• The net death benefit of the policy
• The life expectancy of the insured covered by the policy
• The expected rate of return sought by the investor (financing entity)
• The type of policy to be purchased
• The cost of insurance and other expenses associated with maintaining the policy

Each element related to the pricing calculation is impacted by a number of factors. For example the net death benefit of a life insurance policy is the amount the beneficiary will receive when the insured dies. This figure may be more or less than the face value of the policy and can be affected by cash values of the policy, loan balances secured by the policy values or withdrawals from the policy taken prior to sale. Life settlement companies apply a rigorous and thorough approach to pricing life settlements and collaborate interactively with investors to accurately calculate the price of each policy purchased based on the investor’s requirements.



Life Expectancy

Perhaps the most important variable in the pricing calculation is the life expectancy of the insured. This figure, typically stated in months, is normally produced through an analysis of medical records and other demographic data (age, gender, tobacco use information, family history, etc.) pertaining to the insured. The analysis is performed by one or more “medical underwriters”, small independent companies each with their own proprietary methodology for analyzing medical information, applying actuarial analysis, and then issuing an “LE” or life expectancy and related mortality report. In general, the LE is considered to be the medical underwriter’s estimate as to how many months the insured can be expected to live based on an analysis of the information submitted.

There are currently five medical underwriters who produce most of the life expectancy estimates for the entire life settlement industry: 21st Services, American Viatical Services, Examination Management Services, Inc. (EMSI), Fasano Associates, and ISC Services.

The life expectancy estimate is typically used to determine the period of time over which the investor expects to hold a particular policy, pay premiums to keep the policy “in force”, and track the whereabouts of the insured. It is therefore the most important element in the pricing calculation.



Life Settlement Pricing-Deterministic Method

There are two methods used to calculate the purchase price for an individual life settlement, the deterministic method and the probabilistic method. In the deterministic method, the price for the policy is calculated using a discount rate which is applied to the net death benefit of the policy over the life expectancy of the insured. The discount rate is analogous to the expected rate of return. In addition, the life expectancy estimate is assumed to be correct such that the calculation assumes that the premiums necessary to keep the policy “in force” must be paid over the insured’s life expectancy and the death benefit of the policy will be received at the expiration of the life expectancy period. In other words, the calculation does not consider the possibility that the insured may die earlier or later than expected, a situation which could increase or decrease both the total premium payments required and the actual rate of return earned. Obviously, once the insured dies and the death benefit of the policy is paid out, no future premium payments are necessary. Again, the deterministic method assumes this occurs only at the expiration of the insured’s life expectancy. Therefore, this method represents a less practical approach to pricing life settlements. Clearly insured persons may die at any time and it rarely the case that an insure dies precisely on the expiration of their LE. Given that life expectancy figures are estimates and it is not possible to pinpoint the exact point in time at which a given life will expire, the deterministic approach is a less sophisticated method for pricing life settlements than the probabilistic method.



Life Settlement Pricing-Probabilistic Method

The probabilistic method of pricing a life settlement uses the life expectancy figure to determine the mortality factor applicable to the given life. To do this, the LE is applied to a mortality table, and the mortality factor or rating applicable to the given insured is derived from the table itself. The resulting figure, stated as a percentage, represents an indication as to the degree to which the given life can be considered more or less impaired than a “standard” life having similar characteristics (i.e. age, gender, smoker/non-smoker, etc.). For example, a standard insured (the average life for the given motality table) would carry a mortality rating of 100%. A similar but impaired life bearing a mortality rating of 200% would be considered to have twice the chance of dying early than the standard life. Obviously, any insured can expire at any point in time but, in general, an impaired life (one bearing a greater than standard mortality rating) can be expected to expire earlier than the standard life. In the probabilistic pricing approach, the mortality rating is used to create a range of possible outcomes for the given life and assign a probability that each of the possible outcomes might occur. This listing represents a mathematical curve, known as a mortality curve. This curve is then used to generate a series of expected cash flows over the remaining expected lifespan of the insured and the corresponding policy. Then an Internal Rate of Return (IRR) calculation is used to determine the price of the policy.

Of the two approaches, the probabilistic method is more rational because the calculations acknowledge the fact that there are a range of possible outcomes associated with each insured life. In other words, it is possible for the insured to pass away at any point in time and the effect of each possible outcome on the return earned from the purchase of a policy covering the given life will vary. Obviously, if the insured dies earlier than expected, the return will be higher than if the insured dies when expected (at the LE) or later than expected. In addition, the calculation allows for the possibility that if the insured dies earlier than expected the premiums needed to keep the policy in force will not have to be paid. Conversely, the calculation also considers the possibility that if the insured lives longer than expected, more premium payments will be necessary. Again, based on these considerations, each possible outcome is assigned a probability and the range of possible outcomes is then used to create a price for the policy.

Most institutional investors are inclined to purchase a sufficient number of policies covering a sufficient number of lives to benefit from the law of large numbers. Because of this fundamental insurance concept, the probabilistic method of pricing has becoming the preferred method used by sophisticated investors to price life settlements. Life settlement companies have extensive experience in pricing life settlements using both methods. They modify the pricing models they use in order to quickly and accurately price policies and share pricing data with their clients.



Premium Optimization

One of the key components of the pricing process is the determination of the amount of premiums needed to keep a purchased policy “in force” over the life of the insured. It is critical that these calculations be as accurate as possible to avoid over or underpayment. Overpayment can result in reduced returns to the investor since the excess premium may not be recaptured when the death benefit is collected, while underpayment can result in the policy lapsing. Life settlement companies devote a significant portion of their resources and apply years of experience and the expertise of policy analysts to the premium optimization process.

In order to properly calculate the amount of premium needed to keep a policy in full force and effect while avoiding overpayment, data must be extracted from a variety of documents, each of which is received at different stages in the life settlement evaluation process. The primary document used to develop a premium optimization schedule is the in force illustration. This document contains a number of key pieces of information such as the credited interest rates (guaranteed and current) applicable to the policy’s cash values, the death benefit option currently applicable to the policy, the rating classification applicable to the insured life, minimum premium tests or requirements, and a host of other crucial information.

Policy analysts review the in force illustration as well as the mortality reports and other key data pertaining to the insured to uncover all of the pertinent facts related to the policy. Then, analysts use premium optimization and pricing tools to develop the premium payment schedules necessary to properly assess the overall value of each life settlement policy. These processes are repeated throughout the evaluation, acquisition and closing phases of the underwriting process.

Facts are checked, confirmed and re-checked as each policy moves through the underwriting system. As the quantity and quality of key data improves, analysts continuously evaluate, confirm and re-evaluate the premium and pricing calculations, exchanging data and comparing results with clients to make absolutely certain both the premium optimization and pricing calculations are accurate and verifiable.



Market Conditions

In addition to the analytical processes of pricing and premium optimization, submission and acquisition teams constantly gather market information through daily contact with the community of brokers from whom cases are received. This contact provides valuable if often anecdotal information which is entered into databases for consideration by policy analysts and relationship managers as they evaluate and negotiate offers. By tracking market trends, LE data, competitor offers and a range of other information, life settlement companies are able to maintain a keen sense of market conditions, identify potential trends and thereby consult more effectively with clients as they seek to optimize their competitive position in the market.