Monte Carlo Analysis

We all love clean spreadsheets, but the world refuses to cooperate.

When I help clients build plans in the Real Estate Financial Planner™ software, I never freeze appreciation at 3% and call it “true.”

History doesn’t move in straight lines.

Some years property values jump more than 10%. Some years they fall 5–15%.

Rents do it too.

Inflation drifts and spikes. Mortgage rates are a moving target until you lock. The stock market rarely hands you a steady 8%.

Using one number for each input hides risk, timing, and the range of possible futures.

Monte Carlo Analysis simulates hundreds or thousands of alternate futures.

Each run pulls a new path for appreciation, rents, inflation, rates, and market returns.

Then we analyze the distribution of outcomes: best case, worst case, median, and how often you hit your goals on time.

I call it Alternate Universe Modeling™ because it answers: “What happens to my plan across many plausible futures?”

When I model plans, these inputs do the heavy lifting:

If we want to get “freaky accurate,” we can also vary taxes, insurance, maintenance, CapEx, and vacancy.

But start with the big five.

Start simple.

Assume you rent your home and invest 10% of income into a diversified stock portfolio averaging 8% over the long run.

With static assumptions, you might “hit FI” in roughly 53 years.

But markets don’t hand you 8% each year.

When I rerun this scenario through 1,000 Monte Carlo paths, the FI date spreads out.

In strong sequences, you cross FI a decade earlier.

In poor sequences, you may still be short at year 60.

The lesson: average returns aren’t your experience; sequences are.

Now let’s buy an owner-occupant property with 5% down.

Under static assumptions, you reach FI about 15.5 years faster than renting + stocks.

Monte Carlo still shows a wide range, but two forces help:

When I coach clients, I show how locking a 30-year fixed rate caps one of the most volatile line items you have.

Let’s level up.

You continue renting personally, but you buy up to ten rentals with 20% down whenever you have the cash, investing the rest in stocks.

With static inputs, clients often reach FI ~31 years in.

That’s about 18.5 years faster than renting + stocks and about 3 years faster than buying only an owner-occupant home.

The gap grows over time as rents rise and loans amortize.

Monte Carlo adds realism: sometimes you buy in soft markets with high rates and thin cash flow; sometimes you catch great appreciation and lower rates.

The spread of outcomes is larger, but so is the upside.

This hybrid usually improves both speed and probability.

You stabilize your personal housing cost, then scale rentals.

In my models, the median time-to-FI is faster than either strategy alone, and the probability curve shifts left and up.

Left means earlier.

Up means more runs succeed by each date.

Nomad™ is simple and powerful.

You buy with 5% down as an owner-occupant, live there at least a year (as your lender requires), then move into the next 5% down property and convert the last one to a rental.

Repeat until you hold a portfolio you like.

You acquire rentals with lower down payments, typically better rates, and often better DSCR because you lived in the property first and improved it while there.

Under static assumptions, Nomad™ was about 58 months faster to FI in my sample plan.

Monte Carlo typically shows it’s both faster and more probable than the other approaches tested.

When I build plans with clients, I model:

I check:

I avoid:

Speed is only half the story.

Your standard of living after FI matters.

If you need $10,000/month to be FI but your portfolio produces $20,000, you can live better, reinvest, or derisk.

Probability matters too.

A strategy that averages one year faster but doubles your failure risk is often a poor trade.

Monte Carlo exposes both.

True Net Equity™ adjusts your “paper equity” for selling costs, transaction friction, and taxes.

It’s what you could actually redeploy.

In simulations, I track True Net Equity™ distributions over time to see when 1031s, free-and-clear payoffs, or strategic sales make sense.

Then I test how those moves shift FI probability by year.

That’s how we decide whether to sell three rentals in year 18 to pay off two, or just keep all ten.

Clients always ask, “What should I optimize first?”

In most plans, the big levers are:

I run one-variable sensitivity sweeps to show which lever gives the largest improvement in time-to-FI and probability.

Then we prioritize action steps you control.

A few rules I live by when I model:

When I rebuilt after a business failure in my 20s, the lesson was simple: survive first.

Monte Carlo helps you see survival odds before you wager your future.