City Research Online

Abstract

This article uses a small number of macro factors to model the performance of private US core real estate and associated blended strategies that incorporate listed and private non-core components. The macro factors selected are economic growth, real rate, expected inflation, the term structure, and credit spreads. Private real estate performance was de-smoothed using a nonlinear modeling approach that accounted for differing smoothing effects during identifiable regimes through market cycles. The estimated linear factor loadings are aligned with economic intuition and expectations, including real estate’s inflation hedging characteristics. Using threshold regression modeling to capture nonlinearities in the relationships, a smaller number of the factors were found to be of greater statistical significance. The impact of these factors is found to evolve over time, particularly during phases of market disruption. Although linear factor modeling remains the common approach to estimate risk–return exposures for asset allocation and portfolio risk management processes, the results suggest that these linear models should be adapted to consider these shifting relationships and resulting implications.

Publication Type:	Article
Additional Information:	This article has been published in The Journal of Portfolio Management: https://www.pm-research.com/content/iijpormgmt/51/11/142. Published by Portfolio Management Research.
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School Bayes Business School > Faculty of Finance
SWORD Depositor:	Symplectic Administrator

Export

Downloads