House Price Synchronicity, Banking Integration, and Global Financial Conditions
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

We examine the relationship between house price synchronicity and global financial conditions across 40 countries and about 70 cities over the past three decades. The role played by cross-border banking flows in residential property markets is examined as well. Looser global financial conditions are associated with greater house price synchronicity, even after controlling for bilateral financial integration. Moreover, we find that synchronicity across major cities may differ from that of their respective countries’, perhaps due to the influence of global investors on local house price dynamics. Policy choices such as macroprudential tools and exchange rate flexibility appear to be relevant for mitigating the sensitivity of domestic housing markets to the rest of the world.

Abstract

We examine the relationship between house price synchronicity and global financial conditions across 40 countries and about 70 cities over the past three decades. The role played by cross-border banking flows in residential property markets is examined as well. Looser global financial conditions are associated with greater house price synchronicity, even after controlling for bilateral financial integration. Moreover, we find that synchronicity across major cities may differ from that of their respective countries’, perhaps due to the influence of global investors on local house price dynamics. Policy choices such as macroprudential tools and exchange rate flexibility appear to be relevant for mitigating the sensitivity of domestic housing markets to the rest of the world.

I. Introduction

As global liquidity surged owing to accommodative financial conditions, house prices across advanced and emerging market economies have experienced greater synchronicity. IMF (2018a) finds that nearly 80 percent of countries and cities within a broad set of developed economies have experienced positive house price growth rates in the past decade, while this figure is over 60 percent for emerging market economies and cities. Moreover, over time, median synchronicity in house price gaps—measured by extracting the cyclical component of real house prices—has steadily increased over time across countries and cities (Figure 1). 2

Figure 1.
Figure 1.

House Price Gap Synchronicity Across Countries and Cities

(Closer to zero denotes higher synchronicity)

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ estimates.Note: The Synch1 measure capturing the negative of the absolute difference between the house price gaps between two countries is used (see Annex II for further details). Upper and lower bounds are the 75th and 25th percentiles of the samples respectively. Solid median lines in city-pair panels denote the time span with significantly higher city-sample coverage. Shaded areas correspond to U.S. recession periods.

House price synchronicity is of particular interest given that greater comovement in house prices could amplify the propagation of external shocks. These shocks could be directly transmitted to the domestic economy through channels such as portfolio, balance sheet, and liquidity, or indirectly through risk premium and confidence channels (Allen and Gale 2000; Longstaff 2010). Simultaneous changes in mortgage rates due to global financial conditions could lead to greater house price synchronicity, thus propagating shocks to aggregate demand when financial conditions tighten sharply. At the same time, an increase in global demand for safe assets may compress sovereign spreads where risk is perceived to be low, thereby pushing down mortgage rates and supporting house price booms in those countries (Bernanke et al. 2011). For instance, foreign capital may be a driver of residential property markets in global cities such as London, New York, or Tokyo, especially during “flight to safety” episodes (Badarinza and Ramadorai 2018). In addition, as illustrated in Figure 2, asset managers may rebalance their portfolios to mitigate their losses, thus resulting in dwindling equity price returns (i.e., portfolio channel); this impact could be further amplified due to asset classes such as REITS. In addition, an exogenous shock to house prices may lead to asset fire sales and deleveraging that would result in declining collateral values and hindering the availability of credit in the economy (i.e., bank balance sheet channel). An exogenous shock could also heighten the rollover risk as investors suffering losses may find it difficult to obtain further financing opportunities, thereby affecting the aggregate demand (i.e., liquidity channel). A shock to the financial system in one country could also result in elevated risk premia in other countries, therefore affecting the aggregate demand through indirect channels (i.e., risk premium/confidence channel).

Figure 2.
Figure 2.

House Price Synchronicity and Transmission of External Shocks

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ illustration.

Even though housing is a non-tradable asset, Claessens et al. (2011a)—echoing past research such as Terrones (2004)—points out the presence of high synchronicity in their sample of countries, partially reflecting the importance of global factors such as global interest rates, U.S. business cycles, and global commodity prices. In the same spirit, Hirata et al. (2012) allude to the role of global integration of housing markets across advanced and emerging market economies as a determinant of house price synchronicity.3

Nevertheless, house price synchronicity may also be reflective of the co-movement in economic cycles (in other words, due to business cycle synchronicity). Claessens et al (2011b) notes that business cycles are highly synchronized with house price cycles. Indeed, past research has identified bilateral financial and trade linkages as two possible determinants of business cycle synchronicity between countries (IMF 2013; Kalemli-Ozcan et al. 2013a, 2013b; Duval et al 2016).

