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The world of finance in general and real estate in specific has become increasingly volatile over the past number of years. Events previously considered infrequent and negligible has become more significant if not more frequent. The world is increasingly complex and neo-classic economic theory is being critiqued for its inadequacy of explaining real world events. Events are not, as the theory suggests, insignificant even if they are assumed infrequent.
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To provide an incomplete set of prescriptive advice aimed at
identification of unexpected risks in the commercial real estate
market of Cape Town (South Africa)
David Jansen van Vuuren
Research assignment presented in partial fulfilment
of the requirements for the degree of
Master of Business Administration
at Stellenbosch University
Supervisor: Professor JH Powell
Degree of confidentiality: C December 2013
ii
Declaration
I, David Jansen van Vuuren, declare that the entire body of work contained in this research
assignment is my own, original work; that I am the sole author thereof (save to the extent explicitly
otherwise stated), that reproduction and publication thereof by Stellenbosch University will not
infringe any third party rights and that I have not previously in its entirety or in part submitted it for
obtaining any qualification.
D. Jansen van Vuuren 10 October 2013
14533634
Copyright © 2013 Stellenbosch University All rights reserved
iii
Acknowledgements
I would like to thank my family and friends for their patience and support and John Powell for his
accessibility.
iv
Abstract
The world of finance in general and real estate in specific has become increasingly volatile over the
past number of years. Events previously considered infrequent and negligible has become more
significant if not more frequent. The world is increasingly complex and neo-classic economic theory
is being critiqued for its inadequacy of explaining real world events. Events are not, as the theory
suggests, insignificant even if they are assumed infrequent.
On the opposite spectrum is complexity theory, an allegorical view of market and systems
behaviour. The theory, although adequate in explaining observations, is inadequate for any
meaningful application and decision-making. It essentially asserts unpredictability and the inherent
unknowable future.
This research aims to motivate a hybrid view in between these two polar opposites. The hybrid
perspective is aligned with chaos theory and displays characteristics of both theories uniquely
combined to deliver an analytical in-between view.
This assignment will attempt to deliver two outcomes. The first is a reality matrix aimed at
contextualising the efficiency of the two opposing schools of thought within a level of analysis
(macro, meso and micro). A model will serve as reference for the positioning at meso level of the
applied real estate research proposed in this assignment. The second is the delivery of a list of
prescriptive advice on the identification of risks in the commercial real estate sector of Cape Town,
South Africa.
The central theme of this assignment revolves around the divergence of theory and reality and
delivering an alternative response to neo-classic or complexity theory, although restricted to an
analytical view.
The research methodology closely aligned with the inquiry, falls within the domain of knowledge
management, more specifically, system based knowledge management. Socio-gramming
techniques available to knowledge management are employed in creating a systems diagram of
commercial real estate.
The findings show that commercial real estate as a system is aligned with the characteristics of
chaos theory and continues to provide a list of prescriptive advice on potential risks and mitigation
strategies.
v
Key words
Chaos theory
Commercial real estate
Complexity theory
Cybernetics
Neo-classic theory
Property risk
Risk management
Market volatility
vi
Table of contents
Declaration ii
Acknowledgements iii
Abstract iv
List of tables ix
List of figures x
List of acronyms and abbreviations xi
CHAPTER 1 ORIENTATION 1
1.1. INTRODUCTION 1
1.2. PROBLEM STATEMENT 1
1.3. RESEARCH QUESTIONS 4
1.4. RESEARCH OBJECTIVES 4
1.5. RESEARCH AIM 4
1.6. CHAPTER OUTLINE 4
CHAPTER 2 LITERATURE REVIEW 5
2.1 INTRODUCTION 5
2.2 LITERATURE REVIEW 5
2.2.1 A summative review of neo-classic economic theory 5
2.2.1.1 Normal probability distribution 5
2.2.1.2 Efficient market hypothesis 6
2.2.2 A summative review of complexity economic theory 7
2.2.2.1 Power law distribution 7
2.2.2.2 Nassim Taleb 8
2.2.2.3 Fractal theory 9
2.2.2.4 Chaos theory 9
2.2.3 A summative review of orthodox systems theory 11
2.2.3.1 Cybernetics 11
2.2.3.2 System dynamics 12
2.2.3.3 Open systems 12
2.2.3.4 Chaos 12
2.2.3.5 Complex adaptive systems – variant 1 12
2.2.4 A summative review of radical systems theory 12
2.2.4.1 Dissipative structures 12
2.2.4.2 Complex adaptive systems – variant 2 13
2.2.4.3 Summary of assumptions associated with the two opposite schools of thought 13
2.2.5 Agent-based modelling from a radical perspective 14
2.2.6 Motivation for a reality model and positioning the research 14
2.2.7 Legitimizing knowledge management 16
vii
2.3 CONCLUSION 17
CHAPTER 3 RESEARCH METHODOLOGY 18
3.1 INTRODUCTION 18
3.2 GENERAL METHODOLOGY ASSUMPTIONS 18
3.3 METHODOLOGIES AVAILABLE 19
3.3.1 System dynamics (SD) 19
3.3.2 Qualitative system dynamics (QSD) 19
3.3.3 Qualitative politicised influence diagrams (QPID) 20
3.4 SELECTED STUDY METHOD 21
3.4.1 Nature of the problem 21
3.4.2 System dynamics (SD) 21
3.4.3 Qualitative system dynamics (QSD) 21
3.4.4 Qualitative politicised influence diagrams (QPID) 22
3.5 CONCLUSION 22
CHAPTER 4 DATA COLLECTION 23
4.1 INTRODUCTION 23
4.2 DATA COLLECTION PROCESS 23
4.2.1 Defining the basis for discussion (first session) 23
4.2.2 Preliminary quality control 23
4.2.3 Confirming the basis for analysis (second session) 24
4.2.4 Group interviews 24
4.2.5 Declaration and treatment of bias 25
4.2.6 Final quality control 25
4.3 DESCRIBING THE MODEL 26
4.3.1 Meso-level influence diagram 26
4.3.2 Defining the system variables 27
4.3.3 Describing the loops 29
4.3.3.1 Acquisition expenditure on property loop 29
4.3.3.2 Desirability of location loop 30
4.3.3.3 Building conversion ability loop 30
4.3.3.4 Number of possible investors/buyers loop 30
4.3.3.5 Rate of economic expansion loop 31
4.4 CONCLUSION 31
CHAPTER 5 ANALYSIS 32
5.1 INTRODUCTION 32
5.2 CHARACTERISATION OF LOOPS 32
5.3 VIEWPOINT ANALYSIS 33
5.3.1 Acquisition expenditure on property loop 33
5.3.2 Desirability of location loop 36
viii
5.3.3 Building conversion ability loop 37
5.3.4 Number of possible investors/buyers loop 39
5.3.5 Rate of economic expansion loop 40
5.4 AN INCOMPLETE SET OF PRESCRIPTIVE ADVICE 41
5.5 CONCLUSION 42
CHAPTER 6 CONCLUSION 43
6.1 REALITY MODEL 43
6.1.1 Orthodox perspective: Neo-classic theory 43
6.1.2 Radical perspective: Complexity theory 43
6.1.3 Non-radical orthodox perspective: Hybrid theory 44
6.2 KEY FINDINGS 45
6.2.1 Government failure theme 45
6.2.2 Dependence on debt finance theme 46
6.2.3 Natural disasters theme 46
6.2.4 Market dynamics theme 46
CHAPTER 7 CRITIQUE/FUTURE WORK 47
7.1 LIMITATIONS OF THE PROPOSED MODEL 47
REFERENCES 48
APPENDIX A: MODEL 50
ix
List of tables
Table 1.1: A characteristic and utility comparison of economic and systems theory 2
Table 2.1: Summary of classic market risk theories 5
Table 2.2: Comparing linear and non-linear 10
Table 4.1: Defining system variables 28
Table 5.1: Characterisation of loops 32
Table 5.2: Commercial real estate risks and strategies 41
x
List of figures
Figure 1.1: Opposite economic theories 1
Figure 2.1: Normal distribution (or bell-curve) 6
Figure 2.2: Power law distribution 8
Figure 2.3: Reality model 15
Figure 2.4: Modes of knowledge creation 17
Figure 4.1: Complete influence diagram for meso-level commercial real estate 26
Figure 5.1: Acquisition expenditure on property loop 34
Figure 5.2: Desirability of location loop 37
Figure 5.3: Building conversion ability loop 38
Figure 5.4: Number of possible investors/buyers loop 39
Figure 5.5: Rate of economic expansion loop 40
Figure A.1: Complete influence diagram for meso-level commercial real estate 50
xi
List of acronyms and abbreviations
CAMA computer assisted mass appraisal
CAPM capital asset pricing model
CID city improvement district
CoCT city of Cape Town
CPI consumer price index
EK explicit knowledge
EMH efficient market hypothesis
FDI foreign direct investment
GDP gross domestic product
KM knowledge management
MPT modern portfolio theory
PEST political, economical, socio-cultural and technological
QPID qualitative politicized influence diagrams
QSD qualitative system dynamics
ROI return on investment
SD system dynamics
SBKM systems based knowledge management
SECI socialization, externalization, combination and internalization
TK tacit knowledge
1
CHAPTER 1
ORIENTATION
1.1. INTRODUCTION
This chapter provides an overview of the problem statement, the specific research questions
associated with the problem and overall aim of the research. An outline of the document structure
chapter-by-chapter is also provided.
1.2. PROBLEM STATEMENT
The world has just ended, or so it seemed too many in 2008 when the U.S. subprime crisis served
as the latest reminder of an imperfect financial system. What made this crisis even more significant
than a financial dip in the U.S. is the effect it had on other economies. Globalisation and the
interconnectedness of economies make what happens in one country important and of concern to
another.
An event like this usually, or rather should, prompt a fundamental questioning and inspection into
what caused it and what possible changes to the economic-, banking- or financial system is
required to avoid repeating it. This process of investigation and remediation will lead to either an
improvement of the current system or a radical departure and changeover to a new structure.
From an economic theory point of view, there are several distinctive economic theories aimed at
explaining market observations, although for the purpose of this assignment there are two
predominantly opposing schools of thought, that being neo-classic thinkers on the left-hand side
and complexity thinkers on the right-hand side. The former is an orthodox perspective aligned with
cybernetics systems theory while the latter is a radical perspective aligned with complexity systems
theory.
Figure 1.1: Opposite economic theories
Source: Researcher. Jansen van Vuuren, D. 2013.
The neo-classic view holds that probability distribution takes on a bell-curve shape (normal or
Gaussian distribution) with the majority of events distributed around the mean and deviations (and
most risks) three standard deviations away from the mean (Mandelbrot & Hudson, 2004). In this
view, it is implicitly assumed that unique events are relatively unimportant. From a systems
2
perspective, neo-classic theory is aligned with cybernetics as an orthodox view on how systems
behave. Interactions are average and relationships are linear leading to an equilibrium seeking
system with clear cause and effect links. It is very important to note that this view postulates a self-
regulating (negative interactions) system that is predictable. The future state of this system is
measured against an ideal state. Therefore, an ideal state must be known allowing negative
feedback to correct deviations.
