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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. Deal with limited data.
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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
22
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
33
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
44
Generate forecasts and stress tests for your portfolio
Assess the quality of future vintages
Pre-identify growth opportunities to guide your expansion
55
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
66
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
Sources: Equifax, Moody’s Analytics
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
Net new borrowingNet voluntary pay-offDefaultTotal balance change
High-credit mortgage originations offset declines
Sources: Equifax, Moody’s Analytics
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
Sources: Equifax, Moody’s Analytics
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
Sources: Equifax, Moody’s Analytics
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
Sources: Equifax, Moody’s Analytics
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
Net new borrowingNet voluntary pay-offDefaultTotal balance change
Auto loan balances recovering
Sources: Equifax, Moody’s Analytics
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…
Sources: Equifax, Moody’s Analytics
Balances of new bankcard issuance, $ bil, NSA
1414
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…
Sources: Equifax, Moody’s Analytics
Delinquencies/defaults, % of $ balances, NSA
1515
060606060606060606060606070707070707070707070707080808080808080808080808090909090909090909090909101010101010101010101010111111111111111111111111121212121212121212121212
2
3
4
5
6
7
8
9
10
11
12 AutoBankcardConsumer financeMortgage
…Across all products...
Sources: Equifax, Moody’s Analytics
Delinquent, % of $ volume, NSA
1616
…With regional variation
Sources: Equifax, Moody’s Analytics
Bankcard $ delinquency rate, % of outstanding, NSA
1.58 to 2.74 2.75 to 2.99 3.00 to 4.03
1717
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
Sources: Equifax, Moody’s Analytics
Months since origination
1818
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
Sources: Equifax, Moody’s Analytics
Bankcard write-offs + bankruptcies (green line)
Unemployment rate, %
Bankcard write-offs + bankruptcies default, % of $
1919
• 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
2020
• 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
2121
» 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:
2222
• 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
2323
No data? Rely on industry data exclusively
Sources: Equifax, Moody’s Analytics
Annualized $ default rate for auto loans, August 2012
Credit Score
>700
2424
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
Sources: Equifax, Moody’s Analytics
Lender Profile:
60% NY
25% NJ
15% PA
Credit Score
>700
2525
• 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
2626
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
2727
09
09
09
09
09
09
09
09
09
09
09
09
10
10
10
10
10
10
10
10
10
10
10
10
11
11
11
11
11
11
11
11
11
11
11
11
12
12
12
12
12
12
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
Sources: Equifax, Moody’s Analytics
Annualized $ write-offs for auto loans, NSA
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