How to Deal with Real Estate Booms: Lessons from Country Experiences
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Ms. Deniz O Igan
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Contributor Notes

The financial crisis showed, once again, that neglecting real estate booms can have disastrous consequences. In this paper, we spell out the circumstances under which a more active policy agenda on this front would be justified. Then, we offer tentative insights on the pros and cons as well as implementation challenges of various policy tools that can be used to contain the damage to the financial system and the economy from real estate boom-bust episodes.

Abstract

The financial crisis showed, once again, that neglecting real estate booms can have disastrous consequences. In this paper, we spell out the circumstances under which a more active policy agenda on this front would be justified. Then, we offer tentative insights on the pros and cons as well as implementation challenges of various policy tools that can be used to contain the damage to the financial system and the economy from real estate boom-bust episodes.

I. Introduction

Real estate booms and busts can have far-reaching consequences. These booms are generally accompanied by fast credit growth and sharp increases in leverage, and when the bust comes, debt overhang and deleveraging spirals can threaten financial and macroeconomic stability. These dangers notwithstanding, the traditional policy approach to real estate booms has been one of “benign neglect”. This was based on two main premises. First, the belief that, as for other asset prices, it is extremely difficult to identify unsustainable real estate booms, or “bubbles” (sharp price increases not justified by fundamentals), in a timely manner. Second, the notion that the distortions associated with preventing a boom outweigh the costs of cleaning up after a bust. The recent crisis has challenged (at least the second of) these assumptions.

The bursting of the real estate bubble in the U.S. led to the deepest recession since the Great Depression, and quickly spread to other countries; in particular those with their own homegrown bubbles. Traditional macroeconomic policy rapidly reached its limits, as monetary policy rates approached the zero bound and sustainability concerns emerged on the fiscal front. Despite the recourse to less standard policy tools (ranging from bank recapitalization to asset purchase programs and quantitative easing), the aftermath of the crisis has been characterized by a weak recovery, as debt overhang and financial sector weakness continue to hamper economic growth. It remains true that bubbles are difficult to identify with certainty. But this task can be made easier by narrowing the focus to episodes involving sharp increases in credit and leverage, which are, after all, the true source of vulnerabilities. While early intervention may engender its own distortions, it may be best to undertake policy actions on the basis of a judgment call (as with inflation) if there is a real risk that inaction could result in catastrophe.

Yet, a call for a more preventive policy action raises more questions than it provides answers. What kind of indicators should trigger policy intervention to stop or slow down a real estate boom? Even assuming policymakers were fairly certain that intervention were warranted, what would be the policy tools at their disposal? What are their impacts? What are their negative side effects and limitations? What practical issues (including political economy considerations) would limit their use? This paper explores these questions.

The paper opens with a summary of how real estate boom-bust cycles may threaten financial and macroeconomic stability. Then, it discusses different policy options to reduce the risks associated with real estate booms, drawing upon several country experiences and the insights from an analytical model. The paper concludes with a brief discussion of guiding principles in using public policy measures to deal with real estate booms and busts.

II. The Case for Policy Action on Real Estate Booms

Before the crisis, the main policy tenet in dealing with an asset-price boom was that it was better to wait for the bust and pick up the pieces than to attempt to contain/prevent the boom altogether. Given this prescription, the characteristics of a particular asset class (such as, for example, how purchases are financed and what agents are involved, or whether the asset has consumption value besides investment value) were secondary details. Yet, if the effectiveness of post-bust policy intervention is limited and the costs are large, these details are critical to determine whether it is worth attempting to contain a boom before it reaches dangerous proportions. From this standpoint, several frictions and externalities make the case for early policy intervention in real estate market booms stronger than for booms in other asset classes.

Leverage and the link to crises

From a macroeconomic stability perspective, what matters may be not the boom in itself, but how it is funded. Busts tend to be more costly when booms are financed through credit and leveraged institutions are directly involved. This is because the balance sheets of borrowers (and lenders) deteriorate sharply when asset prices fall.2 When banks are involved, this often leads to a reduced supply of credit with negative consequences for real economic activity. In contrast, booms with limited leverage and bank involvement tend to deflate without major economic disruptions. For example, the burst of the dot-com bubble was followed by a relatively mild recession, reflecting the minor role played by leverage and bank credit in funding the boom.

Real estate markets are special along both these dimensions. The vast majority of home purchases and commercial real estate transactions in advanced economies involve borrowing. And banks and other levered players are actively involved in the financing. Moreover, homebuyers are allowed leverage ratios orders of magnitude higher than for any other investment activity. A typical mortgage loan carries a loan-to-value ratio of 71 percent on average across a global sample of countries. In contrast, stock market participation by individuals hardly ever relies on borrowed funds. When it does, loans are subject to margin calls that prevent the buildup of highly leveraged positions. As a comparison, in the U.S., the ratio of mortgage loans to real estate assets held by the household sector hovered around 45 percent during the 2000s while the ratio of security credit to holdings of corporate equity was less than 5 percent.

During the current crisis, highly-levered housing markets had a prominent role. In particular, the decline in U.S. house prices was at the root of the distress in the market for mortgage-backed securities. When house prices started to fall below the nominal value of loans, both speculative buyers and owner-occupiers that were unwilling or unable to repay their mortgages could not roll them over or sell their properties and started to default (Mayer et al., 2008). As uncertainty about the quality of the underlying loans increased, the value of mortgage-backed securities began to decline. Investors holding these securities and their issuers, both often highly leveraged themselves, found it increasingly difficult to obtain financing and some were forced to leave the market. This, in turn, decreased the available funds for mortgage financing, starting a spiral. The role of the boom and associated leverage in explaining defaults is evident in Figure 1 that plots the increase in delinquency rates against house price appreciation during the boom years for different U.S. regions and also shows the leverage of households. While the size of the house price boom and level of leverage at the end of the boom are correlated, note that the increase in mortgage delinquency rates was more pronounced in regions with higher leverage for similar boom sizes. Further, commercial banks’ exposure to real estate grew rapidly in the 2000s, reaching 54 percent, almost double the steady level hovering around 30 percent observed from 1960 to 1985 (Igan and Pinheiro, 2010). Higher exposure to real estate in a bank’s balance sheet, in turn, is often associated with higher sensitivity of bank stock returns to real estate market developments (Allen et al., 2009) and with greater reduction in lending when the real estate market collapses (Gan, 2007).

Figure 1.
Figure 1.

Leverage: Linking Booms to Defaults

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Sources: Federal Housing Finance Agency, Mortgage Bankers Association, Bureau of Economic Analysis, U.S. Census Bureau.

This pattern is not limited to the U.S. nor is new to this crisis. The amplitude of house price upturns prior to 2007 is statistically associated with the severity of the crisis impact across countries (Figure 2).3 Put differently, the U.S. market may have been the initial trigger, but the countries that experienced the most severe downturns were those with real estate booms of their own. Historically, many major banking distress episodes were associated with boom-bust cycles in property prices (Figure 3). For example, of the 46 systemic banking crises for which house price data are available more than two thirds were preceded by boom-bust patterns in house prices. Similarly, 35 out of 51 boom-bust episodes were followed by a crisis. By contrast, only about half the crises follow a boom-bust in stock prices and only about 15 percent of stock market boom-busts precede systemic banking crises (virtually all of these cases coincide with a real estate boom-bust).4

Figure 2.
Figure 2.

House Price Run-Up and Severity of Crisis

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Source: Claessens et al (2010).
Figure 3.
Figure 3.

House Price Boom-Busts and Financial Crises

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Sources: OECD, Global Property Guide, IMF staff calculations.Note: Crisis dates, shown in gray, are from the 2003 update of the Caprio-Klingebiel Database (1996, 1999) by Caprio, Klingebiel, Laeven, and Noguera, further complemented in Laeven and Valencia (2010).

A distinguishing feature of “bad” real estate boom-bust episodes is the coincidence between the boom and the rapid increase in leverage and exposure of households and financial intermediaries. In the most recent episode, these coincided in more than half of the countries in a 40-country sample (Text Table 1). Almost all the countries with “twin booms” in real estate and credit markets (21 out of 23) ended up suffering from either a financial crisis or a severe drop in GDP growth rate relative to the country’s performance in the 2003-07 period. Eleven of these countries actually suffered from both damage to the financial sector and sharp drop in economic activity. In contrast, of the seven countries that experienced a real estate boom, but not a credit boom, only two went through a systemic crisis and these countries, on average, had relatively mild recessions.

Text Table 1.

Booms, Crises, Macroeconomic Performance

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Notes: The sample consists of 40 countries. The numbers, except in the last column, show the percent of the cases in which a crisis or poor macroeconomic performance happened after a boom was observed (out of the total number of cases where the boom occurred). The last column shows the number of countries in which a boom occurred. A real estate boom exists if the annual real house price appreciation rate during 2000-2006 is above the ad-hoc threshold of 1.5 percent or the annual real house price appreciation rate in the upward phase of the housing cycle prior to the crisis exceeds the country-specific historical annual appreciation rate. A credit boom exists if the growth rate of bank credit to the private sector in percent of GDP is more than the arbitrary cut-off of 20 percent or it exceeds the rate implied by a country-specific, backward-looking, cubic time trend by more than one standard deviation. A financial crisis is a systemic banking crisis as identified in Laeven and Valencia (2010). Poor performance is defined as more than 1 percentage point decline in the real GDP growth rate in 2008-09 compared to the 2003-07 average.

