Transcript Student Loan ABS market

Modelling Mortgage Insurance
Phelim Boyle
14 March 2014
Laval University
7/17/2015
Thanks to Yuchen Mei for helpful
comments
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Outline of Talk
 Introduction to default risk
 Mortgage market structure
 Canadian and US mortgages
 The Alberta experience
 The risk that was ignored
 Projections and stress tests
 Adverse selection and moral hazard
 Summary
Basic Idea
 Buy house for $400,000
 Borrow $380,000
 Assume 30 year amortization period
 Initial Loan to Value (LTV) is 95%.
 Suppose house prices fall by 20%
 House now worth $320,000
 Home owner has incentive to default
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Homeowner defaults
 Suppose homeowner also becomes unemployed
 Does not have income to pay mortgage
 Will stop making payments
 Loan becomes delinquent then defaults
 The lender (bank) seizes the asset
 Bank is owed $380,000 owns asset worth $320,000
 Bank loses (at least) $60,000
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Mortgage Insurance (MI)
 MI exists to cover this loss
 Insurance provided by third party
 Premium is paid by the homeowner
 Insured event is mortgage default
 Bank is the beneficiary
 Insurance mandatory for high ratio mortgages
 Protects lender against this risk
 Could be very critical during severe recession
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Mortgages Canada
 Amortization period 25 years for insured mortgages
 Term is typically five years
 Interest rate reset to current rates every 5 years
 If borrower prepays normally a penalty
 Short terms and penalties discourage prepayments
 These two features reduce interest rate risk
 Good for the Canadian banks
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Mortgages USA
 Typical amortization period 30 years
 Dominant contract 30 year fixed rate contract
 Interest rate fixed for 30 years
 Borrower can prepay without penalty
 Suppose loan is $380,000
 Initial rate is 6% ,monthly payment is $2,300
 If rate drops to 4% payment falls $1,831
 Huge incentive to prepay
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Mortgages USA
 Prepayment risk: big problem for the lender
 Bank funds with short term deposits
 Solution: Securitize the mortgages
 Interest rate risk passed to the market
 In the early days securitized mortgages were insured
 Often by GSEs Fannie Mae and Freddie Mac
 Backed by the full faith of US government?
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Sub Prime Mortgages
 Mortgages granted to more risky borrowers
 Poor credit history : NINJA loans
 Did not qualify for default insurance
 Huge increase in early 2000s
 These were securitized by private sector
 Payments were sliced and diced.
 Safest tranches were rated triple A . Pure magic
 Models using historical data confirmed this!
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Information and Incentives
 Different parties involved in MI
 Lender, borrower, insurer, broker, servicer, investor…
 Parties different information
 Parties different incentives
 Incentives are not always aligned
 Moral hazard: hidden action
 Adverse selection: hidden knowledge
Principal Agent Paradigm
 Principal contracts with agent
 Agent actions are unobservable
 Can only contract on observables
 Trade off risk sharing with incentive provision
 Sub optimal risk sharing: second best
 Possible solutions
 Improve contract design
 Invest in screening and monitoring
Old Ideas in Insurance
 Doctrine of insurable interest
 Use of deductibles
 Incentives for loss reduction
 Use of claims adjusters
 Moral hazard much more prevalent during bad times
 More commercial fires during downturns
 Increased disability claims during recessions
 Term insurance selection
The Alberta Experience
 In 1970`s province was booming.
Population grew at 3% per annum
Run up in house prices
 Very severe recession in 1982-86
Downturn in the oil industry
Severe out migration
Dramatic fall in house prices
 Interest rates fell dramatically
 from 21% in 1981 to 12% in 1983
Alberta Law
 Personal covenant not enforceable
 Law dated back to the Great Depression
 Made it easier for homeowners to default
 Some would sell their house for a dollar
 Dollar dealer (limited co) assumed mortgage, made no
payments and rented out the property
 MICC could not enforce the covenant but CMHC could
 MICC folded in part because of Alberta claims
Lessons
 House prices fell faster than index
 Huge interest rate fall also increased defaults
 Acute moral hazard
 Some defaulting home owners ransacked their property
 Severe adverse selection against MICC
 Wealthier borrowers more prone to use dollar dealers
 Either Type A borrowers or else overstated their net
worth
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Mortgage Insurance Canada
 Dominated by CMHC : 70% of business
 Two private sector firms Genworth and Canada
Guaranty
 CMHC sets the premiums others follow
 Government concerned about risks
 In last few years tightened requirements
 In February, CMHC increased premiums by 15%.
