Property Prices and Bank Risk Taking
Giovanni Dell’Ariccia (IMF and CEPR)
The views expressed in this paper are those of the authors and do not
necessarily represent those of the IMF, its Executive Board, or Management.
Norges Bank Macroprudential Regulation Workshop, Oslo, November 29-30, 2012
Monetary policy to focus on inflation and output gap
Asset prices a concern only through their impact on GDP and inflation (exceptions RBA, Riksbank, some EMs)
Benign neglect approach to boom/busts:
Bubbles difficult to identify
Costs of clean up limited and policy effective
Better clean up than prevent
Bank risk taking important, but job of regulators
Before the crisis …A policy gap
Regulatory policy focused on individual institutions
Limited attention to credit aggregates or asset price dynamics
Ill equipped to deal with booms:
Correlated risk taking
Fire sales and other externalities
Few regulators had necessary tools (exceptions:
Spain/Colombia)
Before the crisis … A policy gap
Macro literature:
Financial intermediation seen as macro neutral
Asset prices (including property prices) did matter. They could accentuate the cycle through financial accelerator
But macro models largely ignored their impact on bank risk taking.
In equilibrium, no bank defaults
Banking literature
Focused on excessive risk taking by intermediaries operating under limited liability and asymmetric information
There are defaults/crises in equilibrium
But there is little attention to macro and monetary policy
Before the crisis … A theory gap
Before crisis … Macro looked
OK
But house prices were rising
rapidly
Standard policies rapidly hit their limits
Limited effectiveness of less traditional policies
Large fiscal and output costs
Multiple banking crises; especially in countries with their own real estate booms
Then the crisis came …
7
House boom/busts and great recession
A closer look at real-estate booms and bank risk taking behavior
Most large banking crises preceded by some form of property price boom
Scandinavia 1990s
Asia 1997
Japan 1990
More recently: US, Spain, Ireland, Iceland, Latvia,…
Property cycles can have macro consequences, even without open banking crises
Borrower debt overhang
But things are worse when credit booms (and lax
standards) are involved
-30 -20 -10 0 10 20 30 -50
-25 0 25 50 75 100
87.52
84.61 50.05
201.06
44.36
169.92 30.93
78.2155.32
90.01 97.91
25.9551.81 98.76
19.9119.5 35.17 109.24
44.31 41.1 139.4
60 65.96
31.07 138.77
59.9425.94 161.8
38.63 166.39
91.67
42.53 60.15
103.8844.4897.9 63.4 157.19
64.19
52.4736.1 85.67 47.11
20.7244.0722.5424.6 39.48
77.79 11.7218.08
107.83
38.21
25.06
35.18 27.4837.75 118.03 172.77
95.59
95.14 148.76
27.24 31.3 60.6448.32
11.99
25.07 82.87 103.85
13.6812.57 54.93
17.6 31.06 14.94 13.33 25.87
33.29
43.34 35.74
49.29 34.4710.47
25.0718.4 22.17
15.8 44.95 47.54
28.84 64.0134.5661.0112.66
20.19
85.93 25 20.56 41.1742.78
71.22
12.8617.2818 36.16
22.73 21.95 14.28 67.46 39.51
22.51 24.61
108.8
Credit Growth and Depth of Great Recession
Change in GDP from 2007 to 2009
Change in credit-to-GDP ratio from 2000 to 2006
Bubble size shows the level of credit-to-GDP ratio in 2006.
Real-estate cycles and bank behavior
Credit constraints – Leverage cycles
Adverse selection and strategic interaction
Bubbles
Govt. guarantees - Risk externalities
Financial Accelerators – Leverage Cycles
Collateralized credit as solution to agency problems (Kiyotaki/Moore, 1997)
Cycle emerges: asset prices credit aggregates investment/demand asset prices
Effect magnified if logic applied to intermediaries (Kiyotaki/Gertler, 2009, Iacoviello, 2011)
Further widening if leverage is cyclical (Adrian/Shin, 2009/Geanakoplos 2010)
Regulation may also contribute (Herring/Wachter, 1999)
But most models do not deal with risk taking
Magnified macro fluctuations
Duration of recession (quarters)
Time to return to trend (quarters)
Source: Claessens/Kose/Terrones, 2008
Adverse selection and strategic effects
Rising house prices reduce incentives to screen borrowers
Even bad borrowers can refinance/sell property
Winner curse reduced in good times:
My competitors screen less
More untested applicant borrowers
Better distribution of applicants
Less incentives to screen
“Conservative” lending punished
Investor pressure on managers (compensation schemes)
Borrowers shop for lax standards
Easy mortgages during U.S.
