Valuation of Goodwill and Other Intangible Assets

Journal Issue

Jun 2002

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The Financial Accounting Standards Board (FASB) recently issued two new statements
that materially change the financial accounting for merger and acquisition (M&A)
transactions. FASB Statement No. 141 is titled "Business Combinations." Statement
142 is titled "Goodwill and Other Intangible Assets."

Statement 141 was issued to improve the generally accepted accounting principles
(GAAP) financial reporting for business combinations. Under Statement 141, the
pooling-of-interests method of accounting for acquisitions is no longer acceptable. All
corporate M&A business combinations will now have to be accounted for under the
purchase method of accounting. Statement 141 is effective for business combinations
initiated after June 30, 2001.

Statement 142 requires that goodwill acquired in a business combination can no
longer be periodically amortized to earnings; rather, the value of acquired goodwill
must be periodically reviewed for possible impairment charges. The FASB believes that
this GAAP change will allow investors to better understand the true economics of a
company's acquired goodwill. The amortization of acquired goodwill will no longer be
allowed after a company's adoption of this statement. Statement 142 must be adopted
for fiscal years beginning after Dec. 31, 2001. However, Statement 142 does
allow for the periodic amortization of a significant number of discrete intangible assets
acquired in an M&A business combination. Discrete intangible assets are those that
may be (1) identified separately and (2) valued separately from acquired general
goodwill. While Statement 142 identifies many categories of discrete intangible
assets, one category of such a discrete intangible is acquired customer lists and
customer relationships.

Customer/client relationships represent a valuable intangible asset to many industrial
and commercial companies. Customer/client relationships may represent the most valuable
asset-tangible or intangible to many service-oriented companies. The expectation of
periodic business from recurring customers/clients can be a substantial component of the
value of service organizations, such as communications, transportation, pipeline,
utilities and cable TV companies. Accordingly, under the provisions of Statement
141, customer-related intangible assets will now be recorded on the GAAP balance
sheets of acquisitive companies. However, there are numerous other reasons to value
a company's customer-related intangible assets. For example, a liquidating company may
sell its customer relationships to a competitor. A debtor-in-possession (DIP)may
license its customer lists to generate cash. In addition, a debtor company may pledge
its customer relationship as collateral for financing or refinancing.

This article discusses the approaches and methods with respect to the
identification, valuation and remaining-useful-life analysis of customer relationship
intangible assets in the bankruptcy and reorganization process. In particular, we will
discuss the importance of—and analytical methods related to—the remaining useful life of
customer relationships. Finally, we will also present a simple illustrative example of
the valuation of customer/client relationships within a bankruptcy context.

<h3>Identification of Discrete Intangible Assets</h3>

There are various definitions of the term "intangible asset." In a purchase-price
allocation, the analyst may have to perform research to determine if a particular
definition is appropriate to the subject analysis, given the purpose and objective of
the valuation. Obviously, relevant judicial precedent and statutory authority should be
consulted in this research. For purposes of this discussion, we will focus on the
economic (and not the accounting) questions that are relevant to the valuation of
discrete intangible assets. From this economic perspective, there are two fundamental
questions that the analyst should consider:

<ol>
<li>What economic phenomena qualify as discrete intangible assets?

</li><li>What economic phenomena manifest—or are indicative of—value in discrete
intangible assets?
</li></ol>

For a discrete intangible asset to exist from an economic perspective, it should
typically possess certain attributes. Some of the more common attributes include:

<ol>
<li>It should be subject to specific identification and recognizable description.

</li><li>It should be subject to legal existence and protection.

</li><li>It should be subject to the right of private ownership, and this private
ownership should be legally transferable.

</li><li>There should be some tangible evidence or manifestation of the existence of
the intangible asset (e.g., a contract, a license, a set of patient files,
a set of client workpapers, a listing of customers, a set of financial
statements, etc.).

</li><li>It should have been created or have come into existence at an identifiable
time or as the result of an identifiable event.

