Understanding the Financial Crisis through Decomposition of Retail Lending Portfolio Performance

Transcription

1 Understanding the Financial Crisis through Decomposition of Retail Lending Portfolio Performance Interthinx, Inc All rights reserved. Interthinx is a registered trademark of Verisk Analytics. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission. The information contained within should not be construed as a recommendation by Interthinx, Inc. or Verisk Analytics for any course of action regarding financial, legal or accounting matters. 1

2 Executive Summary The recent financial crisis resulted in the loss of billions of dollars of home equity, millions of jobs and hundreds of banks. Even as the nation embarks on a slow recovery, it is important to understand the development and progression of this crisis, so that more effective regulatory requirements and portfolio management practices can help prevent similar occurrences in the future. The consumer lending aspect of the recent financial crisis is studied through the decomposition of retail lending portfolio performance into key time-based components along dimensions of origination date (vintage quality), calendar time (exogenous) and age (maturation), and shows that: The origination quality for vintages of non-agency mortgages started deteriorating in 2005 in the midst of a stable economy with rising home prices and low unemployment rates. The worst non-agency mortgages were originated in , while the worst agency mortgages are seen as late as 2008, as consumers turned to agency mortgages when the non-agency market essentially disappeared. Bank-issued credit cards and auto loans also saw deterioration in quality but to a much smaller extent, due in part to a change in the payment hierarchy. The exogenous environment which captures economic trends, seasonality, and policy changes began to deteriorate in mid-2006 as house prices stopped increasing, and the deterioration continued through early 2009 in concert with falling home prices and rising unemployment. The deteriorating environment exacerbated the already poor performance of the lower quality vintages. Long lags in the natural maturation of default likelihood in mortgage loans masked deterioration in originations quality for years prior to the industry downturn. Analyzing the financial crisis through the lens of time-series decomposition also reveals broad insights into more effective portfolio management practices that can be used going forward. These include: Distinguishing between the components driving portfolio performance is essential to making the correct choices in managing the portfolio. Vintage quality should be measured directly rather than relying on proxies such as traditional estimates of creditworthiness which can be misleading during periods of high growth and economic change. Changes in originations have long term consequences for the portfolio while changes in policies related to line management or collections can have immediate effects. Lags in performance relative to originations need to be understood and accounted for because they delay the feedback needed to assess strategy effectiveness. 2

3 Overview The last decade saw a seemingly robust economy with strong GDP growth, record high house prices, and low unemployment in the US transform virtually overnight into a recessionary economy with millions of borrowers underwater, and record high unemployment. Figure 1 provides an annotated timeline of the changes in the House Price Index (HPI) and the Unemployment rate over the last nine years. Lending activity ground to a stand-still as millions of defaulting consumers led to the failure of hundreds of financial institutions. The names (some appropriate, some not) used to describe these events such as mortgage meltdown, subprime crisis and liquidity crisis reflect that retail lending portfolios lay at the heart of this transformation. Figure 1: House Price Index and Unemployment Rate House Price Index (Red) House Price Index (Red) June 2007: HPI at all time high Late 2006/Early 2007: Unemployment Rate as low as 4.4% Oct 2009: Unemployment Rate at 1 January 2011: HPI 2 lower than at peak % 1 8% 6% 4% Unemployment Unemployment Rate Rate (Green) (Green) Extracting insights from retail lending portfolios is complicated by the nonlinear interaction of factors on different time scales. New loans behave differently than seasoned loans; loans that originated at different points in time may exhibit different credit risks due to changes in originations policy or selection effects in the marketplace; borrowers are impacted by environmental conditions such as seasonal factors, policy or regulatory changes, and economic conditions independently of when they originated their loan. At a high level, these effects can be described as maturation (age-based), exogenous (time-based), and vintage quality (based on origination date). Gaining a greater understanding of these independent time-based performance drivers can enable lenders to take appropriate action in a timely manner to mitigate future losses. Standardizing the measures of performance across retail portfolios illustrates the development and progression of the financial crisis in a consistent manner, leading to insights for better portfolio management. 3