On the complete opposite spectrum, complexity economic theory, being aligned with a similar titled
systems theory, postulates an environment where events are distributed on a power law, with non-
average interactions and non-linear relationships. This system is self-organising and remains far
from equilibrium (Stacey, 2000). Agent diversity is heterogeneous that together with the former
characteristics allow for spontaneous emergence of “new and destruction”. This view places
emphasis on its self-organising nature and being inherently unknowable and therefore
unpredictable.
In between these two extremes is the proposed non-radical orthodox perspective. This perspective
postulates a hybrid economic theory that is aligned with chaos systems theory. Events are still
normally distributed and interactions are average, but relationships are non-linear. As a result, the
system is kept far from equilibrium, is self-organising and predictability power is pattern based
(quantitative simulations delivers qualitative behavioural patterns that are never the same in reality,
but generally the same pattern) (Stacey, 2000).
Table 1.1: A characteristic and utility comparison of economic and systems theory
Perspective Orthodox Non-Radical Orthodox Radical
Economic theory Neo-classic Hybrid Complexity
Systems theory Cybernetics Chaos Complexity
Probability distribution Normal Normal Power law
Interactions Average Average Non-average
Relationship Linear Non-linear Non-linear
Regulation Self-regulating Self-organising Self-organising
Equilibrium Equilibrium seeking Far from equilibrium Far from equilibrium
Agent diversity Homogenous Homogenous Heterogeneous
Change External External Internal
Nature of emergence Cause & Effect Emergent Evolve emergent
Importance of unpredictability Predictable Pattern Predictable Unpredictable
Decision making Rational Reason: analog & intuition Irrational
Knowledge Known (Explicit) Tacit & Explicit Unknowable (Tacit)
Risk level Mild Slow Wild
Volatility level Slow Mild High
Risk efficiency (environment) Static Hybrid Dynamic
Scientific method Objective Observer Objective Observer Objective Observer
Decision theory Normative Prescriptive Descriptive
KM methodologies SD QSD QPID
3
Source: Researcher. Jansen van Vuuren, D. 2013.
The neo-classic theory of economic behaviour is increasingly being challenged by authors and
thinkers such as Mandelbrot (2004), Taleb (2012) and more recently and specifically for real
estate, Wyman et al. (2011).
Property and economics are strongly linked and therefore a discussion on the former assumes the
principles of economics as underlying factors. In their report, A new paradigm for real estate
valuation?, Wyman et al. (2011) argue the need for conventional valuation theory to include
complexity economic theory and agent-based modeling to improve current day understanding of
real estate price determination. This calls for a better understanding of how property price
determination works in volatile markets.
The test of any theory is whether it explains real-world observations. If neo-classic theory states
that events are normally distributed and outliers are insignificant, then a financial crisis should be
infrequent and insignificant. However, in reality, a financial crisis might be somewhat infrequent but
is most definitely not insignificant. Some of the primary criticism against neo-classic theory is that it
assumes mild risk, while in reality wild risk is occurring (Mandelbrot & Hudson, 2004). If a theory
does not match the observation, it calls for improvement or it stands to become redundant.
The intention of this assignment is not to add to the criticism of neo-classical theory or to argue its
redundancy; conversely, it is efficient, but only in a certain context. In turn, complexity theory is still
very much in its infancy stage and real-world application is some time away. Although it is very
efficient at describing certain real world events, especially volatile environments, it does not lead to
prediction and never will. It is an abstract theory with allegorical utility.
The assignment will therefore attempt to deliver two outcomes. The first is a reality matrix aimed at
contextualising the efficiency of the two opposing schools of thought within a level of analysis
(macro, meso and micro). The model will serve as reference for the positioning at meso level of the
applied real estate research proposed in this assignment. The second is the delivery of a list of
prescriptive advice on the identification of risks in the real estate sector taken here as Cape Town,
South Africa.
The central theme of this assignment is on the divergence of theory and reality and delivering an
alternative response to neo-classic or complexity theory, although restricted to an analytical view.
4
1.3. RESEARCH QUESTIONS
The research questions that will be addressed in this research report are what does a systems
view of the commercial real estate market in the City of Cape Town look like and whether by
applying a risk perspective to this view, what risk events can cause unexpected surprises?
The final question would be to ask what strategies an investor could implement to improve a risk
position in light of these qualified risk events.
1.4. RESEARCH OBJECTIVES
The first step will be to apply existing systems mapping methods to produce a validated system
model of the commercial real estate market in Cape Town.
The second step will be to apply a risk perspective in analyzing the systems model to produce a
qualitative list of variables that can cause unexpected surprises.
The third step will be to produce strategies aimed at improving an investment position in light of the
risk criteria.
1.5. RESEARCH AIM
To provide an incomplete set of prescriptive advice aimed at identification of unexpected risks in
the commercial real estate market of Cape Town, South Africa.
1.6. CHAPTER OUTLINE
Chapter One introduces the study background, research questions and methodology.
Chapter Two provides a literature review of economic and systems theory.
Chapter Three outlines the research methodologies available and motivates the selection.
Chapter Four describes the data collection process and model.
Chapter Five discusses the loop characterization, conducts a view point analysis and generates an
incomplete set of property risks and associated mitigation strategies.
Chapter Six concludes with the three economic and system perspectives and a summary of key
findings.
Chapter Seven discusses limitations of this study and it also offers some recommendations for
future research.
5
CHAPTER 2
LITERATURE REVIEW
2.1 INTRODUCTION
The focus of this chapter is to provide an overview of economic and systems theory with emphasis
on the two opposing schools of thought. Knowledge management will be legitimized as the domain
relevant to investigate the research problem.
The intent is to highlight the various benefits and shortcomings of each school of thought and
propose a three-by-three matrix postulating the organisation of these into specific levels of reality it
adequately explains.
2.2 LITERATURE REVIEW
2.2.1 A summative review of neo-classic economic theory
Although there are numerous models and theories on market behaviour and risk, the list presented
in the table below are the more widely accepted and influential in terms of the neo-classic finance
view.
Table 2.1: Summary of classic market risk theories
Discovery Accredited Inspired by
Normal Probability Distribution Carl Friedrich Gauss -
Efficient Market Hypothesis (EMH) Eugene F. Fama Louis Bachelier
Capital Asset Pricing Model (CAPM) Jack Treynor, William Sharpe, John Lintner, and Jan Mossin
Harry Markowitz
Modern Portfolio Theory (MPT) Harry Markowitz -
Black-Scholes Fischer Black & Myron Scholes -
Source: Compiled from (Mandelbrot & Hudson, 2004).
2.2.1.1 Normal probability distribution
Carl Friedrich Gauss is accredited with the discovery of normal probability distributions. The
primary idea of normal distributed probabilities is that observations of certain events will over the
long-term cluster around the mean with rare and significant events (outliers) at the edges, thereby
producing a bell-curve shaped distribution (Mandelbrot & Hudson, 2004).
6
Figure 2.1: Normal distribution (or bell-curve)
Source: Adapted from (Mandelbrot & Hudson, 2004, p. 35).
One of the primary arguments used by skeptical empiricists is that of the problem of induction. For
example, if property market price variance over a 12-month period did not decrease by 20%, this
does not allow one to conclude that property prices will not go down by 20%. It only carries the
meaning of not precluding it.
By taking a normal distributed approach to managing risk, it is possible to inaccurately induce that
since property prices do not vary more than 20% in a 12-month period, risk can be adjusted to
accommodate for this variance.
Karl Popper postulates, albeit somewhat negatively, that there are only two types of theories: a.)
theories that are proven wrong through empirical testing (falsified theories), and b.) theories not yet
proven wrong, though exposed to be wrong. The reason he holds this view is that even if property
price movements are not yet proven to exceed a 20% movement in a given period, it does not
preclude the possibility (Taleb, 2004, p. 126).
Humans tend to think in a causal fashion as it is much easier to remember a linked frame of
reference than random unrelated information. Induction therefore allows simplification and
compression, but as a necessity reduces the detection of randomness or non-linearity (Taleb,
2004, p. 130).
Although this is quite strong criticism, the bell-curve is not entirely redundant. In controlled
environments where movements are slow and takes on a linear shape, the bell-curve and any
theory based on similar implicit assumptions, is suitable in dealing with events. This assignment
will argue at a later stage the misalignment of this theory and therefore its inefficiency, rather than
its inherent redundancy.
2.2.1.2 Efficient market hypothesis
Inspired by Louis Bachelier, Eugene F. Fama is accredited with the discovery of the Efficient
Market Hypothesis (EMH) which states, in informal terms, that an efficient market is very good and
7
fast at disseminating and digesting information and reflecting this through adjustments in the price
of a security (Graham, 1973, p. 363).
Benjamin Graham (Graham, 1973, pp. 363-364) argues that profit on a stock exchange is made
through accurately pricing and benefitting from the mispricing of others. So if information is
possessed by an individual on a share that the general market does not, it places that individual in
a more efficient position from a pricing accuracy perspective.
The underlying assumptions for EMH to be true are that price changes are statistically independent
and normally distributed. Mandelbrot (2004, pp. 11-14) disagrees on both these points. The first
argument is that prices are not statistically independent; conversely it has a “memory”. However,
he goes on to point out that there are varying degrees of memory for different types of price series
i.e. some have a weak memory, while others are stronger. Secondly, price changes are not
normally distributed. In reality, the bell-curve is quite inadequate to explain market behaviour and
movements. Again, this will be addressed at a later stage.
2.2.1.3 Capital asset pricing model
The capital asset pricing model (CAPM) aims to calculate the theoretical return of an asset. It
assumes a diversified portfolio and makes provision for non-diversifiable risk (or market risk). The
model is theoretically aligned with neo-classic economic theory (Mandelbrot & Hudson, 2004).
2.2.1.4 Modern portfolio theory
Modern portfolio theory (MPT) places emphasis on the maximization of return for a given degree of
risk or the opposite (minimize risk for a certain expected return). The method is based on
proportional selection of assets to collectively represent a portfolio with lower risk than theoretically
associated with the individual assets. The model is theoretically aligned with neo-classic economic
theory (Mandelbrot & Hudson, 2004).
2.2.1.5 Black-Scholes
The Black-Scholes formula is a mathematical model used to analyse financial derivates. The
primary idea of the model is to strip out risk through accurate pricing when purchasing or selling.
The model sits in between neo-classic and complexity theory (Mandelbrot & Hudson, 2004).
