International direct real estate asset allocation a fuzzy decision making approach

Acknowledgments

I am greatly indebted to my supervisor, Associate Professor Ho Kim Hin, David. He
has been a tremendous source of support, ideas, and useful advice during the three
years we worked together. His energy and keen interest in challenging problems have
been an example and a constant inspiration for me.
I would like to acknowledge gratefully the financial support of the National
University of Singapore (NUS) for granting me the prestigious NUS Research
Scholarship. I am also thankful to Professor Ong Seow Eng, Associate Professor Fu
Yuming, Associate Professor Sing Tien Foo and Associate Professor Tu Yong, for
their concern, willingness to listen, and their constant efforts to make some
suggestions.
My friends at NUS have helped me in numerous ways. To Li Yun, Chen Zhiwei – I
thank you.
The love of my family has been my greatest blessing, and it has helped me to go on in
my most difficult moments. I fondly thank my parents, who have always been an
example of ambition and hard work but also of devotion, caring, and humanity. I am
grateful to my sister for her efforts to support me and take care of my parents. Your
encouragement gave me the strength to pursue my professional goals, and your very
existence reminded me that there is so much more to life. I thank you with all my
heart.

I


Contents
Acknowledgments ..............................................................................................................I
Contents ............................................................................................................................ II
Summary........................................................................................................................... V
List of Tables .................................................................................................................VI
List of Figures.............................................................................................................. VII

Chapter 1 Introduction..................................................................................................... 1
1.1 Background and Motivation.................................................................................. 1
1.1.1 Background ................................................................................................ 1
1.1.2 Motivation .................................................................................................. 2
1.2 Research Question and Issues ............................................................................... 6
1.3 Objectives.............................................................................................................. 7
1.4 Data and Methodology.......................................................................................... 8
1.5 Scope of the Research ........................................................................................... 9
1.6 Potential Results and Contributions ...................................................................... 9
1.7 Structure of This Dissertation ............................................................................. 10
Chapter 2 Literature Review ........................................................................................ 12
2.1 Real Estate........................................................................................................... 12
2.1.1 Definition of Real estate........................................................................... 12
2.1.2 Characteristics of real estate ................................................................... 13
2.1.3 The real estate investing process ............................................................. 13
2.1.4 The benefits of real estate investing ......................................................... 15
2.1.5 Why choose international and direct real estate investing, and not the
real estate investment trust (REITs)?................................................................ 16
2.2 Direct Real Estate Asset Allocation.................................................................... 18
2.2.1 Asset allocation ........................................................................................ 18
2.2.2 Direct real estate asset allocation ........................................................... 20
2.2.3 Asset Allocation Models........................................................................... 37
2.2.4 Shortcomings of traditional asset allocation approaches........................ 43
2.3 Fuzzy Set Theory and Fuzzy Decision Making .................................................. 45

II


2.3.1 Fuzzy set theory........................................................................................ 45
2.3.2 The fuzzy logic benefit.............................................................................. 46

2.3.3 Why must computer software programs enable decision to be made? .... 46
2.3.4 Why fuzzy logic?....................................................................................... 47
2.3.5 Fuzzy logic’s underlying principle........................................................... 47
2.3.6 Why is there a need for an “Inference Engine”?..................................... 48
2.3.7 The Extension Principle ........................................................................... 49
2.3.8 Probability theory and fuzzy set theory.................................................... 50
2.3.9 Popularity of Fuzzy Set Theory Application Research ............................ 50
2.3.10 Fuzzy decision making ........................................................................... 50
Chapter 3 The Fuzzy Strategic Asset Allocation (FSAA) Model............................... 52
3.1 Introduction ......................................................................................................... 52
3.2 FSAA .................................................................................................................. 53
Chapter 4 Fuzzy Tactical Asset Allocation (FTAA) Decision Making Models ........ 62
4.1 Expert judgement and fuzziness.......................................................................... 63
4.2 Optimization in the fuzzy environment............................................................... 65
4.3 Definition of the fuzzy decision (Bellman and Zadeh)...................................... 65
4.4 Flexible Model: Zimmerman’s Symmetric Fuzzy Linear Programming Model 67
4.4.1 Zimmermann’s Symmetric Fuzzy Linear Programming model ............... 67
4.4.2 Fuzzy optimizer ........................................................................................ 73
4.5 FTAA Robust Model: Ramik and Rimanek’s Robust Programming Model..... 74
Chapter 5 Validation of the Fuzzy Asset Allocation Models ..................................... 77
5.1 The Data .............................................................................................................. 77
5.1.1 JLL Data Source ...................................................................................... 77
5.1.2 The Required Data ................................................................................... 78
5.1.3 Some Key definitions used in by JLL REIS-Asia...................................... 80
5.2 Calculation of Total Return Correlation Coefficient .......................................... 84
5.3 The Fuzzy Asset Allocation Models ................................................................... 86
5.4 Comparisons with the Traditional and Fuzzy Asset Allocation Models............. 88
Chapter 6 Conclusion and Future Studies................................................................... 90
6.1 Review of Research Objectives .......................................................................... 90

