Financial Crises

Chapter 12. Policies for Macro-Financial Stability: Managing Real Estate Booms and Busts

Stijn Claessens, Ayhan Kose, Luc Laeven, and Fabian Valencia
Published Date:
February 2014
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Christopher Crowe, Giovanni Dell’Ariccia, Deniz Igan and Pau Rabanal The authors would like to thank Franklin Allen, Olivier Blanchard, Stijn Claessens, and Susan Wachter for useful comments and discussions. Mohsan Bilal and Jeanne Verrier provided excellent research assistance.

Real estate boom-bust cycles 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. Despite these dangers, the traditional policy approach to real estate booms has been one of “benign neglect” (Bernanke, 2002; and Greenspan, 2002), notwithstanding the more proactive approach adopted by a few central banks (Mishkin, 2011). This attitude was based on two main premises. First was the belief that, as with 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 was the notion that the distortions associated with preventing a boom outweigh the costs of cleaning up after a bust. The 2007–09 global economic and financial crisis has challenged (at least the second of) these assumptions.

The burst of the real estate bubble in the United States triggered the worst financial crisis and the deepest recession since the Great Depression. The crisis quickly spread to other countries, especially 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. And despite the recourse to extraordinary measures (ranging from bank recapitalization to asset purchase programs and quantitative easing), the aftermath of the crisis was characterized by a weak recovery, as debt overhang and financial sector weakness continued to hamper economic growth. Bubbles remain hard to spot 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 vulnerability. Although early intervention may engender its own distortions, it may be best to undertake policy action 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 answers. What kind of indicators should trigger policy intervention to stop a real estate boom? If policymakers were fairly certain that intervention were warranted, what policy tools would be at their disposal? What are their impacts? What are their negative side effects and limitations? What practical issues would limit their use? This chapter explores these questions.

It should be recognized at the outset that a more proactive policy stance can help reduce the risks associated with real estate booms, but will inevitably result in its own costs and distortions. With this in mind, the chapter reaches the following conclusions: Policy efforts should focus on booms that are financed through credit and when leveraged institutions are directly involved because the following busts tend to be more costly. In that context, monetary policy is too blunt and costly a tool to deal with the vulnerabilities associated with increased leverage, unless the boom occurs as a result of or at the same time as broader economic overheating. Fiscal tools may be, in principle, effective. But, in practice, they would likely create distortions and are difficult to use in a cyclical fashion. Macroprudential tools (such as limits on loan-to-value ratios) are the best candidates for dealing with the dangers associated with real estate booms because they can be aimed directly at curbing leverage and strengthening the financial sector. But their careful design is crucial to minimizing circumvention and regulatory arbitrage. Furthermore, they will involve a cost to the extent that some agents find themselves rationed out of credit markets.

The chapter 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,1 drawing on several country experiences and the insights from an analytical model.2 The chapter concludes with a brief discussion of guiding principles for using public policy measures to deal with real estate booms.

The Case for Policy Action on Real Estate Booms

Before the 2007–09 global 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 afterward than to attempt to prevent the boom ahead of time (admittedly, this was less true in emerging market economies, which often paid close attention to real estate markets). Given this prescription, the characteristics of a particular asset class (such as how purchases are financed and what agents are involved, or whether the asset has consumption value besides investment value) were secondary details. However, if postbust policy intervention is of limited effectiveness and, thus, the costs associated with a bust are large, these details are critical to determining whether it is worth attempting to contain a boom in the first place. From this standpoint, several frictions and externalities make the case for early policy intervention in real estate market booms more strongly 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 itself, but how it is funded. Busts tend to be more costly when booms are financed through credit and leveraged institutions are directly involved because the balance sheets of borrowers (and lenders) deteriorate sharply when asset prices fall.3 The involvement of banks can lead to a credit crunch with negative consequences for real economic activity. In contrast, economic booms with limited leverage and limited bank involvement tend to deflate without major economic disruptions. For example, the bursting 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 dimensions. The vast majority of home purchases and commercial real estate transactions in advanced economies involve borrowing. And banks and other leveraged players are actively involved in the financing. Moreover, home buyers 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. And when it does, loans are subject to margin calls that prevent the buildup of highly leveraged positions.

Highly leveraged housing markets had a prominent role during the 2007–09 crisis. 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, 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, Pence, and Sherlund, 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 12.1.

Figure 12.1Leverage: Linking Booms to Defaults

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

Note: Bubble size shows leverage (calculated as mortgage credit outstanding divided by household income) in 2007.

This pattern was not limited to the United States, nor was it new to this crisis. The amplitude of house price upturns before 2007 is statistically associated with the severity of the crisis across countries (Figure 12.2; Claessens and others, 2010). 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. And, historically, many major banking distress episodes have been associated with boom-bust cycles in property prices (Figure 12.3; Herring and Wachter, 1999; and Reinhart and Rogoff, 2009). Looking at the 12 infamous episodes, house prices plunged 25 percent in the three-year period from their peak while real GDP dropped by 6 percentage points compared with its level when house prices were at their peak (Figure 12.4).4

Figure 12.2House Price Run-Up and Severity of Crisis

Source: Claessens and others, 2010.

Note: Bubble size shows the change in bank credit from 2000 to 2006. AUS = Australia; AUT = Austria; BGR = Bulgaria; CAN = Canada; CHE = Switzerland; CHN = China; CYP = Cyprus; CZE = Czech Republic; DNK = Denmark; ESP = Spain; EST = Estonia; FIN = Finland; FRA = France; GBR = United Kingdom; GRC = Greece; HRV = Croatia; HUN = Hungary; IND = India; IRL = Ireland; ISL = Iceland; ITA = Italy; KOR = Korea; LTU = Lithuania; LVA = Latvia; NLD = Netherlands; NOR = Norway; NZL = New Zealand; POL = Poland; PRT = Portugal; SVN = Slovenia; SWE = Sweden; UKR = Ukraine; USA = United States; ZAF = South Africa.

