© imacs 2012 printed 28-aug-15 page 1 april 20, 2012new delhi risk scoring and risk based pricing...

© IMaCS 2012Printed 21 Apr 2023

April 20, 2012 New Delhi

Risk Scoring and Risk Based Pricing of Home Loans

ICRA Management Consulting Services Limited

Presentation to National Housing Bank


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


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


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


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


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

Risk Adjusted Return on Capital Employed

Capital Required (regulatory) / Employed (economic)Capital Required (regulatory) / Employed (economic)


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


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%


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


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


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


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


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



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



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

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)


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.


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%


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/


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

DiscussionsDiscussions

© imacs 2012 printed 28-aug-15 page 1 april 20, 2012new delhi risk scoring and risk based pricing...

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