III


6.2 Summary of Key Findings .................................................................................. 90
6.3 Conclusion and Implications............................................................................... 92
6.4 Theoretical Contributions ................................................................................... 93
6.5 Practical Contributions and Policy Implication .................................................. 93
6.6 Limitations of This Research .............................................................................. 93
6.7 Recommendations for Future Studies ................................................................. 96
References ........................................................................................................................ 97
Appendix A fuzzyTECH 5.12e (08-Mar-2000) .......................................................... 103
AppendixB MPT and Efficient Frontier ................................................................... 109
Appendix C The Fuzzy Tactical Asset Allocation Models ........................................ 114

IV


Summary
Although the classical Markowitz mean-variance asset allocation framework can be used
to enable decision-making in international and direct real estate investing, and that many
institutional investors have used it to support their decision-making, such a meanvariance framework still needs to be enhanced in order to capture the multi-causal factors
influencing international and direct real estate investing. A fuzzy decision-making
approach can be a more intuitive and a rigorous alternative in this regard. The aim of this
dissertation is to enhance the classical Markowitz mean-variance asset allocation
framework through making it more appropriate for decision-making in international and
direct real estate investing. This dissertation is concerned with the model formation and
estimation of the institutional investors’ fuzzy strategic asset allocation (FSAA) and
fuzzy tactical asset allocation (FTAA). The dissertation utilizes the Jones Lang Lasalle
Real Estate Intelligence-Asia office-sector dataset in order to integrate the fuzzy decisionmaking approach with the classical Markowitz’s asset allocation mean-variance
framework to provide institutional investors, engaged in international and direct real

estate investing, with a more intuitive way of uniquely and rigorously capturing their
expert judgement in optimal asset allocation. The findings indicate that the new and
estimated FSAA model and FTAA models can be the appropriate ones to enhance
decision-making in international and direct real estate investing.

Keywords: Direct real estate, fuzzy set theory, fuzzy strategic asset allocation, fuzzy
tactical asset allocation, Zimmermann’s fuzzy linear programming, Ramik &
Rimanek’s fuzzy optimization.

V


List of Tables

Table 1.1 Portfolio size of institutional investors across the globe (in billions of US$) .... 2
Table 1.2 Estimates of the size of the investible institutional real estate portfolio in 2000 3
Table 3. 1 Project Statistics.............................................................................................. 53
Table 3. 2 Linguistic Variables ......................................................................................... 55
Table 3. 3 Base Variables.................................................................................................. 55
Table 3. 4 Interfaces.......................................................................................................... 56
Table 3. 5 Definition Points of MBF "EconGthProsp"..................................................... 57
Table 3. 6 Definition Points of MBF "MktLiquidity"....................................................... 57
Table 3. 7 Definition Points of MBF "MktTransparency"................................................ 58
Table 3. 8 Definition Points of MBF "MktVacancy" ....................................................... 58
Table 3. 9 Definition Points of MBF "MktPerformance" ................................................. 59
Table 3. 10 Rules of the Rule Block "RB1"...................................................................... 60
Table 3. 11The fuzzy strategic asset allocation results ..................................................... 61
Table 5. 1 Historical TRs 2000-2005................................................................................ 79
Table 5. 2 Historical Correlations among Asian Country TRs (2000-2005) .................... 79
Table 5. 3 Forecast (Ex ante) TRs, 2006-2010 ................................................................. 80

