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Measure Risk to Manage Risk
“Not everything that can be counted counts… and not everything that counts can be counted.”
— Albert Einstein (1879 – 1955)
Introduction
Community banks find that they are increasingly affected by the practices of major financial institutions and the capital markets. The market’s demand for both equity returns and growth has driven consolidation along with pricing and spread compression. Community banks have reacted by seeking growth, principally from loans in the commercial real estate (CRE) segment. Banks with assets of between $1 billion and $10 billion have seen CRE loans grow from 28% of portfolio to 42%, while concentration levels as a multiple of risk capital have soared.1 At the same time, the growth in secondary markets for financial assets has introduced more liquidity, more standardization, and more sophisticated measurement and management of risk and return.
Bank and other regulators are acutely aware of these trends and, for many years, have been developing and refining systematic responses to ensure the safety of the global financial markets against shocks and event risks like those of the recent past. By enhancing the “state of the possible,” advances in quantitative credit-risk analysis have been at the forefront in the transformation of the capital markets. As one observer noted, “…[T]here has been a revolution in the science of credit risk measurement… Advances in analytics will continue to… [lead] to a significant reappraisal of the wholesale lending process.”2
Regulatory Environment
While the focus has been on the larger commercial banks, it will soon become imperative for independent and community banks to embrace these developments. Indeed, the inter-agency guidance issued by regulators in December 20063 focuses directly on the systematic management of risk in banks’ CRE portfolios. The guidance recognizes community banks’ distinctive competence in real estate lending and does not require specific limits on CRE assets; it does require that institutions with CRE concentrations implement “heightened risk-management practices."4 Best practices in risk management of CRE portfolios are now expected; and that means measuring the credit risks and appropriately allocating capital to manage those risks.
Measuring the Risk
The first step in applying best practices is gaining an understanding of credit risk metrics. The most basic level of risk measurement requires knowing two things: the risk one “expects,” which may be priced and reserved for, and the risk of volatility—“unexpected risk”—that capital must cover. The “priced” risk must be estimated at origination; the ongoing risk in a loan or portfolio may be measured by stress testing. Both benefit from a more analytical approach as a best practice.
The traditional approach to the credit function has been to evaluate the creditworthiness of prospective borrowers using analysis of standard financial statements and other historical data, such as payment history, reputation reports, and other external data, including agency debt ratings if available. The analysis and judgment based on experience combine for a “go/no-go” decision and often a “risk-rating” of a borrower (i.e., categorizing the borrower into a ranking represented by a numerical or alphabetic grading scheme from best to worst credits. Often the assigned risk rating also includes a judgment based on “qualitative” factors and assessment of the value of collateral, especially for CRE subportfolios. It is common practice that, once risk-rated, a credit retains its assigned credit rating unless there is a payment problem or default.
Best practices in measuring risk rely on a dual risk approach: the risk that the obligor will default on the loan is measured separately from the risk of loss, or inadequate recovery of loan principal from the collateral or other claims on the borrower due to the structure and terms of the facility. These are quite distinct risks, and proper risk management requires that they be distinctly measured. The obligor risk is the Probability of Default (PD) and the facility risk is the Loss Given Default (LGD).
The key characteristic of the traditional approach is the reliance on historical data, usually accounting statements. There is nothing wrong with this and, indeed, historical accounting-based information on a borrower’s financial condition and performance is essential for proper credit evaluation. However, by their nature, accounting statements and other historical data provide information about the past. The task of credit evaluation, on the other hand, is to estimate the future ability and willingness of a borrower to meet its obligations.
