A
28.75
#N/A
#N/A
#N/A
#N/A
60.27
42.06
43.37
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
14.75
#N/A
#N/A
54.18
31.89
61.92
85.98
#N/A
#N/A
#N/A
24.27
78.60
#N/A
#N/A
#N/A
30.62
#N/A
#N/A
#N/A
#N/A
58.36
39.21
41.41
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
15.17
#N/A
#N/A
51.00
33.52
65.04
92.56
#N/A
#N/A
#N/A
27.60
81.82
#N/A
#N/A
#N/A
31.81
#N/A
#N/A
#N/A
#N/A
52.28
35.81
41.47
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
18.50
#N/A
#N/A
51.93
29.88
65.45
95.43
#N/A
#N/A
#N/A
30.87
81.66
#N/A
#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
#N/A
#N/A
31.16
#N/A
34.36
#N/A
#N/A
35.80
39.37
39.91
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
39.88
#N/A
#N/A
70.70
24.15
53.14 103.26 #N/A
#N/A
#N/A
27.71
73.49
#N/A
#N/A
#N/A
27.27
#N/A
34.69
#N/A
#N/A
32.60
41.88
38.96
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
42.09
#N/A
#N/A
70.55
23.21
49.27 101.11 #N/A
#N/A
#N/A
27.65
72.25
#N/A
#N/A
#N/A
25.41
#N/A
31.14
#N/A
#N/A
32.38
44.23
38.09
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
44.55
#N/A
#N/A
79.07
24.27
45.26 104.76 #N/A
#N/A
#N/A
29.46
73.04
#N/A
#N/A
#N/A
25.82
#N/A
30.70
#N/A
#N/A
31.43
45.66
44.46
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
38.37
#N/A
#N/A
66.56
23.57
43.75 105.18 #N/A
#N/A
#N/A
30.76
73.46
#N/A
#N/A
#N/A
26.25
#N/A
29.24
#N/A
#N/A
31.16
45.58
51.70
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
32.21
#N/A
#N/A
53.85
23.92
42.70 107.74 #N/A
#N/A
#N/A
31.88
74.98
#N/A
#N/A
#N/A
29.52
#N/A
33.83
#N/A
#N/A
33.03
41.57
59.70
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
32.45
#N/A
#N/A
44.35
27.99
45.11 114.65 #N/A
#N/A
#N/A
35.29
79.25
#N/A
#N/A
31.81
#N/A
30.41
#N/A
#N/A
34.49
46.21
57.34
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
36.87
#N/A
#N/A
45.39
36.97
49.18 120.60 #N/A
#N/A
#N/A
40.48
79.96
#N/A
#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
#N/A
#N/A
41.48
62.48
39.98
#N/A
#N/A
37.99
47.33
47.61
#N/A
#N/A
#N/A
95.05
#N/A
#N/A
42.75
#N/A
#N/A
45.40
49.13
60.43 147.28 #N/A
#N/A
#N/A
52.52
81.68
#N/A
#N/A
#N/A
45.45
63.13
41.32
#N/A
#N/A
36.53
46.47
42.09
#N/A
#N/A
#N/A
94.82
#N/A
#N/A
43.60
#N/A
#N/A
45.10
51.62
58.96 132.56 #N/A
#N/A
#N/A
47.50
77.82
#N/A
#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
#N/A
#N/A
51.26
81.13
44.68
#N/A
#N/A
39.76
45.09
36.73
#N/A
#N/A
#N/A 102.62 #N/A
#N/A
57.07
#N/A
#N/A
40.39
50.67
47.94 113.82 #N/A 132.03 #N/A
39.88
76.74
91.42
#N/A
#N/A
48.83
82.83
50.15
#N/A
#N/A
42.50
47.33
39.06
#N/A
#N/A
#N/A
97.28
#N/A
#N/A
75.65
#N/A
#N/A
39.25
49.09
44.19 106.10 #N/A 127.09 #N/A
39.60
76.82
89.78
#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
#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
#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
#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
#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
#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
25.27
53.89
83.75
91.35
#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
#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
#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
77.33
95.51
68.59
#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
#N/A
43.57
82.99
99.81 101.84 #N/A
#N/A
92.81
87.67
81.58
82.01
82.64
73.87
84.91
89.32
79.55
97.43
77.11 120.99 46.91
#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
98.07 101.01 107.93 97.88 101.39 98.14 103.63 98.77
79.27
90.56
94.24
74.14
95.88 104.34 103.67 94.91
98.89
96.66 120.27 95.51
98.95 115.69 98.21
126.50 87.61
47.39 100.13 48.55 103.99 72.50
81.26
64.39
99.46
81.74
99.01
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
95.56
99.20
28.83
#N/A
88.82
83.89 101.13 87.26
92.95 107.73 94.40
92.20 107.82 97.70 106.45 108.57 95.35
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
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
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
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
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
27
98.07 101.14 95.08 104.61 111.36 93.55
96.48 105.00
#N/A
Figure 4 Evolution of the average and the standard deviation of real house
price annual returns
(b) Moving Average − EMEs
10
5
5
Percent
Percent
(a) Moving Average − AEs
10
0
0
−5
−10
−5
95
00
05
−10
10
15
10
10
5
0
95
00
05
00
05
10
(d) Moving Std. Deviation − EMEs
15
Percent
Percent
(c) Moving Std. Deviation − AEs
95
5
0
10
95
00
05
10
Note. The moving averages and moving standard deviations are computed with a rolling
window of 5 years over the sample 1985.I–2012.IV. The solid lines graph the cross-country
average of the moving average and moving standard deviation of house price annual returns.