Figure 12.3House Price Boom-Busts and Financial Crises: Selected Episodes

Sources: IMF, International Financial Statistics; IMF staff calculations; and Organization for Economic Co-operation and Development, Global Property Guide.

Note: Crisis beginning dates, shown by the vertical lines, are from Caprio and others (2003), further complemented in Laeven and Valencia (2010). Real GDP series are detrended.

Figure 12.4House Price Boom-Busts and Financial Crises: Average Damage

Sources: IMF staff calculations; and Organization for Economic Co-operation and Development, Global Property Guide.

Note: The paths of real house prices and real GDP 8 quarters before and 12 quarters after the house price peak (dated 0) for 12 infamous episodes (see text) are shown. Both real house prices and real GDP are indexed to equal 100 at house price peak.

Another distinguishing feature of “bad” real estate boom-bust episodes seems to be coincidence between the boom and the rapid increase in leverage and exposure of households and financial intermediaries. In the 2007 episode, this coincidence occurred in more than half the countries in a 40-country sample (Table 12.1). 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 the 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 a 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.

Table 12.1Booms, Crises, and Macroeconomic Performance
Type of boomBoom followed by financial crisis (percent)1Boom followed by poor performance (percent)2Boom followed by financial crisis or poor performance (percent)Boom followed by financial crisis and poor performance (percent)Number of countries
Real estate 35377874330
Credit 46778935227
Real estate but not credit297171297
Credit but not real estate10075100754
Sources: IMF, International Financial Statistics; IMF staff calculations; and Organization for Economic Co-operation and Development, Global Property Guide.

A financial crisis is a systemic banking crisis as identified in Laeven and Valencia (2010).

Poor performance is defined as a more than 1 percentage point decline in the real GDP growth rate in 2008–09 compared with the 2003–07 average.

A real estate boom exists if the annual real house price appreciation rate in the upward phase of the housing cycle before 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 percentage 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.

Sources: IMF, International Financial Statistics; IMF staff calculations; and Organization for Economic Co-operation and Development, Global Property Guide.

A financial crisis is a systemic banking crisis as identified in Laeven and Valencia (2010).

Poor performance is defined as a more than 1 percentage point decline in the real GDP growth rate in 2008–09 compared with the 2003–07 average.

A real estate boom exists if the annual real house price appreciation rate in the upward phase of the housing cycle before 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 percentage 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.

Wealth and Supply-Side Effects

Real estate is an important, if not the most important, storage of wealth in the economy. Additionally, the majority of households tend to hold wealth in their homes rather than in equities. Typically, in advanced economies fewer than half of households own stock (directly or indirectly) while home ownership rates hover around 65 percent (Guiso, Haliassos, and Jappelli, 2003).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 and others, 2009). This suggests that fluctuations in house prices create ripples in the economy through their impacts on residential investment, consumption, and credit whereas the reverse effect is not as prominent, implying that the housing sector can be a source of shocks, or at least there is a two-way relationship between house prices and economic activity (IMF, 2011). In advanced economies, recessions that coincide with house price busts tend to be deeper and last longer than those that do not, and their cumulative losses are three times the damage done during recessions without busts (Table 12.2). Again, by contrast, recessions that occur around equity price busts are not significantly more severe or persistent than those that do not (Claessens, Kose, and Terrones, 2008).

Table 12.2Recessions with and without House Price Busts
Recession without bustRecession with bustRecession with

severe bust1
Duration (quarters)3.24.5 **4.6 **
Amplitude (percent)–2.0–3.2 *–4.1 **
Cumulative loss (percent)–3.5–10.4 **–14.0 *
Source: Reproduced from Table 8 in Claessens, Kose, and Terrones (2008).Note: The sample includes 21 Organization for Economic Cooperation and Development countries. Mean values are shown.

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.
Source: Reproduced from Table 8 in Claessens, Kose, and Terrones (2008).Note: The sample includes 21 Organization for Economic Cooperation and Development countries. Mean values are shown.

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, reflecting delays in supply responses to demand shocks and the slow pace of price discovery associated with opaque and infrequent trades as well as illiquidity owing to high transaction costs and the virtual impossibility of short sales. Therefore, real estate prices and construction activity can be expected to display large swings over long periods, even absent the distortions caused by the institutional features of real estate finance and policy actions (Igan and Loungani, 2012).6

Network externalities also complicate the picture. Homeowners in financial distress (particularly those with negative equity) have less incentive 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. The double role of real estate as investment and consumption goods may reduce mobility and increase structural unemployment because households in negative equity may be reluctant or unable to sell and take advantage of job opportunities elsewhere. The preferential tax treatment of home ownership exacerbates this problem by creating a wedge between the cost of owning and renting. Hence, a housing bust may weaken the positive association between employment growth and mobility. Indeed, U.S. regions in which house prices 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).

Policy Options

The 2007–09 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. In several countries, monetary easing, fiscal stimulus, direct support to the financial sector, and special housing market initiatives helped, but could not prevent the largest recession since the Great Depression. Ultimately, there were large costs, including social and human costs caused by foreclosures and job losses associated with the bust. Although 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.7 And, similar to other policy decisions, action may have to be taken under considerable uncertainty when the costs of inaction might 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, which policy lever is best suited to reining in the bad booms? The main risks from real estate boom-bust cycles are associated with increased leverage in both the real (in particular, household) and financial sectors. Thus, policies should, whenever possible, aim at containing these risks rather than containing price increases. In that context, one could think of policies as targeting two main objectives (not to be taken as mutually exclusive): (1) preventing real estate booms and the associated leverage buildup in household and banking sectors altogether, and (2) increasing the resilience of the financial system to a real estate bust. Table 12.3 summarizes available policy measures along with their pros and cons.