</li><li>It should be subject to being destroyed or to a termination of existence
at an identifiable time or as the result of an identifiable event.
</li></ol>

In other words, there should be a specific bundle of legal rights associated with
the existence of discrete intangible assets. For a discrete intangible asset to have
economic value, it should possess certain additional attributes. Some of these additional
attributes include:

<ol>
<li>It should generate some measurable amount of economic benefit to its owner;
this economic benefit could be in the form of an income increment or of a cost
decrement; this economic benefit is sometimes measured by comparison to the amount
of income otherwise available to the intangible asset owner (e.g., the company)
if the subject intangible did not exist.

</li><li>This economic benefit may be measured in any of several ways, including net
income, net operating income, net cash flow and so on.

</li><li>It should be able to enhance the value of the other assets with which it
is associated; the other assets may encompass all other assets of the company such
as tangible personal property, real estate or other intangible assets.

</li></ol>

Economic phenomena that do not demonstrate these attributes typically do not qualify
as discrete intangible assets. Some economic phenomena are merely descriptive or
expository in nature. They may describe conditions that contribute to the existence and
value of identified, discrete intangible assets. But these phenomena do not themselves
possess the requisite elements to qualify as discrete intangible assets.

Examples of such "descriptive" economic phenomena that do not qualify as identifiable
intangible assets include the following:

<ol>
<li>high market share of the firm,

</li><li>high profitability of the firm,

</li><li>general positive reputation of the firm,

</li><li>monopoly position of the firm,

</li><li>market potential of the firm, and

</li><li>other economic phenomena.
</li></ol>

However, while these "descriptive" conditions do not qualify as discrete intangible
assets themselves, they may indicate that the actual intangible assets do have
substantial economic value. For example, while these "descriptive" conditions do not
qualify as discrete intangible assets, they may indicate the existence of—and greatly
contribute to the value of—recurring customer/client relationships.

<h3>Valuation of Customer-related Intangible Assets</h3>

There are several procedures and techniques that may be appropriate when used in the
valuation of discrete intangible assets, such as customer/client relationships. However,
all of these methods can logically be sorted into the three general categories of
analyses: the cost approach, market approach and income approach.

Each of these three approaches (or groups of related methods) has the same
objective: to arrive at a reasonable indication of a defined value for the
customer-related intangible asset. Accordingly, methods that are premised on the same
fundamental economic principles are grouped together into general approaches.
Collectively, the three intangible asset valuation approaches encompass a broad spectrum
of economic theory and property investment concepts.

The cost approach is based on the economic principle of substitution. This economic
principle asserts that an investor will pay no more for an asset than the cost to
obtain—by either purchasing or constructing—an asset of equal utility. For purposes
of this economic principle, utility can be measured in many ways, including
functionality, desirability and so on. The availability of—and the cost of—substitute
assets are directly affected by shifts in supply and demand within the universe of
substitute assets. Unlike fungible tangible assets, there may be no reasonable
substitutes for discrete intangible assets. Accordingly, the cost approach often has
limited application in the valuation of customer/client relationships.

The market approach is based on the related economic principles of competition and
equilibrium. These economic principles conclude that, in a free and unrestricted
market, supply and demand factors will drive the price of an asset to a point of
equilibrium. The principle of substitution also directly influences the market approach.
This is because the identification and analysis of equilibrium prices for substitute
assets will provide market-derived evidence with regard to the value of the subject
discrete intangible asset. Due to a paucity of transactional data, the market approach
often has limited application in the valuation of customer/client relationships.

The income approach is based on the economic principle of anticipation (sometimes
called the principle of expectation). In this approach, the value of the discrete
intangible asset is the present value of the expected economic income to be earned
from the ownership of that intangible. As the name of this principle implies, the
investor anticipates the expected economic income to be earned from the intangible.
This expectation of prospective economic income is converted to a present worth—that
is, the indicated value of the discrete intangible asset. The income approach is
commonly used in the valuation of the customer/client relationships.