The shaded areas graph the cross-country two standard deviations confidence bands.
28
Figure 5 Cross-correlations of real house price annual returns
(a) Advanced Economies
Real GDP
Real Consumption
CPI
Labor Productivity
0.75
0.75
0.75
0.75
0.5
0.5
0.5
0.5
0.25
0.25
0.25
0.25
0
0
0
0
−0.25
−0.25
−0.25
−0.25
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
Real Equity Price
−4−3−2−1 0 +1+2+3+4
−0.5
Short−term Int. Rate
−4−3−2−1 0 +1+2+3+4
Real Eff. Exch. Rate
−0.5
Current Account / GDP
0.75
0.75
0.75
0.75
0.5
0.5
0.5
0.5
0.25
0.25
0.25
0.25
0
0
0
0
−0.25
−0.25
−0.25
−0.25
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
(b) Emerging Economies
Real GDP
Real Consumption
CPI
Labor Productivity
0.75
0.75
0.75
0.75
0.5
0.5
0.5
0.5
0.25
0.25
0.25
0.25
0
0
0
0
−0.25
−0.25
−0.25
−0.25
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
Real Equity Price
−4−3−2−1 0 +1+2+3+4
−0.5
Short−term Int. Rate
−4−3−2−1 0 +1+2+3+4
Real Eff. Exch. Rate
−0.5
Current Account / GDP
0.75
0.75
0.75
0.75
0.5
0.5
0.5
0.5
0.25
0.25
0.25
0.25
0
0
0
0
−0.25
−0.25
−0.25
−0.25
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
−4−3−2−1 0 +1+2+3+4
−0.5
−4−3−2−1 0 +1+2+3+4
Note. The correlation coefficients are computed over the sample 1985.I–2012.IV. Each dot
graphs the cross-country average of the correlation coefficient. The shaded areas graph the
cross-country two standard deviations confidence bands.
29
Figure 6 Moving correlations of real house price annual returns
(a) Advanced Economies
Real GDP
Real Consumption
0.8
0.6
CPI
Labor Productivity
0.8
0.2
0.6
0.6
0
0.4
0.4
−0.2
0.2
0.2
−0.4
0
0.4
0.2
0
−0.2
99
03
07
11
0
Real Equity Price
0.6
0.4
0.4
0.2
0.2
0
0
99
03
07
11
03
07
11
−0.6
Short−term Int. Rate
0.6
−0.2
99
−0.2
99
03
07
11
99
03
07
11
Real Eff. Exch. Rate
−0.2
0
0.2
−0.1
0.1
−0.2
0
−0.3
−0.1
−0.4
99
03
07
11
03
07
11
Current Account / GDP
0.3
−0.2
99
−0.5
99
03
07
11
(b) Emerging Economies
Real GDP
Real Consumption
0.8
0.8
0.6
0.6
0.4
0.4
CPI
Labor Productivity
0.4
0.8
0.2
0.6
0
0.4
−0.2
0.2
0
0.2
99
03
07
11
0
Real Equity Price
0.2
−0.4
99
03
07
11
−0.6
Short−term Int. Rate
99
03
07
11
Real Eff. Exch. Rate
0
0.6
0.6
0.2
0.4
0.4
0.4
0
0.2
0.2
0.2
−0.2
0
0
0
−0.4
99
03
07
11
−0.2
99
03
07
11
−0.2
99
03
07
11
03
07
11
Current Account / GDP
0.6
−0.2
99
−0.6
99
03
07
11
Note. The moving correlations are computed with a rolling window of 10 years over the
sample 1985.I–2012.IV. The solid lines graph the cross-country average of the moving correlation of house price annual returns. The shaded areas graph the cross-country two standard
deviations confidence bands.
