Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Linlin Zhao, Jasper Mbachu, Zhansheng Liu*, Huirong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

100 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number152
Number of pages17
JournalBuildings
Volume9
Issue number6
DOIs
Publication statusPublished - 25 Jun 2019

Cite this