Transcript No Slide Title

Presentation to
National Housing Bank
Risk Scoring and Risk Based Pricing of Home Loans
ICRA Management Consulting Services Limited
April 20, 2012
New Delhi
© IMaCS 2012
Printed 17-Jul-15
Page 1
Risk based pricing enables better risk management
Risk Identification
1. A rating model or scorecard will discriminate good
and bad borrowers
2. Identify risks in the property (collateral)
Risk Measurement
1. Estimate credit losses through models
2. Compute credit risk premium from risk grading
Risk Mitigation
1. Manage anticipated credit losses by provisioning
and risk based pricing
2. Maintain capital to absorb adverse losses
© IMaCS 2012
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Losses
Credit losses can be divided into expected loss and
unexpected loss
Unexpected
Loss
Expected loss
Year
Expected Loss
Average loss in the course of business
Managed by pricing and provisions
Unexpected Loss
Peak losses in excess of expected loss
Managed with capital cushion
Frequency
Credit Loss
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Expected loss is the average loss anticipated in the course of
business
1. Forecast of average level of credit losses a firm reasonably expects to
experience in a year
2. One of the cost components of doing business
3. Managed by pricing and provisioning
4. For e.g.
a. On an average, out of 100 AA borrowers, two of them default at the end
of a normal business year
b. On an average 10% is the loss in the realisation of the asset
5. Rating model or scorecard will help estimate expected loss scientifically
Expected Loss = Probability of Default * Loss Given Default
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Unexpected loss is a peak loss that exceeds expected loss
1. Peak losses do not occur every year but can potentially be very large
2. Capital acts a cushion to absorb unexpected losses
3. Losses can exceed expected losses due to reasons like
a. Economic slowdown, higher interest rates leading to more defaults
b. Correction in property prices leading to negative equity
Capital required = Exposure * Risk Weight
Risk weight is based on loan amount and LTV
© IMaCS 2012
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Risk based pricing
Riskbased
pricing
Interest Rate
The cross-subsidy
1
Good credits
overpriced
Subsidy
2
Typical
Pricing
2
Bad risks
under-priced
1
Risk
Low
High
 Good accounts subsidize poor credit risk accounts
 Risk based pricing can mitigate problem of adverse selection
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Risk Adjusted Return on Capital Employed
Risk Adjusted Return
Income
Interest
Income
Fee and other
non interest
income
Expenses
Interest
Expense
Origination
and servicing
costs
Risk
Adjustment
Provisions /
Expected
Loss
Capital Required (regulatory) / Employed (economic)
Cost heads considered in pricing… as % of exposure
Cost of Funds
Cost of borrowing
Direct and Indirect Costs
 Loan origination and servicing cost
+
 Other overheads
Provisions
Maximum of
 Existing provisions apportioned
 Expected loss computed as
PD * LGD * Exposure
Opportunity Cost of
Regulatory Capital
 Hurdle rate based on RoE
*
 Regulatory capital
© IMaCS 2012
Printed 17-Jul-15
Page 8
Risk based pricing – an example
12.00%
1.00%
10.00%
0.50%
0.90%
0.10%
10.90%
Cost of
capital
Processing
fee
Lending rate
8.50%
8.00%
6.00%
4.00%
2.00%
0.00%
Cost of funds
Overhead
cost
Credit risk
premium
© IMaCS 2012
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Credit risk premium depends on the rating of the borrower
Assumptions
a.
Cost of funds
8.50%
b.
Overhead cost
1.00%
c.
Processing Fee
1.00%
d.
Regulatory capital
12%
e.
Return on Capital
10%
f.
Risk weight for home loans
g.
Cost of capital = d * e * f
Quality of
borrower
Credit risk
Excellent
Negligible 0.30%
Good
Moderate
Poor
PD
50%, 75%
0.60%, 0.90%
LGD
Expected loss =
PD* LGD
Risk based
pricing
10%
0.03%
10.43%
Low
1.00%
10%
0.10%
10.50%
Medium
2.00%
10%
0.20%
10.60%
High
5.00%
10%
0.50%
10.90%
*LGD is assumed as 10% as per Basel guidelines
Processing fee is amortised over 10 years
© IMaCS 2012
Printed 17-Jul-15
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Benefits of a rating model
1. Decision to lend – reduce adverse selection problem
2. In case of lending for a poor credit worthy borrower, what additional
collateral to be sought
3. Measure risk and price loans in a scientific manner
4. Achieve consistency across the organisation
5. Perform analysis of portfolio using risk scores, drivers of risk in the rating
model
© IMaCS 2012
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Explanatory Variables in the Home Loan Model
Quality of
Borrower
Qualitative
Cost of Living
Age
Family structure
Joint/Nuclear
Skill level
Years of Banking
Years of
Experience
Others
Marital
Status
No. of
Residence
dependents
type
Loan to Value
Quantitative
Fixed Obligation /
Income
EMI/NW Loan Amount
Income
© IMaCS 2012
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Quantitative Indicators
Fixed Obligation to Income Ratio (FOIR)
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
FOIR
29.5%
8.5%0.5%
Less than 25%
1.6%
24.5%
0.7%
9.0%
0.5%
25%-40%
28.6%
0.5%
40%-60%
Relative Frequency
Higher the FOIR, lower is the capacity of
the applicant to absorb the negative shock
in net income.
Hence, higher the FOIR, lower is the ability
of the applicant to meet unforeseen
expenses.
60%-80%
1.8%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
Greater than
80%
Default Rate
1.
From the data it is observed that if
FOIR exceeds 60%-80%, default
increases
2.
