KGRI Working Papers
No.9House price and credit cycles: effects of global liquidity and risk
perception

Version1.0
August 2017

Satoshi Tobe1

Keio University Global Research Institute© Copyright 2017 Satoshi Tobe
Graduate School of Economics, Keio University
1

Corresponding author. Graduate School of Economics, Keio University, 2-15-45, Mita,
Minato-ku, Tokyo, 108–8345, Japan. E-mail address: stobe@keio.jp

House price and credit cycles: effects of global liquidity and risk
perception
Satoshi Tobe 1
KGRI project code: S17-06
This version: August 15, 2017

Abstract
This paper explores the determinants of house prices and bank credit. An
empirical analysis of panel data of 22 developed countries during 1990–2015 reveals cross-border bank inflows and risk perception in global
markets as the key drivers of house prices and bank credit. Moreover, it
indicates that the effect of this risk perception on house prices is non-linear,depending on the leverage of local financial systems: it is stronger in
economies with higher leveraged financial sectors. These results suggestthat local financial cycles are exposed to spillover effects of U.S. monetary
policy, and that local banking sectors play key roles in transmitting theeffects to local house price cycles.

Keywords: House Price, Bank Credit, International capital flow, Global Liquidity, Risk
perception
JEL classification: F3

1

Corresponding author. Graduate School of Economics, Keio University, 2-15-45, Mita,

Minato-ku, Tokyo, 108–8345, Japan. E-mail address: stobe@keio.jp.

1. Introduction
This paper empirically analyzes the dynamics of house prices and bank credit. In thepast two decades or more, many developed countries have experienced several boom–
bust cycles in these financial variables. Particularly, the cycles in the late 2000s are
remarkable with regards to the persistence and size of downturns before and after thegreat financial crisis, respectively (Cesa-Bianchi et al., 2015; Hume and Sentance,2009). This recent experience reminds both academics and policy makers that the
dynamics of these financial variables have non-trivial effects on our real economies.
Gourinchas and Obstfeld (2012), Jordà et al. (2015, 2016a) and Schularick and Taylor(2012) reveal that these dynamics contain valuable information on financial stability
and macroeconomic performance after financial crises, even when based on longer
historical perspectives.
In tandem with the cycles, international

capital transactions—especiallycross-border bank flows (lending)—have increased dramatically against a backdrop offinancial integration. Especially in the 2000s, (1) declining federal funds rates and (2)adoption of a common European currency, both of which reduces cross-border banking
costs, facilitated the upward trend (Bruno and Shin, 2015a, 2015b; Hale and Obstfeld,
2016; McGuire and Tarashev, 2007; Shin, 2012). Some literature often calls
cross-border bank inflows “global liquidity.” It has a highly pro-cyclical nature and canamplify local business and financial cycles through tightening or loosing financial
conditions, sometimes impairing financial stability. Moreover, they play a key role in
the international transmission of U.S. monetary policy (Bruno and Shin, 2015b;
McCauley et al., 2015; Rey, 2013, 2016). In this sense, global liquidity increases theconnectedness of local financial markets.
1

Recently, Miranda-Agrippino and Rey (2012, 2015), Rey (2013), and Passari andRey (2015) argue that global financial cycles determine fluctuations in asset prices,credit growth, and international capital transactions, which could explain the global
financial environment described above well. They find cyclical co-movements of thesevariables across a broad sample of countries, and reveal their close ties with a global
common factor, such as risk perception in global financial markets as measured by the
CBOE VIX index. Related literature (e.g., Hirata et al., 2012; Miranda-Agrippino andRey, 2015; Cesa-Bianchi et al., 2017) shows that 20–40% of variation in global asset
prices or credit is explained by a common global factor. Accordingly, it is essential toconsider the effects of global liquidity and global common factors, as well as
country-specific factors, to understand the dynamics of local financial cycles in recent
decades.
This paper explores the interaction among house prices, bank credit, cross-borderbank inflows, and risk perception in global markets. In addition to the effect ofcross-border bank inflows on house prices and bank credit, risk perception in global
markets can be associated with these local financial variables—the easing of risk
perception (the increased risk appetite) in tranquil time produces expansionary effects
on asset prices and operations of financial intermediaries. Moreover, countries may
respond non-linearly to shocks in this risk perception, according to its time orcountry-specific nature. This study performs instrumental variable (IV) regressions byusing quarterly panel data for 22 developed countries between 1990 and 2015. It enables
the exploration of the effects of both country-specific and global common factors onhouse prices and bank credit. Moreover, the method reduces endogeneity concerns, sothat the effects of exogenous (supply-push) change in cross-border bank inflows on
2

house prices and bank credit can be estimated.
Empirical analysis reveals that increases in cross-border bank inflows and an easingof risk perception in global markets induce appreciation in house prices and expansionof bank credit. Moreover, it indicates that the effects of this risk perception on house
prices are non-linear, depending on the leverage of local banking sectors: they arestronger in economies with higher leveraged financial systems. These results indicatethat global liquidity and risk perception in global markets play key roles in explaining
the cycles of house prices and bank credit in developed countries; interestingly, theeffects of this risk perception can vary non-linearly with situations of local financial
systems.
This paper makes three contributions. First, it provides a more complete picture of
key factors inducing boom–bust cycles over the past two decades or more. For example,
the vector autoregression (VAR) analysis of Rey (2013) focuses on a set of variables
similar to that included here, but it has little concern for cross-country variations indomestic factors, which are expected to affect house prices, bank credit, andcross-border bank inflows. In addition, although Avdjiev et al. (2017), Bruno and Shin(2015a), Cerutti et al. (2015), and Cerutti et al. (2017) provide evidence that countries’
capital inflows are tightly associated with global common factors (e.g., VIX or leverageof U.S. financial intermediaries), the ultimate effects of inflows on local economies are
not explored.1
Moreover, Cesa-Bianchi et al.’s (2015) VAR analysis of the causal effects ofcross-border bank inflows on house prices in both developed and emerging economies

1

Calvo et al. (1993, 1996) are the first literature that find U.S. monetary and financial
conditions play dominant roles in explaining capital inflows to emerging economies.
3

does not focus on factors determining inflows, or on the effects of the risk perception in
global markets on house prices. Empirical studies on global imbalances (e.g., Bernanke,
2010; Sà et al., 2014; Sà and Wieladek, 2015) also explore the interaction between (net)inflows and house prices, but have the same shortcomings as Cesa-Bianchi et al. (2015).
This research contributes by analyzing global financial cycles in more detail.
The second contribution is a reduction of the endogeneity concerns. The potential
two-way causality between cross-border bank inflows (more generally, capital inflows)
and local financial variables was insufficiently dealt with by Bernanke (2010) and
Miranda-Agrippino and Rey (2013) in their exploration of the interaction between
capital inflows and house prices. This study performs IV regressions to reduce theendogeneity concern. Moreover, the method enables the joint estimation of thedeterminants of cross-border bank inflows and house prices or bank credit,
strengthening the first contribution.
Third, a novel finding is made on the non-linearity of the effects of risk perception
in global markets measured by VIX. Previous literature provides little systematic
evidence of non-linearity or heterogeneity in the responsiveness of local financial
variables to VIX, focusing on the degree of exchange rate flexibility (Passari and Rey,2015) or financial openness (Miranda-Agrippino and Rey, 2013; Rey, 2013) of local
economies. In contrast, further analysis reveals a higher sensitivity of house prices to
VIX in economies that experience faster bank credit growth compared to their real
economies, or that have larger deviations of the bank credit to GDP ratio from its trend.
This suggests that house price fluctuations are more strongly linked to the U.S. financial
and monetary conditions in economies with larger leverage, because VIX is closely tiedwith federal funds rate shocks, as revealed in Bekaert et al. (2013) and Bruno and Shin
4

