Thai real estate practitioners perceptions of risks

8/6/2019 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:[email protected]

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:[email protected]:[email protected]


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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.

REFERENCES

ACT Insurance Authority (2004). Guide to risk management: AS/NZS 4360: 2004 risk managementstandard, Australian Capital Territory Insurance Authority.

Al-Bahar, J. F., Crandall, C. K. (1990). Systematic Risk Management Approach for Construction projects.

Journal of Construction Engineering and Management, 11(3), 533-546.

APTU (2008). Residential Project Feasibility study; New Bangkok International Airport area,

submitted to National Housing Authority (Thailand), Faculty of Architectural and PlanningThammasat University, Thailand.

AREA (2008). Housing Yellow Page, Agency for Real Estate Affair. Bangkok: Veeruch PrintingCo. Ltd.

ASA Thailand (2008). Bangkok Metropolitan Land usage Plan, Association of Siamese Architects

Under Royal Patronage Website, Thailand [E-text type].

https://reader009.{domain}/reader009/html5/0517/5afcb0578eb45/5afcb06327048.jpg

Baum, A. and Crosby, N. (2008). Property Investment Appraisal, 3rd ed. Oxford: Blackwell

Publishing


13/20

Baccarini, D. and Archer, R. (2001). The risk ranking of projects: a methodology. International Journal ofProject Management, 19, 139-145.

Booth, P., Matysiak, G., Ormerod, P. (2002). Risk Measurement and Management for Real Estate

Portfolios, Report for the IPF, Investment Property Forum (IPF), London.

Brown, R. G. and Matysiak, A. G. (2000). Risk, return and diversification, in Real Estate Investment: A

Capital Market Approach, Essex: Prentice Hall.

Byrne, P. (1996).Risk Uncertainty and Decision-making in Property Development. 2nd ed. London:

Taylor & Francis Group.

Chapman, A. (2008). PEST analysis method and examples with free PEST template [E-text type].

http://www.businessballs.com/pestanalysisfreetemplate.htm

Chen, Z., and Khumpaisal, S. (2008). An Analytic Network Process for Risks Assessment in

Commercial Real Estate Development. Journal of Property Investment and Finance, 27 (3), 238-

258.

Choi, H. H, Cho, N. H, Seo, J. W. (2004). Risk Assessment Methodology for Underground ConstructionProjects. Journal of Construction Engineering and Management, 258-272.

Clarke, J. C. and Varma, S. (1999) Strategic Risk Management: the New Competitive Edge.

International Journal of Strategic Management[On-line serial], 32(4), 414-424.

Crossland, B., et al. (1992).Estimating engineering risk. London: Royal Society.

Flyvbjerg, B., Bruzelius, N. and Rothengatter, W. (2003). Megaprojects and risk: An Anatomy of

ambition. Cambridge: Cambridge University Press.

Frodsham, M. (2007). Risk Management in UK Property Portfolios: A Survey of Current Practice,Investment Property Forum, London [E-text type].

http://www.ipf.org.uk/resources/pdf/research/research_reports/Risk_Management_ Summary.pdf

Gehner E., Halman J. I. M. and de Jonge H. (2006). Risk Management in the Dutch Real Estate

Development Sector: A Survey. Paper presented at 6th International Postgraduate Research

Conference, 6- 7 April University of Salford.

Hargitay, S. and Yu, S. M. (1993). Decision criteria return and risk, Property Investment

Decisions: A quantitative Approach. London: E&FN Spon.

Hilbers, P., Lei, Q. and Zacho, L. (2001). Real Estate Market Developments and Financial SectorSoundness, IMF Working Paper, WP/01/129, International Monetary Fund.

ioMosaic (2002). Designing and Effective Risk Matrix:An ioMosaic Corporation Whitepaper.ioMosaic Corporation, Houston [E-text type].http://archives1.iomosaic.com/whitepapers/risk-ranking.pdf

Jutte, B. (2009). 10 Golden Rules of Project Risk Management [E-text type].

http://www.projectsmart.co.uk/10-golden-rules-of-project-risk-management.html

Kahn, J. (2001). Reporting Statistics in APA Style, Illinois State University [E-text type].

http://my.ilstu.edu/~jhkahn/apastats.html.

Khalafallah, A. M. G. E. I. (2002). Estimating cost contingencies of residential buildings projects using belief

networks, Faculty of Engineering, Cairo University, Cairo, Egypt.
http://www.businessballs.com/pestanalysisfreetemplate.htmhttp://my.ilstu.edu/~jhkahn/apastats.htmlhttp://www.businessballs.com/pestanalysisfreetemplate.htmhttp://my.ilstu.edu/~jhkahn/apastats.html


14/20

Khumpaisal, S. (2007). Risks in construction project procurement process and risks mitigation

methods.Journal of Architectural /Planning Research and Studies, Vol.5, issue 2, , pp.135 - 145

Khumpaisal, S. and Chen Z. (2009). Application of Analytic Network Process to Assess Risks in Urban

Regeneration Projects, Paper presented at SUE-MOT Conference, 22-24 April, Henry Ford College,

Loughborough University, Loughborough, UK.

