alpha and persistence in real estate fund performance

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Alpha and Persistence in Real Estate Fund Performance

Shaun A. Bond & Paul Mitchell

Published online: 5 January 2010

# Springer Science+Business Media, LLC 2009

Abstract This paper investigates whether fund managers investing in the direct real

estate market can systematically and persistently deliver superior risk-adjusted

returns. The research that has been published has typically focused on the

performance of managers trading public real estate securities. Our study draws on

a unique data set of commercial real estate funds collated by the Investment Property

Databank (IPD) in the United Kingdom, covering up to 280 funds over the period

1981 to 2006. The widespread finding is that very few managers appear to be able to

generate excess risk-adjusted returns. Furthermore, there is little evidence of

performance persistence in either fund returns or risk-adjusted fund returns.

Keywords Commercial real estate investment. Fund manager performance

JEL Classification R33 . G11 . G23

Introduction

Alpha is a powerful concept in investment and fund management. It relates to the

delivery of superior risk-adjusted returns, either from an active fund manager or

from an asset class. Investors choose fund managers on the basis of their potential to

deliver alpha, and fund managers are often rewarded on this basis. However, there

has also been a long-running debate across asset classes as to whether or not active

fund management can systematically add value.

J Real Estate Finan Econ (2010) 41:5379

DOI 10.1007/s11146-009-9230-y

S. A. Bond (*)

Department of Finance and Real Estate, College of Business, University of Cincinnati, Cincinnati,

OH 45221-0195, USA

e-mail: [email protected]

P. Mitchell

Paul Mitchell Real Estate Consultancy Ltd, London E11 2SA, UK


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There has been comparatively little research on the extent to which real estate

fund managers can systematically and persistently deliver superior risk-adjusted

returns, and on the magnitude of any such performance. While there has been

research on real estate mutual fund managers trading public real estate securities

(Chiang et al. 2008), and the performance of managers of public real estatecompanies (Brounen et al. 2007), few studies have investigated the performance of

large institutional real estate fund managers investing directly in commercial

property assets. In many countries, such as the United Kingdom, this segment of

the investment market dominates the value of publicly traded real estate funds or

mutual fund-type products aimed at retail investors (see discussions in Chun et al.

2004; Bond et al. 2007).

Why study real estate fund managers? Aside from the interest in this topic by the

professional investment community, there are other reasons to consider that this is a

suitable topic for academic research. Commercial real estate markets seem ideal toconsider the implications of the Grossman and Stiglitz (1980) hypothesis given the

widely held perception of the value of private information in these markets.1

Furthermore, there is considerable evidence to suggest that the returns on

commercial real estate assets exhibit persistence (Lee and Ward 2000; Devaney et

al. 2007). This is potentially important in two respects. Firstly, it is possible that the

artificial smoothing of property valuations may induce persistence in fund returns.2

This could lead to managers being rewarded for high levels of persistent

performance when the apparent persistence is really an artefact of the data

aggregation process. Secondly, as suggested by Key and Marcato (2005), knowledgeof the underlying persistence may point to profitable momentum trading strategies

that could be employed to achieve superior risk-adjusted performance. However, is

not clear that this persistence could be profitably exploited given the high transaction

costs and illiquidity that underlies trading in commercial property assets. Also, such

a study may shed light on the performance of managers of other non-publicly traded

assets, for example private equity fund managers and infrastructure fund managers.

An extensive proprietary database of real estate fund manager performance has

been used in this study. This data on annual fund performance from 1981 to 2006,

which is longer and more extensive than many other studies on real estate fund

performance, allows us to investigate the extent to which real estate fund managers

exhibit a high level of (risk-adjusted) performance and also whether they are able to

maintain good performance, if it is found. By necessity, a secondary objective of this

paper is to investigate the most appropriate form of risk-adjustment in real estate

markets. It is recognised, however, that the form of risk-adjustment adopted in this

paper may not be universally accepted and for this reason performance relative to a

benchmark is also examined in this study.

1 Grossman and Stiglitz (1980) argue that if information is costly, the usual assumption of the efficient

markets hypothesis, that prices reflect all available information, may not hold. It is only worthwhile for

investors to acquire information if they obtain a return from doing so.2 The appraisal-smoothing problem in real estate has been extensively discussed in the literature, see Bond

and Hwang (2007) for a summary of the literature. However, given the long-term nature of real estate

investors and the sample horizons used in this study, such short term persistence effects may not be

relevant to the analysis.

54 S.A. Bond, P. Mitchell


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The next section of this paper presents a literature review on the key academic

studies on alpha and persistence in commercial real estate markets and other asset

classes. Methodology details the methodologies adopted in this study for assessing

alpha and persistence in UK property and for the risk-adjustment model. The Data

describes the IPD database and the specific samples of data used and types ofanalysis undertaken in the study. Performance, Alpha and Persistence in UK

Property Fund Management presents the detailed quantitative analysis of alpha and

persistence and the factors behind it. The conclusions are presented in Conclusion.

Literature Review

One of the earliest attempts to evaluate the performance of mutual fund managers

was by Jensen (1968). Using the Capital Asset Pricing Model (CAPM) as a basis,Jensen showed how a managers superior performance could be captured by the

intercept term (alpha) in the model. In a sample of mutual funds Jensen found that

the average value of alpha was negative, implying that managers underperformed

relative to a risk-adjusted benchmark. A large number of studies followed the work

of Jensen. In a highly cited article, Grinblatt and Titman (1989) identified the

presence of a skilled set of fund managers. They noted that this superior performance

was only found in gross returns. After allowing for the effects of fees the superior

performance diminished. The dampening effect of fees on performance is a recurring

theme in the literature.The question of whether the performance of a manager continues over different

time periods was considered by Hendricks et al. (1993). They found that it was

possible to generate economically significant returns by developing a trading

strategy based on the past performance of fund managers. Showing strongly in the

research was that the performance of managers with icy hands also persists. In

fact, Hendricks et al. point out that the persistence in returns for the poorest

performers was much more prevalent that the persistence in performance for the top

managers. Persistence in performance was also found in studies by Goetzmann and

Ibbotson (1994) and Brown and Goetzmann (1995).

Carhart (1997) provides a critical appraisal of the Hendricks et al. study. In

particular he adjusts for momentum effects in the underlying equity market data and

potential survivorship bias in the data (something which the subsequent article by

Carhart et al. 2002 and a number of articles on hedge funds, notably by Malkeil and

Saha 2005, explore further). While noting some evidence of superior performance,

Carharts overwhelming conclusion is that...most funds under-perform by about the

magnitude of their investment expenses.

More recent evidence using bootstrap techniques has provided support for the

notion that there is a small group of superior fund managers and the superior

performance of these managers persists over time. Kosowski et al. (2006) used

bootstrap techniques to allow for the non-normal distribution of fund alphas and

found evidence that the large positive alphas of the top 10% of fund were unlikely to

arise by chance. Furthermore, the authors found that this performance persisted over

time. In addition Kosowski et al. (2007) found similar evidence of a small group of

elite hedge fund managers that showed persistence in their level of performance.

Alpha and Persistence in Real Estate Fund Performance 55


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Busse and Irvine (2006) also found support for performance persistence using

Bayesian statistical methods. An interesting additional finding was that Bayesian

alphas were useful for predicting future outperformance by fund managers.

Performance and Persistence in Real Estate Funds

One of the few studies that evaluated the topic of performance persistence for

property fund managers was provided by Hahn et al. (2005). Using data on real

estate opportunity funds and a methodology similar to that employed in this study,

the researchers found some evidence of persistence among real estate opportunity

fund managers. In Hahn et als study, performance persistence was measured across

different funds raised by the manager. The research indicates that as much as 20

24% of subsequent fund performance may be related to past fund performance.