2.2.2 A summative review of complexity economic theory
2.2.2.1 Power law distribution
When it comes to fitting a shape to market events at a macro level, power law distribution is
considered more suitable in explaining the observations. As the name indicates, the distribution of
observations takes place at a frequency that varies as the power of some event. Power law applies
equally to positive and negative price changes.
8
Figure 2.2: Power law distribution
Source: Adapted from (Taleb, 2012, p. 437).
The answer to why risk management models is based on normal distribution and not power law, if
the latter is better equipped at explaining observations, is the ease and convenience of the
established methods (Mandelbrot & Hudson, 2004, p. 15). The argument is that normal distribution
works fine most of the time and are only insufficient in times of uncertainty. However, there is a
distinction to be made. If risk can be placed on a spectrum ranging from mild, slow to wild, modern
finance operates on the premise of mild risk while in reality wild risk is occurring (Mandelbrot &
Hudson, 2004, p. 33).
2.2.2.2 Nassim Taleb
The significance of social, economic and cultural life is its inherent unpredictability. This does not
seem acceptable or appropriate for a large percentage of thinkers attempting to make sense of the
world (Parker & Stacey, 1994).
Models and theories such as normal probability distribution, efficient market hypothesis (EMH),
capital asset pricing model (CAPM), modern portfolio theory (MPT), and Black-Scholes are all
examples of attempts made at understanding the underlying workings of economies and more
specifically risk.
Economies go through periods of certainty and uncertainty. Taleb (2012) calls these periods
Mediocristan and Extremistan environments respectively. His overarching premise is becoming
antifragile to extremistan environments by benefitting from it. He defines the word antifragile as
anything that benefits from volatility.
This is similar to stoic philosophy where there is a general indifference to fate i.e. a desire for the
upside, but at the same time being robust to the downside. It is a conditioning to not experience
pain, harm, disappointment unnecessarily. He does confess, however, to domestication and not
elimination of emotions in terms of stoicism, and domestication and not elimination of uncertainty in
terms of risk.
9
His perspective of economic behaviour is that it is not possible to predict anything, that the future is
inherently unknowable and emergent. The best decision tool available for managers are scenarios
and probability based decision-making.
2.2.2.3 Fractal theory
Humans go through two opposite poles of experience. On the one hand there are deterministic
systems with embedded order, planning and certain logic and on the other hand there are
stochastic systems that acts at random, are irregular and unpredictable. Mandelbrot (2004, p. 5) is
accredited with developing a branch of mathematics referred to as fractals or fractal geometry
aimed at explaining observations in natural science.
Although fractal geometry is originally applied to the study of nature, it was extended to provide
theoretical insight in the behaviour of markets and more specifically risk. Mandelbrot do not claim
to provide seamless explanations of market behaviour, though lends another lens through which to
view the world of markets and aims his research at limiting the losses of individuals and not
necessarily empowering them with profit making abilities.
The understanding and study of risk are traditionally viewed from two perspectives. The first is the
most established fundamental (or cause-and-effect) analysis. This perspective holds that in order
to understand risk it is necessary to determine the causes. Therefore, to manage the outcome (or
effects) it is required to know what causes are at work. By an increased knowledge of the various
causes it is possible to predict the outcome (effects) and adapt the risk strategy accordingly. This
proves to be a challenge in practice, as uncovering the causes are a fairly complicated and
intricate task and is often practically infeasible and therefore unknowable. The second view is
technical analysis. Through the study of patterns it is hoped to gain insight into market behaviour
and ultimately a better understanding of risk. Monitoring changes in measures such as volume,
value and various leading or lagging indicators, attempts are made at preempting future market
outcomes. Humans have a tendency to try and uncover patterns and gain increasing insight into
subjects that do not always lend themselves to clarity and lucidity (Mandelbrot & Hudson, 2004, pp.
7-8) (Taleb, 2012). So in terms of modern finance, the central premise is that prices are
unpredictable, but fluctuations can be described through laws of chance (or probability).
2.2.2.4 Chaos theory
Chaos theory is also a departure from classical linear thinking and effectively introduces the fused
thinking of order and disorder, linear and non-linear, regularity and irregularity, the ordinary and the
extraordinary.
Historically, the consensus view in natural sciences was based on certainty (or order) with clear
cause and effect relationships. Things were known and deterministic laws acted as frameworks for
prediction (Newtonian physics). The evolvement of a different kind of thinking that embraced
uncertainty (or disorder) and unpredictability is chaos theory. The motivation behind its
10
development is the inability of traditional models to explain behaviour whether it be in nature,
economics or social. Although there is still underlying order (driven by deterministic laws),
behavioural patterns are now considered from a systemic view. The result is an intricate, multi-
dimensional and paradoxical world view (Parker & Stacey, 1994, p. 11).
As can be seen from table 2.2, non-linear thinking is much more complex than linear thinking
where a single cause can have multiple effects and the aggregate of its system components is
more than the individual parts, i.e. there is a synergistic result.
Table 2.2: Comparing linear and non-linear
Linear Non-Linear
1 cause = 1 effect 1 cause = multiple effects
Sum of its components More than the sum of its components
Source: (Parker & Stacey, 1994, p. 12).
This brings about the recent emphasis from authors such as Taleb (2012) on probabilistic thinking
and a complete disregard for predictability. It is possible to engage in decision making on a
probabilistic basis, even employing scenario planning as thinking models as this is according to
Parker & Stacey (1994, p. 15) “…not planning at all. It is a form of learning intended to improve
skills at responding to events as they occur”.
Any attempt to understand market behaviour will inevitably lead to the concepts of freedom of
choice and constraints. On the one hand there is individual freedom to make choices which will
lead to an unknowable future as the multi-dimensional effect of aggregate decisions of individuals
is not predictable (it is generally accepted that human behaviour is in a non-linear manner). This is
similar to adopt a self-organising, learning and market processes approach. On the other hand
there are rules, regulations, policies, and plans approach which will lead to inactivity.
Non-linear systems are based on the interaction of both positive and negative feedback loops. The
nature of negative feedback loops is to correct or align deviations from a planned outcome to the
actual outcome. Positive feedback has the opposite effect i.e. “it does not cancel out deviations,
rather it reinforces them” (Parker & Stacey, 1994, pp. 25-26).
Linking this to the learning element of a self-organising system approach is the implications of
single and double loop learning. Individuals, groups, companies, society can make decisions
based on either one of these two loops of learning. Single loop learning is corrective decision
making based on feedback of the outcome. This type of learning never challenges the paradigm or
mental model in which the decision is made and usually works in times of certainty. As soon as
there is an increase in uncertainty, the old models become redundant and outmoded requiring a
different type of learning called double loop learning. Double loop learning captures the essence of
challenging paradigms previously accepted as accurate in order to improve the outcome. It is
11
destructive in that it breaks down old mental models in preference to new ones (Parker & Stacey,
1994, pp. 26-27).
It is fair to assume that freedom of choice will not be eliminated from market behaviour or life in
general, resulting in a future that will be unknowable. However, although the future is unknowable,
it does not preclude the utilization of certain responsive measures by shifting the focus from trying
to control the end to concentrating on the means i.e. probabilistic and systemic thinking, scenario
planning. This is the only effective preparatory response to an uncertain outcome (Parker &
Stacey, 1994, p. 17). Non-linear systems are also called dissipative systems and are:
• Irregular (or fractal) in shape and form – positive feedback reinforces changes in the
environment creating fractal patterns of behaviour;
• Self-organising – there is no structure in a conventional sense, but rather one that enables
connection and influence within the system;
• System choices are exponential and unpredictable;
• The system operates on emergence vis-à-vis deterministic (Parker & Stacey, 1994, p. 38).
The importance of discussing dissipative systems is identifying economic and human behaviour as
non-linear systems and that economic self-organisation will lead to unpredictable and emergent
outcomes.
Managers are therefore encouraged to realize their fractal environment and utilize systemic
thinking models while at the same time holding a creative tension as they do not really know what
will emerge or what the outcome will be. However, being flexible and entrepreneurial will contribute
to overall robustness or antifragility (Taleb, 2012).
2.2.3 A summative review of orthodox systems theory
Several of the system theories sit on a spectrum ranging between the orthodox and radical view.
However, the definition of orthodox systems theory adopted in this assignment is that of change
requirements. Systems requiring external or exogenous factors to change are classified as
orthodox while systems that can change because of intrinsic internal or endogenous factors are
classified as complex. It is important to note that systems requiring external influence to change
will respond dramatically if instability is removed, however, radical systems will apply internal
constraints due to the structure of the system and is thereof unaffected.
2.2.3.1 Cybernetics
Cybernetics is a systems theory focused at a macro level of analysis, based on average
interactions that are normally distributed in a linear relationship. The theory operates on negative
feedback (measured against an ideal) and is therefore self-regulating and equilibrium seeking.
Agent diversity is taken as homogenous. Change can only occur through exogenous factors and
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therefore allows a predictability of events based on the known. The method of study associated
with this perspective is that of the objective observer (Stacey, 2000).
2.2.3.2 System dynamics
System Dynamics is a quantitative systems theory aimed at the macro level of analysis. However,
this theory is different from cybernetics in that it is based on non-linear relationships together with
positive feedback (in addition to negative feedback). The result is a self-regulating system but far
from equilibrium. Agent diversity is taken as homogenous. Change can only occur through
exogenous factors and the method of study associated with this perspective is that of the objective
observer (Stacey, 2000).
2.2.3.3 Open systems
Open systems differ from cybernetics and system dynamics in that it is focused at both the macro
and micro level of analysis. Relationship are linear and equilibrium seeking. Change can only occur
from exogenous factors and the method of study associated with this perspective is that of the
objective observer (Stacey, 2000).
2.2.3.4 Chaos
Chaos theory is focused at the macro level of analysis with average normally distributed
interactions though based on non-linear relationships. The system is self-organising or self-
referential, meaning its future state is dependent on its previous state and not an exogenous point
of reference. However, the system still requires exogenous factors to affect change. In terms of
chaos theory it is necessary to think in terms of qualitative patterns related to the system as a
whole. The method of study associated with this perspective is that of the objective observer
(Stacey, 2000).
2.2.3.5 Complex adaptive systems – variant 1
Complex adaptive systems focus at the micro level based on non-average interactions and a self-
organising or self-referential system of regulation. The system operates far from equilibrium with
homogenous agent diversity and an external requirement for change. Predictability is pattern
based and the method of study associated with this perspective is that of the objective observer
(Stacey, 2000).
2.2.4 A summative review of radical systems theory
2.2.4.1 Dissipative structures
Dissipative structure systems theory is focused at a macro level of analysis with non-average
interactions and non-linear relationships. The system is self-organising or self-referential with
heterogeneous agent diversity. Change occurs internally therefore disallowing any predictability
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and aligns itself with the complexity argument of the inherently unknowable. The method of study
associated with this perspective is that of the objective observer (Stacey, 2000).