Table 12.3Policy Options to Deal with Real Estate Booms
Macroeconomic PolicyPotential impactSide effectsPractical issues
Monetary measures
Interest rates Reserve requirementsresponding to property prices and real estate loan growthpotential to prevent booms, less so to stop one that is already in progressinflict damage to economic activity and welfareidentifying “doomed” booms and reacting in time; constraints imposed by monetary regime
Fiscal measures
Transaction/capital gains taxes linked to real estate cyclesautomatically dampen the boom phaseimpair already-slow price discovery processincentive to avoid by misreporting, barter, folding the tax into the mortgage amount
Property taxes charged on market value(could) limit price increase and volatilitylittle room for cyclical implementation
Abolition of mortgage interest deductibilityreduce incentives for household leverage and house price appreciation(potentially) inflict damage on the real estate sector by taking away a sectoral advantagelittle room for cyclical implementation
Regulatory Policy
Macroprudential measures
Differentiated capital requirements for real estate loans Higher risk weights on real estate loansincrease cost of real estate borrowing while building buffer to cope with the downturncosts associated with potential credit rationingmay get too complicated to enforce, especially in a cyclical context; effectiveness also limited when capital ratios are already high
Dynamic provisioning for loans collateralized by real estateincrease cost of real estate borrowing while building buffer to cope with the downturnearnings managementdata requirements and calibration
Limits on mortgage credit growth(could) limit household leverage and house price appreciationloss of benefits from financial deepeningmove lending outside the regulatory periphery
Limits on exposure to real estate sector(could) limit leverage and price appreciation as well as sensitivity of banks to certain shockscosts associated with limiting benefits from specializationshift lending to newcomers for whom exposure limits do not yet bind or are outside the regulatory periphery
Limits on loan-to-value ratio Limits on debt-to-income ratio(could) limit household leverage and house price appreciation while decreasing probability of defaultcosts associated with potential credit rationingcalibration is difficult, circumvention is easy
Sources: IMF country reports; Enoch and Ötker-Robe (2007); Borio and Shim (2007); and IMF staff.
Sources: IMF country reports; Enoch and Ötker-Robe (2007); Borio and Shim (2007); and IMF staff.

It should be recognized at the outset that there is no silver bullet. Each policy will have 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 therefore 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. The narrative in the chapter focuses on residential real estate. However, several (although not all) of the measures discussed could easily apply to commercial real estate booms as well. The chapter examines the potential role of monetary, fiscal, and macroprudential policies. Supply-side housing policies (such as publicly provided housing and land sales) widely used in a few countries (Hong Kong SAR and Singapore in particular) are not discussed because they would be difficult to export to different institutional settings. The benefits and challenges associated with the various policy options are discussed using case 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.

Monetary Policy

Can monetary tightening stop or contain a real estate boom? 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 (the ratio of median household income to income necessary to qualify for a typical mortgage loan) and shrink the number of borrowers that qualify for a loan of a certain amount. Indirectly, to the extent that monetary tightening reduces leverage in the financial sector, it may alleviate the financial consequences of a bust even if it does not stop the boom (Adrian and Shin, 2009;De Nicolò and others, 2010).

However, monetary policy is a blunt instrument for this task. First, it affects the entire economy and is likely to involve 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 at 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 lower when the boom occurs in the context (or as a consequence) of general macroeconomic overheating. Then, the distortions associated with monetary tightening would be minimized. Indeed, when financial constraints are present and real estate is important as collateral, a policy rule reacting to real estate price movements or credit growth (in addition to inflation and the output gap) can trump a traditional Taylor rule but only for booms that occur along with general macroeconomic overheating (see the section below on “Model-Based Evaluation of Policy Options”).

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. If that is the case, tightening may have the perverse effect of leading borrowers (who would have otherwise qualified for standard mortgages) toward more dangerous forms of loans (such as interest-only, variable-rate loans, and in some cases foreign-currency loans).8 Moreover, in the presence of free capital mobility, the effectiveness of monetary policy may be limited, especially for exchange rate regimes that are not fully flexible.

Finally, the effectiveness of a change in the policy rate will also depend on the structure of the mortgage market. In systems in which mortgage rates depend primarily on long-term rates, the effectiveness of monetary policy will depend on the relationship between long and short rates.

To a large extent, empirical evidence supports these concerns, leading to the conclusion 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 precrisis monetary stance had much to do with the real estate boom preceding the 2007–09 crisis. Inflationary pressures were broadly contained throughout the period and the extent of house price booms does not appear to have been correlated with real interest rates or other measures of monetary conditions, except in a subsample of euro area countries (IMF, 2009). This lack of a relationship can be explained in part by the rapid decline in import prices driven by the trade integration of emerging economies—notably China—that may have offset relatively high inflation in nontradables sectors (IMF, 2006). Housing booms were more salient in countries that experienced declines in import prices relative to the general price level. But the relationship between the monetary policy stance and house prices remains weak (albeit more statistically significant) after controlling for this issue (with Taylor residuals based on domestic inflation rather than overall consumer price index inflation). Policymakers would have had to “lean against the wind” dramatically to have had a meaningful impact on real estate prices and credit, with large effects on output and inflation. This intuitive result is confirmed by a panel vector autoregression, which suggests that, at a five-year horizon, a 100-basis-point hike in the policy rate would reduce house price appreciation by only 1 percentage point, compared with a historical average increase of 5 percent per year (see Crowe and others, 2011, for details). But it would also lead to a decline in GDP growth of 0.3 percentage point.