4 Performance Analysis Methodology Interthinx s patented Dual-time Dynamics (DtD) technology employs a two-stage decomposition of historical portfolio performance that is illustrated in Figure 2. The first stage is a nonlinear decomposition into three independent effects along the dimensions of age, time and origination date. The age-based effect is referred to as maturation and quantifies the natural performance of loans as they mature, independent of factors due to the environment and the date of origination. The maturation curve is an age-based series representing idealized performance for all vintages as they age. The effect measured by origination date is referred to as vintage quality and it quantifies the change in performance relative to the maturation curve for accounts that originated on a particular date. Changes in quality are typically due to changes in underwriting criteria or market selection. There is one measure of quality for each vintage. The time-based effect is referred to as the exogenous effect and quantifies the change in performance relative to the maturation curve due to time-varying conditions such as seasonality, policy changes and macroeconomic effects. The exogenous curve undergoes a second stage of decomposition to separate out these factors using a linear decomposition. After subtracting seasonality and discrete impacts due to policy changes or other one-time events the remaining time-varying residual contains macroeconomic structure and random noise. 4

5 1.2% 1. Figure 2: Decomposition of Performance Time Series Rate of Account Flow to 60 DPD 0.8% 0.6% 0.4% 0.2% Months on Books Stage 1: Non-linear Decomposition Rate of Account Flow to 60 DPD 0.8% 0.6% 0.4% 0.2% Vintage, Exogenous Trend, Impact Months on Books Stage 2: Linear Decomposition Seasonality, Impact Exogenous Trend (Seasonality removed), Impact 5

6 Analysis Monthly performance data from July 2005 through March 2012 for all non-agency and agency mortgages, bank-issued credit cards and auto loans originated in the US between Q and Q shows dramatic changes in originations quality ahead of the change in economic conditions. Segmentation by origination score band (super-prime, prime, nonprime, and sub-prime) and the nine Census Divisions provides insight into the variation of lending dynamics along these dimensions. The non-agency mortgage securitization market grew rapidly in the early 2000s with the origination of a large volume of high quality mortgages. By 2005, the non-agency mortgage segment accounted for nearly two trillion dollars in originations a larger share than the combination of agency mortgages, bank-issued credit cards and auto loans. The first sign of deterioration was seen in vintage quality. Mortgage lenders, emboldened by a robust secondary market, began to loosen their underwriting standards and also moved to alternate products (option ARMs, Interest Only, Negative Amortization, Picka-payment etc.) in an attempt to maintain the high origination volume that had become the norm. Additionally, consumers were more willing to take on risk as house prices continued their ascent to record highs in mid Interest rates increased by over one percentage point between mid 2005 and mid 2006, pushing more conservative consumers to the sidelines. The combination of these trends resulted in a steep deterioration in vintage quality throughout the latter half of 2005 and Although vintage quality began to improve in 2007, it remained relatively poor until These delinquency trends are shown in Figure 3a and throughout the rest of this paper in terms of flow rates to 60 days past due (DPD). Origination volumes remained high during most of this time, Figure 3b, resulting in the origination of large volumes of poor quality mortgages. Figure 3: (a) Vintage and (b) Volume Trends for Mortgages Vintage, Change in Delinquency Originations, in Millions (Red) 75% 5 25% -25% Super-Prime Proportion (Green) 6

7 Figure 4 shows that the deterioration in quality started in a strong economic environment. The deterioration in quality seen in mid 2005 came at a time when house prices were still rising at around 10 percent a year, and unemployment rates were less than 5 percent and decreasing at around 5 percent a year. The exogenous trend prior to mid 2006 cannot be calculated with the available data, but since changes in house prices and employment rates two major drivers of the economic environment are stable, the exogenous trend during the time is also likely to be stable. This assumption is indicated by the purple dashed line in Figure 4a. The economic environment started to deteriorate in mid 2006 as house prices stopped increasing, preventing troubled borrowers from refinancing, and this deterioration continued unabated through early 2009 as falling house prices and increasing unemployment rates fueled negative outlooks. This deterioration in the exogenous environment between 2007 and 2009 exacerbated the problems of the poor quality vintages originated in Figure 4: (a) Vintage and Exogenous Trends (with Seasonality Component Removed) for Mortgages, (b) Year-over-year changes in HPI and Unemployment Rate Exogenous Trend: Change in Delinquency (Purple) HPI YoY% Change (Green) % 1 5% -5% Impact Vintage : Change in Delinquency (Blue) Unemployment Rate YoY% Change (Red) 7

