Background/Importance: In the most recent Australian housing boom starting from 2012 till present, prices soared. As at 2016, there is an emergence of a four speed regional property market. Sydney and Melbourne are at top growth of 15%while Perth had experienced downturns of 4.3%(Knight, 2017). It is important to attain understandings of the regional house price dynamics and their spillover effects.

Research questions: This paper poses two research questions:

RQ1.Is there house price spill over effects among the largest four cities in Australia?

RQ2.What are the regional variations between house prices and macroeconomic variables in the largest four cities in Australia?

Research methodology: The paper describes the application of combined enhanced rigorous econometric frameworks, such as Variance decomposition test, Generalised impulse response test, Granger causality, and the Vector Error Correction Model (VECM), to provide an in depth understanding of the regional house price dynamics in Australia.

Findings: The empirical results presented reveal Sydney as the dominant source of the spillover of house prices in the four major cities. It finds changes in house prices in Sydney result in contagious spillover impacts in the house prices of other three main cities due to the transmission mechanism of information. Melbourne house price variance does not come from itself both over the long run and over the short run, which is largely influenced by the contagious spillover from other regions. Spillover effects are identified in these four major housing markets.

Empirical results present evidence that long run relationships exist between various macroeconomic variables and house prices in these cities although heterogeneity was identified.

Originality and value: The foremost contribution of this paper is that it is the first rigorous study of regional house price dynamics in Australia including thorough analyses on macroeconomic relationships and spill over effects both. Additionally, the data set renders the study of particular interest, because it incorporates an analysis of the most recent housing boom (2012–2015 in the datasets).