Research Proposal

1.0 - Introduction

1-1 - Rationale

The construction process has always been

fraught with risk and uncertainty; it remains

one of the sectors most likely to suffer cost

overruns (Memon, Rahman & Azis 2011).

It has been suggested by Dey (2002) and

Hackett, Robinson & Statham (2007, Ch.

6) that this high likelihood of cost overruns

can be attributed to the technical

constraints, number of stakeholders and

t h e c o n s t r u c t i o n p r o c e s s i t s e l f .

Alternatively, Wilkinson & Reed (2008, Ch.

4) along with Atrill (2006, Ch. 10) argue

that the long operating cash cycle and

capital needs involved in construction

projects introduce the main sources of

potential cost overruns.

These sources of cost overruns may be

defined as risk and uncertainty and are

inherent in construction projects. They are

currently assessed using subjective

means, based on risk perception which is

highly influenced by “people’s belief,

a t t i t u d e s , j u d g e m e n t a n d

feelings” (Akintoye & MacLeod,1997). This

is further compounded by the

ent repreneur ia l nature of the UK

construction sector and its reluctance to

adopt formal risk and decision analysis

procedures (Byrne, 1996, Ch. 1).

This subjective and informal attitude to risk

analysis can be seen throughout the

construction process, be it simply adding a

relatively arbitrary percentage to a cost

plan (Kirkham, 2007, Ch. 10 & Ashworth,

2010, Ch. 13) or the perception that risk

analysis and management should only be

used on larger construction projects

(Kirytopoulos, Leopoulos & Malandrakis,

2001). Again, this attitude is compounded

by the blurring of roles in the project team,

Ashworth & Hogg (2007, Ch. 9) argue that

the responsibility for risk assessment and

management is unclear and may be

divided between Quantity Surveyor and

Project Manager, as no formal industry

wide procedure has been adopted.

Ashworth & Hogg (2007) also attribute the

consistent inability of the construction

sector to meet clients needs in terms of

cost, time or quality to a lack of risk

management.

Harry Randle - S09476257 Research Proposal

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The assessment of risk and uncertainty in construction projects

The current financial crisis has had wide

reaching effects on the construction sector;

current estimates for output show negative

figures for the next 18 months (Table 1),

this is to be revised further downwards due

to continued unrest in the Eurozone as it

struggles with severe structural debt

problems.

These structural debt problems along with

the newly implemented BASEL III banking

accord mean banks will be required to hold

significantly more capital from 2013, it is

likely the rate of lending for real estate

projects will decrease over the long term

by -1.3% from an already low figure

(imf.org, 2011). Another consequence is a

tightening of lending criteria as institutions

become more risk averse and understand

the real risks that are inherent in

commercial real estate lending, as

expressed by the Federal Deposit

Insurance Corporation (2006) and Shapiro,

Davies & Mackmin (2009, Ch. 12). The

acknowledgement of the increased risk

posed by real estate lending is especially

important, as exposure to this type of

lending has been increased through the

use of financial instruments such as

synthetic Commercial Mortgage Backed

Securities (CMBS), these instruments

ensure an “investor can gain leverage for

aggressive speculation” (Nomura Sec.,

2006) a view that is echoed by the CRE

Finance Council (2004).

It is likely that lending criteria will remain

centered around the four ‘Cs’ as put

forward by Isaac (2003, Ch. 4). However, it

may be hypothesised that developers and

construction companies with a clear

history and understanding of the Risk

Control Process (Smith, Merna & Jobling

2006, Ch. 4) will find finance easier to

place.

With the above in mind, this study

proposes that a more methodical and

objective approach be taken to the

assessment and management of risk. This

will be done using modelling techniques

that are familiar to the financial industry,

this may partially alleviate fears institutions

have over their exposure to real estate

lending, whilst providing more accurate

cost estimates to clients.


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Fordham (2011)

Table 1 - Experian & CPA Growth Forecasts for Construction Output

1.2 - Benefits

Such a change will not take place without

financial or regulatory incentives. It may be

argued that such incentives are available,

and include:

Accuracy - The risk is modelled on a

project specific basis, allowing project risk

to be accounted for, this will allow a more

accurate risk allowance to be gauged.

Justification - A client or lending

institution will be able to see the

justification of the risk allowance and risk

profile, how it has been calculated and

what is included.

Standardisat ion - The proposed

modelling techniques will be based on

methods already used in the financial

sector, this will allow direct comparison

with other investment types and risk

profiles.