(2015b). Therefore, the finding contributes to existing literature (e.g., McCauley et al.,2015; Rey, 2013, 2016; Passari and Rey, 2015) by capturing non-linearity in an
empirical model of international spillover of U.S. monetary policy.
The reminder of this paper is organized as follows: Section 2 describes data andmethodology, Section 3 presents the estimation results and a discussion thereof, andSection 4 concludes.
2. Data and methodology
2.1 Key variables
This section describes data and methodology. First, visual characterizations areprovided of key variables that are used in an empirical analysis performed later. A
cross-country sample average of key variables is plotted, although the analysis that
follows treats country-specific variables separately. These characterizations aid in thesimplistic understanding of a whole sample trend of key variables. The sample
comprises quarterly data spanning the first quarter of 1990 to the third quarter of 2015
for the 22 developed countries: Australia, Austria, Belgium, Canada, Denmark, Finland,
France, Germany, Greece, Ireland, Italy, Japan, Korea, Netherlands, New Zealand,
Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States.
Figure 1 plots the cross-country average of quarterly growth in house prices (solidline), with upper and lower bounds of a one standard deviation band (shaded area) and a
one-year moving average (dashed line). The figure shows a persistent and steady growthof house prices before the great financial crisis. The one standard deviation bandsuggests that house prices appreciated strongly in many developed countries, especiallyin the 2000s. After the prolonged appreciation, house prices declined sharply during thegreat financial crisis and still showed anemic growth in recent years, compared to the5pre-crisis period.
Similarly, Figure 2 plots the growth of bank credit. According to this figure, bankcredit cycles are short-lived and highly volatile compared to house price cycles.
However, their growth rate in the 2000s is relatively strong compared to that in the otherperiod. It suggests that house price appreciation in the 2000s coincides with an increasein bank credit in average developed countries.
[Insert Figures 1 and 2 here]
To reveal the factors that contributed to the house price and bank credit cycles,
described in Figure 1 and 2, this paper focuses on the roles of cross-border banking
activity and risk perception in global markets. Figure 3 plots the quarterly growth ofcross-border bank claims of all Bank for International Settlements (BIS) reporting
countries on a country’s banking sector, similar to Figures 1 and 2. This series is used asa standard measure for cross-border bank inflows in previous literature (e.g., Bruno andShin, 2015a, 2015b). Such inflows are often called “global liquidity,” because they areinterpreted as the cross-border supply of bank loans. Their dynamics expect to affecteach country’s house prices and bank credit. For example, increases in the inflows canplace upward pressure on house prices and bank credit by (1) lowering interest rates, (2)forming expected appreciation of house prices, and (3) directly or indirectly relaxing
credit constraints by increasing the asset prices used as collateral for bank lending.
As shown in Figure 3, cross-border bank inflows indeed show similar dynamics tothe bank credit described in Figure 2, because the two variables are on both sides ofbanks’ balance sheet. Moreover, especially when we focus on the one-year moving
average, the inflows also seem to be associated with the house price fluctuations
described in Figure 1. To analyze the interaction between the inflows and house prices6

or bank credit formally, cross-border bank inflows are included in baseline empiricalmodels as an endogenous right hand side variable.
[Insert Figure 3 here]
Figure 4 plots the CBOE VIX index (dashed black line, inverted scale). VIX may
contain the following information: (1) uncertainty in global financial markets or risk
appetite of global investors (banks) and (2) realized volatility of risky assets. Thus,
recent literature uses VIX as a valuable proxy for key financial factors. For example,
Forbes and Warnock (2012), Passari and Rey (2015), and Rey (2013) regard VIX as aproxy for risk perception in global markets or the global financial cycle. Moreover,
Adrian and Shin (2014) show that VIX is closely associated with the Value-at-Risk(VaR) constraint. Considering these aspects, the dynamics of VIX are expected to be
associated with the change in house prices and bank credit in each country.
According to Figure 4, VIX shows a cyclical behavior in the sampled period. Most
interestingly, VIX started to decline from 2002, reaching a historically low level in the
mid-2000s. Under this financially eased environment, global investors (banks) increased
risk taking, which may have led to upward pressure on house prices and bank credit.
The following empirical models contain the lagged level and current growth of VIX.2[Insert Figure 4 here]
In reality, the responsiveness of house prices and bank credit to VIX can vary(non-linearly) with situations in each country. To analyze this phenomenon, this study
also focuses on the leverage of banking sectors in each country (local leverage). It
regards the bank credit to GDP ratio as a local leverage measure (proxy). For example,
2

As discussed in Baskaya et al. (2017) and Cerutti et al. (2017), VIX is basically an exogenous
variable for many countries. This is because VIX is originally an index of U.S. stock option
markets that are unlikely to be affected by the dynamics in house prices and bank credit in theother countries.
7

assuming that local banks can raise their funds only by deposit under some capitalconstraints (e.g., VaR constraint), the dynamics of the bank credit to GDP ratio will bestable. This is because bank credit cannot grow faster than the growth of deposits, whichis expected to be approximately equal to GDP growth. However, in reality, banks can
extend their funds through non-core liability, such as short-term wholesale funding(Adrian and Shin, 2010; Brunnermeier et al., 2012; Hahm et al., 2013). Thus, local bankcredit can change regardless of GDP growth. Considering these aspects, the bank credit
to GDP ratio is expected to be a good proxy for local leverage, because the ratio tracks
bank credit that is supported by non-core liability (i.e., leveraged credit).
Some previous literature also regards the ratio as leverage of local banking sectors.
For example, Gourinchas and Obstfeld (2012) and Jordà et al. (2016b) treat the bankcredit to GDP ratio as (internal) leverage. Moreover, Gourinchas and Obstfeld (2012)
and Jordà et al. (2016a) show that the ratio works well as an early warning indicator(EWI), informing of excess leverage or risk taking by local agents. In general,
economies with higher leverage may be sensitive to external shocks in financial
conditions, such as VIX.
Figure 4 also plots a cross-country average of the change in bank credit to GDP ratio.
Local leverage growth has increased since the mid-1990s, and remained at a relatively
high level until occurrence of the great financial crisis. In particular, it recorded
historically high growth rates in the late 2000s. These dynamics suggest that the local
banking sectors in average developed countries aggressively managed their balance
sheets before the recent crisis, after which local leverage growth became nearly zero. To
test whether the responsiveness of house prices and bank credit to VIX is non-linear,depending on situations of local leverage, some empirical models include current and8lagged interaction terms of change in VIX and local leverage growth.
2.2 Other controls
The empirical models discussed in this paper also include country-specific factors tocontrol the effects of local financial conditions and fundamentals (domestic or demand
pull factors). The following factors are included: local stock market volatility, change ininterest rates (short- or long-term rates), GDP growth rate, inflation rate, and change inreal effective exchange rates. All these controls are lagged by one quarter. Appendix
provides further details and data sources on the variables used in the empirical analysis.
2.3 Identification strategy
One of the objectives of this study is to explore the interrelations between
cross-border bank inflows and house prices or bank credit. However, there may be a
two-way causality between the variables, which raises endogeneity concerns when we
estimate the relationships with standard ordinary least squares (OLS) regressions. To
reduce this concern, this study performs IV regressions with house prices or bank credit
as dependent variable and cross-border bank inflows as an endogenous right hand side
variable. This method requires the use of instruments that affect an endogenous variable
but are not related to error terms (or variations in dependent variables). The baselinemodels contain one of the two instruments described below.3
The first instrument is U.S. broker-dealer leverage (lagged level and current growth),
which is defined as the ratio of asset over equity of U.S. broker-dealers. Bruno and Shin(2015a) find that this leverage is the key driver of cross-border bank inflows. Cerutti etal. (2017) take a similar perspective, and reveal that leverage of U.S. financial
3

The identification strategy described here is similar to Tobe (2017), who investigates therelationship between total credit and total capital inflows. This study applies this strategy toinvestigate the determinants of house prices and bank credit, focusing on cross-border bankinflows (i.e., global liquidity).
9intermediaries, including U.S. broker-dealer leverage, performs well in explaining
cross-border bank inflows to each country.
Bruno and Shin’s (2015a) theoretical model partly explains the background to thetight relationship between inflows and leverages. They built a “double-decker model ofglobal banking” that has two types of banks, global and regional, located in a financial
core country (e.g., U.S.) and other countries, respectively. Global banks financethemselves using short-term wholesale funding as well as channel liquidity to regionalbanks through their international banking systems. Then, regional banks provide local
loans using the liquidity from global banks. Given this structure, when the risk
perception in global markets (measured by VIX) or short-term (dollar) interest rate islow, global banks increase their leverage by accumulating short-term funding, leading to
an expansion in cross-border inflows (lending) to regional banks. This can ultimately
increase regional bank credit, and is expected to lead to an appreciation in house prices.
In an empirical exploration of this model, Bruno and Shin (2015a) use U.S.
broker-dealer leverage as a proxy for the leverage of global banks. This is because (1)the broker-dealer sector actively manages their balance sheet through short-term
wholesale funding operations, and (2) data availability on European broker-dealers thatmight also work as global banks is limited. 4 According to their analysis, U.S.
broker-dealer leverage explains a sizable part of the variation in cross-border bankinflows to developed and emerging economies. 5 If results are interpreted more

