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Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector

Author

Listed:
  • Latif Onur Ugur

    (Düzce University)

  • Recep Kanit

    (Gazi University)

  • Hamit Erdal

    (Atatürk University)

  • Ersin Namli

    (İstanbul University)

  • Halil Ibrahim Erdal

    (Turkish Cooperation and Coordination Agency (TİKA))

  • Umut Naci Baykan

    (Republic of Turkey Ministry of Environment and Urbanisation)

  • Mursel Erdal

    (Gazi University)

Abstract

The analysis of a construction project, regarding cost, is one of the most vital problem in planning. Due to its nature, the construction sector is an area of strong competition and estimation works are of vital importance. In recent years the Turkish Republic has started a serious urban regeneration movement in parallel to its economic development. This study is based on the drawings and quantities of 63 detached multi-story reinforced concrete housing unit projects of the Housing Development Administration (TOKI) and the Turkey Residential Building Cooperative Union (TURKKONUT). TOKI is a public company and its projects are that have been applied to 282 separate projects and are being applied to a further 266. On the other side TURKKONUT is a union of 1347 private building cooperative and have been completed 200,000 residential building. The main objective of this study is to improve the estimation accuracy of individual machine learning techniques, namely multi-layer perceptron and classification and regression trees and compares the performance of two machine learning meta-algorithms (i.e., bagging and random subspace) on a real world construction cost estimation problem. The study shows that the estimation accuracy of ensemble models are better than the models that constructed by their base learners and ensemble models may improve individual machine learning models.

Suggested Citation

  • Latif Onur Ugur & Recep Kanit & Hamit Erdal & Ersin Namli & Halil Ibrahim Erdal & Umut Naci Baykan & Mursel Erdal, 2019. "Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1403-1419, April.
    • Handle: RePEc:kap:compec:v:53:y:2019:i:4:d:10.1007_s10614-018-9814-9
    DOI: 10.1007/s10614-018-9814-9
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    References listed on IDEAS

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    1. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    2. Aykut Ekinci & Halil İbrahim Erdal, 2017. "Forecasting Bank Failure: Base Learners, Ensembles and Hybrid Ensembles," Computational Economics, Springer;Society for Computational Economics, vol. 49(4), pages 677-686, April.
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