There are numerous alternative definitions of economic income that may be used in
the valuation of customer/client relationships. Using this valuation approach, the
analyst estimates the intangible asset owner's required rate of return on the investment
that generates the prospective economic income. This required rate of return is a
function of many economic variables, including the risk—or uncertainty—of the expected
economic income.

<h3>Identification of Customer-related Intangible Assets</h3>

The first step in any valuation process is to identify the subject property. In
order for customer/client relationships to have economic value, there should be an
active recurring relationship between the company and the customer (or patient, client,
etc.). First, analysts exclude any "one-time" customers. Such customers may be
merely shopping around for the lowest price, friendliest practitioner, etc. In any
event, they have not established a recurring relationship with the company.

Second, analysts exclude any "retired" customer. "Retired" customers have a recurring
relationship with the company, and the company would not expect to generate future income
from such customers. There is no specific definition as to when a customer has
"retired." The practical definition relates to the type of company (i.e., banking,
insurance, publishing, etc.). In some cases, a customer has "retired" (and the
customer relationship has no intangible value) if the customer has done business with
the company in a year or two. In some cases, it may be a much longer period of
customer inactivity before the customer is considered to be "retired."

Third, there should be some form of personal relationship between the customer and
the company. While this factor is difficult to quantitatively measure, analysts expect
the customer to identify with the company. Because of the recurring relationship, we
would expect the customer, if asked, to be able to specifically identify "his" or
"her" service provider.

Fourth, and likewise, there should be some form of personal relationship between the
service provider and the customer. Just as the customer should be able to identify the
provider, the provider should be able to identify the customer. In other words, the
provider should know something about the customer, such as his/her name, address,
telephone number, customer account number, purchase history, payment history, etc.

To illustrate this point, a McDonald's restaurant and a Kmart store have customers,
and they probably have recurring (i.e., repeat) customers. But they don't have
customer relationships, because they don't collect data regarding individual customers.
Without such a relationship, general retailers cannot directly influence a customer the
way a service organization can. For example, McDonald's generally cannot send a card
to an individual customer to remind him that it is time for his next Big Mac.
However, a data-processing firm or a commercial bank can send marketing notices to
their individual customers.

Fifth, the company should possess or create some form of a file or other tangible
documentation regarding the relationship with the customer. Typically, this file documents
the services provided by the company for the customer. For example, these documents
may include purchase records, service records, credit/payment files, etc. This factor
is important because the customer is more likely to continue a professional relationship
with the company that has his/her records.

Sixth, customer relationships may generally be sold or otherwise transferred. This
does not mean that the actual customers themselves are sold from one company to
another. Rather, the expectation of continued customer loyalty—and recurring customer
income—may be sold from one company to another. Of course, the sale or other transfer
of customer relationships is not an everyday occurrence. Clearly, most companies would
rather maintain their customer relationships than sell their customer relationships.
Nonetheless, customer relationships are bought and sold on occasion—and they may be
bought and sold separately from any other tangible or intangible assets of the company.

<h3>Remaining Useful Life Analysis for Customer-related Intangible Assets</h3>

The next step in the customer relationships valuation is an analysis of remaining
useful life (RUL). As explained below, the estimation of RUL is an integral part
of each valuation approach.

<ul>
<li>Income Approach—RUL analysis should be performed to estimate the projection period
for economic income subject to either yield capitalization or direct capitalization.

</li><li>Cost Approach—RUL analysis should be performed to estimate the total amount of
obsolescence, if any, from the estimated measure of "cost."

</li><li>Market Approach—RUL analysis should be performed in order to select/reject/adjust
"comparable" or "guideline" sale/license transactional data.
</li></ul>

The customer relationships RUL analysis will typically have a direct and predictable
influence on intangible asset value. The expected influence on value is summarized
below.

<ul>
<li>Expected Influence on an Income Approach Valuation—Normally, a longer RUL would
indicate a higher value. The customer relationship value is particularly sensitive
when the RUL is less than 10 years. The customer relationship value is not very
sensitive when the RUL is greater than 20 years.