30
Figure 7 Real house prices boom-bust episodes – Event study
(a) Real House Prices
(b) Output Gap
(Average increase during episodes, percentage)
(Average increase during episodes, percentage)
80.0
6.0
60.0
4.5
40.0
3.0
20.0
1.5
0.0
0.0
Advanced Economies
Emerging Market Economies
Advanced Economies
Emerging Market Economies
(c) Current Account
(d) Real Exchange Rate
(Average increase during episodes, percentage)
(Average increase during episodes, percentage)
0.0
16.0
-1.0
12.0
-2.0
8.0
-3.0
4.0
-4.0
0.0
Advanced Economies
Emerging Market Economies
Advanced Economies
Emerging Market Economies
(e) Capital Inflows
(f) Global Liquidity
(Average increase during episodes, percentage)
(Average increase during episodes, percentage)
4.0
30.0
3.0
22.5
2.0
15.0
1.0
7.5
0.0
0.0
Advanced Economies
Emerging Market Economies
Advanced Economies
Emerging Market Economies
(g) VIX Index
(h) US Real Interest Rate
(Average increase during episodes, percentage)
(Average increase during episodes, percentage)
1.0
0.5
0.0
0.0
-1.0
-0.5
-2.0
-1.0
-3.0
-1.5
-4.0
-2.0
Advanced Economies
Emerging Market Economies
Advanced Economies
Note. Note here.
31
Emerging Market Economies
32
03
06
09
97
Priv. liquidity (left ax.)
94
03
VIX Index (right ax.)
00
06
09
0
12
Note. Official liquidity (Index, 2008=100) is the sum of world international reserves (excluding gold) plus the US M0 both measured
in US dollar, then deflated by the US CPI. Private liquidity (Index, 2008=100) is the external positions of BIS reporting banks for
all countries vis--vis the non-bank sector (measured in millions of US dollars), then deflated by the US CPI.
Off. liquidity (left ax.)
91
13
0.0
88
25
0.1
85
38
0.2
−0.1
50
82
00
0.3
79
97
(b) Official and private global liquidity (log change) & VIX Index (level)
94
10
12
91
0
88
20
50
85
30
100
82
40
150
79
50
200
(a) Official and private global liquidity (level) & VIX Index (level)
Figure 8 Global liquidity measures & VIX index
Figure 9 Impulse response function – Global liquidity shock
(a) Advanced Economies
PRIV. LIQ.
CONS
3.5
RHP
0.3
0.6
3
0.4
Percent Deviation
Percent Deviation
2
1.5
1
Percent Deviation
0.2
2.5
0.1
0
0.5
0.2
0
−0.2
−0.4
−0.1
−0.6
0
−0.5
−0.2
0
10
20
Quarters
30
40
−0.8
0
10
30
40
0
6
0.5
5
0.4
4
3
2
0.1
0
−0.1
−1
20
Quarters
30
40
30
40
30
40
30
40
5
0
−5
−10
−0.2
10
40
10
0.2
0
30
CA
0.3
1
20
Quarters
15
Percent Deviation
0.6
0
10
REER
7
Percent Deviation
Percent Deviation
IRS
20
Quarters
−15
0
10
20
Quarters
30
40
0
10
20
Quarters
(b) Emerging Economies
PRIV. LIQ.
CONS
3.5
RHP
0.6
2
0.4
1.5
Percent Deviation
Percent Deviation
2
1.5
1
Percent Deviation
3
2.5
0.2
0
1
0.5
0.5
−0.2
0
0
−0.5
−0.4
0
10
20
Quarters
30
40
−0.5
0
10
30
40
4
0.4
2
0
−2
−4
10
0.2
0
30
40
0
−10
−20
−30
−0.4
20
Quarters
20
Quarters
CA
−0.2
10
10
20
Percent Deviation
0.6
0
0
REER
6
Percent Deviation
Percent Deviation
IRS
20
Quarters
−40
0
10
20
Quarters
33
30
40
0
10
20
Quarters
Figure 10 Forecast error variance decmoposition – Global liquidity shock
(b) EMEs − PRIV. LIQ
100
90
90
80
80
70
70
Percent Explained
Percent Explained
(a) AEs − PRIV. LIQ
100
60
50
40
60
50
40
30
30
20
20
10
10
0
0
5
10
15
20
25
Quarters
30
35
40
0
0
5
10
15
20
25
Quarters
30
35
40
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) displays the FEVD of PRIV. LIQ. to a PRIV. LIQ. shock in the US model.
34
8
Tables
Table 1 Summary statistics of the annual growth rates of real house prices, real GDP,
and real consumption in advanced economies and emerging economies
Real
House Price
Group
Mean
Median
Max
Min
St. Dev.
Auto Corr.
Skew.
Kurt.
