Understanding the Role of SPIAs in a Retirement Portfolio

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David B. Loeper

Wade Pfau’s recent article, Breaking Free from the Safe Withdrawal Paradigm, was well researched. Its goal was to accurately calculate the benefits of using single-premium immediate annuities (SPIAs) based on certain assumptions (which are explained in his article). I fear, however, that many readers may have not fully grasped the impact of a few key assumptions that drive his results. Both advisors and their clients will benefit from understanding the effect of those assumptions.

Pfau’s analysis relied on these main assumptions:

  • The capital market assumptions (CMAs) of traditional stock and bond allocations and blends thereof
  • The bottom 10th percentile result (which investors have 90% confidence of exceeding) as the basis for optimization of asset allocation and product blends
  • The payout rate on SPIAs and, thus, the return they would deliver at varying mortality rates

Let’s examine the impact of each of these assumptions and then how they work in combination with one another to produce the results of Pfau’s analysis. It is then up to the reader to determine if those assumptions are reasonable.

Capital market assumptions

In any simulation, capital market assumptions are the key for determining the results. When telling a simulation engine what assumptions to use, you are instructing it to “draw a probability distribution that looks like this.”

Plausible but sometimes indefensible CMAs are commonplace in our industry. Despite their importance (as I highlighted in my white paper, Hunting for Black Swans), I fear most readers and journalists don’t have the time, inclination, skills or data to put the impact of CMAs in perspective. Many people will disagree on what the CMAs should be, and Pfau’s fuller published article provided some color around his notions and assumptions. Still, such assumptions are often just accepted at face value based on seemingly plausible narrative. To be fully informed and prevent unintentional consequences, is critical to have a thorough understanding of the CMAs and their cause and effect on any analysis.

Optimizing at the bottom 10% of results

Since CMAs are usually expressed in the form of an arithmetic mean and standard deviation (that results in the probability distribution shape), it is easy to lose perspective of what assumptions actually mean at any specific point in the probability distribution.

Pfau’s article also uses the bottom 10th percentile of results as the basis for calculating his optimization. The CMAs of his analysis have a huge effect in determining the percentile distributions. For our own Wealthcare Capital Management and Financeware.com CMAs, I’ve advocated using a range of the 75th to 90th percentile as a zone that defined “balanced comfort” since the early 2000s. This gives us a sense of whether a plan is overfunded or underfunded (above or below the comfort zone), and we can craft advice to prevent needless sacrifice while maintaining sufficient confidence of exceeding the currently planned goals.

Single-premium annuity payout rate

Pfau disclosed the assumed SPIA payout rate that he used in his analysis, but I could not find whether that payout rate was based on joint- or single-life payouts. For my analysis below, I assumed joint-life mortality probabilities based on David Hultstrom’s mortality calculator. If it was based on a single-life payout, the returns would obviously be lower.

It is relatively easy to calculate the assumed return of the SPIA at various ages and calculate probabilities based on the payout rate and mortality risk. These calculations expose the percentage of clients across the industry whose advisors could be recommending a product that has no chance of doing better or worse than what the SPIA contract offers. (Of course, if the returns are high on the contracts and the capital markets produce results significantly below the contract return, one might question how many insurance companies could survive that liability.)

I calculated SPIA returns using the 5.84% payout rate from the article, joint-life mortality probabilities from Hultstrom’s calculator and the base case of a 65-year-old couple. Below are the returns of the SPIA payout rate at various mortality probabilities:

Time horizon SPIA % return % of buyers doing worse due to joint-life mortality
24 Years 2.61% 52%
30 Years 3.93% 84%
40 Years 4.95% 99%+