Default assumptions
Every plan needs an opinion about the future — what a stock market returns, how bumpy that ride is, and how fast prices rise generally. The workbench starts you off with a set of defaults for all of that. This page is where those numbers come from, what moving one actually does, and how to read the figures the plan shows you against them.
The defaults
| Asset class | Expected return | Volatility |
|---|---|---|
| US stocks | 7% | 15% |
| Intl stocks | 6.5% | 16% |
| Bonds | 4% | 6% |
| Cash | 2.5% | 1% |
| Crypto | 15% | 60% |
| Real estate & alt | 5% | 10% |
Plus two rates that apply across the whole plan rather than to one asset class:
- Inflation — 2.5% a year.
- Medical inflation — 1.5 percentage points above general inflation, compounding only the health insurance line.
The six asset-class rows, plus inflation, are edited in the plan's Assumptions & market model section. Medical inflation isn't there, even though it's also a rate: it's edited in Health insurance, beside the premiums it multiplies (see Households and the plan). Nothing here is fixed — the workbench exists so you can try your own view instead of this one.
Nominal, then real
The figures above are nominal — the return before inflation eats into it, which is how a market return is normally quoted. The simulation doesn't use them directly, though: it takes inflation back off once, and runs the whole plan in today's dollars from there. With the defaults, that means US stocks are actually simulated at a 4.5% real return — 7% nominal, minus 2.5% inflation — not the 7% on the label. For the full arithmetic worked through with numbers, including the monthly figures the engine actually draws from, see How the plan is simulated.
That 4.5% is the figure worth sanity-checking, not the 7%. It's also lower than the roughly 7% real return US equities have averaged over the last century — a plan built on that long-run average alone would be a rosier plan than this one.
Where numbers like these come from
Two kinds of sources typically feed a set of assumptions like these. One is long-run historical return series — decades of actual market data, which is where a figure like "US equities have returned about 7% real over the last century" comes from. The other is forward-looking: large asset managers — Vanguard, BlackRock, J.P. Morgan and their peers among them — publish 10-year capital-market-assumption reports every year, projecting what they expect stocks, bonds and cash to return over the coming decade from today's starting point. Recent reports in that second category have generally sat below long-run history for US equities — a common view is that today's valuations start from a higher level than their historical average, which tends to imply lower returns from here.
The table above isn't a citation-backed blend of either one, though — it's the product's own starting values, picked to sit in a plausible, middle-of-the-road range rather than derived from any specific report or series. (Two rows don't have a clean analog to look up in the first place: crypto and cash sit outside what a capital-market-assumption report is built to estimate.) Read the table as a reasonable place to start and sanity-check, not as something someone else has already vetted for your household.
What each knob does
Moving an assumption changes the simulation in a specific, predictable direction:
- Expected return moves the median path and the success rate the way you'd expect — up with a higher return, down with a lower one — and the effect compounds over however many years you're simulating, so it matters more the longer your plan runs (see Time and the plan).
- Volatility does two separate things, worth naming separately rather than lumping together. It widens the fan chart's bands — more spread between a lucky path and an unlucky one — and it also pulls the median down, not just the low end: compounding a bumpier sequence of returns produces a lower typical outcome than the average return alone would suggest, purely because of how multiplying random numbers together works. That first effect needs no withdrawals at all — a static, untouched portfolio shows it too, and it's sometimes called volatility drag. A plan that's actually drawing money out pays a second, larger cost from the same volatility: withdrawing while the market is down locks in a loss that a portfolio still being added to would have ridden out. That's sequence-of-returns risk, a different mechanism from volatility drag, and Monte Carlo and scenarios walks through a worked example of why the order returns arrive in changes the outcome even when the average is identical. Both effects lower the success rate at the same expected return — it isn't a quirk of this engine.
- Inflation mostly matters as the gap between it and your returns — raising it without touching any return is close to cutting every asset class's return by the same amount, because that gap is what the engine actually simulates against. It isn't the whole story, though: a household with a pension that has no cost-of-living adjustment (see Money in, money out) feels a second effect — a higher inflation rate erodes that check's real value faster too, on top of shrinking the return gap. And not everything in the plan is flat in today's dollars to begin with: health insurance (below) grows in real terms and a no-COLA pension shrinks in them, so inflation is also the rate both of those move against.
- Medical inflation compounds the health insurance line on top of general inflation, and only that line. It has no effect on spending, goals, income, or anything else in the plan.
One correction is already baked into the simulation and isn't a field you can see: the engine multiplies your portfolio's blended volatility by 1.05 before simulating. The blend itself — each class's volatility, weighted by your allocation, added straight up — is actually the volatility you'd get if every asset class moved in perfect lockstep, correlation of 1, the maximum case, rather than an assumption that they move independently: real asset classes usually aren't that correlated day to day, so on an ordinary year this blend already leans conservative. What it can't see is what happens on a bad one. There's no real covariance matrix behind it, each month is drawn from a plain bell curve with no fat tails, and it has no way to represent how normally-diversifying asset classes tend to move together anyway in exactly the crashes that hurt a retirement most. The 1.05 uplift is a flat, modest cushion against those blind spots — fat tails and crash-time correlation spikes — not a precise correction for either one. See What the model leaves out for how this sits alongside the model's other market-side simplifications.
It's blended by what you actually hold
The simulation doesn't treat these six numbers as separate return streams — it blends them by your household's current allocation across US stocks, Intl stocks, Bonds, Cash, Crypto and Real estate & alt, the same mix the portfolio dashboard shows. Two households running identical assumptions with different portfolios get different plans, because the blend itself is different. Change your allocation and the plan updates without you touching a single assumption.