However, due to the nature of fund raising that takes place, it may be difficult forinvestors to profitably exploit this finding because it can be several years before the

performance of a fund is clearly determined.

While not a study on fund performance, Ling (2005) directly addressed the

question of whether participants in the direct real estate market could capitalise on

the perceived market inefficiencies of commercial real estate to produce accurate

market forecasts. He found no evidence that a published consensus forecast,

compiled from the forecasts of institutional real estate owners and managers, was

helpful to investors. While there are many caveats in such as study,3 it does not

appear to support the idea that participants in the commercial real estate market canexploit informational inefficiencies.

A more extensive collection of research is available on the performance of

managers of funds focused on publicly traded real estate securities. Kallberg et al.

(2000) examined the performance of mutual funds that invest only in the REIT

sector. They found that managers do add value through active portfolio management

and estimate that an extra two percentage points annually is added to performance

relative to passive funds. Surprisingly, they found little evidence of performance

persistence. Published at a similar time, Gallo et al. (2000) also found positive

abnormal performance for real estate mutual funds. However, their sample was

smaller than Kallberg et al. and only covered a six-year period. It also provided little

evidence from the multi-factor performance benchmarking models that are now

commonly used in the literature.

Interestingly, other studies, using different time periods and alternative bench-

marking models, have failed to find evidence of positive abnormal performance

(ONeal and Page 2000; Lin and Yung 2004). While not finding significant positive

performance, Lin and Yung did note evidence of short-term performance persistence

among both high and low-performance funds.

Chiang et al. (2008) and Hartzell et al. (2009) have attempted to reconcile the

apparent differences in these earlier studies. Using different approaches, but focusing

closely on the specification of the benchmarking models employed, both sets of

authors conclude that there is little evidence of outperformance by real estate mutual

3 That is, the best forecasters may keep their forecasts private, and owners and managers may have other

objectives in submitting their forecasts, for instance, to make their region look more attractive to investors.



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fund managers. Hartzell et al. in particular provides an extensive discussion of

benchmarking and highlights the importance of introducing factors closely

associated with the real estate market.

Eichholtz et al. (2009) provide a comprehensive study on the performance of

global (listed) real estate mutual funds. Using a similar methodology to that used inthis paper, they found that global and European fund managers were able to add

value, whereas Asian, Australian and North American managers did not. This raises

interesting questions about the way markets operate in these areas. When

performance benchmarks were corrected for investment style, it was found that in

addition to outperformance by global and European managers, the Asian managers

also demonstrated positive performance in excess of their benchmarks.

The literature on the performance of REIT managers has been well summarised in

Brounen et al. (2007). Their study focused on the impact of trading intensity and

acquisitions in understanding the investment performance of publicly-traded realestate firms in the US, UK and Australia. The findings of the study suggest that it is

difficult for managers in these companies to generate outperformance based on an

active trading strategy. However, their conclusions are sensitive to the way in which

trading activity was measured.

While a number of studies have focused on real estate mutual funds, there are

three key points of difference between that set of literature and the current study:

& the nature of the funds examinedinstitutional investment in direct real estate

assets as compared to products which invest in publicly-traded real estate

securities aimed predominately at retail investors;& the size and depth of the sample studiedthis study contains a much larger

sample of funds; and,

& the frequency of the datathis study uses annual performance data as opposed to

monthly data in the mutual fund studies.4

Methodology

This section outlines the main methodological approaches adopted for analyzing the

performance of fund manager returns in this study. An interesting dilemma

encountered with this research is that the multi-factor approach to measuring risk-

adjusted performance is not well developed for commercial real estate funds

investing directly in the real estate market (as opposed to real estate mutual funds

investing in REITs). In keeping with other performance studies where the approach

to benchmarking is not well established (such as hedge funds and private equity

funds), we evaluate funds using both raw returns (no risk adjustment applied), and at

least one form of risk-adjusted returns series. The following discussion of

methodology is based on Brown and Goetzmann (1995) and Carhart (1997).

4 It would have been preferable to use higher-frequency data. However, in the UK such data is much less

extensive than annual data, relating to a small number of funds on a monthly basis (around 70 compared to

over 200 annually) and to a short period (2001 onwards) on a quarterly basis. There are also indications

that higher-frequency data would not have enhanced the analysis.



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However, the selection of models for consideration is limited by the low frequency

of the data set (in this case annual returns).

Two methods dominate the literature on benchmarking, characteristics-matched

benchmarks and regression-based benchmarks.5 However, the application of either

approach to assessing risk-adjusted performance for real estate fund managers has been limited. The one exception to this has been the extensive research on risk

models for real estate mutual funds discussed in the literature review above.

A characteristics-matched approach, while theoretically interesting, is not possible

with the proprietary data set available for this study. Also it is not clear what

characteristics should be used in the sorting procedure. Hence in this study we focus

on regression-based models to assess risk-adjusted performance.

For the period 19812006, three models of the return in excess of the risk-free

interest rate6 were investigated using performance quartiles comprising each years

corresponding funds. Each of these models is described below:

1. A single index model (using the IPD Universe excess total returns index as a

benchmark). The beta in this model simply measures the sensitivity of the funds

return (in excess of the risk-free rate) to that of the IPD Universe.7

2. A version of Sharpes index model (Sharpe 1992) using excess returns in the

IPD office, retail, industrial and the other property sectors. In this case, as

fund managers cannot go short in the property market and the sectors they can

invest in are reasonably defined, the coefficients of the Sharpe index model are

constrained to be positive and also to sum to one, thereby ensuring that the fund

is fully invested.

Sharpes model can be written as:

rpt gp0 XKj1

gpjIjt upt 1

Where

gpj ! 0;j 1; ::;K

PKj1 gpj 1

and pt is serially uncorrelated and distributed as N 0;s2u

.

Annual excess returns on fund p in year t are represented by the variable rpt.Annual excess returns on the four main real estate sectors (office, retail, industrial,

and other) in year t are shown by the variable Ijt (where j indexes sector type, K is

the total number of sectors). The four estimated coefficients for fund p (gpj, j =1 to 4)

in this model represent average portfolio exposures for the fund manager over the

estimation period to the respective sectors.

5 See Chan et al. (2006) for a detailed survey of risk-adjusted benchmarking of fund manager

performance.6 The risk-free rate for each year is determined by the quarterly average three-month Treasury bill rate.7 As explained in The Data, the IPD indices are derived from details voluntarily submitted to the IPD by

funds for benchmarking and performance measurement and attribution.



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3. A model using returns on publicly traded securities (the European Public Real

Estate Association [EPRA] index and a corporate bond index), as factors in a

multifactor model. In this case the coefficients of the factors were not

constrained in the way they were for the Sharpe index model.

In all three models, alpha represents the (expected) excess return, that is, the

overall return (over the risk-free rate) less that due to risk. However, because the risk

factors are different, its value is likely to differ across the three models.

In applying the models it is found that the Sharpe index model (using four

property sectors) provides the highest level of explanatory power. This model is

adopted for the analysis of individual fund returns over ten-year horizons. However,

for five-year horizons, there are insufficient degrees of freedom to apply the Sharpe

Index model, so we use the single-index model, which was narrowly behind the

four-factor model in terms of goodness of fit (R2

typically greater than 0.75) in theinitial investigations.

Attributes of Performance and Persistence

As a final evaluation measure we employ the persistence test of Hendricks et al.

(1993) to determine whether risk-adjusted performance for each fund in the ranking

period is a predictor of risk-adjusted performance in the evaluation period. While

this test has been criticized by Carhart (1997) because of the potential econometric

problems of using a previously estimated variable in a secondary regression, it hassome intuitive appeal and is included along with a range of other fund

characteristics. This regression is intended to investigate whether any known fund

characteristics can be used to predict subsequent fund performance. If such a

regression had strong explanatory power, it may be possible to develop a

profitable trading strategy for picking top performing funds. More discussion of

this approach is included in Performance, Alpha and Persistence in UK Property

Fund Management.

