Housing Finance and Real-Estate Booms
A Cross-Country Perspective
Author: Mr. Eugenio M Cerutti, Jihad Dagher1, and Mr. Giovanni Dell'Ariccia
  • 1 0000000404811396https://isni.org/isni/0000000404811396International Monetary Fund

Contributor Notes

Authors’ E-mail Addresses: ECerutti@imf.org; JDagher@imf.org; GDellAriccia@imf.org
Publication Date:
03 Jun 2015
eISBN:
9781513571393
Language:
English
Keywords:
SDN; credit boom; boom; mortgage market; mortgage; mortgage credit; housing boom; housing credit excess; credit growth; household credit; HH credit boom
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The recent global crisis highlighted the risks stemming from real estate booms. This has generated a growing literature trying to better understand the sources and the risks associated with housing and credit booms. This paper complements and supplements the previous work by (i) exploiting more disaggregated data on credit allowing us to dissociate between firm-credit and household (and in some cases mortgage) credit, and (ii) by taking into account the characteristics of the mortgage market, including institutional as well as other factors that vary across countries. This detailed cross-country analysis offers new valuable insights.

Abstract

The recent global crisis highlighted the risks stemming from real estate booms. This has generated a growing literature trying to better understand the sources and the risks associated with housing and credit booms. This paper complements and supplements the previous work by (i) exploiting more disaggregated data on credit allowing us to dissociate between firm-credit and household (and in some cases mortgage) credit, and (ii) by taking into account the characteristics of the mortgage market, including institutional as well as other factors that vary across countries. This detailed cross-country analysis offers new valuable insights.

I. Introduction

Housing finance is considered one of the villains of the recent global financial crisis. Before the crisis, booming mortgage markets fueled and were supported by rising house prices and economic activity. When the bubble burst, the spiral inverted. Falling house prices led to household debt overhang and tighter lending standards and led several overleveraged financial institutions into distress. This pattern, most evident in the United States, was present in many countries hard hit by the crisis, with some variation in the underlying drivers and innovations that led to the housing boom.1 The recessions and massive increases in public debt that ensued from the fallout in housing led to a renewed debate about financial regulation, consumer protection, and more generally the role asset prices (including the housing market) should play in macro policy decisions.

Yet at least until the crisis, there was a widespread consensus for policies in support of mortgage markets (ranging from interest tax deductibility to publicly supported securitization markets). Many considered access to housing finance as essential to promoting home ownership, which in turn was seen as beneficial to social stability and, ultimately, economic growth.

Then, a tension emerged between increasing access to housing finance and containing the dangers associated with fast-growing mortgage credit. Deeper mortgage markets allow cheaper access to housing credit and promote home ownership. But house-price (real-estate) boom episodes have often ended in busts with important macroeconomic consequences, especially when the boom was financed through fast credit growth.2 This note explores this conflict.

The note first analyzes mortgage markets across countries. It documents the heterogeneity of housing finance institutions. And it asks whether housing finance characteristics (for example, mortgage characteristics); institutional factors (for example, rule of law); and/or macro factors (for example, inflation volatility) can predict cross-country differences in mortgage-market depth. Further, it explores the benefits of housing-finance development for home ownership and, more generally, welfare.

The note, then, turns to the interaction of housing finance and the evolution of house prices, more specifically between credit and house-price booms. In particular, we ask whether housing finance characteristics matter in determining the frequency and amplitude of boom–bust episodes and the probability that they end up badly (for example, are followed by recessions and/or banking crises). And, in that context, we ask whether one can tell bad booms from good ones, ex-ante. The note exploits a new data set on household- and real-estate-related lending; a step forward relative to most of the existing literature that largely relies on aggregate private-sector credit.

The note approaches these questions with the objective of providing policymakers—who need to detect booms and assess their potential macroeconomic impact—with a set of stylized historical patterns that could inform the appropriate policy response. The rest of the note is structured as follows. Section II describes the characteristics of mortgage markets in a large group of advanced economies and emerging markets, and reviews of the benefits of deeper mortgage markets. Section III identifies real-estate boom and credit boom episodes. It then examines their interaction and macroeconomic impact, and factors that might explain how they end. Section IV concludes with a discussion of the policy implications.

II. Mortgage Markets Around the World

The purchase of a house (typically funded with a mortgage) is the largest transaction of most households’ lifetime, and mortgage loans are its main funding tool. This is reflected in the importance of mortgage loans in household credit across countries. In 2011 (the latest year for which we have reliable mortgage data for a broad set of countries), the median share of mortgages in household credit in our sample was about 70 percent, with only six countries below a 40 percent share (Figure 1). Related, countries with a larger share of mortgage to household credit also have a high share in household credit to total credit. That said, the size of mortgage markets in terms of GDP differs sharply across countries, ranging from below 1 percent in Russia and Turkey to about 80 percent in the Netherlands, New Zealand, and Switzerland (based on 2001–05 averages). Also, while the crisis led to some degree of deleveraging, in most countries, mortgage-to-GDP ratios as of 2011 remained above their average for 2001–05. Further, their cross-country ranking and dispersion did not significantly change over the past few years.