4

Cerutti et al. (2017) provide evidence that the leverage of large (commercial) banks in the UK
and Euro area also play a key role in explaining cross-border bank inflows. However, robustnessof the results relies on the sampled period.
5
The double-decker model of global banking explains the background of the cross-border bankinflows from global to regional banks. However, data on the inflows used in Bruno and Shin(2015a) include a broader range of the inflows than their model intend to explain, such as
global-bank-to-global-bankflows,
regional-bank-to-regional-bankflows,
and10

empirically, this leverage works as a time dummy that represents the risk-taking
capacity of global banks, or the ease of short-term dollar funding.
Although U.S. broker-dealer leverage is a valuable measure to track the dynamics ofcross-border bank inflows, this leverage may be irrelevant to dependent variables, atleast directly. As for house prices, leverage of U.S. broker-dealers (or risk taking
capacity of U.S. financial intermediaries) may not affect these in a particular country,
because the broker-dealers are not direct participants in housing markets in each foreign(non-US) economy in many cases. Similarly, the leverage may also be irrelevant to bankcredit in a particular country, because the ultimate suppliers (or decision makers) of
bank credit are regional banks, as described in the double-decker model. Cesa-Bianchi
et al. (2015) also use U.S. broker-dealer leverage as one of the instruments in their VAR
analysis to explore the causal effects of inflows on house prices when they identify
exogenous (supply-push) shocks to cross-border bank inflows. Thus, U.S. broker-dealer
leverage is expected to be a valid instrument to identify supply-push effects ofcross-border bank inflows on local financial variables.
Nevertheless, there is some concern that this leverage may not be irrelevant tovariations in house prices and bank credit in the U.S. This paper reduces this concern byalso using an alternative instrument.6 This second instrument is global flow, defined as
the change in the cross-sectional sample sum of cross-border bank claims of all BIS
reporting countries on a given country’s banking sector, excluding the claims on the
country under consideration. This method follows Blanchard et al. (2015), whoinvestigate the effects of gross capital inflows on GDP growth and the change in private
regional-bank-to-global-bank flows.
6
Another method to deal with this problem is to exclude U.S. data from the sample. The author
confirms that results hold true even when the U.S. data are excluded.
11credit in emerging economies. Considering the findings in Rey (2013), global flow canbe correlated with cross-border bank inflows to each country, because gross capital
inflows—especially cross-border bank inflows—co-move at global level. However,global flow is clearly irrelevant to the variations within the country under consideration,
because data on the country are excluded from the flow. Thus, global flow is also
expected to satisfy the assumptions on valid instruments.
Empirically, the effects of these instruments on endogenous variables may varyacross countries. To deal with this possibility, for half the specifications, the models
contain interaction terms of the instruments with country-specific dummies as thealternative sets of instruments. These specifications may be less restrictive on thesensitivity of the endogenous variables to the instruments. However, the specifications
include a large number of instruments, although they have only one endogenous
variable (i.e., cross-border bank inflows). As a result, they do not tend to pass the test on
over-identification compared to the other specifications, as shown in the next section.
Nevertheless, results are similar across models, so over-identification does not cause asignificant problem in this study.

3. Estimation results and discussion
3.1 Determinants of house prices
First, results on the determinants of house prices are presented. Table 1 presents
results from panel regression models. Models 1A and 1C (Models 1B and 1D) use
global leverage (global flow) as an instrument. Columns (1) and (2) show the results ofthe first- and second-stage regressions of Model 1, respectively.
In the first stage, global leverage (lagged level and current growth) is positively
12correlated with cross-border bank inflows, consistent with Bruno and Shin (2015a). As
for controls, the coefficient for GDP growth is also positively significant, implying
pro-cyclicality of cross-border bank inflows. In this model, VIX is positively correlatedwith the inflows. This is inconsistent with the findings for the global financial cycle, but
the result is not robust as shown in the other models. A possible explanation is that somecountries in the sample are regarded as safe havens. Other controls are insignificant at
this stage, but the F-statistic is sufficiently high (larger than 10). Thus, there is little
concern about weak instruments.7
In the second stage, cross-border bank inflows are positively correlated with house
prices at the 1% significance level, indicating an inflow contribution toward house priceappreciation. In this model, a 1% increase in cross-border bank inflows raises quarterly
house price growth by 0.14%. Moreover, coefficients of current growth and the lagged
level of VIX are negatively correlated with house price, indicating global market risk
perception as another key driver of house price dynamics. Long-term interest rates and
GDP growth show expected signs, although the interest rate coefficient is insignificant.
Results are similar when global flow instead of global leverage is included as an
instrument (Model 1B). Column (3) shows global flow as positively correlated withcross-border bank inflows, indicating that capital inflows co-move across the sampledcountries. The GDP growth rate coefficient remains positively significant. The interest
rate coefficient becomes negative and significant in this model. This result implies
pro-cyclicality of cross-border bank inflows, but is inconsistent with intuition on
uncovered interest parity. If models use total capital inflows instead of cross-border

7

Formally, all the F-statistics shown in this paper exceed the critical value on weak instrumentsthat is presented in Stock and Yogo (2005).
13

bank inflows, the interest rate coefficient becomes positive (but remains insignificant),
as shown in robustness checks. Moreover, the F-statistic remains sufficiently high. In
the second stage, column (4) indicates that the cross-border bank inflow coefficientremains positively significant, although it becomes relatively small compared to Model
1A. In this model, the coefficient for the lagged level of VIX is negatively significant.
The principal results are maintained when the models use the interaction terms of
the instrument with the country-specific dummies, described in the last section as analternative set of instruments (Models 1C and 1D).8 A possible concern is that, in two
out of three over-identified models (Model 1C and 1D), the Sargan–Hansen test rejects
the null hypothesis of the instruments being uncorrelated with the error term. However,
the main results are similar to those of just-identified models and the models that pass
the test. Thus, estimation bias is of little concern, at least with this sample.
[Insert Tables 1 and 2 here]
To explore the sensitivity of house prices to risk perception in global markets,measured by VIX, the models in Table 2 contain the interaction terms of the growth of
VIX, with growth of bank credit to GDP ratio as an additional explanatory variable. The
principal results remain unchanged: coefficients for cross-border bank inflows are
positively significant, and those for VIX are largely negatively significant in the second
stage; however, coefficients for current growth of VIX are marginally insignificant inmodels 2B and 2D. These robustness concerns are cleared, as shown in a later section.
Moreover, coefficients for the lagged interaction terms on house price are negatively
significant in all specifications. This indicates that the effects of VIX on house prices
can vary non-linearly with the conditions of local financial systems: they are stronger in
8

The results of the first stage are omitted because of space constraints.
14

countries with faster expansion of bank credit relative to their real economies. In otherwords, it suggests that house price fluctuations are more strongly linked to U.S.
financial and monetary conditions in economies with a higher local leverage growth,because VIX is closely tied with federal fund rate shocks (see Bekaert et al. (2013) andBruno and Shin (2015b)). Thus, house prices in economies with a larger growth of local
leverage might be more strongly exposed to the spillover effects of U.S. monetary
policy. Furthermore, the result is in line with Jordà et al. (2016a), who suggest that local
boom–bust cycles of credit creation are closely synchronized with the global cycle inmore leveraged countries (measured by bank credit to GDP ratio).
3.2 Determinants of bank credit
Next, the results on the determinants of bank credit are presented. Table 3
summarizes results from panel regression models, similar to Tables 1 and 2. One
noticeable difference is that bank credit models include changes in short-term instead of
long-term interest rates.9 Models 3A and 3C (Models 3B and 3D) use global leverage(global flow) as an instrument. Columns (1) and (2) show the results of the first andsecond stage regressions of Model 3A, respectively.
In the first stage, global leverage (lagged level and current growth) is positively
correlated with cross-border bank inflows at the 1% significance level, consistent withresults in Table 1 and 2. VIX is again positively correlated with the inflows, although
the result is not robust (as shown in the other models). Moreover, the GDP growthcoefficient is positively significant, while that of short-term interest rates is negatively
9