In this paper, building upon the literature on global financial conditions and house prices, we analyze the role of bilateral financial linkages and global financial conditions above-and-beyond that of business cycle synchronicity as a driver of house price synchronicity. We perform bilateral panel data analyses at country-pair level with nearly 50,000 observations and at major city-pair level with nearly 70,000 observations for a broader set of advanced and emerging economies (over 40 economies) and cities (over 70 cities) than previously analyzed. In particular, we aim to address the following questions: (1) Do global financial conditions amplify the house price synchronicity controlling for bilateral macro-financial linkages? (2) Is there an association between bilateral bank linkages and house price synchronicity above-and-beyond that of business cycle synchronicity? (3) What is the role of various institutional factors in either mitigating or amplifying the impact of global financial conditions on house price synchronicity? (4) Do policy tools such as macroprudential policies still turn out to be effective in addressing domestic vulnerabilities in the presence of heightened house price synchronicity?

Our main findings are fourfold. First, the importance of global factors in house price synchronicity as documented in past research still holds when a broader sample of countries and cities with coverage spanning through end-2016 is used. Notably, we find that abundant global liquidity as well as loose financial conditions (in addition to other global factors such as global interest rates) are positively associated with house price synchronicity across country-pairs as well as across major city-pairs. Thus, this paper sheds light on the important role played by mounting financial integration on housing markets across the globe. Second, we find that greater exchange rate flexibility attenuates the positive impact of global factors on house price synchronicity. Third, bilateral relationships such as past co-movement in business cycles and bilateral bank linkages are also positively associated with house price synchronicity. Finally, we find that the macroprudential policies aimed at tackling domestic vulnerabilities may have the additional impact of reducing countries’ house price synchronicity with the rest of the region and the world.

The rest of the paper is structured as follows. Section II describes the data and the construction of the main indicators used in the empirical analyses. Section III presents the main country-level empirical analysis and additional robustness checks. Section IV presents the city-level analysis where we first provide a network analysis on city-level interconnectedness dynamics followed by the empirical analysis. Section V extends the analysis further, looking at the impact of macroprudential policies on house price synchronicity. Section VI concludes.

II. Data and Measurement

This section presents a brief description of the construction of the main variables used in our regression analyses. Further information on underlying data sources, descriptions, and the economies and cities covered in this paper are presented in Annex I.

A. House Price Gap Synchronicity

We employ a measure of house price synchronicity that can be computed at any point in time (in other words at time-series level) rather than as period-wise computations; this measure also provides the additional advantage of not being bound between -1 and 1.

Synchronicity is calculated using the instantaneous quasi-correlation, originally presented by Morgan, Rime, and Strahan (2004) and used in recent business cycle literature (such as Duval et al. 2016; IMF 2013; Kalemli-Ozcan et al. 2013a, 2013b). 4 House price synchronicity (HPsynchijt) between country i and j at time t is measured as follows:

HPsynchijt=(HPgapitHPgapι¯)(HPgapjtHPgapj¯)σiqapσjqap,(1)

where HPgapit and HPgapjt stand for house price gap of country i and j respectively at quarter t and the gaps are measured as explained above. HPgapι¯andHPgapj¯ are the average house price gaps of countries i and j respectively, while σigap,σjgap the standard deviations of house piece gaps of countries i and j respectively.

House price gaps are measured by extracting the cyclical component of real house prices using the band-pass filter of Christiano and Fitzgerald (2003), with the maximum length of 30 years to capture medium-term financial cycles5. The above cyclical components of house prices are then taken as a ratio of the house price levels to obtain house price gaps6.

B. Business Cycle Synchronicity

Business cycle synchronicity (BCS) is analogous to the house price synchronicity measure presented above.

BCSijt=(YgapitYgapι¯)(YgapjtYgapj¯)σiqapσjqap,(2)

where Ygapit and Ygapjt represent output gaps of countries i and j respectively at quarter t and the gaps and measured using Christiano and Fitzgerald band-pass filter (2003), with the maximum length adjusted for business cycles instead of financial cycles. Ygapι¯andYgapj¯ are the average output gaps of countries i and j respectively, while σiqap,σjqap are the standard deviations of output gaps of countries i and j respectively.

