thai real estate practitioners perceptions of risks
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THAI REAL ESTATE PRACTITIONERS PERCEPTION OF RISKS
Sukulpat Khumpaisal
Faculty of Architecture and Planning
Thammasat University
Bangkok,Thailand
Raymond Abdulai
School of the Built Environment
Liverpool John Moores University
Byrom Street, Liverpool,L3 3AF, United Kingdom
Email:R.Abdulai@ljmu.ac.uk
Andrew Ross
School of the Built Environment
Liverpool John Moores University
Byrom Street, Liverpool,L3 3AF, United Kingdom
ABSTRACT
Risk plays a critical role in any investment decision process and therefore its importance cannot be
overemphasised. This paper examines Thai real estate practitioners perception of risks caused by
social, technological, environmental, economic and political (STEEP) factors and the current risk
assessment techniques in the real estate industry. The quantitative research approach is adopted and
specifically, parametric or correlative tests have been carried out. It is based on a pilot survey of 50
Thai real estate practitioners, which was conducted in mid 2009 with a response rate of 78% (39 out
of 50). It has been established that Thai practitioners are concerned with the risks caused by
economic and political factors more than other sources of risk. The study also shows that there is
less evidence of the application of systematic risk assessment techniques that help to deal with
potential risks. In terms of policy implications, the findings have underscored the need for an
appropriate risk assessment model to be developed and implemented in the Thai real estate industry.
INTRODUCTIONRisks are normally associated with every investment vehicle and therefore real estate development
is not an exception. Real estate development has its own risks, especially in relation to the decision-
making process for a new development project. The entire project management process regarding
schedule delay, cost overrun and quality of products are affected by risks (Gehner et al., 2006;
Khallafalah, 2002; Flyvbjerg et al., 2003; PMBOK, 2002). In terms of the nature of real estate
development projects, risks can only be managed within an overall framework of risk management
processes (Blundell et al., 2007; Booth et al., 2002). It is important forrisk assessment techniques to
be based on preferably rigorous quantitative statistical framework as well as subjective analyses of
issues.
mailto:R.Abdulai@ljmu.ac.ukmailto:R.Abdulai@ljmu.ac.uk -
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Broadly, risks are categorised into systematic and unsystematic and there are various techniques
that can be used to assess risk be it systematic or unsystematic. These techniques include Project
Risk Ranking (PRR) and the Construction Risk Management System (CRMS) (Baccarini and
Archer, 2001; Al-Bahar and Crandall, 1990). These techniques have, however, been developed
based on certain parameters. Thus, a technique that might be applicable in one country and have the
desired impact may not be applicable in another country owing to differences in the businessenvironments. Most of the techniques are also subjective in nature, as they are not based on
quantitative statistical measures (Choi et al., 2004). There is therefore a need for risk assessment
techniques that are based on a rigorous and quantitative statistical framework.
Using Thailand as a case study, the objectives of this paper are to: (i) investigate the possible causes
of risks in real estate development projects as well as assess the current risk assessment techniques
employed by Thai real estate developers; and (ii) explore any differences in perceptions between
Thai real estate practitioners and the Western world. The choice of Thailand as a case study is based
on the fact that it was the starting point of the global economic crisis in 1997 (Hilbers et al., 2001;
Warr, 2000). The behaviour of players in the real estate sector towards risks is often cited as theprimary factor responsible for such economic crises. Quigley (2001) and Lauridsen (1998) argue
that the players did not pay enough attention to the impact of risks on their businesses because they
did not have the appropriate techniques that could be used to assess risks and deal with the impact.
In recent years, the current global economic recession has also had significant effects on the entire
Thai business sector. However, it appears that Thai real estate developers are still unaware of
appropriate risk assessment techniques that can be used to potently deal with risks in the changing
business environment (Kritayanavaj, 2007; Pornchokchai, 2007).
