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Household Credit Outlook and Loss Forecasting in the Real World

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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.

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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  • 1. Household Credit Outlook and Loss Forecasting In The Real World Cristian Deritis, Senior Director Erlind Dine, Senior DirectorPresented at Moody’s Analytics Risk Practitioner Conference; Chicago | October 15-18, 2012
  • 2. Economic & Consumer Credit Analytics SolutionsAccess essential expertise on the economic and consumer credit trends that impact your business and investments Economic, Consumer Credit & Economic Research Financial Data Forecasts with Consumer Credit Alternative Analytics Scenarios Risk Management, Strategic Planning & Business / Investment Decisions 2
  • 3. Economic & Consumer Credit Analytics Who we are What we do• 80+ economists, more than 40% of which • Maintain extensive database of have PhD’s (>10 entirely focused on consumer economic, financial and demographic data credit modeling) down to the regional and city level with over 250 million time series covering 200+• 20+ data specialists countries and 600+ cities• Located around the globe • Provide the highest frequency and most up-to- date outlook with monthly updated forecasts of national and regional economies worldwide • London • Forecast alternative macroeconomic • Prague scenarios globally for stress testing and risk management • Sydney • Forecast and stress test clients’ consumer • West Chester credit portfolios with customized models 3
  • 4. Generate forecasts and stress tests for your portfolio Assess the quality of future vintagesPre-identify growth opportunities to guide your expansion 4
  • 5. Households finances better than ever…for some % of disposable income 14.5 19.5 14.0 19.0 13.5 18.5 13.0 18.0 12.5 17.5 12.0 17.0 11.5 16.5 Debt service (L) 11.0 16.0 10.5 Financial obligations (R) 15.5 10.0 15.0 80 82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 Sources: Federal Reserve, BEA, Moody’s Analytics 5
  • 6. Household deleveraging continuesDifference from peak, $ tril 0.0-0.2 Peak: Nov 2007 Peak: Feb 2009-0.4-0.6-0.8 Total-1.0 Auto-1.2 Bankcard Peak: Oct 2008 Mortgage-1.4 Peak: Oct 2008-1.6 0 12 24 36 48 60 Months from peakSources: Equifax, Moody’s Analytics 6
  • 7. Mortgage defaults dominate balance declinesCumulative change in balances from August 2008 peak, $ bil 0 -200 -400 -600 -800-1,000 Net new borrowing-1,200 Net voluntary pay-off Default-1,400 Total balance change-1,600 08 09 10 11Sources: Equifax, Moody’s Analytics 7
  • 8. High-credit mortgage originations offset declines Cumulative change in balances from February 2009 peak, $ bil 0 -50 -100 -150 -200 -250 -300 Net new borrowing Net voluntary pay-off -350 Default -400 Total balance change -450 -500 09 10 11 12 Sources: Equifax, Moody’s Analytics 8
  • 9. No new mortgages for low score householdsCumulative change in balances from August 2008 peak, $ bil 0 -50-100-150-200-250 Net voluntary pay-off-300 Default-350 Total balance change-400 08 09 10 11Sources: Equifax, Moody’s Analytics 9
  • 10. Recent balance growth: Low and SlowBalances, % change yr ago201510 5 0 Auto -5 Mortgage-10 Bankcard Student-15 07 08 09 10 11 12Sources: Equifax, Moody’s Analytics 10
  • 11. Auto loan originations reflect vehicle salesInitial balance of new auto loan and lease issuance, $ bil, NSA115105 95 85 75 65 55 08Q1 08Q3 09Q1 09Q3 10Q1 10Q3 11Q1 11Q3 12Q1Sources: Equifax, Moody’s Analytics 11
  • 12. Auto loan balances recoveringCumulative change in balances from September 2007 peak, $ bil 0 -20 -40 -60 -80-100-120 Net new borrowing-140 Net voluntary pay-off-160 Default Total balance change-180 07 08 09 10 11Sources: Equifax, Moody’s Analytics 12
  • 13. New bankcard volume slower to recover…Balances of new bankcard issuance, $ bil, NSA90 35 Vintage max balance (L)80 Vintage high credit (L) 3070 Utilization rate % (R) 256050 2040 1530 102010 5 0 0 08Q1 08Q3 09Q1 09Q3 10Q1 10Q3 11Q1 11Q3 12Q1Sources: Equifax, Moody’s Analytics 13
  • 14. Performance trends improving…Delinquencies/defaults, % of $ balances, NSA4.0 30-day3.5 60-day3.0 90-day2.5 120-day Default2.01.51.00.50.0 06 07 08 09 10 11 12Sources: Equifax, Moody’s Analytics 14
  • 15. …Across all products...Delinquent, % of $ volume, NSA12 Auto11 Bankcard10 Consumer finance 9 Mortgage 8 7 6 5 4 3 2 06 07 08 09 10 11 12Sources: Equifax, Moody’s Analytics 15
  • 16. …With regional variationBankcard $ delinquency rate, % of outstanding, NSA 1.58 to 2.74 2.75 to 2.99 3.00 to 4.03Sources: Equifax, Moody’s Analytics 16
  • 17. Newer vintages continue to outperformCumulative % of original auto $ balance defaulted or bankrupt12 2007Q1 2007Q310 2008Q1 2008Q3 8 2009Q1 2009Q3 6 2010Q1 2010Q3 4 2011Q1 2 0 1 7 13 19 25 31 37 43 49 55 61 Months since originationSources: Equifax, Moody’s Analytics 17
  • 18. Declines in bankcard default rates to continueBankcard write-offs + bankruptcies (green line)1312 Bankcard write-offs +11 bankruptcies10 default, % of $ 9 8 7 Stress 6 5 Unemployment rate, % 4 Base 3 06 07 08 09 10 11 12 13Sources: Equifax, Moody’s Analytics 18
  • 19. Loss Forecasting In the Real World:Dealing with Limited Data• 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 19
  • 20. CreditForecast.com• 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 20
  • 21. Credit Forecasting Models» Panel data segmented by product, origination Models Consider: vintage, geography (state/metro) and credit score band Life Cycle The age of the» Forecast all performance measures loans – New loan origination volumes – Outstanding balances Vintage Credit Quality and – Delinquency rates, default and bankruptcy State of the economy at rates, prepayment rates origination – Revolving credit utilization – Number of zero-balance accounts Business Cycle Condition of the» Leverage dual-time nature of panel economy every month data for econometric modeling – Moody’s Analytics macro/regional economic data and scenarios – Federal Reserve’s CCAR scenarios 21
  • 22. Example• 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 22
  • 23. No data? Rely on industry data exclusivelyAnnualized $ default rate for auto loans, August 2012 Credit Score >700Sources: Equifax, Moody’s Analytics 23
  • 24. Current profile only? Benchmark with industry data Annualized $ default rate for auto loans, NSA 1.4 US Lender Profile: 1.2 Pennsylvania New Jersey 60% NY 1.0 New York 25% NJ 0.8 Weighted By Lender 15% PA Profile 0.6 0.4 Credit Score 0.2 >700 0.0 06 07 08 09 10 11 12 13 14 Sources: Equifax, Moody’s Analytics 24
  • 25. Some historical data? Calibration model • 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. 25
  • 26. Calibration model exampleAnnualized $ default rate for auto loans, NSA Actual Calibrated Industry (CF) 26
  • 27. Calibration model example – 12 month hold-out Annualized $ write-offs for auto loans, NSA2,0001,800 Actual1,600 Predicted1,400 History1,2001,000 800 600 400 200 0 09 10 11 12 Sources: Equifax, Moody’s Analytics 27

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