C. Bilateral Banking Integration7

Banking integration is measured using bilateral locational banking statistics on residency basis obtained from BIS IBS restricted databases, to be conceptually consistent with balance of payments, national accounts, and external debt statistics. Bilateral banking integration is measured as the logarithm of the sum of bilateral claims of country i vis-à-vis country j and bilateral claims of country j vis-à-vis country i as a ratio of the sum of GDPs of country i and j8:

FININTijt=ln((Aijt+AjitGDPit+GDPjt)*100)(3)

where Aijt is the bilateral claims of country i vis-à-vis country j at quarter t, Ajit is bilateral claims of country j vis-à-vis country i, GDPit is the nominal GDP of country i at time t, and GDPjt is the nominal GDP of country j at time t.

D. Global Financial Conditions

We control for the effect of global financial conditions on house price gap synchronicity as common shocks could propagate through global financial stability-related risks. In our main analyses, we focus on changes in Bank of International Settlements’ (BIS) global liquidity to capture global financial conditions. This measure captures the changes in banks’ cross-border claims denominated in all currencies plus local claims in foreign currency in percent of global GDP. In addition to global liquidity, as robustness checks, we also use global financial conditions index (FCI) and the U.S. FCI estimated in line with IMF (2017). 9 We also use Chicago Board Options Exchange volatility index (VIX), as well as Wu and Xia (2016) and Krippner (2013) U.S. shadow interest rates to capture global financial conditions in robustness specifications.

E. Other Controls

To further assess the impact of global financial conditions and bilateral bank linkages when countries have stronger institutions or when they are at different stages of economic development, we use several institutional characteristics and advanced/emerging market economy dummy variables. In particular, we use indicators for high capital account openness (measured using the Chinn-Ito index which is a de jure measure of financial openness), high exchange rate regime (measured using de facto exchange rate regime indices by Ilzetzki, Reinhart, and Rogoff 2017), and high financial openness (measured using the index developed by Lane and Milesi-Ferretti (2007), which is a de facto measure of financial openness) separately in specifications, where high is defined as a dummy variable that equals 1 when both countries in the country-pair are in the top fifth of the institutional characteristic during a given quarter. Dummy variables for advanced economies, emerging market economies, and advanced-emerging market economies take the value of 1 if both countries in the country-pair are either advanced economies, emerging market economies, or advanced-emerging market economies.

III. Country-Level Analysis

A. Empirical Strategy

This paper employs bilateral country-pair panel data analysis to estimate the impact of business cycle synchronicity, bilateral financial linkages, and global financial conditions on house price synchronicity at country-level10. Our baseline econometric specification presented below is estimated at quarterly frequency from 1990 to 2016, for 40 countries: 11

HPsynchijt=αij+β1BCSijt1+β2FININijt1+β3GL0BALt1+β4INSTijt1×GL0BALt1+β5INSTijt1+tr+εijt(4)

where HPsynchijt is the synchronicity of house price gaps between country-pair i and j at quarter t. BSCij denotes business cycle synchronicity between country i and j. FININTij refers to bilateral financial integration between country i and j.12 GLOBALt is the global factor proxied by the changes in global liquidity. INSTij denote dummies which equal 1 if both countries have a high level of an institutional characteristic (i.e., economic development level, de jure capital account openness, exchange rate flexibility, or de facto financial account openness).13 All regressors are lagged by one quarter. In addition, linear and quadratic time trends (tr) are included. αij is the country-pair fixed effects capturing unobservable time-invariant idiosyncratic factors common to country-pair i and j such as geographic proximity. εijt is the error term.14 Importantly, country-pair fixed effects capture time-invariant supply-side and regulatory considerations that influence house price synchronicity between two countries.

B. Results

Impact of Global Financial Conditions on House Price Gap Synchronicity

In our main analyses, we estimate the impact of global financial conditions (also referred to as the global factor) on house price gap synchronicity using the changes in BIS’ global liquidity variable mentioned in the preceding section as the proxy for the global factor15 and instantaneous quasi-correlation (also mentioned in the previous section)16 as the synchronicity measure for house price and business cycle synchronicity. The results presented in Table 1 show that the global financial conditions are positively associated with house price synchronicity even when controlling for bilateral macro financial conditions including business cycle synchronicity and banking integration (column 4). This impact is also robust across various specifications, including where we control for different institutional characteristics and various error clustering methods are considered (see Tables 24 for robustness checks). This result could provide preliminary evidence for the positive association between the abundance of global liquidity and short-term co-movements in house price gaps.

