Timing, Probability, Convexity

Before You Read This

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.

The Deployment Question

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

• At least three catalyst families
• Mid-to-late lifecycle execution visible
• Absorption alignment in B and B+ housing bands
• Structural friendliness not hostile
• Meaningful remaining pricing delta

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:

If I deploy real equity today, which metro is more likely to outperform?

We modeled two realistic entry points:

• $200,000 equity into a 4-plex
• $400,000 equity into a 10-unit property

Hold periods: 3 years and 5 years. Dallas serves as a repriced control market.

What "Hot" Means (and Why It's Not the Same as Fertile)

"Hot" markets tend to have:

• Higher execution certainty
• More media coverage
• More institutional capital flow
• Earlier repricing

That often comes with:

• Lower entry cap rates
• Less remaining compression
• A tighter downside distribution

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:

• Catalyst stacking that is diversified
• Execution probability that is rising
• Remaining repricing potential
• Absorption alignment in the housing bands investors actually buy

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

Capital Stack Assumptions

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.

What "Outperformance" Means

Outperformance is defined as:

• Higher Median IRR
• Higher 90th percentile IRR
• Lower probability IRR is below 8 percent
• Lower probability of negative return
• Higher probability of outperforming Dallas

All scenarios use local rent growth assumptions, metro-level vacancy context, asset-type leverage, and catalyst-weighted execution modeling.

How Catalysts Enter the Model

Each of the 10,000 simulations includes a catalyst outcome draw: Execute, Partial, or Stall. Probabilities are based on lifecycle stage, stack depth, stack diversity, and durability probability.

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

Indianapolis has broader mid-lifecycle stacking and greater remaining compression potential.

Each catalyst state shifts rent growth mean, vacancy volatility, and exit cap compression probability. Compression is not assumed. It is probabilistic.

4-Plex — $200k Equity — 3-Year Hold

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

4-Plex — $200k Equity — 5-Year Hold

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

10-Unit — $400k Equity — 3-Year Hold

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

10-Unit — $400k Equity — 5-Year Hold

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

The Core Insight

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.

Glossary

• 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.

Appendix — Monte Carlo Framework

• 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:

• Rent growth
• Vacancy volatility
• Expense inflation
• Exit cap rate behavior
• One macro shock event

Catalyst integration:

• Each simulation selects Execute, Partial, or Stall using lifecycle-weighted probabilities
• State selection shifts rent growth mean, vacancy volatility, and exit cap compression likelihood
• Stack diversity reduces stall probability and tail risk

References

• 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