Table 5. 4 Ex-ante Correlations among Asian Country TRs (2006-2010) ....................... 80
Table 5. 5 General information of ten Asian real estate market ....................................... 85
Table 5. 6 Correlation Coefficients................................................................................... 86
Table 5. 7 Covariance Matrix............................................................................................ 86
Table 5. 8 Zimmerman's FTAA Flexible Programming Model Coefficients ................... 87
Table 5. 9 Zimmerman's FTAA Flexible Programming Model Results.......................... 87
Table 5. 10 The Ramik & Rimanek FTAA Robust Programming Model Coefficients ... 87
Table 5. 11 Ramik & Rimanek FTAA Robust Programming Model Results .................. 87
Table 5. 12 Comparision of the different asset allocation models.................................... 88
Table 5.13 Portfolio Risk and Return Comparisons………………..….…….…………..89

VI


List of Figures

Fig 1. 1 Structure of this Dissertation .............................................................................. 10
Fig 2. 1 The Real Estate Investing Process ...................................................................... 14
Fig 2. 2 Participants in the real estate investment............................................................. 15
Fig 2. 3 Opportunity set of portfolios................................................................................ 40
Fig 2. 4 Risk indifference curves for investors A and B................................................... 41
Fig 2. 5 Risk indifference curves for investors A and B with changes in the rate of interest
........................................................................................................................................... 41
Fig 2. 6 Risk indifference curves and the opportunity set................................................ 42
Fig 3. 1 Structure of the Fuzzy Logic System.................................................................. 54
Fig 3. 2 Membership Function of 'temperature' ................................................................ 55
Fig3. 3 MBF of "EconGthProsp" ..................................................................................... 56
Fig 3. 4: MBF of "MktLiquidity"..................................................................................... 57
Fig 3. 5 MBF of "MktTransparency" ............................................................................... 57
Fig 3. 6: MBF of "MktVacancy"...................................................................................... 58

Fig 3. 7: MBF of "MktPerformance" ............................................................................... 59
Fig 4. 1 Decision-making in fuzzy environment............................................................... 66
Fig 5. 1 Efficient frontier of ten Asian real estate market................................................. 85

VII


Chapter 1 Introduction
“Precision is not truth.”
-- Henri Matisse, Impressionist Painter
1.1 Background and Motivation
1.1.1 Background
Real estate investing is a complex human cognitive process involving decision-making
regarding possible uncertain future returns. In an ill-defined and complex environment,
human cognition is often overloaded with many interdependent facets of that
environment, resulting in many instances, a sub-optimal judgment. Investment analysis
comprises several key analytical techniques, namely the discounted cash flow (DCF)
model, portfolio theory and risk analysis that are essentially structured frameworks,
which enable a more precise and certain evaluation of an investment. However, the
success of investment analysis still relies greatly on the reliability and quality of the
inputs to the analytical techniques.

In investment analysis, the precise and crisp result of any of its techniques (models) is
derived on the assumption that the variables in the analysis are deterministic or
probabilistic in nature. This assumption is pseudo accurate and it fails to take into account
unexpected shocks or perturbations that are possible in the real world. Therefore,
investors who rely on sophisticated analytical techniques are not placed in a better
position but are in fact subject to substantial risk. Expert judgment offers an acceptable
alternative to non-naïve models as that judgment, which itself is limited by uncertainty, is
attributable to the vagueness and imprecision inherent to the associated expert’s ex ante

information. Such a limitation is known as cognitive uncertainty or fuzziness. As a result,

1


‘Fuzzy Set Theory’ is incepted to allow a natural and intuitive way of representing
cognitive uncertainty. Fuzzy set theory relaxes the crispness and precision to enable a
robust summary of expert knowledge. The incorporation of fuzzy set theory has made
significant inroads relating to the generalization of traditional investment analysis and its
techniques, thereby opening up a new frontier in structured frameworks for evaluating the
investment market.