Real
GDP
Real
Consumption
AEs
EMEs
AEs
EMEs
AEs
EMEs
2.0%
2.1%
18.3%
-12.5%
6.4%
0.92
0.10
3.20
1.2%
1.5%
27.5%
-34.5%
12.5%
0.86
-0.44
4.34
2.2%
2.5%
7.0%
-5.8%
2.3%
0.83
-1.00
4.91
3.8%
5.0%
13.3%
-13.3%
5.1%
0.87
-1.12
6.15
2.3%
2.4%
7.5%
-3.9%
2.2%
0.81
-0.31
3.88
4.0%
4.7%
16.7%
-16.4%
5.9%
0.79
-0.67
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
Group
Probability
of Boom
into Bust
Probability of
Boom-Bust
per year
Probability
of Boom
per year
Duration of
Boom-Bust
(years)
Duration
of Boom
(years)
AEs
EMEs
ALL
0.442
0.450
0.444
0.022
0.020
0.021
0.051
0.044
0.048
10.1
8.9
9.5
5.5
5.6
5.6
Note. The probability and the duration are computed as averages across countries. AEs are advanced
economies, EMEs are emerging economies, and ALL refers to all countries in the data set. The
boom-bust cycles are computed for each country using real house prices, equity prices and real
effective exchange rates on the largest available sample.
35
36
0.11
60.00
2.30∗
-0.38
(-1.90)∗
-0.55
(-0.51)
4.59
(2.53)∗∗
(4)
0.14
66.00
9.25∗∗∗
0.58
(3.04)∗∗∗
(5)
0.22
66.00
4.17∗∗
-0.70
(-2.21)∗∗
0.88
(2.87)∗∗∗
(6)
0.66
-0.44
1.01
(2.13)∗∗
(8)
Note. All regressions are estimated using a constant, t-test in parenthesis. Significance levels at 1%, 5%, and 10% is denoted by ()∗∗∗ , ()∗∗ , ()∗ ,
respectively.
0.28
62.00
5.89∗∗∗
-0.01
(-1.95)∗
0.02
-0.14
1.21
(3.77)∗∗∗
(7)
0.16
66.00
3.11∗∗
0.19
58.00
1.66
-0.05
(-1.73)∗
-0.09
(-0.73)
4.05
(1.91)∗
(3)
R2
Number of observations
F test
0.16
60.00
3.73∗∗
-5.27
(-2.73)∗∗∗
4.23
(2.47)∗∗
(2)
-0.06
(-1.10)
0.06
60.00
5.51∗∗
2.26
(2.35)∗∗
(1)
Exchange rate flexibility ×
Global liquidity
Financial market depth ×
Global liquidity
Dummy AEs ×
Global liquidity
Exchange rate flexibility ×
Capital inflows
Financial market depth ×
Capital inflows
Dummy AEs ×
Capital inflows
Exchange rate flexibility
Financial market depth
Dummy AEs
Global liquidity
Capital inflows
Explanatory variable
Dependent variable: change in real house price during boom
Table 3 Real house price determinants in boom episodes
Table 4 Correlation between global liquidity measures
Off. Liquidity (level)
Priv. Liquidity (level)
Priv. Liquidity (level)
VIX index (level)
Off. Liquidity (level)
VIX index (level)
0.92
0.99
-0.12
-0.05
-0.30
0.00
0.01
-0.28
-0.41
Off. Liquidity (log diff.)
Priv. Liquidity (log diff.)
Priv. Liquidity (log diff.)
VIX index (level)
Off. Liquidity (log diff.)
VIX index (level)
0.29
0.38
0.43
-0.18
-0.13
0.12
-0.06
-0.23
0.32
Full Sample
Pre-Crisis
Post-Crisis
Full Sample
Pre-Crisis
Post-Crisis
Note. Note here.
37
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40
A
A.1
Appendix. Data Sources
House Prices
The OECD Nominal House Price (Subject: HP.Index. Measure: Index) was collected
for the following countries: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, 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:
Bulgaria, 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 (Morande and Soto (1992)), Taiwan (Synyi).
All house price indices, together with their sources, are displayed in Table A.1.
Seasonal adjustment was performed using Eviews applying the National Bureau’s X12
program on the change of the logged house price series, using the additive option.
The nominal seasonally adjusted indices were then deflated with country-specific CP I
indices (described below).
A.2
Other variables
To construct the database we relied on three main sources: the OECD Analytical
Database, the IMF International Financial Statistics (IFS) Database and Bloomberg.
The variables used are real gross domestic product (RGDP ), consumer price index
(CP I), real consumption (CON S), labor productivity of employed persons (LP ROD),
real equity prices (EQ), short-term interest rates (IRS), real effective exchange rates
(REER), and current account balance as a percent of GDP (CA). Data were collected
in June2013 to cover the period from 1970.I to 2012.IV, unless otherwise 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.
Millions of US dollars, volume estimates, fixed PPPs, OECD reference year, annual levels, seasonally adjusted) 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, United Kingdom, United States.
The IFS Real GDP (Subject: 99BVRzf. Measure: Index GDP VOL., 2005 =
100, seasonally adjusted) was collected for South Africa. The IFS Real GDP (Subject: 99BVPzf. Measure: Index GDP VOL., 2005 = 100, not seasonally adjusted) was
41
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
from the National Bureau of Statistics (NBS) of China. As no institution publishes a
quarterly real GDP Index for China, it has to be derived by a nominal GDP series. The
National Bureau of Statistics (NBS) of China releases a quarterly nominal GDP not
seasonally adjusted. we constructed a quarterly real GDP index as follows. First, we
seasonally adjusted (with the procedure described above) the nominal GDP from NBS.
