How Monte Carlo Modeling Works in LEOFF Helper

What Monte Carlo modeling actually is

Monte Carlo modeling does not ask, "What happens if everything follows one average forecast?" It asks, "What happens if we run the same plan through many different possible futures?"

Instead of projecting one clean path for inflation, portfolio returns, and property growth, the model creates many alternate paths. Each path is a full retirement trial. Then it counts how often the plan still works.

Deterministic planning shows one path. Monte Carlo modeling asks how fragile or durable that path is when the real world behaves differently.

Why Monte Carlo modeling is useful in retirement planning

Retirement plans usually do not fail because one number was off by a tiny amount. They fail because a few bad conditions happen together: inflation runs hot, markets disappoint early, or withdrawals start at the wrong time.

That is why Monte Carlo is useful. It gives you a way to ask:

• How often does this plan still cover expenses?
• How often do assets deplete?
• How exposed is this plan to a weak early retirement sequence?
• Does working longer actually improve the odds in a durable way?

This matters especially for plans with bridge years or heavy dependence on withdrawals. Those are exactly the kinds of plans where average assumptions can look fine while real-world sequence pressure still breaks the result.

Exactly how LEOFF Helper uses Monte Carlo today

As of March 27, 2026, LEOFF Helper runs Monte Carlo as its own separate engine inside the dashboard, not as a chart toggle and not as a replacement for the deterministic projection.

The current dashboard Monte Carlo run uses 250 trials. Each trial reuses the shared retirement projection engine, but feeds it a different year-by-year scenario for inflation and asset growth.

What we vary

• Overall inflation: annual standard deviation `1%`, clipped between `0%` and `8%`, fallback base `3%`.
• Goods and services inflation: annual standard deviation `1%`, clipped between `0%` and `8%`, fallback base `3%`.
• Housing inflation: annual standard deviation `1.2%`, clipped between `0%` and `9%`, fallback base `3.5%`.
• Healthcare inflation: annual standard deviation `1.5%`, clipped between `1%` and `12%`, fallback base `5%`.
• Portfolio returns: annual standard deviation `8%`, clipped between `-18%` and `+18%`, fallback base `5%`.
• Real estate returns: annual standard deviation `3.5%`, clipped between `-10%` and `+10%`, fallback base `3%`.

If a user already entered a base growth or inflation assumption, the Monte Carlo engine shocks that base rate up or down within those bounds rather than ignoring it.

How the yearly paths work

The current engine samples those rates year by year. That means a trial can have a bad market year, then a better one, then another weak one later. It is not one fixed return applied to the whole retirement.

Portfolio accounts receive yearly sampled return paths. Real estate assets also receive yearly sampled growth paths. Inflation paths separately affect overall expenses, goods and services, housing, and healthcare.

What the dashboard counts as success

The Monte Carlo panel focuses on retirement years, not pre-retirement years. For each trial, the engine tracks:

• whether income covers total expenses after retirement begins,
• whether income covers essential expenses,
• whether assets deplete,
• median failure age,
• median asset depletion age, and
• ending net-worth ranges when those ranges are stable enough to display.

That is also why the dashboard recommendation logic has become stricter. The earliest recommended retirement age now needs to do two things:

1. cover expenses without relying on portfolio withdrawals, and
2. clear a Monte Carlo success rate of at least 90%.

What this Monte Carlo model does not do yet

This is important: the current Monte Carlo engine is useful, but it is not yet a full institutional scenario engine.

Right now, LEOFF Helper does not explicitly model:

• a named recession regime,
• clustered bear markets by rule,
• stock-market and real-estate correlation,
• inflation and market correlation,
• deliberate two-year sequences like "down 10% in the first two retirement years," or
• a full capital-markets calibration package.

So yes, the current Monte Carlo can generate bad early years, including market losses and real-estate weakness. But it does that through independent yearly sampling within the configured bounds, not through a hard-coded recession sequence.

If you want the deeper explanation of why that early sequence matters so much, read What a Recession Right Before Retirement Can Do to Your Numbers.

How to read the dashboard Monte Carlo output

The most important number is usually the success rate, not the highest possible net worth. Ask whether the plan stays solvent under many different market and inflation scenarios, not whether one lucky path gets very rich.

A few practical ways to use the Monte Carlo panel:

• Compare nearby retirement ages and see whether the probability improvement is meaningful.
• Watch whether essential-expense coverage remains strong even when total-plan success is lower.
• Use the vulnerability panel and Monte Carlo panel together, not separately.
• Do not confuse a workable age with a durable recommended age.

Bottom line

Monte Carlo modeling is useful because retirement is a sequence problem, not just an average-return problem. Inflation, healthcare, housing, market losses, and withdrawal timing all matter more when they show up in the wrong order.

LEOFF Helper currently uses Monte Carlo to stress-test retirement plans across 250 different market and inflation scenarios, with explicit variance ranges for inflation, portfolio returns, and real estate. That makes the dashboard much more useful than a single-path projection alone, even though the model still has room to grow.