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.

Highlights

  • Th preliminary cost estimation for building projects are usually the basis of project financial feasibility and cost budgeting in the early stages of planning and for effective and efficient project control, monitoring and execution [1]

  • For the building cost series of residential low-rise buildings (LBC), the lowest root mean square error (RMSE) is given from the multivariate transfer function (MTF) model, for a

  • The time-lag effects were identified by using the cross-correlation function (CCF) and transfer function model can model the time-lag effects of explanatory variables on building costs

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Summary

Introduction

Th preliminary cost estimation for building projects are usually the basis of project financial feasibility and cost budgeting in the early stages of planning and for effective and efficient project control, monitoring and execution [1]. Reliable and accurate cost estimation of building projects is very important for project stakeholders. It is common that the final project cost greatly exceeds the initial cost estimates [2]. According to the findings of [3], nine out of ten cost overrun projects were caused by inaccurate cost estimates in the early stages. A fast, inexpensive and comparatively accurate early-stage cost estimation is essential in project decision-making and project feasibility studies [4]. The cost estimates mainly depend on experience of industry professionals and unit cost rate from previous projects. The process is complex, and it is Buildings 2019, 9, 152; doi:10.3390/buildings9060152 www.mdpi.com/journal/buildings

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