Figure 1:
Figure 1:

Share of Mortgages to HH Credit and HH Credit to Total Credit, as of 2011

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Not surprisingly, this cross-country variation reflects heterogeneity in economic and financial development. In univariate regressions, differences in GDP per capita and the credit-to-GDP ratio explain, respectively, more than 50 and more than 60 percent of the variation in the mortgage-to-GDP ratio (Figure 3, left-hand side). Nonetheless, there is still important variation (linked to institutional, cultural, and macroeconomic factors) in the depth of mortgage markets across countries with similar levels of economic development. For example, Austria and the Netherlands have similar GDP per capita, but they exhibit very different mortgage-to-GDP ratios (in 2005, 18 for Austria and 84 percent for Netherlands). This is valid for groups of countries at different income levels as depicted in the right-hand side panel of Figure 3, which divides the sample into three groups (emerging economies, advanced economies with GDP per capita below $30,000, and advanced countries with GDP per capita above $30,000).

Figure 2:
Figure 2:

The Cross-Section of Outstanding Mortgage Debt/GDP, 2001-05 Average

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Figure 3:
Figure 3:

Development Levels and Mortgage Debt/GDP, 2001-05

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

The rest of this section reviews the literature on the benefits of deeper mortgage markets in steady state (that is, ignoring the effects of rapid mortgage credit growth—the focus of section III), and how part of the cross-country heterogeneity in mortgage market depth can be related to institutional and macroeconomic factors as well as specific mortgage characteristics.

A. Benefits of Deep Mortgage Markets

The common wisdom behind government support to housing finance is that deeper mortgage markets benefit homeownership; which in turn is welfare improving. By and large, there is strong support for the argument that deepening and innovations (for example, lower down payment requirements) in mortgage markets favor homeownership. And theoretical as well as empirical evidence exists—especially for the United States—for the associated welfare benefits.

Country-specific institutional and cultural factors make it difficult to identify the contribution of housing-finance development to homeownership. In fact, in our sample, cross-country data present a negative relationship between homeownership and mortgage credit. A country-by-country time series approach overcomes some of these difficulties and typically points to a positive relationship between mortgage credit and homeownership. Figure 4 documents this correlation for the case of U.S. states, which share similar institutional and cultural environments (and for which high quality data are available).

Figure 4:
Figure 4:

Mortgages and Homeownership across U.S. States

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Obviously, even at the single-country level, the relationship could be driven by common trends, and causality is not immediately established. Yet, several papers exploiting microeconomic data find that increases in mortgage availability through innovations and higher LTV have a positive impact on homeownership. For instance, lower down-payment ratios are associated with non-trivially higher homeownership rates for younger households: a decrease in down payment from 40 to 20 percent is associated with an 5 to 8 percentage point increase in the proportion of young households (age 26–35) living in owner-occupied houses (Chiuri and Jappelli, 2003; Chambers, Garriga, and Schlagenhauf, 2009).

Evidence also suggests that homeownership can have a positive effects on welfare factors. In particular, it promotes community investment (for example, DiPasquale and Glaeser, 1999, using data for the United States and Germany) and school attainment (for example, Aaronson, 2000, using data for the United States), and is associated with lower crime incidence and higher levels of life satisfaction (see Rohe and Lindblad, 2013, summarizing U.S. and some European studies).

From an economic perspective, homeownership has been associated with improved saving and investment opportunities for households. Since purchasing a home requires a sizeable down-payment, younger households are more likely to save if homeownership is within reach (Dietz and Haurin, 2003). Further, mortgages commit households to a level of savings that they might not otherwise achieve. This argument weakens somewhat with the advancement of financial innovations that allows households to easily extract equity from their homes.

A second, related, economic argument is that housing is an attractive investment. Indeed, evidence from the United States suggests that the risk-return profile of housing investments compares favorably with that of the stock market (Li and Yang, 2010); albeit risk-return profiles on housing vary significantly across regions.