1.1.2 Motivation
Institutional investors like the insurance companies, banks, corporations and pension
funds, are the primary capital players in today’s investment environment. The
corresponding and teeming volume of funds interested in international real estate
investing is highlighted in Table 1.1.

Table 1.1 Portfolio size of institutional investors across the globe (in billions of US$)
US
Japan
UK
Canada
Netherlands
Switzerland
Australia
Germany
France
Ireland
Hong Kong

Total

Pension funds (1998 data)
7,400
2,285
1,159
585
470
350
205
363
77
46
21
12,961

Insurance companies(1999 data)
3,996
2,216
1,651
164
230
252
179
950
712
38
3
10,391

Source: Henderson Investors (2000)

2


Table 1.2 Estimates of the size of the investible institutional real estate portfolio in
2000
Regions
North America
UK
Continental Europe
Asia
Australasia
South America
Total

Institutional real estate
(US$ billions)
1,598
361
1,262
825
103
131
4,280

Percentage of global portfolio
37.3%
8.4%
29.5%

19.4%
2.4%
3.1%
100.0%

Source: Henderson Investors (2000)
As observed in Table 1.1, the estimated value of the global investment market (i.e.
insurance companies and pension funds around the world) in year 2000 is about US$23
trillion (Henderson Investors, 2000). The estimated total invested institutional direct real
estate market is much smaller at about US$1.3 trillion, which includes the direct real
estate holdings of insurance companies, pension funds and real estate companies in the
major economies.

Recently, industry studies by major investment advisors, including Henderson Investors
(2000), Prudential (1988 and 1990), Jones Lang LaSalle, Lend Lease and AIG, have all
advocated international real estate investing to be the next frontier for institutional
investors, and international real estate to be an alternative and viable investment asset
class.

Given the potentially immense volume of funds that is interested in international real
estate investing, it is not surprising that there has been a significant amount of research
focused on the potential benefits of an international real estate investment strategy.
Investment management and advisory firms clearly support and promote the
diversification benefits of international real estate investing. The diversification benefits
have been instrumental for teeming research, which has amassed many studies and rich
3


data to improve the quality of international real estate investing research. Many studies
on such research adopt the mean-variance, modern-portfolio-theory (MPT) framework

but the adoption of MPT for international real estate investing has been questioned by
researchers.

As some of the earliest critiques, Lizieri and Finley (1995) reiterated that a fund adopting
the mean-variance MPT framework in the context of international real estate investing
would have had a disastrous performance. They suggested several reasons:

Technical problems with the data and the corner solution often results from the
mathematics of modern portfolio theory (MPT).
With unstable returns, the historical mean returns, standard deviations and correlation
coefficients between countries may not be the best way to analyze the (direct) real
estate asset class.
The majority of the(direct) real estate return series used in the most past studies are
short (about 15 years) but the direct real estate investments themselves are
characterized by their long holding period and are not single-period investments.
They lack the liquidity of more traditional asset classes (like common stocks and
bonds).
There are additional risks associated with an international diversification strategy that
includes asset-specific, the domestic/international sector, the domestic/ international
market and currency markets. It is difficult to incorporate these risks into the
traditional mean-variance MPT framework for a mixed-asset portfolio and for the
most part they are ignored in most past research.

4


Published research on the diversification benefits of international real estate investing has
been mixed. Most research seem to believe that international real estate investing would
add diversification benefits to a direct real estate or a mixed asset portfolio, but some
disagree, or believe that other sources of international diversification (such as for

common stock) are superior. Worzala (1992) reiterates that “diversification benefits can
be found from… overseas property” while Chenh, Ziobrowski, Caines, and Ziobrowski
(1999) pose the question of whether international real estate investing is as effective at
providing international diversification as other international asset classes. Regardless, the
one thing they all might agree on is the lack of high quality data on direct and
international real estate markets and past performance. Given the broad nature of the
subject matter, it is understandably hard to find reliable data sources for major markets
that are appropriate for enabling meaningful comparison with one another. As a result,
many studies have focused on the best data that is available.