Then, we used the following formula
1
log(RGDP1 ) = log GDP
for t = 1
CP I1
CP It
GDPt
for t ≥ 2
log(RGDPt ) = log(RGDPt−1 ) + log GDPt−1 − log CP It−1
where CPI is defined in the next subsection. The series displays noisy features in the
first part of the 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
House Price Index inception date, quarterly series were interpolated linearly from the
corresponding IFS annual series (the interpolation procedure is the same used for house
prices). Interpolated data were used in the following periods: Bulgaria from 1990.I to
2001.IV, Colombia from 1988.I to 1993.IV, Indonesia from 1994.I to 1996.IV, Singapore
from 1975.I to 1984.II, Thailand from 1991.I to 1992.IV.
Seasonal adjustment was performed using Eviews applying the National Bureau’s
X12 program 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, Austria, 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 Hungary and 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 longer coverage.
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 program on the change of the log(CP I) using the additive option.
Real Private Consumption
In order to create the 2012 Real Private Consumption Vintage we relied on OECD
Analytical Database, IFS Database and Bloomberg.
42
For those countries for which OECD data is available, the OECD Real Private
Consumption (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, Estonia, Finland, France, Germany, Greece,
Hungary, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal,
Spain, Sweden, Switzerland, United Kingdom, and United States. Seasonal adjustment
was performed using Eviews applying the National Bureau’s X12 program on the change
of the log(CON S) using the additive option.
For the remaining countries, Argentina, Bulgaria, China, Colombia, Croatia, Hong
Kong, Indonesia, Lithuania, Malaysia, Peru, Philippines, South Africa, Thailand, as
no real index was available, IFS Nominal Private Consumption (Subject: code 96F..zf.
Measure: Billions of National 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, we use Bloomberg Real Private Consumption (Subject: SIFCPTE Index. Measure: GDP Private Consumption Expenditure, Constant 2000 Prices, Millions of national currency, not seasonally adjusted) after adjusting the series for seasonality.
Labor Productivity Of Employed Persons
The Labor Productivity Index is constructed with data from HAVER and form the
Conference Board Total Economy Database from Groningen University.21
The HAVER quarterly Labor Productivity Index (Subject: Productivity: Output
per Employed Person (SA, 2000=100). Measure: Index) is collected for the following
countries: Australia, 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) are collected from the Conference Board Total Economy Database from 1970
to 2012. Quarterly series 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 persons employed, using the real output described above.
Equity Price Index
The OECD Equity Price Index (Subject: Share Prices. Measure: Index 2005 =
100) was collected for the following countries: Australia, Austria, Belgium, Canada,
Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Indonesia, Ireland, Italy, Japan, Korea, Netherlands, New Zealand, Norway, Portugal,
Spain, Sweden, Switzerland, United Kingdom, United States.
As IFS data has a longer coverage, Belgium, Denmark, Indonesia, Korea, Norway,
and Spain have 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 from 1973.I to 1977.IV, a quarterly series was interpolated linearly
21
See the Conference Board Total Economy Database here.
43
from the corresponding IFS annual series.
IFS Equity Price Index has been used as well for Hong Kong, Colombia, Malaysia,
Philippines, Singapore, and Thailand. The series for Malaysia, Singapore, and Thailand
have been extended backward with Dees et al. (2007) data set, which has a longer
coverage for these countries.
Short-term Interest Rate
The OECD Short-term interest rate (Subject: Short-term interest rates. Measure:
Per cent per 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.
Where the OECD does not have complete coverage, the changes in short-term interest rates from IFS were used to extend the series backward. The following IFS rates
(Measure: Per cent per annum) were used from 1979.I to the date in brackets: Discount
Rate (Subject: IFS 60zf series) for New Zealand, Norway, Spain, and Switzerland;
Money Market Rate (Subject: IFS 60Bzf series) for Italy; Deposit Rate (Subject: IFS
60Lzf series) for Ireland, and Korea; and Treasury Bill Rate (Subject: IFS 60Czf series)
for Greece.
For the countries for which OECD data was not available, the following IFS shortterm interest rates were used: Discount Rate for Colombia, Peru, Croatia, and Hong
Kong; Money Market 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
the following 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 IFS real effective exchange rate is derived from the
nominal effective exchange rate index, adjusted for 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 effective exchange rate of the Kroon. Measure: Percentage change on the previous
quarter) and for Lithuania (Subject: Real Effective Exchange Rate Indices of the Litas.
Measure: Index).
Finally, we relied on Bloomberg for the remaining countries and, when possible, to
extend backward the previous series with the JP Morgan Real Effective Exchange Rate:
Argentina (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).
