You may recall that a couple of weeks ago, I began to explore Stochastic Modeling concepts, or the idea of adding probability into my models, to get a more complete picture of the risk-return metrics of an investment. I became interested in the idea, after reading a thesis on the subject by Keith Chin-Kee Leung shared with me by one of our readers. The thesis posits that real estate investment professionals should go beyond the discounted cash flow model, an inherently deterministic model, and add variability to our assumptions. Then, with variability added, we could run thousands of iterations (Monte Carlo Simulations) and analyze the set of iterations using basic statistical principles. Thus, we could better understand the possible outcomes of an investment.
You might also find interesting: How to Run Monte Carlo Simulations in Excel
Finding the idea intriguing and using Mr. Leung’s thesis as a starting point, I built my first Monte Carlo simulation module for real estate. I added it to my Apartment Acquisition Model and last weekend shared the model in our Library of Real Estate Excel Models. As promised, I’m following that post up with a short video I recorded on how to use the Monte Carlo Simulation Module.
You can read more about the model, including find the download link, by checking out the post I wrote when I uploaded the model.
Video Tutorial – Using the Monte Carlo Simulation Module
The module can be turned on and off, making this particular apartment model both a deterministic model (model without randomness) and a stochastic model (model with random variation). Probability has been modeled into eight different assumptions (e.g. rent growth, renewal probability), and the model allows for either uniform or normal probability to be used. Keep in mind, this is my first attempt at using probabilistic analysis in real estate, so I’d love to hear your thoughts, comments, and critiques.