The optimum range for lending in terms
of most favorable default experience is
the 40%-60%
© IMaCS 2012
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Page 13
Quantitative Indicators
Loan to Value Ratio (LTV)
50.0%
45.0%
40.0%
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
LTV
47.5%
1.6%
27.5%
17.5%
2.0%
0.0%
Less than 25%
0.4%
5.5%
0.2%
25%-40%
40%-60%
Relative Frequency
1.
Lower the LTV, greater is the
applicants contribution towards
the asset i.e. loss in event of
default increases for the applicant.
0.6%
60%-80%
1.8%
1.6%
1.4%
1.2%
1.0%
0.8%
0.6%
0.4%
0.2%
0.0%
Greater than
80%
Default Rate
The optimum range in terms of most
favorable default experience is the 60-70%
2.
Default rates increase sharply when LTV
is greater than 80%
© IMaCS 2012
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Quantitative Indicators
Age of the Borrower
40.0%
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
1.8%
1.6%
1.5%
1.4%
1.4%
1.2%
19.0%
1.0%
0.9%
0.9%
0.8%
0.8%
0.7%
0.6%
6.4%
0.4%
3.6%
0.2%
0.8%
0.4%
0.0% 0.0%
Less than
25-30
30-40
40-50
50-60
60-70
Greater
25
than 70
34.1%
35.7%
Relative Frequency
The lower age bracket and the
higher age brackets appear more
1.
Default Rate
The 40 -50 years age bracket seems to be
the safest
prone to default
© IMaCS 2012
Printed 17-Jul-15
Page 15
Other important factors which should be considered for
appraisal
Credit Track Record
Past credit record depicts the attitude of the person in honouring his credit
obligation. “Wilful default” are one of the causes for a number of defaults.
Nature of Asset
In Housing Segment, assets gradually appreciate with time unlike many other
assets [Cars, white goods, etc.]. The chance of negative equity will be lesser
and Loan to Value ratio will improve over the period of time.
Collateral Security
Additional collateral security lowers the net exposure of the bank. It increases
the applicants contribution in the asset thus effectively reducing loan to value
ratio
If the Collateral Security is high, in case of default by the applicant, the Loss
Given Default will be lower
© IMaCS 2012
Printed 17-Jul-15
Page 16
Rating models – Does it really work ?
Classification
Predicted
Observed
1
2
3
4
5
6
7
8
9
10
1
5011
1307
566
154
522
179
89
21
17
8
2
96
121
46
5
10
8
5
3
1
0
3
142
162
192
11
30
26
15
10
2
1
4
1
0
1
1
0
0
0
0
0
0
5
9
2
3
0
6
0
0
0
0
0
6
0
0
0
0
0
0
0
0
0
0
7
23
12
2
0
1
0
3
0
0
0
8
1
4
2
0
0
0
0
1
1
2
9
0
2
1
0
0
0
0
0
1
0
Percent
Correct (
allowing
Percent for 1
10 Correct notch +/113 92.90% 94.6%
60
7.20% 95.2%
38 22.60% 28.1%
7
0.60% 6.7%
15
1.00% 1.0%
5
0.00% 0.0%
7
2.50% 2.5%
1
2.80% 2.8%
2
4.20% 16.7%
5 31.30% 31.3%
 6967 out of 9092 customers correctly classified -77% accuracy (73%
accuracy within first 3 grades – refer blue color last column)
Cumulative NPAs
Balance business flexibility with asset quality improvement
Risk Grade 10
9
8
7
6
5
4
Cumulative Lending
3
2
1
The objective is to strike a balance between business objectives (so that not too many cases are
rejected) and potential NPA reduction.
© IMaCS 2012
Printed 17-Jul-15
Page 18
Formation of pools – cost effective way of managing risks
Pool 1
Pool 2
Pool 3
Pool 4
Source of Income
Salaried
Self Employed
Salaried
Self Employed
LTV
50% - 75%
50% - 75%
>75%
>75%
FOIR
< 40%
40% - 60%
40% - 60%
> 60%
Expected Loss
0.1%
0.2%
0.25%
0.3%
Interest Rate
11%
11.10%
11.15%
11.45%
© IMaCS 2012
Printed 17-Jul-15
Page 19
Risk based pricing of mortgage loans – USA
1. Interest rates are determined based on a number of factors like
a. Loan Type, Loan Amount
b. Property Type, Property Use, Property Location
c. Credit Score and History – one of the most important factors
d. Debt to Income Ratio
e. Appraised Value/Purchase Price
f. Loan to Value/Purchase Price
g. Documentation Type
Example:
FICO score >=760 score can fetch 0.375% rebate
FICO score 680-719 will have no fee/rebate
FICO score 660-679 will incur 0.25% cost
Illustration source: http://www.thetruthaboutmortgage.com/mortgage-dictionary/risk-based-pricing-loan/
© IMaCS 2012
Printed 17-Jul-15
Page 20
Risk based pricing system in various countries
USA/ Canada
UK
Australia
Interest Rates are linked to
credit scores and internal
rating models
Interest rates are linked to
credit scores and internal
rating models
Interest rates are linked to
internal rating models
FICO score is widely used
in US
Internal rating models and
scorecards are used widely
than external credit scores
to calculate credit risk and
interest rates
Internal rating models and
scorecards are used widely
than external credit scores
to calculate credit risk and
interest rates
Equifax's ScorePower and
TransUnion's credit score
are popular in Canada
Experian and Delphi scores
Risk based pricing notice to are also referred to
be given to consumers mandated by regulation
External credit scores used
to decide whether loan to be
approved or not and set
limits
© IMaCS 2012
Printed 17-Jul-15
Page 21
Discussions