Part of the problem may be that speculation is unlikely to be stemmed by changes in the monetary policy stance. Some evidence indicates that conditions in the more speculative segment of mortgage markets are little affected by changes in the policy rate. For example, in the United States, 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 (Crowe and others, 2011).

Fiscal Tools

In most systems, a variety of fiscal measures (transaction taxes, property taxes, credits, deductibility of interest payments) bear on the decision to invest in real estate. The net result is often socially driven favorable treatment of home ownership (and sometimes housing-related debt).9 In theory, some of these fiscal tools could be adjusted in a cyclical manner to influence house price volatility while preserving the favorable treatment of homeownership on average during the cycle. However, if the net present value of all future taxes is capitalized in property prices, adjusting taxes countercyclically around the same expected mean would not affect the prices.10 In practice, moreover, cyclically adjusted fiscal measures may be of limited use. First, the evidence on the relationship between the tax treatment of residential property and real estate cycles is inconclusive. Second, technical and political economy problems may complicate implementation.

At the structural level, the tax treatment of housing does not appear to be related across countries to the amplitude of real estate 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 ones (e.g., Japan). Overall, taxation was not the main driver of house price developments during the boom (Keen, Klemm, and Perry, 2010). Furthermore, levels of home ownership (the main excuse for favorable tax treatment of housing) are, if anything, negatively (but not significantly) related to the degree to which the tax system is favorable to owning one’s own home.

In addition, the scope for the use of fiscal tools in a cyclical setting is likely to be limited. 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. Instead, local governments may use lower property or transaction tax rates to attract residents during good times if the burden were to be 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 home buyers 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 make it more difficult to evaluate a property, which already tends to be hard, and also may reduce the mobility of households, with potential implications for the labor market.

Transaction Taxes

Transaction taxes that change with real estate conditions may, in theory, be promising for dealing with booms (Allen and Carletti, 2010). But it should be recognized that these taxes induce considerable distortions in real estate markets and, indirectly, in labor markets through their impact on mobility. On the bust side, the use of time-limited tax credits linked to house purchases in the United States and the suspension of the stamp duty in the United Kingdom helped stabilize the housing market. And, especially in the United States, the price stability and revival of activity disappeared with the expiration of the tax breaks (IMF, 2010). On the boom side, China and Hong Kong SAR 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 transitory.

Property Taxes

Some evidence from the United States suggests that higher rates of property taxation may help limit housing booms as well as short-run volatility around an upward trend in prices (more details can be found in Crowe and others, 2011). A one-standard deviation ($5 per $1,000 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 with annual growth of about 5.6 percent per year). One interpretation of this finding 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. First, municipalities often face pressure to reduce tax rates when markets are booming and tax revenues are high. This implies that some of the negative correlation between prices and taxes may be spurious, and challenges the ability to use property taxes as a countercyclical tool.11 In addition, the results may be specific to the U.S. housing market, the characteristics of which 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 United States, 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.

Mortgage Interest Tax Deductibility

Theoretically, mortgage interest tax deductibility, by encouraging debt financing, may lead to higher household debt and more leveraged loans and, in turn, to more severe financial sector distress during real estate downturns. Empirically, tax reforms that reduce the value of mortgage interest relief have been shown to lead to lower loan-to-value ratios (see Hendershott, Pryce, and White, 2003, for the United Kingdom; and Dunsky and Follain, 2000, for the United States). And they are estimated to cause an immediate decline in house prices of about 10 percent (see Agell, Englund, and Södersten, 1995, for Sweden; and Capozza, Green, and Hendershott, 1996, for the United States). This evidence suggests that a more neutral tax treatment may help make the economy less vulnerable to real estate busts by reducing incentives for leverage and preventing artificially elevated prices and homeownership rates. However, these estimates are based on one-off changes, hinting at the difficulties in using mortgage interest tax deductibility rules in a cyclical way. Furthermore, eliminating interest deductibility will not eliminate booms. Before the recent crisis, some countries that tax mortgage interest experienced rapid growth in prices and household debt levels (such as Australia) while others that allow full deductibility did not have as big a boom (such as Switzerland).

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 either monetary or fiscal policy.

Against the benefit of lower cost, macroprudential measures are likely to present two shortcomings. First, they may be easier to circumvent because they target a specific type of contract or group of agents. When circumvented, these measures can be counterproductive, possibly leading to liability structures that are more difficult to resolve or renegotiate in busts. Second, they may be more difficult to implement from a political economy standpoint. Monetary policy decisions have come to be accepted as a necessary evil thanks to central banks’ increasing credibility and independence. In contrast, the use of macroprudential measures could be considered an unnecessary intrusion into the functioning of markets. The more direct impact of these measures would also complicate implementation because winners and losers would be more evident than if macroeconomic policies were used (although several countries seem to have dealt effectively with this problem).

This analysis focuses 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 of increasing banks’ loan loss provisions during the upswing phase of the cycle; and third, the cyclical tightening and easing of eligibility criteria for real estate loans through loan-to-value (LTV) and debt-to-income (DTI) ratios.12 These macroprudential tools may be able to achieve both objectives: (1) reducing the likelihood or magnitude of a real estate boom (for instance, by imposing measures to limit household leverage), and (2) strengthening the financial system’s ability to withstand the effects of a real estate bust (for example, by urging banks to save in good times for rainy days).

