scholarly journals Modelling Residential Building Costs in New Zealand: A Time-Series Transfer Function Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu

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.

Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang

Cost estimating based on building cost index plays an important role in project planning and management by providing accurate cost information. Recently, tremendous advances in cost estimating has been made but serious inaccuracies in it are still too frequently witnessed. This study aims to improve estimating accuracy for residential building costs in New Zealand. In this study, the New Zealand house prices index is involved in the transfer function models to produce forecasts of building costs for one-storey house, two-storey house, and town house in New Zealand. To demonstrate the effectiveness of the proposed models, this study compares the estimate results of the transfer function models with the univariate ARIMA models. The results indicate that the proposed transfer function models can achieve better outcomes than ARIMA models by considering the causality between building costs and New Zealand house prices. During the modelling process, the better cost estimation approach can be identified, and the movements of building costs are shown.


Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.


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