What proportion of capital should institutional investors decide to devote to direct and
international real estate is subject to uncertainty? More recently, Brounen and Eichholtz
(2003) unsmoothed autocorrelations in domestic private and public real estate markets
adopting methods developed by Geltner (1989a, 1989b) and Giliberto (1990). They use
the resulting returns and confirm that the Sharpe maximizing mixed-asset portfolio
should contain approximately a 10% portfolio weight to real estate. Pension Consulting
Alliance (PCA) remain conservative in relation to the inclusion of direct and international
real estate for strategic investment, preferring instead the tactical use of higher-return
investments in certain markets.

5


In contrast, Prudential Real Estate suggests that costs aside, there are excellent
opportunities for direct real estate abroad if investors shift their thinking somewhat and
consider alternative frameworks. The allocation that Lowrey (2002) recommends
investors should make in international real estate investing is a staggering 20%-30%
portfolio weight, an allocation that he feels is large enough to optimize the diversification
benefits and to maintain “a wide range of potential portfolio strategies”. According to an
MIT survey, average international real estate allocations (within the real estate portfolio)

across pension funds had grow from a 2.1% portfolio weight five years ago to a 7.2%
portfolio weight today (Mullins, 2004).

As a result, there are still puzzles in the asset allocation problem of international real
estate investing. Such puzzles cannot be easily resolved without better data for the direct
and international real estate markets. Then and only then can more rigorous analysis be
conducted to enable the diversification benefits of geographical diversification, real estate
sector diversification and time diversification (time being one of the key sources of risk
or uncertainty). For the latter, it would be imperative to take advantage of different stages
of the direct real estate market cycles through superior real estate market analysis.

1.2 Research Question and Issues
A main and relevant research question to pose is whether we can incorporate fuzzy set
theory into the classical asset allocation models, which optimizes systematic market risk
or uncertainty at the portfolio level, in order to enable direct real estate investment
decision-making to be efficient in international real estate investing?

6


Perhaps, a corresponding investigative research to the above research question can be
undertaken in two parts that are somewhat akin but different from the classical meanvariance MPT framework: a unique and rigorous fuzzy strategic asset allocation (FSAA)
and a unique and rigorous fuzzy tactical asset allocation (FTAA). Both the FSAA and
FTAA should be practical to adopt.

1.3 Objectives
The objectives for the investigative research of this dissertation comprise the following:

1. To improve the traditional mean-variance modern portfolio theory (MPT) framework
for direct real estate asset allocation in international real estate investing.

2. To enable efficient decision-making in international real estate investing for
institutional investors, who are interested in direct real estate investments, as the benefits
of such investments are well documented (Goetzmann and Ibbotson 1990). Unfortunately,
individuals and smaller institutional investors have traditionally had difficulty in
obtaining these benefits since real estate investments prior to the 1990s were dominated
by illiquid units of commingled funds, and by direct investments that were primarily
directed at large institutional investors (Martin 1997, p. 145; Bogle 1994, p. 52).
However, the recent trend towards securitized real estate investments, particularly the real
estate investment trusts (REITs), has increased the availability of liquid and indirect
real estate security investments for individual and small institutional investors (National
Association of Real Estate Investment Trusts, 1998). Therefore, the study in this
dissertation is only concerned with the institutional investors’ decision-making through
direct real estate asset allocation in international real estate investing.

7


1.4 Data and Methodology
Prime office annual total returns, comprising annualized rental yields (real estate
capitalization rates) and capital value (CV) appreciation on a pre-tax and pre-leveraged
basis, are obtained for ten Asian real estate sectors, namely those for the Beijing Central
Business District (CBD), Shanghai CBD, Seoul CBD, Tokyo CBD, Hong Kong Central
& major business districts, Manila’s Makati CBD, Jakarta CBD, Singapore’s Raffles
Place CBD, Kuala Lumpur CBD, and Bangkok CBD. The direct real estate total return
data set is essentially ex post, spanning the period from 2000 to 2005 in US$ terms. As
such, there is no conversion carried out for the total return data set in hedged US$ terms
in contrast to a forecast dataset, where the hedged US$ conversion would be appropriate.

