Forecasting residential building costs in New Zealand using a univariate approach

Abstract

Construction cost index has been widely used to prepare cost estimates, budgets, and bids for construction projects. It can also be regarded as an indicator of cost level, which makes it valuable to public authorities for understanding the conditions in the construction industry. Accurate forecasting of future construction cost index is essential for construction industry at both micro- and macro-level. To improve the accuracy of the cost forecasting, time series modeling techniques are adopted in this study. The performance of the exponential smoothing models and seasonal autoregressive integrated moving average (ARIMA) models for forecasting the building cost of five categories of residential building (one-story house, two-story house, town house, apartment, and retirement village building) in New Zealand is compared. Exponential smoothing models can produce more accurate forecasts for cost series of the one-story house and two-story house in New Zealand, while seasonal ARIMA models outperform exponential smoothing models across the cost series for town house, apartment, and retirement village building. This study contributes toward the development of the current state of knowledge in the area of cost index forecasting for New Zealand and provides insights that should be valuable from the practitioner perspectives.

Time series analysis of building construction cost index in Türkiye

“…The best forecasting model is TES, which accounts for both trend and seasonality components. This result also confirms the literature studies of Ashuri and Lu [10] and Zhao et al [17]. It is crucial to discuss models' forecasting performance on CCI prediction since practitioners should be properly informed regarding the effectiveness of the methods.…”

“…The authors concluded that the grey model and polynomial fitting methods are two effective prediction models for material price prediction in China. Zhao et al [17] forecasted the New Zealand building construction cost index using Holt ES and ARIMA methods. As a result of the study, no significant superiority was found between the models.…”

“…Holt-Winters ES which was first introduced by Holt [22] and Winters [23] is a widely used univariate time-series forecasting model [24]. Holt [22] and Winters [23] first used this method in marketing for short-term sales projections [17]. Holt-Winters helps to model three characteristics of time series respectively; level (a), trend (b), and seasonality (c).…”

A new model for learning-based forecasting procedure by combining k-means clustering and time series forecasting algorithms

“…The conducted research focuses on improving the performance and accuracy of exponential smoothing forecasting method, especially the single exponential smoothing. The proposed new model is based on the single exponential smoothing because it is a simple forecasting method that requires only small sample data and has a comprehensive statistical framework for short-term forecasting ( Khairina et al, 2019 ; Hyndman et al, 2002 ; Zhao, Mbachu & Zhang, 2019 ). M-Competition found that the simplest extrapolation method which is suitable for time series data forecasting is the single exponential smoothing.…”