Retail Real Estate Cycles as Markov Chains

Abstract

Executive Summary. Commercial real estate markets across cities evolve with considerable risk with respect to variables such as rent levels and growth, occupancy levels and trends, capital market developments, and construction conditions. The complex patterns are more understandable with the help of the real estate cycle models reviewed here. Describing the future with a set of alternative cycle points and their anticipated probabilities is an application of a well-developed area of probability theory, Markov chain analysis. Numerical examples show how descriptions of cycle conditions in an initial period can be used to generate risk formatted descriptions of cycle conditions. The results are plausible descriptions of future risks. Standard Markov chain calculations allow the real estate cycle analyst to anticipate the proportion of quarters that a market will reside in each cycle point.