To intimate what follows, the next section looks at the general classification of risks which isfollowed by a section that provides background information of the Thailand real estate development
sector. In section four, the research methodology adopted for the study is described. A comparison
of Thai and Westerns perceptions of risks is made in section five. The empirical data collected is
presented and analysed in the penultimate section whilst the last section concludes the paper.
INVESTMENT RISK AND ITS MANAGEMENT PROCESS
Risk is a concept that denotes a potential negative impact on an asset, project or some characteristic
of value that may arise from some current process or future event (Crossland et al., 1992).
According to Baum and Crosby (2008 risk is the uncertainty of an expected rate of return from aninvestment, while Hargitay and Yu (1993) define risk as the unpredictability of the financial
consequences of actions and decisions. Similarly, risk is the extent to which the actual outcome of
an action or decision may diverge from the expected outcome (Huffman, 2002). Thus, risk is
simply the probability that an investor will not receive the expected return or the deviation of
realisations from expectations.
Risks can be broadly classified into systematic risks and unsystematic risks. Systematic risk
(uncontrollable risk) is the type of risk caused by external factors that affect all investments -
examples include market risk, inflation or purchasing power risk, and interest rate risk (Baum andCrosby, 2008; Brown and Matysiak, 2000). According to these authors, unsystematic or specific
risk refers to risk over which the investor has limited control. Thus, unsystematic risks affect only a
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(Luu, 2008; Khallafalah, 2002). According to Morrison, (2007) regular risks that occur during any
development process can be classified into STEEP factors identified above. The other way to
identify risks is to categorize them in terms of risks external to the project and those that are
internal. The next step after all risks have been identified is initial assessment and the aim is to
assess all consequences for risks that would finally affect the developed project. Tasmanian
Government (2006) has identified the consequences of risks to be: delayed or reduced projectoutcomes; reduced project output quality; extension of timeframes; and increased costs.
Smith (2002) has noted that the risk identification process enables project managers to identify
risks. This process is combined with historical project, industrial checklists and workshop
brainstorming sessions. He recommends that the brainstorming sessions are the appropriate
methods because of they provide the most updated information, which suit real project conditions,
and also equivalent to the value management approach. Raftery (1994) supports the use of
brainstorming techniques to identify project risks as he considers them to be effective. The
decision-makers need to work closely with the project team in order to deal with the internal risks
effectively. They also need to consider the client, the project, the project team and the quality of thedocumentation from the perspectives of the various contractors in anticipation of claims. The
outcomes of the identification process are generally the lists of potential sources of risks, which are
classified based on the impact and likelihood of occurrences (Smith, 2002).
According to Jutte (2009) the risk assessment step is critical in the whole risk management process;
it particularly essential for the decision makers to use the assessment results as information to
support further decision making towards risks. He notes that the earlier the decision makers can
identify and assess risks, the better as it ensures that les time is spent to respond to risks.
Judgements have to be made regarding the positive or negative impact of any risks on theproject as well as opportunities and threats, which may occur during the project progress. However,
the most important rules are to prioritise and analyse risks. This means that decision makers have to
use the information from the risk identification process and the judgements of experts to rank and
level the degree of each projects risks (ACT, 2004; Smith, 2002). This process also includes the
setting of highest impact risk as the first priority in order to respond or mitigate its consequences.
Thus, the risk assessment stage identifies risks as well as assesses the probability of their occurrence
and the consequence of the risks (Wrona, 2009).
Risk Analysis
MacDonald et al. (2004) has defined risk analysis or assessment as a systematic process ofidentifying potential hazards where there is the likelihood that those hazards will cause harm. The
authors note that this process is an important portion of the entire risk management process.
According to Raftery (1994) project risks caused by internal and external factors require systematic,
experienced and creative analysis. Thus, risk assessment is a controllable device that deals with
identified risks and an assessment of their impacts. Generally, risks are analysed in terms of their
likelihood of occurrence and consequences. Decision makers may develop a Risk Matrix to assist
in the determination of the level of likelihood of occurrence and consequences as well as the current
risk level (ACT, 2004). Byrne (1996) observes that this stage of risk analysis is a combination of
three aspects which are: measurement or assessment of probability; use of any indicator to measurethe individual attitudes to risk; and sensitivity and simulation. Various tools and instruments have
been developed to deal with this stage of risk analysis (whether systematic and unsystematic risk
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analysis) and such tools enable decision-makers to use information from sources like secondary
information or panel discussion (Khumpaisal, 2007; Chadborn, 1999).
