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The Subprime Mortgage Crisis: Anatomy of a Market Failure

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The Subprime Mortgage Crisis: Anatomy of a Market Failure The Subprime Mortgage Crisis: Anatomy of a Market Failure Kenneth A. Posner [email protected] 212.761.4524 Morgan Stanley does and seeks to do business with companies covered in Morgan Stanley Research. As a March 10, 2008 result, investors should be aware that the firm may have a conflict of interest that could affect the objectivity of Morgan Stanley Research. Investors should consider Morgan Stanley Research as only a single factor in making their investment decision. Customers of Morgan Stanley in the US can receive independent, third-party research on companies covered in Morgan Stanley Research, at no cost to them, where such research is available. Customers can access this independent research at www.morganstanley.com/equityresearch or can call 1-800- 624-2063 to request a copy of this research. For analyst certification and other important disclosures, refer to Disclosure Section. 1 $400 bn in mortgage losses Forest Fire - Minnesota, USA Source: World Prout Assembly Source: “Leveraged Losses: Lessons from the Mortgage Market Meltdown,” David Greenlaw et al, 2/29/2008 2 Damage Report 3 Mortgage Lenders 800 100 200 300 400 500 600 700 0 12/31/99 4/30/00 8/31/00 NEWC NDE CFC NC WM 12/31/00 C 4/30/01 8/31/01 12/31/01 4/30/02 8/31/02 12/31/02 4/30/03 8/31/03 12/31/03 4/30/04 8/31/04 12/31/04 4/30/05 8/31/05 Sourc e 12/31/05 : Factse 4/30/06 t, M o 8/31/06 rgan Stanley Res 12/31/06 4/30/07 e ar 8/31/07 ch 4 12/31/07 GSEs 100 120 140 160 180 20 40 60 80 0 12/31/1999 6/30/2000 12/31/2000 FNM FR 6/30/2001 E 12/31/2001 6/30/2002 12/31/2002 6/30/2003 12/31/2003 6/30/2004 12/31/2004 6/30/2005 Sourc 12/31/2005 e : Factse 6/30/2006 t, M o 12/31/2006 rgan Stanley Res 6/30/2007 12/31/2007 e ar ch 5 Mortgage Insurers 300 MTG PMI 250 RDN 200 150 100 50 0 4/30/2000 8/31/2000 4/30/2001 8/31/2001 4/30/2002 8/31/2002 4/30/2003 8/31/2003 4/30/2004 8/31/2004 4/30/2005 8/31/2005 4/30/2006 8/31/2006 4/30/2007 8/31/2007 12/31/1999 12/31/2000 12/31/2001 12/31/2002 12/31/2003 12/31/2004 12/31/2005 12/31/2006 12/31/2007 Source: Factset, Morgan Stanley Research 6 Bond Insurers 150 200 250 300 100 50 0 12/31/1999 6/30/2000 MBI AB 12/31/2000 K 6/30/2001 12/31/2001 6/30/2002 12/31/2002 6/30/2003 12/31/2003 6/30/2004 12/31/2004 6/30/2005 Sourc 12/31/2005 e : Factse 6/30/2006 t, M o rgan 12/31/2006 Stanley Res 6/30/2007 e ar ch 12/31/2007 7 Broker-Dealers 450 BSC 400 MER 350 C 300 250 200 150 100 50 0 6/30/2005 6/30/2006 6/30/2007 6/30/2000 6/30/2001 6/30/2002 6/30/2003 6/30/2004 12/31/2004 12/31/2005 12/31/2006 12/31/2007 12/31/1999 12/31/2000 12/31/2001 12/31/2002 12/31/2003 8 Source: Factset, Morgan Stanley Research Over $400 billion in market cap destroyed Mkt Cap % of Mkt Cap (In $Bn) Mkt Cap At Peak Destroyed Destroyed WM 44.5 28.9 64.9% NCC 26.0 15.7 60.4% CFC 26.7 22.9 85.7% NDE 3.4 2.7 81.3% NEWC 3.2 3.2 100.0% MTG 8.1 6.9 84.7% PMI 4.4 3.6 83.4% RDN 5.4 4.7 87.5% FNM 89.2 58.7 65.9% FRE 50.7 31.6 62.2% ABK 9.8 8.7 88.6% MBI 9.8 8.0 81.4% BSC 25.0 15.5 61.9% MER 85.5 40.8 47.7% C 282.2 152.6 54.1% Total 673.9 404.5 60.0% Source: Factset, Morgan Stanley Research 9 Balance sheets not looking so strong 2000 FNMA 1800 MGIC WM 1600 AXP 1400 WFC read p 1200 ABK s S D 1000 ar C 800 1-ye 600 400 200 0 2007 2008 