The main results hold even when the models include long-term interest rates. However,coefficients for the interest rates become positive, although they are insignificant. Long-term
interest rates may work as a proxy for the return of lending for banks in empirical models, atleast in this sample. That is why long-term interest rates are associated with higher bank credit.
For better understanding and readability, specifications on bank credit include short-term insteadof long-term interest rates throughout this paper.
15

significant, indicating pro-cyclicality of cross-border bank inflows. The exchange rate
coefficient is positive, as revealed in Bruno and Shin (2015a), but insignificant.
In the second stage, cross-border bank inflows are positively correlated with bankcredit at the 1% significance level. This indicates that the inflows contribute towardcredit creation. In this model, a 1% increase in the inflows induces a 0.62% higher
quarterly growth of bank credit. Moreover, VIX (lagged level and current growth) is
negatively correlated with bank credit. This result indicates that risk perception in
global markets is also a key driver of credit growth. In contrast, the coefficients ofcountry-specific factors are insignificant or have unexpected signs, except for the
short-term interest rate, which implies that credit growth is not strongly supported by acountry’s domestic conditions. This is consistent with the fact that boom–bust cycles ofcredit creation coincide with stable GDP growth and low inflation rates (called “great
moderation”). Moreover, the Sargan–Hansen test cannot reject the null hypothesis of the
instruments being uncorrelated with the error term in all specifications, indicating that
instruments empirically satisfy the assumption of validity.
[Insert Tables 3 and 4 here]
These results almost hold true when a model uses global flow as an instrument(Model 3B). As shown in column (3), global flow is positively correlated withcross-border bank inflows. Moreover, coefficients of VIX lose significance, whereas
those of local stock market volatility become negative and significant in the first stage,
implying that country-specific risk factors affect the inflows. Similarly, column (4)shows that the signs of the coefficients of cross-border bank inflows and VIX remainunchanged and are significant, whereas country-specific factors still have mainly
ambiguous effects on bank credit. In addition, these results hardly change when the16models use the interaction terms described in the previous section as an alternative setof instruments (Models 3C and 3D).10
To explore the sensitivity of bank credit to VIX, the models in Table 4 contain theinteraction terms of growth of the VIX with growth of bank credit to GDP ratio as anadditional explanatory variable. The principal results remain unchanged: coefficients forcross-border bank inflows are positively significant, and those for the VIX are
negatively significant in the second stage. Moreover, coefficients for the additional
interaction terms on bank credit are negatively significant in models 4A and 4C.
However, this result on the interaction terms is not robust, as shown in robustness
checks. Thus, in contrast to house prices, there is little evidence that the effects of VIX
on bank credit are non-linear, depending on the conditions of local financial systems.
3.3 Robustness checks and extensions
The results presented in the previous sections are robust to various changes. First,
we perform a different estimation method. Table 5 provides the results on thedeterminants of house prices when the models are estimated by the standard OLS
method with lagged right hand side variables. According to columns (1) to (4), results
are similar to those of Table 1. Lagged cross-border bank inflows are positively
correlated with house prices, which implies that the inflows lead to subsequent
appreciation in house prices. In addition, lagged growth of VIX is negatively significant.
These results are also consistent with the results by IV regressions. As for controls, theGDP growth rate coefficients remain positive and significant. Coefficients for local
stock market volatility become negatively significant, implying that country-specificrisk factors affect house price cycles. Moreover, as shown in columns (5) to (8), the10

The results of the first stage are omitted because of space constraints.
17

interaction terms of VIX with growth of local leverage are negatively significant in all
specifications. These results strengthen the results presented in Table 2.
[Insert Table 5 here]
Similarly, Table 6 presents the results when the determinants of bank credit are
estimated by the OLS method. Columns (1) to (4) show the robustness of the key
results: coefficients for cross-border bank inflows are positively significant, and thosefor growth of VIX are negatively significant. Moreover, coefficients for interest rates
remain negatively significant, and those for GDP growth rate become positively
significant. In contrast, as can be seen in columns (5) to (8), the interaction terms of thegrowth of VIX with that of local leverage are insignificant in all specifications. These
results indicate that there is little evidence that the sensitivity of bank credit to VIX ishigher in economies with larger local leverage growth, highlighting the difference
between the results on house prices and those on bank credit.
[Insert Table 6 here]
Second, results hold true when the models use alternative local leverage measures.
The models in Table 7 use the deviation of bank credit to the GDP ratio from its trend(calculated by the one-year moving average) instead of the growth of the ratio. This
alternative measure enables a focus on the level instead of the growth of local leverage.
Table 7 shows the robustness of the key results: cross-border bank inflows are positively
correlated with house prices and bank credit, VIX is negatively correlated with the twovariables, and coefficients for the interaction terms of VIX with local leverage measure
are negatively significant only when house prices are used as dependent variable.
[Insert Table 7 here]
Third, the results hold even when the models use total capital inflows instead of18cross-border bank inflows. A focus on the latter is valuable for understanding the effects
of global liquidity, but might miss the total effects of capital inflows on house prices.
Specifications using total capital inflows can reduce this concern.1112 Tables 8 and 9
show that this extension does not change the key results. Coefficients for gross capital
inflows are positively significant in all specifications, corroborating the global
imbalance hypothesis that states (net) capital inflows induce appreciation in house
prices (e.g., Bernanke, 2010; Sà et al., 2014; Sà and Wieladek, 2015). Results on VIX
and its interaction terms are consistent with the results shown in other tables.
[Insert Tables 8 and 9 here]
Finally, results hold when the models use winsorized data (available on request).
Winsorizing reduces the undesirable effects of outliers. In this sample, the procedure
mainly excludes the sample during the great financial crisis of the late 2000s. This
reduces concern that these results rely on relationships in a severe crisis period.

4. Conclusion
This paper explored the determinants of house prices and bank credit, focusing on
cross-border banking activity and risk perception in global markets. Empirical analysis
comprised IV regressions with quarterly panel data of 22 developed countries over two
decades. The results indicate that the key drivers of house prices and bank credit arecross-border bank inflows (i.e., global liquidity), which are closely tied with U.S.
Tobe (2017) explores the effects of total capital inflows on total credit, which provides
additional robustness checks.
12 Data on capital inflows are divided by external liability to avoid the undesirable
effects of outliers that undertake large cross-border capital transactions relative to thescale of their real economy (e.g., Ireland and Switzerland). Results hold true even when
inflows are normalized relative to GDP. However, F-statistics in the first-stage
regressions in that case often decrease below 10, which raises concerns with regard to
weak instruments. Trimming outliers by winsorizing cannot fully resolve this problem.
11

19

broker-dealer leverage, and VIX-measured risk perception in global markets. Moreinterestingly, they also indicate that linkage between house prices and VIX are
non-linear, depending on leverage of local financial systems.
The results have some implications for international transmission channels of U.S.
monetary policy. Previous literature, such as Bekaert et al. (2013) and Bruno and Shin(2015b) revealed that federal fund rate shocks could explain a large part of the variationin U.S. broker-dealer leverage and VIX. Considering these facts, house prices and bankcredit are directly or indirectly exposed to cross-border spillover effects of U.S.
monetary policy through cross-border banking activity and risk perception in global
markets. In other words, country-specific factors, including monetary policy, cannot
fully anchor or smooth local financial cycles.
Thus, the results also suggest that house prices in economies with higher leveraged
financial sectors seem to be more sensitive to shocks in U.S. monetary policy. This
non-linearity in responsiveness to the shocks or spillover effects implies the
productiveness of macro-prudential policy. For example, some counter-cyclicalregulations on banks’ balance sheets could partly insulate local house price cycles fromthe spillover effects of U.S. monetary policy, as well as smooth or restrain excess
fluctuations in local banking activities.
Finally, further research could include an extension of the sample to emerging
economies, for which cross-border bank lending is a key funding source. Moreover,policy makers in these economies often underline the undesirable effects of global
liquidity and the spillovers of monetary policy in financially developed core countries.
In addition, extending the sample would enable us to compare the determinants of house
prices and bank credit between the two country groups. Thus, exploring the effects of20

cross-border bank inflows and VIX on house prices and bank credit in emerging
economies will be another interesting topic. This would contribute to a more detailed
understanding of the global financial cycle.