Response and Mitigation
It is really important to identify mitigation strategies very early in any project and this is because
practical risk mitigation strategies reduce the chance that a risk will be occur and/or reduce the severity
of the risk if it occurs (Byrne, 1996).
From the above three stages, the risk management process is crucial since it allows for the
determination of quantitative or qualitative values of risk related to a concrete situation and any
recognized threat or hazard. The figure below shows the management flow chart
Figure 1: Risk Management Flowchart
Source: AS/NZS 4360: 2004 Risk Management Standard (ACT 2004)
According to the risk management flowchart above, the first thing to do is to establish a framework
for risks by decision makers and based on this, risks are identified. The decision makers have to
analyse risks to determine the existing controls as well as determine the likelihoods and
consequences of the risks that may occur during the project development process (Byrne, 1996).
Thus, it is necessary for decision makers to rank the level of risk based their probability of
occurring and their consequences. Risks and then assessed based on comparison of risks against the
established criteria and risk criteria are constructed based on the classification of risks. Thecategories of risks are varied in accordance with the perception of the decision makers or by the
current project situation (Pidgeon, 1992).
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Another popular risk management or assessment model used by real estate developers is Risk
Assessment Matrix (RAM). The RAM describes the likelihood and consequence of each risk in a
matrix format that is generally accepted by many decision makers owing to its simplicity and the
fact that it provides more understanding of projects at every level (Younes and Kett, 2007; Rafele et
al., 2005; ioMosaic, 2002; Kindinger, 2002). However, RAM demerits. One disadvantage relates to
the data used in the calculation; the data is based on personal opinions and not on reliable sources
with a strong theoretical basis. RAM also measures the likelihood and consequences of risk based
on a single criterion, and it is therefore not suited to real estate developers aiming to understand the
correlation and the effects of each factor (Chen and Khumpaisal, 2008). Booth et al. (2002) and
Frodsham (2007) note that there is a need for an idealistic risk assessment model that can analyse
the impact of risks in a quantitative format to be introduced in the real estate sector. According to
the authors, such a model would allow the synthesis of risk assessment criteria and comparisons
among factors, and would also help developers to structure the decision-making process.
It is against this background that the Analytic Network Process (ANP) model has been introducedas an alternative risk assessment technique to respond to these requirements. The model adopts the
principles of Multi Criteria Decision Making (MCDM) and it is developed based on the grounded
theories of Analytic Hierarchy Process (AHP). The ANP model is a powerful and flexible decision-
making tool that helps investors or decision makers to set priorities and make the best decision
when both qualitative and quantitative aspects of a decision need to be considered (Saaty, 2005;
Cheng and Li, 2004). Chen et al. (2006), Saaty (2005) and Cheng et al. (2005) summarise the
construction of the ANP model as follows:
Decomposing the problem into a hierarchy in which the highest level of the structure
denotes the primary goal of the problem and the lowest level refers to the alternatives; Inviting experts to conduct pair-wise comparisons of each element with regard to their
respective adjacent higher level. The scale of interval employed in this pair-wise comparison
is usually the 9-point scale of measurement;
Calculating the relative importance weights (eigenvectors) in each pair-wise comparison
matrix and computing the consistency of the comparison matrices;
Placing the resulting relative importance weights (eigenvectors) in pair-wise comparison
matrices within the super-matrix (un-weighted); conducting pair-wise comparisons on the
clusters; weighting the un-weighted super-matrix, by the corresponding priorities of the
clusters, which becomes the weighted super-matrix; and Adjusting the values in the super-matrix so that it can achieve column stochasticity. This
means that the decision maker will take the resultant relative importance weights
(eigenvectors) and place them in the matrix.