Table 1.

House Price Gap Synchronicity at Country Level and Global Factors

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Source: Authors’ estimates.Note: GFC Dummy = a dummy variable that equals 1 during 2008–09, and zero otherwise. Post-GFC Dummy = a dummy variable that equals 1 during 2010–16, and zero otherwise. All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications 5 through 9, but are not shown above (specifically, dummy variables for EMEs-EMEs, EMEs-AEs, high capital account openness, high exchange rate regime, high financial openness are included in specifications 5 through 9, but not shown). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are three-way clustered (at country i, country j, and date), with the exception of regression (10), in which errors are two-way clustered (at country i, country j). AEs = advanced economies; EMEs = emerging market economies; FE = fixed effects; GFC = global financial crisis. *** p < 0.01; ** p < 0.05; * p < 0.1.
Table 2.

House Price Gap Synchronicity at Country Level and Global Factors─Robustness Checks: Global Factors

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Source: Authors’ estimates.Note: All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications, but are not shown above (specifically, dummy variables for high exchange rate regime). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are clustered as described above; FE = fixed effects.*** p < 0.01; ** p < 0.05; * p < 0.1.
Table 3.

House Price Gap Synchronicity at Country Level and Global Factors─Robustness Checks: Additional Controls

article image
Source: Authors’ estimates.Note: All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications, but are not shown above (specifically, dummy variables for high exchange rate regime). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are clustered as described above; FE = fixed effects.*** p < 0.01; ** p < 0.05; * p < 0.1.
Table 4.

House Price Gap Synchronicity at Country Level and Global Factors─Robustness Checks: Clustering of Standard Errors

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Source: Authors’ estimates.Note: All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications, but are not shown above (specifically, dummy variables for high exchange rate regime). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are clustered as described above; FE = fixed effects.*** p < 0.01; ** p < 0.05; * p < 0.1.

Moreover, the impact of the global financial conditions on house price synchronicity appears to be higher between advanced economies than in country-pairs that are emerging market economies (column 5). While the impact of the global financial conditions in advanced economies is statistically significant and positive, neither emerging market economies’ nor advanced-emerging market economy-pairs’ impact is statistically significant at conventional levels when standard errors are clustered in the most stringent manner.

Institutional characteristics such as higher exchange rate flexibility appear to be attenuating the positive association between global financial conditions and house price synchronicity (column 7). This impact is statistically significant at 1 percent confidence interval. Moreover, it is robust to various controls, as presented in Tables 25. We also find an attenuating effect of de jure financial openness (i.e., Chinn-Ito index of capital account openness) on the global financial conditions’ impact on house price synchronicity, but the impact of this interaction term is not statistically significant at conventional levels (column 6). Results in columns 4 to 6 are also presented in figure 3, where we have standardized the coefficients for comparability across specifications.

Table 5.

House Price Gap Synchronicity at City Level and Global Factors─Two-Way Clustering

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Source: Authors’ estimates.Note: GFC Dummy = a dummy variable that equals 1 during 2008–09, and zero otherwise. Post-GFC Dummy = a dummy variable that equals 1 curing 2010–16, and zero otherwise. All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications 5 through 9, but are not shown above (specifically, dummy variables for EMEs-EMEs, EMEs-AEs, high capital account openness, high exchange rate regime, high financial openness are included in specifications 5 through 9, but not shown). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are two-way clustered (at country ij, and date). AEs = advanced economies; EMEs = emerging market economies; FE = fixed effects; GFC = global financial crisis.*** p < 0.01; ** p < 0.05; * p < 0.1.

Furthermore, the positive impact of global liquidity on house price synchronicity was substantially higher prior to the global financial crisis (GFC). This may provide evidence to the association between the global house price boom that occurred preceding the GFC and the abundance of global liquidity accumulated during that period.

Figure 3.
Figure 3.

Impact of Global Financial Conditions on House Price Synchronization

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ estimates.Note: Global financial conditions are proxied by the BIS global liquidity variable mentioned in the previous section. Synchronicity is measured by the quasi correlation of gaps. Shaded bars denote joint significance of the F-test at or above 90 percent. Patterned bars denote interaction terms that are statistically significant. Coefficients are standardized. Standard deviation of the country-level dependent variable is approximately 0.85 (see Annex Table 1.3). AEs = advanced economies; EMEs = emerging market economies; FX = exchange rate.