2008 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 1/10/ 2/10/ 3/10/ 4/10/ 5/10/ 6/10/ 7/10/ 8/10/ 9/10/ 1/10/ 2/10/ 10/10/ 11/10/ 12/10/ Source:Market1, Morgan Stanley Research 10 ABX AAA prices for different vintages 120 100 80 60 40 ABX ABX 20 _AAA_06- ABX _AAA_06- ABX _AAA_07- 0 _AAA_07- 1 1/19/2006 2 2/19/2006 1 3/19/2006 2 4/19/2006 5/19/2006 6/19/2006 7/19/2006 8/19/2006 9/19/2006 10/19/2006 11/19/2006 12/19/2006 1/19/2007 2/19/2007 3/19/2007 4/19/2007 5/19/2007 6/19/2007 7/19/2007 8/19/2007 Sou 9/19/2007 rc e: Mark 10/19/2007 11/19/2007 it, Mo 12/19/2007 rg an Stanl 1/19/2008 2/19/2008 ey Res ear ch 11 The root of the problem HEL 30 yr FRM Cum Losses 5% 4% 3% 2% 1% 0% 0 102030405060 Loan Age 2000 2001 2002 2003 2004 2005 2006 12 Not just subprime…. Alt-A Jumbo prime 2.5% 0.30% Alt-A Fixed Jumbo Fixed 2.0% 0.25% 0.20% 1.5% 0.15% 1.0% 0.10% 0.5% 0.05% 0.0% 0.00% Jan- Feb- Mar- Apr- May- Jun- Jul- Aug- Sep- Oct - Nov- Dec- Jan- Feb- Mar - Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 07 Source: Loan Performance, Morgan Stanley Research 13 Home prices now falling House Price Index (HPI) 15% Real Home Prices (Inflation Adjusted) 10% 5% 0% 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 -5% -10% Source: OFHEO, Morgan Stanley Research 14 Turnover rates heading downwards 12% Turnover (new and existing) 10% 8% 6% 4% 2% 0% 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 Year Turnover = new and existing home sales as % of stock of housing units 15 Source: U..S. Bureau of Census, Morgan Stanley Research Welcome to Extremistan 16 Mediocristan Extremistan When your sample is large, no single Inequalities are such that one single instance will significantly change the observation can disproportionately total impact the total Examples Examples • Distribution of height, weight, IQ in a • Distribution of wealth, income large population of people • Book sales by author • Mortality rates • Academic citations • Damage caused by earthquakes Typical Function Typical Function • Gaussian (“Normal”) curve • Power law Source: Nassim Taleb, The Black Swan, 2007 17 The 1906 Great San Francisco Earthquake Source: Science Photo Library 18 Subprime losses surprised to the upside -1 std = 2% +1 std = 7% Expected mean = 5% Current expectation: 21% a 6 std dev shock 19 Mis-specifying subprime losses Dependent variable = F{Independent variables} Subprime losses • FICO score • Loan-to-value ratio • Loan type • Documentation •No data for new loan types •No data for extreme macro environments • Property type •Interaction between terms not understood • Home prices 20 Understanding subprime loss rates: The basic model Home prices Sub-prime losses Underwriting standards 21 The credit cycle Home prices Availability of credit WARNING Sub-prime losses Independent variables may be correlated Underwriting standards 22 New products played a large role 5,000 4,000 HEL ) 3,000 Alt-A Bn Subprime ($ Prime 2,000 1,000 0 2001 2002 2003 2004 2005 2006 2007 2008E Source: “Leveraged Losses” by Greenlaw etc., Morgan Stanley Research 23 The liquidity boom Home prices Availability Global of credit liquidity WARNING Sub-prime losses Volatility of independent variables may be non-stationary Underwriting standards 24 Securitization has substantially reshaped US financial markets Percent 60 Intermediated • Driven by advances in 55 Through Securities information technology, Markets securitization has Intermediated transformed the