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Standard
Premium
(Current)
Standard
Premium
(Effective
May 1,
2014)
Up to and
including
65%
0.50%
0.60%
Up to and
including
75%
0.65%
0.75%
Up to and
including
80%
1.00%
1.25%
Up to and
including
85%
1.75%
1.80%
Up to and
including
90%
2.00%
2.40%
Up to and
including
95%
2.75%
3.15%
Loan-toValue Ratio
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What factors impact the risk
 Borrower attributes
 Loan characteristics
 Collateral
 Economic conditions
 Institutional features
 Legal environment
 Moral hazard and adverse selection
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Some special features
 Long underwriting cycle
 Long profitable periods then recessions
 Local (Alberta 82-86) or nationwide US 2007-2009
 BIS report stated
 Supervisors should require mortgage insurers to build
long-term capital buffers and reserves during the
valleys of the underwriting cycle to cover claims during
its peaks
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Risk Management
 Sound underwriting practices
 Policy design: reduce moral hazard and adverse selection
 Risk diversification
 Charge enough premium
 Hold enough capital
 Loss mitigation
 Need reliable procedure to measure the risks
 Use stochastic models
 Example CIA Task Force on segregated Funds
Financial Stability Board Principles
 Verification of income and other financial
information
 Reasonable GDS ratios
 Appropriate LTV ratios
 Lenders should not relax LTV during booms
 Effective collateral management
 Sound appraisals. No Down Payment Assistance
 Lenders should assess risk independently
Down Payment Assistance
 Non profits help people find affordable housing.
 Provides gift : typically 3%-6% of the purchase price
 Home buyer must have an approved FHA home loan
 Assume house price $400,000 buyer has no cash
 Assume buyer gets gift of $25,000
 Borrower can get a mortgage for $375,000
 LTV is 93.75%
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What did happen
 DPA often seller (builder) funded
 Seller makes a donation of $25,000 to the non profit
 Seller raises the house price to $425,000
 Borrower takes out a mortgage of $400,000
 Now the recorded LTV is 94%
 However the true LTV is 100%
 Next slide shows cumulative claim rates for these loans
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Origination
Fiscal Year
No Gift
Relative
Non-profit,
Religious, or
Community
6.37
8.19
18.94
16.69
9.83
5.78
6.88
17.54
15.20
8.06
5.79
7.89
19.44
16.24
11.27
7.48
9.12
20.82
14.89
12.20
10.43
12.08
23.52
18.16
15.04
12.90
14.31
24.41
17.26
21.84
13.89
14.22
24.19
18.20
17.15
10.44
9.26
16.59
13.61
9.82
Government
Employer
2001
2002
2003
2004
2005
2006
2007
2008
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Model inputs
 Current portfolio
 Projected new business
 Loan characteristics
 Borrower attributes
 House price information
 Geographical location
 Economic Scenarios
 Claim cost components
Loan Level Models
 Each loan modeled separately
 Exposed to different risk factors
 Linked to projected economic variables
 Loan includes several options
 Option to default, prepay cure
 Option to default like a put
 Options do not average out if policies grouped
 Illustrate with put option example
House Price Indices
 Used to project claims
 Index is an average
 Suppose 100 houses at $200,000 each
 50 increase in one year by 20% to $240,000 each
 50 decrease in one year by 20% to $160,000 each
 The average price one year later is $200,000
 The HPI shows a zero increase
Put option example
 We have 10 different stocks
 Put option on each stock
 Each put has a strike price of 100
 Payoff at maturity is max(100 − S1,0) where S1 is the
price of first stock at maturity
 If stock price =90 payoff is 10
 If stock price =110 ,payoff is zero
Put option example
 Assume five of the stock prices are 90 and five are 100
 The ten options have total combined payoff =50.