Wealth and supply-side effects

Real estate is an important, if not the most important, storage of wealth in the economy. For instance, in the U.S., real estate constitutes roughly a third of total assets held by the non-financial private sector. Additionally, the majority of households tend to hold wealth in their homes rather than in equities: only half of American households own stock (directly or indirectly) while homeownership rate hovers around 65 percent. This comparison is not specific to the U.S. and actually is more striking in some European economies, e.g., 23 percent of French households hold stocks but 56 percent are homeowners and the respective ratios in the U.K. are 34 and 71 percent (see, for instance, Guiso et al., 2003). Therefore, for a shock of similar magnitude, the wealth effect of changes in house prices is much larger than those in other asset prices (Case et al., 2005).5

In addition, the supply-side effects associated with house price dynamics can be substantial. The construction sector, a significant contributor to value added, takes property prices as a signal and adjusts production accordingly. As a result, the interaction between real estate boom-busts and economic activity is not limited to financial crises, but extends to “normal times”. In most advanced economies, house price cycles tend to lead credit and business cycles (Igan et al., 2009). This suggests that fluctuations in house prices create ripples in the economy through their impact on residential investment, consumption, and credit while the reverse effect is not as prominent, implying that the housing sector is a source of shocks. In the U.S., a sharp decline in the abnormal contribution of residential investment to growth is a good predictor of recessions (Leamer, 2007). More generally, in advanced economies, recessions that coincide with a house price bust tend to be deeper and last longer than those that do not. Cumulative loss in GDP during recessions associated with housing busts is three times the damage done during recessions without busts (Text Table 2). Again, by contrast, recessions that occur around equity price busts are not significantly more severe or persistent than those that do not (Claessens et al., 2008).

Text Table 2.

Recessions with and without House Price Busts

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Source: Reproduced from Table 8 in Claessens, Kose, Terrones (2008). Notes: The sample includes 21 OECD countries. Mean values are shown. Duration is expressed in quarters. Severe busts are those that are in the top half of all house price bust episodes. * and ** indicate that the difference between means of recessions with house price busts and recessions without house price busts is significant at the 10 percent and 5 percent levels, respectively.

Illiquidity, opacity, and network effects

Boom-bust cycles are an intrinsic feature of real estate markets given the delay in supply response to demand shocks and the slow pace of price discovery due to opaque and infrequent trades as well as illiquidity owing to high transaction costs and the virtual impossibility of short sales.6 These features frequently lead to deviations from equilibrium. In other words, even in the absence of distortions introduced by institutional features of real estate finance systems and policy actions, real estate prices and construction activity can be expected to display large swings over long periods (Ball and Wood, 1999; Igan and Loungani, 2010).7

Network externalities also complicate the picture. Homeowners in financial distress (and in particular in negative equity) have diminished incentives to maintain their properties and do not internalize the effects of this behavior on their neighbors. Similarly, foreclosures (and the associated empty houses) tend to diminish the value of neighboring properties beyond their effect through fire sales. These dynamics highlight the indivisibility (because of which distressed sales weigh heavily on prices as sellers cannot put only a portion of their property on the market) and the double role of real estate as investment and consumption good. These characteristics may also impose constraints on the economy’s adjustment mechanisms: households with negative equity in their homes may be reluctant or unable to sell and take advantage of job opportunities elsewhere, reducing mobility and increasing structural unemployment. The preferential tax treatment of homeownership exacerbates this problem, by creating a wedge between the cost of owning and renting, and hence, inducing the loss of this tax shelter when an owner becomes a renter. Indeed, U.S. regions where house prices have declined more, pushing an increasing number of households into negative-equity territory, experienced sharper declines in the mobility rate (defined as the portion of households that move from the region to another region). This relationship survives when changes in the mobility rate are regressed on changes in employment and house prices and their interaction.8 Hence, a housing bust may weaken the positive association between employment growth and mobility.9 Micro data from the European Community Household Panel confirms this effect. The probability of an individual moving is adversely affected if the household has a mortgage, potentially hurting job creation (Boeri and Garibaldi, 2010).

III. Policy Options

The crisis has lent some support to the camp favoring early intervention in real estate boom-bust cycles. Policy proved to be of limited effectiveness in cleaning up the mess and there have been large costs associated with the bust. While the issue remains of distinguishing “bubbles”—that is, price misalignments relative to economic fundamentals—from large or rapid movements in prices, better yardstick indicators (such as price-income and price-rent ratios, measures of credit growth, and leverage) can be developed to guide the assessment of the risks posed by a run-up in prices and the decision to take action against “bad” booms. Similar to other policy decisions, action may have to be taken under considerable uncertainty when the costs of inaction can be prohibitively high.

If we accept the notion that intervention may be warranted even though it is often difficult to separate good from bad booms, the question arises as to which policy lever is best suited to reining in the latter. The main risks from real estate boom-bust cycles are associated with increased leverage in both the real (in particular, households) and financial sectors. In that context, one could think of policies as targeting two main objectives (not to be taken as basis for a mutually exclusive categorization): (i) preventing real estate booms and the associated leverage build-up at household and banking sectors altogether, (ii) increasing the resilience of the financial system to a real estate bust. Table 1 gives a summary of policy measures available to achieve these objectives along with their pros and cons.

Table 1.

Policy Options to Deal with Real Estate Booms

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It should be recognized at the onset that there is no silver bullet. Each policy will entail costs and distortions, and its effectiveness will be limited by loopholes and implementation problems. Broad-reaching measures (such as a change in the monetary policy rate) will be more difficult to circumvent, and hence potentially more effective, but will typically involve greater costs. More targeted measures (such as maximum loan-to-value ratios) may limit costs, but will be challenged by loopholes, jeopardizing efficacy.

What follows are explorations. We examine the potential role of monetary, fiscal, and macroprudential policies. We discuss the benefits and challenges associated with the various policy options, using cases studies of countries with experience in the use of particular measures and, where possible, cross-country evidence. Finally, policy options are also examined through the lens of a stylized theoretical model.

A. Monetary Policy

Can monetary policy tightening stop or contain a real estate boom? Arguably, an increase in the policy rate makes borrowing more expensive and reduces the demand for loans. In addition, higher interest payments lower the affordability index (defined as the ratio of median household income to income necessary to qualify for a traditional mortgage loan10) and reduce the number of borrowers that can qualify for a loan of given size. Moreover, indirectly, to the extent that monetary tightening reduces leverage in the financial sector, it may reduce the financial consequences of a bust even if it does not stop the boom (Adrian and Shin, 2009; De Nicolo et al., 2010).

Yet, monetary policy is a blunt instrument for this task. First, it affects the entire economy and is likely to entail substantial costs if the boom is limited to the real estate market. Put differently, a reduction in the risk of a real estate boom-bust cycle may come to the cost of a larger output gap and the associated higher unemployment rate (and possibly an inflation rate below the desired target range). Obviously, these concerns are minimized when the boom occurs in the context (or as a consequence) of general macroeconomic overheating. In that context, the distortions associated with monetary tightening would be minimized. Indeed, when financial constraints are present and real estate represent an important vehicle for collateral, a policy rule reacting to real estate price movements (in addition to inflation and the output gap) can actually improve over a traditional Taylor rule by reducing welfare loss (see Section IV).

A second concern is that, during booms, the expected return on assets (in this case, real estate) can be much higher than what can be affected by a marginal change in the policy rate. It follows that monetary tightening may not directly affect the speculative component of demand.11 If that is the case, it may have the perverse effect of leading borrowers (who would have otherwise qualified for standard mortgages) towards more dangerous forms of loans (such as interest-only, variable-rate loans, and in some cases foreign-currency loans).12 Further affecting the efficacy of monetary policy is free capital mobility: real estate booms are often associated with strong capital inflows, especially in emerging markets, and higher interest rates may create complications on this front.13

Finally, the effectiveness of a change in the policy rate will also depend on the structure of the mortgage market. In systems where mortgage rates depend primarily on long-term rates the pass-through from the policy rate to the real estate market may be limited, depending on the relationship between long and short rates.

To a large extent, empirical evidence supports these concerns, leading to the bottom line that monetary policy could in principle stop a boom, but at a very high cost.

At first glance, there is little evidence across countries that the pre-crisis monetary stance had much to do with the real estate boom. Inflationary pressures were broadly contained throughout the period and changes in house prices do not appear correlated with real interest rates or other measures of monetary conditions. For instance, in advanced economies, the extent of the recent house price boom was uncorrelated with estimated Taylor residuals14, except in a subsample of eurozone countries (IMF, 2009).

An explanation for this lack of a relationship may be in the rapid decline in import prices driven by the integration of low-cost emerging market economies—notably China—into global production chains that may have offset relatively high inflation in nontradable sectors.15 Indeed, plotting nominal house price growth in the 10 years to the end of 2008 against the change in the relative price level of imports over the same time period shows an extremely robust negative relationship across countries (Figure 5, top panel), particularly once one conditions on the asymmetric impact of ECB monetary policy in the core and periphery of the eurozone. These simple scatter-plots suggest that housing booms were indeed more salient in countries which experienced a decline in import prices relative to the general price level. This, in turn, suggests that buoyant conditions extended beyond the housing sector and the domestic economy as a whole was overheating.

Figure 4.
Figure 4.

Policy Responses and Effectiveness in Dealing with Real Estate Booms

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Sources: IMF country reports; Hilbers, Otker-Robe, and Pazarbasioglu (2007); Borio and Shim (2007); Laeven and Valencia (2010); authors’ calculations.Notes: The colors of country names indicate the effectiveness of policy response in terms of the impact on real estate price and credit growth rates and the incidence of a systemic banking crisis. A score of 1 is assigned if the percent decline in a growth rate is in the top quintile of the cross-country distribution. A score of 0 (1) is assigned if the boom episode was followed by a (borderline) systemic crisis and a score of 2 is assigned if there were no crisis. Hence, the final score (i.e., the sum of all scores) range from 0 to 4, 4 being the best outcome with largest decline in the magnitude of the boom and avoidance of a crisis. Red, orange, yellow, green, and dark green correspond to scores of 0, 1, 2, 3, and 4, respectively. * indicates that the episode is incomplete but there has been no crisis so far. For each country, the period during which the policies were implemented reflects the country experience in Table 3. China, Hong Kong SAR, Malaysia, and Thailand have employed different combinations of policy tools during different episodes: China monetary and macroprudential tools in 2005-07 and all three sets of tools in 2009-10; Hong Kong SAR macroprudential tools in 1991-97 and macroprudential and fiscal tools in 2009-10; Malaysia monetary and macroprudential tools in 1994-97 and macroprudential and fiscal tools in 2009-10; Thailand macroprudential tools in 2003 and 2010.
Figure 5.
Figure 5.