The model has been used in several areas of construction research and practice since the late 1970s,
including construction planning, location selection and environmental impact assessment (Chen et
al., 2005; Cheng et al., 2005). Recently, Chen and Khumpaisal (2008) used the ANP model to
assess risks in Liverpool commercial real estate projects and the study shows that the ANP model is
an effective model to assess risks.
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AN OVERVIEW OF THAILANDS REAL ESTATE DEVELOPMENT
The collapse of the global economic crisis in 1997 was caused by the downfall of Thailands real
estate development business (Warr, 2000). Quigley (2001) and Lauridsen (1998) have observed that
the key reasons for this crisis could be traced to financial institutions and real estate developers
who it is argued lacked monetary discipline and neglected risks in real estate business as well as the
lack of practical risk assessment and management techniques to resolve the consequences of risks.
Vanichvatana (2007) and Kritayanavaj (2007) have predicted that the future trend of the Thai real
estate sector will be similar to the circumstances in the 1997 crisis, as practical risk assessment
techniques are yet to be developed. This prediction is supported by the incidents of the current
global recession (2007 2010) and the US sub-prime crisis, which has significantly affected the
Thai real estate sector owing to the shortage of housing purchasing demand and less funding
injected into the housing and residential sub-sector. Despite the fact that Thai real estate developers
have experienced this crisis and acknowledged its main causes, they are still less concerned with
risks and their effects on real estate projects. Pornchokchai (2007) and Kritayanavaj (2007) note
that this is because of the lack of appropriate knowledge to assess, identify and understand the risks
as well as the fact that they are only interested in realising a maximum return from their investment.
This article focuses on the real estate development projects in the Bangkok Metropolitan Area
(BMA) and vicinity (see Figure 2). This is the heart of the Thai economic and political system, with
the highest density of housing projects in comparison to the rest of Thailand (REIC, 2009;
ONESDB, 2007). This area also has the highest number of real estate developers approximately
250 (APTU, 2008).
Figure 2: Map of the Case Study Area
RESEARCH METHODOLOGY
In the social sciences, there are mainly three research approached that can be employed to conduct
research, which are quantitative, qualitative and mixed methodologies. Truthfulness or realitytypifies quantitative and qualitative methodologies, but it is the criteria for judging it that differ. The
quantitative research methodology considers knowledge to be real that can be objectively measured
Pathumtani
Nontaburi
Bangkok
Samutprakarnn
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whilst the qualitative research methodology views reality as something that can be subjectively
measured (Creswell, 2007). The mixed methodologies combine the quantitative and qualitative
methodologies in a single project. These approaches were considered in terms of their
appropriateness giving the objectives of the study and the quantitative approach was finally
adopted. Surveys were used as the strategy of enquiry and the case study philosophical approach
was adopted (with Thailand as the case study).
Questionnaire was designed and administered to the surveys participants who were randomly
selected from an established sampling frame. The questionnaire was designed based on the
researchers experience in Thailand real estate sector and relevant literature and the reliability of the
questionnaire was verified by the experts opinions prior to the administration of the questionnaire.
Fifty (50) sets of questionnaires were distributed to the sampled survey participants in June 2009
via post. The questionnaire consisted of 29 questions divided into four sections and covered
various aspects like respondents characteristics, real estate projects that participants had engaged
in, decision makers roles and perception of participants towards risks. Thirty nine (78%)
responded to the questionnaires and the data was analysed using SPSS. Parametric statisticaltechniques like Independent T-Test, ANOVA and Rank Correlation analyses were carried out.