</li><li>Expected Influence on a Cost-approach Valuation—Normally, a longer RUL means
a higher value. Normally, a shorter RUL means a lower value.

</li><li>Expected Influence on a Market Approach Valuation—The "market" should indicate
an acceptance for the customer relationships RUL. If the subject RUL is different
from guideline sale/license transactions, then adjustments to the transactional
multiples may be required. If the subject RUL is substantially different from
guideline sale/license transactions, then this may indicate a lack of marketability
of the subject.
</li></ul>

The following list presents the common determinants, or factors, that influence
intangible asset RUL:

<ul>
<li>legal

</li><li>contractual

</li><li>functional

</li><li>technological

</li><li>economic

</li><li>analytical
</li></ul>

Each of these RUL determinants should be considered in the analysis of customer/client
relationships. Typically, for customer/client relationships, the determinant that
indicates the shortest RUL deserves primary consideration.

<h3>The Analytical RUL Method</h3>

With regard to customer/client relationships, the analytical method often provides
the best indication of RUL. There are two procedures related to the application of
the analytical method to customer/client relationships RUL estimation:

<ol>
<li>estimation of a historical customer/client attrition rate, and

</li><li>development of survivor curves based on historical attrition rates.
</li></ol>

In the analytical method, "survivor curves" are used to estimate the mortality or the
decay rate of a group of similar assets (e.g., customer/client/subscriber
relationships) as those assets age. The analytical method—and the survivor curve
theory—is similar to the mortality theory used by insurance company actuaries in order
to estimate the human life span. RUL analysis is the process of estimating the
behavior of a group of assets (e.g., customers) by fitting a "test group" of the
assets (e.g., customers) to various survivor curves. In that way, by selecting
the survivor curve that best "describes" the historical decay patterns of the test
group, the future mortality behavior of each customer in the group can be estimated.

In a typical survivor curve, at age zero, 100 percent of the customer group
is still surviving. As time passes, members of the customer group "retire" (i.e.,
are no longer customers of the company). Therefore, the percent of the customer group
surviving decreases. This creates the downward sloping characteristic of the survivor
curve. A survivor curve can be any mathematical function of age that can accurately
depict the test group's mortality pattern.

The age at which 50 percent of the original customer group still survives is
defined as the "average life." That is, a new customer relationship would have an
expected life of the average life of the customer group. In reality, customers are
"live" (i.e., active) across a wide range of possible time units. However, the
expected life (i.e., the mean life) for a new customer relationship is the average
life for the customer group.

The objective of RUL analysis is to estimate the specific RUL of each customer
relationship. RUL is defined as the amount of time before a customer is expected to
"retire" (and no further economic income can be expected from servicing that
customer). An important procedure for estimating a customer's RUL is to calculate the
"probable life" for each customer within the customer group. The probable life is the
age at which a customer will "retire," given that it has already reached its current
age. By subtracting the current age of a customer from its probable life, the RUL
of the customer can be estimated. That is:

<center>
RUL = Probable Life minus Current Age.
</center>

The mathematical definition of the probable life of a customer relationship is the
area under the survivor curve (i.e., using calculus, the integral) to the right
of the current age of that customer. Every survivor curve has a corresponding probable
life curve. For any customer relationship that is already "x" age units (e.g.,
months, years, etc.) years old, this relationship can be summarized in the following
form:

<center><table cellpadding="10">
<tbody><tr><td>Probable Life of the Survivor Customer Relationship =</td>
<td valign="bottom">∫</td>

<td valign="top">∞ Survivor 
χ Curve</td></tr></tbody></table></center>

There are several sets—or series—of survivor curve mathematical functions that may be used
in the analytical method, including:

<ul>
<li>Iowa-type curves (the exponential function is a special case of this type of
survivor curve)

</li><li>Weibull distributions (Iowa-type curves themselves are a special case of this
type of survivor curve)

</li><li>Gompertz-Makeham curves

</li><li>Polynomial equations
</li></ul>

All of these mathematical functions should be considered when selecting the
best-fitting survivor curve relative to a specific set of customer age-characteristic
data. In summary, by selecting a survivor curve that accurately depicts the past
decay performance of a customer group, the future retirement pattern of the customer
group can be estimated. From this retirement pattern, the RUL of each customer
relationship can be calculated.