2.2.4.2 Complex adaptive systems – variant 2
Variant 2 of complex adaptive systems are based on non-average interactions being held far from
equilibrium with heterogeneous agent diversity and a self-organising or self-referential method of
regulation. This leads to a radically unpredictable system at the edge of chaos delivering new and
destructive change (many small changes and few large though significant). The system dynamics
is inherently unknowable and the method of study associated with this perspective is that of the
objective observer (Stacey, 2000).
2.2.4.3 Summary of assumptions associated with the two opposite schools of thought
Before moving on to the next section, it would be prudent to conclude on some explicit
assumptions (Yates & Worzala, 2013).
The premise for neoclassic economic theory is:
• Rational expectations;
• Decision-making practices; and
• Equilibrium conditions.
The areas neoclassic economic theory is inadequate to address include:
• Economic growth;
• Cost-benefit analysis;
• Human behaviour (the agent problem);
• Networks;
• Emergence; and
• Evolution (or innovation).
The areas that complexity economic theory can assist with are:
• Non-linear and upredictable effects;
• Capacity to balance order and chaos in what is called “the edge of chaos” i.e. the area
between order and randomness.
Arguments against complexity economic theory are:
• Ill-defined;
• Too grandiose;
• Non-empirical; and
• Speculative.
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Systems theory range between orthodox models that are linear, equilibrium seeking and lacking in
microdiversity to radical models that are non-linear, far from equilibrium and full of microdiversity
(Stacey, 2000).
2.2.5 Agent-based modelling from a radical perspective
There are several key insights to agent-based modelling from a radical perspective:
The first insight is the dynamic at the edge of chaos. The dynamic is a paradox of simultaneous
stability and instability. Information flow is high but not overly, diversity of agents is rich but not
overly. Interestingly enough, agent diversity is a primary requirement for the emergence of new.
The second insight is the emergence of “new and destruction” (Stacey, 2000). The dynamic at the
precipice, the edge of chaos, results in the emergence of novelty, but can also lead to destruction
or extinction. There are many small events but a few large events can cause large-scale
destruction.
The third insight is an extension of the second. The agents in self-organising systems interact on a
basis of local principles and these interactions are a function of chance events. This leads to
emergence or spontaneity.
The fourth insight is unpredictability. The edge of chaos shrouds cause & effect, hiding links and is
self-referential. The system does not allow for any predictability and essentially remain
unknowable.
The fifth and final insight is perspective. Orthodox agent-based modellers place emphasis on
individual interaction while Radical modellers place emphasis on system and context.
2.2.6 Motivation for a reality model and positioning the research
Neo-classic theory is fairly suitable and adequate at explaining events occurring at a micro
economic level. The problem arises when applying it to the macro level when observations are
misaligned to the predicted outcomes of the theory.
Complexity theory adequately describes observation of events at a macro-economic level,
however, the primary critique is that it is allegorical in nature and being a relatively new theory,
practical application is still some time away.
Beinhocker (2006) as quoted in Yates & Worzala (2013) says: “Complexity economics views the
economy as a complex adaptive system consisting of many agents interacting in a variety of ways,
forming coherent social structures, and interacting with their environment at many levels, covering
micro, meso and macro scales.”
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The proposed reality model captures this then as follow:
Figure 2.3: Reality model
Source: Researchers. Powell, J.H., Jansen van Vuuren, D. 2013.
The model operates on two dimensions. The vertical dimension describes an economic and
systems perspective while the horizontal dimension describes the overall perspective, economic
theory, systems theory, probability distribution, relationship and knowledge.
The interaction of these two sets of dimensions delivers various realities, although for the purposes
of this assignment only four is considered:
• Aligned reality: Neo-classic economic theory, more specifically normal distribution, is aligned
with reality at a micro level. When risk is assumed as static, models analysing information
can be useful for decision making.
• Misaligned reality: At a macro economic level, neo-classic theory are misaligned to reality
and does not deliver the observed reality.
• Allegorical reality: Complexity economic theory are suitable at explaining events occuring at
the macro economic level. The limitation is that the reality delivered here is allegorical in
nature and decision making at best is probabilities attached to scenarios. Economic
decisions are not inherently structured on this basis.
• Analytical reality: The proposed achievable reality is the analytical reality positioned at the
meso level. Decision making will be by analog and intuition recognising patterns.
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2.2.7 Legitimizing knowledge management
This assignment proposes to investigate the workings of a meso-level commercial real estate
system. In order to understand and study the system, a diagram explaining the various interactions
is required.
In order to define knowledge a distinction between data, information and knowledge is required.
Data is seen as the cellular level of information while information can be regarded as data that
forms part of a specific context and placed in a certain category (Swart & Powell, 2006, p. 11).
Only once information is applied does it become knowledge. According to Nonaka (1994, p. 15),
knowledge requires action, is subjective and belief driven. Knowledge is a result of personalisation
by the individual after processing it through his/her own personal beliefs, mental models, world
views or values.
It is possible to obtain real estate information, for example, by going to property conferences,
studying a real estate course or conversing with colleagues. This information then becomes
knowledge when it is applied in activities such as buying or selling of property, managing property,
reviewing an application and extending credit at a financial institution.
Knowledge can be distinguished into two categories i.e. tacit- (tk) and explicit knowledge (ek). The
characteristics of tacit is described as “acquired through practice, manifest only through action,
difficult to transfer, inseparable from individuals, personal belief, values, [or] ideas floating in
someone’s head” (Kane, et al., 2006, p. 142). The characteristics of explicit is described as
“rationalisation of information, capable of storage and transmission, can be articulated, factual,
represented in the form of documents, designs, formal language, objective and rational knowledge”
(Kane, et al., 2006, p. 142).
Stated differently, tacit knowledge is that part of knowledge that is unseen, intangible, obtained
through experience, embedded in an individual’s actions, and not entirely transferable. Explicit
knowledge in turn is that part of knowledge easily and readily seen, tangible, made available and
accessible at an external level, and entirely transferable.
Epistemology is the philosophical theory of knowledge addressing the dual types of knowledge i.e.
tacit and explicit, while ontology is the arrangement and organisation of knowledge in a hierarchical
manner that specifically relates to individual, group, organisational and inter-organisational levels.
The significance of combining the two dimensions of epistemology and ontology is the resulting
four quadrant spiral model, also known as the SECI of knowledge conversion as developed by
Nonaka (1994, pp. 18-19). The model attempts to explain how existing knowledge can be
converted or new knowledge created through the four modes of knowledge conversion.
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Figure 2.4: Modes of knowledge creation
Source: (Nonaka, 1994, pp. 18-19).
The process of knowledge conversion (or creation) is arranged into four quadrants of interaction
entitled socialization, externalization, combination and internalization. Each of these quadrants
represents an interaction from and to:
• From tacit knowledge to tacit knowledge through the process of socialization;
• From tacit knowledge to explicit knowledge through the process of externalization;
• From explicit knowledge to explicit knowledge through the process of combination; and
• From explicit knowledge to tacit knowledge through the process of internalization.
Since the knowledge regarding the workings of this system is tacitly located within the agents or
participants active therein, it needs to be accessed through the process of externalization and
distilled into the form of a systems map.
2.3 CONCLUSION
This chapter gives an overview of the characteristics of the two opposing schools of thought for the
neo-classic and complexity thinkers both in terms of economic and systems theory. The
perspective in between these two theories, better known as chaos theory is discussed in order to
address the theoretical parameters associated with this view and the characterisation thereof for
the application and analysis to the research questions.
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CHAPTER 3
RESEARCH METHODOLOGY
3.1 INTRODUCTION
This chapter starts with a discussion on general methodology assumptions, proceeds to discuss
the various methodologies available to systems-based research, characterises the nature of the
problem and subsequently motivates the most appropriate research method.
3.2 GENERAL METHODOLOGY ASSUMPTIONS
According to Forrester (1994), all decisions are based on models. More specifically, nearly all
social and economic activities are influenced and controlled by mental models. A very important
benefit of mental models is that it holds vast sets of information not captured anywhere (tacit
knowledge). However, mental models are not necessarily true or accurate reflections of reality.
Rather it is most often the aggregate of subjective assumptions and experiences of an individual
over time.
The question then emerges whether it is the possible to change a mental model. Forrester (1994)
argues that system dynamics, as a thinking framework, allows for a method of learning through
immersion into the workings of a system and correction of mistakes observed over time holding the
power to change a person’s mental model.
Apart from changing mental models, other human shortcomings include incompleteness, internal
contradictions and the inability to draw dynamic conclusions. This can, however, be addressed by
computer models and builds the case for computer-aided diagrams.
A few points of premise need to be established before proceeding to the methodology discussion.
Firstly, economics is a system and understanding the workings of economic behaviour will require
a systemic perspective. The meaning of a system adopted here is the identification of components
acting together to produce a result or behaviour that is not possible individually (separately). The
emphasis lies on the interaction. Systems modelling are an appropriate method when dealing with
complexity and interconnectedness (Powell, 2001, pp. 2-3).
Secondly, since mental models influence most economic activities, any attempt at addressing a
problem will necessarily need to be informed by individuals possessing practical knowledge of the
system being described.
Finally, informants often do not know they possess the knowledge to address the problem; the
challenge is to extract it in the right order. The explication of knowledge into a systems map
addresses the inability of mental models to make dynamic conclusions. Although Forrester was
referring to quantitative modelling and the logic required by computer software, the wider sense is
assumed here i.e. qualitative modelling.
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3.3 METHODOLOGIES AVAILABLE
In terms of knowledge management methodologies there are three primary techniques of enquiry:
i) System Dynamics (SD) based on influence diagrams (ID)
ii) Qualitative System Dynamics (QSD)
iii) Qualitative Politicised Influence Diagrams (QPID)
3.3.1 System dynamics (SD)
Jay Forrester founded the field of system dynamics during the 1950s. The tradition of system
dynamics is that a problem can only be analysed and used for advice delivery, based a fully
quantified model (Coyle, 2000, p. 225). This is not only the tradition, but also the focal point of this
method, arguing that any attempt to understanding a dynamic system is through quantified
simulation.
Quantitative models have certain limitations and shortcomings:
• The accuracy of the system to reflect reality is dependent on exhaustive variable
identification which is not always practically feasible;
• The level of mathematical competence required limits its use;
• Conclusions based on a quantitative approach could differ from a qualitative model;
• The uncertainties with quantified variables are argued away by placing the emphasis on
general patterns of behaviour as opposed to the actual number;
• The model does not address competing parties and interests;
• When modelling, the variables used should reflect real-world observation. If the model
cannot adequately reproduce the real and reasonable system behaviour, due to uncertainties
about the “concepts, social presssures and sources of information that control the actual
decisions”, it is better to limit the analysis to a qualitative level (Forrester, 1961, p. 63) (Coyle,
2000, p. 233).
