The valuation of real estate assets and the financial feasibility analysis of real estate development projects typically involve a lot of uncertainties such as the expected monthly rental income or the sales prices of the units, the vacancy rate, the construction or renovation costs, the maintenance costs, the exit yield, etc… Furthermore, real estate investment analysis is typically performed using discounted cash flow (DCF) models (NPV analysis) over long term periods, often anywhere between 10 to 30 years, thereby making the prediction of the value of the real estate asset or the profit margin of a real estate development a real challenge.
In this webinar we’ll demonstrate how @RISK can help to improve such real estate valuation or feasibility models by applying Monte Carlo simulation using appropriate probability distribution for representing the uncertainties related to the various inputs. On the basis of a probabilistic DCF model we’ll illustrate how the range of the predicted value of a multi-apartment building can be determined as well as how likely it is that one can achieve a targeted internal rate of return (IRR) for a residential real estate development project. During the webinar we’ll also briefly touch on some of the other powerful features of @RISK such as Tornado diagrams, goal seek and advanced sensitivity analysis.
Of course, the use of @RISK, the principles of the underlying probabilistic models, as well as the illustrated features can equally be applied to examples and cases outside of the real estate sector.