Disclaimer: Views expressed in the presentation and during the talk are those of the
presenter and should not be ascribed to the IMF.
Before the crisis…
 Financial sector a black box, macro-finance nexus rarely in
forefront
 Asset prices a concern only through their impact on GDP
and inflation
 Monetary policy to focus on inflation and output gap
(exclusively in AE, more flexible in EMs)
 Benign neglect approach to boom/busts:
 Bubbles difficult to identify
 Costs of clean up limited and policy effective
 Better clean up than prevent
Then came the crisis…
 Bust had enormous
consequences
 Standard policies rapidly hit
their limits
 Limited effectiveness of less
traditional policies
 Large fiscal and output costs
Need to reconsider “consensus”
 Benign neglect approach may be dead
 But, problems and trade offs with more interventionist
strategy remain:
 Bubbles difficult to detect in real time
 Risks associated with pricking bubbles
 Traditional policies may be ineffective
 And have large costs
Real estate booms are dangerous
 Not all asset-price booms should be target of policy
 But how to choose?
 Emerging consensus: culprit is leverage (Nasdaq crash
was fine)
 Real estate markets are special:
 Leverage (link to crises)
 Large storage of wealth
 Major supply-side effects
 Network externalities
Boom, Leverage, and Defaults
Real Effects of Housing Busts
Figure 2. House Price Run-Up and Severity of Crisis
Cumulative decline in GDP f rom start to end of recession
10
IND
0
AUS
CHN
NZL
CAN
FRA
GRC
CHE CYP
PRT
AUT
USA
KOR
NLD
CZE HRV
HUN
DNK SWE
BGR
FIN
SVN
-10
ZAF
ESP
GBR
NOR
ITA
POL
y = -0.0416x - 4.1152
R² = 0.1496
IRL
ISL
UKR
EST
-20
Bubble size shows the change in bank
credit f rom 2000 to 2006.
LTU
LVA
-30
-20
0
20
40
Source: Claessens et al (2010).
60
80
100
120
140
160
Change in house prices f rom 2000 to 2006
180
200
220
240
Leverage and Link to Crises: Recent Episode
Booms, Financial Instability, Macroeconomic Performance
Followed by …
Boom
systemic
banking
crisis
significant drop
in real GDP
growth
either
both
Real estate
53%
77%
87%
43%
Credit
67%
78%
93%
52%
Real estate but not
credit
29%
71%
71%
29%
Credit but
not real estate
100%
75%
100%
75%
Both
61%
78%
91%
48%
Neither
27%
18%
45%
0%
50
150
Leverage and Link to Crises:
Examples from the Past
cps_gdp
30
40
Philippines
1997
0
10
20
50
cps_gdp
100
Thailand
1997
1970
1980
year
1990
1960
2000
1970
1980
year
1990
2000
1980
year
1990
2000
60
100
1960
cps_gdp
40
Chile 1982
0
20
40
20
cps_gdp
60
80
Finland
1991
1960
1970
1980
year
1990
2000
1960
1970
Calling it in real time: Should have, would have,
could have?
 Deviation from house price
“fundamentals”
 Price-income ratio
 Price-rent ratio
 Error-correction models
 Turning point models
 Monitoring credit indicators
 Leverage
 Credit growth rates
 Measures of lending standards
Measures of “overvaluation”
(as reported in October 2008 WEO)
Did predictions come true?
Back to Earth
15
Actual change in house prices, 2008-09
CAN
AUS
10
5
NOR
BEL
SWE
GBR
FRA
JPN
NLDITA
FIN
NZL
USA
0
DEU
KOR
-5
ESP
-10
DNK
-15
-20
IRE
-25
-30
-35
-30
-25
-20
-15
-10
Change in house prices,
predicted in Oct. 2008 WEO
Sources: IMF staff calculations, and OECD.
-5
0
5
10
Recognize an elephant when you see one…
250
1000
900
200
800
150
600
Home Prices
500
100
400
300
Building Costs
Population
50
200
Interest Rates
0
1880
1900
Source: Robert Shiller.
1920
1940
1960
1980
2000
100
0
2020
Population in Millions
Index or Interest Rate
700
Monetary Policy:
(Sometimes) Effective at a (large) cost
 Make borrowing more expensive and may limit leverage
and risk taking
 But:
 Too blunt: costly for the entire economy (unless in context of
general overheating)
 Issues for small open economies
 Effect on speculative component may be limited
 Panel VAR suggests impact on house prices at considerable
cost to GDP growth
 100 basis points reduce house price appreciation by 1 but also lead
to a decline of 0.3 in GDP growth
Fiscal Tools:
Distortionary and limited cyclical use
 Debt-financed ownership favored:
 allow deductibility of mortgage interest (DMI)
 do not tax imputed rents and capital gains fully
 But:
 No link between favorable treatment and the crisis
 Cyclical use is difficult and violates tax smoothing
 Evidence:
 Structurally, removal of DMI may help reduce leverage
 Cyclically, transaction taxes may help


