Modelling seasonality in Australian building approvals

• Main Author: Harry M Karamujic
• Format: Article
• Language: English
• Published: UTS ePRESS 2012-02-01
• Series: Construction Economics and Building
• Subjects: New housing building approvals; univariate structural time series modelling; out of sample forecasting; stochastic and deterministic trend; stochastic and deterministic seasonality.
• Online Access: https://learning-analytics.info/journals/index.php/AJCEB/article/view/2323

The paper examines the impact of seasonal influences on Australian housing approvals, represented by the State of Victoria[1] building approvals for new houses (BANHs). The prime objective of BANHs is to provide timely estimates of future residential building work. Due to the relevance of the residential property sector to the property sector as whole, BANHs are viewed by economic analysts and commentators as a leading indicator of property sector investment and as such the general level of economic activity and employment. The generic objective of the study is to enhance the practice of modelling housing variables. In particular, the study seeks to cast some additional light on modelling the seasonal behaviour of BANHs by: (i) establishing the presence, or otherwise, of seasonality in Victorian BANHs; (ii) if present, ascertaining is it deterministic or stochastic; (iii) determining out of sample forecasting capabilities of the considered modelling specifications; and (iv) speculating on possible interpretation of the results. To do so the study utilises a structural time series model of Harwey (1989). The modelling results confirm that the modelling specification allowing for stochastic trend and deterministic seasonality performs best in terms of diagnostic tests and goodness of fit measures. This is corroborated with the analysis of out of sample forecasting capabilities of the considered modelling specifications, which showed that the models with deterministic seasonal specification exhibit superior forecasting capabilities. The paper also demonstrates that if time series are characterized by either stochastic trend or seasonality, the conventional modelling approach[2] is bound to be mis-specified i.e. would not be able to identify statistically significant seasonality in time series. According to the selected modeling specification, factors corresponding to June, April, December and November are found to be significant at five per cent level. The observed seasonality could be attributed to the ‘summer holidays’ and ‘the end of financial year’ seasonal effects. [1] Victoria is geographically the second smallest state in Australia. It is also the second most populous state in Australia. Australia has six states (New South Wales, Queensland, South Australia, Tasmania, Victoria, and Western Australia), and two territories (the Northern Territory and the Australian Capital Territory). [2] A modelling approach based on the assumption of deterministic trend and deterministic seasonality.