Kritayanavaj, B. (2007). Housing Bubble. Government Housing Bank Journal, 1(1), 70-76.

Lauridsen, S., L. (1998). The Financial Crisis in Thailand: Causes, Conduct and Consequences.Journal of World Development, 26(8), 1575-1591.

Macdonald, J., Knopman, D., Lockwood, J. R., Cecchine, G. and Willis, H. (2004). Unexploded

Ordnance: A Critical Review of Risk Assessment Methods, RAND Corporation, United States

Army, USA

Matson, J. (2000). Cooperative Feasibility Study Guide, United States Department of Agriculture,

Rural Business Cooperative Service [E-text type]. http://www.rurdev.usda.gov/rbs/pub/sr58.pdf

Morrison, L., J., (2007). The STEEP Factors, University of North Carolina at Chapel Hill [E-text

type]. http:// horizon.unc.edu/onramp/

Millington, A.F., (2000).Risk and Uncertainty, and Risk control, in Property Development.London: EG Books.

Nezhad, G., H., and Kathawala, Y. (1990). Risk Assessment for International Investment.

Management Research News, 13(1),pp. 1-8

Office of the National Economic and Social Development Board: NESDB (2007) Thailand in Brief:

2006, Office of the National Economic and Social Development Board, Bangkok, Thailand [E-text

type]. http://www.nesdb.go.th/Default.aspx?tabid=136

Pidgeon, N., Hood, C., Jones, D., Turner, B. and Gibson, R. (1992). Risk Perception, London:

Royal Society.

Pornchokchai, S. (2007). Rethinking the Real Estate Cycle. Government Housing Bank Journal,

1(1), 48-59.

Project Management Institute (2002). A Guide to the Project Management: Body of Knowledge,

Automated Graphic Systems, Charlotte, North Carolina, U.S.A.

Quigley, M. J. (2001). Real Estate and the Asian Crisis. Journal of Housing Economics, 10, 129-161

Rafele, C., Hillson. D. and Grimalai, S. (2005). Understanding Project Risk Exposure Using the

Two-Dimensional Risk Breakdown Matrix, Paper presented at Project Management Institution

Global Congress, Edinburgh, Scotland [E-text type]. http://www.risk-doctor.com/pdf-files/pmi-e-

rbmpaper.pdf.

Raftery, J. (1994).Risk Analysis in project management. London: E & FN Spon.

REIC (2009). Summary of Thailand Real Estate Condition, Real Estate Information Centre,

Thailand [E-text type].http://www.reic.or.th/SummaryRealEstate/SummaryRealEstate_index.asp
http://www.rurdev.usda.gov/rbs/pub/sr58.pdfhttp://www.reic.or.th/SummaryRealEstate/SummaryRealEstate_index.asphttp://www.reic.or.th/SummaryRealEstate/SummaryRealEstate_index.asphttp://www.rurdev.usda.gov/rbs/pub/sr58.pdfhttp://www.reic.or.th/SummaryRealEstate/SummaryRealEstate_index.asp


15/20

Ross, D. A. (2005). A Model of Contingency Factors Affecting Contractors Economic

Organisation of Projects, PhD. Thesis, School of Construction and Property, University of Salford,

Salford, UK

Smith, J. N. (2002). Managing risk in construction projects. In Kelly, J., Morledge, R. and

Wilkinson, S. (Eds.)Best Value in Construction. , pp. 100-115 Oxford: Blackwell.

Smith, J. N., Merna, T. and Jobling, P. (2006). Managing risk in construction projects. Oxford:Blackwell.

Spaulding, W. C. (2008). Risk [E-text type]. http://thismatter.com/money/insurance/risk.htm

Tasmanian Government (2006). MBT Step 7 Risk Management: Developing a risk management

plan[E-text type]. http://www.projectmanagement.tas.gov.au/

Thompson, A. (2005). Business Feasibility Study Outline, Entrepreneurship and business

innovation: The art of successful business start- ups and business planning.

Vanichvatana, S. (2007).Thailand Real Estate Market Cycles: Case Study of 1997 Economic

Crisis., Government Housing Bank Journal, 1(1), pp. 38-47

Luu, V.T., Soo-Yong, K., N. V. T. and Ogunlana, S. O. (2008). Quantifying schedule risk in

construction projects using Bayesian Belief Networks. International Journal of Project

Management.

Warr, G. P. (2000). What Happened to Thailand? Oxford: Blackwell.

Wrona, V. (2009). Your Risk Management Process: A Practical and Effective Approach[E-text type].

http://www.projectsmart.co.uk/your-risk-management-process-a-practical-and-effective-approach.html

Younes, E., Kett, R. (2007). Hotel investment risk: what are the chances?Journal of Retail and

Leisure Property, 6(1), 69-78.

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


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


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


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


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


Cumulative

Percent

1.8. Type of the real estate projects


17/20

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


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


18/20

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


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


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


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


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


Cumulative

Percent

3.2. The likelihood of each STEEP and affect to real estate project

3.2.1. Social risk


19/20

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


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


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


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


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


20/20

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.

Thai real estate practitioners perceptions of risks

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