What are the benefits and limitations of using Monte Carlo simulation for scenario analysis in CRE?

1 What is Monte Carlo simulation?

Monte Carlo simulation is a statistical technique that uses random sampling to generate a large number of possible scenarios based on a set of inputs and distributions. For example, instead of using a single value for the vacancy rate, you can use a range of values with a certain mean and standard deviation, and let the simulation randomly pick a value for each scenario. By repeating this process thousands of times, you can create a distribution of outcomes that reflects the uncertainty and variability of the real world.

2 How to use Monte Carlo simulation for CRE?

To use Monte Carlo simulation for CRE, you need to have a financial model that calculates the key metrics and indicators of your project or portfolio, such as net operating income (NOI), cash flow, internal rate of return (IRR), net present value (NPV), and equity multiple. You also need to identify the variables that affect these metrics, such as market rents, operating expenses, cap rates, interest rates, leverage, and holding period. Then, you need to assign a probability distribution to each variable, such as normal, uniform, lognormal, or triangular, depending on the shape and characteristics of the data. Finally, you need to run the simulation using a software tool, such as Excel, @RISK, Crystal Ball, or ARGUS Enterprise, that can generate random samples from the distributions and output the results in the form of histograms, charts, tables, and statistics.

5 How to improve Monte Carlo simulation for CRE?

Monte Carlo simulation is not a perfect or foolproof method for CRE scenario analysis, but it can be improved by following some best practices. To ensure reliable and relevant data sources and methods, you should collect and analyze the data regularly to reflect changing market conditions and trends. Additionally, you should use appropriate distributions and parameters for each variable, testing their validity and robustness with goodness-of-fit tests, backtesting, and scenario testing. You should also use a reasonable number of iterations and scenarios to ensure the convergence and stability of the simulation results. To present the results, you should use clear visualizations and reports to highlight key findings, insights, and recommendations. Monte Carlo simulation should be used as a supplement to your expert knowledge and intuition, while incorporating other qualitative and quantitative methods to enhance your scenario analysis.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?