 Suppose we have put option on the portfolio
 Assume strike is 1000
 Payoff at maturity is
max(1000 − (S1 + S2 + ..S10 ) ,0) = 10 max(100 – S𝐴, 0)
 Where S𝐴 is average stock price.
S𝐴 = (S1 + S2 + ..S10 ) / 10
Put option example
 As before 5 of the stock prices are 90 and 5 are 110
 The average stock price, S𝐴 =100
 The payoff of put on the average is zero
 Payoff of the put on the portfolio is zero
 Portfolio of options worth more than option on portfolio
 Think of each put option as an MI contract
 Grouping reduces the option value
Model Structure
 Framework to estimate liabilities and future claims
Model estimates future claim incidence.
 The transitions can include
 Active to delinquent
 Delinquent to claim
 Delinquent to cure
 Other transitions
 Estimate claim severity
 Loss given default
Multiple State Transition Model
DELINQUENT
ACTIVE
CLAIM
Model Structure
 Transitions estimated using logistic regression.
 Explanatory variables include
 Borrower attributes
 Loan characteristics
 Collateral details
 Type of loan
 Other risk factors
 Integrate with dynamic economic variables
Model Evaluation
 Is methodology sound
 Are assumptions reasonable? Can they be validated?
 Do relationships make economic sense?
 Estimation and calibration
 Are results intuitive?
 Are its limitations understood?.
 Sensitivity tests and back testing ?
How have models fared
 Not very well in the US during crisis
 Default risk was gravely underestimated
 Data did not include extreme house price declines
 Correlations increased significantly
 Decline in house prices underestimated
 Most of us did not know we were in a bubble
 Model built on benign experience
Evolution of models
 Changes in agents’ incentives over time
 Securitization attracts loans that rate high on reported
hard data
 Less incentive to collect soft data
 Interest rates set more on the hard data
 Defaults tend to increase
 Models underestimate the defaults
Securitization a good idea
 Started in 1970’s
 Loans packaged and sold to investors
 Motivation get rid of prepayment risk
 Increase supply of housing finance
 First ones Ginnie Mae then Fannie and Freddie
 Payments guaranteed by government
 Emergence of private label MBS
 Various tranches rated by rating agencies
Securitization: the darker side
 System rife with incentive conflicts
 Originate and retain
 Originate and sell
 Many agents involved with different information and
different incentives
 Appraisers, borrowers, lenders and insurers
 Poorly capitalized, state-chartered nonbank mortgage
brokers stepped in to help originate loans, especially to
low-income households
Ed Kane
A new layer of agents developed between lenders and
safety-net managers to provide an alternative to insured
deposit financing of riskless securities. These would-be
financial alchemists (accountants, appraisers, investment
banks, derivatives dealers, credit raters, statistical modelbuilders, credit insurers, and financial servicers) cooperated
in overstating collateral values and understating
institutional leverage and other risks.
Examples
 Over appraisal of collateral
 Down payment assistance loans
 Mortgage broker made more profit from complex loans, low
documentation and less informed borrowers.
 Lenders incentives: origination and securitization
 Quality of mortgages declined over time
 There is soft information not captured by the numbers
The 620 FICO Puzzle
 FICO scores generally good predictors of delinquencies.
 Keys et al (2010) looked at loans with FICO around 620
 Securitization weakens banks’ incentives to screen and
monitor loans
 Rule of thumb securitize if FICO >620
 Securitized loans have higher delinquency rates around
the cut off point.
 Delinquency rates for different book years
The ratings game
 Ratings agencies paid by issuer
 Inherent conflict of interest
 Encouraged ratings inflation
 Majority of AAA rated securities on sub prime were re
rated to junk status
 Managers of rating groups were expected by their
supervisors and ultimately the Board of Directors of
Moody’s to build, or at least maintain, market share
(Wall Street and the Financial Crisis )
Economic scenarios
 Models rely on projected economic scenarios
 Predict claim incidence and claim severity by linking to
future macro variables
 These include house prices, interest rates and
unemployment rates
 Macro variables modeled as stochastic processes
 Simulate a range of economic outcomes
 Aggregate information on each loan to get distribution
House Price Indices
 House price variable of double importance
 Impacts claim incidence and claim severity
 What factors impact house prices?