The analysis concerning the impact of bilateral linkages on house price gap synchronicity is presented in Annex II. Using an alternative measure of house price synchronicity, which captures the medium-term dynamics through differences in house price gaps, we find evidence that both business cycle synchronicity and bilateral banking integration are positively and robustly associated with house price synchronicity.

C. Robustness Checks

In addition to the results presented above, various robustness checks were performed, with the main findings broadly unchanged. For instance, alternative proxies for global financial conditions including the U.S. financial conditions index (FCI), Global FCI, CBOE volatility index (VIX), U.S. shadow interest rates (Wu and Xia 2016; Krippner 2013) are used, where the global financial conditions and the high exchange rate regime interaction terms are still found to be statistically significant with the coefficient sign and the size broadly unchanged (Table 2).17 Specifications above were also estimated by replacing BCS with interest rate synchronicity to investigate the role of synchronized monetary policies in contributing to house price synchronicity. We find interest rate synchronicity to be a statistically significant driver of house price synchronicity on its own when either synchronicity measure is used (either synch1 or quasi correlation). However, the statistical significance of interest rate synchronicity above and beyond other financial factors such as the global liquidity and bilateral banking linkages is only robust to less stringent manners of standard error clustering (Table 3, columns 3–6). At the same time, trade integration was included as an additional control, but found not to be statistically significant (Table 3, columns 7–8). When equity price synchronicity is included as an additional control, the main results presented in the previous section remain broadly unchanged (Table 3, columns 9–10). However, equity price synchronicity itself does not consistently have a statistically significant relationship with house price synchronicity.

Various clustering alternatives were employed (clustering at country-pair level, two-way at country i and country j, two-way at country-pair and time level, and without clustering, Huber/White/sandwich estimator), and as expected, the level of significance improves under less restrictive clustering options (Table 4). Additional time controls, such as year fixed effects and linear time trends, were also considered with little changes to the main conclusions. Finally, further robustness checks were employed by dropping one country-pair at a time as well.

IV. City-Level Analysis

While house prices synchronicity may vary among country-pairs owing to their degree of exposure to bilateral linkages and global financial conditions as identified in the preceding section, house prices in major cities18 may move in tandem due to increasing global presence even if their country-level house prices may not portray such dynamics. To dig deeper into city-level house price synchronicity, we first explore house price interconnectedness dynamics through a network analysis, and then move on to analyzing the drivers of city-level house price synchronicity empirically.

A. Network Analysis: House Price Interconnectedness at City Level

Our network analysis uses the spillovers approach developed by Diebold and Yilmaz (2014) (see Annex III for detailed methodology) controlling for global financial conditions (proxied by the U.S. FCI). In fact, comparing the network analysis at country-level and city-level confirms that cities that are attractive to global investors may be at the core of the network and closer to other cities such as financial centers even if the respective countries are at the periphery (Figure 4). For instance, Tokyo and Rome are centrally located in the vicinity of global financial centers such as New York and London in the city-level network map below (Figure 4, Panel 2), while Japan and Italy are located at the periphery of the country-level network map (Figure 4, Panel 1).

Figure 4.
Figure 4.

House Price Interconnectedness Among Countries vs. Cities

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ estimates.Note: The figure is based on a vector autoregression of country-level/city-level house price growth rates (quarter over quarter) controlling for global factors, spanning 1990:Q1 to 2016:Q4 for country-level and 2004:Q1 to 2017:Q2 for city-level. For methodology details, see Annex III. See the footnote 18 for city selection criteria, conditional on data availability. Node size is based on the city’s total outward spillovers. Pink nodes represent advanced economies and gray nodes represent emerging market economies. Arrows’ thickness is based on link distribution. Only links above the 50th percentile for country-level and 66th percentile for city-level are considered. The figure layout is based on the algorithm by Fruchterman and Reingold (1991), and plotted using the “qgraph” R package. Ack = Auckland; Ams = Amsterdam; Bgt = Bogotá; Brl = Berlin; Brs = Brussels; Dbl = Dublin; Dub = Dubai; HKG = Hong Kong SAR; Hls = Helsinki; Jkr = Jakarta; Lim = Lima; Lnd = London; Mdr = Madrid; Mmb = Mumbai; Mnl = Manila; Msc = Moscow; Mxc = Mexico City; NYC = New York City; Osl = Oslo; Prs = Paris; Rom = Rome; Sel = Seoul; SGP = Singapore; Shn = Shanghai; Snt = Santiago; Stc = Stockholm; Syd = Sydney; Tky = Tokyo; Trn = Toronto; Vnn = Vienna. Following Morgan Stanley Capital International markets classification criteria, Korea (and thus Seoul) is classified as an emerging market economy; moreover, Korea (and thus Seoul) was not classified as an advanced economy in the IMF’s World Economic Outlook country classification at the beginning of our sample period, which starts in 1990.

