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Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach

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  • Goh Bee Hua
  • Teo Ho Pin

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.

Suggested Citation

  • Goh Bee Hua & Teo Ho Pin, 2000. "Forecasting construction industry demand, price and productivity in Singapore: the Box-Jenkins approach," Construction Management and Economics, Taylor & Francis Journals, vol. 18(5), pages 607-618.
    • Handle: RePEc:taf:conmgt:v:18:y:2000:i:5:p:607-618
    DOI: 10.1080/014461900407419
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    Cited by:

    1. Johannes Tshepiso Tsoku & Nonofo Phokontsi & Daniel Metsileng, 2015. "Forecasting South African Gold Sales: The Box-Jenkins Methodology," Proceedings of International Academic Conferences 2704589, International Institute of Social and Economic Sciences.
    2. Eda Ustaoglu & Carlo Lavalle, 2017. "Examining lag effects between industrial land development and regional economic changes: The Netherlands experience," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-34, September.
    3. Eda Ustaoglu & Filipe Batista e Silva & Carlo Lavalle, 2020. "Quantifying and modelling industrial and commercial land-use demand in France," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(1), pages 519-549, January.

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