On the negative side, higher homeownership rates are associated with reduced labor mobility (see for example, Dietz and Haurin 2003 for a review). Also, the size of the benefits of homeownership might be smaller than what U.S.-based evidence suggests (see DiPasquale and Glaeser, 1999, on community investment). In countries such as Germany and Switzerland—where home ownership rates are low but rental contracts are often long-term—strong communities, low crime, and high social capital are often present. Therefore, policies that increase or even subsidize homeownership (for example, from tax exceptions to government intervention in mortgage markets to increase financial depth) should be based on country-specific cost-benefit analyses.3

B. Factors Associated with Cross-Country Differences in Mortgage Markets

Mortgage market development is tightly linked to overall financial development, which in turn is related to the quality of institutions and particularly the legal framework that governs financial contracts (see, among others, Beck and Levine, 2008). Macroeconomic factors can also play a role in explaining cross-country differences in housing finance. For example, high inflation volatility (which increases the volatility of returns on nominal contracts) can have an adverse impact on the development of financial markets (Warnock and Warnock, 2008 and 2012).

In addition, specific mortgage-market characteristics may contribute to some of the cross-country differences in market depth. In some countries, the mortgage market receives little-to-no support from the government while, in others, households are given strong incentives (mainly through tax deductions) that tilt their decisions toward ownership and indebtedness and away from renting. Differences also exist in default laws, the maturity of loans, their relative size, and the types of funding used by lenders. We collected data on the following six house financing characteristics for the countries in our sample for 2005 or the closest year to 2005 for which data are available (See Tables in Annex I).

Maximum Observed LTV: The country-specific upper limit of LTV can serve as a proxy of borrowing constraints (especially for new borrowers). In many instances the maximum observed LTV corresponds to its legal limit (when such limit exists).4 As shown in Figure 5, most countries seem to be in the 70–80 and 90–100 LTV buckets. The maximum observed LTV ranges from 70 percent (Colombia, Hong Kong, and Hungary) to 125 percent (the Netherlands). The median maximum observed LTV is 83 percent.

Figure 5:
Figure 5:

Cross-Country Differences in LTV and Maturities of Mortgages

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Term to Maturity: The maturity of mortgage loans varies between 7 years (Turkey) and 45 years (Sweden), with a median of 25 years. This heterogeneity is also likely to be linked to differences in financial development and home affordability.

Interest Type: Mortgage rates can be fixed through the life of a loan, or vary over time with changes linked to key interest rates in the economy. In our sample, the standard mortgage rate is variable in 30 countries, fixed in 12 countries; while in the remaining 14 countries both contracts are observed. Variable rates are more common in emerging economies (Figure 5).

Funding Model: There is also heterogeneity across countries’ funding models (for example, retail deposits, covered bonds, securitization) used for financing mortgages. Moreover, different funding models can be present simultaneously within the same country. In this context, we differentiate between countries that use retail deposits as the primary source of financing and others that rely more on alternative funding sources. In most of our sample (44 countries), banks funded mostly by retail deposits play a major role in mortgage lending (Figure 6).5

Figure 6:
Figure 6:

Cross-Country Differences in Funding Model

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Degree of Lender Recourse on Mortgages: The rights of lenders to pursue a borrower’s assets (other than the house securing the mortgage) in case of a default, referred to as the right to recourse, also varies across countries (and sometimes, across jurisdictions within the same country, such as across different states in the United States). In about 44 of the 53 countries in the sample, there is full recourse on mortgages. Full recourse increases borrowers’ incentives to honor the terms of the contract and has been associated with lower default rates (see Duygan-Bump and Grant, 2008, for evidence from Europe).

Mortgage Interest Tax Deduction: In 33 of the countries in our sample, households are allowed to deduct mortgage interest payments from their taxable income. Interest deductibility is more common in advanced economies than in emerging market countries (about two-thirds versus half of the cases in our sample; Figure 6). And it varies substantially from country to country. In many cases, deductions are capped to a maximum (for example, Poland and South Korea), and the United States and Norway are the only cases that allow for nearly full deductibility without taxing imputed rents (IMF, 2011). Government support to mortgage markets can go beyond interest rate deductibility, and include subsidies (for example, first-time buyers or other selected groups), a government agency providing guarantees and/or loans, capital-gain tax deductibility, and state-owned institutions playing a major role in the mortgage market (IMF, 2011). But cross-country data availability limits our focus to interest deductibility.

Following and augmenting the current literature on the importance of institutional and macroeconomic factors, we analyze the correlation of the average mortgage depth over 2000–05, measured as the ratio of total mortgage debt to GDP, with institutional, macroeconomic, and house financing characteristics (See Annex II for further details). Although the cross-sectional nature of the data makes it difficult to attribute a definitive causal interpretation, similar to Warnock and Warnock (2012), we find that institutional factors, such as the strength of legal rights and ease of registering property, are statistically significantly correlated with the size of mortgage markets (see the cross-country differences in Box 1). The analysis also highlights a positive relationship with per-capita GDP, which is also a good proxy for hard-to-measure differences in institutional quality and in the level of financial development. More interestingly in our context, housing finance characteristics—LTV, term-to-maturity, and funding model—also contribute to explaining part of the cross-country variation in the depth of mortgage markets relative to a specification based solely on macro and institutional variables. However, the fit of the model improves by only about 10 percent when adding house finance characteristics on top of the institutional (for example, ease of registering property) and GDP per capita, which remain statistically significant.

