May 24, 2021
When measuring a business interruption loss, the policy instructs the forensic accountant/claim preparer to consider the historical experience of the business and the probable experience had the loss not occurred. For most losses, the historical experience of the business may yield a realistic view of what would have occurred, “but-for” the loss. However, when there has been a significant change in the market and/or business, claim preparers must seek alternative bases to consider the change in their loss measurement. By using statistical analysis of related indices, we can accurately measure complicated losses in compliance with the terms and conditions of the insurance policy in force.
Following an insurable peril, an important step in the claims process is to estimate what would have happened “but-for” the event. Business interruption claims can be complicated and resource consuming. To reduce the burden, policyholders often hire forensic accountants to help measure their losses and support their recovery while providing guidance in the claims process. Many policies reimburse the policyholder for claim preparation (professional) fees incurred to prepare their claim. In many instances, one can utilize pre-loss metrics and financial performance to project “normal” performance during the loss period. There are instances, however, in which past performance is not representative of what would have happened, “but-for” the loss. In these cases, a historical approach might not be appropriate, and a different approach should be considered. This article will focus on how best to consider business changes and non-loss related changing market dynamics when measuring a business interruption claim.
Nearly all insurance policies with time element coverage state some variation of the following language:
In determining the amount of time element loss as insured by this policy, the experience of the business before the occurrence and the probable experience thereafter had no such occurrence occurred shall be considered.
Some policies also specify that the change in demand caused by the event itself should be considered in the projection of “normal,” such as impacts resulting from widespread events like hurricanes, earthquakes, and other acts of God. For this article, the discussion is limited to changing market conditions that are not impacted by the loss itself but occur before, during, or after a separate insurable event.
As stated in the policy language above, the business interruption claim should consider the prior experience of the business and the likely experience during the loss, which would include any changes to the business or market. For many businesses and loss scenarios, the prior experience of the business may be a proper indicator of the probable experience thereafter. However, when a business change or market change has occurred, it may result in materially different expectations for the probable experience thereafter. The historical performance of the business will not necessarily equal the probable experience during the loss period, because a myriad of factors may cause differences.
In one example, consider a company that had recently engaged with a new and significant customer and was set to begin shipping product the month after a loss occurred. By looking only at historical performance, the resulting projection would not include this new customer, as illustrated below. This would understate the projected revenue and the true impact of the loss.
Alternatively, imagine the same company suffered a loss prior to a pre-planned extensive expansion project that would have curtailed production by 50% for two months. It is unlikely that a projection based solely upon historical performance would properly consider this planned partial shutdown. In this scenario, using historical performance as a basis for the projection would overstate the loss.
In addition to company specific considerations which might impact the probable experience had no loss occurred, there are situations when changing market dynamics may impact the “but-for” loss projection.
In the normal course of business, some industries rely heavily on current market dynamics and industry indices in the operation of their business. For example, in the petrochemical industry, companies rely on current market data as a basis for establishing contract pricing, demand planning and other functions. Industry indices are an integral tool in how companies manage their businesses. Therefore, when measuring a loss in a particular industry, simply utilizing historical operating performance likely would not provide an accurate depiction of a forward-looking projection.
In other instances, a market movement may impact most or all industries. Some examples of significant market changes include the post-September 11, 2001 market decline, the financial crisis of 2008 and eventual recovery, and the COVID-19 pandemic. This article is focused on measuring losses stemming from discrete incidents occurring before, during or after market changes that influenced the industry, not the market changes themselves. In these examples, the market changes may cause both positive and negative impacts to a business, depending on the industry. As such, the calculation of damages stemming from losses occurring before, during or after these events must consider the effect of the current market conditions on the business, had no loss occurred.
In the next example, the table below displays the year-over-year growth of a “fast-casual” restaurant as of February 2020. Assume that on March 1, 2020, this restaurant suffered a significant fire that would result in its closure through October 2020.
Based upon the historical performance of the business, one would likely conclude that for March 2020 – October 2020, this restaurant’s revenues would have been above the prior year’s levels, as the green dotted line displays approximately 6% growth on a year-over-year basis.
However, due to COVID-19, in March 2020, many restaurants were forced to close and upon reopening, modified their business model to a drive-thru and/or take-out only model. Due to this type of operational change, a historical view would not provide an accurate projection of what this restaurant’s sales and cost structure would have looked like, “but-for” the fire (loss event).
