Risk Intelligence Module
The Monte Carlo simulation doesn't guess a single outcome — it models thousands of them, so you can see not just whether a deal looks good, but how confidently good it looks.
The core idea
Traditional underwriting picks a best-guess rent, a vacancy rate, and an expense estimate, then runs the math. The problem: those are three bets disguised as facts. If any one of them is off, so is your conclusion — and you won't know by how much.
Monte Carlo simulation takes a different approach. Instead of one set of assumptions, it runs the deal thousands of times, each time drawing slightly different (but realistic) values from each variable. The result isn't a single projected return. It's a full picture of the probability landscape.
The process
For each variable — rent growth, vacancy, expenses — we don't enter a single number. We enter a range and a most-likely value. For example: rent growth is usually 3%, but could realistically fall anywhere between 0% and 7% depending on market conditions.
The engine samples each variable independently, thousands of times, drawing values that respect the shape of each distribution. Each simulation produces one complete outcome — a projected cash flow, cap rate, or return on investment. This is the "casting thousands of fishing lines" step.
The 10,000 outcomes are assembled into a distribution — a curve showing which results cluster in the middle and which tail off toward the edges. From that, we can say: "In 80% of simulated scenarios, this property cash-flows positively." That's a decision-ready number.
What's being simulated
Every variable below has uncertainty baked into its definition. The simulation respects that uncertainty rather than hiding it.
Reading the output
When the simulation finishes, the platform draws a distribution of outcomes. The tall middle of the curve is where most results land. The tails — thin at the edges — represent the unlikely extremes. Three zones tell the story at a glance.
Scenario summary
Rather than showing you the full curve every time, the platform condenses the simulation into three named scenarios — each anchored to a specific percentile of simulated outcomes.
| Scenario | Percentile | What it means | Sample IRR |
|---|---|---|---|
| Bear | 10th | Only 10% of simulations came out worse than this. The floor of realistic expectations. | 2.1% |
| Base | 50th | The median outcome — half of all simulations did better, half did worse. Your central estimate. | 8.4% |
| Bull | 90th | Only 10% of simulations came out better than this. The realistic upside, not a fantasy. | 14.7% |
Probability at a glance
The simulation can answer plain questions with plain probabilities. For a sample ZIP code assessment, here's what 10,000 runs found:
Sample output only. Actual probabilities vary by ZIP code, market score, and deal structure.
The bottom line
Traditional underwriting creates an illusion of certainty — one model, one number, one answer. Monte Carlo replaces that illusion with honest probability. You see not just whether a deal pencils out, but how robust that conclusion is under pressure.
A deal that returns 9% in the base case but 1% in the bear case is very different from one that returns 9% in the base case and 6% in the bear. Both look identical on a single-point model. Only the simulation tells them apart.