In

Asia,

the

Jones

Lang

Lasalle

Real

Estate

Intelligence

Service

(JLL REIS-Asia), which is based in Singapore, is the sole service provider that maintains
a reliable transaction-based set of indicators for the market performance of the prime
office sectors in ten countries of the Asian region. JLL REIS-Asia also produces 5-year
total return forecasts in local currency terms for each of the markets and several key real
estate market indicators (i.e. market demand growth, completions, vacancy, rental change,
yield and capital value growth). In effect, the JLL REIS-Asia data set is a research asset
class index type of database, as opposed to a peer universe type.

In general and in terms of research methodology, the following step-wise procedure can
be adopted:

1. Examine the behavioral uncertainty behind international real estate investing in direct
real estate investments.

8


2. Incorporate fuzzy set theory into the strategic asset allocation process.
3. Incorporate fuzzy set theory into the tactical asset allocation process.
4. Validate the above behavioral uncertainty and the corresponding process models

1.5 Scope of the Research
The research study in this dissertation is scoped along the following confines:

1. International and direct real estate investments (excluding corporate real estate).

2. Fuzzy strategic asset allocation (FSAA) and fuzzy tactical asset allocation (FTAA)
formulation and estimation.

3. Inter-sector portfolio diversification but within a direct real estate portfolio because
direct. real estate is found to be an effective portfolio diversifier, even more so when both
domestic and international real estate sectors (assets) are considered.

1.6 Potential Results and Contributions
Fuzzy set theory can be incorporated into traditional mean-variance MPT asset allocation
models and can therefore improve the efficiency of asset allocation decision-making in
international real estate investing for direct real estate investments. Thus, the
contributions of this dissertation are achievable in the following ways:

•

Modern and new ideas with regard to fuzzy set theory and fuzzy logic can be
introduced into the international real estate investing process for direct real estate
investments.

9


•

The study in this dissertation can incorporate human and intuitive thinking into
the direct real estate asset allocation process within the international context.

•

New and direct real estate asset allocation models are developed in international
real estate investing.

1.7 Structure of This Dissertation
This dissertation consists of six chapters and Fig 1.1 shows the relationships among the
chapters. The chapters are outlined below.

Chapter 1 Intruduction
Introduction
Chapter 2 Direct Real Estate Asset Allocation & Fuzzy
Optimization Application to Finance: A Literature Review

Chapter 3 Fuzzy Strategic Asset
Allocation (FSAA) Decision
Making Models

Chapter 4 Fuzzy Tactical Asset
Allocation (FTAA) Decision
Making Models

Chapter 5 Results Comparison and Interpretation &
Models Validation
Chapter 6 Conclusions and
Future Studies

Fig 1. 1 Structure of this Dissertation
Source: Author, 2007
From Figure 1, Chapter 1 introduces the motivations, research questions and objectives of
this dissertation.
Chapter 2 reviews the related literature of direct real estate asset allocation, within
international real estate investing, and the extension of fuzzy set theory, fuzzy logic and
fuzzy optimization in this regard.

10


Chapter 3 discusses the fuzzy strategic asset allocation (FSAA) models, where four
macro market indicators are examined in such models.

Chapter 4 discusses the development of fuzzy tactical asset allocation (FTAA) models. In
addition, the flexible programming and the robust programming models are examined in
depth.

Chapter 5 discusses the estimation, results and makes a comparison between different
asset allocation models. Furthermore, these models are validated. This chapter also
demonstrates the practical use of fuzzy asset allocation in international real estate

investing for direct real estate investments.

Chapter 6 concludes this dissertation.

11


Chapter 2 Literature Review
“It is the mark of an instructed mind to rest satisfied with that degree of
precision which the nature of the subject admits, and not to seek
exactness where only an approximation of the truth is possible.”
---- Aristotle, Ancient Greek Philosopher
Chapter 2 is concerned with reviewing the related literature on direct real estate asset
allocation within international real estate investing. This chapter also extends the
conceptions of fuzzy set theory, fuzzy logic and fuzzy optimization to international and
direct real estate asset allocation.

2.1 Real Estate
2.1.1 Definition of Real estate
It is first imperative to properly define real estate itself and according to Graskamp, “Real
estate is Space and Money over Time.”