III. Housing Finance and Real-Estate Booms

There are several reasons why real-estate and mortgage markets sit at the nexus of macroeconomic and financial stability. First, size matters. As seen in the previous section, real-estate-related lending accounts for a large share of household credit and often a major portion of a financial sector’s activities. Second, leverage matters. Through mortgages, households are allowed leverage limits much higher than with other asset classes. Further, real estate is collateral for not only households and construction companies, but also firms in other sectors. And major mortgage lenders are typically commercial banks, which are themselves leveraged. In this context, mortgage markets might become excessively large or increase swiftly due to lax lending standards or distorted incentives (for example, implicit leverage subsidies linked to interest deductibility) harboring vulnerabilities for the overall economy.

This section explores the relationship between mortgage credit, house-price dynamics, and real-sector performance; and the extent to which it is influenced by housing finance characteristics. Since long time-series on mortgage credit are lacking in many countries, we rely instead on household credit. Mortgage debt typically represents a large share of household debt (the median mortgage-to-household credit ratio is about 70 percent in our sample), and the two variables are highly correlated.6 This finding suggests that approximating mortgage credit with household credit is appropriate given the data constraints.7

A. Defining and Identifying Credit Booms and Real-Estate Booms

There is no widely accepted definition of what constitutes a real-estate or a credit boom. These episodes are generally defined as large and persistent deviations of these variables from some historical norm. And previous literature has employed different definitions of historical norm (different filters, different time windows, country-specific or not) and different approaches to measure these deviations (different thresholds, real versus nominal growth, absolute levels and values relative to GDP).8

Here we focus on the real growth of both house prices and credit and define boom episodes as deviations from a country-specific historical norm. Specifically, we identify episodes by comparing the real evolution (measured at year-over-year rates) of credit and house prices at quarterly frequency. We classify an episode as a boom if the following two conditions are satisfied: (i) the real growth rate of credit (house prices) is greater than 10 (5) percent, or two standard deviations of the country-specific distribution of credit (house prices) real growth rates in a given quarter; and (ii) the real growth rate of credit (house prices) is above 10 (5) percent or one standard deviation of the country-specific distribution of credit (house prices) real growth rates for a period of at least two years. The first condition ensures that a boom episode contains at least a quarter with an annual growth rate above 10 percent or two standard deviations for credit, and above 5 percent or two standard deviations in the case of house prices. The second conditions rules out very short-lived spikes in credit and house prices.9

Cross-Country Institutional Differences Related to Housing Markets

There is substantial heterogeneity in terms of countries’ institutional factors. The legal-right index from the World Bank’s Doing Business reports (WBDB), which is a proxy of the extent to which the country’s bankruptcy and collateral laws facilitate lending, shows that most emerging economies and a few advanced economies in our sample display relatively low legal-right indexes (chart, top left-hand-side panel). The ease-of-registering property index also shows high heterogeneity across countries (bottom panel). Finally, cross-country differences are not as sharp with regard to the WBDB credit-information index, which measures lenders’ access to standardized and informative sources of borrowers’ history and creditworthiness.

A01bx01ufig1
Source: World Bank, Doing Business, 2005 data.1/ It measures the extent to which the country’s bankruptcy and collateral laws facilitate lending.2/ It measures the depth of lenders’ access to standardized and informative sources of credit information on potential borrowers.3/ It measures the costs of registering a property.

Occurrence of credit booms

We apply this definition to the 53 countries for which house-price, household-credit, and corporate-credit data are available on a quarterly basis. The sample starts as far back as the 1970s, for some countries, and extends to 2012. We focus our attention on three different types of credit booms: (i) household-credit booms; (ii) corporate-credit booms; and (iii) overall private-sector-credit booms. Although private credit is the sum of household and corporate credit, we include it separately in the analysis. First, it is a useful benchmark as the variable most often used in previous studies on credit booms. Second, private-credit booms generally coincide with generalized credit overheating episodes in which both household and corporate credit are booming.

Based on our definition, we find 83 household-credit booms, 68 corporate-credit booms, and 67 private-credit booms during the period 1970–2012. Reflecting the composition of our sample, most episodes (about 60 to 65 percent) occur in advanced countries. However, once we control for this bias, emerging markets appear to be in a boom state more often than advanced economies (the portion of quarterly observations classified as booms is roughly double that of advanced economies).