Opportunity - A Construction Consultant

that offers this service has the opportunity

to be involved early on in the project and is

best placed to offer advice on risk

mi t iga t ion and va lue eng ineer ing

strategies. Thus, winning extra work in

fields that may not currently be defined as

core business.

1.3 - Aims and Objectives

Aim

To develop a theoretical model to identify

and quantify risk factors in the construction

sector.

Objectives

To review risk assessment methods

currently used in the construction sector.

To produce a model based on research

that will quantify identified risk factors.


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2.0 - Literature Review

2.1 - Risk and Uncertainty

Risk and uncertainty are central to construction. The bespoke nature of buildings ensure each project has its own risk profile which brings with it a new set of challenges (Kirkham, 2007 Ch. 4). Unfortunately, it may be argued that the construction industry sometimes fails to truly understand these terms and what they represent, using them in a rather colloquial manner (Byrne, 1996, Ch. 3).

Uncer ta in ty - I n t he con tex t o f construction, uncertainty usually refers to estimating uncertainty (Smith, Merna & Jobling, 2006, Ch. 1). Akintoye & MacLeod (1997) go a little further and propose that c o n s t r u c t i o n c o n t a i n s f e w t r u e uncertainties, only varying degrees of risk. Uncertainties present should be converted to risks using a Judgmental Risk Analysis Process (Oztas & Okmen, 2005).

Risk - A scenario that exists where a range of probable outcomes are known, each impact capable of being quantified. Nemuth (2008) expresses risk as:

Risk = Probability x Impact

It should be noted that the construction sector’s view of risk is simplistic when compared to that of the financial sector, this is surprising given the collaboration that is needed between the sectors. Jorion‘s FRM Handbook (2009, Ch. 1) classifies risks into 3 sub-categories:

Known Knowns - Risks that can be properly identified and measured.

Known Unknowns - Risks that arise through volatility or correlations (Crouhy, Galai & Mark, 2005, Ch. 14).

Unknown Unknowns - This category is reserved for systemic risks. Byrne (1996) m a y i n c o r r e c t l y c l a s s t h e s e a s uncertainties, they are better defined as Knightian Uncertainties or immeasurable risks.

The above sub-categories receive no more than a cursory mention in Smith, Merna & Jobling (2006) and remain absent from most other literature, including Byrne (1996). Possibly the most in depth study of risk factors that are foreseeable at pre-tender stage was undertaken by Elhag, Boussabaine & Ballal (2005), 67 factors where identified, along with a Severity index. This comprehensive study will form the basis of the risk model under study.


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2.2 - Risk Management Process

To consider the ident ificat ion and quantification of risk, first the control and management process must be understood. The construction industry uses a simple 4 stage process as shown in figure 1, Nemuth (2008) confirms this risk control process is used in the construction industry, irrespective of project size. This appears to be a simplification of the risk control and management process as instituted in BS6079-3 (BSI, 2000).

This theoretical process in translated into various stages with no single individual taking control (Ashworth & Hogg, 2007), it may be hypothesised this is because the risk control process spans all RIBA stages.

In practice these stages might be interpreted as:

Risk Identification - Ashworth & Hogg (2007) say this stage is usually fulfilled during a risk workshop. This workshop will usually take the form of an open discussion where all members of the design team will come together and assess both the project specific and systematic risks (McLaney, 2009, Ch. 6).

Risk Analysis - In this stage both Byrne (1996) and Smith, Merna & Jobling (2006) concur that the results from the previous stage should be analysed with various scenarios, probabilities and costings being attributed to each risk.

Risk Response - This study will accept the proposed risk response procedures of Ashworth & Hogg (2007, Ch. 6). This SADE (Shr ink, Accept, Distr ibute, Eliminate) response is used industry wide - It is also referred to by other authors as ERIC and TTTT.

Risk Review - Risk is a function of time and probability (Byrne, 1996), thus, as the project progresses so does the overall risk profile and exposure to risk. For this reason the risks must be reviewed and updated regularly (Akintoye & Macloeod, 1997)


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Figure 1 - The Risk Control Process

Smith, Merna & Jobling (2006)

2.3 - Monte Carlo Simulation

The Monte Carlo Simulation (MCS) is used as a probabilistic analytical technique for the quantification of risks in a project at the risk analysis stage. The mathematics are complex and are studied in more detail by Glasserman (2003, Ch. 1). The MCS is essentially a random number generator that is able to relate those random numbers to probability distributions given by the user, this produces outcomes in the form of relative probability distributions. This allows those involved in the analysis of risk to study possible scenarios and outcomes based on inputs and probability.