House Price Boom, Relative Import Prices, and Taylor Residuals

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Notes: The relative price level of imports is measured as the ratio of the implicit price deflator for imports of goods and services to the GDP deflator, taken from the IMF’s WEO database. The top left chart plots the simple bivariate relationship; the top right chart shows the partial relationship, controlling for the asymmetric impact of ECB monetary policy inside the eurozone, which appears to have been a key factor in driving the divergent house price dynamics within this region. The ECB sets a single monetary policy for the entire eurozone, but since economic conditions vary significantly across the currency area, this policy is too tight in some countries and too loose in others, relative to policy set optimally for each country. For instance, domestic demand growth was extremely weak at the core (Germany), but very robust in some countries in the periphery (e.g. Ireland and Spain), and policy was thus too tight for the former and too loose for the latter. As a crude way of capturing this asymmetry, the regression underlying the top right chart includes, in addition to the relative price term, a dummy variable that takes the value of -1 for Germany, 1 for eurozone countries other than Germany (including Denmark as its currency is tightly pegged to the euro), and 0 for non-eurozone countries. This variable is a statistically and economically significant determinant of the magnitude of house price appreciation; together the two variables account for more than three quarters of the total cross-sectional variation in the extent of the housing boom across our set of 19 advanced economies. The Taylor residual shown in the bottom charts is based on 5-year averages covering quarterly data for 22 countries since the start of the 1990s. The bottom left chart uses CPI inflation to construct the Taylor residual; the bottom right chart uses the GDP deflator. In each case observations for the last two periods, covering the period after 2000 that generally coincides with the peak of the housing boom, are shown in gray; earlier observations are shown in black.

After controlling for this issue by computing the Taylor residuals based on domestic inflation rather than overall CPI inflation, the relationship between the monetary-policy stance and house prices is robust and statistically significant (Figure 5, bottom panel).16 But the slope of this relationship suggests that economic significance is weak and it would be very costly to use monetary policy to stop a boom: the policymakers would have to “lean against the wind” dramatically to have a meaningful impact on real estate prices, with large effects on output and inflation.

This intuition is confirmed by a panel vector autoregression.17 This exercise suggests that, at a 5-year horizon, a 100 basis point hike in the policy rate would reduce house price appreciation by 1 percentage point. But it would also instigate a decline in GDP growth of 0.3 percentage points. To put things in context, between 2001 and 2006, real house prices rose 48 percent in a global sample of 55 countries. A 500-basis-points tightening would have cut the boom by roughly 5 percentage points to 43 percent (still well above the historical average of 27 percent increase in house prices over a five-year period). And it would have reduced real GDP growth by 1.5 percentage points over this 5-year period.18

Consistent with these estimates, the experiences of Australia and Sweden suggest that marginal changes in the policy rate are unlikely to tame a real estate boom. They were among the few countries that used monetary policy to ‘lean against the wind’ during the global real estate boom. Australia increased the policy rate by 300 basis points between April 2002 and August 2008 while Sweden had a 325 basis-point hike between December 2005 and September 2008.19 This tightening notwithstanding, house prices in both countries increased substantially, gaining 80 percent in real terms between 2000 and 2007.

Part of the problem may be that speculation is unlikely to be stemmed by changes in the monetary policy stance. Indeed, there is some evidence that conditions on the more speculative segment of mortgage markets are little affected by changes in the policy rate. For example, in the U.S., denial rates (calculated as the proportion of loans originated to applications received) in the market for prime mortgages appear highly related to changes in the Federal Funds rate, with banks becoming more choosy when the rate increases. In contrast, denial rates for subprime loans (typically more linked to speculative purchases) do not seem to move systematically with monetary policy (Figure 6).

Figure 6.
Figure 6.

Mortgage Loan Granting and Monetary Policy

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Sources: Home Mortgage Disclosure Act database, Federal Reserve Board.Notes: All values are expressed in percent. Subprime lenders are identified based on the list prepared by the Department of Housing and Urban Development. Denial rate is calculated as the number of denied mortgage loan applications divided by the total number of applications.

B. Fiscal Tools

Most tax systems involve favorable treatment of debt-financed home ownership.20 In theory, for households to be indifferent between renting and owning, rent should equal the user cost of owning (Poterba, 1984). Complicating this equation, aside from rent controls, is the fact that tax treatment of owner-occupied housing may significantly alter the latter. Under neutral treatment, imputed rents and capital gains would be fully taxed and mortgage interest payments would be fully deductible. In reality, however, imputed rents and capital gains are seldom taxed (e.g. two out of three OECD countries treat these as tax-exempt). Transaction, capital gains, and property taxes, all indirect ways of taxing imputed rents, partially offset the bias towards ownership created by this treatment but mortgage interest tax relief is often large enough to undo this partial offset.21 Moreover, mortgage interest tax relief encourages levered property purchases.

Can tax treatment of home ownership and housing-related debt be adjusted in a cyclical manner to curb house price increases? Hypothetically, in a tax system where imputed rents and capital gains are exempt while mortgage interest and property taxes are deductible, increasing property taxes or trimming down mortgage interest tax relief could reduce house prices, especially for higher-income households that are subject to higher marginal income tax rates.22 Property taxes, arguably, are better suited for cyclical implementation since it is administratively easier to reset tax rates than to abolish/re-establish or change mortgage interest deductibility rules and they can be tailored better to local real estate market dynamics.23 A better tool yet may be cyclical transaction taxes (of which capital gains taxes and registration fees are two components) that could, in theory, automatically dampen the boom phase of the real estate cycle as well as discourage speculative activity.

Transaction taxes

The level of transaction taxes does not appear to have a clear relationship with house price dynamics. In theory, one would expect higher transaction taxes to “thin” the market but, at the same time, to reduce the probability of bubbles by limiting speculative activity. Empirically, the relationship remains ambiguous. On the one hand, Belgium, with its high transaction taxes reaching as high as 16.5 percent, has had very modest house price movements with quarterly appreciation not exceeding 2 percent during upturns and depreciation not falling below -2 percent during downturns since the 1970s. On the other hand, Japan also with substantial transaction taxes, experienced one of the most notable real-estate bubbles to date (for more on the Japanese real estate bubble and land taxation, see IMF, 2001).

Transaction taxes/subsidies that change with real estate conditions may be, in theory, more promising.24 On the bust side, the use of time-limited tax credits linked to house purchases in the U.S. and the suspension of stamp duty in the U.K. helped stabilize the housing market. And, especially in the U.S., the stabilization in prices and revival of activity disappeared with the expiration of the tax breaks (IMF, 2010). On the boom side, China and Hong Kong SAR have recently introduced higher stamp duties to dampen real estate prices and discourage speculation. Their experience, however, indicates that transaction volume responds more than prices do (suggesting that the associated collateral costs are high) and the impact of the introduction of the tax may be transient.

Property taxes

To address the question of how property taxes affect price dynamics we use data from the U.S. covering 243 metropolitan areas.25 Based on an instrumental variables strategy, which can provide a causal interpretation running from tax rates to house price growth, a one standard deviation ($5 per $1000 of assessed value) increase in property tax rates is found to be associated with a 0.9 percentage point decline in average annual price growth (compared to annual growth of around 5.6 percent per year).26 Moving from the minimum tax rate in the sample (some $2.60 per $1000) to the maximum ($26) is estimated to cut average price growth by 4.3 percentage points per year. The impact on price volatility around the trend growth rate is similar: a one standard deviation increase in tax rates leads to a reduction in the standard deviation of house price growth of around 0.8 percentage points, around one quarter of the average level of volatility in the sample.

This evidence suggests that higher rates of property taxation can help limit housing booms as well as short-run volatility around an upward trend in prices. One interpretation is that property taxes, indirectly taxing imputed rent, may mitigate the effect of other tax treatments favoring homeownership and perhaps reduce speculative activity in housing markets. Of course, caveats apply in deriving implications from this evidence in the international context. The results may be specific to the U.S. housing market, whose characteristics differ markedly from those in many other advanced economies, let alone emerging markets. Moreover, property tax rates clearly did not cause (or prevent) the emergence of a national housing boom in the U.S., although they may have limited its impact on some areas, and the impact at the national level of a hypothetical national property tax might be very different from the localized impact of local taxes. Finally, endogeneity of tax rates remains an important issue: municipalities often face pressure to reduce tax rates when markets are booming and tax revenues are high, challenging the ability of policymakers to use property taxes as a countercyclical tool.27

Mortgage interest tax deductibility

Tax reforms that reduce the value of mortgage interest relief can lead to lower loan-to-value ratios, pointing to the role of tax incentives favoring debt-financed homeownership (see Hendershott et al., 2003, for the U.K. and Dunsky and Follain, 2000, for the U.S.). Tax reforms advocating removal or reduction of this tax shelter are estimated to cause around 10 percent immediate decline in house prices (see Agell et al., 1995, for Sweden and Capozza et al., 1996, for the U.S.). Yet, all of these are one-off changes, hinting at the difficulties in using mortgage interest tax deductibility rules in a cyclical way.

Overall, evidence on the relationship between the tax treatment of residential property and real estate cycles is inconclusive. At the structural level, tax treatment of housing does not appear to be related to the cross-country variation in the amplitude of housing cycles: during the most recent global house price boom, real house prices increased significantly in some countries with tax systems that are highly favorable to housing (such as Sweden) as well as in countries with relatively unfavorable tax rules (such as France). Similarly, appreciation was muted in countries with both favorable systems (e.g. Portugal) and unfavorable (e.g. Japan).

