Regression modelling of risk impacts on construction cost flow forecast

Henry Odeyinka, John Lowe, Ammar Kaka

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    Purpose – Significant risk factors inherent in construction cost flow forecast were identified in this study. The aim of this paper is to develop regression models to assess the impacts of the identified risks on the baseline forecast at the in-progress stage of construction.Design/methodology/approach – Two stages were involved in data collection. The first was astructured questionnaire survey administered on 370 UK contractors to identify significant riskfactors inherent in cost flow forecast. The second stage was the collection of forecast and actual cost flow data from 55 case study projects. Variations between these pair of data sets were measured at 30 per cent, 50 per cent, 70 per cent and 100 per cent completion periods. Respondents were then requested to score on a Likert type scale, the extent of occurrence of the significant risk factors in the case study projects. This pair of data sets were used in regression modelling.Findings – Significant risk factors were identified from the questionnaire survey analysis as:changes to initial design, variation to works, production target slippage, delay in agreeingvariation/dayworks and delay in settling claims among others. Using the identified significant risk factors and the periodic variability measurements, multiple linear regression models were developed. The models were promising in that they helped to establish the fact that the phenomenon under consideration could be modelled. They also provided some insights in explaining the observed variability between the baseline cost flow forecast and actual cost flow based on risk impacts.Research limitations/implications – The developed models showed a promising level of accuracy but also indicated that the phenomenon under consideration is not strictly linear and may need to explore some other form of modelling.Practical implications – The developed models provide invaluable information to the constructioncontractors regarding the likely impacts of significant risk variables on cost flow baseline forecast at different stages of construction so that a pro active risk response can be put in place.Originality/value – This study makes an original contribution of providing a modelling insight intothe phenomenon of how risks inherent in construction could impact the baseline cost flow forecast at different stages of construction. The information is invaluable in making pro active risk response.Keywords: Construction projects, Cost flow, Contractors, Regression modelling, Risk factors,United Kingdom, Risk management, Construction industry, Costs
    LanguageEnglish
    Pages203-221
    JournalJournal of Financial Management of Property and Construction
    Volume17
    Issue number3
    DOIs
    Publication statusPublished - 1 Nov 2012

    Fingerprint

    Modeling
    Costs
    Construction costs
    Risk factors
    Questionnaire survey
    Contractors
    Slippage
    Data flow
    Key words
    Risk management
    Linear regression model
    Design methodology
    Multiple linear regression
    Regression model
    Construction industry
    Inherent risk
    Data collection
    Construction project

    Keywords

    • Construction projects
    • Cost flow
    • Contractors
    • Regression modelling
    • Risk factors
    • United Kingdom
    • Risk management
    • Construction industry
    • Costs

