Estimating Commercial Property Fundamentals from REIT data

David Geltner (), Anil Kumar () and Alex M. Van de Minne ()
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David Geltner: Massachusetts Institute of Technology (MIT)
Anil Kumar: Aarhus University
Alex M. Van de Minne: University of Connecticut

The Journal of Real Estate Finance and Economics, 2025, vol. 71, issue 3, No 4, 453-475

Abstract: Abstract In this paper we propose a new methodology for the estimation of fundamental property asset-level investment time series performance and operating data based on real estate investment trusts (REITs). The methodology is particularly useful to develop publicly accessible operating statistics for investment real estate, such as income or expenses per square foot. Commercial property operating statistics are relatively under-studied from an investment perspective. We show how the methodology can be used to 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 the quarterly frequency for multiple specific geographic markets from 2004 through 2018. We show illustrative empirical results for Los Angeles offices and Atlanta apartments. The methodology allows estimation of actual quantity levels, not just dimensionless index numbers (longitudinal relative). It allows for an “additive” model structure that is more parsimonious, thereby addressing 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 (search for similar items in EconPapers)
JEL-codes: C01 C11 R30 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11146-023-09962-z

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