Quantitative credit default models use the advances in theory and the availability of data and data-processing technology to quantify credit default risk in a particularly useful way. Not only are historical accounting data used, but industry data and macroeconomic measures are included. In contrast to accounting measures, many of these are market measures and inherently forward-looking estimates that are highly correlated with the default rate—exactly the feature desired for measuring credit-default risk. For example, Standard & Poor’s Credit Risk Tracker™ North America model, which is based on financial and market data on 17,000 private companies and with over six million data points, has the best predictive record of any private firm, credit-default model.5
Default Probability
A firm’s PD is the probability that it will default within a given time horizon, typically one year. Because the methodology for calculating PD precisely calibrates the calculated measure to the actual observed default behavior of firms in the CRT™, it is both highly reliable and an actual default probability, not simply a “ranking” system. PD measures provide a continuous spectrum of default risk, which can be mapped to any internal risk-rating system. Figure 1 below displays the Estimated Default Frequency (EDF) measure on a logarithmic scale calibrated to S & P’s public debt ratings, a color-coded “go/no-go” ranking system and a typical bank-internal, risk-rating system.
Figure 1. Probability of Default Scale.
Clearly, the PD measure provides a degree of precision not found in the conventional risk-rating approach. The “ordinal” rating systems order credits in terms of relative risk, but the PD displays precisely how much
difference in credit quality exists between two credits. It is apparent, for example, that a risk-rating 4 credit, typically a BB equivalent, with a PD of approximately .8% is more than three times as risky as a risk-rating 3, BBB- equivalent at about .25% PD. Moreover, a major issue for most bank-internal risk-rating systems is the “lumpiness” in risk discrimination that they exhibit. It is not uncommon for these systems to have as much as 75% of the portfolio graded into only two risk-rating categories, usually the middle of the “pass” range of risk grades, say, grades 3 and 4 as shown above in Figure 1. Measuring the risk more precisely allows the bank a much more granular structure of risk discrimination, permitting more accurate provisioning and pricing.
Risk-based Provisioning
There are several other valuable features of market-based approaches to determining PD (e.g., they are objective, transparent, and consistent across business lines and geographies). Perhaps the most important is the opportunity to institute risk-based provisioning which accurately reflects the risk associated with each transaction.
Accurate provisioning requires the use of a precise measure of Expected Loss (EL) to calculate the bank’s Allowance for Loan and Lease Losses (ALLL) as the basis for management’s judgment about what reserves to take. The basic formula for EL on a loan is simply:
EL = PD * EAD * LGD
where:
PD = Probably of Default
EAD = Exposure at Default (Unamortized Balance)
LGD = Loss Given Default (%)
For example, a borrower with a PD of .625%, a risk rating 4 or equivalent to a BB/BB- credit, with a facility having an expected LGD of $.50 on the dollar of EAD would have an EL of:
.625% x 100 x 50% = .3125% = 31.25 basis points.
Here the bank’s “local knowledge” of real estate as collateral is important in assessing LGD and ensuring a comfort-level with recovery estimates and workout prospects. It is obviously a critical element in the calculation of EL. These simple analytics can be used to optimize loan loss reserves on a portfolio or subportfolio basis, using the community bank’s data and experience to estimate the LGD and calculate the EL metric. A more realistic ALLL is then likely; and assets are more effectively syndicated, if necessary, with a consistent and objective basis for determining and pricing risk.
In addition, they can also be used to screen existing subportfolios to determine the degree of any “mispricing,” (i.e., the extent to which the pricing structure does not, in fact, reflect risk). Figure 2 below shows the results of an analysis of actual pricing compared to calculated risk-based pricing.6 The pricing spread achieving the bank’s hurdle rate is indexed
Figure 2. Actual vs. Risk-based Pricing.
as “zero” with the stated spread and the “risk-adjusted” spread netted against the hurdle-rate spread. This type of analysis will reveal possible pricing opportunities where better credits are “overpriced” and worse credits “underpriced” on a risk-adjusted basis. Syndication or securitization decisions can be made for assets that may be dragging down portfolio risk/return dynamics, and funding costs can, thereby, be optimized as well.
Stress Testing and Risk Migration
One basic method of measuring the “unexpected” risk in a portfolio is stress testing. Stress testing is a sensitivity analysis—modeling the behavior of a portfolio or of individual obligors systematically as assumptions about key performance indicators are changed to reflect extreme “shocks.” For CRE portfolios, these include such “macro” factors as the level of interest rates, the capitalization rate of rentals/net operating income (NOI), regional vacancy rates, and general economic conditions.7 In turn, these factors affect PD and of LGD and, ultimately, EL in an extreme scenario—one that could affect the institution’s capital. Figure 3 below is a schematic of the stress-testing process.