The MCS is widely used in econometrics to model single variables (Barreto & Howland 2006), and is central to financial engineering and risk management (Jorian, 2009), in the financial sector it is used to simulate random variables that are assumed to follow the Markov Process, including stock prices, market risk, investment yields and VaR. Lending institutions also use the MCS to measure credit risk and default rates (Glasserman, 2003, Ch. 9).

Currently, the MCS does not form a central part of risk analysis in the construction sector. Ashworth and Hogg (2007, Ch. 6)

state it should only be used to model uncertain variables and inputs are largely subjective. Such a position is inline with longstanding opinions in the construction sector. However, current MCS modelling software has become considerably easier to use, the availability and quality of data has increased along with the knowledge of t h e r i s k s u n d e r t a k e n w i t h i n t h e construction process. Smith, Merna & Jobling (2006, Ch. 6) show how even a simplistic MCS can now be integrated into the risk analysis process. This position is being increasingly confirmed as papers such as those from Nemuth (2008) and Oztas & Okmen (2005) confirm. As the construction industry becomes more dependent on the financial industry it may be hypothesised that the financial industry will require risk assessment methods that it understands and are inline with its own, hence the move and interest in MCS.

This study will focus on 5 probability distributions, normal, beta, rectangular, triangular and PERT. These will be used as they best model the risks present in construction, they rely on and reflect the ‘most likely’ nature of construction at the earlier stages.


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3.0 - Methodology

Methodology is the study of method and must answer the fundamental questions of ‘Why, How and What’ is being researched, the method used must also be justified and fit the proposal (Farrell, 2011).

In this proposal the ‘Why’ has already been out l ined - to ga in a bet ter understanding of construction risk and contingency allowances.

‘How’ the research will be conducted presents more of a problem. It would be ideal to compare pre-tender cost estimates with final accounts to assess the amount of contingency allowance used, the project team could be interviewed to gain an insight into why those contingencies were used and to explain anomalies, the results could then be modelled with a view to forecasting project risk. A method such as this presents its own problems, such as collation and subjectivity of data, time, ethical dilemmas and the realisation that the construction sector may not be willing to accept that risk assessment techniques need to be improved.

The “How’ will therefore be undertaken by way of triangulation (Blaxter, Hughes & Tight, 2006). A method that will combine

the use of objective historic data with a subjective understanding gained through review of current literature.

“What” is being assessed is simply risk. The risks present will be take from the extensive study by Elhag, Boussabaine & Ballal (2005) and the MCS model will be produced using Palisade software’s @Risk Suite. The model will then be tested using a case study to check for accuracy.

3.1 - Stage 1 - Literature Review

The first stage of this study will be an in-depth literature review. Primary and secondary sources will be used, the aim of which is to gain a thorough understanding of risk. If other modelling techniques are highlighted (such as hedonic price modelling via ANN or ARIMA) they will be considered.

3.2 - Stage 2 - Model Production

The second stage of this study will be the production of a model to assess and forecast risk and contingency allowance.

3.3 - Stage 3 - Verification

The model will be verified against a case study and the results considered.


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5.0 - References

Akintoye, A. and MacLeod, M. (1997) Risk Analysis and Management in Construction, I n t e r n a t i o n a l J o u r n a l o f P r o j e c t Management, Vol. 15, No. 1.

Ashworth, A. (2010) Cost Studies of Buildings, 5th Edition, Essex: Pearson Education.

Ashworth, A. and Hogg, K. (2007) Willis’s Practice and Procedure for the Quantity Surveyor, 12th Edition, Oxford: Blackwell Publishing.

Atrill, P. (2006) Financial Management for Decision Makers, 4th Edition, Essex: Pearson Education.

Barreto, H. and Howland, F. (2006)

Introductory Econometrics: Using Monte

Carlo Simulation, Cambridge: Cambridge

University Press.

Blaxter, L., Hughes, C. and Tight, M.

(2006) How to Research, 3rd Edition,

Berkshire: McGraw Hill Education.

British Standard Institute (2000) BS6079-3 - Project Management - Guide to the Management of Business Related Project Risk, London: British Standard Institute.

Byrne, P. (1996) Risk, Uncertainty and Decision-Making in Property Development, 2nd Edition, Oxon: Taylor & Francis.

Cosimaro, T. and Hakura D. (2011) Bank Behavior in Response to Basel III: A Cross-Country Analysis . [ONLINE] Available at: http://www.imf.org/external/pubs/ft/wp/2011/wp11119.pdf. [Accessed 07 November 11].