8 It is important to distinguish between originations quality and the exogenous environment particularly when the trends for the two diverge in order to take appropriate management action. For instance, if the deteriorating exogenous environment in the timeframe was mistakenly attributed to origination quality, a lender may have responded by making their origination strategies more conservative rather than managing their exposure to existing loans. As a result, they would have missed out on high quality originations while losses from their existing portfolio would continue to grow unabated. Starting in early 2007, there was a marked increase in the proportion of super-prime originations, as indicated by the green line in Figure 3b. However, moving the mix of originations to higher credit scores was less beneficial than may have been expected because the deterioration in quality was by far the worst for the super-prime originations as shown in Figure 5. The large deterioration for super-prime mortgages is likely due to high scoring consumers being subjected to less stringent due diligence on qualifying aspects such as appraisal review, and income and asset verification. The increased deterioration at high credit scores highlights the value of monitoring quality directly. While credit scores can be a good proxy for quality, when other factors such as the depreciation of property value become strong drivers of delinquency the similarity between quality and credit scores can decrease. Figure 5: Vintage Trends for Mortgages by Score Band Vintage, Vintage Change, in Delinquency Sub-prime Non-prime Prime Super-prime There is a large regional variation in the deterioration of vintage quality in the timeframe. The regions that experienced the greatest deterioration include the Pacific Census Division which is dominated by California (shown in red in Figure 6), the Mountain Census Division that contains Nevada and Arizona, and the South Atlantic Census Division which contains Florida, Figure 7. In hindsight, this seems obvious as these states are poster children for the mortgage crisis with the largest house price declines and foreclosure rates. However, knowing this information in early 2006 when their economies were booming would have enabled national lenders to shift their originations to more stable regions. The regions that deteriorated the least include the West South Central Census Division which is dominated by Texas (shown in green in Figure 6), and the East South Central and West North Central Census Divisions. 8

9 15 Figure 6: Vintage Trends for Mortgages by Census Division Vintage : Change in Delinquency Pacific Census Division West South Central Census Division Census Regions and Divisions of the United States WA WEST MIDWEST NORTHEAST ME Figure 7: Worst Vintage for Mortgages by Census Division PACIFIC CA OR PACIFIC NV AK ID UT AZ MT WY MOUNTAIN CO NM ND SD NE TX KS OK MN WEST NORTH CENTRAL WEST SOUTH CENTRAL IA MO AR LA WI IL IN MI EAST NORTH CENTRAL MS AL KY EAST SOUTH CENTRAL SOUTH OH GA WV SC FL DC VA PA MD SOUTH ATLANTIC TN NC NY MIDDLE ATLANTIC DE NEW ENGLAND VT NH MA NJ CT RI Miles PACIFIC CENSUS DIVISION WORST VINTAGE QUALITY, CHANGE IN DELINQUENCY HI Miles PACIFIC 117% MOUNTAIN 94% SOUTH ATLANTIC 77% MID ATLANTIC 65% NEW ENGLAND 62% EAST NORTH CENTRAL 61% WEST NORTH CENTRAL 35% EAST SOUTH CENTRAL 2 WEST SOUTH CENTRAL 18% 9

10 All consumer lending products show a lag between originations and natural peaks in default. Mortgage loans show the longest lags, making it difficult to gather feedback on strategy effectiveness. The poor quality originations from the timeframe did not hit their peak in delinquency until early 2010, by which time the exogenous environment was beginning to mend, Figure 8. The reason for the delayed manifestation of high delinquency is the maturation process that loans go through: they start with low delinquency rates soon after origination, and these rates increase sharply at first and more gradually later on, as shown by risk band and product type in Figure 9. The lag between origination and high delinquency does provide a window in which a lender could sell off poor quality loans, however if the loans stay on the books, the losses are delayed but still inevitable. Figure 8: Delinquency Rate for Mortgages Vintage (Blue) and Exogenous Trend (Purple): Vintage (Blue) and Exogenous Trend (Purple): % 6% 4% 2% 60+ Delinquency Rate (Green) 60+ Delinquency Rate (Green) Figure 9: Account Flow to 60 DPD Maturation Trends for Mortgages by (a) Score Band and (b) Product Rate of Account Flow to Rate 60 DPD of Account Flow to 60 DPD Rate of Account Flow Rate to 60 DPD of Account Flow to 60 DPD 5% 4% 3% 2% 1% 12% 1 8% 6% 4% 2% Sub-prime Non-prime Prime Super-prime Fixed Rate Mortgages ARM (Initial Reset < 5 years) ARM (Initial Reset >= 5 years) Months on Books Months on Books 10