The Data

This section introduces the source of the data used in Performance, Alpha and

Persistence in UK Property Fund Management to analyse the persistence of property

performance and the characteristics of the sample of data used. It also considers the

extent of any bias associated with the data.

The IPD Fund Database

In common with most studies of this type, our analysis focuses on the performance

of specific funds rather than fund management houses or individual fund managers.

In doing this, it draws on the records collated by the Investment Property Databank

(IPD) since 1981. Funds voluntarily submit their details to the IPD for independent

performance measurement and benchmarking. The IPD (2007) estimates that its

records cover 55% of professionally managed investment property in the UK. This



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definition excludes, amongst others, small private landlords, owner-occupied

properties, and funds that own the operating business as well as the property (e.g.

hotels, hospitals).

Potential biases in such manager universe indices are widely noted in the

practitioner and academic literature. In the IPDs database, such biases may be both positive and negativefor example, resulting from some collective skill that

professionally managed funds add or, alternatively, from those who eschew

benchmarking (such as opportunistic or absolute return funds). In this later respect,

Hahn et als (2005) conclusion that some US real estate opportunity fund managers

persistently out-perform is notable.

The histories of funds newly entering the database may be retrospectively added.

Similarly, the historic records of funds expiring are also retained up to their last full

calendar year. Funds expire not only when they wind up but also when they merge

or split, the portfolio management company changes, or when there is a substantialchange to the name of the fund. Unfortunately the database does not identify the

reason why funds wind up.

The IPD performance data relate to the underlying property assets and in this

respect are ungeared (unlevered). However, funds exposures to indirect holdings,

which may be geared (levered), such as property unit trusts, REITs etc, are also

included. Property unit trusts, REITs and other listed property companies are

included in the database but, in the same way as other portfolios, the data relate to

the unlevered performances of the underlying property assets.

Returns are measured net of property management costs (letting, rent reviewand general property management costs). However, they do not take account of

fund management costs, for which no estimates are available. For a general,

balanced fund, interviews with investors and investment consultants suggest that

fund management costs are typically in the range 2540 basis points, with

performance-related fees increasing and (less commonly) reducing these very

marginally. Other than this marginal effect from performance-related fees, inter-

views with investors suggest that the best fund managers charge only marginally

higher fees (510 bps).

The growing number of specialist vehicles and value-added funds (albeit, in

accounting for about 14% of funds, still in the minority) tend to charge slightly

higher base fees than the general funds; performance-related fees are also understood

to be higher. Surveys by the European Association for Investors in Non-listed Real

Estate Vehicles (INREV), suggest that fund management fees for valued-added

funds are marginally higher at around 60 bps.

Given this small variation in fees amongst the type of funds in the IPD universe,

we do not believe the inability to net fund management fees out of our estimates of

relative performance and alpha will significantly alter our conclusions.

Quantifying Fund and Benchmark Performance

In contrast to IPD practice, in this study funds are combined on an unweighted basis

to form sample averages. This is consistent with the approaches adopted in studies of

other asset classes and treats each fund equally, avoiding biases brought about by

differences in size.



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All funds in the IPD database are included in the analysis until they expire, but on

the condition that they are in existence for the duration of a qualifying period (i.e.

the ranking period defined below).

Relative performances of funds against the benchmark are calculated in line with

the IPDs methodology, i.e. as the ratio of the fund return to the benchmark return.

Ranking and Evaluation Periods

This analysis is based on fund performance over three, five and ten-year periods.

Performance over one particular period is then compared with the following period.

The initial period is termed the ranking period, the following one the evaluation

period. In total, 12 sets of data are examined:

& one set of ten-year data (19871996 ranking period vs 19972006 evaluation

period);

& four sets of five-year data (19821986 vs 19871991, 19871991 vs 19921996,

19921996 vs 19972001, 19972001 vs 20022006); and,

& seven sets of three-year data (19831985 vs 19861988, 19861988 vs 1989

1991, 19891991 vs 19921994, 19921994 vs 19951997, 19951997 vs

19982000, 19982000 vs 20012003, and 20012003 vs 20042006).

Funds are ranked into quantiles (specifically quartiles and deciles) according to

their performance over each ranking period and each evaluation period. Performance

of the set of funds in each quantile is calculated as the unweighted fund average.A benchmark return for all the three, five, and ten-year periods is calculated as the

annual average of the unweighted average of all funds in the sample under

investigation. Such benchmarks will differ to the IPD Universe, primarily because

this study does not include all funds in each period and because fund returns are

unweighted.

Fund Numbers, Attrition, Creation and Bias

Previous studies of persistence in other asset classes (see, for example Carhart et al.

2002) have identified the survivorship bias generated by excluding funds which

subsequently die. This study limits survivorship bias by including some funds

which subsequently expire. However, some bias still may exist on account of the

following:

1. Funds are excluded at the outset if they do not survive throughout the whole of

the ranking period. As Table 1 shows, the number of funds affected is relatively

small up until the mid-1990s but this increases and is more substantial

(representing about 35% of the number of funds) from the late 1990s. In

general, the worst performing funds have a greater probability of expiring, so the

benchmark return used will be marginally inflated and the ranking of the funds

in this studys sample could be overstated.

2. Funds not in existence at the beginning of each periods analysis are excluded

from the analysis. Table 1 shows that the number of funds excluded in this way

is large, comparable to the total number which qualify for inclusion in the



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sample. Bias could arise because newly formed funds appear initially to out-

perform mature funds8; hence if the newly formed funds are excluded from the

analysis, it will tend to flatter the ranking and the relative performance of mature

funds in this studys sample. Of those included in the analysis, the proportion of

funds in the upper quartiles is likely to be exaggerated, and the proportion in the

lower quartiles understated; the estimates of persistence will be correspondingly

affected.

A detailed discussion on the impact of these omissions on the data used in the

study is contained in the appendix.

Characteristics of Funds

The IPD Universe encompasses a range of fund types. Table 2 presents details of

these for a number of the samples used in the analysis. Segregated pensions are the

largest fund type throughout, and have broadly maintained their weighting.

However, the weight of the second and third largest fund types in the 1980sthe

life funds and the unit-linked life and pension fundshas diminished over the last

decade or so. The pooled pension funds have also declined in significance.

8 This is inferred by comparing the performances of the evaluation and ranking periods of the same period

(e.g. the set of funds in the evaluation period 19921997 with those of the ranking period 19921997)

the latter includes the funds created during the previous ranking period.

Table 1 Number of funds in the sample compared to number in the IPD Universe

10-year horizon 5-year horizons

19872006 1982

1991

1987

1996

1992

2001

1997

2006

Funds existing at start of analysis 200 141 200 242 277

Funds without full ranking period history 30 0 5 29 88

Funds in starting sample 170 141 195 213 189

Funds ceasing to exist during evaluation period 85 4 25 77 49

Surviving funds at end of analysis 85 137 170 136 140

Funds starting during the entire analysis period

(net of deaths)

195 105 107 134 140

Funds in existence at end of analysis 280 242 277 270 280

The first row of the table shows, for each window of analysis, the number of possible funds that exist in

the IPD fund database. Row two of the table shows how many funds are excluded because they do not

cover the entire history of the ranking period. Hence, when the data in row two is subtracted from row

one, the number of funds included in the sample for the ranking period is shown in row three. The fourth

row displays the number of funds that cease to exist during the evaluation and row fifth shows how many

funds are still in existence at the end of the period of analysis. Row six shows the number of funds started

over the period of analysis (this included both the ranking period and the evaluation period) and this row is

added to row five to show how many funds exist in the IPD fund database at the end of the period of

analysis (shown at the top of each column). Note that while funds are not included if they start up during

one part of the analysis, the fund may be included in a subsequent horizon window



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By contrast, the unregulated property unit trusts and other unitized funds and, to a

lesser extent, the other category (which covers property companies, charities, and

traditional institutions such as the Crown) has grown in importance.

