Purpose This paper aims to explore principal drivers affecting prices in the Australian housing market, aiming to detect the presence of housing bubbles within it. The data set analyzed covers the past two decades, thereby including the period of the most recent housing boom between 2012 and 2015. Design/methodology/approach The paper describes the application of combined enhanced rigorous econometric frameworks, such as ordinary least square (OLS), Granger causality and the Vector Error Correction Model (VECM) framework, to provide an in-depth understanding of house price dynamics and bubbles in Australia. Findings The empirical results presented reveal that Australian house prices are driven primarily by four key factors: mortgage interest rates, consumer sentiment, the Australian S&P/ASX 200 stock market index and unemployment rates. It finds that these four key drivers have long-term equilibrium in relation to house prices, and any short-term disequilibrium always self-corrects over the long term because of economic forces. The existence of long-term equilibrium in the housing market suggests it is unlikely to be in a bubble (Diba and Grossman, 1988; Flood and Hodrick, 1986). Originality/value The foremost contribution of this paper is that it is the first rigorous study of housing bubbles in Australia at the national level. Additionally, the data set renders the study of particular interest because it incorporates an analysis of the most recent housing boom (2012-2015). The policy implications from the study arise from the discussion of how best to balance monetary policy, fiscal policy and macroeconomic policy to optimize the steady and stable growth of the Australian housing market, and from its reconsideration of affordability schemes and related policies designed to incentivize construction and the involvement of complementary industries associated with property.

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This paper develops an algorithm for a "shortest route" network problem in which it is desired to find the path which yields the shortest expected distance through the network. It is assumed that if a particular arc is chosen, then there is a finite probability that an adjacent arc will be traversed instead. Backward induction is used and appropriate recursion formulae are developed. A numerical example is provided.

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Summary This article derives a simple upper bound for the sample standard deviation that could be useful in guarding against gross errors of calculation.