Property-Level Performance Attribution: Investment Management Diagnostics and the Investment Importance of Property Management

Abstract

Feng and Geltner describe a field demonstration of a new technique for property-level performance attribution based on the since-acquisition internal rate of return of the investment. The internal rate of return is parsed among the components of initial yield, cash flow change, and yield change, each of which (along with the overall internal rate of return) is compared against a NCREIF-based custom benchmark for each property over the same investment horizon.Among these three, cash flow change particularly reflects the investor’s property-level operational management over the holding period. The analysis is then applied across the Client Fund’s entire property portfolio to derive diagnostic indications about the strengths and weaknesses of the Fund’s investment management. The technique is applied to 42 institutional-quality property investments from acquisition through disposition spanning 1983–2008. In this case, the Client Fund is found to show strength (relative to NCREIF peers) in acquisition, but relative weakness in long-term post-acquisition operational management at the property level. The authors also find wide dispersion in outcomes at the property level, with a clear management pattern in the outlier, most successful cases of superior property-level operational management. Hence, improving property-level operational management emerges in the example portfolio as a key to better overall investment performance. Feng and Geltner speculate that this pattern may often be the case in large institutional funds due to their financial strength, which draws a first look at the best deals, but is offset by disadvantages in subsequent long-run operational management and disposition.

TOPICS: Real estate, big data/machine learning, equity portfolio management