In recent years, Australia has experienced high rates of immigration. We investigate the effect that this has had on housing prices at the postcode level. The endogeneity of immigrant inflows is accounted for using the Bartik shift-share approach. Using data from the censuses in 2006, 2011, and 2016, we find that an immigrant inflow of 1% of a postcode's population raises housing prices by around 0.9% per year. As a result, Australian housing prices would have been around 1.1% lower per annum had there been no immigration. The size of this effect is broadly consistent with that found for other countries. The effects of immigration on housing prices were larger in the more recent part of the period examined and strongest in the states of New South Wales and Victoria, and the cities of Melbourne and Adelaide. Chinese and Indian immigrant groups are shown to have a strong positive influence on prices.

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Purpose The purpose of this paper is on developing and implementing a model which provides a fuller and more comprehensive reflection of the interaction of house prices at the suburb level. Design/methodology/approach The authors examine how changes in housing prices evolve across space within the suburban context. In doing so, the authors developed a model which allows for suburbs to be connected both because of their geographic proximity but also by non-spatial factors, such as similarities in socioeconomic or demographic characteristics. This approach is applied to modelling home price dynamics in Melbourne, Australia, from 2007 to 2018. Findings The authors found that including both spatial and non-spatial linkages between suburbs provides a better representation of the data. It also provides new insights into the way spatial shocks are transmitted around the city and how suburban housing markets are clustered. Originality/value The authors have generalized the widely used SAR model and advocated building a spatial weights matrix that allows for both geographic and socioeconomic linkages between suburbs within the HOSAR framework. As the authors outlined, such a model can be easily estimated using maximum likelihood. The benefits of such a model are that it yields an improved fit to the data and more accurate spatial spill-over estimates.

Australia's economy, like most national economies, is made up of several regional sub‐economies. How these regional economies interact is not well understood but is relevant to macroeconomic policy setting. We outline and estimate a flexible Global Vector Autoregression model using quarterly data on house prices, output, unemployment and population for the eight Australian states and territories from 1986 to 2016. Using region‐to‐region impulse response functions from this model we quantify the influence of shocks in one region on another. Our results highlight the high degree of influence of New South Wales on other regions over and above its size.