22
Note that the real effective exchange rate is constructed such that a decline of the index is a
depreciation (or a gain in competitiveness).
44
Current Account to GDP Ratio
In order to create the 2012 Current Account to GDP Ratio Vintage we relied on
OECD Analytical 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
download the IFS Current Account balance (Subject: ALDZF. Measure: Millions of
USD) and IFS nominal 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 in current USD. Finally, we compute the ratio
between the Current Account balance and GDP in current USD.
45
Figure A.1 Real house price indices - Advanced economies
Australia
Austria
Belgium
Canada
150
40
150
40
150
20
150
30
100
20
100
20
100
7
100
13
50
0
50
0
50
−7
50
−3
0
70
79
88
97
06
−20
15
0
70
79
Denmark
88
97
06
−20
15
0
70
79
Finland
88
97
06
−20
15
0
70
79
France
88
97
06
−20
15
Germany
150
40
150
40
150
15
150
10
100
20
100
13
100
7
100
3
50
0
50
−13
50
−2
50
−3
0
70
79
88
97
06
−20
15
0
70
79
Greece
88
97
06
−40
15
0
70
79
Iceland
88
97
06
−10
15
0
70
79
88
Ireland
97
06
−10
15
Italy
150
15
150
30
150
50
150
60
100
5
100
10
100
17
100
27
50
−5
50
−10
50
−17
50
−7
0
70
79
88
97
06
−15
15
0
70
79
Japan
200
40
150
25
100
10
50
−5
0
70
79
88
97
88
97
06
−30
15
0
70
79
Luxembourg
06
−20
15
88
97
06
−50
15
0
70
79
Netherlands
88
97
06
−40
15
New Zealand
150
20
150
50
150
30
100
7
100
17
100
13
50
−7
50
−17
50
−3
0
70
79
Norway
88
97
06
−20
15
0
70
79
Portugal
88
97
06
−50
15
0
70
79
Spain
88
97
06
−20
15
Sweden
150
40
150
10
150
40
150
20
100
20
100
3
100
20
100
0
50
0
50
−3
50
0
50
0
70
79
88
97
06
−20
15
0
70
79
Switzerland
88
97
06
−10
15
0
70
79
United Kingdom
88
97
06
−20
15
United States
150
20
150
40
150
10
100
7
100
20
100
0
50
−7
50
0
50
0
70
79
88
97
06
−20
15
0
70
79
88
97
06
Index (left axis)
−20
15
0
70
−10
79
88
Yearly Growth Rate (right axis)
46
97
06
−20
15
0
70
−20
79
88
97
06
−40
15
Figure A.2 Real house price indices - Emerging economies
Argentina
Brazil
150
50
100
0
Bulgaria
200
20
150
13
100
50
79
88
97
06
−100
15
50
100
0
5
−50
50
50
0
70
Chile
150
0
70
China
88
97
06
100
150
50
100
0
−50
50
−3
79
200
−10
15
0
70
79
Colombia
88
97
06
−100
15
0
70
−50
79
Croatia
88
97
06
−100
15
Czech Republic
150
10
150
20
150
40
150
30
100
3
100
7
100
20
100
17
50
−3
50
−7
50
0
50
3
0
70
79
88
97
06
−10
15
0
70
79
Estonia
88
97
06
−20
15
200
40
150
50
150
15
100
0
100
−10
50
−35
50
−50
88
97
06
−100
15
0
70
79
Indonesia
20
150
0
100
−20
50
−40
79
88
97
97
06
−60
15
97
06
−20
15
0
70
79
06
−60
15
88
97
06
−10
15
India
150
50
150
20
100
17
100
7
50
−17
50
−7
0
70
79
Israel
200
0
70
88
88
Hungary
100
79
79
Hong Kong
200
0
70
0
70
88
97
06
−50
15
0
70
79
Korea
88
97
06
−20
15
Latvia
150
20
150
20
150
60
100
7
100
7
100
20
50
−7
50
−7
50
−20
0
70
79
Lithuania
88
97
06
−20
15
0
70
79
Malaysia
88
97
06
−20
15
0
70
79
Malta
88
97
06
−60
15
Mexico
150
50
150
20
150
30
150
10
100
17
100
10
100
13
100
5
50
−17
50
0
50
−3
50
0
0
70
79
88
97
06
−50
15
0
70
79
Morocco
88
97
06
0
70
79
Peru
150
4
100
88
97
06
−20
15
200
50
150
15
150
25
100
0
100
0
50
88
97
06
−4
15
0
70
−15
79
88
97
79
06
50
−30
15
47
0
70
88
97
06
−5
15
Poland
30
−1
79
0
70
Philippines
200
1
50
0
70
−10
15
150
60
100
33
50
7
−25
79
88
97
06
−50
15
0
70
79
88
97
06
−20
15
Figure A.2 Real House Price Indices - Emerging Economies (Cont’d)
Russia
Serbia
Singapore
Slovakia
150
60
150
40
150
100
150
40
100
27
100
7
100
50
100
20
50
−7
50
50