financial 50 Share of Private markets in the US for a Through Depository Institutions Nonfinancial wide range of asset Debt Outstanding classes, at the expense 45 of traditional bank (%) financing 40 • Rating agencies, investment banks, 35 guarantors played a major role in the growth of this market 30 25 80 83 86 89 92 95 98 01 04 07 Source: Morgan Stanley Fixed-income Research 25 CDO issuance supported securitization growth 600 500 400 300 $ Billlion 200 100 - 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Source: Morgan Stanley Fixed-income Research 26 Liquidity also benefited from monetary policy • More vigorous Fed action in the Volcker and Greenspan era led to heightened interest rate volatility, although it dampened economic volatility • Aggressive rate cuts in 2001-2 undoubtedly helped boost real estate fundamentals 27 Should we think of a mortgage …as a claim on a hard asset? 28 …or as a small piece of the global capital markets? 29 Competitive pressure and breakdown of governance Home prices Availability Global of credit liquidity Sub-prime losses Underwriting standards Competitive pressure 30 Breakdown of governance – along the value chain Aggressive •Beholden to marketing fixed-income profits •Poor risk mgmt Borrowers Brokers Originators Securitizers Investors •Cut standards to keep sales force Relied on rating •Strong-armed agencies, appraisers guarantors 31 Mortgage brokers: a major force, should they be regulated? Thousands 60 50 40 30 20 10 0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 S 2004 o urce: 2005 Wh ol 2006 e sal e A 2007E c ce ss Broker 2008E St u d ie s 32 Problems with market governance No one sees the entire market Time lags in emergence of losses 33 The bust Home prices Availability Global of credit liquidity Sub-prime losses Underwriting standards 34 Underwriting standards now super tight Percent 80 Mortgage Prime Mortages 60 Subprime 40 20 0 -20 Net Percentage of Banks Reporting a Tightening -40 90 92 94 96 98 00 02 04 06 Source: Federal Reserve 35 Looking Ahead 36 Global capital flows and home prices Real Housing Prices and Banking Crises 135 • We may blame brokers, lenders, 130 and rating agencies for the subprime market crash… 125 US 120 • ….but the run-up in US home 115 prices also reflects global capital inflows Index 110 Avg Bank Crisis 105 • Our home price experience tracks similar appreciation in 100 Big 5 Crisis “Big 5” crises of post-war 95 period… t-4 t-3 t-2 t-1 T t+1 t+2 t+3 • Suggesting we could be in for a Source: Reinhart, Rogoff 2008 post-shock slump of similar severity 37 Market expects home prices to decline over 20% in the next three years 4.0% Y/Y Change 2.0% 1.9% 1.9% 0.0% 2007A 2008 2009 2010 2011 2012 -2.0% -2.4% -4.0% -4.9% -6.0% -8.0% -7.6% -10.0% -12.0% -11.9% -14.0% Source: Radar Logic, Morgan Stanley Research 38 Global capital flows and GDP Real GDP Growth per Capita and Banking Crises • However, US economic (PPP basis) growth wasn’t as strong in 5 the run-up as was the case 4 in the “Big 5” financial 3 crises t Avg Banking Crisis n 2 rce • So hopefully the economic 1 US Pe 0 aftermath for us will be -1 Top 5 Crisis more moderate -2 t-4t-3t-2t-1 t t+1t+2 Source: Reinhart, Rogoff 2008 39 Disclosure section Morgan Stanley ModelWare is a proprietary analytic framework that helps clients uncover value, adjusting for distortions and ambiguities created by local accounting regulations.
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