This document analyzes commercial vehicle loan pools in India. It finds that: 1) Pools with greater non-commercial vehicle exposure (>40%) and higher weighted average seasoning tend to have better collection efficiency and lower credit enhancement. 2) Collection efficiencies have declined each year from 2008-2012 for all originators' pools. AU Finance pools have the lowest cumulative collection efficiency. 3) For AU Finance pools specifically, shorter tenures (<3 years) and higher weighted average seasoning (>3 months) correlate with better average collection efficiency. Pools containing CV, auto and tractor loans have the lowest efficiency. 4) Across all originators, the originator is found to have a greater impact on

1. Commercial Vehicles

2. Contents

Commercial Vehicles – An Overview

Objective of the Analysis

Preliminary Analysis of CV Pools – All Originators

Preliminary Analysis of CV Pools – Only AU Financiers Ltd.

Detailed Analysis of CV Pools – All Originators

Market Outlook

Conclusion

3. Commercial Vehicles – An
Overview

4. Commercial Vehicles

What is a Commercial Vehicle (CV)?
A Commercial vehicle is a type of motor vehicle that may be used for transporting goods or passenger.

Importance on CV Industry in India
Trucks can access remote and hilly areas where rail lines cannot be constructed.
The CV industry enables quick, easy departure of goods and accepts smaller loads than railways.
The industry has captured short-haul goods transport and a major chunk of long-haul goods transport where quick delivery
from railways is essential.

Major Classification of CV Industry
Light Commercial Vehicle (LCV) – Goods & Carriage Vehicle with light capacity with Minimum Permissible Capacity (MPC)
of 7 – 11 tons
Medium Commercial Vehicle (MCV) – Single Axle Vehicle used mainly for the carriage of perishable goods with MPC of 16
tons of Mass
Heavy Commercial Vehicle (HCV) – Dingle Axle Vehicle used mainly for the carriage of non-perishable goods with MPC of
17 to 25 tons of Mass

Major CV Financiers in India
Cholamandalam Finance, Sundaram Finance, AU Finance, Shriram Transport Finance, Magma, L&T, M&M, Religare, Tata
Motors, Reliance

5. Objective of the Analysis

6. Objectives

Identify the factors which significantly affect the pool performance (delinquencies)

Illustrate the way in which these factors affect the delinquencies

7. Preliminary Analysis of CV
Pools – All Originators

8. Data Used – An Overview

Securitized CV pool details from the Pool Performance reports (Sep’12) of Rating Agencies (CARE, ICRA and CRISIL)

Total number of Transactions (Data Points)
-
190

Period of Transactions
-
2008-2012

Number of Originators
-
16

Major Originators used for Analysis

Shriram
-
58 Transactions

Magma
-
34 Transactions

AU
-
21 Transactions

Tata Motors
-
18 Transactions

Sundaram
-
13 Transactions

Religare
-
12 Transactions

Reliance Cap
-
9 Transactions

9. Collection Details & Methodology Used
Parameters Collected for Analysis:

Transaction Details
: Transaction month, Transaction Name, Originator, Transaction Structure,
Type of Transaction, Rating Assigned for Seniors and Junior

Initial Pool Details
: Pool Principal, Pool Composition, Credit Enhancement (% of Initial POS),
EIS (% of Initial POS)

Pool Performance Details
: Cumulative Collection Efficiency (CCE)
Methodology Used:

Collection of data from the Pool Performance (Sep’ 12) reports of Rating Agencies

Identification of factors, which are to be used for the analysis of transactions

Regression Analysis of these factors with respect Sep’12 CCE and Scatter plots to capture trends

Observe the significance level of factors which affects CCE

10. Findings from Preliminary Analysis of CV Pools (All Originators)
 Pools having greater NCV Exposure (>40%) tend to
have better collection efficiency and lower credit
Enhancement (Figure 1)
 Cumulative Collection Efficiencies of transactions are
continuously declining with respect to the year of
transactions during 2008-2012 (Figure 2)
 Au pools seems to have the lowest CCE when
compared to its competitors in CV Industry (Figure 3)
CCE by year of securitization
60
52
Figure 1
100%
52
99%
50
98%
40
28
30
28
96%
20
10
97%
95%
94%
2
0
70
60
50
40
30
20
10
0
Originators and CV Analysis
93%
2008
2009
2010
Total Transaction
2011
2012
Year Collection Efficiency
No. of securities
WCE
Figure 3
Figure 2
100%
98%
96%
94%
92%
90%
88%

11. Preliminary Analysis of CV
Pools – AU only

12. Data & Methodology Used
Data Used:

Securitized CV pool details from the Pool Performance reports (Sep’12) of Rating Agencies (CARE, ICRA and CRISIL)

Total number of Transactions (Data Points)
-
21

Period of Transactions
-
2010-2012
Parameters Collected for Analysis:

Transaction Details
: Transaction month, Transaction Name, Originator, Transaction Structure,
Type of Transaction, Rating Assigned for Seniors and Junior

Initial Pool Details
: Pool Principal, Pool Composition, Credit Enhancement (% of Initial POS), EIS (% of
Initial POS), Weighted Average Seasoning (Months), Tenure (Years)