0
50
0
0
70
79
88
97
06
−40
15
0
70
−27
79
Slovenia
88
97
06
−60
15
0
70
79
South Africa
88
97
06
−50
15
0
70
79
Taiwan
88
97
06
−20
15
Thailand
150
20
150
50
150
20
150
20
100
7
100
17
100
7
100
7
50
−7
50
−17
50
−7
50
−7
0
70
79
88
97
06
−20
15
0
70
79
Ukraine
88
97
06
−50
15
100
150
30
100
50
100
10
50
0
50
−10
79
88
97
79
88
Uruguay
150
0
70
0
70
06
−50
15
0
70
79
88
97
06
Index (left axis)
−30
15
Yearly Growth Rate (right axis)
48
97
06
−20
15
0
70
79
88
97
06
−20
15
Figure A.3 Boom-bust in real house prices – Advanced economies
Australia
Austria
Belgium
Canada
150
150
150
150
100
100
100
100
50
50
50
50
0
1970
1980
1991
2001
2012
0
1970
1980
Denmark
1991
2001
0
1970
2012
1980
Finland
1991
2001
2012
0
1970
France
150
150
150
100
100
100
100
50
50
50
50
1980
1991
2001
2012
0
1970
1980
Greece
1991
2001
0
1970
2012
1980
Iceland
1991
2001
2012
0
1970
150
150
100
100
100
100
50
50
50
50
1991
2001
2012
0
1970
Japan
1980
1991
2001
0
1970
2012
Luxembourg
200
1980
1991
2001
2012
0
1970
Netherlands
150
2001
2012
1991
2001
2012
2001
2012
Italy
150
1980
1980
Ireland
150
0
1970
1991
Germany
150
0
1970
1980
1980
1991
New Zealand
100
150
80
150
100
100
60
100
40
50
50
0
1970
50
20
1980
1991
2001
2012
0
1970
1980
Norway
1991
2001
0
1970
2012
1980
Portugal
1991
2001
2012
0
1970
Spain
150
150
150
100
100
100
100
50
50
50
50
1980
1991
2001
2012
0
1970
Switzerland
1980
1991
2001
0
1970
2012
United Kingdom
150
150
100
100
100
50
50
50
1980
1991
2001
2012
0
1970
1980
1991
2001
1991
2001
2012
United States
150
0
1970
1980
0
1970
2012
49
1980
1991
1991
2001
2012
2001
2012
Sweden
150
0
1970
1980
2001
2012
0
1970
1980
1991
Figure A.4 Boom-bust in real house prices – Emerging economies
Argentina
Brazil
150
Bulgaria
200
Chile
100
250
80
200
60
150
40
100
20
50
150
100
100
50
50
0
1970
1980
1991
2001
2012
0
1970
China
1980
1991
2001
0
1970
2012
1980
Colombia
150
2012
100
80
80
60
60
40
40
50
1980
1991
2001
2012
0
1970
Estonia
1980
1991
2001
0
1970
2012
1991
2001
2012
20
1980
Hong Kong
200
1980
Czech Republic
20
0
1970
0
1970
100
100
50
2001
Croatia
150
100
1991
1991
2001
2012
0
1970
1980
Hungary
1991
2001
2012
2001
2012
2001
2012
2001
2012
2001
2012
India
150
150
150
100
100
100
50
50
50
150
100
50
0
1970
1980
1991
2001
2012
0
1970
1980
Indonesia
1991
2001
0
1970
2012
1980
Israel
250
1991
2001
2012
0
1970
1980
Korea
1991
Latvia
150
150
150
100
100
100
50
50
50
200
150
100
50
0
1970
1980
1991
2001
2012
0
1970
Lithuania
1980
1991
2001
0
1970
2012
1980
Malaysia
100
1991
2001
2012
0
1970
1980
Malta
1991
Mexico
150
150
150
100
100
100
50
50
50
80
60
40
20
0
1970
1980
1991
2001
2012
0
1970
1980
Morocco
1991
2001
0
1970
2012
Peru
150
1980
1991
2001
2012
1980
Philippines
200
200
150
150
100
100
50
50
1991
Poland
150
100
100
50
0
1970
0
1970
50
1980
1991
2001
2012
0
1970
1980
1991
2001
0
1970
2012
50
1980
1991
2001
2012
0
1970
1980
1991
Figure A.4 Boom-bust in real house prices – Emerging economies (Cont’d)
Russia
Serbia
100
Singapore
150
150
100
100
50
50
Slovakia
100
80
80
60
40
60
40
20
0
1970
20
1980
1991
2001
2012
0
1970
Slovenia
1980
1991
2001
0
1970
2012
1980
South Africa
100
1991
2001
2012
0
1970
Taiwan
1980
1991
2001
2012
2001
2012
Thailand
150
150
150
100
100
100
50
50
50
80
60
40
20
0
1970
1980
1991
2001
2012
0
1970
1980
Ukraine
150
100
100
50
50
1980
1991
2001
2012
2001
2012
0
1970
Uruguay
150
0
1970
1991
2001
2012
0
1970
1980
1991
51
1980
1991
2001
2012
0
1970
1980
1991
52
Note. Note here.
