Catching Up on Your Retirement Savings – A Look at the Numbers

A number of powerful steps can help you save enough to generate the income you’re likely to need.

Saving for retirement late in your career can be challenging—you have less time to narrow any gap between the savings you have and the savings you might need by the time you stop working. The actions you take now can have a powerful impact on your future well-being. “Although they may require adjusting your lifestyle today,” advises Christine Fahlund, CFP®, a senior financial planner with T. Rowe Price, “the actions can be very effective in narrowing the financial gap between where you are currently and where you wish to be once you are fully retired.”

Adding Up Retirement Income

“Your goal should be to generate 75% of your income at the time of your retirement from savings and Social Security or other sources, such as a pension,” notes Fahlund. The chart shows the steps a hypothetical investor could take to reach this goal. 

The Target: $4,688 A Month

The hypothetical investor in this example will need to receive $4,688 a month in today’s dollars in the first year of retirement from all sources to replace 75% of a preretirement income of $75,000. To reach the target, the investor could take the following steps:



* Estimated in today’s dollars, assuming both an inflation rate and a discount rate of 3% annually at age 50. Given those assumptions, today’s dollars reflect the purchasing power your income would have at different ages. In actuality, all of these figures would be considerably larger if shown in their future dollar values.Keep in mind that all examples are for illustrative purposes only and do not represent the performance of any particular investment or imply precision of Social Security or withdrawal estimates. Social Security benefits were estimated with an assumed work history —your work history and corresponding benefits will vary.



Explaining Monte Carlo Analysis Used in Hypothetical Example Above

The following is an explanation of the Monte Carlo simulation analysis used in the article above.

Monte Carlo Simulation

Monte Carlo simulations model future uncertainty. In contrast to tools generating average outcomes, Monte Carlo analyses produce outcome ranges based on probability, thus incorporating future uncertainty.

Material  Assumptions Include:

  • Underlying long-term rates of return for the asset classes are not directly based on historical returns. Rather, they represent assumptions that take into account, among other things, historical returns. They also include our estimates for reinvested dividends and capital gains.
  •  These assumptions, as well as an assumed degree of fluctuation of returns around these long-term rates, are used to generate random monthly returns for each asset class over specified time periods.
  • The monthly returns are then used to generate 10,000 scenarios, representing a spectrum of possible return outcomes for the modeled asset classes. Analysis results are directly based on these scenarios.

Material Limitations Include:

•  The analysis relies on return assumptions, combined with a return model that generates a wide range of possible return scenarios from these assumptions. Despite our best efforts, there is no certainty that the assumptions and the model will accurately predict asset class return ranges going forward. As a consequence, the results of the analysis should be viewed as approximations, and users should allow a margin for error and not place too much reliance on the apparent precision of the results.• Extreme market movements may occur more often than in the model.

•  Some asset classes have relatively short histories. Actual long-term results for each asset class going forward may differ from our assumptions, with those for classes with limited histories potentially diverging more.

•  Market crises can cause asset classes to perform similarly, lowering the accuracy of our projected return assumptions and diminishing the benefits of diversification (that is, of using many different asset classes) in ways not captured by the analysis. As a result, returns actually experienced by the investor may be more volatile than projected in our analysis.

•  The model assumes no month-to-month correlations among asset class returns (correlation is a measure of the degree in which returns are related or dependent upon each other). It does not reflect the average duration of bull and bear markets, which can be longer than those in the modeled scenarios.

•  Inflation is assumed to be constant, so variations are not reflected in our calculations.

•  The analysis assumes a diversified portfolio, which is rebalanced monthly. Not all asset classes are represented, and other asset classes may be similar or superior to those used.

•  Taxes on withdrawals are not taken into account, nor are early withdrawal penalties.

•  The analysis models asset classes, not investment products. As a result, the actual experience of an investor in a given investment product (e.g., a mutual fund) may differ from the range of projections generated by the simulation, even if the broad asset allocation of the investment product is similar to the one being modeled. Possible reasons for divergence include, but are not limited to, active management by the manager of the investment product, or the costs, fees, and other expenses associated with the investment product. Active management for any particular investment product—the selection of a portfolio of individual securities that differs from the broad asset classes modeled in this analysis—can lead to the investment product having higher or lower returns than the range of projections in this analysis.

Model Portfolio Construction and Initial Withdrawal Amount

•  The primary asset classes used for this analysis are stocks and bonds. An effectively diversified portfolio theoretically involves all investable asset classes, including stocks, bonds, real estate, foreign investments, commodities, precious metals, currencies, and others. Since it is unlikely that investors will own all of these assets, we selected the ones we believed to be the most appropriate for long-term investors.

•  Results of the analysis are driven primarily by the assumed long-term, compound rates of return of each asset class in the scenarios. Our corresponding assumptions, all presented in excess of inflation, are as follows: 4.9% for stocks and 2.23% for bonds.

•  Investment expenses in the form of an expense ratio are subtracted from the return assumptions as follows: for stocks, 0.70%; and for bonds, 0.60%. These expenses represent what we believe to be a reasonable approximation of investing in these asset classes through a professionally managed mutual fund or other pooled investment product.

•  The modeled asset class scenarios and withdrawal amounts may be calculated at, or result in, a simulation success rate. Simulation success rate is a probability measure and represents the number of times our outcomes succeed (i.e., has at least $1 remaining in the portfolio at the end of retirement).

IMPORTANT: The projections or other information generated by the T. Rowe Price Retirement Income Calculator regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. The simulations are based on assumptions. There can be no assurance that the projected or simulated results will be achieved or sustained. The charts present only a range of possible outcomes. Actual results will vary with each use and over time, and such results may be better or worse than the simulated scenarios. Clients should be aware that the potential for loss (or gain) may be greater than demonstrated in the simulations.The results are not projections, but they should be viewed as reasonable estimates. Source: T. Rowe Price Associates, Inc.