The higher frequency of household-credit booms is reflected in Figure 7, which shows the proportion of countries that are experiencing credit booms during each quarter.10 The higher occurrence of credit booms after the 1980s could be at least partly attributed to the wave of banking and mortgage deregulation as well as to financial innovations such as the rise in securitization (for more details see IMF, 2008, and Muellbauer and Murphy, 1997). The picture also suggests that booms tend to come in bunches, suggesting that global factors play at least some role (Mendoza and Terrones, 2008).

Figure 7:
Figure 7:

Occurrence of Credit Booms during 1970-2012

(as percentage of countries in the sample)

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Occurrence of house-price booms

We identify 85 house-price booms.11 Most countries in the sample experienced at least one of these episodes. And, as shown in Figure 8, the all-time peak in the relative occurrence of house-price booms was during the period just before the recent global financial crisis (in 2005, there were booms in more than half of the sampled countries). Not surprisingly, booms were rare in the immediate aftermath of the global financial crisis: less than 10 percent of the countries were experiencing a credit boom as of 2012:Q4.12

Figure 8:
Figure 8:

Occurrence of House-price Booms and Credit Booms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Household-credit booms and house-price booms tend to occur together. Moreover, they seem more in sync than private-credit booms and house-price booms. This aligns with findings that household credit is a better proxy for understanding house-price fluctuations than the commonly used private-sector credit.13

B. Interaction between Real-Estate Booms and Credit Booms

The rest of this section explores the historical relationship between credit booms and house-price booms and the macroeconomic performance around these episodes. Then, it studies the factors that can help predict whether house-price booms will turn into recessions and/or financial crises.

Can credit booms help predict house-price booms?

Regression analysis suggests that household-credit booms are good predictors of real-estate booms (see Annex IV). The presence of a household-credit boom increases the probability of a real-estate boom to 57 percent from an unconditional probability of 29 percent. Across specifications, household-credit booms are better predictors of house-price booms than private-credit booms. In contrast, the level of household debt to GDP (a proxy for household leverage) is negatively correlated with the occurrence of real-estate booms. Global factors simultaneously driving house-price booms across countries are captured by the evolution of the Federal Funds rates and Chicago Board Options Exchange Market Volatility Index (VIX). Lagged GDP growth is positively associated with the probability of a real-estate boom, suggesting that these booms tend to start during or after buoyant economic growth.

With respect to housing finance characteristics, the analysis indicates that the higher the maximum observed LTV, the higher the probability of a real-estate boom. This most likely captures the effect of relaxed lending standards on house prices, and is supported by other studies (Crowe and others, 2011; IMF, 2011; and Cerutti, Claessens, and Laeven, 2015) that have found a positive relationship between LTV limits and house-price increases over time. Hence, to the extent that this coefficient can be given a causal interpretation, LTVs appear to be a well-targeted tool for limiting real-estate price fluctuations.

Classifying real-estate booms based on the evolution of credit

So far the analysis has highlighted that credit booms, especially household-credit ones, are good predictors of real-estate booms. In the rest of the section, we further explore this interaction by identifying the characteristics and consequences of real-estate booms as a function of whether they coincided with: (i) no credit booms; (ii) only corporate-credit booms, (ii) only household-credit booms, and (iv) private (twin household and corporate) credit booms.

Most real-estate booms in the sample coincide with private (twin household and corporate) credit booms.14 Table 1 shows that 49 of the 85 real-estate booms coincide with private-credit booms. There are 16 real-estate booms accompanied by household-credit (but not aggregate) booms. And only two house-price booms in the sample are associated with booms limited to corporate credit (Hong Kong 2004:Q1–05:Q4 and Singapore 2006:Q3–08:Q2). While they may make for interesting case studies, these findings are too few in number for meaningful comparison with the other type of house-price booms. Finally, 18 real-estate booms happened without any type of credit booms.15

Table 1:

Characteristics of House-price Booms

article image
Source: IMF staff estimations based on Bank for International Settlements; central bank data; Haver Analytics; and IMF, International Financial Statistics.Notes: A total of 85 house-price booms were identified. In addition of the 83 house-price booms presented in the table, there are two house-price booms which were associated with corporate credit booms (Hong Kong 2004:Q1–05:Q4 and Singapore 2006:Q3–08:Q2). They are not reflected in the table because their small sample does not provide enough observations for meaningful comparisons with the other type of house-price booms.

Real-estate booms associated with different types of credit episodes also differ with regard to size and duration. On average, house-price booms accompanied by private-credit booms (about 18 quarters) last longer than the other two cases (about 14 quarters). The average real increase in house prices during booms accompanied by a private-credit boom (14 percent) is higher than in episodes with only household-credit booms (9 percent) and without any credit boom (10 percent). Similarly, as also shown in Figure 9, the average real household-credit growth is much larger in the case of house-price booms accompanied by private (twin household-corporate) credit booms (about 21 percent) than in the case of house-price booms with only household-credit booms (11 percent) or without any credit booms (5 percent).