boom
Source: Dell’Ariccia, Igan, and Laeven 2009
0 20 40 60 80 100 120 140 160
-50 0 50 100 150 200 250
19.33 24.18
16.59
22.13 51.77
25.02
32.33 40.92 78.18
54.84
19.27
63.99
12.13
32.00
20.81 8.49
10.749.94
18.25 29.81
40.35
18.54 9.22
24.24 22.50
16.48
26.18 32.34
9.31 8.34
20.75
21.83 28.34
47.98
25.90
9.29 10.71
38.06
14.85
34.57
30.38 13.61 18.03
13.38
24.07 37.43
18.61
31.28
8.41
12.43
24.39
Subprime Boom and Defaults
House price appreciation, 2000-06
Change in mortgage delinquency rate, 2007-09
Bubble size shows the percentage point change in the ratio of mortgage credit outstanding to household income from 2000 to 2006.
Bubbles
Normal times: prices reflect fundamentals
Bubble: speculative motive allows for deviation from fundamentals (Allen/Carletti, 2011)
Banks may fund speculators:
Govt. guarantees
Risk shifting (limited liability)
Can’t separate speculators from “legitimate” consumers
Increasing recourse to instruments with correlated risks
U.S.: teaser-rate/interest-only loans
East Europe: FX denominated loans
Interest-only loans and boom
Source: Barlevy and Fisher (2011)
Credit and housing booms in East
Europe
FX lending and credit boom
Strategic complementarities
Government guarantees
Do not want to die alone (Farhi/Tirole, 2012)
Greenspan put
FX in Eastern Europe
Risk taking externalities
Poor incentives structure if systemic banks take excessive risk
Correlated risk taking
Self fulfilling equilibria
Ex-post …
Macro bailouts did occur
If benign neglect is dead, then what?
Asset price booms difficult to spot
But other policy decisions also taken under uncertainty
Booms involving leveraged agents more dangerous
Real estate case
Objectives?
Prevent unsustainable booms altogether
Alter lender/borrower behavior
Increase resilience to busts
No silver bullet
Broader measures: hard to circumvent but more costly
Targeted tools: limited costs but challenged by loopholes
A new policy consensus?
23
Natural place to start
Credit highly correlated with monetary aggregates
Increase cost of borrowing, decrease loan demand, lower collateral values
Risk-taking channel
Potential issues
Conflict of objectives
Impact on balance sheets
Capital inflows (especially in SOEs)
Switch to riskier lending (FX, IO loans)
Monetary policy
Evidence of risk shifting
Source: Landier et al. 2011
Prudent stance can:
Reduce overheating
Buffer for bailout/stimulus during a potential bust
Reduce incentives for leverage (deductibility, FAT)
Time lags make it an impractical tool
Some measures hard to use countercyclically
“Tax planning”, circumvention, calibration
Little evidence of effectiveness in stopping booms… …but fiscal room critical in busts
Fiscal policy
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
Mixed evidence on effectiveness
Macro-Prudential Tools
Hong Kong: Limited Effectiveness of
LTV Limits
Korea: Effective LTV Limits, but
Difficult Calibration?
Conclusions
Benign neglect might be dead, so …
Emerging consensus that leveraged bubbles (real estate in particular) are dangerous
What to do. Still many open questions:
Monetary policy remains blunt instrument
Fiscal impractical. Perhaps helpful on liability structures
Macroprudential tools promising …
But it will take time:
Develop new macro models
Design/calibrate macroprudential tools
Build institutions to control them
Limited liability and speculators
q
1-q
H-P(1+r)
L-P(1+r)
q
1-q
H-P(1+r)
L-P(1+r) Unlevered
consumer
Levered speculator