B. Empirical Strategy

The determinants of city-level house price synchronicity are analyzed using a bilateral panel data analysis, where we specifically estimate the impact of country-level measures such as business cycle synchronicity and bilateral financial linkages, and global financial conditions on house price synchronicity within major cities. The analysis is estimated at quarterly frequency from 2004 to 2016 for over 70 major cities19. The econometric specification for the city-level analysis takes the following form:

HPsynchijt=αij+β1BCSijt1+β2FININijt1+β3GLOBALt1+β4INSTijt1×GLOBALt1+β5INSTijt1+tr+εijt(5)

where HPsynchijt is the synchronicity of house price gaps between city-pair i and j at quarter t. αij stands for city-pair fixed effects and tr stands for quadratic and linear time trends. GLOBALt-1stands for global financial conditions proxied by changes in the BIS’ global liquidity in percent of global GDP. All other regressors are country-level variables that are defined in the section on the country-level analysis.

C. Results

Similar to our country-level analysis, we use the changes in BIS global liquidity to proxy for the global financial conditions and instantaneous quasi-correlation to measure city-level house price gap synchronicity. We find that global financial conditions are positively associated with city-level house price gap synchronicity; this impact is statistically significant even if standard errors are clustered using a more stringent form of multi-way clustering, while the significance level improves from a 10 percent confidence level to a 1 percent confidence level if two-way clustering is employed instead (column 4 in Tables 5 and 6).

Table 6.

House Price Gap Synchronicity at City Level and Global Factors─Multi-Way Clustering

article image
Source: Authors’ estimates.Note: GFC Dummy = a dummy variable that equals 1 during 2008–09, and zero otherwise. Post-GFC Dummy = a dummy variable that equals 1 curing 2010–16, and zero otherwise. All regressors are lagged by one quarter. Institutional characteristics dummies are included in specifications 5 through 9, but are not shown above (specifically, dummy variables for EMEs-EMEs, EMEs-AEs, high capital account openness, high exchange rate regime, high financial openness are included in specifications 5 through 9, but not shown). High = a dummy variable that equals 1 when both countries are in the top fifth of the institutional characteristic. Standard errors (in parentheses) are three-way clustered (at country i, country j, and date). AEs = advanced economies; EMEs = emerging market economies; FE = fixed effects; GFC = global financial crisis.*** p < 0.01; ** p < 0.05; * p < 0.1.

In line with our country-level findings, city-level analysis also confirms that higher exchange rate flexibility tends to be attenuating the positive association between the global factor and the city-level house price synchronicity; this impact is statistically significant at a 5 percent confidence level even when more stringent form of standard error clustering is used (column 7 in Tables 5 and 6).

Furthermore, the impact of global financial conditions on city-level house price synchronicity is higher among city-pairs residing within advanced economies than that of city-pairs residing either within emerging economies or advance-emerging economy pairs (column 5 in Tables 5 and 6). While the impact for advanced economies is statistically significant even when more stringent forms of standard errors are used, the interaction term for advance-emerging pairs is significant only when a less stringent form of clustering is used (such as two-way clustering at country-pair and time level; column 5 in Table 5). The interaction term for emerging economies is not statistically significant when two-way clustering is used.

In contrast to our country-level analysis, the city-level empirical findings suggest that greater financial openness at country-level tends to amplify the positive association between global financial conditions and city-level house price synchronicity. In other words when a de jure measure of financial openness is used (i.e., Chinn-Ito index of capital account openness). However, we find that this impact is not statistically significant if standard errors are clustered in a more stringent manner (column 6 in Table 6). We fail to find statistically significant results when a de facto measure of financial openness is used (i.e., Lane and Milesi-Ferretti (2007) measure of financial openness).