Looking at other housing market indicators, the tax treatment of housing is not significantly related to the ratio of mortgage debt to GDP, while levels of homeownership (the main excuse for favorable tax treatment of housing) are, if anything, negatively related to the degree to which the tax system is favorable to owning one’s own home (although the relationship is, again, not statistically significant). Other research has painted a similarly ambiguous picture. For instance, in the eurozone, more favorable tax treatment of housing may be associated with greater house price volatility (van den Noord, 2005). However, in a broader sample of economies, taxation was not the main driver of house price developments during the recent global housing boom (Keen et al., 2010).

Summarizing, even if fiscal tools can, in a one-off setting, dampen volatile house price dynamics and the build-up of vulnerabilities associated with debt-financed homeownership, scope for the use of such tools in a cyclical setting is likely to be limited given the political economy angle of fiscal policy perceived to be interventionist. In addition, the institutional setup in most countries separates tax policy from monetary and financial regulation policies, making it extremely hard to implement changes in tax policies as part of a cyclical response with financial stability as the main objective. Moreover, local governments may use lower property or transaction tax rates to attract residents during good times if the burden in the case of a bust is shared with other jurisdictions. The ability of cyclical transaction taxes to contain exuberant behavior in real estate markets may be further compromised if homebuyers do not respond to these taxes fully, because they consider them to be an acceptable cost for an investment with high returns and consumption value. Also, during a boom phase, the incentives to “ride the bubble” may increase efforts to circumvent the measure by misreporting property values or folding the tax into the overall mortgage amount. Finally, as with most tax measures, the distortions created by a cyclical transaction tax may hurt the price discovery process, which tends to be rather slow in real estate markets already, and also the mobility of households with potential implications for the labor market.

C. Macroprudential Regulation

At least in theory macroprudential measures such as higher capital requirements or limits on various aspects of mortgage credit could be designed to target narrow objectives (for instance, household or bank leverage) and tackle the risks associated with real estate booms more directly and at a lower cost than with monetary or fiscal policy.

Against the benefit of a lower cost, these measures are likely to present two critical shortcomings. First, they may be easier to circumvent as they target a specific type of contracts or group of agents. When this happens, these measures can be counterproductive, as they may lead to liability structures that are more difficult to resolve/renegotiate in busts. Second, they may be more difficult to implement from a political economy standpoint. Over time, monetary policy decisions have come to be accepted as a necessary evil thanks to central banks increasingly achieving credibility and independence. Using measures that were previously confined to the realm of micro-prudential supervision to achieve systemic results would likely be considered as an unnecessary intrusion into the functioning of markets. The more direct impact of these measures would also complicate implementation as winners and losers would be more evident than in the case of macro policies.

We focus our analysis on three specific sets of measures. First, capital requirements or risk weights that change with the real estate cycle. Second, dynamic provisioning, that is, the practice to increase banks’ loan loss provisions during the upswing phase of the cycle. And third, the cyclical tightening/easing of eligibility criteria for real estate loans through loan-to-value (LTV) and/or debt-to-income (DTI) ratios.28 Unlike monetary and fiscal policy options that mostly aim at preventing or pricking bubbles, some of these macroprudential tools may be able to achieve both objectives: (i) reducing the likelihood and/or magnitude of a real estate boom (for instance, by imposing measures to limit household leverage), and (ii) strengthening the financial system against the effects of a real estate bust (for example, by urging banks to save in good times for rainy days).

An important caveat is in order before we start our analysis. A major limitation in assessing the effectiveness of macroprudential tools stems from the fact that macroprudential policy frameworks are still in their infancy, and only a handful of countries have actively used them.29 For example, out of 36 countries for which information is available, as of September 2010, 81 percent have reported not having any restrictions on the type of mortgage loans (e.g. interest-only or negative amortization loans, which are more likely to be used by speculators) financial institutions can extend to borrowers (Text Table 3). While limits on loan-to-value and debt-to-income ratios are reported to exist in roughly half of the countries, a closer look reveals that these are often applicable only to a subset of mortgages (e.g. those insured by the government) or are recommended best practice guidelines rather than strictly-enforced rules. Only 3 countries have reported actively using such limits to respond to real estate market developments. On the tools that are more “defensive” in nature than “preventative”, a similar picture emerges: a mere 11 percent of the countries in the survey have applied cyclically-adjusted real-estate-specific risk weights with 14 percent requiring institutions to provide countercyclical loan loss provisioning on real estate loans (sometimes through a rules-based framework, sometimes at the discretion of the supervisory authority).30 In addition to the lack of a track record, the fact that these measures have been typically used in combination with macroeconomic policy tools complicates further the challenge to attribute observed developments to a specific policy measure.

Text Table 3.

Survey-Based Assessment of Policy Frameworks as of September 2010

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Notes: The numbers correspond to the proportion of respondents giving a particular answer. Country-by-country responses to this brief in-house survey are in Appendix Table 1.

Yet, much can be learned from case studies. Following the Asian crisis, some countries in the region (particularly, Hong Kong SAR, Korea, Malaysia, and Singapore) have taken a more heavy-handed approach to deal with risks posed by real estate booms. Spain, following a period of significant credit growth, put in place a dynamic provisioning framework as early as July 2000. Countries in Central and Eastern Europe have experimented with various measures to control the rapid growth in bank credit to the private sector in the 2000s. Table 2 presents some stylized facts on the effects of various policy responses to real estate (and credit) booms. Table 3 gives more details on particular country cases, some of which are used in the discussion that follows on the efficacy of different tools. On the whole, success stories appear to be few, perhaps to some extent reflecting the learning curve in expanding the policy toolkit, improving the design of specific tools, and sorting out implementation challenges (Figure 4). The cases that appear to have the most success (that is, slowing down the boom and avoiding systemic crisis in the bust) almost always have involved some macroprudential measures.

Table 2.

Stylized Facts on Policy Responses to Real Estate Booms: Stocktaking

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Notes: The table gives a snapshot, it is not meant to be a comprehensive and detailed list of cases where authorities took one or more of the measures listed to address credit/real estate developments. Bolivia and Peru have also put in place a dynamic provisioning framework and Romania had employed a battery of policy measures to address rapid credit growth; yet these countries are not included in the table due to lack of house price data. Dynamic provisioning in China and India is discretionary rather than rules-based.
Table 3.

Country Experiences with Various Policy Responses

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Sources: IMF country reports; Hilbers, Otker-Robe, and Pazarbasioglu (2007); Borio and Shim (2007). Note: Country selection based on occurrence of real estate boom and policy measures taken in response.

Higher capital requirements/risk weights

Background

Capital regulation has a procyclical effect on the supply of credit. During upswings, better fundamentals reduce the riskiness of a given loan portfolio, improving a bank’s capital adequacy ratio and its ability to expand its asset holdings. In a downturn, the opposite happens, possibly leading to deleveraging through fire sales. The broadly-discussed proposal of procyclical capital requirements could help reduce this bias.31 Further, by forcing banks to hold more capital in good times, it would help build buffers for future losses (see Gordy and Howells, 2006, and references therein).

In the context of real estate, the procyclical element of capital regulation is largely absent. In most countries, existing rules do not take collateral values into consideration or reflect the heterogeneity among loans backed by real estate, other than the commercial-residential distinction. Under Basel II’s standard approach, risk weights for property loans are fixed (50 percent for residential mortgages and 100 percent for commercial property loans).32 As a result, mortgage loans with predictably different default probabilities (for instance, because of different LTV ratios or their exposure to different aggregate shocks) are often bundled in the same risk category and no adjustment is made through time to account for the real estate cycle.33 In this context, capital requirements or risk weights linked to real estate price dynamics could help limit the consequences of boom-bust cycles. By forcing banks to hold more capital against real estate loans during booms, these measures could build a buffer against the losses associated with busts. And, by increasing the cost of credit, they might reduce demand and contain real estate prices themselves.34 Finally, weights could be fine-tuned to target regional booms.

Implementation challenges

A few caveats are in order. First, absent more risk-sensitive weights, an across-the-board increase in risk weights (or capital requirements) carries the danger of pushing lenders in the direction of riskier loans (this is essentially the risk-shifting effect identified by models in the spirit of Stiglitz and Weiss, 1981). Thus, the introduction of procyclical risk weights for real estate loans should be accompanied by the implementation of a finer cross-sectional risk classification as well. Second, as with any other measure increasing the cost of bank credit (when credit is in high demand), procyclical risk weights may be circumvented through recourse to non-bank intermediaries and off-balance sheet activities. Third, these measures will lose effectiveness when actual bank capital ratios are well in excess of regulatory minima (as often happens during booms). Fourth, while improving the resilience of the banking system to busts, tighter requirements are unlikely to have a major effect on credit availability and prices. Put differently, they are unlikely to reduce vulnerabilities in the real (household) sector. Finally, regulators may be reluctant to allow banks to reduce risk weights during a bust (when borrowers become less creditworthy).

Evidence

The empirical evidence on the effectiveness of these measures is mixed. In an effort to contain the rapid growth in bank credit to the private sector and the associated boom in asset markets, several countries have raised capital requirements and/or risk weights on particular groups of real estate loans. Some attempts (such as the cases of Bulgaria, Croatia, Estonia, and Ukraine) have failed to stop booms and the associated post-bust damage to the financial sector; others (such as the case of Poland) were at least a partial success (Table 3).35

Yet, it is not easy to say why measures taken in one country may have been more effective than those taken elsewhere or how much other developments account for the observed changes. Furthermore, even in countries where tighter capital requirements appeared to produce some results on controlling the growth of particular groups of loans, real estate price appreciation and the overall credit growth remained strong.