The space dimension- covering fundamental policy analysis, housing markets
(especially user cost and sub-markets).
The cash flow dimension- covering securitized real estate (i.e. uncoupling cash flows
from physical real estate), the real estate investment trust (UBS DoRE public talk Oct
2004: real estate itself comprises 12% of global investment assets, tremendous
growth in REIT in North American, Europe and Asia), and asset-backed
securitization.
Space & Money dimensions- covering the pricing of attributes, technology and cash

flow.

12


2.1.2 Characteristics of real estate
Next, the characteristics of real estate can be defined to include the following:
Immobility: fixed in location
Heterogeneity: unique, scarce (particular use at a particular time and location)
Durability: services, good loan collateral
Sizeable investment outlay: financing
Indivisibility of investment
Illiquid: long transaction process

Studies relating to the characteristics of real estate have been considerably hampered by
the lack of reliable data. In comparison with the equities market, our knowledge of even
the distributional characteristics of real estate returns and performance are relatively
limited. This has been the result of data and information confidentiality and data
inadequacy. This situation is gradually changing, and good quality data is now more
readily available and our understanding of real estate market analysis this sector is
improving.
2.1.3 The real estate investing process
When one investor makes a real estate investing decision, he should systematically
analyze the factors and contingencies that impact the value of a real estate investment. It
is widely accepted by the real estate investment management community that investment
in real estate is about sacrificing present consumption for future benefits; and that the real
estate value is the present worth of rights to the future benefits arising from ownership.

13

At the same time, the real estate investor must identify his objectives, goals and
constraints. The basic objective for the investors would be wealth maximization while the
investor makes his investing decision, based on the mean-variance (return-risk) i.e. the
risk pricing principle. This typical real estate investing process is depicted in Fig 2.1..
STEP1: Identify investors’
objectives, goals, and
constraits
Risk-return
preferences

Wealth
STEP2: Analyze investment
climate and market conditions
Market
environment

Legal
environment

Financing
environment

Tax
environment

STEP3: Develop financial
analysis
Operating
decision

Income
taxation

Financing decision

Reversion
decison

Tax planning

Wealth
taxation

STEP4: Apply decisionmaking criteria
Rules of Thumb
techniques

Discounted cash
flow techniques

Traditional valuation
techniques

STEP5: Investment decision

Fig 2. 1 The Real Estate Investing Process
Source: Jaffe and Sirman 1995; Author, 2007
In this thesis, we investigate how to undertake that real estate asset allocation decisionmaking when the institutional investor invests in the international and direct real estate
market, to optimize the systematic market risk for maximizing return at the direct real
estate portfolio level and given that the specific risks of the real estate assets (or sectors)

cancel out one another.

14


As illustrated in Fig 2.2, there are many participants that invest in real estate investment
mainly comprise the equity investor, the mortgage investor, the tenant-occupier and the
government that regulates their behavior and space-user restriction.

Mortgage Lender

y
or
ss
i
om
Pr

Equity Investor

1.Individual
2.Corporation
3.Partnership
4.REIT
h
t
l
a
e
W

d
n
a
e
m
o
c
n
I

Types
1.Shortterm
2.Long-term

Sources
1.S&L
2.Insurance
3.Bank
4.Individual
5.REIT

te
No

eg
a
tgr
oM
Property Rights
Cash Flow

tn
e
umc
oD

tb
e
D

ec
i
rv
e
S

REAL ESTATE
INVESMENT

Re
st
ri
Le cti
nd ons
er
s on
Mortgage Laws

Restrictions on
use:

taxation,
eminent domain,
police power

Government

1.Federal
2.Stale
3.Local

Landlord-Tenant
n
o
i
t
a
x
a
T

Lea
se
Doc
ume
nt

Laws

Tenant

1.Residential
2.Commercial
3.Industrial
4.Special purpose
5.Others

r
Use
on
n
o
cti
tri
Res

d
na
em
o
cn
I

ht
a
eh
W

no
i
ta

x
aT

Fig 2. 2 Participants in the real estate investment
Source: NUS lecture
So, although there are many participants in the real estate investing process, this
dissertation is concerned with institutional investors that are interested and are actively
engaged in the international and direct real estate asset allocation process.
2.1.4 The benefits of real estate investing
Several key investment and financial benefits result from real estate investing and they
include the following:

