Household Credit Outlook and Loss Forecasting in the Real World

Household Credit Outlook and Loss Forecasting In The Real WorldCristian Deritis, Senior DirectorErlind Dine, Senior Director

Presented at Moody’s Analytics Risk Practitioner Conference; Chicago | October 15-18, 2012

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Economic & Consumer Credit Analytics Solutions

Access essential expertise on the economic and consumer credit trends that impact your business and investments

Risk Management, Strategic Planning & Business / Investment Decisions

Economic, Consumer Credit & Financial Data

Forecasts with Alternative Scenarios

Economic Research

Consumer Credit Analytics

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Economic & Consumer Credit Analytics

Who we are

• 80+ economists, more than 40% of which have PhD’s (>10 entirely focused on consumer credit modeling)

• 20+ data specialists

• Located around the globe

• London

• Prague

• Sydney

• West Chester

What we do

• Maintain extensive database of economic, financial and demographic data down to the regional and city level with over 250 million time series covering 200+ countries and 600+ cities

• Provide the highest frequency and most up-to-date outlook with monthly updated forecasts of national and regional economies worldwide

• Forecast alternative macroeconomic scenarios globally for stress testing and risk management

• Forecast and stress test clients’ consumer credit portfolios with customized models

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Generate forecasts and stress tests for your portfolio

Assess the quality of future vintages

Pre-identify growth opportunities to guide your expansion

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80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 1210.0

10.5

11.0

11.5

12.0

12.5

13.0

13.5

14.0

14.5

15.0

15.5

16.0

16.5

17.0

17.5

18.0

18.5

19.0

19.5

Debt service (L)Financial obligations (R)

Households finances better than ever…for some % of disposable income

Sources: Federal Reserve, BEA, Moody’s Analytics

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0123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

TotalAutoBankcardMortgage

Peak: Oct 2008

Peak: Nov 2007Peak: Feb 2009

Peak: Oct 2008

Household deleveraging continues

Sources: Equifax, Moody’s Analytics

Difference from peak, $ tril

Months from peak

77

08 09 10 11-1,600,000

-1,400,000

-1,200,000

-1,000,000

-800,000

-600,000

-400,000

-200,000

0

Net new borrowingNet voluntary pay-offDefaultTotal balance change

Mortgage defaults dominate balance declines


Cumulative change in balances from August 2008 peak, $ bil

88

09 10 11 12-500000

-450000

-400000

-350000

-300000

-250000

-200000

-150000

-100000

-50000

0


High-credit mortgage originations offset declines


Cumulative change in balances from February 2009 peak, $ bil

99

-400000

-350000

-300000

-250000

-200000

-150000

-100000

-50000

0

Net voluntary pay-off

Default

Total balance change

No new mortgages for low score households


Cumulative change in balances from August 2008 peak, $ bil

1010

070707070707070707070707080808080808080808080808090909090909090909090909101010101010101010101010111111111111111111111111121212121212121212

-15

-10

-5

0

5

10

15

20

AutoMortgageBankcardStudent

Recent balance growth: Low and Slow


Balances, % change yr ago

1111

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

10Q1

10Q2

10Q3

10Q4

11Q1

11Q2

11Q3

11Q4

12Q1

12Q2

55

65

75

85

95

105

115

Auto loan originations reflect vehicle sales


Initial balance of new auto loan and lease issuance, $ bil, NSA

1212

07 08 09 10 11-180000

-160000

-140000

-120000

-100000

-80000

-60000

-40000

-20000

0


Auto loan balances recovering


Cumulative change in balances from September 2007 peak, $ bil

1313

08Q1

08Q2

08Q3

08Q4

09Q1

09Q2

09Q3

09Q4

10Q1

10Q2

10Q3

10Q4

11Q1

11Q2

11Q3

11Q4

12Q1

0

10

20

30

40

50

60

70

80

90

0

5

10

15

20

25

30

35Vintage max balance (L)

Vintage high credit (L)

New bankcard volume slower to recover…


Balances of new bankcard issuance, $ bil, NSA

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060606060606060606060606070707070707070707070707080808080808080808080808090909090909090909090909101010101010101010101010111111111111111111111111121212121212121212121212

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.030-day60-day90-day120-dayDefault

Performance trends improving…


Delinquencies/defaults, % of $ balances, NSA

1515

060606060606060606060606070707070707070707070707080808080808080808080808090909090909090909090909101010101010101010101010111111111111111111111111121212121212121212121212

2

3

4

5

6

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8

9

10

11

12 AutoBankcardConsumer financeMortgage

…Across all products...