The city-level analysis also confirms that the global financial conditions were positively associated with city-level house price synchronicity prior to the global financial crisis (column 10 in Tables 5 and 6).

V. Extensions: The Impact of Macroprudential Policies

In this section, we focus on the relationship between macroprudential policies (MPPs) and house price synchronicity with regional and global cycles.20 MPPs targeted at dampening the accumulation of domestic vulnerabilities in the financial and housing sectors may have indirect effects of weakening the correlation of house price cycles, thereby leaving room for policymakers to regain control over local house price dynamics.

Macroprudential tools, which have been used more actively since the global financial crisis (Alam et al. 2018; Cerutti, Claessens, and Laeven 2015), aim at curbing leverage and reducing financial vulnerabilities in order to decrease the likelihood of domestic asset bubbles and financial crises. MPPs are usually domestically targeted, with a large share of measures focused on domestic credit and housing market conditions. However, in countries experiencing deeper financial integration and where business cycles are more intertwined at the regional and global levels, house prices are, in part, driven by other factors, such as capital flows from global investors and by global financial conditions.21 Thus, the relationship between macroprudential tools and house price synchronicity might be ambiguous because it may be offset by other factors.

Recent empirical literature (Vandenbussche, Vogel, and Detragiache 2015; Cerutti, Dagher, and Dell’Ariccia 2015) suggests that the role of macroprudential policies in mitigating house prices is less clear and may vary according to policy type. For instance, measures targeting housing finance (Akinci and Olmstead-Rumsey 2017) and those that complement monetary policy (Bruno, Shim, and Shin 2017) seem to be most effective in mitigating house price growth. In contrast, there is no robust evidence for policies such as risk-weighting and provisioning requirements (Kuttner and Shim 2016).

A. Empirical Strategy

The analysis gauges the effectiveness of macroprudential tools in reducing house price synchronicity across 41 countries from the second quarter of 1990 through the last quarter of 2016. More specifically, the following panel regression specification is estimated, with i denoting country and t representing quarter:

HPSi,t=ρBCSi,t1+βMPPi,t1+γXi,t1+αi+i,t(8)

where αi denotes country fixed effects. The dependent variable HPS refers to house price cycle synchronicity (instantaneous quasi-correlation) with either the regional or the global cycle. PCS is business cycle synchronicity with the region or the rest of the world. X is a vector of controls (including global financial conditions, financial integration with the region or the world, and institutional characteristics). MPP is a macroprudential tool (such as limits to loan-to-value ratios or debt-to-income ratios, or fiscal-based measures that include sellers’ and buyers’ stamp duty taxes) or a macroprudential group index (such as loan-targeted, supply-side [capital, general, loans], or demand-side tools).22

B. Results

House price growth evolved differently after the adoption of demand-side MPPs such as loan-to-value (LTV) limits, depending on the level of synchronicity (Figure 5). Before the adoption of these policies, house prices grew similarly in countries with high or low house price synchronicity. Following the adoption of MPPs, house price growth declined in both groups of countries, but the decline was stronger and more sustained in low-synchronicity countries. These simple patterns suggest that policymakers may have more control over the dynamics of the housing markets in low-synchronicity countries. At the same time, it suggests that a high degree of synchronicity does not render MPPs ineffective. This could be the case if the financial factors behind house price synchronicity operate, at least partially, through local financial intermediaries.

Figure 5.
Figure 5.

Average House Price Growth and Demand-side Macroprudential Policies

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ estimates.Note: The figure depicts the average year-over-year house price growth for high-synchronicity and low-synchronicity countries within a period of plus or minus five quarters around the implementation of demand-side macroprudential policies (MPPs). Demand-side MPPs include limits to debt-service-to-income and loan-to-value (LTV) ratios. Total number of demand-side events is 47, and t = 0 is identified as the first quarter in which demand-side MPPs were implementated within the plus-or-minus-five-quarter window. Synchronicity is based on the quasi-correlation of house price gaps with the global cycle. A country is classified in the high-synchronicity group when its average syncronicity (over the sample period) with the global cycle is above the 50th percentile in the sample, and vice versa.