    Cite this

    Odeyinka, Henry ; Lowe, John ; Kaka, Ammar. / Regression modelling of risk impacts on construction cost flow forecast. In: Journal of Financial Management of Property and Construction. 2012 ; Vol. 17, No. 3. pp. 203-221.
    @article{01cb53798a93479a8f76d07b3bd8beb7,
    title = "Regression modelling of risk impacts on construction cost flow forecast",
    abstract = "Purpose – Significant risk factors inherent in construction cost flow forecast were identified in this study. The aim of this paper is to develop regression models to assess the impacts of the identified risks on the baseline forecast at the in-progress stage of construction.Design/methodology/approach – Two stages were involved in data collection. The first was astructured questionnaire survey administered on 370 UK contractors to identify significant riskfactors inherent in cost flow forecast. The second stage was the collection of forecast and actual cost flow data from 55 case study projects. Variations between these pair of data sets were measured at 30 per cent, 50 per cent, 70 per cent and 100 per cent completion periods. Respondents were then requested to score on a Likert type scale, the extent of occurrence of the significant risk factors in the case study projects. This pair of data sets were used in regression modelling.Findings – Significant risk factors were identified from the questionnaire survey analysis as:changes to initial design, variation to works, production target slippage, delay in agreeingvariation/dayworks and delay in settling claims among others. Using the identified significant risk factors and the periodic variability measurements, multiple linear regression models were developed. The models were promising in that they helped to establish the fact that the phenomenon under consideration could be modelled. They also provided some insights in explaining the observed variability between the baseline cost flow forecast and actual cost flow based on risk impacts.Research limitations/implications – The developed models showed a promising level of accuracy but also indicated that the phenomenon under consideration is not strictly linear and may need to explore some other form of modelling.Practical implications – The developed models provide invaluable information to the constructioncontractors regarding the likely impacts of significant risk variables on cost flow baseline forecast at different stages of construction so that a pro active risk response can be put in place.Originality/value – This study makes an original contribution of providing a modelling insight intothe phenomenon of how risks inherent in construction could impact the baseline cost flow forecast at different stages of construction. The information is invaluable in making pro active risk response.Keywords: Construction projects, Cost flow, Contractors, Regression modelling, Risk factors,United Kingdom, Risk management, Construction industry, Costs",
    keywords = "Construction projects, Cost flow, Contractors, Regression modelling, Risk factors, United Kingdom, Risk management, Construction industry, Costs",
    author = "Henry Odeyinka and John Lowe and Ammar Kaka",
    note = "Reference text: Akintoye, A. and Fitzgerald, E. (2000), “A survey of current cost estimating practices in the UK”, Construction Management and Economics, Vol. 18 No. 2, pp. 161-72. Akintoye, A.S. and MacLeod, M.J. (1997), “Risk analysis and management in construction”, International Journal of Project Management, Vol. 15 No. 1, pp. 31-8. Association for Project Management (2006), APM Body of Knowledge, 5th ed., Association for Project Management, High Wycombe. Berdicevsky, S. (1978), “Erection cost flow analysis in public projects”, MSc thesis, Technion-Israel Institute of Technology, Haifa. Berny, J. and Howe, R. (1983), “Project management control using real time budgeting and forecasting models”, Construction Papers, Vol. 2, pp. 19-40. Boussabaine, A.H. and Elhag, T. (1999), “Applying fuzzy techniques to cash flow analysis”, Construction Management and Economics, Vol. 17, pp. 745-55. Boussabaine, A.H. and Kaka, A.P. (1998), “A neural networks approach for cost flow forecasting”, Construction Management and Economics, Vol. 16, pp. 471-9. Boussabaine, A.H., Thomas, R. and Elhag, T.M.S. (1999), “Modelling cost flow forecasting for water pipeline projects using neural networks”, Engineering, Construction and Architectural Management, Vol. 6 No. 3, pp. 213-24. Bromilow, F.J. and Henderson, J.A. (1977), Procedures for Reckoning the Performance of Building Contracts, 2nd ed., CSIRO, Division of Building Research, Highett, Special Report. Bufaied, A.S. (1987), “Risks in the construction industry: their causes and their effects at the project level”, PhD thesis, University of Manchester, UMIST, Manchester. Drake, B.E. (1978), “A mathematical model for expenditure forecasting post contract”, Proceedings of the Second International Symposium on Organisation and Management of