This evidence highlights the implementation challenges associated with these tools: with limited effect on overall credit growth and household leverage, lenders and borrowers may find new, less regulated types of credit. At times, these may go beyond regulatory perimeters (e.g. loans extended by finance companies or mortgage brokers rather than commercial banks) or even national borders (e.g. financing provided by banks in neighboring countries).

Dynamic provisioning

Background

Dynamic provisioning (the practice of mandating higher loan loss provisions during upswings) can help limit credit cycles.36 The mechanics and benefits are similar to those of procyclical capital requirements. By forcing banks to build (in good times) an extra buffer of provisions, it can help cope with the potential losses that come when the cycle turns (see, for example, the case of Spain; Table 3). It is, however, unlikely to cause a major increase in the cost of credit, and thus to stop a boom. That said, one advantage over cyclical capital requirements is that dynamic provisioning would not be subject to minimums as capital requirements are, so they can be used when capital ratios maintained by banks are already high.

Provisioning for property loans could be made a specific function of house price dynamics. In periods of booming prices, banks would be forced to increase provisioning, which they would be allowed to wind down during busts. As in the case of risk weights, provisioning requirements could depend on the geographical allocation of a bank’s real estate portfolio.

Implementation challenges

As noted, this type of measure is primarily targeted at protecting the banking system from the consequences of a bust. Consequently, it is not meant to have a significant impact on credit and contain other vulnerabilities associated with a boom, such as increases in debt and leverage in the household sector. In addition, practical issues and unintended effects such as calibration of rules with rather demanding data requirements and earnings management (which may raise issues with tax authorities and securities markets regulators) should be discussed in each country’s context to design a framework that best fits the country’s circumstances. There are also other shortcomings, similar to those of procyclical risk weights (being primarily targeted at commercial banks, dynamic provisioning may be circumvented by intermediaries outside of the regulatory perimeter). Lastly, application of the measure to domestically-regulated banks only may hurt their competitiveness and shift lending to banks abroad, raising cross-border supervision issues.

Evidence

The experience with these measures suggests that they are effective in strengthening a banking system against the effects of a bust, but do little to stop the boom itself. Spain led the countries who have adopted some form of countercyclical provisioning and constitutes an interesting case study to provide a preliminary assessment on its effectiveness.

As member of the eurozone, Spain’s ability to respond to a booming local real estate market with macroeconomic policy is limited. Starting in 2000 and with a major revision in 2004, the Bank of Spain required banks to accumulate additional provisions based on the ‘latent loss’ in their loan portfolios (for more details on the Spanish dynamic provisioning framework, see Saurina, 2009). Dynamic provisions forced banks to set aside, on average, the equivalent of 10 percent of their net operating income. Yet, household leverage grew by a still high 62 percent in Spain. At the end of 2007, just when the real estate bust started, total accumulated provisions covered 1.3 percent of total consolidated assets, in addition to the 5.8 percent covered by capital and reserves (so far, the value of the housing stock decreased by roughly 15 percent in real terms). Hence, Spanish banks had an important buffer that strengthened their balance sheet when real estate prices started to decline and the economy slipped into recession.

Limits on loan-to-value and debt-to-income ratios (LTV and DTI)

Background

A limit on LTV will prevent the build-up of vulnerabilities on the borrower side (in particular in the household sector). Containing leverage will reduce the risks associated with declines in house prices. Put differently, the lower the leverage, the greater the drop in prices needed to put a borrower into negative equity. In turn, this will likely result in fewer defaults when the bust comes, as more borrowers unable to keep up with their mortgages will be able to sell their houses. In addition, in case of default, lenders will be able to obtain higher recovery ratios. On the macro front, a limit on LTV will reduce the risk that a large sector of the real economy ends up with a severe debt overhang. In addition, it will reduce the pool of borrowers that can obtain funding (for a given price) and thus will reduce demand pressures and contain the boom.

Similar to limits on LTV, DTI limits will rein in the purchase power of individuals reducing the pressure on real estate prices. In particular, they will be effective in containing speculative demand (they will screen out borrowers that would only qualify for a mortgage on the assumption the house would be quickly turned around). They will also reduce vulnerabilities as borrowers will have an ‘affordability’ buffer and will be more resilient to a decline in their income or temporary unemployment.

Implementation challenges

One practical issue with implementing LTV limits is that lenders are generally quick to find ways to circumvent the restrictions. For instance, in the U.S., during the housing boom, the practice of combining two or more loans to avoid the mortgage insurance (which kicked in when LTV exceeded 80 percent) became common.37 A ban on second liens or LTV applied to a borrower’s overall exposure would improve effectiveness. Similarly, an obvious way to get around a DTI limit would be to extend sequential loans and report the ratios separately. In Hong Kong SAR, where regulators impose maximum limits on the debt service ratio (which takes into account the payments the borrower has to make on non-mortgage loans as well), supervisors often encounter cases where lenders choose not to report all outstanding debt obligations. In addition to minimize the effect of these limits, circumvention may entail significant costs, as it results in more liability structures that can complicate debt resolution during busts (for example, in the U.S., it is often second-lien holders to object to restructuring).

The coverage perimeter of these measures is also an issue: in Korea, lower LTV limits for loans with less than three years of maturity spurred a boom in loans originated with maturity of three years and one day. Circumvention may also involve shifting of risks not only across mortgage loan products but also outside the regulatory perimeter through expansion of credit by non-bank, less-regulated financial institutions and/or by foreign banks (which may result in increased currency mismatches as the proportion of FX-denominated loans rises).

The narrow target nature of these measures may increase political economy obstacles (as happened in the case of Israel38), particularly since the groups more impacted by LTV and DTI limits tend to be those more in need of credit (poorer and younger individuals). In addition, unlike with more “macro” measures, the consequences of these limits are immediate and transparent. Beyond these political economy considerations, LTV and DTI limits, by rationing sensitive groups out of credit markets, will entail a cost in terms of diminished intertemporal consumption smoothing and lower investment efficiency.39

To contain these costs, countries have adopted more targeted approaches (trying to protect more vulnerable groups and aiming at those they consider to be market-destabilizing speculators). For instance, Korea differentiates the limits across regions based on the extent of house price appreciation.40 China and Singapore impose lower limits on second mortgages in an effort not to hurt owner-occupiers. Hong Kong SAR has cut-offs on property values to target the high-end segment of the real estate market. Although less controversial, these limits also receive criticism and opposition from property developers and lenders. In countries where these groups have political influence, effective execution of LTV and DTI limits may still pose a challenge. Even when the policymaker can surmount such political economy problems, calibrating and timing the changes in limits is unlikely to be an easy task as demonstrated by the recent Korean experience, where bubble fear rapidly turned into concern that the real estate markets had become too weak because the macroprudential policy response was too strong, forcing the authorities to reverse the tightening of eligibility criteria.

Evidence

Establishing a causal link running from LTVs to price and credit dynamics is a hard task. In a simple cross-section of 21 (mostly) developed countries, maximum LTV limits are positively related to house price appreciation between 2000 and 2007 (Figure 7). Back-of-the-envelope calculations suggest a 10 percentage point increase in maximum LTV allowed by regulations to be associated with a 13 percent increase in nominal house prices. Regressions on a panel of U.S. regions from 1978 to 2008 deliver a smaller impact of LTV at loan origination: roughly 5 percent increase in house prices for a 10 percentage point increase in LTV.41 Duca et al. (2010) construct a series for LTV faced by first-time home buyers and estimate a cointegration model of house price-to-rent ratios at the national level for the U.S. between 1979 and 2007. Their results imply an impact of 8-11 percent on house prices from a 10 percentage point increase in LTV for first-time home buyers, assuming rents remain constant.

Figure 7.
Figure 7.

Maximum LTV and House Prices

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Sources: EMF, BIS, OECD, UNECE, ECLAC, IADB, IUHF, IUT, national statistics, and central bank statistics.

It must be noted that there are major drawbacks with this analysis. In the international sample, a major concern is the lack of time dimension. In many countries, there is no data available for multiple points in time. Even when data availability is not the problem, very few countries have time variation in maximum LTV allowed since this is not an active part of the regulatory agenda. Another issue, which also applies to the estimates based on U.S. data analysis, is that in many cases there are no mandatory maximum limits in practice and the values reported are simple guidelines for mortgage insurance or prudential concerns. In fact, the data for U.S. regions are the actual LTVs because there is not variation across regions in the LTV guidelines. Hence, because of the feedback loop between mortgage credit availability and house price movements, endogeneity remains a concern.

That said, a review of the experience of countries that experimented with mandatory LTV limits changing in response to real estate market developments also suggests that they can be quite effective but perhaps for short periods (see the cases of Korea and Hong Kong SAR; Table 3). For instance, when the Korean authorities introduced LTV limits in September 2002, month-on-month change in house prices went down from 3.4 percent to 0.3 percent immediately and remained low until April 2003. Subsequent reductions in LTVs were also followed by significant drops in house price appreciation rate. A similar pattern applies to DTI limits, with month-on-month change dropping from 2.3 percent in July 2005 to 0.2 percent in August 2005 with the introduction of the measure. Interestingly, the measures had a much smaller or no impact on prices in ‘non-speculative’ areas where the limits were untouched. The impact on year-on-year changes, however, has been smaller since prices tend to start increasing at a faster pace again after the first immediate reaction. In Hong Kong SAR, prudent lending practices guided by LTV and DTI limits have been credited with pausing the house price boom briefly in 1994 and guarding the system against the fallout from the crash in 1997 (Wong et al., 2004).42

IV. Model-Based Evaluation of Policy Options

The objective of this section is to provide a quantitative evaluation of the policy trade-offs discussed in the previous sections. To that purpose, it builds a dynamic stochastic generalized equilibrium (DSGE) model that takes into account a housing sector and credit markets.43 The advantage of DSGE models over traditional macroeconomic models, especially in analyzing the impact of policy shocks and structural changes, is that they are less subject to the Lucas critique since they rely on optimization of microeconomic decisions. The disadvantage, in addition to computational challenges, is that they do not have the capability to replicate nonlinear dynamics often observed in macroeconomic aggregates, especially in a crisis context. Hence, the analysis actually looks at policy responses to any fluctuation in house prices, not to unsustainable booms.44 The model-based analysis supports the view that tools that are narrower in focus (by addressing a specific rigidity) can perform better.