Argentina
Australia
Austria
Belgium
Brazil
Bulgaria
Canada
Chile
China
Colombia
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hong Kong
Hungary
Iceland
India
Indonesia
Ireland
Israel
Italy
Japan
Korea
Latvia
Country
Descr.
Apartments in Buenos Aires City - average price
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
21340 - Residential Real Estate Collateral Value Index
RESIDENTIAL PROPERTY PR., EXIST.FLATS (BIG CITIES),PER SQ..M,Q-ALL NSA
Residential property prices, new and existing dwellings
Morande and Soto, 1992
RESID. PROPERTY PRICES, (BEIJING), PER SQ.M, M-ALL NSA (Average existing and new)
HPI - For three major colombian cities - Quarterly data
AVERAGE PRICES OF NEW DWELLINGS SOLD PER 1 m2
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
RESID. PROPERTY PRICES, ALL DWELLINGS, PER SQ.M.,Q-ALL
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
RESIDENTIAL PROPERTY PR.,ALL DWELLINGS, PER SQUARE M., M-ALL NSA
RESID. PROPERTY PRICES, ALL DWELLINGS, PER SQ.M.,Q-ALL NSA
RESIDENTIAL PROP.PR.,ALL DWELLINGS(GR. REYKJAVK),PER SQ.M,M-ALL NSA
RESIDEX - Median residential property price across 20 cities
RESIDENTIAL PROPERTY PRICES, NEW HOUSES (BIG CITIES), PER DWELLING,NSA
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
RESIDENTIAL PROPERTY PRICES, EXISTING FLATS, PER SQUARE M., Q-ALL, NSA
Table A.1 House prices description
ARKLEMS
OECD
OECD
OECD
CBB
BIS
OECD
Paper
BIS
NCB
CBS
OECD
OECD
BIS
OECD
OECD
OECD
OECD
BIS
BIS
BIS
NHB
BIS
OECD
OECD
OECD
OECD
OECD
BIS
Source
53
Note. Note here.
Lithuania
Luxembourg
Malaysia
Malta
Mexico
Morocco
Netherlands
New Zealand
Norway
Peru
Philippines
Poland
Portugal
Russia
Serbia
Singapore
Slovakia
Slovenia
South Africa
Spain
Sweden
Switzerland
Taiwan
Thailand
Ukraine
United Kingdom
United States
Uruguay
Country
Descr.
RESIDENTIAL PROPERTY PR., ALL DWELLINGS, PER SQUARE M., Q-ALL, NSA
RESIDENTIAL PROPERTY PRICES, ALL DWELLINGS, Y-ALL & Res. Prop. Prices
RESIDENTIAL PROP. PRICES, ALL DWELLINGS, PER SQ. M., Q-ALL Y-ALL NSA
PROPERTY PRICES INDEX (BASED ON ADVERTISED PRICES)
RESIDENTIAL PROPERTY PRICES, ALL DWELLINGS, PER DWELLING, Q-ALL NSA
RESIDENTIAL PROPERTY PRICES, EXIST.DWELLINGS, PER SQUARE M., Q-ALL NSA
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
La Molina, Miraflores, San Borja, San Isidro y Surco
Prime 3BR Condominium Price - Makati CBD
RESIDENTIAL PROPERTY PRICES,(BIG CITIES), PER SQ.M.,Q-ALL NSA
Residential property prices, new and existing dwellings
RESIDENTIAL PROPERTY PRICES,EXISTING DWELLINGS,PER SQUARE M,Q-ALL NSA
Prices of dwellings of new construction
price index, private residential, Singapore
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
RESIDENTIAL PROPERTY PR.,ALL MIDDLE-SEGMENT HOUSES,PER DWELL.,M-ALL SA
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Sinyi House Price Index, National
RESID. PROPERTY PR., ALL DETACHED HOUSES(GR.BANGKOK),PER SQ.M,M-ALL SA
Average Price of Apartments, Kiev (US $ per sq. m.)
Residential property prices, new and existing dwellings
Residential property prices, new and existing dwellings
Indice Gral. de Precios m2 de Laspeyres
Table A.1 House prices description (Cont’d)
BIS
OECD
BIS
NCB
BIS
BIS
OECD
OECD
OECD
NCB
Colliers
BIS
OECD
BIS
NSO
NSO
OECD
OECD
BIS
OECD
OECD
OECD
Synyi
BIS
Blagovest
OECD
OECD
INE
Source