Figure 9:
Figure 9:

Change in House Prices, and JH Credit during House-price Rooms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Note: Type 0 is house-price boom with no credit boomType 1 is house-price boom with only HH credit boomType 2 is house-price boom with private credit boom

Macroeconomic performance during real-estate booms

As discussed previously, real economic activity, aggregate credit, and house-price fluctuation are closely linked through wealth effects and the financial accelerator mechanism (see, among others, Bernanke and Gertler, 1989; Kiyotaki and Moore, 1997; Gilchrist and Zakrajsek, 2008, Mian and Sufi, 2011, Quint and Rabanal, 2014). In an upturn, better growth prospects improve borrower creditworthiness and collateral values. Lenders respond with an increased supply of credit and, sometimes, looser lending standards. More abundant credit allows for greater investment and consumption and further increases house prices and collateral values. In a downturn, the process is reversed.

In this context, not surprisingly, economic activity is significantly higher during real-estate booms compared to non-boom years (See Table 2 and Figure 10). Real GDP growth during housing booms is higher than during non-boom periods by about 1¼ to 2 ½ percent. These differences are statistically significant (see p-values in the lower part of Table 2). In addition, the different performances among housing boom types shows that housing booms that coincide with private-credit booms register higher (statistically significant) real-GDP growth than episodes accompanied by household-credit booms or occurring in the absence of a credit boom.

Table 2:

Macroeconomic Performance during House-price Booms

article image
Source: IMF staff estimations based on Bank for International Settlements; central bank data; Haver Analytics; and IMF, International Financial Statistics.Notes: A total of 85 house-price booms were identified. In addition of the 83 house-price booms presented in the table, there are two house-price booms which were associated with corporate credit booms (Hong Kong 2004:01–05:04 and Singapore 2006:Q3–08:Q2). They are not reflected in the table because their small sample does not provide enough observations for meaningful comparisons with the other type of house-price booms.
Figure 10:
Figure 10:

Average Growth of Real GDP during House-price Booms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Note: Type 0 is house-price boom with no credit boomType 1 is house-price boorn with only HH credit boomType 2 is house-price boom with private credit boom

Consistently, consumption and investment growth are higher during house-price booms with private-credit booms than in tranquil times (see p-values of joint coefficient test for “Type 2 and 3 are the same” in Table 2). There is also evidence of an appreciation of the exchange rate with housing booms that were accompanied by credit booms. This is consistent with the literature (for example, Dell’Ariccia and others, 2012) that highlights that credit booms are associated with real exchange-rate appreciations and current-account deteriorations.

Finally, the analysis shows that inflation typically remains subdued and is not much different from levels that prevail in tranquil times. This is in line with the recent empirical evidence documenting how credit and asset-price imbalances can grow under seemingly tranquil macroeconomic conditions (low and stable inflation and output gap). It suggests that, should monetary policy lean against the wind to contain these kind of episodes, a tradeoff might emerge (at least in the short-run) with its traditional price-stability objective. (See Bayoumi et al., 2014, for a review of the debate on the role of monetary policy in containing asset-price booms.)

Performance in the aftermath of real-estate booms

House prices generally decline after real-estate booms (although not in all cases), and in several cases, the adjustment is substantial. Figure 11 (left-hand side) displays the correction in house prices observed within a three-year window after the end of real-estate booms. Real-estate booms that occur with private-credit booms (type 2 in the figure) tend to be followed by the largest falls in house prices. Further, there is little evidence that these sharp declines are systematically followed by rebounds (Figure 11, right panel).

Figure 11:
Figure 11:

Change in House Prices after House-price Booms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Note: Type 0 is house-price boom with no credil boomType 1 is house-price boom with only HH credit boomType 2 is house-price boom with private credit boom

Drops in house prices are generally accompanied by recessions (in our sample, 49 out of 78 house-price booms ended up in recessions).16 Yet, there is substantial heterogeneity in the performance of real GDP post real-estate booms (see Figure 12). This largely depends on whether a bust occurs. Indeed, output losses in recessions accompanied by housing busts are two to three times greater than in “normal” recessions (Claessens, Kose, and Terrones, 2008).

Figure 12:
Figure 12:

Lowest Annual Change in Real GDP after House-price Booms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Note: Type 0 is house-price boom with no credit boomType 1 is house-price boom with only HH credit boomType 2 is house-price boom with private credit boom

Can we tell bad real-estate booms from good ones?