The output of the stress test is an analysis of the change in the portfolio’s overall risk—its risk migration—as measured by a change in its EL. Risk migration measures the “drift” in a portfolio’s overall PD as some obligors remain as originally risk-rated while others deteriorate (and a few improve or fully amortize). There is an “expected” drift, but more importantly, the risk migration from shocks is a key to capital adequacy measurement. Additionally, those subportfolios, or concentrations of obligors and even individual obligors with the potential to react adversely in the greatest magnitude to extreme or “unexpected” changes in “macro” factors, may be identified for monitoring or other active risk management.
Figure 3. Stress Testing.
This ability to measure risk migration is the key to satisfying the regulatory agencies’ concerns that community banks have an “effective framework” for managing the concentration of CRE assets in their portfolios. Management can demonstrate not only that current provisioning is adequate for the actual risks in the portfolio—as actually measured objectively, transparently, and consistently—but also that capital adequacy covers the portfolio even in a “stressed” economic environment.
Conclusion
The use of the new risk-management technologies by community banks with substantial CRE portfolio concentrations will certainly help satisfy regulators’ concerns. But in addition to the meeting the challenge of increased scrutiny around CRE concentrations, there is an opportunity as well. The adoption of advanced risk metrics developed in recent years and increasingly used by banks and other financial institutions provides the link between credit risk and market pricing, leading to potentially superior performance. The community banks, which likely have superior asset knowledge, may be especially well suited to introduce risk-based pricing and provisioning. They may introduce the technology in stages and move toward portfolio management practices, replacing the traditional “originate-and-hold” model, at least in part, with an “underwrite-and-distribute” model. The payoff is likely to be substantial: “Aggregate capital requirements are expected to drop for those firms that apply advanced measurement approaches… to credit risks. Through this reduction, capital costs will also be reduced, bringing increased profit margins… better priced products, and market share.”8
© 2007 Inmatrix, Inc All rights reserved.
Robert F. Cunningham
June 2007
References
1 The New Commercial Real Estate Guidance, American Bankers Association, 2007, p.4.
2 “Active Credit Portfolio Management,” Andrew Kuritzes, Erisk, July 1, 1998.
3 Concentrations in Commercial Real Estate Lending, Sound Risk Management Practices, December 12, 2006; OTS, December 14, 2006.
4 Ibid. Section II.
5 Credit Risk Tracker™ North America Technical Documentation, Standard & Poor’s, September 2006, p. 5.
6 “The Basel II Accord and Risk-Based Pricing,” Equipment Lease and Finance Foundation, February 23, 2004, p.21.
7 The New Commercial Real Estate Guidance, American Bankers Association, 2007, p.10.
8 “Turning Basel II Compliance Into a Competitive Advantage,” Christopher McLaughlin, Bank Technology News, Vol. 17, No. 8 (August 2004), p. 48.
By Robert F. Cunningham.
Robert F. Cunningham is senior vice president, Client Services at Inmatrix Inc. Mr. Cunningham leads risk consulting for Inmatrix clients, applying the company’s analytical products to the development of superior risk metrics, primarily for community banks and the SME business segment. Most recently, he led Avalon Analytics, a consultancy he founded to assess risk and pricing issues in commercial finance companies; he worked principally with Moody’s/KMV and a regional bank.
Mr. Cunningham has over 20 years experience in corporate finance at major financial institutions, including PruCapital, Citicorp, and Keybank, for both small-ticket “program” and large-ticket structured transactions. His background includes roles in origination, structuring, and credit and risk analysis, with extensive experience in pricing and negotiating complex asset-based programs.
Mr. Cunningham received a Bachelor of Arts cum laude from Dartmouth College and a Masters in Business Administration from Harvard Business School.
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