Acknowledgments
The author appreciate the helpful comments of Kazuhito Ikeo, Kentaro Iwatsubo, Yasuo
Maeda, Se-Jin Min, Masaya Sakuragawa, Kwanho Shin, Shumpei Takemori, LeranWang, and participants at the International Trade and Finance Association 27th
conference, The Korea Money & Finance Association Annual Conference, and Annual
Meeting of Japan Society of Monetary Economics. Financial support from Keio
Economic Society is greatly acknowledged.

Appendix. Variables and Data Sources
ΔHouse: Residential property price index (log difference). Source: BIS.
ΔCredit: Total bank credit to the private non-financial sector (log difference). Source:
BIS.
Bank_Flow: Cross-border bank claims of all BIS reporting countries on the banking
sector in a given country (log difference). Source: BIS.
Inflow: Gross capital inflow (divided by external liability). Source: IMF.
Global_Leverage: U.S. broker-dealer leverage defined as the ratio of asset over equity(logged). Source: FRB.
ΔGlobal_Leverage: U.S. broker-dealer leverage defined as the ratio of asset overequity (log difference). Source: FRB.
Global_Bank: Sample sum of cross-border bank claims of all BIS reporting countries
21

on the banking sector, excluding claims on the country under consideration (logdifference). Source: BIS.
Global_Flow: Sample sum of gross inflow, excluding the inflow to the country underconsideration (divided by sample sum of external liability, excluding the country underconsideration). Source: IFS.
VIX: CBOE VIX index of implied volatility of S&P index options (logged). Source:
FRED.
ΔVIX: CBOE VIX index of implied volatility of S&P index options (log difference).
Source: FRED.
Stock_Vol: 12-month standard deviation of return of local stock price index (logged).
Source: FRED.
ΔLong_Rate: Change in interest rate on a 10-year government bond. Source: OECD.
ΔShort_Rate: Change in interest rate on a three-month government bond. Source:OECD.
ΔGDP: GDP growth rate. Source: OECD.
Inflation: Inflation rate. Source: IFS.
ΔREER: Real effective exchange rate (log difference). Source: BIS.
ΔCredit/GDP: Ratio of total bank credit to the private nonfinancial sector to GDP (logdifference). Source: BIS.
CreditDev: Deviation of the bank credit to GDP ratio from its trend (calculated using afour-quarter moving average). Source: BIS.

References
Adrian, T., Shin, H.S., 2010. Liquidity and Leverage. J. Financ Intermed 19, 418–437.
22

Adrian, T., Shin, H.S., 2014. Procyclical Leverage and Value-at-Risk. Rev.Financ. Stud.
27(2), 373–403.
Avdjiev, S., Hardy, B., Kalemli-Ozcan, S., 2017. Gross capital inflows to banks,
corporates, and sovereigns. NBER Working Paper No 23116.
Baskaya, Y. S., Di Giovanni,J., Kalemli-Ozcan, S., Ulu, M.F., 2017. International
spillovers and local credit cycles. NBER Working Paper No 23149.
Bekaert, G., Hoerova, M., Lo Duca, M., 2013. Risk, uncertainty and monetary policy. J.
Monetary Econ. 60, 771–788.
Bernanke, B. S., 2010. Monetary policy and housing bubble. Remarks at the Annual
Meeting of the American Economic Association, Atlanta, Georgia, January 3,
2010.
Blanchard, O., Ostry, J.D., Ghosh, A.R., Chamon, M., 2015. Are capital inflows
expansionary or contractionary? Theory, policy implications, and some evidence.
International Monetary Fund Working Paper Series 15–17.
Brunnermeier, M.K., De Gregorio, J., Lane, P., Rey, H., Shin, H.S., 2012. Banks andcross-border capital flows: policy challenges and regulatory responses. Committee
on International Economic Policy and Reform.
Bruno, V., Shin, H.S., 2015a. Cross-border banking and global liquidity. Rev. Econ.
Studies 82, 535–564.
Bruno, V., Shin, H.S., 2015b. Capital flows and the risk-taking channel of monetary
policy. J. Monetary Econ., 71, 119–132.
Calvo, G., Leiderman, L., Reinhart, C.M., 1993. Capital inflows and real exchange rate
appreciation in Latin America. Staff Pap. Int Monet. Fund 40(1), 108–151.
Calvo, G., Leiderman, L., Reinhart, C.M., 1996. Inflows of capital to developing
23

countries in the 1990s. J. Econ. Perspect. 10(2), 123–139.
Cerutti, E., Claessens, S., Puy, D., 2015. Push factors and capital flows to emerging
markets: why knowing your lender matters more than fundamentals. IMF Working
Paper No 127.
Cerutti, E., Claessens, S., Ratnovski, L., 2017. Global liquidity and cross-border bankflows. Econ. Policy 32(89), 81–125.
Cesa-Bianchi, A., Cespedes, L.F., Rebucci, A., 2015. Global liquidity, house prices, andthe macroeconomy: evidence from advanced and emerging economies. J. Money
Credit Bank. 47(2), 301–335.
Cesa-Bianchi, A., Martin, F.E., Thwaites, G., 2017. Foreign Booms, Domestic Busts:
the global dimension of banking crises. Bank of England Staff Working Paper, No.
644.
Forbes, K.J., Warnock, F.E., 2012. Capital flow waves: surges, stops, flight andretrenchment. J. Int. Econ. 88(2), 235–251.
Gourinchas, P., Obstfeld, M., 2012. Stories of the twentieth century for the twenty-first.
Am. Econ. J-Macroecon. 4(1), 226–265.
Hahm, J., Shin, H.S., Shin, K., 2013. Non-Core Bank liabilities and Financial
Vulnerability. J. Money Credit Bank. 45(S1), 3–36.
Hale, G. Obstfeld, M., 2016. The Euro and the geography of international debt flows. J.
Eur. Econ. Assoc. 14(1), 115–144.
Hirata, H., Kose, M.A., Otrok, C., Terrones, M.E., 2012. Global house pricefluctuations: synchronization and determinants. NBER International Seminar on
Macroeconomics 2012.
Hume, M., Sentence, A., 2009. The global credit boom: challenges for macroeconomics
24and policy. J. Int. Money Finance 28, 1426–1461.
Jordà, Ò., Schularick, M., Taylor, A.M., 2015. Leveraged bubbles. J. Mon. Econ. 76,
S1–S20.
Jordà, Ò., Schularick, M., Taylor, A.M., 2016a. The great mortgaging: housing finance,crisis and business cycles. Econ Policy 31(85), 107–152.
Jordà, Ò., Schularick, M., Taylor, A.M., 2016b. Macrofinancial history and the new
business cycle facts. NBER Working Paper No 22743.
Marinda-Agrippino, S., Rey, H., 2012. World asset markets and global liquidity. Paper
presented at the Frankfurt ECB BIS Conference, Mimeo, London Business School.
Marinda-Agrippino, S., Rey, H., 2013. Funding flows and credit in carry tradeeconomics. Paper presented at the conference on “Liquidity and Funding Markets,”
Federal Reserve of Australia, August 19–20.
Marinda-Agrippino, S., Rey, H., 2015. World asset markets and the global financial
cycle. NBER Working Paper No 21722.
McCauley, R., McGuire, P., Sushko, V., 2015. Global Dollar credit: links to U.S.
monetary policy and leverage. Econ. Policy. 30(82), 188–229.
McGuire, P., Tarashev, N., 2007. International banking with the Euro. BIS Quart. Rev.
47–61.
Passari, E., Rey, H., 2015. Financial flows and the international monetary system. Econ.
J. 125(584), 675–698.
Rey, H., 2013. Dilemma not trilemma: the global financial cycle and monetary policy
independence. A Symposium sponsored by the Federal Reserve Bank of Kansas
City, Jackson Hole, Wyoming, August 22–24.
Rey, H., 2016. International channels of transmission of monetary policy and the25Mundellian trilemma. NBER Working Paper No 21852.
Schularick, M., Taylor, A.M., 2012. Credit booms gone bust: monetary policy, leverage
cycles, and financial crises, 1870–2008. Am. Econ. Rev, 102(2), 1029–1061.
Shin, H. S., 2012. Global banking glut and loan risk premium. IMF Econ. Rev. 60(2),155–192.
Stock, J., Yogo, M., 2005. Testing for weak instruments in linear IV regression, in:Andrews, D.W.K. (Ed.), Identification and Inference for Econometric Models,Cambridge University Press, New York, pp. 80–108.
Sá, F., Towbin, P., Wieladek, T., 2014. Capital inflows, financial structure and housing
booms. J. Eur. Econ. Assoc. 12(2), 522–546.
Sá, F., Wieladek, T., 2015. Capital inflows and the housing boom. J. Money Credit Bank.
47(1), 221–260.
Tobe, S., 2017. Domestic credit growth, international capital inflows, and risk
perception in global markets. Econ. Bull. 37(2), 631–636.

