Using Monte Carlo Simulation to Support a Retail Real Estate Investment Decision Master’s Thesis

The academia and professional organizations in the field of real estate have raised discussion about adding probabilistic features into real estate valuations to take into account the uncertain characteristics of any valuation. Currently in the real estate sector the value of a property is usually calculated by an appraiser using a discounted cash flow model (DCF-model) to reach a single point valuation. The valuation of the appraiser is often falsely interpreted as an absolute truth, even though no cash flow model can be exactly certain unless the future can be correctly predicted. A more sophisticated application of the DCF analysis can be used to achieve a probability distribution of single point valuations. This application uses a tool that simulates the valuation process multiple times. It includes defining the input variables as ranges of possible values to be used in the valuation. This method is called Monte Carlo simulation. This master’s thesis looks to clarify how DCF is used when evaluating potential real estate investments, what are its main disadvantages, and can the decision process be enhanced using Monte Carlo simulation. These questions are answered by conducting a literature study where the frame of reference for the theoretical study is built and a case study where the acquisition of Shopping Centre Arabia is reviewed. In the revision process people involved in the acquisition process are interviewed and the material available for the initial real estate investment analysis is examined and developed to create a Monte Carlo simulation model. The results created by the model are compared with the results produced with a traditional DCF model during the acquisition process. The main disadvantage of using only DCF calculation to assess a real estate investment target is that DCF does not take into account the uncertainty that the input variables are subject to. In the empiric study it was recognized that a MCS model can support an initial analysis based on direct capitalization calculation or DCF calculation. MCS model provides numerical data about the uncertainty of the market value calculation results of a standard DCF calculation and therefore measures the level of comfort that the analyst has towards the DCF calculation.