Housing unaffordability within the major capital cities in Australia: A spatial analysis of microsimulated data

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Description

Housing unaffordability within the major capital cities in Australia: A spatial analysis of microsimulated data

Most of the statistical local areas (SLAs) in a major city are relatively smaller at geographic size but typically compacted with vast numbers of households and/or populations. Capital city areas are usually considered as the places where issues of housing unaffordability are at acute level in Australia. Characteristics of different cities (and various regions within a city) vary with many factors including the geographic location, population size, socioeconomic conditions, governances and social welfares etc. This paper provides a through spatial analysis of housing unaffordability estimates in eight major capital cities of Australia by using a microsimulated dataset. Findings reveal that almost two thirds of Australian households experiencing housing unaffordability are residing in Sydney, Melbourne, Brisbane, Perth, Adelaide, Hobart, Canberra and Darwin. Among these capital cities, Sydney and Melbourne have about forty per cent of the total estimate in Australia and Sydney alone has one-third of the total estimate of housing unaffordability for these eight cities. The distributive patterns of housing unaffordability estimates varied with cities, statistical local areas within a city and by household’s tenure types. For instance, Canberra has much lower estimated numbers and percentages of households experiencing housing unaffordability, compared to all other cities. On the other hand, Sydney has much higher level estimates having hotspots SLAs in Canterbury-Bankstown, Fairfield-Liverpool, Blacktown and Gosford-Wyong areas. Moreover, overall households level estimates in different cities are largely biased by the private renter households and the estimates show somewhat mixed increasing trends for some capital cities in Australia.
Period06 Feb 2013
Event typeOther
Degree of RecognitionRegional

Keywords

  • Applied Statistics
  • Spatial microsimulation