Your retirement vs. average assumptions

Published: Mar 14, 2013 4:45 a.m. ET




What do retirement planners and fortune tellers have in common? More than we would like to admit.

Think about it. Every time you calculate an estimate for how much money is needed to retire you make certain assumptions that are nothing more than predictions about the future:

  • · How long will I live?
  • · What will be my rate of inflation during retirement?
  • · How will my investment portfolio perform during retirement?

Nobody can ever know the answers to these questions; yet, you can't calculate a retirement estimate without answering them. Unless you have a crystal ball or connection to the Higher Power, it can't be done accurately because it is a forecast about an unknowable future. It is impossible, and therein lies the problem.

Retirement planners attempt to get around this problem by applying assumptions derived from historical research. We use average expectancy, statistics, and random sampling to replace exact forecasts. Unfortunately, this approach is nothing more than a historical optimization that may (or may not) hold true in the future. More importantly, statistical validity cannot be applied to an individual retirement because it's attempting to apply conclusions proven valid for large populations to a singular event. There is no statistical validity for an individual retirement because it's a sample size of one.

For example, the common approach to answering the life expectancy assumption for individuals is to consult average life expectancy tables. This solution sounds logical on the surface except for the fact that you are no more likely to die on a statistical average date than any other date. Life expectancy data is only valid for the IRS or insurance companies dealing with large pools of people. It has zero scientific validity for you or me individually.

The reality is your date with destiny is not a probabilistic outcome. Nobody is 50% dead at age 83. Your individual lifespan has no statistical validity; therefore, any attempt to forecast a single expected lifespan based on probability is a fundamentally flawed approach, yet retirement planners make this mistake every day. It's an example of how statistics can deceive.

Fortunately there are rational solutions to managing individual life expectancy risk that don't require you to forecast the future:

  • · You could budget your retirement savings for a statistically average death while living in your fully-paid-for home. If you outlive the odds then you can reverse mortgage or sell your house to harvest any money needed to fund your remaining years.
  • · Alternatively, you could purchase deferred annuities and transfer the longevity risk to the insurance company where it is statistically valid.
  • · Finally, you could assume life expectancy at the 95% confidence interval to encompass nearly all reasonable outcomes, but that has the drawback of reducing spending below optimal levels for the majority of retirees.

In short, it is dangerous to build any individual retirement plan around historical average expectancy based on large populations because it's not statistically valid for that one individual. That is true with life expectancy and it is equally true with investment return expectations.

For example, the common practice when forecasting investment return is to use either long term historical averages or Monte Carlo simulations, neither of which accurately represents what an individual retiree can expect for the following reasons:

  • · Individual Variation: Research proves most individual investors and professional money managers significantly underperform the averages net of expenses thus making average return assumptions unrealistic (unless your portfolio exactly matches the indexes and has zero expenses).
  • · Average vs. Compound: The only return you can spend is compound return, and the compound return is significantly less than average return for volatile assets such as stocks. For example, according to fellow Retirementor Wade Pfau, the average return from stocks net of inflation was 8.6% but the compound return was significantly less at 6.5%. This is critically important because many retirement planners use average return expectations even though it is a statistical artifact that overestimates expectancy.
  • · Sampling Error: Both Pfau and another RetireMentor, Michael Kitces, have demonstrated the amount of money needed to retire is highly dependent on your investment return during the critical first 15 years of retirement. Unfortunately, the probability that you will experience anywhere close to historical average returns during those critical first 15 years is unlikely. The variability of 15-year returns is so large that any average historical return assumption can be dangerously misleading for real world retirement planning.

Bottom line is the industry standard practice of using statistical solutions such as average expectancy or Monte Carlo simulations to plan individual retirements is an attempt to appear scientific where no science can actually exist. It is a dangerous practice because every retirement is a singular event; therefore, it can never be planned statistically.

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