Performance, Alpha and Persistence in UK Property Fund Management

This section assesses the extent of performance persistence in UK real estate funds

and quantifies the magnitude of any out-performance (alpha). It also examines the

characteristics of funds according to performance and the factors behind perfor-

mance. A brief overview of the ranges of annual performance across funds is

presented, followed by an examination of the subsequent performances of cohorts of

funds ranked according to their initial performance.

The bulk of the analysis examines performance over horizons longer than 1 year,

i.e. three, five, and ten-year periods. The first part considers the persistence of

relative performance and the magnitude of such performance and follows the

methodology described in Methodology.

The subsequent part considers the persistence of risk-adjusted performance,

having first quantified the extent of Jensens alpha across property funds. This is

done because some out-performance may be compensation for risk rather than

reflecting genuine fund manager skill. This analysis draws on the risk models

introduced in Methodology. Finally, the characteristics of and factors behind

performance and alpha are explored.

Table 2 Distribution of funds by type in the sample

Funds continuously in existence during:

10 years 198796 5 years 19972001 3 years 20012003

Life and general insurance funds 18% 15% 14%

Unit-linked life & pension funds 26% 20% 18%

Segregated pension funds 37% 37% 34%

Pooled pension funds 8% 5% 3%

Unregulated PUTs & other unitised funds 4% 13% 18%

Other (property companies, charities etc.) 7% 10% 13%

TOTAL 100% 100% 100%

To demonstrate the types of funds in the sample, this table shows the composition of the sample by fundtype and for the different sample periods shown in the table. Unlike studies, such as Brounen et al. (2007),

property companies form only a small part of the sample. The largest category of funds is segregated

pension funds. These are separate accounts of pension funds, either managed in-house or by a third party;

pooled pension funds are co-mingled schemes run by fund managers. Unit-linked life and pension funds

are schemes associated with life insurance and pension policies run by the life insurance companies and

whose performance is linked directly to that of the property fund; these contrast with the life and general

funds of the insurance companies where property forms part of a multi-asset exposure and where policy

holders returns are calculated differently. Unregulated property unit trusts (PUTs) etc. are collective

investment schemes marketed at institutional and retail investors



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Relative Performance and its Persistence

Figure 1, Panels AD illustrate the performance of funds grouped according to their

initial ranking. For example, all the funds in the IPD Universe in 1987 are ranked

according their performance in 1987 and allocated to a quartile; the (unweighted)performance for each quartile in 1987 and for the same set of funds in subsequent

years is then calculated. Note that there will be some attrition in the number of funds.

Similar calculations are undertaken using 1992, 1997 and 2002 as starting points.

The results are presented relative to the unweighted average performance of all

funds.

By definition, the relative performance in the starting year of the top quartile is

relatively strong, with the converse applying to the bottom quartile. For the 1987 and

1992 cohorts, Panels A and B reveal that this initial relative performance very

quickly dissipates, to the extent that each cohorts performance fluctuates closely

around the average after one or two years. The same is true for the 1982 cohort,

which is not illustrated. In these cases, one years strong (or weak) performance

A : Subsequent relative performance of quartile fundsin 1987

B : Subsequent relative performance of quartile fundsin 1992

C : Subsequent relative performance of quartile fundsin 1997

D : Subsequent relative performance of quartile fundsin 2002

-10

-8

-6

-4

-2

0

2

4

6

8

10

1987 1989 1991 1993 1995 1997 1999 2001 2003 2005

Relativeperformance(%)

Top quartile

2nd quartile

3rd quartile

Bottom quartile

-10

-8

-6

-4

-2

0

2

4

6

8

1992 1994 1996 1998 2000 2002 2004 2006

Relativeperformance(%)

Top quartile

2nd quartile

3rd quartile

Bottom quartile

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Relativeperforman

ce(%)

Top quartile

2nd quartile

3rd quartile

Bottom quartile

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

2002 2003 2004 2005 2006

Relativeperforman

ce(%)

Top quartile

2nd quartile

3rd quartile

Bottom quartile

Fig. 1 Performance and Persistence in Real Estate Fund Returns: The graphs above show the

performance of fund grouped by quartiles across the sample period. Panel A displays the average

(unweighted) relative-performance of funds grouped into quartiles based on the performance in 1987. In

subsequent years the average performance of the funds based on the 1987 sorting is calculated and thedifferent colour lines trace the path of the performance of the quartiles. Similarly Panel B displays the

performance of quartiles based on a sorting of funds existing in 1992. Panel C displays the initial

subsequent performance of funds existing in 1997 and sorted into quartiles based on performance in that

year. Interestingly there is evidence that the performance ranking is maintained for the first 4 years of the

sample and this ranking is also found to hold in the final 2 years of the sample. Persistence in initial return

rankings is also found in Panel D based on funds existing in 2002. However, by 2005 the initial fund

performance has converged and the return for funds across quartiles is virtually indistinguishable



14/27

tends not to persist. However, this is less the case for the 1997 and 2002 cohorts

where the performance of the top and bottom quartiles seems to persist for longer (2

to 3 years), after which it fluctuates around the average.

There are similar findings at the more extreme ends of the return distributiontop

and bottom decile funds eventually fluctuate around the average fund performance,reaching such a state quickly in the 1980s and first half of the 1990s but taking

longer from the second half of the 1990s.

This graphical analysis presents visual clues on the extent of persistence in UK

real estate fund performance. The following sections explore this more robustly.

The Persistence of Medium Term Relative Performance

The analysis examines the persistence of relative performance over three, five, and

ten-year horizons. The three and five-year horizons correspond to the periods whichinvestors typically judge property performance, while the ten-year horizon is a more

demanding test encompassing a number of property market cycles.

The study does not examine in detail persistence over a one-year horizon as it can

be both impractical (given propertys illiquidity) and inefficient (on account of

transaction costs) to rebalance portfolios over such a short horizon. It might also be

expected that appraisal smoothing of asset values may bias one-year persistence

tests. Consistent with this rationale, and the findings of Lee (2003), Figure 1

suggests (particularly for the 1997 and 2002 cohorts) relatively high levels of

persistence over one-year horizons for both the best and the worst performers.The findings on medium-term performance persistence are presented in varying

degrees of detail. Tables 3 and 4 below present the averages of each set of horizons

(i.e. of all the sets of three, five, and ten-year periods which are examined). Full

Table 3 Proportions remaining in top quantile rankingsrelative performance

Top decile in

both periods

Top quartile in

both periods

Top half in

both periods

Including expired funds

10 year horizon 12% 16% 26%

All 5 year horizons 16% 30% 44%


Excluding expired funds




This table compiles information from the individual transition matrices for each ranking and evaluation periodacross the sample period (covering all ten-year, five-year and three-year horizons). The first column of data

shows the proportion of funds that remain in the top decile for both the ranking and evaluation periods.

Similarly the second column of data shows the percent of funds that remain in the top quartile in the ranking

and evaluation periods, and the final column shows the proportion of funds with above median performance in

each of the ranking and evaluation periods. The first three rows of data present information from the transition

matrices that explicitly accounts for the funds that terminate during the evaluation period. The final three rows

show information only from those funds that remained in operation during the evaluation period



15/27

details for each of the specific three, five, and ten-year periods are available in

supplementary tables from the contact authors website.

Table 3 shows the proportion of funds in the top 10%, 25% and 50% in the first

three, five, or ten-year period retaining such rankings in the following three, five, or

ten-year period. Table 4 shows the corresponding proportions for the poorest

performing funds.