Delinquent, % of $ volume, NSA

1616

…With regional variation


Bankcard $ delinquency rate, % of outstanding, NSA

1.58 to 2.74 2.75 to 2.99 3.00 to 4.03

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1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 610

2

4

6

8

10

122007Q12007Q32008Q12008Q32009Q12009Q32010Q12010Q3

Newer vintages continue to outperformCumulative % of original auto $ balance defaulted or bankrupt


Months since origination

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06 07 08 09 10 11 12 133

4

5

6

7

8

9

10

11

12

13

Base

Stress

Declines in bankcard default rates to continue


Bankcard write-offs + bankruptcies (green line)

Unemployment rate, %

Bankcard write-offs + bankruptcies default, % of $

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• Regulators are requiring more banks to stress test portfolios

• A few years of observations are not enough to build reliable models for loss forecasting and stress testing • Business cycle is about 8 years long on average

• Options:• Use shorter, available history assuming larger confidence bands

• Rely on regional heterogeneity to identify the business cycle

• Use industry-level data to fill-in the data gaps and build models

• Focus on leveraging CreditForecast.com data today

Loss Forecasting In the Real World: Dealing with Limited Data

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• CreditForecast.com, a joint service from Equifax and Moody’s Analytics, provides data on loan volume and performance

• 100% monthly extract of credit report data

• Segmented by Product, Origination Vintage, Metro/State Geography, Credit Score at Origination and Current Credit Score

• Volume and performance forecasts under a variety of economic scenarios, econometrically determined using a vintage approach

• Product-geography-vintage-credit score segments allow for apples to apples comparison for benchmarking history and forecasts

CreditForecast.com

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» Panel data segmented by product, origination vintage, geography (state/metro) and credit score band

» Forecast all performance measures– New loan origination volumes

– Outstanding balances

– Delinquency rates, default and bankruptcy rates, prepayment rates

– Revolving credit utilization

– Number of zero-balance accounts

» Leverage dual-time nature of panel data for econometric modeling– Moody’s Analytics macro/regional

economic data and scenarios

– Federal Reserve’s CCAR scenarios

Credit Forecasting Models

Life CycleThe age of the

loans

VintageCredit Quality and

State of the economy at origination

Business CycleCondition of the

economy every month

Models Consider:

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• Regional bank operating in New York, New Jersey, Pennsylvania

• Started originating auto loans in 2000

• Exited the business in 2006, selling off the portfolio and servicing

• Re-entered the business in 2009.

• Performance observations are available starting from 2009

Example

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No data? Rely on industry data exclusively


Annualized $ default rate for auto loans, August 2012

Credit Score

>700

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Annualized $ default rate for auto loans, NSA

0606060606060606060606060707070707070707070707070808080808080808080808080909090909090909090909091010101010101010101010101111111111111111111111111212121212121212121212121313131313131313131313131414141414141414141414140.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4US

Pennsylvania

New Jersey

New York

Weighted By Lender Profile

Current profile only? Benchmark with industry data


Lender Profile:

60% NY

25% NJ

15% PA

Credit Score

>700

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• CreditForecast historical data and econometric models allow for reliable estimation of business cycle effects

• Assume that the relationship between industry and firm’s data is described by Beta function based on examination of history:

• Flexible specification with 2 parameters, a and b

• Estimate a and b to minimize the distance between the firm’s and industry data historically

• Apply the inverse relationship to generate bank-specific forecasts

• Enhance procedure for multiple product-vintage-region-score segments. Customize to availability of data in bank portfolio.

Some historical data? Calibration model

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Calibration model example

Charge Off Rate, %, Vintage 2007Q2

Charge Off Rate, Industry (Spliced), Baseline, Vintage 2007Q2

Charge Off Rate, Industry, Calibrated, (Spliced), Baseline, Vintage 2007Q2

0.000

0.001

0.002

0.003

0.004

2009M22009M4

2009M62009M8

2009M102009M12

2010M22010M4

2010M62010M8

2010M102010M12

2011M22011M4

2011M62011M8

2011M102011M12

2012M22012M4

2012M6

Annualized $ default rate for auto loans, NSA

Actual

Industry (CF)

Calibrated

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0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

Actual

Predicted

History

Calibration model example – 12 month hold-out


Annualized $ write-offs for auto loans, NSA

Household Credit Outlook and Loss Forecasting in the Real World

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