Real estate fund active management
| Pages | 494-516 |
| Published date | 07 September 2015 |
| DOI | https://doi.org/10.1108/JPIF-06-2014-0043 |
| Date | 07 September 2015 |
| Author | Stephen Lee,Giacomo Morri |
| Subject Matter | Property management & built environment,Real estate & property,Property valuation & finance |
Real estate fund
active management
Stephen Lee
Cass Business School, City University London, London, UK, and
Giacomo Morri
SDA Bocconi School of Management, Bocconi University, Milan, Italy
Abstract
Purpose –The purpose of this paper is to analyse the performance of UK property funds using the
dual sources of active management, Active Share and tracking error, to distinguish between the types
of active management styles used by funds.
Design/methodology/approach –The authors use data on 38 UK real estate funds and classify
them into five active management categories using the dual sources of active management, Active
Share and tracking error. Then, the authors compare their return performance against Active Share,
tracking error, fund size and leverage. Therefore the paper is able to answer two of the fundamental
questions of investment: does active management add value and what form of active management,
stock selection or factor risk, is better at adding value to the fund?
Findings –There are three main conclusions. First, the approach of Cremers and Petajisto (2009) and
Petajisto (2010) is able to classify real estate funds in the UK on their management activity into
categories that makes intuitive sense and seem stable over time. Second, balanced funds show
relatively low Active Shares and particularly low tracking errors, due to the benefits of property-type
diversification. In contrast, specialists funds display higher Active Shares and both low and high
tracking errors depending on their stock-picking approach; diversified or concentrated. Third, an
analysis over different time periods confirmed that funds in the sample essentially remained in the
same categories within the sample period, even during markedly different market return periods.
This implies that investors need to constantly monitor changes in the market and switch between fund
management styles, if at all possible.
Research limitations/implications –The analysis was only based on 38 funds with complete data
over the sample period and the relationship between fees and active management was not examined,
even though ultimately investors are concerned with returns after management fee. It would be
instructive therefore if the number of funds and time period was expanded to see if the results are
robust and to see whether management fees outweigh the benefits of active manager.
Practical implications –The findings should enable investors to make a more informed investment
decisions in the future.
Originality/value –To the best of the author’s knowledge this is the first paper to apply the dual
sources of active management, Active Share and tracking error, in the UK real estate market.
Keywords Impact on performance, Tracking error, Active share, Better classification of funds,
Real estate funds, Sub-period analysis
Paper type Research paper
Introduction
The traditional way to measure the active management of a fund is to calculate its
tracking error: as measured by the standard deviation of the difference in a fund’s
returns vs its benchmark returns (see inter alia, Alford et al., 2003; Higgins and Ng,
2009; Higgins, 2010). However, tracking error alone is an inadequate measu re of fund
active management since even very actively managed funds can in fact have rather low
tracking errors. Therefore judging the activity level of fund management based solely
on tracking error can be misleading. In addition, there exists plenty of “closet indexing ”
among these so called actively managed funds. Closet indexing is referred to when
Journal of Property Investment &
Finance
Vol. 33 No. 6, 2015
pp. 494-516
©Emerald Group Publishing Limited
1463-578X
DOI 10.1108/JPIF-06-2014-0043
Received 23 June 2014
Revised 25 May 2015
Accepted 28 May 2015
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1463-578X.htm
494
JPIF
33,6
a fund that claims to be actively managed, and therefore charges high management
fees, in fact acts like a passive index fund by closely replicating some benchmark index.
Finally while tracking error volatility makes sense and is easy to calculate, it only infers
what the manager is doing at the portfolio level and does not tell you how the tracking
errors were generated. For instance, Cremers and Petajisto (2009) argue that the two
distinct approaches to active management, stock selection or factor risk, can produce
substantially different tracking errors.
Cremers and Petajisto (2009) suggest that a fund’s level of active management
would be better understood by examining the actual holdings in the portfolio and
comparing those holdings to its benchmark index, which the authors call Active Share.
The authors argue that there are two reasons why Active Share is a useful method to
measure fund’s active management. First, since an active manager can only add value
relative to the benchmark by deviating from it, Active Share can help in identifying
managers capable of delivering positive αthrough their stock-picking activity. Second,
Active Share can also be combined with the traditional method of measuring the active
management of a fund, tracking error, to provide a more comprehensive way to
measure active management. In other words, by using the double sources of active
management, Active Share and tracking error, Cremers and Petajisto (2009) are able to
classify the funds into one of five investment management strategies categories: pure
indexers, closet indexers, diversified stock pickers, concentrated stock pickers and
factor bet funds.
To the best of the author’s knowledge the dual sources of active management,
Active Share and tracking error, have not been applied in the real estate market so this
paper is the first to apply such an approach to classify real estate funds in the UK into
the five active management categories identified by Cremers and Petajisto (2009). Then,
after the fund categorisation, we compare their return performance against Active
Share, tracking error, fund size and leverage. As such the paper proposes a style
classification of property funds different to that used by the Investment Property
Databank (IPD) and is able to more easily identify the form of active management that
contributes most to fund performance. In addition, the paper analyses the consistency
over time of this classification and as such our results will be of interest to financial
investors and consultants in revealing which factors should be considered in sele cting
real estate funds according to the point in the cycle. In other words, the paper
provides answers to the two of the fundamental questions of investment: does active
management add value and what form of active management, stock selection or factor
risk, is better at adding value to the fund?
The remainder of the paper is structured as follows. The next section discusses the
difference between active and passive management. This is followed by a review of the
approaches used to measure active management. Fourth section outlines the research
design. The next section presents the fund data. Sixth section presents the fund
classification results for the overall sample period, while the next section tests the
robustness of the classification results in three sub-periods. Eighth section then
compares the performance of funds against their level of active management, while the
final section presents the conclusions.
Active and passive management
Fund managers have two ways to exercise their business. They can operate an active
fund management policy, or, on the other hand they may be more passive. Investors
investing in actively managed funds seek expertise from the fund managers who are
495
Real estate
fund active
management
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