Navigating Business Income Losses in Uncertain Times
How claims analysts deal with today’s uncertainty when trying to predict relatively short operational periods, often based on limited, economically impacted or patchy historical results.
December 20, 2010
Evaluating business income claims can be challenging even in times of relative economic stability and prosperity. As forensic accountants, we are typically engaged in complex matters often involving large conglomerates with numerous business segments, multiple revenue drivers and complex overhead allocations and cost structures. In other cases, we are hired to evaluate newer businesses lacking established history or trends. Uncertain economic times add a new level of complexity making difficult cases even more challenging.
As we all know, the past few years have been far from certain or stable. Though many of us might rather forget, I’m sure we all remember the softening of the real estate market, sluggish retail sales, rising unemployment and declining consumer confidence that began in some cases as early as 2006/2007. By 2008, many economists were calling the times recessionary, though there was still much more to come.
The second “Black Monday” in the past century came on September 15, 2008. On this day, Lehman Brothers filed for bankruptcy, Merrill Lynch was taken over by Bank of America and the Dow Jones Industrial Average (DJIA) lost five percent overnight and another 45 percent over the next several months. Although the slide had begun much earlier, from that date forward, all bets were off. The United States (U.S.) entered uncharted waters as the federal government spent nearly $1 trillion to bail out a number of larger corporations that were considered “too big to fail.”
In terms of magnitude, this latest recession is unparalleled in length or depth, surpassed only by the Great Depression of the 1930s. In the housing market, annual new housing starts fell by a record 80 percent from 2006 to 2009. Furthermore, total home sales fell by 35 percent during the same period, while housing values dropped by as much as 50 percent in certain regions.
In addition to the housing market, growth in the Gross Domestic Product (GDP) fell from a high of six percent per annum in 2006 to minus-two percent in 2009. And with the decline in housing and GDP, the labor market began to suffer with unemployment rising from just under five percent in 2006 to over 10 percent in early 2010. Simply put, the nation went through a perfect economic storm, the past three years to four years with virtually no sectors spared. And the worst may not be over yet as economists are now starting to talk about a “double dip” recession later this year.
With this economic stage set, the predictive power of analysts has never been more challenged, particularly in the area of business income claims analysis.
There’s an old saying in our business; “historical performance is often the best indicator of future performance.” This may still hold true in situations measured over very long periods of several years, decades or longer. However, business income losses are generally measured in periods of weeks or months, rarely extending beyond one year. So when analyzing these much shorter time frames, analysts are faced with the very real situation that the only thing certain may be uncertainty itself.
So how do we as claims analysts deal with this uncertainty when trying to predict relatively short operational periods, often based on limited, economically impacted or patchy historical results? Simply put, we have to look beyond the basic financial history. Two of the more important factors to consider are:
When examining how a company is impacted by the overall economy, we are mostly concerned with gross revenue or sales. The determination of how a company’s sales are affected by changes in the external environment is commonly referred to as sensitivity or correlation analysis. In broader economic terms, the concept is often referred to as the elasticity of demand. The elasticity of demand is an economic principle that gauges the sensitivity of a subject to changes in price or other economic conditions. As a general rule, appliances, cars, confectionary and other nonessentials show elasticity of demand whereas most necessities (basic food, medicine, essential clothing, etc.) show relative inelasticity of demand.
As you might imagine, the level of sensitivity to changing economic conditions is not always intuitively obvious. Certain segments of the entertainment industry often perform better in a down economy, such as movie theatres and some casinos and gaming establishments. Conversely, other businesses that would seemingly be adversely affected or neutral may behave quite differently when put to the test. As such, the presence or degree of sensitivity cannot be assumed or estimated; but rather, must be quantified in clear and supportable terms. Although sensitivity can be measured a number of ways, one of the more commonly used methods is correlation analysis.
By way of example, we recently worked on a business income loss for a retail auto parts store in Southern California. At the onset of our review, it was thought the business might likely be unaffected by the economic downturn or that it may even perform better or be counter-cyclical. This preliminary and somewhat intuitive thinking seemed reasonable as consumers might make more auto repairs themselves rather than face steep repair fees or, worse, the costly purchase of a new automobile.
However, based on our review and analysis, we found that this auto-parts store was, in fact, acutely impacted by the economic downturn beginning precisely in September 2008 and continuing through mid-2010. Although it is difficult to pinpoint exactly why the business behaved this way, we noted that the subject company was located in a smaller community that was deeply impacted by the economic downturn. Additionally, research also indicated that new car financing and purchase incentives, such as: “Cash for Clunkers,” zero-money down, zero- or very low-interest rates and delayed payment start dates sometimes entice consumers to purchase new autos rather than repair old.
As a result of our research and analysis, we concluded that this business had suffered a significant downturn in sales that closely paralleled the overall condition of the domestic economy. Knowing and understanding this correlation was very helpful in developing our sales projection for the several-month loss period in 2009/2010.
Another interesting factor we have recently encountered in these times is that various segments of the same industry may react quite differently to changing economic conditions. For example, in the restaurant industry, the fast-food segment often behaves very differently than the casual-dining or fine-dining segments. Typically, casual dining parallels the economy closely, while fast food is often inversely affected. Fine dining is generally the more stable of the three (or somewhat inelastic) as it relies largely on higher-wealth individuals, business entertainment and corporate travel.
In addition to measuring financial sensitivity, it is also important to understand where the subject company lies in the economic timeline or cycle. This requires the analyst to determine whether the particular company or industry has historically followed the leading edge, middle line or trailing edge of economic cycles. If the company or industry is fairly new, this may require further analysis to find similarly situated companies or comparable industries. Similar to sensitivity described above, time series correlation is often used to determine where a company lies in the economic cycle.
Based on our experience and research, semi-conductors and certain other high-technology sectors typically populate the leading edge of the economic cycle, often used as leading indicators by those forecasting the future economic climate. Conversely, businesses with long-service timelines, such as insurance companies or law firms, often find themselves at the trailing edge of economic cycles. And of course, somewhere in the middle lies the bulk of the domestic economy, comprising largely commodity manufacturing, retail sales and service.
Knowing where a business falls in the economic timeline can be essential in predicting future results, particularly in prolonged downturns similar to that experienced these past years. Although the overall economy might finally be showing signs of recovery, businesses that lie on the trailing edge of the economy may not see the light for some time. Conversely, leading edge companies might already be showing signs of recovery, making an upward trend factor more appropriate and reasonable for business income losses occurring this year.
Once we understand how a business is likely to behave in these uncertain times, we can then relate the subject to relevant external metrics. This approach is often referred to as “Yard Sticking” or “Bench Marking.” The yard-stick approach essentially involves finding an external point of comparison that has typically correlated well with the subject company. These external points of comparison could come from a number of potential sources, including: publicly-traded companies, industry or market indexes, general economic metrics or indicators, trade commodities or even consumption metrics such as gasoline prices. The more suitable the match, the more predictive power in the projection.
The subject of navigating income-loss claims, particularly in these trying times, is admittedly a large one. Therefore, while we have outlined some of the factors considered and techniques used by forensic accountants, other considerations and methods may be appropriate, depending on the facts and circumstances of the case. However, regardless of what specific techniques or methods are used, the important point to realize is that history is not necessarily the best indicator of future performance, particularly in these uncertain times.
Nathan P. Weber, CPA, CFF is a senior manager at RGL Forensics in San Diego. He can be reached at 619-236-0377.