Construction, Technion Israel Institute of Technology, Haifa, pp. 163-83. Eldin, N. (1989), “Cost control systems for PMT use”, Transactions of the AACE, pp. F3.1-F3.5. Evans, R.C. and Kaka, A.P. (1998), “Analysis of the accuracy of standard/average value curves using food retail building projects as case studies”, Engineering, Construction and Architectural Management, Vol. 5 No. 1, pp. 58-67. Hardy, J.V. (1970), “Cash flow forecasting for the construction industry”, MSc Report, Dept. of Civil Engineering, Loughborough University of Technology, Loughborough. Hudson, K.W. (1978), “DHSS expenditure forecasting method”, Chartered Surveyor – Building and Quantity Surveying Quarterly, Vol. 5, pp. 42-5. Hwee, N.G. and Tiong, R.L. (2002), “Model on cash flow forecasting and risk analysis for contracting firms”, International Journal of Project Management, Vol. 20, pp. 351-63. Ireland, V. (1983), “The role of managerial actions in the cost, time and quality performance of high rise commercial building projects”, PhD thesis, University of Sydney. Kaka, A.P. (1990), “Corporate financial model for construction contractors”, PhD thesis, Department of Civil Engineering, Loughborough University of Technology, Loughborough. Kaka, A.P. (1996), “Towards more flexible and accurate cash flow forecasting”, Construction Management and Economics, Vol. 14 No. 1, pp. 35-44. Kaka, A.P. (1999), “The development of a benchmark model that uses historical data for monitoring the progress of current construction projects”, Engineering, Construction and Architectural Management, Vol. 6 No. 3, pp. 256-66. Kaka, A.P. and Price, A.D.F. (1991), “Net cash flow models: are they reliable?”, Construction Management and Economics, Vol. 9, pp. 291-308. Kaka, A.P. and Price, A.D.F. (1993), “Modelling standard cost commitment curves for contractors’ cash flow forecasting”, Construction Management and Economics, Vol. 11, pp. 271-83. Kenley, R. and Wilson, O.D. (1986), “A construction project cash flow model – an idiographic approach”, Construction Management and Economics, Vol. 4, pp. 213-32. Kenley, R. and Wilson, O.D. (1989), “A construction project net cash flow model”, Construction Management and Economics, Vol. 7, pp. 3-18. Khrosrowshahi, F. (1991), “Simulation of expenditure patterns of construction projects”, Construction Management and Economics, Vol. 9, pp. 113-32. Khosrowshahi, F. (2000), “A radical approach to risk in project financial management”, Proceedings of the 16th Annual ARCOM Conference, Glasgow Caledonian University, 6-8 September, pp. 547-56. Khosrowshahi, F. and Kaka, A. (2007), “A decision support model for construction cash flow management”, Computer-Aided Civil and Infrastructure Engineering, Vol. 22, pp. 527-39. Kinnear, P.R. and Gray, C.D. (2011), IBM SPSS 18 Statistics Made Simple, Psychology Press, New York, NY. Laufer, A. and Coheca, D. (1990), “Factors affecting construction planning outcomes”, Journal of Construction Engineering and Management, Vol. 116 No. 6, pp. 135-56. Lowe, J.G. (1987), “Cash flow and the construction client – a theoretical approach”, in Lansley, P.R. and Harlow, P.A. (Eds), Managing Construction Worldwide, Vol. 1, E & FN Spon, London, pp. 327-36. Moavenzadeh, F. and Rossow, J. (1976), “Risks and risk analysis in construction management”, Proceeding of the CIB W65, Symposium on Organisation and Management of Construction, 19-20 May, US National Academy of Science, Washington, DC. Nurosis, M.J. (2009), PASW Statistics 18 Statistical Procedures Companion, Prentice-Hall, Upper Saddle River, NJ. Odeyinka, H.A. (2003), “The development and validation of models for assessing risk impacts on cash flow forecast”, PhD thesis, School of the Built and Natural Environment, Glasgow, Caledonian University, Glasgow. Park, H.K., Han, S.H. and Russell, J. (2005), “Cash flow forecasting model for general contractors using moving weights of cost categories”, Journal of Management in Engineering, Vol. 21No. 4, pp. 164-75. Peer, S. (1982), “Application of cost flow forecasting models”, Journal of the Construction Division ASCE, Vol. 108, CO2, pp. 226-32. Project Management Institute (2008), A Guide to the Project Management Body of Knowledge, 4th ed., Project Management Institute, Atlanta, GA. Sidwell, A.C. and Rumball, M.A. (1982), “The prediction of expenditure profiles for building projects”, in Brandon, P.S. (Ed.), Building Cost Techniques: New Directions, E. & F.N Spon, London, pp. 324-38. Winch, G.M. (2010), Managing Construction Projects, 2nd ed., Wiley-Blackwell, Chichester. Zoisner, J. (1974), Erection Cost Flow Analysis in Housing Projects as a Function of its Size and Construction Time, MSc thesis, Technion-Israel Institute of Technology, Haifa.",
    year = "2012",
    month = "11",
    day = "1",
    doi = "10.1108/13664381211274335",
    language = "English",
    volume = "17",
    pages = "203--221",
    journal = "Journal of Financial Management of Property and Construction",
    issn = "1366-4387",
    number = "3",

