Estimating Returns on Commercial Real Estate: A New Methodology Using Latent‐Variable Models

Abstract

Despite their widespreao use as benchmarks of U.S. commercial real estate returns, indexes produced by the National Council of Real Estate Investment Fiduciaries (NCREIF) are subject to measurement problems that severely impair their ability to capture the true risk–return characteristics–especially volatility–of privately held commercial real estate. We utilize latent‐variable statistical methods to estimate an alternative index of privately held (unsecuritized) commercial real estate returns. Latent‐variable methods have been extensively applied in the behavioral sciences and, more recently, in finance and economics. Unlike factor analysis or other unconditional statistical approaches, latent variable models allow us to extract interpretable common information about unobserved private real estate returns using the information contained in various competing measures of returns that are measured with error. We find that our latent‐variable real estate return series is approximately twice as volatile as the aggregate NCREIF total return index, but less than half as volatile as the NAREIT equity index. Overall, our results strongly support the use of latent‐variable statistical models in the construction of return series for commercial real estate.