3.3.2 Qualitative system dynamics (QSD)
Qualitative system dynamics (QSD) is an extension of SD. From the 1980s, qualitative models
emerged without the added simulation component. According to Wolstenholme and Coyle (1983),
the description of a system can precede simulation in an aid to describe and better understand the
problem. Coyle (2000, p. 226) specifically states that this does not necessarily mean reliable
inferences can be drawn from a complex systems diagram.
Various arguments and views have been produced around the validity of qualitative mapping.
Some hold very strong views about the utility of quantitative models while a more balanced view is
presented by Richardson (1999). He argues that a quantitative simulation model is always better
than a qualitative model. However, there are instances when a map can provide certain insight
without the necessity of a model.
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QSD as a methodology offers certain flexibility and utility over SD:
• Practically feasible and conceptually exhaustive;
• Accessbile to a wider range of users.
3.3.3 Qualitative politicised influence diagrams (QPID)
QPID is a variant of QSD and includes actors or agents affecting the strength of interactions
(Coyle, 2000) (Swart & Powell, 2006). This method makes allowance for the influence of agents in
the system that is not addressed in QSD.
The benefit of a politicised diagram is the understanding it affords to the various interactions.
Knowing which actor influences what variable, provides additional insight into the overall system
behaviour and the ability to formulate strategic responses to achieve personal, enterprise or
economic goals.
“[A]ny system modelling approach which fails to reflect the effects of human behaviour upon the
business system is unlikely to be adequately rich in its representation.” (Powell & Bradford, 2000,
p. 187). Extending this to a slightly broader application, any human activity system should
represent the influence of human behaviour.
Powell (2001, pp. 8-9, 15-18) has compiled a guideline or a set of “grammar” rules for QSD
mapping, however, this has been slightly adapted to accommodate relevancy to both quantitative
and qualitative mapping.
Firstly, when starting the influence diagram, care should be exercised in stating a clear and
unambiguous question.
Secondly, only valid variables should be captured. A valid variable can be quantitative such as
revenue, inventory volume or rate of new products or qualitative with the requisite of being scalable
such as willingness to spend, desirability of location or access to finance. The latter is considered
scalable since it can be qualitatively motivated as high or low, whereas non-scalable variables are
static and should therefore be excluded.
Thirdly, as soon as more than two variables are captured in the diagram and a relationship exists,
the causal arrow will have to be drawn in. The arrow runs from the cause to the effect. Emphasis
should be placed on the distinction between causality and correlation. There should be a clear
causal-relationship and not only correlation. In a quantitative model, stock and flows are used.
Finally, polarity should be indicated at the end of the causal arrow i.e. positive or negative polar
points. This is true for both quantitative and qualitative modelling. Variables moving together,
whether it be up or down, carries a “+” sign, while variables moving in opposite directions to each
other carries a “–“ sign. The connection can be strong or weak and not necessarily linear. In some
cases, “±” signs may be used, though this adds certain ambiguity.
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3.4 SELECTED STUDY METHOD
There does not seem to be a very clear line between when to use a quantitative or when to use a
qualitative model. This does not mean that it does not matter which model is used, but rather
highlights the necessity of first understanding the problem.
An influence diagram has the ability to capture very complex problems in a concise manner. As
described before, the first step in system dynamics is mapping an influence diagram to represent a
system. The second step is to study the diagram and feedback loops and its relevancy to the
problem. Only once this is completed does the decision to quantify or potentially add actors arise.
A rigorously drawn influence diagram is not only descriptive of a problem, it can form the basis for
quantification and simulation modelling if required.
3.4.1 Nature of the problem
The intention is to develop an incomplete set of prescriptive advice aimed at identification of
unexpected risks in the commercial real estate market of Cape Town (South Africa). The problem
can be described as:
• Markets are potentially chaotic;
• Markets move from one state to another: periods of stable equilibrium, periodic equilibrium
and chaos;
• Forecasting is impossible at a macro level, however, pattern prediction is possible;
• Similar actions do not lead to the same state.
3.4.2 System dynamics (SD)
A system dynamics model is generally useful and more robust than a qualitative approach. The
nature of the problem requires the identification of variables that can cause unexpected surprises
based on certain decisions. A simulated quantitative model can deliver qualitative patterns;
however, this falls without the scope of this assignment. The intention is to develop a prescriptive
list and does not require quantification at this stage.
Selection decision: not appropriate due to scoping limitations.
3.4.3 Qualitative system dynamics (QSD)
The focus of the research is in developing a prescriptive list of advice. As mentioned before, the
first step in system dynamics is mapping an influence diagram to represent a system. The second
step is to study the diagram and feedback loops and its relevancy to the problem.
QSD is appropriate in this regard.
Selection decision: selected and appropriate for the research question.
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3.4.4 Qualitative politicised influence diagrams (QPID)
Skarzauskiene (2010, p. 53) in reference to Senge (1990) believes that “the essence of systems
thinking is to:
• Understand interrelations, but not linear cause-effect relations;
• See processes of changes, but not static states; and
• See and understand context.”
This seems similar to Forrester (1994) who does not believe that cause-and-effect is closely tied in
time and space. Rather, in complex systems the cause is further removed from the effect
misleading causal determination.
The QPID method can accommodate non-linearity, dynamic conditions and the context of agent
influence. However, the intention of the research is not to identify who is responsible for certain
interactions and the strength of the relationship, but rather to focus on system variables.
Selection decision: not appropriate for the research question.
3.5 CONCLUSION
A discussion of the three methods available to systems-based research is given. More specifically,
these three methods are system dynamics (SD) based on influence diagrams (ID), qualitative
system dynamics (QSD) and qualitative politicised influence diagrams (QPID). An evaluation of the
nature of the problem in light of the three methods and associated suitability results in the selection
of qualitative system dynamics (QSD) as a research method.
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CHAPTER 4
DATA COLLECTION
4.1 INTRODUCTION
This chapter will discuss the data collection process that took place over two focus group sessions.
Participants related to the commercial real estate industry from diverse backgrounds were selected
to inform the process. The first session focused on defining the basis of discussion, more
specifically selecting the unit of analysis and producing a basic model. After this session, the
research supervisor and researcher cleaned the model and prepared an agenda to assist in
identifying any variable omitted from the model in the first session. Subsequently, the second
session was held with the aim of validating any additional variables to the model and finding
consensus that the model is an adequate representation of the commercial real estate market in
Cape Town. The chapter closes with a description of the model and defining the various system
variables.
4.2 DATA COLLECTION PROCESS
4.2.1 Defining the basis for discussion (first session)
In order to initiate the process of developing a system maps during the first group session, the unit
of analysis had to be decided on. The positioning of the research (at meso level) and the domain
(real estate) was known and decided upon prior to the first group session, however, consensus
was reached on commercial real estate in Cape Town metropolitan, South Africa as the unit of
analysis.
The aim of this stage is also to develop a basic model to illustrate the principles and process of
model building, however, the participants caught on to the process quickly and a reasonably
comprehensive model was generated.
4.2.2 Preliminary quality control
After the first group session, a meeting was scheduled with the research supervisor of this report to
inspect the diagram for any inconsistencies, missing elements and to discuss the validity of a risk
perspective when analysing the model.
During the meeting the various variables represented in the diagram were confirmed as consistent.
Two additional variables were added and marked for validation when presenting the model at the
second group meeting. These variables include desirability of investment and fashionability of
location. Cost of transport and locational economic advantages are identified as the primary input
drivers for desirability of location.
An agenda for the second group meeting was designed:
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• Validate the added variables (desirability of investment and fashionability of location);
• Unpack existing variables/mechanisms further with specific reference to value of a single
property, desirability of location and operating expenses;
• Utilise a PEST analysis (Political, Economic, Socio-Cultural and Technological), Porter’s 5
Forces and industry barriers as tools to aid the discussion in identifying omitted system
variables.
A viewpoint analysis question was raised with the supervisor detailing the intent of identifying
inefficiencies and resultant risks associated with the various loops in the diagram. This was
confirmed as a suitable approach.
4.2.3 Confirming the basis for analysis (second session)
A follow-up group data collection session was arranged. The following outcomes were achieved
during this meeting:
• An overview of the methodology and mapping process was given to a new participant as
three of the previous participants could not attend the second session and a balance of
backgrounds had to be maintained;
• The model as produced during the first meeting and the added mechanisms were validated;
• The PEST analysis, Porters 5 Forces and industry barriers were applied as tools to discuss
commercial real estate with the aim of identifying ommited system variables. The information
produced from these exercises serves as a basis of analysis for identifaction and conversion
to system variables.
4.2.4 Group interviews
Two meetings were arranged for the data collection process. Each meeting lasted approximately
two and a half hours. For the purpose of this research this group was referred to as the commercial
real estate focus group.
The interviews were unstructured as this allowed for a facilitated discussion and emergence of
thought disallowed by other techniques. The sessions were not recorded and would have been of
value in hindsight.
The participants for the first meeting included:
• Managing director and professional valuer of a private property consultancy firm;
• Two real estate consultants (property valuers);
• A commercial real estate broker;
• A credit and relationship manager for a major financial institution;
• A senior manager of human settlements for government; and
• An operations manager for a bio-tech company (unrelated industry participant).
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The meeting was facilitated by the research supervisor, a professor with specialisation in strategy
and knowledge management. The reseacher participated by asking intermittent clarification
questions.
The participants for the second meeting included:
• Managing director and professional valuer of a private property consultancy firm;
• Two real estate consultants (property valuers);
• A commercial real estate broker; and
• A property manager of a listed property fund.
The researcher opened the second meeting, presented the model and validation of additional
mechanisms, and facilitated the PEST, Porters 5 Forces and industry barriers discussion. The
research supervisor was also present and both facilitated and participated in the discussion.
4.2.5 Declaration and treatment of bias
The researcher owns and manages a family business offering real estate valuation and
consultancy, business consulting and e-learning services.
The participant described as managing director for a private consulting firm is related to the
researcher as his father, business co-owner and professional mentor. He also acts as mentor to
the two real estate consultants (property valuers). As a result, there are two classes of bias
associated among these four participants.
The first is a professional bias. The managing director has mentored the researcher and two real
estate consultants (property valuers) in his capacity as a professional valuer, contributing to a
shared view. The second is an income bias. All four informants receive some form of income from
the business.
In order to address the bias of the researcher, the research supervisor acted as independent group
facilitator for both interview sessions. The supervisor has no financial interest and is not active
within the real estate industry as a professional, providing therefore an unbiased and unrelated
view. The researcher’s role in the data collection was restricted to observation and the asking of
clarification questions.
The bias associated with the managing director and two real estate consultants (property valuers)
is addressed by validation of the remaining informants who are considered entirely independent to
the inquiry.
4.2.6 Final quality control
The information produced during the second meeting was investigated for potential mechanism
omitted or overlooked in the system diagram.