    }

    Regression modelling of risk impacts on construction cost flow forecast. / Odeyinka, Henry; Lowe, John; Kaka, Ammar.

    In: Journal of Financial Management of Property and Construction, Vol. 17, No. 3, 01.11.2012, p. 203-221.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Regression modelling of risk impacts on construction cost flow forecast

    AU - Odeyinka, Henry

    AU - Lowe, John

    AU - Kaka, Ammar

    N1 - Reference text: Akintoye, A. and Fitzgerald, E. (2000), “A survey of current cost estimating practices in the UK”, Construction Management and Economics, Vol. 18 No. 2, pp. 161-72. Akintoye, A.S. and MacLeod, M.J. (1997), “Risk analysis and management in construction”, International Journal of Project Management, Vol. 15 No. 1, pp. 31-8. Association for Project Management (2006), APM Body of Knowledge, 5th ed., Association for Project Management, High Wycombe. Berdicevsky, S. (1978), “Erection cost flow analysis in public projects”, MSc thesis, Technion-Israel Institute of Technology, Haifa. Berny, J. and Howe, R. (1983), “Project management control using real time budgeting and forecasting models”, Construction Papers, Vol. 2, pp. 19-40. Boussabaine, A.H. and Elhag, T. (1999), “Applying fuzzy techniques to cash flow analysis”, Construction Management and Economics, Vol. 17, pp. 745-55. Boussabaine, A.H. and Kaka, A.P. (1998), “A neural networks approach for cost flow forecasting”, Construction Management and Economics, Vol. 16, pp. 471-9. Boussabaine, A.H., Thomas, R. and Elhag, T.M.S. (1999), “Modelling cost flow forecasting for water pipeline projects using neural networks”, Engineering, Construction and Architectural Management, Vol. 6 No. 3, pp. 213-24. Bromilow, F.J. and Henderson, J.A. (1977), Procedures for Reckoning the Performance of Building Contracts, 2nd ed., CSIRO, Division of Building Research, Highett, Special Report. Bufaied, A.S. (1987), “Risks in the construction industry: their causes and their effects at the project level”, PhD thesis, University of Manchester, UMIST, Manchester. Drake, B.E. (1978), “A mathematical model for expenditure forecasting post contract”, Proceedings of the Second International Symposium on Organisation and Management of Construction, Technion Israel Institute of Technology, Haifa, pp. 163-83. Eldin, N. (1989), “Cost control systems for PMT use”, Transactions of the AACE, pp. F3.1-F3.5. Evans, R.C. and Kaka, A.P. (1998), “Analysis of the accuracy of standard/average value curves using food retail building projects as case studies”, Engineering, Construction and Architectural Management, Vol. 5 No. 1, pp. 58-67. Hardy, J.V. (1970), “Cash flow forecasting for the construction industry”, MSc Report, Dept. of Civil Engineering, Loughborough University of Technology, Loughborough. Hudson, K.W. (1978), “DHSS expenditure forecasting method”, Chartered Surveyor – Building and Quantity Surveying Quarterly, Vol. 5, pp. 42-5. Hwee, N.G. and Tiong, R.L. (2002), “Model on cash flow forecasting and risk analysis for contracting firms”, International Journal of Project Management, Vol. 20, pp. 351-63. Ireland, V. (1983), “The role of managerial actions in the cost, time and quality performance of high rise commercial building projects”, PhD thesis, University of Sydney. Kaka, A.P. (1990), “Corporate financial model for construction contractors”, PhD thesis, Department of Civil Engineering, Loughborough University of Technology, Loughborough. Kaka, A.P. (1996), “Towards