mortgages, markets and what we know so far detroit chapter of the institute of internal auditors
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MORTGAGES, MARKETS AND WHAT WE KNOW SO FAR Detroit Chapter of the Institute of Internal Auditors. Robert Van Order University of Michigan. Basic Observation: Big increase in foreclosures. Why?. Subprime ARM Defaults are Very Different from Prime and Subprime FRM. – Recession. - PowerPoint PPT PresentationTRANSCRIPT
MORTGAGES, MARKETS AND WHAT WE KNOW SO FAR
Detroit Chapter of the Institute of Internal
Auditors
Robert Van OrderUniversity of Michigan
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Basic Observation: Big increase in foreclosures. Why?
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Subprime ARM Defaults are Very Different
from Prime and Subprime FRM Loans 90 days or more delinquent or in foreclosure (percent of number)
Source: Mortgage Bankers Association(Quarterly data not seasonally adjusted;1998Q1-2007Q3)
Prime Conventional
FHA & VA
SubprimeFRM
– Recession SubprimeARM
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Stylized Facts and Gatherings from Various Data Sources
Credit Risk: A Few Propositions Recent History: Especially Large Early Payment
Defaults: Can it be rate adjustments? Changing Loan Characteristics: Hard vs. Soft Data,
Technical Change and the Two Decades. Economic Conditions Market Structure: The rise of subprime Securitization: The rise of non-agency securities
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0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
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10
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25
30
35
40
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0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%0 5
10
15
20
25
30
35
40
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Age of Loan in Number of Months From Origination Date
Cumulative REO Rates Are Showing Poor Performance of Recent Origination VintagesCould it have been ARMs?
Cu
mu
lati
ve R
EO
Rat
e as
a S
har
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f N
um
ber
of
Lo
ans
Ori
gin
ated
Alt-A Subprime
2002 2003 2004 2005 2006 2007
Source: Loan Performance, a subsidiary of First American Real Estate SolutionsNote: the last twelve points on each origination year cohort contain fewer loans progressively as loans issued at earlier dates always age faster. Data through December 2008.
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Credit Risk
Underwriting models and history suggested scorecards and diversification worked
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Relative Default ProbabilitiesNote the “Nonlinearity” as you move NortheastMore sensitive to mistakes.
LTV <70 LTV 71-80 LTV 81-90 LTV 91-95
FICO <6200.96 4.8 11.04 19.68
FICO 620-6790.46 2.3 5.29 9.43
FICO 680-7200.2 1 2.3 4.1
FICO >7200.08 0.4 0.92 1.64
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Price Performance Matters. So Does (Did?) Diversification
Default Probability vs. House-Price AppreciationState/Origination Year and National/Origination Year Cohorts (1985-1995)
80% Loan-to-Value, 30-Year Fixed-Rate Home-Purchase Mortgage
NV 1985
HI 1994
AZ 1985
CA 1989
CA 1990
DC 1995
AK 1986
0%
5%
10%
15%
20%
25%
-30% -10% 10% 30% 50% 70% 90% 110% 130%
5-Year Cumulative House-Price Appreciation
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efau
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ate Individual States National
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So looking back, you would have thought that controlling FICO and LTV was a big deal, you couldn’t have a credit problem without changes in FICO-LTV distribution, and a diversified portfolio would perform well.
But Performance Got Really Bad. Especially in Early Months
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Underwriting has changed over time, but not in ways you might have thought.
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High LTVs went up in 90s. Fell lately
LTV Trends
0.010.020.030.040.050.060.070.080.090.0
1973
1976
1979
1982
1985
1988
1991
1994
1997
2000
2003
Average LTV
% LTV>90
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Recent observables haven’t changed all that much (Tales?)
Loan Characteristics at Origination for Different Vintages: Alt-A and Subprime Demyanyk, Yuliya and Otto Van Hemert, “Understanding the Subprime Mortgage Crisis”(2007)
2001 2002 2003 2004 2005 2006 Average Loan Size (*$1000) 151 168 180 201 234 259
FRM (%) 41:4 39:9 43:3 28:2 25:1 26:1
ARM (%) 0:9 1:9 1:3 4:3 10:3 12:8
Hybrid (%) 52:2 55:9 54:7 67:3 62:0 46:2
Balloon (%) 5:5 2:2 0:8 0:2 2:6 14:9
Refinancing (cash out) (%) 52:1 51:2 51:6 47:9 45:7 44:8
FICO Score 620 630 641 645 653 654
Loan-to-Value Ratio (%) 79:3 79:4 79:2 79:3 78:5 78:3
Debt-to-Income Ratio (%) 37:8 38:1 38:2 38:5 39:1 39:8
Documentation Dummy (%) 68:5 63:4 59:8 57:2 51:8 44:7
Initial Rate (%) 9:4 8:3 7:3 6:7 6:6 7:2
Margin for ARM and Hybrid (%) 6:2 6:3 5:9 5:3 5:0 4.9
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We Seem To Have Two Explanations Left
Economic Conditions. Structure and Moral Hazard
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The Case-Shiller Index
Single-family Construction
400
700
1,000
1,300
1,600
1,900
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
1- to 4-Family Housing Starts (thousands of units, SAAR)
Sources: Bureau of Census, Freddie Mac
– Recession
Third Quarter 2005 record: 1.8 million units
Forecast
Fourth Quarter 2007:
0.9 million units
Source: Freddie Mac Purchase-Only Conventional Mortgage Home Price Index (Annualized Quarterly Rates for 3rd Quarter 2007)
Price Changes by State: Third Quarter 2007
Pacific -5.8%
Mountain
0.4%
West South Central
4.9%
East South Central
-0.1%
South Atlantic -2.7%
Middle Atlantic
-0.9%
New England
-3.6%East North
Central
-3.8%
West North Central
-0.8%
> 5% Quarterly Change
< 0% Quarterly Change
0 – 5% Quarterly Change
< -5% Quarterly Change
DC
United States -2.2%(3rd Quarter Annualized Growth)
How favorable were economic conditions?
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The structure of the Market Has Changed
More Subprime and Alt-A Non Agency Securitization
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Subprime used to be about 10% of originations, but it’s share increased a lot after 2003
Market shares
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1 2 3 4 5
2001-2005
FHA/VA
Conforming
Subprime+Alt-A
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Securitization ChangesNote the nonagency share went up after the subprime share went up and around the time the vintages got worse.
Non Agency Share of MBS
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
Non Agency Share ofMBS
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SUBPRIME SECURITIZATION
Credit risk is more important than for Agency securities. The risk has been handled (poorly) by structuring.
So securitization could have been a big part of the problem, because it is so susceptible of moral hazard/asymmetric information.
Recall that to some extent the recent subprime loans didn’t look that bad on paper. Hard vs. soft information.
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Subprime Foreclosures Started: 4-yr Distributed Lag of Multipliers
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As an aside there have been spillovers that don’t match with actual risk.
About half way through the eventual increases in defaults
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