A caveat is in order: 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 (Table 12.4; Borio and Shim, 2007; CGFS, 2010). And these measures have typically been used in combination with macroeconomic policy and direct interventions in the supply side of housing markets (such as in Singapore), further complicating the challenge of attributing outcomes to specific tools.

Table 12.4Survey-Based Assessment of Policy Frameworks as of September 2010(proportion of respondents giving a particular answer, percent)
Monetary policy frameworkTax systemRegulatory structure
Credit growth explicitly considered?Property prices explicitly considered?Transactions tax?Mortgage interest deductibility?On which financial institutions can extend mortgage loans?On types of mortgages?On loan-to-value ratio?On debt-to-income ratio?On mortgage credit growth rate?Real-estate-specific loan loss provisioning?Real-estate-specific risk weights?Full recourse on mortgages?
Directly (not through, e.g., the rent component of CPI)148
Subject to restrictions Cyclically based64441111
Source: Survey of country authorities conducted by IMF staff.Note: Compiled responses from 36 countries. Country-by-country responses to this brief in-house survey are in Crowe and others, 2011. CPI = consumer price index.
Source: Survey of country authorities conducted by IMF staff.Note: Compiled responses from 36 countries. Country-by-country responses to this brief in-house survey are in Crowe and others, 2011. CPI = consumer price index.

But much can be learned from case studies. Following the Asian crisis of the late 1990s, some countries in the region took a more heavy-handed approach to dealing with risks posed by real estate booms. Countries in Central and Eastern Europe experimented with various measures to control the rapid growth in bank credit to the private sector in the first decade of the 2000s. Others put in place dynamic provisioning frameworks. Table 12.5 summarizes policy experiences with real estate booms (a detailed account of country cases is in Crowe and others, 2011). On the whole, success stories appear to be few, perhaps reflecting the learning curve in expanding the policy toolkit, improving the design of specific tools, and sorting out implementation challenges. However, when policy succeeded in slowing down a boom and avoiding a systemic crisis in a bust, some macroprudential measures were almost always involved.

Table 12.5Stylized Facts on Policy Responses to Real Estate Booms: Stocktaking
MeasureIssue addressedCountryImpact
Monetary tighteningRapid credit growth or real estate boomCroatia, Iceland, Latvia, Ukraine; Australia, Israel, Korea, SwedenNot always effective, capital flows and currency switching risk are major limitations
Maintaining a flexible and consistent foreign exhange policyRapid credit growthCzech Republic, PolandForeign-exchange-denominated credit growth slowed down in Poland but not in the Czech Republic
Fiscal tightening or removal of incentives for debt financing (e.g., mortgage interest tax relief)Rapid credit growth or real estate boomEstonia, Netherlands, Poland, United Kingdom; Lithuania, SpainLimited effect on house prices, slightly more on household leverage
Additional/higher transaction taxes to limit speculative activityReal estate boomChina, Hong Kong SAR, SingaporeSome effect on transaction acitivity, but not long lasting
Higher/differentiated capital requirements or risk weights by loan typeRapid credit growth or real estate boomBulgaria, Croatia, India, Poland, NorwayNot always effective, some side effects of shifting the risk elsewhere in the system
Tighter/differentiated loan classification and provisioning requirementsRapid credit growth or real estate boomBulgaria, Croatia, Greece, Israel, UkraineLimited effect
Dynamic provisioningResilience to cyclical downturn/bustChina, Colombia, India, Spain, UruguaySo far so good on bank distress, small or no impact on credit conditions
Tightening eligibility requirements, e.g., limits on loan-to-value ratiosReal estate boomChina, Hong Kong SAR, Korea, Malaysia, Singapore; SwedenShort-lived effect on prices and mortgage activity
Sources: Borio and Shim (2007); IMF country reports; Enoch and Ötker-Robe (2007); and IMF staff.Note: The table gives a snapshot; it is not meant to be a comprehensive and detailed list of cases in which 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 used a battery of policy measures to address rapid credit growth; yet these countries are not included in the table because of lack of house price data. Dynamic provisioning in China and India is discretionary rather than rules based. In the entries in the “Country” column, countries following a semicolon implemented the measure under question only in the recent bust phase.
Sources: Borio and Shim (2007); IMF country reports; Enoch and Ötker-Robe (2007); and IMF staff.Note: The table gives a snapshot; it is not meant to be a comprehensive and detailed list of cases in which 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 used a battery of policy measures to address rapid credit growth; yet these countries are not included in the table because of lack of house price data. Dynamic provisioning in China and India is discretionary rather than rules based. In the entries in the “Country” column, countries following a semicolon implemented the measure under question only in the recent bust phase.

Higher Capital Requirements Or Risk Weights

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 assets. In a downturn, the opposite happens, possibly leading to deleveraging through fire sales. Countercyclical capital requirements could help reduce this bias. Furthermore, by forcing banks to hold more capital in good times, buffers would be built against future losses (see Gordy and Howells, 2006, and references therein).13

For real estate loans, the countercyclical 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). As a result, mortgage loans with predictably different default probabilities (for instance, because of different LTV ratios or exposure to different aggregate shocks) are often bundled in the same risk category and no adjustment is made to account for the real estate cycle.14 Thus, capital requirements or risk weights linked to real estate price dynamics could help limit the consequences of boom-bust cycles. These measures could build buffers against the losses incurred during busts by forcing banks to hold more capital against real estate loans during booms. And by increasing the cost of credit, they might reduce demand and contain real estate prices themselves.15 Finally, weights could be fine-tuned to target regional booms.

