Key Takeaways
- Monte Carlo simulation replaces single-point assumptions with probability distributions, producing a range of possible investment outcomes.
- Key outputs include mean/median NPV or IRR, probability of loss, and percentile ranges (10th–90th).
- The width of the output distribution reveals total risk; the downside tail reveals worst-case exposure.
- Correlations between variables (e.g., rent growth and vacancy) must be modeled to avoid unrealistic scenarios.
- The quality of Monte Carlo results depends entirely on the quality of input assumptions — poorly calibrated distributions produce misleading output.
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Test Your Knowledge
1.What is the primary advantage of Monte Carlo simulation over traditional DCF analysis?
2.A Monte Carlo simulation shows P(IRR < 0) = 12% and a 10th percentile IRR of -2.1%. What does this mean?
3.Why is it important to model correlations between variables in a Monte Carlo simulation?