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

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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.

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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.

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Key words

Chaos theory

Commercial real estate

Complexity theory

Cybernetics

Neo-classic theory

Property risk

Risk management

Market volatility

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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

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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

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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

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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

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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

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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

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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

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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.

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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).

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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

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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.

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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.

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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

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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

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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

42

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.

43

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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Forrester, J., 1961. Industrial Dynamics. Cambridge: MIT Press.

Forrester, J., 1994. University of Jaén. [Online]

Available at:

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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.