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

Latif Onur Ugur (), Recep Kanit (), Hamit Erdal (), Ersin Namli (), Halil Ibrahim Erdal (), Umut Naci Baykan () and Mursel Erdal ()
Additional contact information
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

Computational Economics, 2019, vol. 53, issue 4, No 6, 1403-1419

Abstract: 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.

Keywords: Bagging; CART; Multi-layer perceptron; Ensemble machine learning; Random subspace; Construction project; Construction cost (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-018-9814-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:53:y:2019:i:4:d:10.1007_s10614-018-9814-9

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-018-9814-9

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().