Required Savings Calculator (Monthly Rate for Your Goal)
Most savings calculators answer the forward question — save $X a month and see where you land. This one answers the question people actually have: I want $X in N years, so how much do I need to save every month? It gives two kinds of answer. First, an exact closed-form rate under three return scenarios (conservative, expected, optimistic), so you see how sensitive the number is to your assumptions. Second — the part almost no free tool offers — an inverse Monte Carlo rate: the smallest monthly amount that reaches your goal in at least your chosen share (say 80%) of 3,000 simulated market futures. Everything runs in today's dollars with real returns, so your target keeps its purchasing power. It is a planning model, not financial advice.
Your goal
Everything is in today's dollars: use a real (inflation-adjusted) return. The probabilistic rate is the smallest monthly amount that hits your target in at least 80% of 3,000 seeded market simulations — identical inputs always give identical results.
Required monthly savings
$763.19/mo
for 80% success — this rate reaches $100,000 in 80.00% of simulated market paths
Conservative
$629.23
3% return
Expected
$554.62
5% return
Optimistic
$484.50
7% return
Path to your goal — expected scenario
Balance by year
Deterministic schedule at the expected-scenario rate ($554.62/mo). The balance grows each year, then twelve contributions are credited at year end.
Compare scenarios
Run the same calculation with two or three input sets side by side. Differences are highlighted; every number comes from the same tested formula as the calculator above.
| Input | Scenario A | Scenario B |
|---|---|---|
| Target Amount | ||
| Current Savings | ||
| Years | ||
| Expected Real Return Pct | ||
| Volatility Pct | ||
| Success Target Pct |
How it works
The deterministic answer inverts the future-value formula. Under the model's annual convention — the balance grows by the year's return, then twelve monthly contributions are credited at year end — the goal is reached when S·(1+r)^n + C·((1+r)^n − 1)/r equals your target, so the required annual contribution is C = (target − S·(1+r)^n) · r / ((1+r)^n − 1), divided by twelve for the monthly rate (at 0% it reduces to spreading the shortfall evenly). This is computed at your expected real return and at two points below and above it, floored at 0% and capped at 12%.
The probabilistic answer simulates 3,000 market paths, each drawing one random real return per year from a normal distribution with your expected return as the mean and your volatility as the standard deviation. A path succeeds if its final balance reaches the target. The calculator then bisects on the monthly contribution — testing every candidate against the exact same 3,000 simulated futures, which keeps the search stable — until it finds, within $1 a month, the smallest rate whose success share clears your chosen threshold.
Expect the Monte Carlo rate to sit noticeably above the deterministic expected rate. That is not a bug: the average path is not good enough when you need 80% of paths to succeed, and volatility drags compounded growth below the simple average even before dispersion is considered. The gap between the two numbers is effectively the price of confidence — raise the success target or the volatility and it widens; set volatility to zero and the two answers converge to the same figure.
Frequently asked questions
Why is the Monte Carlo rate higher than the expected-scenario rate?+
Two compounding reasons. First, volatility drag: a portfolio that swings ±15% a year compounds to less than the same average return delivered smoothly, so even the median simulated path lands below the deterministic projection. Second, dispersion: the deterministic rate only gets the average path to the target, which roughly means succeeding about half the time — demanding success in 80% or 90% of paths means funding the below-average futures too. The difference between the two numbers is what buying that extra confidence costs per month.
How reliable is the success probability?+
It comes from a deliberately simple model: annual real returns drawn independently from a normal distribution, identical every year. Real markets have fat tails, clustered bad years, and valuation effects, so the model can understate crash risk; and it knows nothing about your taxes, fees, or whether you will actually contribute every month. Treat the probability as a gauge for comparing scenarios and sizing a margin of safety, not as a forecast. The simulation is seeded, so identical inputs always reproduce the same answer. This is not financial advice.
What return, volatility, and success target should I use?+
Enter a real (inflation-adjusted) return, because the model keeps everything in today's dollars — historically a diversified stock-heavy portfolio has delivered roughly 4–7% real with volatility around 15–18%, while cash and short bonds sit near 0–1% real with low volatility. Match the assumption to where the money will actually sit, and for goals under about five years favour low-volatility assumptions, since there is little time to recover from a bad sequence. On the success target, 80–90% is a common planning range; pushing toward 95% gets expensive fast. None of this is a guarantee or advice — assumptions in, assumptions out.