MPPs are also associated with a reduction in house price synchronicity (Figure 6, Panel 1 and Annex Table 4.1); in fact, tighter macroprudential tools targeting bank capital and credit conditions are found to be associated with lower house price synchronicity. Since these tools mostly affect local financial intermediaries and domestic demand, this finding also suggests that factors driving house price co-movement operate, to some degree, through these channels. The relationship between capital-based measures, which include countercyclical capital buffers, and house price synchronicity seems the most highly negative. Likewise, loan-targeted measures, including LTV limits, and supply-side loan-targeted tools, such as limits on foreign currency, are found to lessen correlations with the global and regional house price cycles. The adoption of fiscal-based measures, such as ad valorem and buyer’s stamp duty taxes that could potentially deter global investors from engaging in speculative real estate purchases is also associated with a decline in synchronicity, but to a lesser extent than other MPPs.23 When looking only at periods with credit booms, the results are both qualitatively and quantitatively similar, although the relationships are slightly less significant (Figure 6, panel 2 and Annex Table 4.2).

Figure 6.
Figure 6.

Impact of Macroprudential Measures on House Price Synchronicity

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ estimates.Note: Figure depicts estimated average effects of macroprudential tools on house price synchronicity with the regional cycle (green) and global cycle (red). Shaded bars show statistically significant standardized coefficients, at the 10 percent confidence level. Estimated panel regressions use data for 41 countries (panel 1) spanning over 1990:Q2 – 2016:Q4 period. Regressions control for business cycle synchronicity, financial integration, and global financial conditions. All regressors are lagged one quarter. Supply side (loans) consists of limits on credit growth, loan loss provisions, loan restrictions, and limits on foreign currency loans. Supply side (capital) consists of capital requirements, conservation buffers, the leverage ratio, and the countercyclical capital buffer. Supply side (general) consists of reserve requirements, liquidity requirements, and limits on foreign exchange positions. Demand-side includes limits to debt-service-to-income and LTV ratios. All loans measures include demand side and supply side (loans). Fiscal-based measures include taxes such as ad valorem, seller’s and buyer’s stamp duty, or other taxes.

VI. Conclusions

Using various proxies for global financial conditions, this paper confirms that the abundance of liquidity owing to accommodative financial conditions is positively associated with house price synchronicity at country and city levels. While higher house price synchronicity may benefit countries in some cases, positive association with global financial conditions could also suggest a stronger transmission of external shocks into the domestic economy or to major cities within an economy. Moreover, house price synchronicity dynamics among major cities may vary from that of their respective countries’ owing to the attractiveness of these cities to global investors. Our analysis also finds that the positive association of global financial conditions with house price synchronicity was stronger preceding the global financial crisis.

Countries with more flexible exchange rate regimes, on average, may possess the ability to attenuate the positive impact of global financial conditions on house price synchronicity. Moreover, our empirical analysis suggests that major cities located in countries with more flexible exchange rate regimes possess the ability of attenuating the impact of global financial conditions on city-level house price synchronicity as well.

Finally, we find that house price growth in countries that experience lower house price synchronicity with the rest of the world, on average, are more sensitive to macroprudential policies that are aimed at reducing domestic vulnerabilities, compared to high synchronicity countries. However, our empirical analysis suggests that macroprudential policies intended at addressing domestic vulnerabilities also possess the unintended effect of reducing house price synchronicities, thereby allowing policymakers to regain partially control over local house price dynamics.

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Annex I: Data Sources, Coverage, and Summary Statistics

Annex Table 1.1.

Data Sources

article image
Source: Authors.Note: FCI = financial conditions index; PCA = principal component analysis; VIX = Chicago Board Options Exchange Volatility Index.
Annex Figure 1.1.
Annex Figure 1.1.

Sample Coverage

Citation: IMF Working Papers 2018, 250; 10.5089/9781484385692.001.A001

Source: Authors’ calculations.Note: Cities selected are the largest cities based on population, and overlap with the top 50 cities for global investors identified by Cushman & Wakefield (2017). The sample comprises over 70 cities based on the top 30 cities for global investors in Cushman & Wakefield’s (2017) Global Capital Markets 2017 report’s economic scale, financial center, technology hub, and innovation pillars are also used in robustness checks. If none of the cities in a country (where data are available) are chosen based on the four pillars stated above, the largest city by population in the country is included. Moreover, an additional sample with 44 major cities is also constructed.
Annex Table 1.2.

List of Economies and Cities in the Analysis

article image
Source: Authors’ calculations.

See the Annex Figure 1.1 note above for city selection criteria. Cities with asterics are included in the smaller sample.

Annex Table 1.3.

Standard Deviations of the Variables Used in Empirical Analyses

article image
Source: Authors’ calculations.