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The following variables were added:
• Functional obscolescence;
• Number of possible investors/buyers;
• Cost of land;
• Building conversion ability;
• Foreign direct investment (FDI);
• Competence of investor/buyer.
The diagram was sent electronically to the various participants for validation and confirmed to be
an accurate description of the commercial real estate system in Cape Town, South Africa.
4.3 DESCRIBING THE MODEL
4.3.1 Meso-level influence diagram
The validated and final influence diagram of commercial real estate is illustrated below:
Figure 4.1: Complete influence diagram for meso-level commercial real estate
Source: Created by commercial real estate focus group. July 2013.
The various short and descriptive terms visible in the diagram are the system variables. Causal
arrows are drawn to illustrate relationships between the various variables. A blue arrow typifies a
positive relationship while a red arrows a negative relationship. An alternative description is
feedback loops. A negative feedback loop is ideal-based, continuously measuring the state of the
system against an ideal and feeding back the difference of reality. A system only comprising of
negative feedback loops are self-regulating from a systems perspective. For example, although not
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illustrated in the diagram, the condition of a property has a negative influence on operating
expenses, a negative influence on the return on investment of a property and in turn a negative
influence on condition. The assumption therefore, is that there is a certain ideal return on
investment and the condition of the property is regulated, through timeous maintenance, to achieve
this.
The positive feedback loops are reinforcing and amplifies results. For example, the quality of
tenants positively influences the adjacent quality of tenants that in turns influence the quality of
tenants. This is a positive upward spiralling loop.
The diagram consists of both positive and negative feedback loops qualifying this map as a self-
organising (or referential) system with non-linear relationships though average interactions that are
normally distributed. This implies that markets are potentially chaotic, move from periods of stable
equilibrium to periodic equilibrium and chaos, forecasting is impossible at macro level and similar
actions do not lead to the same state.
Where variables form part of a greater feedback loop, direct relationships have not been drawn into
the diagram.
4.3.2 Defining the system variables
The model consists of forty variables with specific definitions attached to each. A clarification of
terms is required to ensure transparency and consistency:
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Table 4.1: Defining system variables
System variable Definition
Value of single property The value of a single commercial property in Cape Town, South Africa
Appetite for capital expenditure The willingness to incur capital expenses in improving the property
Capital investment The generally accepted accounting definition of capital investment
Condition The continuous physical state of repair of the subject property (maintenance)
Quality of tenants The degree of worth associated with tenants
Quality of adjacent tenants The degree of worth associated with adjacent tenants
Security of income Rental income stability of a single or multi-tenanted commercial property
Variability of lease expiry Continuous lease expiry variance of a single multi-tenanted commercial property
Rent on asset Rental income on a single commercial property (asset)
Desirability of location Overall relative prominence of property location
Fashionability of location Degree of coolness associated with the micro location
Locational economic advantage Economic advantages specifically associated with the unique location
Cost of transport Cost of available modes of transport
Cost of land Acquisition cost of available land
Operating expenses Property expenses in the normal course of operation
Rates and taxes Property assessment rates, city improvement district (CID) levies and other municipal taxes
Utilities Cost of water and electricity
Cost of insurance Property insurance
Building conversion ability The architectural design and building technology employed at the time of construction impacts on the future conversion ability of the building
Functional obsolescence Changes in building technology. Also includes changes in buyer preferences/tastes
Availability of information The general availability of information in the market place, but also information deliberately/strategically withheld by a corpus of investors
Number of possible investors/buyers The degree of fragmentation or consolidation of investor/buyer groups
Competence of investors/buyers Agent (investor/buyer) ability to recognise patterns, emotional intelligence and willingness to learn
ROI on property Gross property return
Expected ROI on property Future gross property return
Desirability of investment Attractiveness of investment
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Foreign direct investment Directly invested foreign funds (excluding any secondary trading)
Acquisition expenditure on property Cost of acquiring and transferring commercial property ownership
Cost of new build New construction cost of a commercial property
Rate of new build Rate of new property construction (observed or measured by passed building plans)
Availability of alternative properties The amount of similar / equivocal stock available on the market
Regulatory constraint Policy making and agenda setting
Access to finance Accessibility of debt finance (offered loan-to-value ratio, interest rate, credit terms)
Rate of economic expansion Rate of economic growth as measured in gross domestic product (GDP) terms
Cost of money Prime interest rate
Confidence in the economy The degree of confidence in future economic performance
Inflation rate As measured by consumer price inflation (CPI)
Tax rate Individual and legal entity tax rate
Alternative investment opportunities Availability of alternative investment opportunities with similar risk profiles
ROI on alternative investment ROI on alternative investment opportunities competing for investor/buyer capital
Source: Created by commercial real estate focus group. July 2013.
4.3.3 Describing the loops
There are five dominant loops illustrated in the influence diagram. Some of the variables are not
directly reflected in the causal chain, but has a prominent influence on the system. The five loops
with the direct and indirect causal chain are listed below:
4.3.3.1 Acquisition expenditure on property loop
• Cost of new build;
• Rate of new build;
• Availability of alternative properties;
• Value of single property:
o Availability of information.
• Appetite for capital expenditure:
o Tax rate;
o Inflation rate;
o Cost of money;
• Capital investment:
o Access to finance.
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• Condition;
• Operating expenses:
o Rates & taxes;
o Utilities;
o Cost of insurance.
• ROI on property;
• Expected ROI on property;
• Desirability of investment.
4.3.3.2 Desirability of location loop
• Quality of tenants;
• Security of income;
• Rent on asset;
• Value of single property:
o Availability of information.
• ROI on property;
• Expected ROI on property;
• Desirability of investment;
• Acquisition expenditure on property.
4.3.3.3 Building conversion ability loop
• Functional obsolescence;
• Quality of tenants;
• Security of income;
• Rent on asset;
• Value of single property;
• Appetite for capital expenditure;
• Capital investment.
4.3.3.4 Number of possible investors/buyers loop
• Value of single property;
• ROI on property;
• Expected ROI on property;
• Desirability of investment;
• Foreign direct investment:
o Regulatory constraint.
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4.3.3.5 Rate of economic expansion loop
• Regulatory constraint;
• Access to finance.
4.4 CONCLUSION
This chapter discussed the data collection process addressing various aspects such as the basis
of collection (unstructured), discussion and analysis (commercial real estate in Cape Town, South
Africa). Quality control, more specifically the adding and validation of system variables and
research biases, are also addressed.
There are five dominant loops referred to in the model, namely the acquisition expenditure on
property loop, desirability of location loop, building conversion ability loop, number of possible
investors/buyers loop, and finally the rate of economic expansion loop. The loops consist of various
variables that were all defined in table 4.1 for consistency and clarification of terms.
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CHAPTER 5
ANALYSIS
5.1 INTRODUCTION
This chapter provides a characterisation of the five dominant loops selected from chapter 4 as the
basis of analysis, and then proceeds to apply a chaotic viewpoint analysis. The fundamental
question asking during the analysis of each loop is “What can go wrong?”. These events are then
classified as possible risks for which associated mitigation strategies are prescribed in table 5.2.
5.2 CHARACTERISATION OF LOOPS
The following table summarises and characterises the loops found in the influence diagram:
Table 5.1: Characterisation of loops
Loop Causal Chain Type Speed Strength
Acquisition expenditure on property loop
Cost of new build > Rate of new build > Availability of alternative properties > Value of single property > Appetite for capital expenditure > Capital investment > Condition > Operating expenses > ROI on property > Expected ROI on property > Desirability of investment
Reinforcing Medium Strong
Desirability of location loop
Quality of tenants > Security of income > Rent on asset > Value of single property > ROI on property > Expected ROI on property > Desirability of investment > Acquisition expenditure on property
Reinforcing Fast Strong
Building conversion ability loop
Functional obsolescence > Quality of tenants > Security of income > Rent on asset > Value of single property > Appetite for capital expenditure > Capital investment
Reinforcing Medium Medium
Number of possible investors/buyers loop
Value of single property > ROI on property > Expected ROI on property > Desirability of investment > Foreign direct investment
Reinforcing Fast Strong
Rate of economic expansion loop
Regulatory constraint > Access to finance Reinforcing Medium Strong
Source: Created by commercial real estate focus group. July 2013.
All five loops are reinforcing. This implies that commercial property as a system can spiral beyond
control and potentially explain the occurrence of market bubbles. As an asset, it is believed that
property values always increase over the long-term. Although the system is predominantly
reinforcing i.e. small inputs delivering amplified results, it does not suggest stability. Rather to the
contrary, since the various variables in the system are non-linear in relationship, being a mixture of
positive and negative links, the system is kept far from equilibrium.
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Speed refers to how fast or slow a change can be affected by intervention. Strength refers to the
degree of contribution the loop will have on the performance of the system.
The five loops are of varying speed and strength with most loops being medium to fast in speed
and medium to strong in strength.
5.3 VIEWPOINT ANALYSIS
In analysing the various loops, the research intent is aimed at identifying possible sources of
unexpected risks. The question asked in each case is simply put, “What can go wrong?”. By asking
this question, it is potentially possible to gain some insight into the future sources of unexpected
events. It is recognised that each agent’s environment is contextual and adjustment for uncertainty
would therefore vary on a case-by-case basis.
5.3.1 Acquisition expenditure on property loop
This reinforcing loop is a composite of two interacting dynamics. The first is the cost consideration
of building a new property and the second the capital investment required to maintain or improve
the property in order to deliver desirable returns.
The first dynamic takes into account the cost of building a new property and assumes that the
market will not pay more for a property than what it would cost to build. This is considered a fair
assumption and therefore maintains pressure on the built environment to keep costs in check
making extraordinary (building) cost inflation infeasible from a market point of view. However, the
cost of money also has an impact on the developer cost margins. The implicit assumption here is
that money or capital will be available from lending institutions. Occasions can arise where no
capital is available and developers will be required to either halt new development or be self-
funded. The latter can be done through various arrangements such as supplier payment terms,
internal cash flow prioritisation and stacking of projects, and even rental agreements with the end-
user while retaining ownership.
The cost of new build affects the rate of new build. The traditional view is that lower cost will lead to
an increase in building projects. However, this does not accommodate competitive measures such
as controlling the amount of stock available on the market to create a higher demand and
subsequently a higher price. The rate of new buildings influences the availability of alternative
properties, which has a negative impact on the value of a single property. In a neo-classic market,
an increase in the availability of alternative properties will lower, ceteris paribus, the value of a
single commercial property and vice versa.
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Figure 5.1: Acquisition expenditure on property loop
Source: Created by commercial real estate focus group. July 2013.