DATA PRESENTATION AND ANALYSIS
Attributes of Respondents
The survey data revealed that the respondents occupy various positions in real estate companies:
36% (14 out of 39) were quantity surveyors or estimators whilst project managers/directors and
engineers/architects constituted 25% (9 out of 39) each. Fifty-six percent (22 out of 39) were
decision makers regarding risks but only 43% (17 out of 39) had any risk assessment experience in
real estate projects and 15% (6 out of 39) had used any risk assessment models before. Only 10% (4out of 39) were aware of AHP or ANP. Most of them (56% or 22 out of 39) had undergraduate
degree and their working experiences ranged from 0 to 5 working years. Sixty-one percent of the
respondents (24 out of 39) were involved in low rise /housing residential projects whilst 15% were
involved in hotel projects; 10% (4 out of 39) were involved in high-rise residential projects and
retail projects constituted 2.6% (1 out of 39). It was also found that 25% (10 out of 39) of the
participants were located outside of Bangkok Metropolitan Area (BMA) and the same percentage of
projects were located within Bangkok Metropolitan Area (BMA)
Satisfaction Regarding Current Risk Assessment TechniquesThere was a low response rate to a question that bordered on practitioners satisfaction with the
current risk assessment techniques as only six respondents representing 15.40% answered that
question. The statistics in Table 1 shows a mean value of 3 (using the Likert scale where 1 is very
dissatisfied; 3 is neutral; and 5 is very satisfied) amongst these respondents. Thus, it implies that the
respondents were neither satisfied nor dissatisfied with the current risk assessment techniques. To
verify these results, the independent T-test was conducted to test the equality of the mean of this set
of respondents. Results derived by T-Test shows that the significance level is 1.0, meaning that
there is no significant difference between the Means.
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Table 1: Statistics on Satisfaction towards Current Risk Assessment Models
Experience in risk
assessment N Mean Std. Deviation Std. Error Mean
Satisfaction in model usage Yes 6 3.00 0.6325 0.2582
No 1 3.00 . .
Satisfaction in model' s effectiveness Yes 6 3.00 0.6325 0.2582
No 1 3.00 .
The independence T-Test was then conducted in order to verify the differences between the mean of
these two groups of respondents. The value derived by this test gives a significance level is 1.00 (as
shown in Table 2), which means there was no significant difference between these variables, and no
difference between each mean. Perhaps a larger-scale survey than this with a higher response rate could
produce more insights on this issue.
Table 2: T-test Value of Satisfaction towards Current Risk Assessment Models
t-test for Equality of Means
t Df.
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval
of the Difference
Lower Upper
Satisfaction in
model use
Equal variances assumed 0.00 5 1.00 0.00 0.6831 -1.7560 1.7560
Equal variances not
assumed . . . 0.00 . . .
Satisfaction in
model' s
effectiveness
Equal variances assumed 0.00 5 1.00 0.00 .06831 -1.7560 1.7560
Equal variances not
assumed . . . 0.00 . . .
There were 10 survey participants who responded the question that bordered on risk assessment
techniques currently being used in their projects. It was established that the panel discussion is the
most popular technique that is used as 70% (7 out of 10) of the decision makers had used it whilst
20% (2 out of 10) employed secondary information from reliable sources. Those who usedbackground experience represented the remaining 10%. The results show that Thai practitioners still
rely on non-systematic risk assessment techniques, which is unlikely to provide the precise
information enough to make a decision towards risk in the real estate sector.
The Practitioners Perceptions of Risks that Emanate from STEEP Factors
Descriptive frequency and correlation tests were used to assess the perceptions of respondents regarding
risks caused by STEEP factors. The percentages of their opinions in terms of the consequences of risks
and the likelihood of their occurrence are summarised in the Tables 3 and 4 below.