The previous analysis has shown that the end of a real-estate boom is often related to significant falls in house prices and economic recessions. The question then arises whether these “bad” booms can be distinguished from “good” ones (those that do not end up in recessions) ahead of time. We address this question by exploring whether a boom’s characteristics, such as duration, associated type of credit boom, and housing finance characteristics, can help predict whether it will lead to a recession.17

About two-thirds of the booms in our sample end badly according to the above criteria. And this proportion is about the same in advanced economies and emerging markets (Figure 13). The timing of the booms shows that not only did more booms occur since 2000, but also that a much larger proportion ended in recessions. The role of the 2007–09 global financial crisis is evident in the right-hand-side panel of Figure 13. There does not seem to be a clear relationship between a boom’s duration and the probability that it will end up badly.

Figure 13:
Figure 13:

Good and Bad Booms

Citation: Staff Discussion Notes 2015, 012; 10.5089/9781513571393.006.A001

Note: The total number of booms that can be identified as good orbad is 78.1/ 33 house price boom episodes (out of 78) are shown since funding model characteristics are availabe only since 2000.

The type of credit boom accompanying a real-estate boom sheds some light on how it could end (Figure 13). Real-estate booms accompanied by private-credit booms (twin household-corporate credit) are more likely to end in recessions than housing booms accompanied by only household-credit booms or without any credit booms. The funding model of housing finance also seems to matter. In countries that finance housing credit primarily through bank retail deposits, booms have a lower probability of ending in recessions. Perhaps this reflects the fact that access to wholesale markets generally increases the leverage of mortgage lenders, increasing the potential sharp deterioration of lenders’ balance sheets in a bust (as during the global financial crisis).18 A more formal analysis summarized in Annex V confirms these findings, for the period after 2000 when house finance characteristics are available.

IV. Policy Implications

The recent global financial crisis has placed the housing market at the center stage of economic policy discussions on financial stability. While the advantages of a deep mortgage market cannot be ignored, it is now also widely recognized that housing credit excesses happen and that their far-reaching negative consequences warrant a reassessment of how macroeconomic policy should look at real-estate market developments. Against this background, we analyzed how mortgage-market depth varies across countries, and the dynamic relationship between housing finance and house-price and credit boom-bust episodes.

The findings in this note indicate that cross-country differences in the depth of mortgage markets are associated with institutional elements (for example, collateral and bankruptcy laws that define the legal rights of borrowers and lenders, and the ease of registering property), macroeconomic factors (for example GDP per capita), and some housing finance characteristics. These differences suggest that there is room for policy action. As suggested in the literature (for example, Warnock and Warnock, 2012), several emerging and advanced countries could improve access to house financing through better legal frameworks and more stable macroeconomic environments. Deeper and more inclusive (for example with low down-payment ratios) mortgage markets are correlated with higher home ownership. And higher home ownership rates are linked to social benefits, such as higher school attainment, higher social capital, lower crime, and higher levels of life satisfaction and psychological health.

However, some of the housing finance characteristics that favor mortgage market deepening, by increasing access and affordability, may also promote fast credit growth and eventually entail greater risks to financial stability. Relaxed lending standards, such as higher LVRs and longer maturity of mortgages, seem to correlate with “excessively” rapid house credit growth and costly boom-bust cycles. We also find that house-price booms that are funded through wholesale markets are more likely to end badly (that is, in recessions).

In this context, housing finance regulation—which is nowadays a widely accepted part of the macroprudential policy arsenal—could play a role in reducing the frequency and severity of housing boom episodes. Unlike monetary policy that requires an overall increase in interest rates to dampen household/mortgage credit, if effective, macroprudential policies could target directly household leverage and indebtedness and the risk profile of mortgage originators and investors (Dell’Ariccia and others, 2012).

While these findings confirm previous work identifying macroprudential policies as useful tools for containing systemic vulnerabilities, our historical analysis also highlights that the role of monetary policy should not be downplayed. About 60 percent of the real-estate booms in our sample occurred together with private-credit booms. Moreover, in those episodes, the occurrence of a private-credit boom was not only associated with simultaneous household- and corporate-credit booms, but also with rapid and broad economic growth. These signs of overheating in other sectors could call for monetary policy tightening (Crowe and others, 2011; Dell’Ariccia and others, 2012; IMF, 2013b) after weighing the potential benefits and risks to financial stability. Monetary policy tightening could have both positive and negative effects on financial stability, and these need to be weighed before resorting to such policy during a boom. On the risks side, monetary policy can weaken financial conditions of households and firms, increase the interest rate burden, induce deleveraging and reduce the value of legacy assets. However, by reducing the leverage (or the rate of increase in leverage) monetary policy can strengthen financial stability over the medium term. However, the absence of inflation pressure during many real-estate booms calls for close consideration of proposals in favor of including real-estate prices in monetary policy response functions (Iacoviello, 2005; Aspachs-Bracons and Rabanal, 2011)

Finally, dealing with real-estate booms effectively requires a broad mix of policies that goes beyond the use of macroprudential and monetary policies, and may also involve realignment of incentives over the long run. Well-paced country-specific measures to strengthen supply-side responses would mitigate the impact of demand shocks over the long run. Abrupt supply-side modification at the peak of house-price booms or at the beginning of house-price busts could exacerbate the correction in house prices. More generally, the policy mix should also include measures to minimize distortions linked to special treatment of housing and homeownership.