The second dynamic takes into account the appetite for capital expenditure associated with
ownership. The following five drivers directly affect the appetite for capital expenditure:
i) Value of single property: The state of repair of a property positively influences the appetite to
invest capital for improvement delivering an increased value. The dynamic of cost and value
are at play here. Over or under-capitalising can have undesirable and infeasible financial
consequences.
ii) Tax rate: An increasing tax rate can positively or negatively influence the appetite for capital
expenditure. It is positive in assuming debt financed capital expenditure, the interest is tax
deductible. It is negative as companies adjust to increasing tax liabilities. Taking this a step
further, the tax rate could escalate drastically as was the case during war times. However,
there is also the likelihood of non-market political agendas.
iii) Inflation rate: Discussed in the first paragraph is that of building cost inflation, however, this
does not address consumer price inflation. Hedging against consumer inflation is an
investment objective of many investors/buyers. The radical perspective is that of a
hyperinflation environment.
iv) Cost of money: Already discussed in the first paragraph.
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v) Confidence in the economy: Two aspects influence confidence in the economy. The first is
cyclical economic activity. During times of up or down markets, investor/buyer confidence
(and the market as a whole) are relative to the environment. The second and more radical
aspect is investor/buyer diffidence with government (sovereign debt) failure.
The appetite for capital expenditure and access to finance influences the level of capital invested.
Capital investment improves the overall condition of the property that in turn increases the value
and decrease operating expenses.
Operating expenses suppresses the return on investment. However, with an increased investment
in building efficiency, lower operating costs can result in higher returns. There are three prominent
property expenses.
The first is rates and taxes. The City of Cape Town (CoCT) uses a mass appraisal system called
Computer Assisted Mass Appraisal (CAMA) to value properties within their administrative region
for rates and taxes purposes (City of Cape Town, 2012). One of the primary reasons for using this
system is its cost efficiency in handling large volumes of properties. However, there are
inefficiencies associated with this approach that can lead to significant errors of over or under
valuation. Holding commercial property as an investment therefore requires active monitoring and
action.
The second is utilities which can be separated into two categories i.e. water and electricity. In
terms of water, the possibility exists of weather changes affecting water shortage or rationing.
Older, less modern commercial property will use more amounts of water than their counterparts
(green buildings) affecting costs and levies. In terms of electricity, it is no longer required to attach
a probability on electrical supply failure or constraints, as it is an everyday reality. Companies have
invested in independent electrical supply sources such as generators, solar panels, efficient
lighting and various other techniques. The increasing cost of electricity is a reality and requires pro-
active solutions.
The third is insurance. Depending on the location, some sea front or riverbank commercial property
can be susceptible to flood or storm damage, properties located in electric belts can be
increasingly susceptible to storms, or properties located in on quarry can be exposed to landslip or
shifting foundations. The challenge with insurance is not the traditionally known risks such as these
mentioned above, but the occurrence of events in areas not anticipated before. Unfortunately, it is
not possible at this stage to know the where and the what, but as these events occur, the luck of
the draw will be with those not holding property in an area that has become exposed to a certain
event.
Operating expenses has a negative influence on the return of investment of a property that in turn
affects the expected return and desirability of the investment.
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The following summary lists possible sources of unexpected risks:
• Unavailability of money or capital for new construction or capital expenditure (renovations,
refits, or maintenance);
• Competitive risk of property stock control i.e. the number of available investment
opportunities;
• Tax increase due to the potentiality of war (civil or governmental) or other non-market
political agendas;
• Tax increases due to inefficiencies in the property rates assessment system;
• Managing investments in a hyperinflation environment;
• Investor/buyer diffidence (and the market as a whole) in the case of government failure;
• Weather changes resulting in water shortage and rationing with accompanying costs and
levies for water-dependent or less efficient buildings;
• Electricity shortage, a reality in South Africa, and increasing costs;
• Uncertainty of property location exposure to natural events and changes in patterns.
5.3.2 Desirability of location loop
This reinforcing loop starts with desirability of location that has three additional variables not shown
on the diagram i.e. fashionability of location, cost of transport and locational economic advantages.
Fashionability of location has both a positive and negative influence on the desirability of a
location. The coolness of being in a certain location could be building up or breaking down.
Cost of transport refers to all modes of transport. The price of private vehicles can increase
exponentially due to shortage of steel. The cost of fuel can increase unsustainably due to changes
in the oil market. Taxi or bus drivers’ going on strike will require alternative transport arrangements
that can be costly. Corporates dependent on flying executives and managers to various meeting
points can be susceptible to high aviation costs or the unavailability of flights due to a natural
disaster or a major accident on the landing strip.
The unique locational economic advantage of a commercial building can be sustainable
competitive advantage in relation to other competing properties. However, there is always the risk
on an unrelated substituting risk driven by technology that can eradicate the economic edge. This
should not be interpreted as the unilateral demise of physical locational advantage, but rather the
increasing complexity and channel management of modern day business.
Therefore, the degree of desirability of a certain location attracts, ceteris paribus, a certain quality
of tenant with higher quality tenants normally associated with good locations and vice versa. The
quality of a tenant affects the quality of adjacent tenants creating a reinforcing loop spiralling either
upwards or downwards.
37
Security of income is a function of the quality of tenants and associated lease expiry profiles.
Building managers try to ensure a degree of uniformity in timing of leases. The conclusion of a
lease is one aspect while financial underperformance and liquidation is another. In order to
manage the latter, a deep understanding of each tenant’s business is required. Tenants can be
required to report to the property owner’s internal or external (third party service provider)
management team composed of various professionals. Understanding each sub-business in terms
of a network or system, would deliver greater insight and potentially lower the risk of income loss.
Figure 5.2: Desirability of location loop
Source: Created by commercial real estate focus group. July 2013.
The security of income negatively affects rent on asset achieved and directly influences the value
of the property, return on investment, expected return on investment and desirability of investment.
The return on investment of alternative opportunities competes with the desirability of investment
and can negatively affect the willingness to acquire or invest in a certain property.
The following summary lists possible sources of unexpected risks:
• Change in coolness of an area/location;
• The dependence on a single mode of transport can result in inaccessibility or sporadic high
costs;
• Technological advancement substituting or competing with locational economic advantage;
• Sub-business (tenant) risk and the need to understand sub-trends and business changes to
effectively manage the tenant mix and security of income.
5.3.3 Building conversion ability loop
The ease of building conversion ability will determine the amount of capital investment required
over a period to ensure staying abreast with building technology and space market trends.
Traditionally, the speed of change in building technology and space market trends are medium to
slow. With the rise of environmental consciousness, there is an increasing trend for green
buildings. Developers are designing and developing innovative building designs incorporating the
38
latest building technology. Each new green building is trying to set an even higher benchmark for
sustainability. It has become a fashion to certify green buildings as corporates seek to report green
initiatives and social responsibility.
Figure 5.3: Building conversion ability loop
Source: Created by commercial real estate focus group. July 2013.
Green buildings impact negatively on non-green buildings in overall market appeal. If two buildings
are located adjacent to the other, the green building is able to demand a higher rental, attract
better quality tenants and benefit from lower operating expenses and higher returns in the long-
term. The effect of the green building on the non-green building is not neutral as the latter will not
be able to maintain rental levels, but actually lower rentals in an attempt to attract tenants and
thereby negatively affecting long-term returns and value of the property.
In order to compete, non-green building will be required to invest in renovating and refitting the
improvements. Buildings that fall behind in the renovation or are too outdated in terms of design
and finishes will decline and eventually be demolished and redeveloped i.e. where cost of
redevelopment is less than the cost of renovation.
Apart from building technology, socio-cultural changes also affects building use. The increasing
popularity of shared workspaces is changing the traditional view of space market management.
Individual freelancers or micro businesses require workspace on demand with fitted services such
as internet connectivity, call filtering, or boardroom facilities. This requires a change in building
configuration (types of services offered) and how income is managed (short-term contracts and
marketing strategy for building).
39
The following summary lists possible sources of unexpected risks:
• Fashionability of green buildings and the impact on non-green buildings;
• Socio-cultural changes in the traditional use of commerical space affecting tenant profile,
duration, cost, and building configuration.
5.3.4 Number of possible investors/buyers loop
The number of investors/buyers influences the value of a single property assuming that the greater
the number, ceteris paribus, the higher the value and vice versa. Although the value increases, the
expected return on investment is put under pressure due to the increasing competition.
Investors include both local and foreign interested parties to a transaction. The latter can be
advantaged or disadvantaged depending on the type of foreign policy pursued by government.
Foreign investors can potentially have access to cheaper finance and exchange rate benefits if
South Africa is not competitive enough at an international level.
Two other variables not included in the diagram are availability of information and competence of
investor/buyer.
The availability of information can potentially be limited to a small number of investors, thereby
creating a distinct advantage in the transaction. For example, there are only a handful of listed
property firms competing for top value properties. Information regarding these properties could
potentially be limited to local players, thereby placing foreign investors at a disadvantage.
Figure 5.4: Number of possible investors/buyers loop
Source: Created by commercial real estate focus group. July 2013.
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Investor/buyer competence also influences the return on investment on property. The ability to
recognise patterns, emotional intelligence and willingness to learn will impact over property results.
The following summary lists possible sources of unexpected risks:
• Foreign policy pursued by government allowing competitive (theoretically equal) or anti-
competitive (in favour of local or in favour of foreign) interactions;
• The level of foreign direct investment taking into account access to alternative/cheaper
sources of finance and exchange rate benefits;
• Manipulation of information availability by a select number of market participants;
• Investor/buyer competence (or incompetence).
5.3.5 Rate of economic expansion loop
There is a reinforcing loop between the rate of economic expansion and access to finance i.e. if the
economy is expanding it does so through increasing availability and accessibility to finance, if the
economy is contracting finance dries up.
It seems counter intuitive to provide cheap access to finance during times of expansion and closing
access during times of contraction. The effect is a constant over or under regulation.
Figure 5.5: Rate of economic expansion loop
Source: Created by commercial real estate focus group. July 2013.
Depending on the economic policy pursued by government, regulation can constrain or facilitate
access to finance and subsequently economic growth.
The following summary lists possible sources of unexpected risks:
• Misaligned finance and economic growth policy resulting in continuous over and under
regulation;
• Non-market political agendas limiting access to finance.