11 The lag associated with the loan maturation process has further important implications. Due to this lag, changes in originations have long-term consequences for the portfolio, whereas changes in policies related to line management or collections can have immediate effects. Also, it is important to account for the lack of immediate feedback created by this lag when assessing the effects of origination strategies, as otherwise an effective strategy can be discarded before enough time passes to reap the benefits. This provides another compelling reason for tracking vintage quality which can be assessed with confidence within a few months (rather than a few years) of origination. Coming out of the financial crisis, the near-term outlook for non-agency mortgages is generally optimistic. Vintage quality improved dramatically between 2007 and 2009, and these quality improvements have held steady going forward, albeit on a much lower volume (Figure 3). The exogenous environment started improving in early 2009 (Figure 4) and continues to do so as house prices continue to stabilize. Unemployment rates have started to fall, and decreasing interest rates have allowed many borrowers to refinance into lower payments. Within this picture of general health, there are a few warning signs that should be noted. In particular, a close look at Figure 5 shows that the quality of sub-prime mortgages has been on a deteriorating trend since early The worst agency mortgage vintages were originated in late 2007 and again after a brief respite in early 2008 associated with a refinancing boom in late 2008, over a year later than when the worst non-agency mortgages were originated. This may have been due to consumers who couldn t get non-agency mortgages, because of tightening lending standards, turning to agency mortgages instead. We have already seen that despite the misnomer of sub-prime crisis the deterioration in loan quality occurred across all credit score bands. Similarly, despite being thought of as purely a mortgage crisis, the deterioration in quality was seen across a number of lending products. Figure 10 shows the change in quality across auto loans, credit cards and mortgages. The deterioration for both agency and non-agency mortgages is much greater than for credit cards and auto loans, and this trend is likely indicative of a change in payment hierarchy, where consumers who traditionally gave their mortgage payments priority changed to making their other debt payments ahead of the mortgage in order to maintain cash flow. 75% 5 Auto Loans Credit Cards Figure 10: Vintage Trends by Product Vintage 25% -25% -5-75% Agency Mortgages Mortgages

12 Conclusions Examining the financial crisis through the lens of time-series decomposition of retail lending portfolio performance sheds light on a number of insights pertaining to portfolio analysis and management practices: Originations quality and the exogenous environment trends are independent of each other. It is important to distinguish between the two, particularly in periods where they diverge, as it may otherwise result in ineffective or even inappropriate action. For instance, if the deteriorating exogenous environment in the timeframe was mistakenly attributed to origination quality, a lender may have responded by making their origination strategies more conservative rather than managing their exposure to existing loans, with the result that they would miss out on high quality originations, while losses from their existing portfolio would continue to grow. Credit scores alone do not illustrate loan quality. The deterioration of vintage quality across all credit score bands with the greatest deterioration occurring for super-prime originations highlights the value of monitoring quality directly. When other factors such as adverse selection or the depreciation of property value became strong drivers of delinquency, quality provides a more effective measure than credit scores. Changes in originations have long-term consequences. There is a lag between origination and peak delinquency due to the loan maturation process. As a result, changes in originations have long term consequences for the portfolio while changes in policies related to line management or collections can have immediate effects. Origination strategies need to account for the maturation lag. It is important to account for the lack of immediate feedback created by the lag between originations and high delinquency when assessing the effects of origination strategies, as otherwise an effective strategy can be discarded before enough time passes to reap the benefits. This lag also provides another compelling reason for tracking decomposed vintage quality which can be assessed with confidence within a few months (rather than a few years) of origination. The analysis of retail lending portfolio performance using time-series decomposition can predict how much of future losses will come from the quality of underwriting, age of the loans and economic environmental impacts. This improved approach to forecasting and stress testing portfolios against separate originations strategies and economic factors will help lenders determine what percentage of risk is within their control and what is not. Further, more accurate forecasting models that benchmark performance against industry data and stress test under different economic scenarios will help guard against future financial crises. References House Price Index data from Federal Housing Finance Agency. Unemployment Rate data for the Bureau of Labor Statistics. 12

13 About Interthinx Interthinx, a Verisk Analytics (Nasdaq:VRSK) subsidiary, is a leading national provider of comprehensive risk mitigation solutions focusing on mortgage fraud, collateral risk and valuation, regulatory compliance, forensic loan audit services, loss mitigation, and loss forecasting. With more than 20 years of experience in customizable risk evaluation technology, Interthinx offers proven and effective predictive analytics to the residential mortgage industry through its experience with millions of loan applications and fraud incident data from thousands of monthly loan reviews. Throughout the mortgage life cycle, the Interthinx suite of services can increase the value of client portfolios with its comprehensive and holistic approach to loan quality and compliance. Winner of multiple awards for technology, Interthinx helps clients reduce risk, increase operational efficiencies, satisfy regulator demands, manage data verification, remain compliant, and mitigate loan buybacks. For more information, visit or call