during busts
less so during booms with impact falling on transaction volumes
rather than prices
Transaction Value Growth in Selected Asian Real Estate Markets
(In percent, year-on-year)
400
600
China
Hong Kong SAR
Korea
Singapore (right scale)
300
200
450
300
Recent
data
100
0
150
0
-100
Source:National authorities, CEIC, IMF staff estimate. Note: Korea data represent units of transactions
Jun-10
Mar-10
Dec-09
Sep-09
Jun-09
Mar-09
Dec-08
Sep-08
Jun-08
-150
Macroprudential Tools:
Promising but still at infancy, learning by doing
 Most ‘experiments’ in emerging markets, particularly Asia
 Common tools:
 Maximum LTV/DTI limits
 Differentiated risk weights on high-LTV loans
 Dynamic provisioning
 Discretion rather than rule-based
 Potential issues
 Circumvention
 Calibration
 Political resistance
Macropru in practice
 Range of triggers:
 Rapid price and credit growth, overvaluation
 Household leverage
 Bank concentration, nonbank activity
 NPLs by vintage, “exotic” loans and speculative activity (FXdenominated, high LTV, long tenor, multiple loans at a time, etc)
 Calibration:
 No magic numbers (LTV 60–85%, DTI 30–50%)
 Variation by loan type
 Frequent changes (often in response to leakages)
 Exemptions for certain groups
 Enforcement:
 Narrow gap between announcement and implementation
 Often not discussed with stakeholders
 Decision power:
 CB involved in many cases
 Multi-agency or committee approach not common
Mixed evidence on effectiveness (so far)
 Promising: reduction in procyclicality of credit and negative
link to incidence of booms and booms turning bad
 More success in building up buffers than preventing a boom
 Analysis of household surveys point to an impact on
expectations
 Emerging Europe case indicates effectiveness of some (CAR
and non-standard liquidity measures)
 Latin America case shows moderate, transitory effect
Use of LTV and DTI since 2000
Countries that changed LTV
10
Bangladesh
China
Hong Kong
India
Indonesia
Korea
Malaysia
Nepal
Singapore
Thailand
8
6
Countries that changed DTI
6
Bulgaria
Cyprus
Hungary
Israel
Netherlands
Norway
Poland
Romania
Spain
5
4
Argentina
Bahamas
Brazil
Canada
Chile
4
2
Serbia
Netherlands
Romania
Norway
Poland
Oman
Pakistan
0
3
2
Saudi Arabia
Kuwait
Hong Kong
Korea
Bahamas
Canada
1
0
Africa
Asia &
Pacific
Europe
Middle East Western
& Central Hemisphere
Asia
Africa
Asia &
Pacific
Europe
Middle East Western
& Central Hemisphere
Asia
0
Serbia
Saudi Arabia
Romania
Poland
20
Netherlands
40
Kuwait
80
Korea
100
Hong Kong
Limits on Loan-to-Value ratios
Canada
120
Bahamas
Argentina
Bahamas
Bangladesh
Brazil
Bulgaria
Canada
Chile
China
Cyprus
Hong Kong
Hungary
India
Indonesia
Israel
Korea
Malaysia
Nepal
Netherlands
Norway
Oman
Pakistan
Poland
Romania
Singapore
Spain
Thailand
Range of LTV and DTI
Limits on Debt-to-Income ratios
70
60
50
40
60
30
20
10
0
“Effect” of LTV and DTI
 Macro, cross-country data supports some impact on both credit and
house price growth
 Different story with disaggregated/micro, country-specific data
 HKG: effect on household leverage, not house price
 KOR: short-lived effect on both mortgage and house price, LTV more effective than DTI
 ROM: effect on consumer credit, not house price
 MYS: effect on speculative borrower activity only, not on house price
Hong Kong
Volatile house prices,
conservative LTV limits