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Table 3: Perceptions of STEEP Factors Consequences (%)
Very High (%) High (%) Neither high nor
low (%)
Low (%) Very Low (%) Not responded
(%)
Social 15.4 17.9 30.8 17.9 5.1 12.8Technological 10.3 12.8 33.3 17.9 12.8 12.8
Environmental 2.6 30.8 28.2 17.9 7.7 12.8
Economical 46.2 20.5 5.1 2.6 12.8 12.8
Political 23.1 30.8 10.3 10.3 10.3 15.4
Table 4: Perceptions towards the Likelihood of Risk Occurring from STEEP Factors (%)
Very High (%) High (%) Neither high nor low (%)
Low (%) Very Low (%) Not responded(%)
Social 15.4 12.8 33.3 17.9 7.7 12.8
Technological 15.4 12.8 23.1 23.1 12.8 12.8
Environmental 5.1 20.5 35.9 15.4 10.3 12.8
Economic 38.5 20.5 7.7 10.3 5.1 12.8
Political 23.1 25.6 20.5 10.3 7.7 15.4
The results from the Table 3 indicate that Thai practitioners prioritised risks caused by economic
and political as they considered them to have the strongest impact on the progress of their project
whilst social and technological risks were considered to have a low impact on projects. In terms of
the STEEP risks likelihood of occurrence, Table 4 shows that the likelihood of economic risk
occurring is the highest, followed by political, social and technological in that order. Environmental
issues are considered to have the least impact on real estate projects in terms of the consequences
and regarding likelihood of occurrence it they also ranked the lowest.
Correlation tests were then carried out to determine the correlation between each STEEP factor and the
perception of practitioners. The results are summarised in Table 5. From the Table, The there were 8
variables which were strongly correlated (p < 0.05) while the rest did not show a significant
correlation. This shows that Thai practitioners are more concerned with risks caused by economic
and political factors that other sources of risks.
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Table 5: The Correlation of STEEP Factors
Based on the findings from the empirical data above, secondary data was then used to make a
comparison between Thai and Western real estate practitioners (using Dutch and British) in order to
determine whether there are differences between their perceptions of STEEP risks and how they
assess risks in real estate projects. The perceptions are compared and ranked in Table 6 below
whilst a comparison of risk assessment techniques are indicated in Table 7.
Table 6: Comparison of Practitioners Perceptions towards Risks Emanating from STEEP Factors
R Thai Dutch British
1 Economic (32%) Political
(34%)
Economic
(35%)
2 Political (26%) Technological
(31%)
Technological
(22%)
3 Social (16%) Economic
(15%)
Social (20%)
4 Environmental
(16%)
Environmental
(13%)
Environmental
(13%)5 Technological
(11%)
Social (7%) Political (9%)
S Khumpaisal
(2009)
Gehner et al.
(2006)
Khumpaisal
and Chen
(2009)
Table 7: Comparison of Risk Assessment Techniques
Risk assessment techniques Western Thailand
Non systematic techniques (i.e. workexperience/intuition, probabilistic) 58% 80%
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Systematic / pragmatic techniques (i.e.
sensitivity analysis, matrix, model,
Monte Carlo, reliable 2nd sources)
42% 20%
Source: Gehner et al.
(2006)
Khumpaisal
(2009)
From Table 7 nearly a half (42%) of the Western practitioners employ systematic risk assessment
techniques such as sensitivity analysis, assessment checklists or risk premium. Regarding Thai
practitioners, 80% use non-systematic assessment methods, particularly, the panel discussion
techniques, which provide less precise details as to how to deal with risks in the real estate sector.
Generally, the results of the survey are varied depending on the definition of risks by survey
participants and their experiences in dealing with real estate projects as well as the environment
surrounding their developed projects.
CONCLUSION
There are various sources of risks in real estate and this paper has examined real estatepractitioners perception of risks caused by social, technological, environmental, economic and
political factors as well as the risk assessment techniques using Thailand as a case study. The
quantitative research methodology has been adopted and the results show that Thai practitioners are
more concerned with the risks caused by economic and political factors than other sources of risk. It
has also been established that there is less evidence of the application of systematic risk assessment
techniques that help to deal with potential risks. The findings have therefore underscored the need
for an appropriate systematic risk assessment model to be developed and implemented in the Thai
real estate industry.