26

Figure 1. House Price Index (quarterly growth).
NOTES: The solid line is the cross-country average of the quarterly growth rate in house prices.
The shaded area represents the one standard deviation band. The dashed line is the one-year
moving average.

Figure 2. Bank Credit to Non-financial Private Sector (quarterly growth).
NOTES: The solid line is the cross-country average of the quarterly growth rate of bank credit
to the non-financial private sector. The shaded area represents the one standard deviation band.
The dashed line is the one-year moving average.

27

Figure 3. Cross-border Bank Inflow.
NOTES: The solid line is the cross-country average of the quarterly growth rate of cross-borderbank claims of all BIS reporting countries on the banking sector. The shaded area represents theone standard deviation band. The dashed line is the one-year moving average.

Figure 4. CBOE VIX Index and Bank Credit to GDP Ratio.
NOTES: The dashed black line is the level of VIX (inversed right hand side axis). The solid lineis the cross-country average of the quarterly growth rate of bank credit to GDP ratio. The shaded
area represents the one standard deviation band. The dashed grey line is the one-year moving
average.

28Model 1A
1st stage 2nd stage
Bankt
ΔHouse t(1)(2)

Model 1B
1st stage 2nd stage
Bankt
ΔHouset(3)(4)

Model 1C
2nd stage
ΔHouset(5)Model 1D
2nd stage
ΔHouse t(6)0.139***(0.021)

0.051***(0.013)0.120***(0.018)0.055***(0.014)-0.004**(0.001)-0.003(0.002)0.000(0.001)-0.254(0.166)
0.247***(0.050)-0.026(0.132)0.010(0.019)0.368***(0.072)0.014***(0.005)0.026-0.004**(0.002)-0.005**(0.002)0.000(0.001)-0.254(0.207)0.182***(0.056)-0.019(0.139)0.003(0.020)0.346***(0.072)0.013**(0.006)0.021
YY
288.1755.665(0.093)
0.171
1976
22

-0.004**(0.001)-0.003(0.002)0.000(0.001)-0.254(0.168)0.245***(0.051)-0.025(0.133)0.009(0.019)0.367***(0.073)0.014***(0.005)0.026
YY
215.8234.967(0.028)
0.237
1976
22

Endogenous variable
Bank_Flowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.086***(0.011)0.245**(0.041)0.907***(0.065)Global_Bankt
Control variable
VIXt-1ΔVIXtStock_Volt-1
ΔLong_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔHouse t-1
Constant
AR(1)Fixed effectLiner trend
F-statWald χ2
Sargan-Hansen test
(p-value)
R2
Observation# Country

0.019**(0.007)0.025***(0.007)-0.002(0.004)-0.069(0.710)0.713***(0.177)-0.079(0.331)0.084(0.064)0.157(0.107)-0.344***(0.050)0.016-0.004**(0.002)-0.005**(0.002)0.001(0.001)-0.255(0.219)0.165***(0.062)-0.018(0.141)0.001(0.021)0.340***(0.070)0.012**(0.006)
0.020

0.010
(0.006)0.007(0.007)-0.003(0.003)-1.124***(0.353)0.574***(0.172)0.114(0.323)0.083(0.060)0.119(0.095)-0.029(0.019)
0.019

Y
Y
25.68
-

Y
Y
250.58

36.43
-

2.337
(0.126)
0.098
0.154
1976
22

214.23
-

0.220

0.239
1976
22

Table 1. IV Regressions on House Prices
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 1A (Model 1D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

29Model 2A
1st stage 2nd stage
Bankt
ΔHouse t(1)(2)

Model 2B
1st stage 2nd stage
Bankt
ΔHouset(3)(4)

Model 2C
2nd stage
ΔHouset(5)Model 2D
2nd stage
ΔHouse t(6)0.138***(0.022)0.051***(0.013)0.118***(0.017)0.054***(0.014)-0.004**(0.001)-0.003(0.002)-0.105(0.077)-0.280**(0.116)0.000(0.001)-0.237(0.166)0.250***(0.050)-0.019(0.131)0.008(0.018)0.366***(0.072)0.014***(0.005)0.026-0.004**(0.002)-0.005**(0.002)-0.071(0.089)-0.251*(0.103)0.000(0.001)-0.240(0.206)0.187***(0.056)-0.015(0.136)0.001(0.020)0.344***(0.072)0.013**(0.006)0.020
YY
364.8755.599(0.094)
0.176
1976
22

-0.004**(0.001)-0.003(0.002)-0.104(0.077)-0.278**(0.115)0.000(0.001)-0.237(0.167)0.248***(0.051)-0.019(0.131)0.007(0.018)0.365***(0.072)0.014***(0.005)0.025
YY
341.8834.506(0.032)
0.242
1976
22

Endogenous variable
Bank_Flowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.086***(0.011)0.245***(0.041)0.907***(0.065)Global_Bankt
Control variable
VIXt-1ΔVIXt
ΔVIXt*ΔCredit/GDP t
ΔVIXt-1*ΔCredit/GDP t-1
Stock_Volt-1
ΔLong_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔHouse t-1
Constant
AR(1)Fixed effectLiner trend
F-stat2

Wald χ
Sargan-Hansen test
(p-value)
R2
Observation# Country

0.019**(0.007)0.025***(0.007)-0.602(0.372)-0.389(0.487)-0.002(0.004)-0.031(0.709)0.711***(0.179)-0.048(0.322)0.082(0.065)0.152(0.101)-0.345***(0.050)0.016-0.004**(0.002)-0.005**(0.002)-0.060(0.093)-0.242**(0.101)0.001(0.001)-0.241(0.218)0.168***(0.061)-0.013(0.138)0.000(0.001)
0.338***(0.070)0.012**(0.006)
0.019

0.010
(0.007)0.008(0.007)-0.570(0.402)-0.398(0.479)-0.003(0.003)-1.087***(0.351)0.574***(0.174)0.144(0.319)0.080(0.062)0.115(0.089)-0.029(0.019)
0.019

Y
Y
42.29
-

Y
Y
329.59

69.07
-

1.955
(0.162)
0.100
0.157
1976
22

366.9
-

0.221

0.244
1976
22

Table 2. IV Regressions on House Prices with Interaction Terms of Local Leverage
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 2C (Model 2D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

30

Model 3A
1st stage 2nd stage
Bankt
ΔCreditt(1)(2)

Model 3B
1st stage 2nd stage
Bankt
ΔCreditt(3)(4)

Model 3D
2nd stage
ΔCreditt(5)

Model 3D
2nd stage
ΔCreditt(6)0.624***(0.079)0.864***(0.046)

0.543***(0.058)0.874***(0.041)0.007-0.008*(0.006)(0.005)0.004-0.027***(0.007)(0.007)-0.007** 0.012***(0.003)(0.003)-0.381*** -0.274*(0.123)(0.155)0.605***-0.198(0.150)(0.209)0.175
0.268(0.236)(0.295)0.000-0.043(0.069)(0.079)0.103**-0.056**(0.040)(0.023)-0.0110.000(0.020)(0.015)
0.008
0.009
Y
Y
38.62
865.34-0.006*(0.003)-0.020***(0.006)0.006***(0.002)-0.357**(0.140)0.131(0.171)0.275(0.235)-0.065(0.064)0.009(0.022)0.008(0.011)
0.008
YY
511.1748.593(0.257)
0.191
2179
22

-0.008*(0.005)-0.027***(0.007)0.012***(0.003)-0.271(0.156)-0.209(0.210)0.268(0.297)-0.042(0.080)-0.058**(0.024)0.000(0.015)
0.009
Y
Y
901.6
22.767(0.356)
0.181
2179
22

Endogenous variable
Bank_Flowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.081***(0.011)
0.239***(0.036)0.851***(0.060)Global_Bankt
Control variable
VIXt-1ΔVIXtStock_Volt-1
ΔShort_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔCreditt-1
Constant
AR(1)Fixed effectLiner trend
F-statWald χ2
Sargan-Hansen test
(p-value)
R2
Observation# Country

