© imacs 2012 printed 28-aug-15 page 1 april 20, 2012new delhi risk scoring and risk based pricing...
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© IMaCS 2012Printed 21 Apr 2023
Page 1
April 20, 2012 New Delhi
Risk Scoring and Risk Based Pricing of Home Loans
ICRA Management Consulting Services Limited
Presentation to National Housing Bank
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Risk based pricing enables better risk management
1. A rating model or scorecard will discriminate
good and bad borrowers
2. Identify risks in the property (collateral)
Risk IdentificationRisk Identification
Risk MeasurementRisk Measurement
Risk MitigationRisk Mitigation
1. Estimate credit losses through models
2. Compute credit risk premium from risk grading
1. Manage anticipated credit losses by provisioning
and risk based pricing
2. Maintain capital to absorb adverse losses
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Credit losses can be divided into expected loss and unexpected loss
YearYear
Unexpected Loss
Los
ses
Los
ses
Expected loss
Peak losses in excess of expected loss Managed with capital cushionPeak losses in excess of expected loss Managed with capital cushion
FrequencyFrequency
Credit Loss
Expected LossExpected Loss Average loss in the course of business Managed by pricing and provisionsAverage loss in the course of business Managed by pricing and provisions
Unexpected LossUnexpected 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 DefaultExpected 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 WeightCapital required = Exposure * Risk Weight
Risk weight is based on loan amount and LTVRisk weight is based on loan amount and LTV
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Risk based pricing
Good accounts subsidize poor credit risk accounts Risk based pricing can mitigate problem of adverse selection
The cross-subsidy
Typical Pricing
Good credits overpricedGood credits overpriced
Risk-basedpricing
Bad risks under-pricedBad risks under-priced
Subsidy
Low HighRiskRisk
Inte
rest
Rat
eIn
tere
st R
ate
11
22
11
22
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Risk Adjusted Return on Capital Employed
Capital Required (regulatory) / Employed (economic)Capital Required (regulatory) / Employed (economic)
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Cost heads considered in pricing… as % of exposure
Cost of FundsCost of Funds
Direct and Indirect CostsDirect and Indirect Costs
ProvisionsProvisions
Opportunity Cost of Regulatory CapitalOpportunity Cost of Regulatory Capital
Loan origination and servicing cost + Other overheads
Maximum of Existing provisions apportioned Expected loss computed as PD * LGD * Exposure
Cost of borrowing
Hurdle rate based on RoE * Regulatory capital
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Risk based pricing – an example
8.50%
1.00%0.50%
0.90%
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
Cost of funds Overhead cost
Credit risk premium
Cost of capital
Processing fee
Lending rate
0.10% 10.90%
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Credit risk premium depends on the rating of the borrower
Quality of borrower Credit risk PD LGD
Expected loss = PD* LGD
Risk based pricing
Excellent Negligible 0.30% 10% 0.03% 10.43%
Good Low 1.00% 10% 0.10% 10.50%
Moderate Medium 2.00% 10% 0.20% 10.60%
Poor High 5.00% 10% 0.50% 10.90%
*LGD is assumed as 10% as per Basel guidelines
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 50%, 75%
g. Cost of capital = d * e * f 0.60%, 0.90%
Assumptions
Processing fee is amortised over 10 years
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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
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Explanatory Variables in the Home Loan Model
Fixed Obligation /
Income
IncomeLoan AmountEMI/NW
Quality of Borrower
Cost of Living
Age
Skill level
Years of
ExperienceOthers
Family structure
Joint/Nuclear
Years of Banking
Marital
Status
No. of
dependents
Residence
type
Loan to Value
Qualitative
Quantitative
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Quantitative IndicatorsFixed Obligation to Income Ratio (FOIR)
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.
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%
8.5% 9.0%
29.5% 28.6%24.5%
0.5% 0.5% 0.5%0.7%
1.6%
0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%
0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%
Less than 25% 25%-40% 40%-60% 60%-80% Greater than 80%
FOIR
Relative Frequency Default Rate
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Quantitative IndicatorsLoan to Value Ratio (LTV)
Lower the LTV, greater is the applicants contribution towards the asset i.e. loss in event of default increases for the applicant.
1. 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%
2.0% 5.5%
17.5%
47.5%
27.5%
0.0%0.2%
0.4%0.6%
1.6%
0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%
0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%40.0%45.0%50.0%
Less than 25% 25%-40% 40%-60% 60%-80% Greater than 80%
LTV
Relative Frequency Default Rate
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Quantitative IndicatorsAge of the Borrower
The lower age bracket and the
higher age brackets appear more
prone to default
1. The 40 -50 years age bracket seems to be
the safest
0.8%6.4%
34.1% 35.7%
19.0%
3.6% 0.4%
1.4%
0.9% 0.9%0.7% 0.8%
1.5%
0.0% 0.0%0.2%0.4%0.6%0.8%1.0%1.2%1.4%1.6%1.8%
0.0%5.0%
10.0%15.0%20.0%25.0%30.0%35.0%40.0%
Less than 25
25-30 30-40 40-50 50-60 60-70 Greater than 70
Relative Frequency Default Rate
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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
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Rating models – Does it really work ?
ClassificationPredicted
Observed 1 2 3 4 5 6 7 8 9 10Percent Correct
Percent Correct ( allowing for 1 notch +/-
1 5011 96 142 1 9 0 23 1 0 113 92.90% 94.6%2 1307 121 162 0 2 0 12 4 2 60 7.20% 95.2%3 566 46 192 1 3 0 2 2 1 38 22.60% 28.1%4 154 5 11 1 0 0 0 0 0 7 0.60% 6.7%5 522 10 30 0 6 0 1 0 0 15 1.00% 1.0%6 179 8 26 0 0 0 0 0 0 5 0.00% 0.0%7 89 5 15 0 0 0 3 0 0 7 2.50% 2.5%8 21 3 10 0 0 0 0 1 0 1 2.80% 2.8%9 17 1 2 0 0 0 0 1 1 2 4.20% 16.7%
10 8 0 1 0 0 0 0 2 0 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)
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Balance business flexibility with asset quality improvement
Risk Grade 10 9 8 7 6 5 4 3 2 1Cumulative Lending
Cum
ulat
ive
NP
As
The objective is to strike a balance between business objectives (so that not too many cases are rejected) and potential NPA reduction.
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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%
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Risk based pricing of mortgage loans – USA
1. Interest rates are determined based on a number of factors like a. Loan Type, Loan Amountb. Property Type, Property Use, Property Locationc. Credit Score and History – one of the most important factorsd. 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
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/
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Risk based pricing system in various countries
USA/ Canada UK Australia
Interest Rates are linked to credit scores and internal rating models
FICO score is widely used in US
Equifax's ScorePower and TransUnion's credit score are popular in Canada
Risk based pricing notice to be given to consumers - mandated by regulation
Interest rates are linked to credit scores and internal rating models
Internal rating models and scorecards are used widely than external credit scores to calculate credit risk and interest rates
Experian and Delphi scores are also referred to
Interest rates are linked to internal rating models
Internal rating models and scorecards are used widely than external credit scores to calculate credit risk and interest rates
External credit scores used to decide whether loan to be approved or not and set limits
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