CRE Finance Council (2004) Borrowers Guide to CMBS [ONLINE] Available at: h t t p : / / w w w . c r e f c . o r g /I n d u s t r y R e s o u r c e s . a s p x ?id=3348&terms=borrowers+guide+to+CMBS. [Accessed 07 November 11].

Crouhy, M., Galai, D. and Mark, R. (2005) The Essentials of Risk Management, Berkshire: McGraw Hill.

Dey, P. (2002) Project Risk Management: A Combined Analytic Hierarchy Process and Decision Tree Approach, Cost Engineering, Vol. 44, No. 3, March.


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http://www.imf.org/external/pubs/ft/wp/2011/wp11119.pdf




http://www.crefc.org/IndustryResources.aspx?id=3348&terms=borrowers+guide+to+CMBS








Elhag, T., Boussabaine, A. and Ballal, T. ( 2 0 0 5 ) C r i t i c a l D e t e r m i n a n t s o f Construction Tendering Costs: Quantity Surveyors’ Standpoint, International Journal of Project Management, Vol. 23, pp 538-545.

Farre l l , P. (2011) Wri t ing a Bui l t Environment Dissertation. Oxford: Wiley-Blackwell.

Federal Deposit Insurance Corporation (2006) Concentrations in Commercial Real Estate Lending, Sound Risk Management Practices [ONLINE] Available at: http://www.fdic.gov/regulations/laws/federal/2006/06notice1212.html. [Accessed 07 November 11].

Fordham, P. (2011) Market Forecast: Stuck in the Mud [ONLINE] Available at: http://www.building.co.uk/data/market-forecast/marke t - fo recas t -s tuck - in - the -mud/5026742.article. [Accessed 07 November 11].

Glasserman, P. (2003) Monte Carlo

Methods in Financial Engineering, London:

Springer.

Hackett, M. (Ed.), Robinson, I. (Ed.) and Statham G. (Ed.) (2007) The Aqua Guide to Procurement, Tendering & Contract Admin is t ra t ion , Oxford : B lackwel l Publishing.

Isaac, D. (2003) Property Finance, 2nd Edition, Hampshire: Palgrave MacMillan.

Jorion, P. (2009) Financial Risk Managers Handbook - FRM Part I / Part II, 6th Edition, Sussex: John Wiley & Sons.

Kirkham, R. (2007) Ferry and Brandon’s Cost Planning of Buildings, 8th Edition, Oxford: Blackwell Publishing.

Kirytopoulos, K., Leopoulos, V. and

Malandrakis C. (2001) Risk Management:

A Powerful Tool for Improving Efficiency of

Project Orientated SMEs. Manufacturing

Information Systems: Proceedings of the

4th SMESME International Conference:

Denmark.

McLaney, E. (2009) Business Finance: Theory and Practice, 8th Edition, Essex: Pearson Education.


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http://www.fdic.gov/regulations/laws/federal/2006/06notice1212.html






http://www.building.co.uk/data/market-forecast/market-forecast-stuck-in-the-mud/5026742.article








Memon, A., Rahman, I. and Azis, A. (2011) Preliminary Study on Causative Factors Leading to Construction Cost Overrun, International Journal of Sustainable Construction Engineering & Technology, Vol. 2, No. 1, June.

Nemuth, T. (2008) Practical Use of Monte Carlo Simulation for Risk Management within the International Construction Industry, Grauber, Schmidt and Proske: Proceedings of the 6th International Probabilistic Workshop: Darmstadt.

Nomura Securities (2006) Fixed Income Research: Synthetic CMBS Primer [ O N L I N E ] A v a i l a b l e a t : h t t p : / /www.secur i t izat ion.net /pdf /Nomura/SyntheticCMBS_5Sept06.pdf. [Accessed 07 November 11].

Oztas, A. and Okmen, O. (2005) Judgmental Risk Analysis Process Development in Construction Projects, Building and Environment, Vol. 40, pp 1244-1254

Shapiro, E., Davies, K. and Mackmin D. (2009) Modern Methods of Valuation, 10th Edition, London: EG Books.

Smith, N., Merna, T. and Jobling, P. (2006) Managing Risk in Construction Projects,

2nd Edition, Oxford: Blackwell Publishing.

Wilkinson, S. and Reed, R. (2008) Property Development, 5th Edition, Oxon: Routledge.


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http://www.securitization.net/pdf/Nomura/SyntheticCMBS_5Sept06.pdf






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