Implementation Challenges

Several caveats exist in implementing countercyclical capital regulation to curb real estate booms. 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.16 Thus, the introduction of countercyclical risk weights for real estate loans should be accompanied by the implementation of a finer cross-sectional risk classification. Second, as with any other measure that increases the cost of bank credit (when credit is in high demand), countercyclical risk weights may be circumvented through recourse to nonbank intermediaries, foreign banks, and off-balance-sheet activities. Third, these measures will lose effectiveness when actual bank capital ratios are well in excess of regulatory minimums (as often happens during booms). Fourth, although they would improve the resilience of the banking system to busts, tighter requirements are unlikely to have a major effect on credit availability and prices, that is, 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).


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 or risk weights (or both) on particular groups of real estate loans. Some attempts (such as in Bulgaria, Croatia, Estonia, and Ukraine) failed to stop the boom; others (as in Poland) were at least a partial success.17

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

Dynamic Provisioning

Dynamic provisioning (the practice of mandating higher loan loss provisions during upswings and one of the elements in Basel III) can help limit credit cycles.18 The mechanics and benefits are similar to those of countercyclical capital requirements. Forcing banks to build (in good times) an extra buffer of provisions can help them cope with the potential losses that occur when the cycle turns (see, for example, the case of Spain). However, it is unlikely to cause a major increase in the cost of credit, and thus to stop a boom. That said, one advantage over countercyclical capital requirements is that dynamic provisioning would not be subject to minimums as capital requirements are, so it 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 with 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 to contain other vulnerabilities associated with a boom, such as increases in debt and leverage in the household sector. In addition, practical issues, such as calibration of rules with rather demanding data requirements, and unintended effects, such as earnings management (which may raise issues with tax authorities and securities markets regulators) should be discussed in each country’s context, and frameworks should be designed that best fit each country’s circumstances. There are also other shortcomings, similar to those of countercyclical risk weights. (Being primarily targeted at commercial banks, dynamic provisioning may be circumvented by intermediaries outside of the regulatory perimeter.) Last, application of the measure only to domestically regulated banks may hurt their competitiveness and shift lending to banks abroad, raising cross-border supervision issues.


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. Spain led the countries that have adopted countercyclical provisioning and constitutes an interesting case study for a preliminary assessment of its effectiveness. 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.19 Dynamic provisioning 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 (for some perspective, the value of the housing stock has decreased by roughly 15 percent in real terms). Hence, Spanish banks had an important buffer that strengthened their balance sheets when real estate prices started to decline and the economy slipped into recession. The recession has deepened as the problems in peripheral Europe escalated, making it difficult to say whether the Spanish economy would have suffered even more if dynamic provisioning had not been in place.

Limits on Loan-to-Value and Debt-to-Income Ratios

A limit on LTV can help prevent the buildup of vulnerabilities on the borrower side (in particular in the household sector). Containing leverage reduces the risks associated with declines in house prices; that is, the lower the leverage, the greater the drop in prices needed to put a borrower into negative equity. In turn, fewer defaults should occur when the bust comes because more borrowers unable to keep up with their mortgages will be able to sell their houses. In addition, in default situations, 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 purchasing power of individuals, reducing the pressure on real estate prices. Limits on DTI will be effective in containing speculative demand by screening out borrowers who would qualify for a mortgage only on the assumption that the house would be quickly resold. Limits will also reduce vulnerabilities because borrowers will have an “affordability” buffer and will be more resilient to a decline in their income or temporary unemployment.

Implementation Challenges

Careful design of these measures is key to limiting circumvention. For instance, in the Republic of Korea, lower LTV limits for loans with less than three years to maturity spurred a boom in loans originated with maturity of three years and one day. During the housing boom in the United States, the practice of combining two or more loans to avoid mortgage insurance (which kicked in when LTV exceeded 80 percent) became common.20 An LTV limit 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 also takes into account the payments the borrower has to make on nonmortgage loans), supervisors often encounter cases in which lenders choose not to report all outstanding debt obligations.

Circumvention may result in significant costs, possibly resulting in liability structures that can complicate debt resolution during busts (for example, in the United States, holders of second liens often object to restructuring). In addition, circumvention may involve the shifting of risks not only across mortgage loan products, but also to outside the regulatory perimeter, through expansion of credit by nonbank, less-regulated financial institutions or by foreign banks (which may result in increased currency mismatches as the proportion of foreign-currency-denominated loans rises).

As with monetary policy, calibration of these tools involves a learning process. And clear communication strategies must be developed to improve their efficiency. Frequent intervention and excessively sharp changes in the limits may lead to confusing signals and carry the risk of generating policy-induced real estate cycles.

The narrow target of these measures may increase political economy obstacles (as happened in Israel),21 particularly because the groups more highly affected 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. Nevertheless, several Asian countries successfully introduced various versions of these measures. Beyond these political economy considerations, LTV and DTI limits, by rationing sensitive groups out of credit markets, will also diminish intertemporal consumption smoothing and lower investment efficiency.


Establishing a causal link running from LTVs to price and credit dynamics is difficult. At the cross-country level, a major concern is the lack of a time dimension: In many countries, multiple data points are not available. Even when data availability is not a problem, very few countries have time variation in maximum allowable LTV because it has not been an active part of the regulatory agenda. Another issue is that the values reported are simple guidelines for mortgage insurance or prudential concerns—there are no mandatory maximum limits. Hence, because of the feedback loop between mortgage credit availability and house price movements, endogeneity remains a concern.

