Student Loan ABS market
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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
7/17/2015
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