Tables 3 and 4 show a mixed picture of persistence. The statistical tests emphasise

this. None of the ten-year tests are significant. The overall distributions are

significant in only one (out of four) of the five-year horizons, and in two (out of

seven) of the three-year horizons; at the aggregated level, both the three and the

five-year distributions are significant at the 5% level. Excluding expired funds

from the transition probabilities results in none of the five-year horizons being

significant and only one of the three-year horizons being significant; the aggregated

three and five-year horizons remain significant. Only two (out of seven) of the

three-year cross-product ratios are significant at the 5% level, although the aggregated

ratio for the three-year horizons is significant. The indications are strongest for the best

performers over three and five-year horizons where the transition probabilities for

those consistently in the top quantile are relatively high.

Furthermore, Table 3 indicates that those experiencing top-decile performance

have a particularly high probability of repeating such good performance. Persistence

also exists amongst those in the top quartile although this is less striking than

amongst the top decile performers (subsequent checks show that the second decile

performers have a lower probability of staying in the top two deciles than the top

decile performers). There is no evidence of persistence within the top 50% of funds.

Table 4 Proportions remaining in bottom quantile rankingsrelative performance

Bottom decile in

both periods

Bottom quartile in

both periods

Bottom half in

both periods

Including expired funds




Excluding expired funds




This table compiles information from the individual transition matrices for each ranking and evaluation periods across the sample period (covering all ten-year, five-year and three-year horizons). The first

column of data shows the proportion of funds that remain in the bottom decile for both the ranking and

evaluation periods. Similarly the second column of data shows the percent of funds that remain in the

bottom quartile in the ranking and evaluations periods, and the final column shows the proportion of funds

with below median performance in each of the ranking and evaluation periods. The first three rows of data

present information from the transition matrices that explicitly accounts for the funds that terminate during

the evaluation period. The final three rows show information only from those funds that remained in

operation during the evaluation period



16/27


17/27

the ten-year horizon the lowest performing funds outperformed during the evaluation

period.

An interesting interpretation of the performances in the columns entitled

evaluation is that they represent the relative return of investing, costlessly, in the

previous periods hierarchy of funds. For example, a strategy of investing in the topquartile group of funds from the previous five-year period would have yielded only

0.4% per annum in excess of the benchmark. It is clear that, after accounting for

transaction costs (which in the UK total 7.5% of capital value for a round-trip), a

strategy of switching investment into a fund which performed well in the previous

period on average will not have paid off.

Risk-Adjusted Performance and its Persistence

The above results provide an analysis of the persistence in property returns notadjusted for risk. This evidence is important in understanding the dynamics of total

returns generated by fund managers. However, many investors are interested not

only in total returns but in total returns adjusted for risk. Methodology considered

possible models that could be used in adjusting for the risk exposures of fund

managers. There is evidence that even simple models can account for a large amount

of the variation in individual fund manager performance. Accordingly, the factor

models identified in Methodology are employed in the following analysis.

The estimates of Jensens alpha from the ten and five-year models described in

Methodology

are summarised in the following section. Then, as in the section above,this information is used to examine the transition between performance quantiles.

The first stage in the analysis applied the four property sector Sharpe model to

two ten-year periods (198796 and 19972006) to evaluate the performance of UK

real estate fund managers. The four IPD sectors (retail, offices, industrials, and other

property) represent the benchmarks for the model. Each equation is estimated for a

ten-year period. Hence, there is a general caveat that the model has limited degrees

of freedom. Table 6 shows the resulting estimates of alpha over the ranking period

(19871996) and the evaluation period (19972006). Interestingly, only the top

Table 6 Estimates of 10-year alpha for UK property fund managers

Quartile ranking 1987 to 1996 1997 to 2006

Quartile 1 0.92 1.86




Top decile 1.93 2.63

Bottom decile 4.39 1.74

Using the style model of Sharpe (1992) estimates of fund outperformance (alpha) are shown over two ten-year

horizons. All funds are allocated to quartiles and the (unweighted) average outperformance measure is

computed for each quartile along with the top and bottom decile. In the first period, only the funds in the top

quartile showed evidence of outperformance. In the second ten-year period both the first and second quartiles

recorded positive alpha values



18/27

quartile (and decile) funds have an average alpha value that is positive in the first

period. In the second period both the first and second quartiles have an average value

of alpha that is greater than zero.

Table 6 shows the average alpha values for each quartile, which are determined

based on performance levels in the ranking and evaluation period. That is, in each period the alpha measure is calculated and each fund ranked accordingly. While

some funds will appear in the same quartile for the evaluation period as they did in

the ranking period, many will not.

Figure 2 below tracks the performance of managers in each quartile from the

ranking period to the evaluation period. A strong tendency towards convergence is

noted in the chart. The general ordering of managers by quartile was maintained

(deciles are shown by the darker lines), with the top quartile managers in the ranking

period, as a group, still outperforming the other managers. However the dispersion

of outcomes between groups was markedly reduced.One problem with using the Sharpe Index model is the requirement to use ten-year

data periods to estimate the model. This period length is chosen to balance the

demands to provide sufficient degrees of freedom with ensuring at least two periods to

analyze a managers performance. To allow for greater analysis of manager

performance by period, a set of models using a single index model (the IPD Universe

index) is estimated to obtain a five-year alpha measure. By estimating the models in

five-years periods, a greater number of intervals can be studied with a larger number of

managers included in the sample. The same caveat applies to this analysis as applied

above; estimating an econometric model with only a small number of observations

-5.0%

-4.0%

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

Ranking period Evaluation period

Alpha

Top decile

Top quartile

2nd quartile

3rd quartile

Bottom quartile

Bottom decile

Fig. 2 Average fund alpha, according to initial (

ranking period

) performance quantile, 10 year horizons.This chart shows the average fund alpha by quartile in the ranking and evaluation periods. That is, the

(unweighted) average of fund alpha based on the quartile assigned in the ranking period is compared to the

(unweighed) average of the funds in those same quartiles in the second ten-year evaluation period. The alpha

measures are generated from a Sharpe (1992) style model and capture fund performance against four

benchmarks of property-type sector performance. The chart shows considerable convergence of fund

performance between the ranking and evaluation periods. However, there is some evidence that the

general ordering of the initial quartiles is maintained



19/27

will give rise to large confidence intervals around each parameter estimate. The

magnitude of alpha is not estimated with a great deal of precision.

Table 7 shows the estimated magnitudes of alpha by quartiles for UK property

fund managers, based on the single index model and using five-year time windows.

The results presented show the average value of alpha ranked for each quartile whenmanagers are sorted by performance. The orderings established in the ranking

periods are maintained when calculating average alpha values for the corresponding

evaluation periods.

There is some evidence to suggest that managers performance, as measured by

alpha, is counter-cyclical. Alpha is higher during the boom periods of 198791 and

20022006 and lowest for the period covering the early 1990s (i.e. 199296). Apart

from the 199296 period, the top quartile managers, as a group, went on to record

the highest average level of performance in the evaluation period (albeit at a lower

level than before). This did not occur with the top decile managers, who had averageperformance measures below in the bottom decile group for subsequent performance

in the recent 199296 and 19972001 evaluation periods. This result must be

carefully interpreted as the number of surviving funds in each decile is small, and the

results may be driven by just one or two adverse cases.

Figure 2 and Table 7 therefore portray a similar pattern of convergence in alpha as

in the earlier analysis of raw, relative performance. The convergence in Jensens

alpha between five-year horizons is greater than that for relative performance but

such convergence is less pronounced for Jensens alpha than relative performance for

the ten-year horizons.

The Persistence of Risk-Adjusted Performance

To highlight trends in the persistence of risk-adjusted performance, we calculate the

same types of transition matrices used earlier to examine raw relative performance.