That said, considering that bubbles are hard to identify in real time from fundamentals-driven movements and that even non-boom fluctuations in house prices have an impact on aggregate economic activity through wealth and supply-side effects, the model-based analysis can help us understand and quantify the trade-offs associated with different policies. All in all, the analysis in this section supports the view that, in theory, tools that are closer to the target (such as macroprudential measures that alter interest rate spreads or the quantity of credit on particular markets) and narrower in focus perform better.

The model involves three components to introduce a housing sector and credit markets into the standard macroeconomic set-up. First, households, make decisions about how much to invest in a durable good (which in this instance is housing), in addition to choosing their consumption of nondurable goods. Housing has a double function in this economy: it provides a flow of services and is the main vehicle for accumulating wealth. Second, in order to create the need for a credit market, there is a distinction between two types of households. (If all agents in the economy behave the same way, then credit is in zero net supply and asset and bond prices only represent shadow prices without actual transactions taking place.) Hence, we introduce agents that are more impatient than others and have preference for consuming early and for borrowing. Third, the lending rate is modeled as a spread over the policy rate that depends on the balance sheet position of potential borrowers, a banking sector markup, and a policy instrument. Hence, for example, rising house prices raise market valuations of borrowers’ collateral, improve their balance sheet position, and therefore lead to a fall in lending rates even if monetary policy has not eased. Credit market conditions can change—because of, say, changes in perceptions of risk—which could lead banks to adjust their markups and thus alter the lending spread.45

The feedback loop between credit spreads, house prices and balance sheets of households helps accelerate a rise in residential investment, nondurable consumption, and prices, and is a main source of concern for the policymaker because of its impact on output and inflation. Therefore, the policymaker may choose to alter the course of this feedback loop by adjusting the policy instrument, which may take the form of a macroprudential tool such as a limit on loan-to-value ratios or a fiscal tool such as tax deductibility of mortgage interest.

The model has conventional New Keynesian features; in particular, prices and wages do not adjust immediately. To make the model more realistic, we assume that consumption and residential investment adjust slowly, and it is costly for workers to shift from producing consumption goods to building houses, and vice versa. The presence of these nominal and real frictions means that monetary policy can stabilize the economy, because it influences interest rates and, hence, spending on durables and housing. The presence of financial frictions means that not only monetary policy but also macroprudential policy can play a role by responding to credit market conditions.

The objective is to determine which policy regime is better at stabilizing the economy in the face of pressures on the housing market—policies that can help prevent financial vulnerabilities, rather than help pick up the pieces after a bust. The conclusions that can be drawn from this analysis depend crucially on which shocks drive the house price boom. To illustrate the importance of correctly identifying the drivers of the housing boom, we test the policy regimes with two shocks: a financial shock that prompts a relaxation in lending standards, and a positive productivity shock that leads to an increase of income and demand for both nondurables and housing.46

Effectiveness of monetary policy

Our starting point is to assume that the central bank follows a standard Taylor-type rule, whereby the central bank raises rates whenever CPI inflation is running above target, or when the economy is expanding at a faster rate than its fundamentals suggest (i.e., the output gap is positive). We set the coefficients that determine the reaction to deviations from the targeted inflation and output as 1.5 and 0.5, respectively, as standard in the literature. Then, as alternative policy rules, we include the reaction to nominal house price inflation, nominal mortgage credit growth, or both.

The top panel of Table 4 shows the results of this exercise. For the case of a productivity shock, the inclusion of either house prices or credit in the policy rule would lead to an improvement of welfare mostly due to a decline of output gap volatility, yet it looks like reacting only to real estate prices would suffice. Intuitively, this is because the shock in this case reflects a change in one of the fundamentals, namely, income, that drives house prices. By responding to real estate prices, the policymaker can smooth out the adjustment path and limit the accelerator effect to output. In the case of a financial shock, there is merit to responding to both real estate prices and credit. This stems from the increased strength of the feedback loop: the financial shock creates a “credit boom” and pushes house prices up with immediate consequences for output while, in the case of a productivity shock, the push comes from fundamentals and credit fluctuations are mere responses to readjustment in house prices. In other words, the optimal policy choice of responding to credit reflects the merit of attacking directly to the source that triggers the feedback loop.

Table 4.

Performance of Policy Rules in a DSGE Model

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Note: Welfare is measured as the un-weighted sum of the variances of the output gap and CPI inflation around the steady state.

If both productivity and financial shocks are present, an interesting finding emerges: reacting to credit is superior to reacting to real estate prices. The mechanism behind this result hinges upon the characteristics of the feedback loop. The rise in house prices is driven both by the shift in fundamentals due to the productivity shock and by cheaper credit because of the financial shock. Output fluctuations can be reduced by monetary policy accommodating the effect of the shocks on house prices, but a better option is to directly respond to credit growth as it helps keep in check the push on house prices while at the same time containing the relaxation in credit conditions.47

All in all, a moderate expansion of central bank’s objectives to include real estate prices as one of the monetary policy targets could result in an improvement of welfare. When faced with a financial shock, reacting to credit growth may also be justified to stabilize the output gap.

Effectiveness of fiscal policy

Taxes on homeownership and housing transactions, in principle, can curb demand for housing and tame exuberance in real estate markets. To analyze the effectiveness of capital gains/property taxes and cyclical transaction taxes, we assume that, on top of the standard Taylor rule, the policymaker can impose a property tax on home-owners. The tax receipts are paid back as a lump sum to households. The policymaker can set two parameters in the tax rule. First, the steady-state level tax rate, and second, the cyclical reaction of the tax rate to house price inflation. Using tax rates to reduce house-price volatility and the associated accelerator effects implies small welfare improvements (middle panel in Table 4). This suggests that fiscal policy is much less effective than using other (extended monetary and regulatory) policy tools. This limited effect results from the fact that high property taxes are needed to have some bite in reducing the volatility of house prices. But, the reduced house price volatility comes with a trade-off, namely, a wedge between real house prices and their fundamental price. The inefficiency associated with such distorted prices leads to a smaller improvement in welfare.

Fiscal policy could also be used to deal with real estate booms given the differential treatment of debt-financed homeownership in many countries. In particular, interest payments on mortgages are, at least partially, tax-deductible. By creating a wedge between debt-financed ownership and non-debt-financed ownership, mortgage interest tax deductibility affects the user cost of housing. This could affect credit conditions and, hence, house price dynamics, by inducing a discrepancy between the (after-tax) lending rate and the deposit rate. Yet, in this model, this is isomorphic to any other tool that would directly affect the lending spread; for example, countercyclical LTV would achieve the same purpose. We study policy tools that have a direct impact on the lending spread under macroprudential policy tools next.

Effectiveness of macroprudential policy

In the model, policies that can directly affect the spread that financial intermediaries charge between lending and deposit rates may also help stabilize the cyclical movement in the economy. One such tool to achieve this would be to change the LTV in a countercyclical manner: by lowering the LTV when house prices increase, the supervisory authority would be forcing the volume of credit to be cut down, increasing the spread and hence reducing the accelerator effect.48

In order to demonstrate how this macroprudential rule works, Figure 8 shows the impulse-response to a 1 percent permanent reduction in the steady-state LTV. Initially, interest rate spreads increase 25 basis points, and credit decreases on impact by 0.3 percent.49 The increase in lending rates leads to a decline in private consumption (0.15 percent), consumer prices (0.02 percent), residential investment (0.2 percent) and real house prices (0.07 percent). Because of this contractionary effect, the central bank provides support by cutting the policy rate (which equals the deposit rate in this economy) and this helps cushion the downturn. Over the medium term, residential investment and house prices return to their initial values and credit is permanently reduced by 1 percent.50

Figure 8.
Figure 8.

Impulse Response to a One-Percent Permanent Reduction in the Loan-to-Value Ratio

Citation: IMF Working Papers 2011, 091; 10.5089/9781455253302.001.A001

Moving from the effect of a permanent change in LTV to how systematic cyclical changes in LTV can help stabilize the business cycle, we examine whether welfare can be improved if LTV is tied to certain observables such as credit growth and house price inflation. We specify a cyclical LTV rule that reacts linearly to fluctuations in those variables, much in the spirit of standard monetary policy rules. The results are in the bottom panel of Table 4.

Under a productivity shock, introducing a macroprudential instrument that reacts countercyclically to one of the two indicators brings important welfare gains, mostly due to the fact that the volatility of the output gap is greatly reduced. We run a horse-race by optimizing the coefficients of the rule with respect to the two indicators, and find that having the LTV react to nominal credit growth is superior than having it react to house price fluctuations. One interpretation of this finding is that the macroprudential instrument directly addresses the financial friction in the model, so it is optimal to have it react to excessive credit growth: by increasing lending rates countercyclically, the macroprudential instrument is able to slow down the feedback loop between credit, house prices, and spreads. Even when the coefficients on the Taylor rule are optimized, there is a role for the macroprudential instrument to react to nominal credit growth. The macroprudential policy could in principle be able to perfectly offset a financial shock by changing the LTV requirement aggressively and, hence, induce a change in the lending spread of the exact opposite magnitude as the financial shock. Of course, in a world where there are implementation delays, this would not be possible.

When both shocks hit, under the original Taylor rule, a strong reaction to deviations of nominal credit growth from steady-state values would lead to significant welfare improvements. Compared to all policies that start with the original coefficients of the Taylor rule, the macroprudential instrument delivers the highest welfare. When the Taylor rule is optimized, expanding monetary policy to react to real estate market developments and using the macroprudential tools deliver virtually the same outcome.