The graph below demonstrates the year-over-year performance of comparable fast-casual restaurants, both pre and post COVID-19. In this illustration, the comparable restaurants experienced an initial decrease in sales due to temporary closures, as seen below in March and April 2020. However, after April, they experienced an eventual recovery and improvement over pre-loss trends, as consumers flocked to drive-thru and/or take-out restaurants during the pandemic. The experience depicts a departure from the historical experience of the business and industry due to COVID-19, which is the reason that we cannot simply use the loss location’s historical results as a basis for projecting the “but-for” loss scenario.
Changing market conditions can be accounted for using various methodologies, including market indices and, in the case of chain restaurants, benchmarking to comparable locations which have been impacted by the changing market condition but not by a discrete loss event.
Each industry has its own set of indices and/or external market information which can be leveraged to measure performance. Some of the indices that are used in these types of analyses are as follows:
- Smith Travel Research (STR) Reports
- Bureau of Labor Data/Statistics
- Construction Statistics (housing starts, etc.)
- Financial Data (Stock Market, Dow, etc.)
- Commodity Pricing
- Consumer Price Index
- Comparable Locations (if unaffected by the loss)
Regardless of the metric utilized, it is important to demonstrate the relationship between the business and the index or statistic. To do this, one must statistically test the correlation between the two datasets during timeframes that are not impacted by the loss event itself (either historically or post-recovery). Further, one needs to study the magnitude of a change on the index and its relative impact to the business, based on historical market share and causal relationship. While indexing to a market is a generally acceptable projection methodology, one must be mindful of other factors that may cause the business to move differently than the market, such as constraints on capacity and resource availability.
Continuing with the restaurant example above, the below table displays the historical performance of the loss location, as compared to two comparable locations (Locations 1 and 2).
The index is calculated by taking the loss location’s revenue divided by the total of the two comparable locations. As displayed above, the loss location displays consistent performance against the two comparable locations, with an index ranging from 0.5746 (Min) – 0.6159 (Max).
This results in an R-Squared, also referred to as the coefficient of determination, equaling .9719, where 1.0 represents perfect correlation. In this illustration, historically 97.19% of the variability in the loss location’s revenue is explained by the revenues achieved at the comparable locations.
Based upon this relationship, it may be reasonable to utilize the actual performance of the comparable locations from March 2020 – October 2020, multiplied by the historical index factor (in this case, 0.5897 pre-loss index), in order to project revenue for the loss period, thereby factoring in the expected impact of COVID-19 on the loss location.
However, before using this index, it important to understand how the index may have been impacted by the market change or the loss itself. In this example, it is important to ensure that the alternate locations are in close proximity such that they were impacted similarly as the loss location would have been by COVID-19 (same city, state, civil authority actions, etc.) but not too close such that the results were positively or negatively impacted by the fire itself. As an example, if the comparable locations are near the loss location, customers may shift their business to these locations, resulting in mitigation.
Without demonstrating correlation and determining the causal relationship of the index versus the business being analyzed, the resulting projection may be called into question. At times, while an index may seem intuitively correlated to the performance of a company, it may be overly simplistic, and minimal statistical correlations may exist. This may be due to additional factors impacting the performance of the business.
For example, it may seem intuitive to utilize temperature to project the expected number of golfers on a given day, but historically there may be no significant statistical relationship, as other factors can influence a golfer’s decision. For example, on a Saturday in May, the temperature may be a perfect 75 degrees Fahrenheit, but with heavy thunderstorms in the forecast, a golf course may be empty. While temperature may be one factor that figures into a golf course’s daily golf rounds, it may not be the only one. This example illustrates that while there may be strong, positive correlation between two variables, there may be additional variables that should be considered.
Once one has considered the factors above and concluded that the index or indices are correlated with the historical performance of the business, it is possible to use the actual performance of the index during the loss period to project the “but-for” performance of the loss location. Any market conditions impacting the performance of the index should be factored into the resulting projection for the loss location.
When measuring a business interruption loss, the policy instructs the forensic accountant/claim preparer to consider the historical experience of the business and the probable experience had the loss not occurred. For most losses, the historical experience of the business may yield a realistic view of what would have occurred, “but-for” the loss. However, when there has been a significant change in the market and /or business, claim preparers must seek alternative bases to consider the change in their loss measurement. By using statistical analysis of related indices, the forensic accountant/claim preparer can accurately measure complicated losses in compliance with the terms and conditions of the insurance policy in force.