With these caveats in mind, the scant existing empirical evidence suggests that these limits hold promise. For example, 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 12.5). And rough calculations suggest that a 10 percentage point increase in maximum LTV allowed by regulation is associated with a 13 percent increase in nominal house prices. Regressions on a panel of U.S. states from 1978 to 2008 suggest a weaker association between house price appreciation and a given LTV at loan origination: roughly a 5 percent increase in house prices for a 10 percentage point increase in LTV. Duca, Muellbauer, and Murphy (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 United States between 1979 and 2007. Their results suggest a 10 percentage point increase in LTV for first-time home buyers has an impact of 8–11 percent on house prices, assuming rents remain constant.

Figure 12.5Maximum Loan-to-Value Ratio and House Prices

Sources: Bank for International Settlements; Economic Commission for Latin America and the Caribbean; European Mortgage Federation; Inter-American Development Bank; International Union for Housing Finance; International Union of Tenants; national statistics, and central bank statistics; Organization for Economic Cooperation and Development; United Nations Economic Commission for Europe.

Note: Maximum loan to value allowed refers to new mortgage loans and, in most cases, shows the limits above which additional requirements such as mortgage insurance would apply.

A review of the experience of countries that experimented with changing mandatory LTV limits in response to real estate market developments also suggests that doing so can be quite effective. For instance, when the Korean authorities introduced LTV limits in September 2002, the month-over-month change in house prices decreased from 3.4 percent to 0.3 percent immediately and remained low until April 2003. Subsequent reductions in LTV ratios were also followed by significant drops in the house price appreciation rate. A similar pattern applies to DTI limits, with the month-over-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 “nonspeculative” areas in which the limits were untouched. The impact on year-over-year changes, however, has been smaller because prices tend to start regaining their faster pace after the first immediate reaction.22 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, Fung, and others, 2004; 2011).

Model-Based Evaluation of Policy Options

This section provides a quantitative evaluation of the policy trade-offs discussed in the previous sections, using a dynamic stochastic general equilibrium (DSGE) model that incorporates a housing sector and credit markets. DSGE models have become popular tools for analyzing optimal policy under credit market frictions (e.g., Kannan, Rabanal, and Scott, 2009; Angelini, Neri, and Panetta, 2010). But a main disadvantage is that they do not have the capability to replicate the nonlinear dynamics often observed in a crisis context, nor can they incorporate bubbles in a tractable way. Hence, the analysis in this section deals with house price fluctuations that come from fundamentals and that reflect the expected present discounted value of rents. That is, these are booms that reflect general macroeconomic overheating. However, even in this context, the analysis supports the view that tools that are narrower in focus (by addressing a specific rigidity) can perform better (Table 12.6).

Table 12.6Performance of Policy Rules in a Dynamic Stochastic General Equilibrium Model
Type of shock
Original Taylor81010
Monetary policy+ reaction to real estate prices155
+ reaction to mortgage credit363
+ reaction to both prices and credit133
Fiscal policy+ constant tax10109
+ cyclical tax788
+ both taxes877
Macroprudential policy+ rule on real estate prices636
+ rule on mortgage credit421
+ rule on both prices and credit411
Source: IMF staff calculations.Note: Policy rules are compared with the original Taylor rule and ranked by their welfare costs under each shock scenario.Rank 1 corresponds to the rule that would deliver the largest welfare improvement and is highlighted. When two rules deliver roughly the same improvement, they are assigned the same rank.
Source: IMF staff calculations.Note: Policy rules are compared with the original Taylor rule and ranked by their welfare costs under each shock scenario.Rank 1 corresponds to the rule that would deliver the largest welfare improvement and is highlighted. When two rules deliver roughly the same improvement, they are assigned the same rank.

The model has conventional New Keynesian features: prices and wages do not adjust immediately. Households make decisions on how much to invest in housing, in addition to choosing their consumption of nondurable goods. To make the model’s dynamics more realistic, consumption and residential investment are assumed to 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 by affecting interest rates and, hence, spending on both nondurables and housing. Credit is introduced by assuming that some agents are more impatient than others and prefer to consume early by borrowing. The lending rate is modeled as a spread over the policy (or deposit) rate that depends on the balance sheet position of potential borrowers. This assumption generates a feedback loop between credit spreads, house prices, and net worth of households. The spread also depends on a banking sector markup (i.e., a financial shock) and a policy instrument, which may take the form of a macroprudential or a fiscal tool. A main shortcoming is that the banking sector markup is exogenous and independent of the balance sheet of the banks.

The objective is to determine which policy regime is better at stabilizing the economy in the face of pressures on the housing market. 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, policy regimes are examined under two shocks: a financial shock that prompts a relaxation in lending standards, and a positive productivity shock that leads to an increase in income.23

Effectiveness of Monetary Policy

Suppose the central bank follows a standard Taylor (1993)-type rule, whereby it raises rates whenever consumer price index 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). This rule can be expanded by including reactions to nominal house price inflation, nominal mortgage credit growth, or both. When the economy is hit by a productivity shock, this augmented rule leads to an improvement in welfare, mostly due to a decline in output gap volatility, especially when the rule responds to house prices. This outcome occurs because the shock reflects a change in one of the fundamentals, income, that drives house prices. For a financial shock, welfare is improved by responding to real estate prices and credit because policy directly targets the source that triggers the feedback loop.

If both productivity and financial shocks are present, reacting to credit is superior to reacting to real estate prices. Reacting to credit is better because the optimal response to credit developments is broadly the same for both shocks, whereas the optimal response to changes in house prices is very different across shocks. It follows that when shocks are difficult to identify, the best option is to directly respond to credit growth because it helps keep in check the push on house prices while at the same time containing the relaxation in credit conditions.

