Forecasting Real Estate Cycle Risks in Portfolios of Office Properties Across Cities

Abstract

Executive Summary. Relatively low-level Markov chain methods and widely available information allow this extension of real estate cycle risk analysis to office portfolios across cities initially in different cycle conditions. Examples include evaluation of cycle conditions at the end of a holding period and for cash flows from operations across a span of quarters. Standard spreadsheet functions serve to provide examples of changes in real estate cycle prospects, including before/after changes in portfolio weights, applying mean-variance dominance, mean-semivariance dominance, and stochastic dominance analysis.