Table 8 provides some indication of persistence in alpha. Compared to the transition

Table 7 Estimates of alpha for UK property fund managers based on a single index model

Ranking

period

quantile

19821986 sample 19871991 sample 19921996 sample 19972001 sample

Ranking Evaluation Ranking Evaluation Ranking Evaluation Ranking Evaluation

Top decile 6.86 0.73 8.76 1.18 4.12 0.22 6.38 1.75

Top quartile 4.08 1.00 5.94 0.65 3.05 0.11 4.55 2.64

2nd quartile 0.46 0.31 1.69 0.20 1.05 0.79 1.79 0.80

3rd quartile 1.63 0.57 0.24 0.23 0.38 0.30 0.25 0.11

4th quartile 4.55 0.07 3.03 0.59 2.96 1.28 3.88 0.34

Bottom decile

5.93

0.10

4.46 0.26

4.63 1.58

6.20 0.53

This table shows the (unweighted) average fund alpha over a five year horizon calculated from a single

factor model. Each funds alpha is ranked by quartile, along with the top and bottom decile, in the ranking

period. Each funds subsequent performance is traced through to the evaluation period. For example, the

first two columns show the average fund alpha by quartile for the 19821986 period. The evaluation

period reports the average alpha of the funds (calculated from the quartile developed during the ranking

period), during the period 19871991



20/27

matrix for raw relative performance, the transition probabilities for above- and

below-average funds are higher on a risk-adjusted basis.For example, the probability that a top quartile manager will remain an above

median manager (first or second quartile performance) is 44%. Compare this to the

probability that a top quartile manager would turn in a below median performance, at

15% and an element of persistency is observed.9 Similarly, the probability of a bottom

quartile manager remaining a bottom quartile manager in the evaluation period is

21%. The probability that a bottom quartile manager will return an above median

performance in the evaluation period is only 7% (44% for a top quartile manager).

Two tests of persistence are reported for the ten-year performance analysis.

Firstly, the cross-product ratio test shows that the performance persistence apparent

on visual inspection of Table 8 is confirmed. The cross-product ratio is 2.84, which

is statistically significant at the 5% level of significance. Secondly, a chi-square test,

is highly significant (chi-square = 27.9, df = 12).

A similar analysis is undertaken for the five-year results based on alpha derived

from a single index model (results not shown). The tests of persistence are conducted

using both the individual five-year periods and also an aggregated set of results

covering all five-year periods. The cross-product ratio test on the aggregated results is

not statistically significant (1.33, p-value=0.08), implying no persistence in manager

performance. However, the chi-square test is significant (chi-square 22.9, df = 12,

p-value=0.02), although it should be noted that when the expired funds are excluded

from the test the results change and show no indication of persistence in quartile

rankings (chi-square 11.1, df=9, p-value=0.27). It is also notable that persistence in

Table 8 Transition probabilities for fund manager risk-adjusted performance: 10-year samples, 1987

1996 to 19972006

Evaluation period quartile

Top 2nd 3rd Bottom Expired Total

Ranking period quartile Top 21% 23% 7% 9% 40% 100%

2nd 17% 12% 24% 7% 40% 100%

3rd 7% 14% 9% 12% 58% 100%

Bottom 7% 0% 10% 21% 62% 100%

All 13% 12% 12% 12% 50% 100%

Table 8 shows the transition probabilities for fund manager performance based on 10-year horizons and

alpha derived from the Sharpe index model discussed in Methodology. For each fund in the sample,

alpha is estimated and the funds sorted into quartiles, based on the period 19871996. The alpha of eachfund is then calculated for the evaluation period (19972006), and the funds are sorted into quartiles based

on performance in the evaluation period. From the frequency counts of funds being sorted into new

quartiles given their performance in the ranking period, a transition probability can be calculated. For

example, in this table the first cell shows that a top quartile fund in the ranking period had a 21%

probability of remaining a top quartile fund in the evaluation period. The table also includes a column,

titled expired, to account for funds that operated in the ranking period but did not operate (or ceased to

operating during) the evaluation period

9 If there was no persistence, the probabilities of remaining above or below median is 25%. It does not

equal 50% in this calculation as expired funds are included in the analysis.



21/27

alpha amongst the very best (top decile) performers over five years is less than in the

analysis of raw, relative performance.

When the statistical tests are conducted on each five-year sub-sample, the results

show some sensitivity to the time frame chosen. There is strong evidence of

persistence in manager performance for funds in the ranking period 19972001,when evaluated over the period 20022006. In the earlier periods no test of

persistence is statistically significant at the 5% significance level.

Over five-year horizons, evidence of persistence is therefore weaker for risk-

adjusted returns than for relative performance.

The Attributes and Predictability of Performance and Alpha

The results from the previous section suggest that, at least for the ten-year

performance interval, there is evidence of persistence in alpha but not in relativeperformance. For the five-year results, the evidence is even more mixed, with the

conclusion of persistence dependent on the time period chosen. If this information is

to be useful to investors, it would be helpful to know whether there is any way top

performing managers can be identified. In this section we investigate a model of the

predictability of fund alpha and relative performance. To avoid bias from

contemporaneous relationships between the variables in the model we only use

lagged values of the explanatory variables. The model estimated is:

ai

j g0 g1LCVi

j1 g2DLFi

j g3DSPFi

j g4DPPFi

j g5DULi

j g6DUNi

j g7STRUCTi

j

g8PROPSCORi

j1 g9HIi

j1 g10EYi

j1 g11DEVRATi

j1 g12NETINVi

j1 ei

j

Where:

j a j subscript indicates the time period of a variable. Period j-1 refers

to the 1987 to 1996 interval and j refers to the 1997 to 2006 interval

i is a superscript indicating each property fund selected for analysis

LCV is the natural log of a funds capital value (direct & indirects)

averaged over the relevant time period (eg. 19871996)

DLF is a dummy variable to indicate that the fund is a Life & General

Insurance Funds (IPD type codes 2 & 3)

DSPF is a dummy variable to indicate that the fund is a Segregated Pension

Funds (IPD type code 4)

DPPF is a dummy variable to indicate that the fund is a Pooled Pension

Funds (IPD type code 5)

DUL is a dummy variable to indicate that the fund is a Unit Linked Life or

Pension Funds (IPD type code 7)

DUN is a dummy variable to indicate that the fund is an Unregulated PUTs

and other unauthorised funds (IPD type codes 6 & 9)

STRUCT is a variable created by IPD representing the return attributed to

allocation to 11 segments (shopping centres, City of London offices,

South East industrial etc) for each fund averaged over the each period

PROPSCOR is a variable created by IPD representing the return attributed to stock

selection for each fund averaged over the period



22/27

HI is a Herfindahl index to measure the investment focus (or

specialisation) of each fund. The index for each fund is created using

the following formula:

HIi XNl1

S2l

where Sl is the funds annual percentage capital value weighting to

sector l averaged over the period

EY is a variable comprising the average all-property equivalent yield for

each fund (averaged over the period). The equivalent yield is the

discount rate, which equates the future income flows to the gross

capital value

DEVRAT is a variable created as the ratio of total development expenditure

divided by the total capital value of each fund. Then take the average

of this ratio over the period examined, e.g. 19871996

NETINV is a variable defined as the ratio of net investment divided by the total

capital value of each fund. Then take the average of this ratio over

each of the ten-year periods

e is a random error term that is N 0; s2e

; and Cov eij; ekj

08i 6 k

The variables included in the model are limited to those available in the IPD

database. Management house, individual fund manager, investment process etc and

changes in these might all be factors associated with and predictive of performance, butit is not possible to incorporate this information due to limitations in the data available.

The model is estimated for the 1997 to 2006 period, but the value of the

explanatory variables all relate to the 1987 to 1996 period. This regression is helpful

if an analyst wishes to determine, on an ex-ante basis, which managers may

outperform in the subsequent ten-year period. Surprisingly the model shows a

reasonable level of explanatory power. Approximately 35% of the variation in fund

alpha (37% for raw performance) is directly attributable to the fund characteristics

established in the earlier period. The model can also be interpreted as a version of

the Hendricks et al. (1993) paper, in which the test of significance of the laggedalpha term can be viewed as a test of persistence.