Effectiveness of Fiscal Policy

Taxes on home ownership and housing transactions, in principle, can curb demand for housing and tame exuberance in real estate markets. Consider a property tax imposed on homeowners, to whom the tax receipts are paid back as a lump sum. 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. In either case, the welfare improvements are small. High property taxes would be needed to have some bite in reducing the volatility of house prices (and the associated accelerator effect), but high taxes would lead to highly distorted prices—hence, the overall small welfare improvement.

Effectiveness of Macroprudential Policy

In the model, policies that can directly affect the spread between lending and deposit rates can help stabilize the cycle. For instance, by lowering the maximum LTV when house prices increase, the supervisory authority can lower the volume of credit, increasing the spread between lending and deposit rates and thus reducing the accelerator effect. To assess the efficacy of this macroprudential rule, the analysis looks at 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.24 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). The central bank provides support by cutting the policy rate (which equals the deposit rate in this economy), which helps cushion the downturn. Over time, residential investment and house prices return to their initial values and credit is permanently reduced by 1 percent.

The next question is whether welfare can be improved if the LTV is tied linearly to certain observables such as credit growth and house price inflation. Under either shock, the macroprudential instrument brings important welfare gains, mostly because volatility of the output gap is greatly reduced. It turns out that an LTV reacting to nominal credit growth is superior to one linked to house price fluctuations: the macroprudential instrument directly addresses the financial friction in the model, therefore it is optimal to have it react to excessive credit under either or both shocks.


Determining 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 for achieving 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.

Provisional policy recommendations, from the evidence reviewed and the analysis, depend on the characteristics of the real estate boom in question (see Figure 12.6). If property prices are out of sync with income and rent, and if leverage is increasing rapidly, taking action is advisable.25 In deciding which policy option to choose, policymakers should adopt a wider view of the economy and complement targeted measures with broader macroeconomic tightening if the boom is a part of or a reflection of general overheating in the economy.

Figure 12.6Dealing with Real Estate Booms

Sources: IMF country reports; and IMF staff.

This leads to the following tentative core principles that could guide policymakers in designing an effective toolkit for dealing with real estate booms:

  • 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 measures aiming to increase the resilience of the financial system and with well-defined resolution frameworks to hasten the cleanup in the aftermath of bubbles that survive the first line of defense.

  • Minimize distortions caused by special treatment of housing and homeownership and strengthen the supply-side response to mitigate the impact of demand shocks in the longer term.

Two important questions will need to be answered when it comes to applying these principles in practice. First, what are the potential complementarities and conflicts between monetary and macroprudential policies and what policy design framework can best accommodate them? Undoubtedly, there is a complex relationship between the objectives of macroeconomic and financial stability and the respective primary objectives of monetary and macroprudential policy. Take, for instance, 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 spill over to other types of loans. 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 a buildup 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, in a discretionary framework 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 quickly accomplish a certain degree of prudence, and time inconsistency is not an issue. The choice of framework would need to weigh these pros and cons. We leave these questions to future research.


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The focus is on cyclical policies; a discussion of the impact of structural measures is in IMF (2011).

A more detailed analysis of country cases is in Crowe and others (2011).

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

The infamous episodes comprise Spain in the early 1980s; Australia, Denmark, Finland, Norway, and Sweden in the late 1980s; Japan in the early 1990s; Malaysia and the Philippines in the late 1990s; and Ireland, Spain, and the United States in the first decade of the 2000s.

Although stock market fluctuations are typically larger, the wealth loss associated with real estate busts tends to be larger because of spillover effects. 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 brought about a 15 percent decline in the value of real estate assets, or US$3.7 trillion. However, total wealth lost stands at US$10 trillion or 13 percent of end-2006 total household assets.

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

Although leverage is the real target, price misalignment ratios can also act as helpful indicators because of the aforementioned two-way relationship between credit and prices. Also, in practice, these ratios can signal vulnerabilities as more households stretch their finances to pay for housing services.

For instance, Brzoza-Brzezina, Chmielewski, and Niedzwiedzinska (2007) find that in the Czech Republic, Hungary, and Poland, monetary tightening led to decreased domestic-currency lending but accelerated foreign-currency-denominated loans.

See, for instance, Cremer and Gahvari (1998) for the economics of tax treatment of owner-occupied housing.

Further to this point, adjusting taxes according to the cycle violates the principle of tax smoothing, which minimizes the excess burden of the taxes.

Also, despite their impact on prices, neither transaction nor property taxes directly get to credit and leverage.

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

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

Fixed risk weights are applicable only under the standard approach of Basel II. Under the internal-rating-based approach, regulators (and banks) can split loans into subcategories based on several risk indicators and vary risk weights accordingly. A few countries have applied higher risk weights to high-LTV loans (see Table 3 in Crowe and others, 2011, for more on country-by-country policy actions and their outcomes).

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.

This is essentially the risk-shifting effect identified by models in the spirit of Stiglitz and Weiss (1981).

Evidence on exposure limits is scant. Many countries have constant exposure limits, but there is no apparent relationship between the level of these limits and real estate boom-bust episodes.

As has been the case for capital requirements, procyclicality of regulations governing loan loss provisions was subjected to criticism before the crisis (e.g., Laeven and Majnoni, 2003).

For more details on the Spanish dynamic provisioning framework, see Saurina (2009).

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 and a down payment (which could be as low as zero).

“Bank of Israel May Increase Housing-Loan Provisions to Slow Rising Prices” 2010 (

This pattern, potentially an indication of the difficulty of calibrating these rules in practice, may elicit frequent intervention by policymakers.

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

BIS (2010) 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. Therefore, the exercise can also be thought of as increasing capital requirements.

Additional uncertainty may be involved because leverage can be a lagging indicator. Hence, keeping a close watch on credit origination, including by nonbank financial intermediaries, is warranted.

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