0.016**-0.007*(0.006)(0.004)0.023*** -0.022***(0.007)(0.007)-0.0060.008***(0.004)(0.002)-0.394** -0.336**(0.142)(0.142)0.778***
0.047(0.142)(0.168)0.077
0.273(0.246)(0.249)0.002-0.060(0.076)(0.069)0.124***-0.007(0.039)(0.029)-0.314***0.005(0.045)(0.012)
0.007
0.008
Y
Y
21.47
534.31
0.113(0.736)
0.096
0.188
2179
22

0.205

0.181
2179
22

Table 3. IV Regressions on Bank Credit
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 3C (Mode 3D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

31Model 4A
1st stage 2nd stage
Bankt
ΔCreditt(1)(2)

Model 4B
1st stage 2nd stage
Bankt
ΔCreditt(3)(4)

Model 4C
2nd stage
ΔCreditt(5)

Model 4D
2nd stage
ΔCreditt(6)0.627***(0.078)0.864***(0.045)0.542***(0.057)0.873***(0.041)0.008-0.008*(0.006)(0.005)0.004-0.026***(0.007)(0.007)-0.328-0.451(0.451)(0.370)-0.4550.385(0.421)(0.360)-0.007*0.012***(0.003)(0.003)-0.368*** -0.264*(0.117)(0.158)0.605***-0.214(0.153)(0.210)0.178
0.289(0.240)(0.289)0.000-0.046(0.069)(0.079)0.101**-0.052*(0.039)(0.022)-0.0110.000(0.020)(0.015)
0.008
0.009
Y
Y
55.33
1123.15-0.007*(0.003)-0.019***(0.006)-0.538*(0.299)0.259(0.243)0.007***(0.002)-0.343**(0.141)0.116(0.171)0.297(0.230)-0.068(0.063)0.013(0.022)0.008(0.011)
0.008
YY
875.5948.007(0.277)
0.193
2179
22

-0.009*(0.005)-0.026***(0.007)-0.449(0.374)0.389(0.363)0.012***(0.003)-0.262*(0.158)-0.223(0.210)0.289(0.291)-0.045(0.079)-0.054**(0.023)0.008(0.015)0.009
YY
1170.422.520(0.370)
0.183
2179
22

Endogenous variable
Bank_Flowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.081***(0.011)
0.239***(0.036)0.851***(0.060)Global_Bankt
Control variable
VIXt-1ΔVIXt
ΔVIXt*ΔCredit/GDP t
ΔVIXt-1*ΔCredit/GDP t-1
Stock_Volt-1
ΔShort_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔCreditt-1
Constant
AR(1)Fixed effectLiner trend
F-statWald χ2
Sargan-Hansen test
(p-value)
R2
Observation# Country

0.016**-0.007*(0.006)(0.004)0.024*** -0.021***(0.007)(0.006)-0.334-0.515*(0.441)(0.309)-0.3860.293(0.425)(0.267)-0.0060.008***(0.004)(0.002)-0.381** -0.322**(0.142)(0.145)0.776***
0.029(0.144)(0.168)0.082
0.295(0.244)(0.244)0.002-0.062(0.077)(0.068)0.122***-0.004(0.039)(0.028)-0.315***0.006(0.046)(0.012)
0.007
0.008
Y
Y
33.93
766.870.076(0.783)
0.097
0.190
2179
22

0.206

0.183
2179
22

Table 4. IV Regressions on Bank Credit with Interaction Terms of Local Leverage
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 4C (Model 4D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

32

Bank_Flowt
VIXt-1ΔVIXt-1
ΔVIXt-1*ΔCredit/GDP t-1
ΔVIXt-2*ΔCredit/GDP t-2
Stock_Volt-1
ΔLong_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
Constant
AR(1)Fixed effectLiner trend
R2
Observation# Country

Dependent Variable: ΔHouse t
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
0.021**
0.021**
0.020**
0.020**(0.009)(0.009)(0.008)(0.008)
0.002
0.002
0.002
0.002
(0.002)(0.002)(0.002)(0.002)-0.012*** -0.011*** -0.012*** -0.010*** -0.012*** -0.011*** -0.012*** -0.010***(0.002)(0.001)(0.001)(0.001)(0.002)(0.001)(0.001)(0.001)-0.307*-0.307*-0.317*-0.318*(0.173)(0.170)(0.179)(0.176)-0.160** -0.157** -0.166*** -0.162**(0.057)(0.056)(0.058)(0.057)-0.004**-0.003*-0.005**-0.003*-0.005**-0.003*-0.005** -0.004**(0.001)(0.001)(0.002)(0.001)(0.001)(0.001)(0.002)(0.001)-0.254-0.249-0.264-0.259-0.232-0.226-0.242-0.236(0.171)(0.174)(0.165)(0.168)(0.167)(0.170)(0.161)(0.164)0.549*** 0.540*** 0.571*** 0.562*** 0.543*** 0.534*** 0.564*** 0.555***(0.073)(0.072)(0.072)(0.071)(0.074)(0.074)(0.074)(0.073)-0.016-0.0210.000-0.006-0.002-0.0080.012
0.006(0.129)(0.131)(0.131)(0.132)(0.133)(0.135)(0.135)(0.137)0.038
0.038
0.049**
0.049**
0.036
0.036
0.046*
0.047**(0.024)(0.024)(0.022)(0.022)(0.024)(0.024)(0.022)(0.022)
0.004
0.010
0.004
0.011**
0.004
0.010
0.004
0.011***(0.007)(0.003)(0.007)(0.004)(0.007)(0.003)(0.007)(0.004)
0.049
0.051
0.051
0.053
0.050
0.052
0.051
0.054
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
0.123
0.121
0.115
0.113
0.130
0.128
0.123
0.120
1979
1979
1979
1979
1962
1962
1962
1962
22
22
22
22
22
22
22
22

Table 5. OLS Regressions on House Prices
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively.

33

Bank_Flowt
VIXt-1ΔVIXt-1
ΔVIXt-1*ΔCredit/GDP t-1
ΔVIXt-2*ΔCredit/GDP t-2
Stock_Volt-1
ΔShort_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
Constant
AR(1)Fixed effectLiner trend
R2
Observation# Country

Dependent Variable: ΔCreditt
(4)
(5)
(6)
(7)
(8)
0.032**
0.032**(0.014)(0.014)
0.001
0.001
0.002
(0.003)(0.003)(0.003)-0.004*-0.005**-0.004*-0.004*-0.003-0.004*-0.002(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)(0.002)
0.056
0.055
0.047
0.046
(0.174)(0.174)(0.178)(0.178)-0.216-0.214-0.226-0.223(0.159)(0.160)(0.164)(0.165)-0.003-0.002-0.003-0.003-0.003-0.003-0.004-0.003(0.003)(0.002)(0.003)(0.002)(0.003)(0.002)(0.003)(0.002)-0.462*** -0.462*** -0.453*** -0.453*** -0.439*** -0.439*** -0.429*** -0.430***(0.143)(0.143)(0.138)(0.139)(0.133)(0.133)(0.129)(0.129)0.732*** 0.728*** 0.765*** 0.761*** 0.722*** 0.717*** 0.754*** 0.748***(0.160)(0.155)(0.159)(0.154)(0.165)(0.160)(0.164)(0.160)
0.258
0.256
0.285
0.282
0.229
0.226
0.253
0.250
(0.180)(0.181)(0.175)(0.175)(0.180)(0.180)(0.176)(0.176)
0.018
0.018
0.034
0.034
0.016
0.016
0.031
0.032
(0.044)(0.044)(0.041)(0.042)(0.044)(0.044)(0.041)(0.042)0.011
0.015**
0.012
0.016**
0.009
0.014**
0.009
0.015**(0.010)(0.006)(0.010)(0.006)(0.010)(0.006)(0.010)(0.006)
0.006
0.006
0.006
0.006
0.006
0.006
0.007
0.006
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
0.033
0.033
0.030
0.030
0.030
0.030
0.028
0.028
2179
2179
2179
2179
2159
2159
2159
2159
22
22
22
22
22
22
22
22
(1)
0.032**(0.014)0.001(0.003)-0.005**(0.002)(2)0.032**(0.014)(3)

Table 6. OLS Regressions on Bank Credit
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively.