more flexible and accurate cash flow forecasting”, Construction Management and Economics, Vol. 14 No. 1, pp. 35-44. Kaka, A.P. (1999), “The development of a benchmark model that uses historical data for monitoring the progress of current construction projects”, Engineering, Construction and Architectural Management, Vol. 6 No. 3, pp. 256-66. Kaka, A.P. and Price, A.D.F. (1991), “Net cash flow models: are they reliable?”, Construction Management and Economics, Vol. 9, pp. 291-308. Kaka, A.P. and Price, A.D.F. (1993), “Modelling standard cost commitment curves for contractors’ cash flow forecasting”, Construction Management and Economics, Vol. 11, pp. 271-83. Kenley, R. and Wilson, O.D. (1986), “A construction project cash flow model – an idiographic approach”, Construction Management and Economics, Vol. 4, pp. 213-32. Kenley, R. and Wilson, O.D. (1989), “A construction project net cash flow model”, Construction Management and Economics, Vol. 7, pp. 3-18. Khrosrowshahi, F. (1991), “Simulation of expenditure patterns of construction projects”, Construction Management and Economics, Vol. 9, pp. 113-32. Khosrowshahi, F. (2000), “A radical approach to risk in project financial management”, Proceedings of the 16th Annual ARCOM Conference, Glasgow Caledonian University, 6-8 September, pp. 547-56. Khosrowshahi, F. and Kaka, A. (2007), “A decision support model for construction cash flow management”, Computer-Aided Civil and Infrastructure Engineering, Vol. 22, pp. 527-39. Kinnear, P.R. and Gray, C.D. (2011), IBM SPSS 18 Statistics Made Simple, Psychology Press, New York, NY. Laufer, A. and Coheca, D. (1990), “Factors affecting construction planning outcomes”, Journal of Construction Engineering and Management, Vol. 116 No. 6, pp. 135-56. Lowe, J.G. (1987), “Cash flow and the construction client – a theoretical approach”, in Lansley, P.R. and Harlow, P.A. (Eds), Managing Construction Worldwide, Vol. 1, E & FN Spon, London, pp. 327-36. Moavenzadeh, F. and Rossow, J. (1976), “Risks and risk analysis in construction management”, Proceeding of the CIB W65, Symposium on Organisation and Management of Construction, 19-20 May, US National Academy of Science, Washington, DC. Nurosis, M.J. (2009), PASW Statistics 18 Statistical Procedures Companion, Prentice-Hall, Upper Saddle River, NJ. Odeyinka, H.A. (2003), “The development and validation of models for assessing risk impacts on cash flow forecast”, PhD thesis, School of the Built and Natural Environment, Glasgow, Caledonian University, Glasgow. Park, H.K., Han, S.H. and Russell, J. (2005), “Cash flow forecasting model for general contractors using moving weights of cost categories”, Journal of Management in Engineering, Vol. 21No. 4, pp. 164-75. Peer, S. (1982), “Application of cost flow forecasting models”, Journal of the Construction Division ASCE, Vol. 108, CO2, pp. 226-32. Project Management Institute (2008), A Guide to the Project Management Body of Knowledge, 4th ed., Project Management Institute, Atlanta, GA. Sidwell, A.C. and Rumball, M.A. (1982), “The prediction of expenditure profiles for building projects”, in Brandon, P.S. (Ed.), Building Cost Techniques: New Directions, E. & F.N Spon, London, pp. 324-38. Winch, G.M. (2010), Managing Construction Projects, 2nd ed., Wiley-Blackwell, Chichester. Zoisner, J. (1974), Erection Cost Flow Analysis in Housing Projects as a Function of its Size and Construction Time, MSc thesis, Technion-Israel Institute of Technology, Haifa.