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5.4 AN INCOMPLETE SET OF PRESCRIPTIVE ADVICE
The following table summarises a list of possible risks and counter-strategies:
Table 5.2: Commercial real estate risks and strategies
No. Risk Strategy
1 Unavailability of money or capital for new construction or capital expenditure (renovations, refits, or maintenance)
Maintain larger cash reserves
Change payment terms (customer & supplier)
Formalised barter trading
2 Competitive risk of property stock control i.e. the number of available investment opportunities
Invest in an area relatively free of competitive control
3 Tax increase due to the potentiality of war (civil or governmental) or other non-market political agendas
Transform business model to be war enduring or disinvest
4 Managing investments in a hyperinflation environment
Do not hold any cash, engage capital in investments
5 Investor/buyer diffidence (and the market as a whole) in the case of government (sovereign debt) failure
Do not have government as a tenant
Understand tenant’s business – should not be delivering products/services to government
6 Weather changes resulting in water shortage and rationing with accompanying costs and levies for water-dependent or less efficient buildings
Invest in greening of building if cash is available (grey water system, plumbing efficiencies)
Implement water use policies
7 Electricity shortage and increasing costs Invest in greening of building (design, technology)
8 Uncertainty of property location exposure to natural events and changes in patterns i.e. an area previously not known for a certain disaster becomes increasingly prone for the same
Do not be located on a river bed, sea front, quarry or mineral rich area
9 Change in coolness of an area/location Be area/location independent
10 The dependence on a single mode of transport can result in inaccessibility or sporadic high costs
Ensure familiarity with multiple transport systems
Create technology backup solution
11 Technological advancement substituting or competing with locational economic advantage
Create technology independent advantage
12 Sub-business (tenant) risk and the need to understand sub-trends and business changes to effectively manage the tenant mix and security of income
Research team (internal or external contracted party) to continuously research and advise selection and tenant mix decisions
13 Fashionability of green buildings and the impact on non-green buildings
Invest in green buildings Invest in convertible non-green buildings
14 Socio-cultural changes in the traditional use of commercial space affecting tenant profile, duration, cost, and building configuration
Management to make tenant mix decision i.e. traditional leases or short-term or combination
15 Foreign policy pursued by government allowing competitive (theoretically equal) or anti-competitive (in favour of local or in favour of foreign) interactions
Competitive: business as usual
Anti-competitive (local): advantageous
Anti-competitive (foreign): partner
16 The level of foreign direct investment taking into account access to alternative/cheaper sources of finance and exchange rate benefits
Source finance abroad with value proposition of local knowledge and superior investor returns
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17 Manipulation of information availability by a select number of market participants
Create internal competitive intelligence team or hire competitive intelligence company
18 Investor/buyer competence (or incompetence) Do not bid/purchase higher than internal feasibility
19 Misaligned finance and economic growth policy resulting in continuous over and under regulation
Be cycle independent (as much as possible)
20 Non-market political agendas limiting access to finance
Maintain larger cash reserves
Source: Researcher. Jansen van Vuuren, D. 2013.
5.5 CONCLUSION
Through the analysis of the five dominant loops, a list of twenty risk events is identified. These
events all stem from the dynamics of the model and are therefore possible; however, the
probability of each event is not discussed as this is marked for further research. The utility of risk
events listed in table 5.2 for commercial real estate participants is the identification of risk events,
but also a list of possible mitigation strategies that can be implemented to lower overall risk
exposure. It therefore serves as a guide for decision-making in terms of new investments, deciding
to disinvest and day-to-day management of commercial real estate.
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CHAPTER 6
CONCLUSION
6.1 REALITY MODEL
The focus of the research is to propose a reality matrix organising neo-classic and complexity
theory into levels of analysis it adequately describes. Instead of arguing in favour of complexity
theory and critiquing neo-classic theory, both is recognised to offer certain utility while a hybrid
view is proposed. The properties of the latter are associated with chaos theory, though an
understanding of what this entails is required for decision-making and risk management.
6.1.1 Orthodox perspective: Neo-classic theory
The research takes neo-classic theory as departure point for describing the financial or real estate
events observed in reality. According to this perspective, events are normally distributed with
average interactions based on linear relationships. The system or market are self-regulating and
equilibrium seeking with homogenous agent diversity. Cause and effect links are clear and known
allowing prediction and decision-making from a rational basis. Change is external to the system
requiring intervention.
The proposed hybrid perspective of this research shares some of the system properties, but differs
in other areas. The hybrid economic theory perspective shares a normal probability distribution of
events with average interactions although recognizes that relationships are non-linear. As
illustrated in the influence diagram, both positive and negative interactions are observed. The
system is self-organising with its future state a function of its present or former state, different to
neo-classic which is self-regulating. This characteristic on regulation recognizes the pattern
movement of property prices i.e. periods of increasing property prices and periods of decreasing
property prices. Prices are not high the one day, low the next and high the following, it flows and is
a function of its previous state.
As a result, the system is kept far from equilibrium and only allows pattern predictability. Decision-
making is still rational, but using analog and intuition.
6.1.2 Radical perspective: Complexity theory
The radical opposite is complexity theory which states that events are power law distributed, non-
average and non-linear. The system is self-regulating, far from equilibrium and based on
heterogeneous agent diversity. Change in this perspective is internal, evolving spontaneously.
Events are entirely unpredictable, in fact, it is unknowable and assumes irrational decision making.
Macro observations are potentially aligned with this perspective; however, it offers no practical
utility. Since nothing is knowable and everything is unpredictable, no decision value can be
extracted from this perspective apart from probability based assessments.
44
The hybrid perspective taken in the research, shares the self-organising nature of the system and
being far from equilibrium with non-linear relationships. It differs in other aspects. Commercial
property is considered normally distributed with average interactions. Property prices increase over
the long-term. Non-average movements would disallow this observation. Change and emergence
are not internal or spontaneous i.e. property construction and finance is emergent but not
spontaneous. These items are man-made and only a reality because of the economic and social
need for it.
6.1.3 Non-radical orthodox perspective: Hybrid theory
The proposed theory characteristics argue in favour of potentially chaotic markets with market
movements from one state to another i.e. periods of stable equilibrium, periodic equilibrium and
chaos. Relationships are non-linear (positive and negative) while interactions are average and
normally distributed. Change remains external to the system as property is not considered to be
spontaneous, rather it is human design. Agent diversity is homogenous and similar actions do not
lead to the same state.
Forecasting is impossible at a macro level, but simulated pattern prediction is possible. Decision
making is rational, but through analog and intuition requiring both tacit and explicit knowledge. The
overall risk efficiency is a hybrid (best alternative to static efficiency of neo-classic theory and
allegorical dynamic efficiency of compexlity theory) with the associated scientific method that of an
objective observer.
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6.2 KEY FINDINGS
The systems representation of commercial real estate illustrated five reinforcing loops namely,
acquisition expenditure on property loop, desirability of location loop, building conversion ability
loop, number of possible investors/buyers loop, and rate of economic expansion loop. For a
detailed description of the model, please see chapter 4. A larger version of the model can also be
seen in Appendix A.
One of the prominent findings is the reinforcing nature of the various loops. This suggests that
commercial property as a system can spiral beyond control and potentially explain the occurrence
of market bubbles. Since property requires external intervention, the stability of the system is a
function of agent decision-making. Therefore, if agents or system participants decide not to
intervene, the nature of the system will spin out of control, as is observed in practice with the
occurrence of market bubbles.
Since only one loop is medium in strength while the others are all strong, the impact of each loop is
significant. Prioritising, therefore, certain loops over others in terms of risk source identification is
not necessarily helpful. The model is qualitative; deeper insight can possibly be gained if a
quantitative model is simulated based on patterns.
Twenty risks have been identified from the various loops and interactions. For each of these risks,
general strategies are proposed. The table of risks and strategies (see table 5.2) will not be
reproduced here, though the major themes can possibly conclude the research discussion (this is
not exhaustive, only to summarise).
6.2.1 Government failure theme
The first theme is government failure with possible risks such as dependence on government for
supply of electricity, formulation of free market (or non-market) legislation with specific reference to
foreign direct investment, taxes, property and ownership, peace (civil or governmental war), as a
tenant (directly), as a main source of income for a tenant (indirectly) and cost of transport (aviation,
rail and bus services).
There are a number of possible strategies that, in no specific order, could lower risk, namely the
transformation of business models to be (war) enduring, non-government tenant policy,
understanding the tenant’s business (linkages to government and percentage contribution to
revenue for offering government products or services), investing in greening of building (design
and technology), ensuring familiarity with multiple transport systems and the creation of technology
backup solution (where physical travelling could possibly be avoided) and partnering with foreign
investors if policies are in their favour.
46
6.2.2 Dependence on debt finance theme
The second theme is dependence on debt finance for new construction or capital expenditure
(renovations, refits or maintenance) and misaligned finance and economic growth policy resulting
in continuous over and under regulation.
Possible strategies to address these risks if finance is just not available is to maintain larger cash
reserves, change of payment terms with customers and suppliers, implement formalised barter
trading and be cycle independent as much as possible.
6.2.3 Natural disasters theme
The third theme is natural disasters with possible risks such as weather changes resulting in water
shortage and rationing with accompanying costs and levies for water-dependent or less efficient
buildings or the uncertainty of exposure to natural events and changes in patterns i.e. an area
previously not known for a certain disaster becomes increasingly prone for the same.
Possible strategies to address these risks are to invest in greening of building if cash is available
(grey water system, plumbing efficiencies), implement water use policies, and not investing in
properties located on a riverbed, sea front, quarry or mineral rich area.
6.2.4 Market dynamics theme
The fourth theme is market dynamics with possible risks such as competitors controlling
information availability, change in coolness of an area/location, technological advancement
substituting or competing with locational economic advantage and the fashionability of green
buildings and the impact on non-green buildings.
Possible strategies to address these risks are to invest in an area relatively free of competitive
control, create internal competitive intelligence team or hire a competitive intelligence company, be
area/location independent, create technology independent advantage, invest in green buildings
and convertible non-green buildings.
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CHAPTER 7
CRITIQUE/FUTURE WORK
7.1 LIMITATIONS OF THE PROPOSED MODEL
The research methodology employed was qualitative system dynamics. In light of the proposed
hybrid theory (chaos theory), pattern predictability should be possible. A quantitative simulated
model aimed at providing pattern predictability and testing will improve the research.
The research focused on commercial real estate in Cape Town, South Africa. The relevancy and
applicability to commercial real estate in other metropolitans such as Johannesburg, Durban or
Port Elizabeth is not tested. The model therefore lacks scaling and universality.
The probability of events is not investigated, disallowing as a result prioritisation of risks and
decision-making value for investors/buyers.
There is also a diagnostic limitation to employing SD diagrams for identifying chaotic systems
behaviour. The studying of chaotic behaviour in a systems map is necessarily subject to the
researcher’s view and interpretation (read imagination) of chaotic events stemming from the
various system variables. The prescriptive advice in this assignment is therefore incomplete.
48
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Beinhocker, E., 2006. The Origin of Wealth: Evolution, Complexity and the Radical Remaking of
Economics. Boston, MA: Harvard Business School.
City of Cape Town, 2012. General Valuation 2012. [Online]
Available at: http://www.capetown.gov.za/en/propertyvaluations/Pages/GeneralValuation2012.aspx
[Accessed 8 October 2013].
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APPENDIX A:
MODEL
Figure A.1: Complete influence diagram for meso-level commercial real estate
Source: Created by commercial real estate focus group. July 2013.
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