Delinquency Review of a Large Housing Finance Company’s Home Loan Portfolio

Akshat Singh

24 July 2015

2

Agenda

1 Executive Summary

3 Insights

4 Key Recommendations

2 Approach to Data Analysis

3

Executive Summary

Determine the normal distribution curve for bucket

position to see which are the riskiest districts and the

safest districts

After locating these districts, assess the normal

distribution for the loan to income ratio in these

districts - both risky and safe

Ascertain if a correlation/causality can be found

between the bucket position and income

Issue / Requirement

The Muthoot Housing Finance Company is part of a 127 year old business house in India with interests in Financial

Services, IT, Media, Healthcare, Education, Power and Infrastructure with revenues of over USD 4 B

In order to glean insights on delinquency patterns and potential causes for delinquency, I conducted an analysis of

sample data of the housing finance company’s portfolio across various districts

Common statistical methods for causality and correlations were used

Overview

Khed, Palani, Calicut and Dindigul have high average

delinquency and standard deviation and hence are

risky areas to give a loan in

Vasai, Coimbatore, Baroda and Rajkot have low

average delinquency and standard deviation and

hence are safe areas to give loans in

It is seen that for the riskier districts LTI Ratio is higher

than 20

A weak negative correlation between bucket position

and income was found for Khed. No correlation could

be found for the other risky districts

Analysis and recommendations

4

Approach to Data Analysis

Topic Details

Scope

Review of sample data of Muthoot Housing Financing

Company’s portfolio to develop insights on possible causes

of delinquency

Report Analysed Sample disbursal data from districts

Data points Data of bucket position, loan value, LTV and income was

analysed for specific districts

Analytics tools

and

methodology

Normal distribution curve and regression analysis

5

Review of normal distribution of delinquency for

districts that appear to have a lower risk portfolio

Vasai- mean delinquency = 0.25

Standard deviation = 0.52Coimbatore- mean delinquency = 0.16

Standard deviation = 0.37

Baroda mean delinquency = 0.15

Standard deviation = 0.53

Rajkot mean delinquency =0.42

Standard deviation = 0.49

It can be predicted that 47.5% of the

data will lie between 0.25 and 1.29

It can be predicted that 47.5% of the

data will be between 0.16 and 0.9

0

0.2

0.4

0.6

0.8

1

-2 -1 0 1 2

0

0.2

0.4

0.6

0.8

1

1.2

-2 -1 0 1 2

0

0.2

0.4

0.6

0.8

-2 -1 0 1 20

0.2

0.4

0.6

0.8

1

-2 -1 0 1 2 3

It can be predicted that 47.5% of

the data will lie between 0.15

and1.21

It can be predicted that 47.5% of

the data will lie between 0.42 and

1.40

6

Review of normal distribution of delinquency for

districts that appear to have a higher risk portfolio

Khed- mean delinquency = 4.13

Standard deviation = 2.35Palani- mean delinquency = 2.39

Standard deviation – 4.59

Calicut mean delinquency = 0.98

Standard deviation = 2.32

Dindigul mean delinquency =1.49

Standard deviation = 2.30

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

-20 -10 0 10 20

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

-5 0 5 10 15

It can be predicted that 47.5% of the

data will lie between 4.13 and 8.83It can be predicted that 47.5% of the

data will be between 2.39 and 11.57

0

0.05

0.1

0.15

0.2

-10 -5 0 5 10

0

0.05

0.1

0.15

0.2

-10 -5 0 5 10

It can be predicted that 47.5% of the

data will lie between 0.98 and 5.62

It can be predicted that 47.5% of the

data will be between 1.49 and 6.09

7

Review of normal distribution of LTI ratios for districts

that appear to have a higher risk portfolio

Khed- mean LTI ratio= 25.6

Standard deviation =29.8

Palani- mean LTI ratio= 22.6

Standard deviation – 3.0

Calicut mean LTI = 20.6

Standard deviation = 8.7

Dindigul mean LTI =20.7

Standard deviation = 5

It can be predicted that 47.5%

of the data will lie between

and 25.6 and 85.2

It can be predicted that 47.5 of the

data will be between 22.6 and 28.6

0

0.005

0.01

0.015

-100 -50 0 50 100 1500

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 10 20 30 40

0

0.01

0.02

0.03

0.04

0.05

-20 0 20 40 600

0.02

0.04

0.06

0.08

0.1

0 10 20 30 40It can be predicted that 47.5%

of the data will lie between

20.6 and 38

It can be predicted that 47.5% of the

data will be between 20.7 and 30.7

8

Review of the data for correlations and causality

between bucket position and income

Palani- graph showing data between bucket

position and income

Khed negative correlation between bucket

position and income

No clear correlation can be

found for Palani

The slope is -0.002752

The degree of correlation is 0.28

-5

0

5

10

15

20

0 20000 40000 60000 80000

Dindigul – graph showing data

between bucket position and

income

0

5

10

15

0 100000 200000 300000

0

1

1

2

2

3

3

4

4

5

0 100000 200000 300000

0

5

10

15

20

25

0 50000 100000

Calicut – graph showing data between

bucket position and income

No clear correlation can be seenNo clear correlation can be seen

9

Key Recommendations

It is risky to give big loans in Khed, Palani, Calicut or Dindigul because of the

high range in which the delinquency is predicted to fall.

It is safe to give loans in Vasai, Baroda, Coimbatore and Rajkot because of

the low range in which the delinquency is predicted to fall.

In Khed and Virudhunagar a negative correlation can be seen between

income and bucket position i.e. low income leads to high bucket position.

It is a weak correlation but it is till useful in seeing that higher the income,

lower the bucket position

It can be seen in the risky districts , the mean loan to income ratio is found to

be above 20 so loans shouldn't be given more than 20 times the income of

the recipient in these areas