15


Correlation coefficient is low among the direct real estate sectors or assets (the direct
real estate sector diversification and the geographical diversification).
Pride of direct real estate ownership, locally and abroad.
Reduce / control operational costs (a financial benefit at the direct real estate project
level).
Steady rental yield and long-term capital growth (an investment benefits at the direct
real estate portfolio level).
Inflation hedge and tax shelter (an investment benefits at the direct real estate
portfolio level).
Risk diversification (an investment benefits at the direct real estate portfolio level).
Financial leverage (a financial benefit at the direct real estate project level, consistent
with the capital asset pricing model and theory).
2.1.5 Why choose international and direct real estate investing, and not the real
estate investment trust (REITs)?
It is worthwhile noting the adage that as property “lives forever”, and no one knows

exactly what the future cash flows from any property will be for all years into the future,
commercial real estate is certainly a “risky asset” (Geltner, 1989). In the 1984 special
issue on valuation published by the Journal of the American Real Estate and Urban
Economics Association (AREUEA), its editor Lusht surveyed 71 professional appraisers,
asking them what valuation topics they considered most important for research. The
second most frequently cited response was “Estimating risk and determining the proper
discount rate”.

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In short, there is no clear consensus on how “risky” commercial real estate properties are,
even on a relative basis compared to common stocks, and there seems to be considerable
confusion even regarding how to measure or think about this issue. We seem to know
much less about risk in direct real estate returns than we do about risk in stock market
returns, even though direct real estate is of comparable magnitude to the stock market in
capitalized value, and in some ways the direct real estate assets are much simpler and
easier to understand than modern industrial corporations.

The basis, obtainable upon empirically and observable data like cash flows and the
appraisal returns, is thus famed more for the purpose of developing our intuition about the
nature of direct real estate return risk, rather than the required empirical analysis.

So, why not simply do an empirical study of the returns to indirect securitized real estate
portfolios, like the REITs, which trade on the stock exchanges. Securitized real estate and
their portfolios offer regular and frequently true returns data, based on stock prices and
dividends, and so offer a more direct and theoretically accurate data source of returns. A
short answer to the above question is that this dissertation’s investigation is not only
primarily empirical but it is also seeking to understand the nature and determinants of
direct real estate return risk rather than to describe what that risk has historically been ex

post. However, several studies have analyzed the indirect securitized REIT returns, and
the REIT data does provide an important source of empirical information about indirect
real estate risk and returns (Smith & Shulman, and Burns & Epley.) In general, such
studies have found that REIT returns behave much like the typical common stock returns,
similar in particular to the common stocks of public utility companies. REITs generally

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have higher than average yields and lower than average volatility but have their REIT
betas to be smaller than average and significantly positive, all with respect to the stock
market. REIT returns are found to be highly correlated with the overall stock market
return. At the same time, there are not very many equity REITs and many of them have
small and changing portfolios, and/or have not existed nor have they been publicly traded
for very long.

Therefore, it is difficult enough to obtain very clear and specific information about real
estate return risk through studying the REITs alone. Perhaps, more serious is the widely
held perception among real estate academicians and practitioners that “REITs are not
Real Estate”, in the sense that the REIT risk and return characteristics are perceived to
differ significantly from those of direct real estate and even from those of the direct real
estate portfolios, which underpin the REITs’ values. Various explanations offered to
account for the widely held perception, ranging from arguments that the “stock market is
inefficient” to arguments that the REIT return risk reflects the intangible REIT’s
management risk more than the risk of the tangible direct real estate assets themselves
which the REIT owns. Another possible explanation is that the direct real estate assets in
turn are very heterogeneous, such that even seemingly large and diversified direct real
estate portfolios can differ significantly in their risk and return profile.

2.2 Direct Real Estate Asset Allocation

2.2.1 Asset allocation
In general, asset allocation in finance theory refers to the process of securing the most
favorable return and risk trade off involving competing interests that are concerned with

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