This analysis is directional, not deterministic. The Monte Carlo framework does not predict exact returns. It stress-tests relative positioning under different catalyst execution paths. The goal is not to forecast precise IRRs, but to understand which metros have the highest probability of durable repricing given current capital investment trends.

If catalyst execution diverges materially from expectations, the board will move.

This is a living framework — not a static forecast.

On this week’s board, Columbus and Indianapolis remain at the top of the Now tier.Both clear the hard gates:

But they are not the same. Columbus is later in its cap rate curve. Indianapolis retains more remaining pricing delta. That distinction is not academic. It changes what “outperform” means depending on hold period and how leverage actually works by asset type. So instead of debating which market is “hot,” we modeled the deployment question:

We modeled two realistic entry points:

Hold periods:

Dallas serves as a repriced control market.

“Hot” markets tend to have:

That often comes with:

That can be good, especially at shorter hold periods.

But our framework is not trying to identify temperature. It is trying to identify fertility.

Fertility means:

Hot is often later cycle.Fertile is often mid lifecycle. That is the tension between Columbus (probability) and Indianapolis (opportunity).

We modeled realistic acquisition structures.

4-Plex Scenario

Equity deployed: $200,000; Purchase range: ~$800k property before closing and repairs

10-Unit Scenario

Equity deployed: $400,000: Purchase range: ~$1.3M property before closing and repairs

These are not the same properties across markets. In Columbus, lower entry cap rates mean higher price per unit. In Indianapolis, higher entry cap rates mean more income per dollar deployed.

Leverage is consistent within each asset class across metros. Equity requirements reflect realistic scaling.

Outperformance is defined as:

All scenarios use:

Each of the 10,000 simulations includes a catalyst outcome draw:

Probabilities are based on: lifecycle stage; stack depth; stack diversity; durability probability

Intel in Columbus is further along than Lilly in Indianapolis.That increases short-term execution probability.

Indianapolis has:

Each catalyst state shifts:

Compression is not assumed. It is probabilistic.

Interpretation: Columbus carries higher near-term probability because its flagship catalyst is further along.

Interpretation: At five years, Indianapolis’ remaining pricing delta becomes more influential.

Interpretation: With larger equity and lower leverage sensitivity, Columbus still wins near-term probability.

Interpretation: Over longer holds, Indianapolis benefits more from remaining repricing potential.

Columbus equals higher probability in shorter windows. Indianapolis equals greater convexity in longer windows. Both outperform a repriced control market in most simulations.

Timing determines the winner.

Cap Rate Compression- Decrease in exit cap rate relative to entry.

Convexity- Asymmetric upside if catalysts execute successfully.

Probability - Percentage of simulations in which an outcome occurs.

Pricing Delta - Remaining repricing potential implied by catalyst progression.

Catalyst Stack- Multiple independent capital investment families influencing demand durability.

10,000 simulations per scenario

Hold periods: 3 and 5 years

Equity:

4-plex: $200k; 10-unit: $400k; Consistent LTV within each asset class.

Modeled baseline variables:

Catalyst integration:

Each simulation selects Execute, Partial, or Stall using lifecycle-weighted probabilities.

State selection shifts:

Stack diversity reduces stall probability and tail risk.

CBRE U.S. Cap Rate Survey 2023–2024

JLL Capital Markets Outlook

Cushman and Wakefield MarketBeat

Federal Reserve Economic Data

U.S. Census Bureau Population Estimates

Intel Ohio development filings

Eli Lilly Indianapolis expansion filings

Indiana Economic Development Corporation

Ohio Department of Development