Estimating Commercial Property Fundamentals from REIT data

Abstract

In this paper we propose a new methodology for the estimation of fundamental property-level investment real estate time series performance and operating data using real estate investment trust (REIT) data. The methodology is particularly useful to develop publicly accessible operating statistics, such as income or expenses per square foot. Commercial property operating statistics are relatively under-studied from an investment perspective. To demonstrate the methodology and its usefulness, we estimate the time series of property values, net operating income, cap rates, operating expenses and capital expenditures, per square foot of building area, by property type (sector) at a quarterly frequency for multiple specific geographic markets from 2004 through 2018. We show illustrative results for Los Angeles offices and Atlanta apartments. The methodology is essentially an extension and enhancement of the so-called "Pure Play" method introduced by Geltner and Kluger (1998). It enables easy derivation of important basic data that should be useful for academic and industry practitioner analysts, derived from high quality stock market based information. The extensions and enhancements introduced here to the prior methodology allow estimation of actual quantity levels rather than just longitudinal relative values (index numbers). They also avoid the need for any data source other than published REIT data. Our methodology allows for an "additive" model structure that is more parsimonious to address the need for granular market segmentation. We also introduce a Bayesian framework that allows the estimation of reliable time series even in small markets.

Keywords: Real Estate Price Indices, Commercial Real Estate, REITs, Structural Time Series Modelling, Bayesian Inference, Real Estate Operating Statistics, Capital Expenditures, Operating Income and Expense

JEL Classification: R30, C01, C11