 Forces of supply and demand
 Dual nature of housing
 Durable consumption good and also an asset
 Supply adjusts slowly.
 Long run factors and short run factors
Factors impacting house prices
 Long run factors
 Demographic shifts
 Growth in per capita income
 Over the medium term supply adjusts slowly
 Over short term demand depends on interest rates and
credit conditions
 Medium term supply curve is steeper than long run
supply curve
Modelling House Prices
 Modeling supply and demand is the ideal
 This process could generate bubbles
 Viewed as too hard to do
 Pragmatic approach. Use a reduced form model
 House prices assumed to depend on interest rates,
mortgage rates unemployment rates and past house
prices.
House Price Volatility
 Option theory predicts that value of option to default is
an increasing function of the volatility of the collateral
 Could measure the historical volatility of house prices in
various regions
 Yang, Lin and Cho(2000) analyze impact of cross sectional
dispersion
 They show how to compute the additional dispersion
and include it in the model
Diversification
 Assume we have N risks
 Each has variance σ2 all correlations are equal to ρ
 The variance of the portfolio is 𝑁σ2 1 + ρ 𝑁 − 1
 As ρ tends to zero this variance tends to 𝑁σ2
 As ρ tends to one this variance tends to 𝑁2σ2
 During the crisis correlations increased
 Diversification works in normal times but fails in extreme
conditions
Housing market is procyclilcal
 Housing has booms and busts
 Rising house prices increase value of collateral
 Mortgage terms are liberal, high LTVs
 Borrowers increase leverage to finance consumption
 Some spend these new funds on housing
 Feedback to house prices creates a spiral
 Consumers and banks become highly levered
 System is very vulnerable to a bust in house prices
Interest rate models
 Interest rates are important in MI
 Impact on mortgages and affordability
 Also relation to HPI changes (can vary over time )
 Interest rate drops have a big impact on prepayments
 Prepayments are very important in the USA
Interest rate models
 Hard to construct stochastic models of interest rates
 Long rates have declined steadily since 1980
 Hard to model with one factor stochastic process with
constant terms
 Takes three factors to provide a good fit
 Then life gets complicated
 Need a wide range of behaviour to model prepayments
 Current rates are historically low
Interest rate models
 Hard to construct stochastic models of interest rates
 Long rates have declined steadily since 1980
 Hard to model with one factor stochastic process with
constant terms
 Takes three factors to provide a good fit
 Then add regime switching. Life gets complicated
 Need a wide range of behaviour to model prepayments
 Current rates are historically low
P measure or Q measure?
 Interest model should be in the real world measure
 Contrast with risk neutral measure using for pricing
derivatives.
 Consider parallel with equities
 Black Scholes model is in risk neutral measure. Uses the
risk free rate.
 Optimal portfolio selection uses actual return on stock.
This corresponds to the real world measure.
Stress tests
 Federal Reserve Bank of Boston Conferences
 Stress tests designed to measure financial impact of
extreme but plausible(?) (i.e., tail risk) adverse scenarios
 Does insurer have enough capital and liquidity to
withstand such scenarios?
 Challenge to design appropriate and internally consistent
sets of shocks and incorporate feedback effects
 A one percent tail event depends on the model
Stress tests
 Company stress test may differ from supervisors
 How do you know if it is severe enough ?
 Avoid derailment of the process by “that’s
unprecedented / impossible / unrealistic” criticisms
 Reverse stress tests. Find the scenarios that will ruin you
 In the MI case things may be much worse in the tails
 More opportunities for moral hazard
 Loss ratios, claims costs will increase
Kerry D Vandell
 My experience involving much of the recent litigation
representing the fallout from the financial crisis is that
virtually none of the lenders, ratings agencies, monoline
or mortgage insurers, or analysts ran scenarios that
embedded expectations of 40% house price declines
within any scenario tested. Had they done so, their
models would have predicted default rates of the same
order of magnitude of what actually occurred
Summary
 Surveyed some current issues in Mortgage Insurance
 Long profitable periods followed by extreme losses
 Moral hazard, adverse selection always lurking in the
background
 Problems with House Price Indices
 How much capital to hold?
 Stress tests