monte-carlo-return-simulator

To run a Monte Carlo simulation for CRE investment returns, provide three-point uncertainty estimates for your inputs and execute the 5,000-trial setup to generate a probabilistic distribution of levered cash flows, IRR, and equity multiples.

VaR and CVaR in commercial real estate risk analysis are tail-risk metrics quantifying potential losses within a simulated return distribution. They are calculated alongside IRR and equity multiples by converting uncertainty inputs into probabilistic cash flow outcomes.

You can model value-add and opportunistic CRE deals with different hold periods. The simulator converts three-point uncertainties into year-by-year NOI, debt service, and terminal value to generate levered cash flows across various deal structures and timelines.

To handle correlation between inputs in a Monte Carlo cash flow model, the simulator applies Cholesky decomposition to the correlation matrix. This ensures correlated variables maintain realistic dependencies during the 5,000-trial distribution fitting process.

The best way to underwrite CRE tail risk using probabilistic returns is converting three-point estimates into a full simulated distribution. This calculates cash-on-cash, IRR, equity multiple, VaR, and CVaR while applying scenario overlays for sensitivity analysis.

IRR convergence during stochastic real estate modeling can fail due to unstable simulated cash flow sequences. The simulator addresses this with robust IRR convergence handling to ensure reliable metrics across the generated distribution of returns.