Hong Kong: Fighting to rein in a boom
160
New loans approved
Prices
170
150
150
140
130
110
90
70
2009 - Mar 2009 - May
October 2009:
Maximum LTV for properties over
HK$20 million lowered to 60
percent, maximum loan size for
mortgage insurance eligibility
reduced and non-owner-occupied
properties disqualified.
August 2010:
LTV for properties over HK$12 million
lowered to 60 percent, applications for
mortgage insurance exceeding 90% LTV and
50% DTI suspended, maximum loan size for
mortgage insurance eligibility if LTV>90%.
130
120
110
2009 - Jul
2009 - Sep 2009 - Nov 2010 - Jan
2010 - Mar 2010 - May
2010 - Jul
Korea
Limits on LTV and DTI to
curb price increases in
‘speculation zones’
Korea: Effective but difficult to calibrate?
6%
6
Month-on-month house price changes in 'speculation zones' (LHS)
5%
Policy rate (RHS)
September 2002:
Introduced LTV limits
4%
5
4
3%
2%
September 2009:
Tightened DTI
October 2003:
Lowered LTV in
speculative areas
1%
3
February 2007:
Tightened DTI
2
0%
June 2003:
Lowered LTV in
speculative areas
-1%
-2%
2000 - Jan
July 2009:
Lowered LTV in
non-speculative
areas
August 2005:
Introduced DTI limits
1
0
2001 - Apr
2002 - Jul
2003 - Oct
2005 - Jan
2006 - Apr
2007 - Jul
2008 - Oct
2010 - Jan
Tentative policy taxonomy
 Macroprudential tools first line of defense especially if
concern about a single sector
 Target leverage
 Strengthen balance sheets
 Monetary policy definitely to be involved when there are
other signs of overheating
 Fiscal tools hard to use cyclically
 But removing distortions may help at the structural level
Could macroprudential tools have prevented the Euro Zone crisis?
 Greece and (to lesser extent) Portugal classic fiscally
driven crises:
 Large fiscal deficits
 Relatively low growth (and very low productivity growth)
 Large current account deficits
 But Spain, Ireland, Latvia different
 Prudent fiscal (at time of crisis, plenty of fiscal room)
 But buoyant private sector
 Asset price bubbles and credit booms
 Large current account deficits (especially Spain/Latvia)
 Common currency a constraint for all
Bottom line
 Strong association between real estate boom-busts and financial
crises/recessions
 Leverage is key
 Macroprudential policy promising but still learning (by doing):
 When to take action


Deviation from yardsticks (valuation ratios, leverage, credit growth)
Bubbles difficult to spot but many policy decisions are taken under such
uncertainty
 Objectives


Prevent unsustainable booms and leverage buildup
Increase resilience to busts
 No silver bullet


Broader measures: hard to circumvent but more costly
Targeted tools: limited costs but challenged by loopholes
Important Open Questions
 Rules versus discretion
 Far away from IT standards
 Risks associated with excessively interventionist policy
 Making “constrained discretion” operational?
 Who does what?
 Where should macro-prudential authority reside?
 Evidence (we need more!)
 Effectiveness : Differences across instruments, between emerging and




industrial countries, based on position in the business cycle?
Calibration: Optimal level for LTV limits?
Circumvention: Leakages and waterbed effects?
Relationship among instruments: To what extent are these
independent tools?
Timing: Best leading indicators to predict a real estate market crisis?
Any differences across countries? Main data challenges?