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Annex I—Data Sources and House Finance Characteristics

Table A1.1–

Data Sources and Period Coverage

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Annex II: Mortgage Market Depth and Institutional, Macroeconomic and Housing Finance Factors

This Annex studies the correlation between countries’ mortgage market depth and institutional, macroeconomic and housing finance factors. More specifically, it presents results from variations on the following cross-sectional regression:

MCY      i=α+β⁢    institutionsi+γ⁢    Macroi+δ⁢    Housing Financei+∈i

in which MCY      i is the ratio of mortgage credit to GDP. The institutional variables include: the legal-rights index, the credit-information index, and the ease-of-registering property index from the World Bank’s Doing Business database. All these variables are 2001–05 averages. The macroeconomic variables include the average GDP per capital (in log) and volatility of inflation over the same sample period. The housing finance variables captures the characteristics discussed in Section II.B (see also Table A1.2) and include: a dummy indicating whether interest payments are tax deductible, the maximum observed LTV, a dummy indicating whether there is full recourse on mortgage debt, a variable ranging from 1 to 3 increasing in the popularity of fixed-rate mortgages (vs. variable rate), the maturity of a typical mortgage contract (in years), and a dummy variable indicating whether mortgage lending is dominated by retail funded institutions.

Table A1.2–

Institutional and House Finance Characteristics

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The first column of Table 1 introduces the institutional variables. It shows that the legal rights index is a variable very significantly associated with a deeper mortgage market. The second column adds macro variables as controls. Not surprisingly, higher GDP per capital is strongly and significantly associated with deeper mortgage markets. Also there is evidence that the ease of registering a property is associated with deeper mortgage markets.

Columns (3) to (10) introduce the housing finance characteristics. The results are suggestive of a positive relationship between maximum observed LTV and the size of the mortgage market (see column 4 and the robust regression results in column 10). From a theoretical standpoint, an increase in the maximum LTV would, everything else constant, increase the total stock of mortgages, through both an intensive margin effect (larger mortgages) and an extensive margin effect (increasing the demand for loans). Obviously, the relationship goes also in the other direction, with deeper mortgage markets potentially allowing greater LTV ratios.

The typical duration of mortgage contracts is positively correlated with the depth of the mortgage market (column 7), but this result is not robust. Finally, countries where the main originators are banks that fund themselves primarily with retail deposits have significantly lower mortgage to GDP ratios (column 8). Non-retail sources of funds may help alleviate a bank’s liquidity and maturity mismatch problems related to mortgage lending. Indeed they have also been linked to higher leverage in the banking sector (see Hahm, Shin, and Shin 2011).

In summary, although it is difficult to attribute a definitive causal interpretation to these regressions, housing finance characteristics—LTV, term to maturity, and funding model—contribute to explaining about an additional 10 percent of the cross-country variation in the depth of mortgage markets relative to a specification based solely on institutional and macro variables.

Table A2.1:

OLS Regressions of Mortgage Credit to GDP on Institutional and Other Variables

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Note: This table reports the estimates from a linear regression of mortgage debt to GDP (average 2001-05) on economic, institutional, and housing finance variables. The last regression in column (10) reports the result from a robust regression. See text for variable definitions. ***, **, * indicates statistical significance at the 1, 5, and 10 percent, respectively.

Annex III: Robustness Analysis of Booms Definitions

This Annex explores whether the findings in this note are robust to the specific definition of boom episodes discussed in Section III.

Existing literature employs various approaches to identify credit and house-price booms and alternative thresholds. To some extent, this is more art than science. Here we compare our baseline boom episodes to the boom dummies generated by using two different filters (a backward-looking cubic trend and a Hodrick-Prescott filter) and different thresholds (separating one-quarter of the real growth rate distribution of each variable and using a minimum boom duration of six quarters instead of eight quarters). In general, the list of episodes we identify is not very sensitive to the methodology used. The major booms are captured under all methodologies. As expected, differences appear in small- and medium-sized booms where different thresholds matter more (Table A3.1).

Table A3.1

- Correlations across Different Methodologies

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