    PY - 2012/11/1

    Y1 - 2012/11/1

    N2 - Purpose – Significant risk factors inherent in construction cost flow forecast were identified in this study. The aim of this paper is to develop regression models to assess the impacts of the identified risks on the baseline forecast at the in-progress stage of construction.Design/methodology/approach – Two stages were involved in data collection. The first was astructured questionnaire survey administered on 370 UK contractors to identify significant riskfactors inherent in cost flow forecast. The second stage was the collection of forecast and actual cost flow data from 55 case study projects. Variations between these pair of data sets were measured at 30 per cent, 50 per cent, 70 per cent and 100 per cent completion periods. Respondents were then requested to score on a Likert type scale, the extent of occurrence of the significant risk factors in the case study projects. This pair of data sets were used in regression modelling.Findings – Significant risk factors were identified from the questionnaire survey analysis as:changes to initial design, variation to works, production target slippage, delay in agreeingvariation/dayworks and delay in settling claims among others. Using the identified significant risk factors and the periodic variability measurements, multiple linear regression models were developed. The models were promising in that they helped to establish the fact that the phenomenon under consideration could be modelled. They also provided some insights in explaining the observed variability between the baseline cost flow forecast and actual cost flow based on risk impacts.Research limitations/implications – The developed models showed a promising level of accuracy but also indicated that the phenomenon under consideration is not strictly linear and may need to explore some other form of modelling.Practical implications – The developed models provide invaluable information to the constructioncontractors regarding the likely impacts of significant risk variables on cost flow baseline forecast at different stages of construction so that a pro active risk response can be put in place.Originality/value – This study makes an original contribution of providing a modelling insight intothe phenomenon of how risks inherent in construction could impact the baseline cost flow forecast at different stages of construction. The information is invaluable in making pro active risk response.Keywords: Construction projects, Cost flow, Contractors, Regression modelling, Risk factors,United Kingdom, Risk management, Construction industry, Costs

    AB - Purpose – Significant risk factors inherent in construction cost flow forecast were identified in this study. The aim of this paper is to develop regression models to assess the impacts of the identified risks on the baseline forecast at the in-progress stage of construction.Design/methodology/approach – Two stages were involved in data collection. The first was astructured questionnaire survey administered on 370 UK contractors to identify significant riskfactors inherent in cost flow forecast. The second stage was the collection of forecast and actual cost flow data from 55 case study projects. Variations between these pair of data sets were measured at 30 per cent, 50 per cent, 70 per cent and 100 per cent completion periods. Respondents were then requested to score on a Likert type scale, the extent of occurrence of the significant risk factors in the case study projects. This pair of data sets were used in regression modelling.Findings – Significant risk factors were identified from the questionnaire survey analysis as:changes to initial design, variation to works, production target slippage, delay in agreeingvariation/dayworks and delay in settling claims among others. Using the identified significant risk factors and the periodic variability measurements, multiple linear regression models were developed. The models were promising in that they helped to establish the fact that the phenomenon under consideration could be modelled. They also provided some insights in explaining the observed variability between the baseline cost flow forecast and actual cost flow based on risk impacts.Research limitations/implications – The developed models showed a promising level of accuracy but also indicated that the phenomenon under consideration is not strictly linear and may need to explore some other form of modelling.Practical implications – The developed models provide invaluable information to the constructioncontractors regarding the likely impacts of significant risk variables on cost flow baseline forecast at different stages of construction so that a pro active risk response can be put in place.Originality/value – This study makes an original contribution of providing a modelling insight intothe phenomenon of how risks inherent in construction could impact the baseline cost flow forecast at different stages of construction. The information is invaluable in making pro active risk response.Keywords: Construction projects, Cost flow, Contractors, Regression modelling, Risk factors,United Kingdom, Risk management, Construction industry, Costs

    KW - Construction projects

    KW - Cost flow

    KW - Contractors

    KW - Regression modelling

    KW - Risk factors

    KW - United Kingdom

    KW - Risk management

    KW - Construction industry

    KW - Costs

    U2 - 10.1108/13664381211274335

    DO - 10.1108/13664381211274335

    M3 - Article

    VL - 17

    SP - 203

    EP - 221

    JO - Journal of Financial Management of Property and Construction

    T2 - Journal of Financial Management of Property and Construction

    JF - Journal of Financial Management of Property and Construction

    SN - 1366-4387

    IS - 3

    ER -