In the analytical method, the procedure used to select an appropriate survivor curve
is called "curve fitting." The basic concept is to find the survivor curve that best
depicts (i.e., fits) the customer group's prior retirement pattern. The following
procedures are typically involved in selecting the best fit survivor curve:

<ol>
<li>Selection of a sample population of "retired" (inactive) customers: A
statistically valid random selection of the most recent retired customers is
generated. The key information needed for the retired customer sample is the
start date and the retirement date of each retired customer relationship.

</li><li>Selection of a sample population of "live" (active) customer relationships:
A statistically valid random selection of active customer relationships is generated.
The key information needed for the live customer sample is the start date of the
customer relationship.

</li><li>Creation of the survivor table: A survivor table is created by using the
random samples of retired and live customer relationships described above. A survivor
table indicates the percent surviving of the sample customer group at a given age.
The percent surviving at a given age "x" is:

Percent Surviving at age x = [percent surviving at age (x-1)] X [1 - retirement rate at age (x)]

The retirement rate at any given age is the ratio of number of customers that
retired during the age divided by the number of customers exposed to retirement at
the beginning of the age interval. The number of customers exposed to retirement
is simply the number of active customer relationships at the beginning of the age
interval.

</li><li>Plotting of the survivor table: By selecting the pairs of coordinates
(x,y), where x is the age and y is the percent surviving, an "actual" data
curve is plotted.

</li><li>Selection of best-fit survivor curve: All predetermined survivor curves are
plotted on the same graph as the "actual" (i.e., survivor table) data described
above. These curves are called the "ideal" curves. The difference between the actual
percent surviving (i.e., the survivor table) and the "ideal" percent surviving
is the "fitting error" at the particular age being examined. By summing all the
squares of the fitting errors for a given survivor curve, a ranking factor
describing the "fit" of the curve can be ascertained. The errors are squared both
(1) to remove the "canceling" effect of negative fitting errors and (2) to put
more emphasis on large errors. As a formula, the curve fitting procedure described
above is:

<center><table cellpadding="5"><tbody><tr><td><center>
Ranking
</center></td>
<td><center>
n
</center></td>
<td><center>
[survivor table (age i)
</center></td></tr>

<tr><td><center>
Factor =
</center></td>
<td><center>
∑
</center></td>
<td><center>

minus survivor curve
</center></td></tr>

where "n" is the number of entries in the survivor table selected for the fitting.
The method described above is called the stub curve or stub period fitting process.
</li></ol>

All potential survivor curves are fitted over a logical range of average lives,
and a ranking factor is assigned to each fitting. The best fit curve is the
survivor curve at the specified average life that has the smallest ranking factor.
This procedure is typically called minimizing the sum of the squared errors. As each
potential survivor curve is "fitted," a correlation coefficient is determined. The
correlation coefficient is a ranking from -1 to +1 that describes how well the
potential survivor curve fits the actual survivor table data. A correlation coefficient
of +1 suggests that the potential survivor curve—at the average life being
fitted—accurately predicts the customer sample's past retirement pattern. A correlation
coefficient of -1 suggests that the potential survivor curve being fitted is not a
good estimator of the sample customer group's actual past retirement pattern.

Once a "best-fit" survivor curve has been selected, the RUL for all active customer
relationships can be calculated using the procedure described above. The RUL represents
the remaining number of time periods that the company is expected to enjoy an economic
benefit from the customer.