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APPENDICES
APPENDIX A: Descriptive statistics
1.1. Positions held by respondents in real estate development projects
Position
10 25.6 25.6 25.6
5 12.8 12.8 38.5
10 25.6 25.6 64.1
14 35.9 35.9 100.0
39 100.0 100.0
Project Manager/ DirectorProject Coordinator
Engineer/ Architect /
Designer
Other
Total
Valid
Frequency Percent Valid Percent
Cumulative
Percent
1.2. The decision-maker role in the real estate project
Decision Maker
22 56.4 57.9 57.9
16 41.0 42.1 100.0
38 97.4 100.0
1 2.6
39 100.0
yes
No
Total
Valid
0Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
http://thismatter.com/money/insurance/risk.htmhttp://www.projectsmart.co.uk/your-risk-management-process-a-practical-and-effective-approach.htmlhttp://thismatter.com/money/insurance/risk.htmhttp://www.projectsmart.co.uk/your-risk-management-process-a-practical-and-effective-approach.html -
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1.3. Working experiences (years)
Working Experience (Years)
17 43.6 43.6 43.6
12 30.8 30.8 74.4
5 12.8 12.8 87.2
3 7.7 7.7 94.92 5.1 5.1 100.0
39 100.0 100.0
0-5
6-10
11-15
16-2021 above
Total
ValidFrequency Percent Valid Percent
Cumulative
Percent
1.4. Experience in risk assessment
Experience in risk assessment
17 43.6 45.9 45.9
20 51.3 54.1 100.0
37 94.9 100.0
2 5.1
39 100.0
yes
No
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
1.5. Used of any risk assessment models/ techniques
Used of any model
6 15.4 19.4 19.4
25 64.1 80.6 100.0
31 79.5 100.0
8 20.5
39 100.0
yes
No
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
1.6. If they did not employ risk assessment model, how could they assess risks in real estate project?
How to assess if no model
1 2.6 10.0 10.0
7 17.9 70.0 80.0
2 5.1 20.0 100.0
10 25.6 100.0
29 74.4
39 100.0
By working experience
Panel discussion
Secondary informaiton
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
1.7. The knowledge in Analytical Network Process (ANP) or Analytical Hierarchical Process (AHP)
Knowledge in AHPANP
4 10.3 11.4 11.4
31 79.5 88.6 100.0
35 89.7 100.0
4 10.3
39 100.0
yes
No
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
1.8. Type of the real estate projects
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Type of project
24 61.5 66.7 66.7
4 10.3 11.1 77.8
1 2.6 2.8 80.6
1 2.6 2.8 83.3
6 15.4 16.7 100.0
36 92.3 100.0
3 7.7
39 100.0
Low rise / housing project
highrise condominium/
apartment
retail
commercial
other
Total
Valid
0Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
APPENDIX B: Statistical analysis of data
2.1. Questionnaires reliability
Reliability Statistics
.644 31
Cronbach's
Alpha Nof Items
2.2. T-test to verify Mean of respondents who used the risk assessment models.
Group Statistics
6 3.0000 .63246 .25820
1 3.0000 . .
6 3.0000 .63246 .25820
1 3.0000 . .
Experience in risk
assessment
yes
No
yes
No
Satisfaction in model
Satisfaction in model'
s effectiveness
N Mean Std. Deviation
Std. Error
Mean
Independent Samples Test
Levene's Test forEquality ofVariances t-test for Equality of Means
F Sig. t df Sig. (2-tailed)
MeanDifference
Std. ErrorDifference
95% Confidence Intervalof the Difference
Upper Lower
Satisfactionin model
Equalvariancesassumed
. . .000 5 1.000 .00000 .68313 -1.75604 1.75604
Equalvariances notassumed
. . . .00000 . . .
Satisfactionin model' seffectiveness
Equalvariancesassumed
. . .000 5 1.000 .00000 .68313 -1.75604 1.75604
Equalvariances notassumed
. . . .00000 . . .