V. Conclusion

The correct policy response to real estate booms is, like many other policymaking decisions, an art more than a science. Of the policy options considered, macroprudential measures appear to be the best candidates to achieve the objective of curbing real estate prices and leverage because of their ability to attack the problem at its source, their adaptability to accommodate the specific circumstances in different locations at different times, and their added benefit of increasing the resilience of the banking system.

As a whole, the core principles in guiding policymakers to design an effective policy toolkit to deal with real estate booms emerge as follows:

  • Widen the policy perspective to recognize imbalances that do not necessarily show up in traditional measures of inflation targets and output gaps

  • Recognize the local features of real estate markets and use targeted macroprudential tools rather than across-the-board monetary policy responses to respond to excessive and destabilizing movements in prices and activity

  • Complement measures aiming to reduce the risk of bubbles with those aiming to increase the resilience of the financial system and well-defined resolution frameworks to speed up the cleaning in the aftermath of bubbles that survive the first line of defense

  • - Minimize distortions due to special treatment of housing and homeownership and strengthen supply-side response to mitigate the impact of demand shocks in the longer horizon

When it comes to applying these principles in practice, two important questions arise. First, what are the potential complementarities and conflicts between monetary and macroprudential policies and what is the best policy design framework to accommodate these? Undoubtedly, there is a complex relationship between the objectives of macroeconomic and financial stability, the primary objectives of monetary and macroprudential policy, respectively. Take the option of raising capital requirements for loans secured by real estate, which would increase the cost of borrowing in this segment through interest rate changes, which could also spillover to other loan types. Any kind of credit rationing that may stem from this move could also alter real activity. Both consequences are in the realm of monetary policy. In turn, recent studies show that loose monetary policy can fuel risk-taking incentives and build-up of leverage, which could warrant tighter macroprudential rules. Given these interactions, the best option may be to consider the macroprudential policy framework alongside, not apart from, the monetary policy decision.

Second, should the macroprudential framework be based on discretion or rules? On the one hand, a discretionary framework has the advantage that the measures could be better calibrated to particular situations and circumvention may be less likely because of the temporary nature of the measure (less incentive, less time to learn). On the other hand, a rules-based framework could be better because political economy problems may be less severe (no fight to put measures in place during a boom), adjustment of private agents’ behavior to the new framework may already accomplish a certain degree of prudence, and time inconsistency is not an issue. At the end, the best option may be a design with robust rules that allow discretion when needed.

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Appendix

Stocktaking on the Current Status of Policy Frameworks

Gathered from various national and international sources, Appendix Table 1 gives a country-by-country account of monetary, fiscal, and regulatory frameworks in the context of real estate markets as of September 2010.

Appendix Table 1.

Policy Frameworks in the Context of Real Estate Booms by Country as of September 2010

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P-VAR Analysis of Monetary Policy

To analyze the relationship between monetary policy conditions and house price changes (examined in Section III.A) in a more formal setting, we run a panel vector autoregression (P-VAR) on quarterly data spanning 22 countries over the 1990-2007 period. The P-VAR includes six macroeconomic variables, ordered as GDP, CPI, house prices, the nominal effective exchange rate, the import price deflator, and the policy interest rate. All variables except the policy rate are in natural logarithms. In addition, all variables are de-trended by taking the residuals from a linear-quadratic time trend and country-specific intercepts. Estimation is via generalized method of moments (GMM) following Love and Ziccino (2006). The ordering of variables implies that policy responds contemporaneously to all the other variables in the system, but that other macroeconomic variables respond only with a lag to policy. This seems reasonable, except perhaps in the case of the exchange rate. Results are robust to an alternative ordering with the policy rate ordered after house prices but before the exchange rate and import prices. Note that the ordering implies that import prices respond contemporaneously to exchange rate changes; hence, the exogenous shocks to import prices should be unrelated to exchange rate developments.

Of particular interest are the responses of the other variables to exogenous import price shocks (e.g. as brought about by opening up of the Chinese economy). Following a negative shock to import prices, monetary policy is relaxed, output increases, house prices rise. Some of the house price response can be attributed to the output channel: a decrease in import prices acts as a positive supply shock, pushing up potential and actual output. Since house prices co-move with output, house prices will tend to rise even as the price of goods decreases. However, part of the impact of the import price shock is transmitted via monetary policy, since the house price response to a policy relaxation is positive and significant.

Looking at the share of the variation in house prices that can be accounted for by the different structural shocks identified in the P-VAR, the share that can be attributed to import price shocks increases steadily over time. At a 10-year horizon, more than 16 percent of total house price variation is due to import price shocks (greater than the share for any other variable apart from shocks to house prices themselves), with monetary policy shocks accounting for a further 8 percent. Import price shocks also appear to be a significant determinant of the monetary policy stance, accounting for around 13 percent of the variance in the policy rate at each horizon and exceeding the variance share attributable to shocks to the domestic price level. Thus, the monetary policy response appears to play a key role in transmitting import price shocks to the housing market.

Appendix Table 2.

Results of Regression Analyses

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Notes: In Panel A, we analyze price developments in the U.S. at the Metropolitan Statistical Area (MSA) level. MSAs are agglomerations of counties, typically centered on a large urban conurbation. Counties are included in a particular MSA (or, in the majority of cases, in no MSA) based on factors such as commuting patterns. This means that an MSA typically corresponds to a unified local housing market. Price growth and volatility are measured by the mean and standard deviation, respectively, of change in log annual average price index. In the 2SLS estimation, the excluded instruments are latitude and commuting time (column III) and latitude and the Wharton Residential Land Use Regulatory Index (column VI). In Panel B, we analyze price developments in the U.S. at the state level. House price data come from FHFA (formerly OFHEO), information on property tax rates is provided by NHBA. LTVs are as reported at loan origination and are obtained from the Monthly Interest Rate Survey of the FHFB. Other data sources include U.S. Census Bureau and BEA. Robust standard errors are in brackets. ***, **, * correspond to significance at the 1, 5, 10 percent level, respectively.
1

This paper accompanies SDN/11/02: “Policies for Macrofinancial Stability: Options to Deal with Real Estate Booms.” We would like to thank Mohsan Bilal and Jeanne Verrier for research assistance and participants at the BOK-IMF Workshop on Managing Real Estate Booms and Busts for valuable comments.

2

As illustrated in models in the tradition of Kiyotaki and Moore (1997), the collateral role of property magnifies the swings as real estate cycles become highly correlated with credit cycles. A potentially destabilizing two-way amplification process develops between rising house prices and credit boom during the upswing, and declining prices and a credit crunch during the downturn.

4

See, for instance, Herring and Wachter (2000) and Reinhart and Rogoff (2008) for more on the link between real estate booms and banking busts.

5

Stock markets are typically susceptible to larger fluctuations; therefore, one could expect the wealth loss associated with stock market busts to be bigger in absolute terms. For instance, during the dot-com bust, the value of American households’ equity holdings declined by 44 percent or US$5.4 trillion. The real estate bust that started at the end of 2006 has so far brought about a 15 percent decline in the value of real estate assets held by households, wiping out US$3.7 trillion off their wealth. Interestingly, however, the total wealth lost during the real estate bust, standing at US$10 trillion or 13 percent of end-2006 total household assets, exceeds that lost during the dot-com bust, which reached US$2.8 trillion or 6 percent of end-2000 total assets, because of the spillover to other asset markets from the housing downturn.

6

The slope of the supply curve is particularly relevant for the characteristics (such as frequency and magnitude) of boom-bust cycles and how these cycles affect the rest of the economy. If the supply curve is flat, effect of a demand shock on prices is small and the spillovers to aggregate activity occur mainly through the increase in quantities, e.g. construction value added, limiting the spillovers through leverage (which could threaten financial stability). If the supply curve is steep, prices move more and, while direct impact on economic activity may be limited, leverage would amplify spillovers with implications for both macroeconomic and financial stability.

7

Another factor that could delay adjustment of prices to fundamentals in real estate markets is the existence of large set of investors with adaptive expectations (Case and Shiller, 2003; Piazzesi and Schneider, 2009).

8

This simple panel-data analysis uses data from the U.S. Bureau of Economic Analysis, Federal Housing Finance Agency, and Internal Revenue Service covering U.S. states from 2000 to 2008.

9

Ferreira et al. (2010) also find evidence of a negative relationship between negative equity and mobility micro data from the American Housing Survey. It should, however, be noted that a recent study challenges their findings arguing that certain observations were systematically left out (Schulhofer-Wohl, 2010).

10

A traditional mortgage loan here is a 30-year, 20 percent down, fixed-rate, fully-amortizing contract. The lender is assumed to require mortgage payments not to exceed 25 percent of borrower’s income. This reflects common practices in U.S. mortgage markets while the characteristics of a traditional mortgage may vary rom one country to another.

11

Another way of stating this argument follows from thinking of house prices as the sum of two components: fundamentals-driven and bubble. The fundamentals-driven component would be, more or less mechanically, affected by monetary policy. But the bubble component tends to have a life of its own with less responsiveness to policy rates as inefficiencies in real estate markets allow positively serially correlated returns (see, for instance, Case and Shiller, 1989), making speculation more attractive and bubbles harder to break once they have started. See Allen and Carletti (2010) for more on the role of speculation in real estate bubbles, as well as on the role monetary policy in real estate markets.

12

See, for instance, Brzoza-Brzezina et al. (2007) for evidence that, in the Czech Republic, Hungary and Poland between 1997 and 2007, restrictive monetary policy led to a decrease in domestic currency lending but simultaneously accelerated foreign-currency-denominated loans.

13

It is beyond the scope of this paper to discuss capital inflows and their relationship with asset price booms, we refer the interested reader to Ostry et al. (2010) and references therein for the associated literature and policy recommendations.

