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Author:

Zhao, Linlin (Zhao, Linlin.) | Mbachu, Jasper (Mbachu, Jasper.) | Liu, Zhansheng (Liu, Zhansheng.)

Indexed by:

SSCI EI Scopus SCIE

Abstract:

Cost estimating based on a building cost index plays an important role in project planning and cost management by providing accurate cost information. However, an effective method to predict the building cost index of New Zealand is lacking. This study proposes a transfer function method to improve the forecasting accuracy of the building cost index. In this study, the New Zealand house price index is included in the transfer function models as an explanatory variable to produce cost forecasts. The proposed method is used to estimate the building cost index of residential buildings including one-story houses, two-story houses, and town houses in New Zealand. To demonstrate the effectiveness of the proposed method, this study compares the cost forecasts generated from the transfer function models and the autoregressive integrated moving average (ARIMA) models. The results indicate that the proposed transfer function method can achieve better outcomes than ARIMA models by considering the time-lag causality between building costs and New Zealand house prices. The proposed method can be used by industry professionals as a practical tool to predict project costs and help the professionals to better capture the inherent relationships between cost and house prices.

Keyword:

Author Community:

  • [ 1 ] [Zhao, Linlin]Beijing Univ Technol, Coll Architecture & Civil Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Liu, Zhansheng]Beijing Univ Technol, Coll Architecture & Civil Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Mbachu, Jasper]Bond Univ, Fac Soc & Design, Gold Coast, Qld 4226, Australia

Reprint Author's Address:

  • [Zhao, Linlin]Beijing Univ Technol, Coll Architecture & Civil Engn, Beijing 100124, Peoples R China

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Source :

MATHEMATICAL PROBLEMS IN ENGINEERING

ISSN: 1024-123X

Year: 2020

Volume: 2020

ESI Discipline: ENGINEERING;

ESI HC Threshold:115

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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