APPENDIX C: The perceptions of STEEP factors3.1. The consequence of each risk to real estate projects
3.1.1. Social risk
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Level of Social risk to project
6 15.4 17.6 17.6
7 17.9 20.6 38.2
12 30.8 35.3 73.5
7 17.9 20.6 94.1
2 5.1 5.9 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.1.2. Technological risk
Level of Techological risk to project
4 10.3 11.8 11.8
5 12.8 14.7 26.5
13 33.3 38.2 64.7
7 17.9 20.6 85.3
5 12.8 14.7 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.1.3. Environmental risk
Level of Environmental risk to project
1 2.6 2.9 2.9
12 30.8 35.3 38.2
11 28.2 32.4 70.6
7 17.9 20.6 91.2
3 7.7 8.8 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.1.4. Economic risk
Level of Economical risk to project
18 46.2 52.9 52.9
8 20.5 23.5 76.5
2 5.1 5.9 82.4
1 2.6 2.9 85.3
5 12.8 14.7 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.1.5. Political risk
Level of Political risk to project
9 23.1 27.3 27.3
12 30.8 36.4 63.6
4 10.3 12.1 75.8
4 10.3 12.1 87.9
4 10.3 12.1 100.0
33 84.6 100.0
6 15.4
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.2. The likelihood of each STEEP and affect to real estate project
3.2.1. Social risk
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Frequency of Social risk to project
6 15.4 17.6 17.6
5 12.8 14.7 32.4
13 33.3 38.2 70.6
7 17.9 20.6 91.2
3 7.7 8.8 100.0
34 87.2 100.0
5 12.839 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.2.2. Technological risk
Frequency of Technological risk to project
6 15.4 17.6 17.6
5 12.8 14.7 32.4
9 23.1 26.5 58.8
9 23.1 26.5 85.3
5 12.8 14.7 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.2.3. Environmental risk
Frequency of Environmental risk to project
2 5.1 5.9 5.9
8 20.5 23.5 29.4
14 35.9 41.2 70.6
6 15.4 17.6 88.2
4 10.3 11.8 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.2.4. Economic riskFrequency of Economicl risk to project
15 38.5 44.1 44.1
10 25.6 29.4 73.5
3 7.7 8.8 82.4
4 10.3 11.8 94.1
2 5.1 5.9 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid Percent
Cumulative
Percent
3.2.5. Political risk
Frequency of Political risk to project
9 23.1 26.5 26.5
10 25.6 29.4 55.9
8 20.5 23.5 79.4
4 10.3 11.8 91.2
3 7.7 8.8 100.0
34 87.2 100.0
5 12.8
39 100.0
Very HIgh
HIgh
Medium
Low
Very low
Total
Valid
.00Missing
Total
Frequency Percent Valid PercentCumulative
Percent
3.3. One-way ANOVA to test the Mean Value
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ANOVA
1.448 4 .362 .246 .910
42.670 29 1.471
44.118 33
7.666 4 1.917 1.394 .261
39.863 29 1.375
47.529 33
5.466 4 1.367 1.343 .278
29.504 29 1.017
34.971 33
8.221 4 2.055 .981 .433
60.750 29 2.095
68.971 33
5.927 4 1.482 .794 .539
52.255 28 1.866
58.182 32
6.762 4 1.691 1.203 .331
40.767 29 1.406
47.529 33
5.056 4 1.264 .694 .602
52.826 29 1.822
57.882 33
5.028 4 1.257 1.110 .371
32.854 29 1.133
37.882 334.628 4 1.157 .710 .592
47.254 29 1.629
51.882 33
1.531 4 .383 .218 .926
50.939 29 1.757
52.471 33
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
Total
Between Groups
Within Groups
TotalBetween Groups
Within Groups
Total
Between Groups
Within Groups
Total
Level of Social risk to
project
Level of Techological
risk to project
Level of Environmental
risk to project
Level of Economical
risk to project
Level of Political risk to
project
Frequency of Social risk
to project
Frequency of
Technological risk to
project
Frequency of
Environmental risk to
project
Frequency of Economicl
risk to project
Frequency of Political
risk to project
Sum of
Squares df Mean Square F Sig.
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