14

The Taylor Rule posits that monetary policy should respond to inflation and the output gap and, using the suggested coefficients in the classical version, can be expressed as: interest rate = 1 + 1.5*inflation + 0.5*output gap. The difference between the actual policy interest rate and that suggested by the rule, i.e., the Taylor residual, gives a rough measure of the policy stance with negative values suggesting “too loose” policy. In estimating the Taylor residuals, the following assumptions are maintained: an equilibrium real interest rate of 200 basis points, an inflation target of 2 percent, and a zero output gap in equilibrium.

15

IMF (2006) finds that globalization reduced CPI inflation by more than 1 percentage point in some advanced economies during some periods, particularly in the wake of the Asian crisis in 1998 and again after the 2001 recession. Assuming that monetary policy followed a classical Taylor Rule based on CPI, this would have tended to reduce policy interest rates by up to 200 basis points. This point is particularly critical because well-founded models for analyzing monetary policy in open economies (e.g. Galí and Monacelli, 2005; Benigno and Benigno, 2006) emphasize that optimal policy responds primarily to inflation in the price of domestically produced goods, not to CPI inflation.

16

Using median regressions (a technique that minimizes problems associated with outliers) of house price growth on each of the two Taylor residuals, including in each case time fixed effects to control for global factors driving the real estate cycle, we find that the relationship for the GDP-deflator-based Taylor residual is statistically significant while the apparent negative relationship for the CPI-based residual is not. This echoes IMF (2009), which looked at Taylor residuals using CPI inflation and found only limited evidence for a relationship between monetary policy and the housing boom.

17

Details of the estimation and a discussion of the impulse responses are in the Appendix.

18

This back-of-the-envelope calculation may be underestimating the effectiveness of a large hike on house prices because of potential non-linearity.

19

The Reserve Bank of Australia, on its media release dated May 8, 2002, stated that “The strong rises in house prices […] have also been associated with a rapid expansion in household debt, a process that carries longer-term risks […] To persist with a strongly expansionary policy setting […] could fuel other imbalances such as the current overheating in the housing market […]” in communicating its decision to raise the cash rate target (the full statement is available at http://www.rba.gov.au/media-releases/2002/mr-02-10.html). Sveriges Riksbank, on its press release dated January 20, 2006, said “As before, there is also reason to observe that household indebtedness and house prices are continuing to rise rapidly. Against this background, the Executive Board decided to raise the repo rate by 0.25 percentage points […]” (the full statement is available at http://www.riksbank.com/templates/Page.aspx?id=20017). See also Bloxham et al. (2010) and Giavazzi and Mishkin (2006) for more on Australian and Swedish monetary policies, respectively.

20

See, for instance, Cremer and Gahvari (1998) and Bourassa and Grigsby (2000) for more on the economic reasons that shape tax treatment of owner-occupied housing.

21

Note that the extent of this partial offset will depend on various factors such as deductibility of capital gains and the extent of pass-through from property taxes to rents.

22

Formally, R = UC = P[(1- τm )(i + τp )+ β +m + d -π] where R is the rent, UC is the user cost, P is the house price, τm is the marginal tax rate i is the nominal mortgage interest rate, and τp is the property tax rate on owner-occupied houses. i measures the cost of foregone interest that the homeowner could have earned on an alternative investment. β, d, and m are the recurring holding costs consisting of the risk premium on residential property, depreciation, and maintenance. π is the expected capital gains.

23

In addition, to the extent that the value of the tax shelter is factored into house prices, reduction or abolishment of mortgage interest deductibility may lead to a higher house price level in the short run.

24

See Allen and Carletti (2010) for a theoretical framework in which transaction taxes that are higher the greater the rate of real estate price appreciation can limit speculative motivations.

25

Teasing out significant causal relationships from a small cross-country sample is confounded by a number of challenges: results can be sensitive to which countries are included in the sample, and standard econometric problems of endogeneity (extracting the correct causal relationship from the estimated correlation) and omitted variable bias (controlling for all the other factors that vary between countries and could affect price developments) are pronounced. Differences in national house price definitions and methodologies create additional noise. Focusing instead on one country has a number of advantages: many of the housing market determinants that vary across countries (such as monetary policy, central government fiscal policy, and the treatment of mortgage loans in the banking system) are relatively constant across geographical areas within a country. Data comparability issues are minimized as the house price measurement is standardized across areas. At the same time, there is substantial variation in property tax rates, which are set at the local government level.

26

The results are presented in Appendix Table 2, Panel A.

27

On a related note, while they may have an impact on prices, neither transaction nor property taxes get directly to credit and leverage.

28

Other measures not discussed here include cyclical ceilings on portfolio exposure to real estate, speed limits on real estate lending, and restrictions on certain type of loans. These tools have been used even more sparingly.

29

Note that, of course, some of these tools have been used before, but mostly with microprudential objectives.

30

Reflecting the commonly-accepted ‘benign neglect’ approach prior to the crisis, not only the macroprudential policy frameworks but also the macroeconomic policy frameworks do not currently incorporate mechanisms to directly respond to real estate market developments. The proportion of countries that report explicitly considering mortgage credit growth or property price appreciation in policy decisions is less than 15 percent.

31

The discussion focuses on the price-related measures of capital regulation but exposure limits would have similar implications working as a quantity-based measure.

32

Risk weights are generally set higher for commercial real estate loans than residential real estate loans, given the higher risk profile of commercial real estate due to more volatile price dynamics and the dependence of borrower’s ability to service the debt on rental income.

33

Fixed risk weights are applicable only under the standard approach of Basel II. Under internal-rating-based approach, regulators (and banks) can split loans into sub-categories based on several risk indicators and vary risk weights accordingly. Indeed, a few countries have experimented with applying higher risk weights to high-LTV loans (see Table 3 for more information).

34

Obviously, the increase in the cost of borrowing may have a side effect: credit rationing may set in, reducing welfare gains associated with access to finance.

35

Evidence on exposure limits is more limited: only Malaysia tried decreasing the maximum real estate exposure but the measure came in too late in April 1997, just before the Asian crisis hit the markets. In a more structural sense, many countries have constant exposure limits but there is no apparent relationship between the level of these limits and real estate boom-bust episodes.

36

As it has been the case for capital requirements, procyclicality of regulations governing loan loss provisions has been subject to criticism before the crisis (see, for instance, Laeven and Majnoni, 2003).

37

With these “piggyback” loans the first lien would cover 80 percent of the home value and the remainder would be split between a second lien loan and a downpayment (which could be as low as zero).

39

Another concern related to credit rationing that may be associated with LTV limits is that, by changing the status of second liens, such limits may constrain borrowers’ ability to use second mortgages for starting new businesses or other small business financing. Yet, according to survey evidence, only a small portion of home equity lines of credit were used for such purposes in the U.S.: the main purposes were home improvement, financing of real estate (e.g. to reduce downpayment on first home or to purchase second home), durable goods purchase, and debt consolidation (http://money.cnn.com/2003/10/01/commentary/everyday/sahadi/).

40

An area is designated as ‘speculative’ in Korea if the following criteria are met: (i) nominal house prices rose more than 1.3 times the nationwide inflation rate over the past month and (ii) either the average appreciation rate over the past 2 months is higher than 1.3 times the average national appreciation rate over the past 2 months, or the average appreciation rate over the past 12 month is higher than the average national HPI appreciation rate over the past 36 months.

41

These regressions are presented in Appendix Table 2, Panel B.

42

Actions in Hong Kong SAR taken over the past few months appear to be less effective though, but the reason for that may be the fact that the boom in this case appears to be driven less by domestic demand/supply and credit conditions and more by external factors, in particular, the capital flows from mainland China. IMF (forthcoming) looks into the issue of managing capital inflows using different policies.

43

Such models have increasingly become the standard in studying the role of credit market frictions and macroprudential tools in a macroeconomic setting (see, for instance, Christiano et al., 2009; IMF, 2009; Cúrdia and Woodford, 2010; Iacoviello and Neri, 2010; and Angelini et al., 2010). See Kannan et al. (2009) for more on the theoretical underpinnings of the particular model used here.

44

At this time, designing a tractable model of bubbles that can be incorporated into a medium-scale macroeconomic model remains a challenge. All house price fluctuations in the model come from fundamentals and reflect the expected present discounted value of rents. It should also be noted that the model is based on a closed-economy framework, potentially underestimating the aggregate effects associated with house price increases. While these caveats may limit the ability to derive strong policy conclusions, the model still offers an organized framework to discuss implications of various policy options.

45

Note that banks are modeled as financial intermediaries that simply channel funds from savers to borrowers. A main shortcoming of the model is that the banking sector markup is exogenous and independent of the balance sheet of the banks.

46

Nolan and Thoenissen (2009) show that total factor productivity and financial shocks explain a large fraction of the fluctuation in main U.S. macroeconomic variables.

47

A concern could be that the improvement in welfare reflects the far-from-optimal coefficients on the reaction to the output gap and inflation. Hence, we optimize all coefficients of the Taylor rule and still find a role for reacting to real estate prices and mortgage credit.

48

Other policy proposals have considered the effects of changing capital and liquidity requirements (BIS, 2010; Angelini et al., 2010). As the BIS (2010) document acknowledges, all these macroprudential policies have the effect of working through the spread between lending and deposit rates.

49

The BIS (2010) document estimates that an increase of 1 percent in bank capital requirements also leads to an increase of 25 basis points in the spread between lending rates and the cost of funds for banks. Hence, the exercise here can also be thought of as an increase in capital requirements.

50

Note that, in the long run, prices are set to equal their marginal cost of production, and hence it is not possible for regulatory policy to affect house prices in the long term by restricting the amount of credit.

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How to Deal with Real Estate Booms: Lessons from Country Experiences
Author:
Mr. Pau Rabanal
,
Mr. Christopher W. Crowe
,
Mr. Giovanni Dell'Ariccia
, and
Ms. Deniz O Igan