34

Bank_Flowt
VIXt-1ΔVIXt-1
ΔVIXt-1*CreditDevt-1
ΔVIXt-2*CreditDevt-2
Stock_Volt-1
ΔLong_Rate t-1

ΔHouset
(1)
(2)
(3)
(4)
0.019**
0.020**(0.008)(0.008)0.003
0.003(0.002)(0.002)-0.012*** -0.010*** -0.012*** -0.010***(0.002)(0.001)(0.001)(0.001)-0.003*** -0.003*** -0.003*** -0.003***(0.000)(0.000)(0.000)(0.000)-0.002*** -0.002*** -0.002*** -0.002***(0.000)(0.000)(0.000)(0.000)-0.005*** -0.004** -0.005*** -0.004**(0.001)(0.001)(0.002)(0.001)-0.220-0.214-0.230-0.224(0.155)(0.159)(0.149)(0.153)

ΔShort_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
Constant
AR(1)Fixed effectLiner trend
R2
Observation# Country

0.522***(0.069)-0.002(0.145)0.033(0.025)0.003(0.007)
0.053
Y
Y
0.130
1928
22

0.511***(0.068)-0.011(0.148)0.034(0.024)0.011***(0.003)
0.055
Y
Y
0.127
1928
22

0.543***(0.068)0.012(0.147)0.044*(0.023)0.004(0.007)
0.055
Y
Y
0.123
1928
22

0.532***(0.067)0.002(0.150)0.044*(0.023)0.012***(0.003)
0.057
Y
Y
0.120
1928
22

(5)
0.030**(0.013)0.007*(0.003)-0.011***(0.003)0.000(0.001)-0.002(0.002)-0.005(0.003)ΔCreditt(6)(7)
0.031**(0.013)0.007**(0.003)-0.007** -0.010***(0.003)(0.003)0.000
0.000(0.001)(0.001)-0.002-0.002(0.002)(0.002)-0.002-0.005*(0.002)(0.003)(8)-0.006**(0.002)0.000(0.001)-0.002(0.002)-0.002(0.002)-0.381*** -0.383*** -0.371*** -0.373***(0.110)(0.112)(0.106)(0.107)0.746*** 0.728*** 0.778*** 0.760***(0.165)(0.161)(0.167)(0.163)0.290
0.273
0.315*
0.297(0.181)(0.179)(0.177)(0.175)
0.014
0.016
0.029
0.031
(0.042)(0.043)(0.040)(0.041)0.001
0.015**-0.0010.016**(0.010)(0.006)(0.010)(0.006)
0.007
0.006
0.007
0.007
Y
Y
Y
Y
Y
Y
Y
Y
0.035
0.034
0.033
0.032
2119
2119
2119
2119
22
22
22
22

Table 7. OLS Regressions with Alternative Measure for Local Leverage
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively.

35

Model 8A
1st stage 2nd stage
Inflowt
ΔHouse t(1)(2)

Model 8B
1st stage 2nd stage
Inflowt
ΔHouset(3)(4)

Model 8C
2nd stage
ΔHouset(5)Model 8D
2nd stage
ΔHouse t(6)0.168***(0.043)0.090***(0.027)0.065**(0.025)0.079***(0.023)-0.003*(0.002)-0.004*(0.002)0.001(0.001)-0.226**(0.091)0.188***(0.056)-0.110(0.126)0.002(0.015)0.425***(0.077)0.004(0.006)0.056-0.003*(0.002)-0.004*(0.002)0.000(0.001)-0.218**(0.093)0.210***(0.067)-0.105(0.125)0.003(0.016)0.434***(0.075)0.008(0.006)0.052
YY
315.4377.021(0.000)
0.290
1732
22

-0.003*(0.002)-0.004*(0.002)0.001(0.001)-0.223**(0.094)
0.198***(0.057)-0.108(0.125)0.002(0.016)0.429***(0.076)0.006(0.005)
0.054
YY
192.6638.248(0.005)
0.276
1732
22

Endogenous variable
Inflowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.065***(0.015)0.179***(0.036)
0.716***(0.101)

Global_Flowt
Control variable
VIXt-1ΔVIXtStock_Volt-1
ΔLong_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔHouse t-1
Constant
AR(1)Fixed effectLinear trend
F-stat0.004(0.007)0.005(0.005)-0.006(0.005)0.274(0.645)0.688***(0.206)0.173(0.487)0.011(0.081)
0.203(0.119)-0.153**(0.060)0.050-0.003(0.002)-0.004*(0.002)0.002(0.001)-0.253***(0.088)0.119*(0.066)-0.123(0.140)0.000(0.015)0.397***(0.075)-0.005(0.009)
0.066

0.009
(0.008)0.008(0.006)-0.002(0.005)0.069(0.698)0.305(0.214)0.211(0.531)
0.015(0.077)0.218*(0.118)-0.002(0.025)0.051

YY

Wald χ2
Sargan-Hansen test
(p-value)
R2
Observation# Country

17.00
-

Y
Y
194.13

14.08
-

2.518
(0.112)
0.125
0.177
1732
22

205.43
-

0.191

0.263
1732
22

Table 8. Determinants of House Price Using Total Inflows
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 9C (Model 9D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

36Model 9A
1st stage 2nd stage
Inflowt
ΔHouse t(1)(2)

Model 9B
1st stage 2nd stage
Inflowt
ΔHouset(3)(4)

Model 9C
2nd stage
ΔHouset(5)Model 9D
2nd stage
ΔHouse t(6)0.169***(0.044)0.090***(0.027)0.063**(0.025)0.077***(0.023)-0.003*(0.002)-0.004*(0.002)-0.184**(0.092)-0.177**(0.083)0.000(0.001)-0.209**(0.091)0.189***(0.057)-0.099(0.126)0.000(0.015)0.423***(0.075)0.004(0.006)0.053-0.003*(0.002)-0.004*(0.002)-0.200**(0.084)-0.182**(0.080)0.000(0.001)-0.199**(0.092)0.213***(0.068)-0.093(0.125)0.001(0.016)0.432***(0.073)0.008(0.006)0.048
YY
358.2878.450(0.000)
0.297
1732
22

-0.003*(0.002)-0.004*(0.002)-0.192**(0.091)-0.179**(0.083)0.000(0.001)-0.204**(0.093)0.201***(0.058)-0.096(0.125)0.001(0.016)
0.428***(0.074)0.006(0.005)
0.051
YY
334.6038.475(0.005)
0.282
1732
22

Endogenous variable
Inflowt
Instruments
Global_Leverage t-1
ΔGlobal_Leverage t

0.066***(0.015)0.180***(0.037)0.717***(0.099)Global_Flowt
Control variable
VIXt-1ΔVIXt
ΔVIXt*ΔCredit/GDP t
ΔVIXt-1*ΔCredit/GDP t-1
Stock_Volt-1
ΔLong_Rate t-1
ΔGDP t-1
Inflationt-1
ΔREERt-1
ΔHouse t-1
Constant
AR(1)Fixed effectLinear trend
F-stat2

Wald χ
Sargan-Hansen test
(p-value)
R2
Observation# Country

0.004(0.007)0.006(0.005)-0.694**(0.249)-0.183(0.332)-0.006(0.005)0.311(0.648)0.685***(0.199)0.213(0.488)0.009(0.081)0.202*(0.115)-0.155**(0.061)
0.051-0.003(0.002)-0.004*(0.002)-0.136(0.095)-0.165*(0.088)0.001(0.001)-0.239***(0.089)0.120*(0.066)-0.115(0.140)-0.001(0.015)0.395***(0.073)-0.005(0.009)
0.064

0.009
(0.008)0.009(0.006)-0.652**(0.266)-0.201(0.276)-0.002(0.005)0.106(0.699)0.303(0.206)0.248(0.533)0.012(0.077)0.218*(0.115)-0.002(0.025)
0.053

Y
Y
17.48
-

Y
Y
208.45

16.57
-

2.233
(0.135)
0.126
0.179
1732
22

271.84
-

0.192

0.267
1732
22

Table 9. Determinants of House Price Using Total Inflows with Interaction Terms
Note: Standard errors clustering at the country level are reported in parentheses. ***, **, and *denote significance at the 1, 5, and 10% levels, respectively. Model 10C (Model 10D) uses
interaction terms of global leverage (global flow) with country-specific dummies as
instruments.

37