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Bresnen, M and Marshall, N (2000) Motivation, commitment and the use of incentives in partnerships and alliances. Construction Management and Economics, 18(05), 587-98.

Crosthwaite, D (2000) The global construction market: a cross-sectional analysis. Construction Management and Economics, 18(05), 619-27.

Fong, P S-W and Choi, S K-Y (2000) Final contractor selection using the analytical hierarchy process. Construction Management and Economics, 18(05), 547-57.

Holm, M G (2000) Service management in housing refurbishment: a theoretical approach. Construction Management and Economics, 18(05), 525-33.

Hoxley, M (2000) Are competitive fee tendering and construction professional service quality mutually exclusive?. Construction Management and Economics, 18(05), 599-605.

Hua, G B and Pin, T H (2000) Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach. Construction Management and Economics, 18(05), 607-18.

  • Type: Journal Article
  • Keywords: accuracy; Box-Jenkins approach; construction demand; forecasting; productivity
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/014461900407419
  • Abstract:

    In academic research, the traditional Box-Jenkins approach is widely acknowledged as a benchmark technique for univariate methods because of its structured modelling basis and acceptable forecasting performance. This study examines the versatility of this approach by applying it to analyse and forecast three distinct variables of the construction industry, namely, tender price, construction demand and productivity, based on case studies of Singapore. In order to assess the adequacy of the Box-Jenkins approach to construction industry forecasting, the models derived are evaluated on their predictive accuracy based on out-of-sample forecasts. Two measures of accuracy are adopted, the root mean-square-error (RMSE) and the mean absolute percentage error (MAPE). The conclusive findings of the study include: (1) the prediction RMSE of all three models is consistently smaller than the model’s standard error, implying the models’ good predictive performance; (2) the prediction MAPE of all three models consistently falls within the general acceptable limit of 10%; and (3) among the three models, the most accurate is the demand model which has the lowest MAPE, followed by the price model and the productivity model.

Landin, A (2000) ISO 9001 within the Swedish construction sector. Construction Management and Economics, 18(05), 509-18.

Loosemore, M and Tan, C C (2000) Occupational stereotypes in the construction industry. Construction Management and Economics, 18(05), 559-66.

Love, P E D, Mandal, P, Smith, J and Li, H (2000) Modelling the dynamics of design error induced rework in construction. Construction Management and Economics, 18(05), 567-74.

Munns, A K and Al-Haimus, K M (2000) Estimating using cost significant global cost models. Construction Management and Economics, 18(05), 575-85.

Nicholas, J, Holt, G D and Mihsein, M (2000) Contractor financial credit limits: their derivation and implications for materials suppliers. Construction Management and Economics, 18(05), 535-45.

Odeyinka, H A (2000) An evaluation of the use of insurance in managing construction risks. Construction Management and Economics, 18(05), 519-24.