Pool Performance Details
: Cumulative Collection Efficiency (CCE)
Methodology Used:

Collection of data from the Pool Performance reports of Rating Agencies & Identification of factors, which are to be used for
the analysis of transactions

Single & Multiple Regression of these factors with CCE, Scatter plots for trend capture

Observe the significance level of factors affecting the delinquencies

13. Findings from Preliminary Analysis of CV Pools (AU Only)
 Average CCE is better for the pools with Short Tenure
(<3 years) and High WAS (> 3months)
 Average CCE is the lowest (around 92%) when the pool
mix is CV,AUTO and Tractors when compared to other
mixes
 Average CCE, with respect to CV and Auto mix, is
ranging around 94%.
*CI in Figure 1 & 2 represent Confidence Interval
Figure 2
Figure 1

14. Detailed Analysis of CV Pools –
All Originators

15. Data Used – An Overview

Securitized CV pool details from the Pool Performance reports (from Jun’ 08 to Dec’12) of Rating Agencies (CARE, ICRA
and CRISIL)

Total number of Transactions (Data Points)
-
194

Period of Transactions
-
2008-2012

Number of Originators
-
14

Quarterly data is collected for Cumulative Collection Efficiency and Quarterly Average Collection, starting from the first data
to the latest one available.

Major Originators used for Analysis

Shriram
-
58 Transactions

Magma
-
34 Transactions

AU
-
21 Transactions

Tata Motors
-
18 Transactions

Sundaram
-
13 Transactions

Religare
-
12 Transactions

Reliance Cap
-
9 Transactions

16. Collection Details & Methodology Used
Parameters Collected for Analysis:

Transaction Details
: Transaction month, Transaction Name, Originator, Transaction Structure,
Type of Transaction, Rating Assigned for Seniors and Junior

Initial Pool Details
: Pool Principal, Pool Composition, Credit Enhancement (% of Initial POS), EIS (% of
Initial POS), Weighted Average Seasoning (Months), Average Loan to Value, Average
Loan Size

Pool Performance Details
: Cumulative Collection Efficiency (CCE), Quarterly Average Collection, 90+ & 180+
Delinquency Loss
Methodology Used:

Collection of data from the Pool Performance reports of Rating Agencies & Identification of factors, which are to be used for
the analysis of transactions

Single & Multiple Regression of these factors with 90+ and 180+ delinquencies

Observe the significance level of factors affecting the delinquencies

17. Findings from Detailed Analysis of CV Pools (All Originators)
 In the transactions, Originators have greater impact on
delinquencies than the other parameters (Figure 1)
 None of the other pool parameters are found to have
significant impact on the pool performance in multiple
regression (Figure 1)
 Individual regression results suggest that more % of
NCV (less % of UCV), high Weighted Average
Seasoning and high Loan Size have positive impact on
delinquencies (Figure 2)
Figure 1 – Multiple Regression with 180+ Delinquency
Figure 2 – Individual Regression with 180+ &
90+ Delinquency

18. Limitations & Future Works
Limitations

Data Collected for only those transactions that are done after June 2008.

Reporting format was not uniform across all the rating agencies.

Inferences are based on small sample size.
Future Works

Time Series analysis of Delinquencies and Cumulative Collection Efficiency of earlier transactions to date and also from the
perspective of an originators can be done

All the originators should be analysed according to the individual assets of the pools (type of underlying vehicles).

Analysis of relation between the closely related independent factors viz. Loan size and % of NCV so as to figure out that
which of the two factors actually impacts the performance

Comparing the rating rationales, for similar rated transaction with different credit enhancement or vice versa, more deeply to
understand the factors seen by rating agencies

19. Market Outlook

20. Recent Study by ICRA

Asset Quality of CV

90+ DPD 6.8% and 180+ DPD 3.8%


UCV –
NCV -
90+ DPD 6.2% and 180+ DPD 1.5%
Macro-Economic Indicators



Correlation between GDP vs. 90+ DPD = -83% (-91% with three months lag of 90+dpd)
Correlation between IIP vs. 90+ DPD = -91%
Portfolio Analysis


TN & Maharashtra contributed around 35% of total disbursement

MHCV weaker performance than other asset classes

High Tenure Contract (3 Years) showed weaker performance

Contracts originated in Orissa and Goa is affected a lot because of Mining Issues


In FY13, Average Tenure is 43 months
Contracts with higher interest rates performed weakly
Repossession Loss

37% in New LCV and 39% in New MHCV

Loss due to repossession in UCV is slightly lower than New CV’s (Probably because of Lower LTV)

Repossession Rates in FY13 is ranging around 30-50%

Resale Value is reduced by 10-20% due to demand decline and discount on new vehicles

21. Conclusion

22. Concluding Remarks
 Originators play an important role compared to other parameters when it comes to the
performance of CV Pools.
 Also, with fair degree of confidence, it can be said following parameters have an
impact in a pool’s performance

% of NCV Exposure

Weighted Average Seasoning

Tenure of the pool

Loan Size (May be correlated with other factors like % of NCV)
 GDP and IIP are the major indicators of CV industry
 LCV - Safer bet than MCVs and HCVs

23. Questions ??