Two interesting conclusions are apparent from Table 9. First, the equivalent yield

of the fund in the earlier period is a significant predictor both of fund alpha and of

fund performance in the following period. As mentioned earlier this may indicate an

uncontrolled for risk factor. Second, the IPD property score measure is significant

and negative for alpha (albeit not significant for performance). This may indicate

mean-reverting behaviour in the performance of specific assets.

The regression analysis in this section is repeated using the single-index model

over five-yearly periods (results not shown but available from the contact author).

Generally the five-year regression results do not support the hypothesis that alpha or

performance persists in fund manager returns.10 The equivalent yield of a fund was

10 The lagged value of alpha is only significant (at the 10% level) in one of the four periods considered for

analysis; lagged performance was significant at the 10% level in two out of the four 5-year periods

considered.



23/27

found to be a consistent predictor both of fund alpha and of fund performance in

subsequent five-year periods. The variable net investment was also found to be a

significant predictor in three of the four five-year periods examined for alpha (less so

for performance). However, the sign of its coefficient is not consistent, with both

negative and positive values being observed.

Conclusion

This paper finds that evidence of systematic medium or long-term out-performance

(alpha) amongst real estate fund managers is not compelling. It is periodic and at

best focused on a small number of funds. Evidence of persistent poor performance is

also not compelling. There is no clear evidence that these tendencies have changed

over the past few years although any developments associated with the recent

expansion in real estate fund management may take time to be realized.

Table 9 Determinants of fund alpha/performance, as a function of lagged alpha/performance and lagged

fund characteristics: 1997 to 2006 sample period

Alpha Performance

Coefficient t-ratio Coefficient t-ratio

Intercept 0.47 0.12 6.28 1.20

alpha(1)/performance(1) 0.12 1.31 0.10 0.85

Size (capital value) 0.03 0.20 0.17 0.77

Fund type*:

Life & general insurance 3.50 4.70 4.31 4.39

Segregated pension 3.15 4.67 3.53 3.96

Pooled pension 3.61 4.54 4.37 4.16

Unit-linked life & pension

3.49 4.79

4.13 4.29

Unregulated PUTs & other unauthorised 3.71 3.94 4.38 3.52

IPD attribution scores:

Structure 0.11 0.56 0.03 0.13

Property 0.20 2.15 0.16 1.34

Investment focus 0.60 0.23 1.50 0.44

Equivalent yield 0.41 2.48 0.84 3.83

Development exposure 0.86 0.09 4.45 0.35

Net investment 0.62 0.45 1.37 0.76

R2 0.35 0.37

Coefficients significant at 5% level shown in bold. The table shows estimation results for a cross-sectional

equation explaining fund performance. Two sets of results are provided, the first uses fund alpha as the

dependent variable (shown in first two columns), the second uses total fund performance as the dependent

variable. In each case the estimated coefficients of the variables are shown along with the t-statistics. All

explanatory variables are measured with respect to the previous ten-year period (1987 to 1996), compared

to the value of the dependent variable (either fund alpha or performance), which is measured for the 1997

to 2006 period. The dummy variable for fund type omits the fund type property companies, traditional

institutions and charities



24/27

In line with these conclusions, the subsequent performances of top and second-

quartile funds are, on average, less exceptional. For the three and five-year horizons,

there is still out-performance (and alpha) but it is relatively modest. Over 10-year

horizons, the earlier relative performance advantage is totally eroded, whereas for

risk-adjusted alpha it is still there but marginal. This conclusion is tentative, as themost appropriate way to benchmark fund performance remains an area for further

research.

In investigating whether fund characteristics may explain either risk-adjusted or

relative performance, it was found that the portfolio yield was an important indicator

of future fund performance. However, it is recognised that this variable may proxy

for an unidentified source of risk and this reinforces the need for further research on

the most appropriate form of risk adjustment. A high development exposure for a

fund is not found to impact fund performance in a statistically significant way.

Finally, there are no strong conclusions to emerge about fund manager type. The preponderance of segregated pension funds (and traditional institutions) with

persistent top-decile performance is interesting and may be signalling something

different about the style of some of this group.

Acknowledgements The research was funded and commissioned under the auspices of the Investment

Property Forums Research Programme (20082009). The authors acknowledge the cooperation of the

Investment Property Databank, in particular Malcolm Frodsham and Roberto Diaz. The authors are also

thankful for helpful comments received from Katrina Bond, Kevin Chiang, Piet Eichholtz, John Glascock,

Charles Ward, and an anonymous referee, as well as participants at the European Real Estate Society

Annual Conference 2008, the Maastricht-MIT Real Estate Symposium 2008, and the American RealEstate and Urban Economics Association Annual Meeting 2009.

Appendix Potential Survivorship Bias in the Dataset

This appendix provides additional information about the impact of the exclusion

of funds without a full history in the ranking period. Table 1 provides a break-

down of the data set to show the number of funds omitted from the ranking period

for each of the five and ten-year intervals used in this study because they do not

survive for the complete time of the ranking period (e.g. in the 19871991 ranking

period five funds were omitted for this reason). In addition, new funds entering the

sample are not included in the analysis if they are not in existence at the start of the

ranking period; however, if the funds survive for the full length of the evaluation

period they will be included in the subsequent window of analysis.

To explore the impact such exclusions may have on this study we first examine

the average returns of the funds excluded to see if they differ from the funds

remaining in the sample. For each set of ranking and evaluation periods Table 10

shows the average of the funds excluded to assess if there is a systematic bias

associated with the omissions. The first three columns focus on the five-year ranking

periods (19821986, 19871991, 19921996, 19972001), with column one

displaying the (unweighted) annual averages for the funds included in the analysis.

The second column displays the annual averages for those funds existing in the first

year of the ranking period but subsequently excluded because they did not survive

for the complete five years of the ranking period (the number of funds involved is



25/27

Table 10 Assessing survivorship influences in the sample

Funds in Analysis

(ranking period

average)

Ave of funds

not surviving

ranking

period

Annual average

of funds

including non-

surviving funds

Funds in Analysis

(evaluation period

average)

Ave of funds starting

after first year of

ranking with full

evaluation history

Annual average

of funds

including new

funds omitted

1982 8.5 All survived 8.5 Not applicable Not applicable Not applicable

1983 8.2 8.2

1984 9.8 9.8

1985 9.4 9.4

1986 10.3 10.3

1987 22.6 16.1 22.4 22.0 23.7 22.5

1988 30.6 25.9 30.6 29.7 32.7 30.6

1989 17.1 2.5 17.0 16.5 18.1 17.0

1990

7.6

7.6

7.8

7.1

7.61991 0.1 0.1 1.0 2.7 0.1

1992 0.7 3.0 0.9 0.9 0.7 0.9

1993 19.4 23.0 19.9 20.0 19.4 19.9

1994 12.7 6.5 12.2 12.3 12.7 12.4

1995 3.6 0.9 3.4 3.0 4.8 3.4

1996 9.4 9.4 9.4 9.3 9.4

1997 16.3 14.4 15.7 15.0 17.1 15.5

1998 12.1 10.1 11.6 11.1 13.3 11.6

1999 14.6 13.4 14.4 14.1 14.6 14.3

2000 11.0 11.0 11.0 10.9 11.1 10.9

2001 7.3 7.3 7.2 7.7 7.3

2002 Not applicable Not applicable Not applicable 9.8 Not applicable 9.8

2003 11.0 11.0

2004 18.6 18.6

2005 20.1 20.1

2006 19.2 19.2

The first three columns of this table examine whether the funds not included in the ranking period (due to

expiring before the end of the ranking period), have a systematic impact on the overall average returns of

the funds in the study. Column one shows the (unweighted) average annual return for funds currentlyincluded in the study. Column two displays the (unweighted) average annual return of those funds existing

in the first year of the ranking period but ceasing to exist before the end of the five-year ranking period.