<h3>Customer Relationships Valuation</h3>

Numerous measures of economic income are relevant to the customer relationships
valuation. Some of the common measures of economic income include the following:

<ul>
<li>gross or net revenues

</li><li>gross income (or gross profit)

</li><li>net operating income

</li><li>net income before tax

</li><li>net income after tax

</li><li>operating cash flow

</li><li>net cash flow

</li><li>several others (such as incremental income)
</li></ul>

Given the different measures of economic income that may be used, an essential
procedure in the customer relationships valuation is to ensure that the present-value
discount rate or the direct capitalization rate used in the analysis is derived on a
consistent basis with the measure of economic income used. Although there are at least
as many valuation methods as there are measures of economic income, all of the methods
have similar conceptual underpinnings and similar practical applications. All of the
customer-relationship valuation methods may be grouped into two analytical categories:
(1) those that rely on direct capitalization, and (2) those that rely on yield
capitalization.

In a direct-capitalization analysis, the analyst estimates the appropriate measure
of economic income for one period (i.e., one period future to the valuation date)
and divides that measure by an appropriate investment rate of return. The appropriate
investment rate of return is called the direct capitalization rate. The capitalization
rate is derived for a specified finite period of time, depending on the RUL of the
customer relationships.

In a yield-capitalization analysis, the analyst projects the appropriate measure of
economic income for several discrete time periods into the future. This projection of
prospective economic income is converted into a present value by the use of a present
value discount rate, which is the investor's required rate of return, or
yield-capitalization rate, over the economic income projection period. The discrete
projection period depends on the RUL of the customer relationships.

<h3>Illustrative Example of Customer-relationship Valuation</h3>

This illustrative example will present the valuation of customer/client relationships.
Alpha Beta is a long-distance telephone services reseller, with both commercial and
residential recurring customer/client relationships. Alpha Beta is in bankruptcy, and
it has pledged the value of its intangible assets as collateral on its secured debt.
This example will estimate the value of the customer-related intangible asset of Alpha
Beta, as of Dec. 31, 2002—i.e., the date of the bankruptcy filing.

The following table summarizes the valuation of the Alpha Beta recurring
customer/client relationships. An income-approach method is illustrated in the table.
Specifically, the yield-capitalization method (using net cash flow as the appropriate
measure of economic income) is illustrated. In this example, the average RUL of the
Alpha Beta customer relationships is determined to be three years. This conclusion
was based on an analysis of the historical "placements" and "retirements" of the
company's customer relationships. In addition to the RUL of three years, the analysis
indicated that the "survivor curve" and "retirement rate" of the customer relationships
was estimated by an exponential function. As presented on the table, the indicated
value of the Alpha Beta customer relationships intangible value is $8 million. This
is the value of this intangible asset collateral for the Alpha Beta secured
creditors.

<h3>Summary and Conclusion</h3>

This article discussed the valuation of customer/client relationships as intangible
assets that are part of a bankruptcy estate. As in the appraisal of real estate,
there are three approaches to the valuation of discrete intangible assets such as
customer/client relationships: cost, market and income. The income approach is most
often applicable to the valuation of customer/client relationships.

In the income approach, the value of customer relationships is based on the
economic income earned by the company servicing the subject customers. Some of the
common measures of economic income include operating income, net income, operating cash
flow and net cash flow. The selected measure of economic income is capitalized
(through direct capitalization or yield capitalization) by an appropriate capitalization
rate in order to estimate the customer/client relationship value. The RUL of the
customer relationships will obviously impact the valuation results. For example,
customer relationships with an RUL of three years will have a lower value than the
same customer relationships with an RUL of 15 years, all other factors held equal.
There are several methods to estimate the RUL of intangible assets. However, for
customer/client relationships, the analytical method is the most common method.

Customer/client relationships are an important intangible asset of many
service-oriented companies. Therefore, the valuation of this discrete intangible asset
will be an integral component of any bankruptcy or reorganization analysis.

Journal Authors

Robert F. Reilly

Journal Date

Saturday, June 1, 2002 - 12:00

Bankruptcy Rule

2002