Column three shows the overall (unweighted) average annual return in each year including the funds that

were omitted in column two. On average the change is small but clearly the funds omitted from the study

have lower average return than the funds remaining in the sample. Columns four to six examine the

possible impact from omitting funds from the evaluation period, that were not in existence at the start of

the ranking period. Column four shows the (unweighted) average annual fund return in the evaluation

period for the funds included in the analysis in Performance, Alpha and Persistence in UK Property Fund

Management. The (unweighted) average annual return in the evaluation period for new funds that did not

exist at the start of the ranking period (but had a complete history in the evaluation period) is shown in

column five. On average the newer funds have a higher average annual return than the existing funds inthe data sample. The final column shows the average annual fund return including both the existing funds

in the study and the funds omitted because of their start date. Table 1 provides additional information

about the number of funds entering and exiting the data set



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shown in Table 1). Inspection of the columns shows that the excluded funds do have

a lower average return than the funds remaining in the analysis. Column three shows

the corrected annual average return for all funds including the previously omitted

funds. Generally the impact of excluding funds without a full history in the ranking

period is minor, as the number of non-surviving funds was small relative to theoverall number of funds during the first part of the sample. The greatest impact is

seen to occur in the late 1990s period (the 19972001 horizon). Note that funds that

have a full history for the ranking period but subsequently expire during the

evaluation period have been included in all analyses reported in this paper.

The final three columns of Table 10 assess the impact of the decision to exclude

funds that begin after the first year of the ranking period and have a full history for

the subsequent evaluation period (for example, a fund that commences in 1999 and

remains in operation until 2006 will be excluded from the 19972001 ranking

period). Inspection of columns four and five shows that excluding the newer fundsfrom the analysis may bias downward the average performance in the evaluation

period (as the newer funds have a higher average performance). Column six shows

the corrected fund averages during the ranking period.

The slight difference in fund average performance between columns three and six

reflects the fact that a small number of funds may have existed for a short period of

time outside of the key starting dates for the five-year windows (e.g. a fund that

starts in 1998 and lasts only two years).

Therefore, even though the economic impact of the omissions appears to be small,

there is clearly the potential for bias in the tests of persistence. In particular, theomission of funds that performed below average in the ranking period, and those that

performance above average in the evaluation period, may bias tests of persistence

downward. To evaluate this potential bias, we focus on the five-year period that is

likely to be most affected by the bias (ranking period 19921996 and evaluation

period 19972001). In calculating the chi-square test and cross-product ratio test, the

chi-square test showed most impact (rising from 7.19 to 15). However, neither the

revised chi-square test nor the cross-product ratio test (which showed little change),

altered the conclusions drawn from the main part of the study.

References

Bond, S. A., Hwang, S., Mitchell, P., & Satchell, S. E. (2007). Will Private Equity and Hedge Funds

Replace Real Estate in Mixed-Asset Portfolios?. Journal of Portfolio Management, Special Real

Estate Issue, 7484.

Bond, S. A., & Hwang, S. (2007). Smoothing, nonsynchronous appraisal and cross-sectional aggregation

in real estate price indices. Real Estate Economics, 35, 349382.

Brounen, D., Eichholtz, P., & Ling, D. C. (2007). Trading intensity and real estate performance. Journal of

Real Estate Finance and Economics, 35, 449474.

Brown, S., & Goetzmann, W. N. (1995). Performance persistence. Journal of Finance, 50, 679

698.Busse, J. A., & Irvine, P. J. (2006). Bayesian alphas and mutual fund persistence. Journal of finance, 61,

22512288.

Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52, 5782.

Carhart, M. M., Carpenter, J. N., Lynch, A. W., & Musto, D. K. (2002). Mutual fund survivorship. Journal

of Finance, 15, 14391463.

Chan, K. C., Dimmock, S. G., & Lakonishok, J. (2006). Benchmarking Money Manager Performance:

Issues and Evidence, NBER Working Paper 12461.



27/27

Chiang, K. C. H., Kozhevnikov, K., Lee, M.-L., & Wisen, C. H. (2008). Further evidence on the performance

of real estate funds: the case of real estate mutual funds. Real Estate Economics, 36, 4762.

Chun, G. H., Sa-Aadu, J., & Shilling, J. D. (2004). The role of real estate in an institutional investors

portfolio revisited. Journal of Real Estate Finance and Economics, 29, 295320.

Devaney, S. P., Lee, S. L., & Young, M. S. (2007). Serial persistence in individual real estate returns in

the UK. Working Paper, University of Reading.Eichholtz, P., Kok, N., & Margaritova, A. (2009). Investment Style and Performance in The Global Real

Estate Mutual Fund Market, paper presented at the RERI annual meeting, April 2009.

Gallo, J. G., Lockwood, L. J., & Rutherford, R. C. (2000). Asset allocation and the performance of real

estate mutual funds. Real Estate Economics, 28, 165184.

Goetzmann, W. N., & Ibbotson, R. G. (1994). Do winners repeat? Patterns in mutual fund return behavior.

Journal of Portfolio Management, 20, 918.

Grinblatt, M., & Titman, S. (1989). Mutual fund performance: an analysis of quarterly portfolio holdings.

Journal of Business, 62, 393416.

Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The

American Economic Review, 70, 393408.

Hahn, T. C., Geltner, D., & Gerardo-Lietz, N. (2005). Real estate opportunity funds. Journal of Portfolio

Management, Special Real Estate Issue, 143153.Hartzell, J. C., Muhlhofer, T., & Titman, S. (2009). Alternative benchmarks for evaluating REIT mutual

fund performance. Real Estate Economics.

Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: short-run persistence of

relative performance 19741988. Journal of Finance, 48, 93130.

Investment Property Databank. (2007). The IPD Index Guide, Edition 2.

Jensen, M. C. (1968). The performance of mutual funds in the period 194564. Journal of Finance, 23,

389416.

Kallberg, J., Liu, C. L., & Trzcinka, C. (2000). The value added from investment managers: an

examination of REIT funds. Journal of Financial and Quantitative Analysis, 35, 387408.

Key, T., & Marcato, G. (2005). Direct investment in real estate: momentum profits and their robustness to

trading costs. Journal of Portfolio Management. Special Real Estate Issue, 55

69.Kosowski, R., Naik, N. Y., & Teo, M. (2007). Do hedge funds deliver alpha? A Bayesian and bootstrap

analysis. Journal of Financial Economics, 84, 229264.

Kosowski, R., Timmermann, A., Wermers, R., & White, H. (2006). Can mutual fund Stars really pick

stocks? New evidence from a bootstrap analysis. Journal of Finance, 61, 25512595.

Lee, S. L., & Ward, C. W. R. (2000). Persistence of UK real estate returns: A Markov Chain analysis,

Working Paper, Department of Land Management, University of Reading.

Lee, S. (2003). The Persistence of Real Estate Fund Performance paper presented at the 19th Annual

American Real Estate Society Meeting, April 2003.

Lin, C. Y., & Yung, K. (2004). Real estate mutual funds: performance and persistence. Journal of Real

Estate Research, 26, 6993.

Ling, D. C. (2005). A random walk down main street: can experts predict returns on commercial real

estate. Journal of Real Estate Research, 27, 137154.Malkeil, B. G., & Saha, A. (2005). Hedge funds: risk and return. Financial Analysts Journal, 61, 8088.

ONeal, E., & Page, D. (2000). Real estate mutual funds: abnormal performance and fund characteristics.

Journal of Real Estate Portfolio Management, 6, 23947.

Sharpe, W. F. (1992). Asset allocation, management style and performance measurement. Journal of

Portfolio Management, 719.


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