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Abstract
Recognising the existence of socio-economic and demographic disparities across metropolitan cities such as Greater Sydney, this study gauges the determinants of homeownership affordability in the different regions of Greater Sydney using local government area (LGA) data over 1991–2016 with a system generalised method of moments (GMM) and a panel error correction model (ECM). The results of the study showed that the determinants of homeownership affordability vary across the regions of Greater Sydney. Although house price and median personal income are the key drivers of homeownership affordability across all regions, the difference in the magnitude of these determinants between regions have also been documented. Specifically, Western Sydney is more sensitive to income and house price change than the other regions. In addition, Western Sydney is also sensitive to other determinants (i.e. housing supply, residential population, median rent, and housing investors), while no comparable evidence is found for the other regions. This clearly highlights the differences across regions and the importance of submarket considerations in the analysis of homeownership affordability. The implications of the study have also been discussed.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 See Appendix 1 for the LGAs that make up each region
2 The results are not reported for brevity.
3 Yates (Citation2008) and Kutty (Citation2005) also reported that household consumption of non-housing goods and services potentially depends on the proportion of income spent on housing-related expenses. As such, low-income families often have limited household non-shelter expenditure due to their low residual income.
4 If the results show that the determinants of homeownership affordability are homogenous and do not vary across different regions, it refutes the Shelter Poverty Theory. It also highlights the complexity in explaining housing affordability using the Shelter Poverty Theory.
5 We use the median house price because, as reported by Housing NSW (Citation2016), this measure of central tendency is not significantly affected by unusually high or low values.
6 For intercensal periods, the aggregate of building approvals was added progressively to the reported census housing stock. A similar interpolation technique was used by Liu and Otto (Citation2017).
7 The panel unit root results show that the series are first-differenced stationary or I(1)
8 Elasticity measures the level of change in affordability when there is a change in any of the regressors (e.g. income), holding everything else constant. For example, a 1% increase in income would improve affordability by 1.21% in the western region.
9 We re-run the tests with a disaggregated dataset (SA3). Again, this robustness check shows that the determinants of homeownership affordability vary across the regions of Greater Sydney. This reflects the importance of submarket considerations in homeownership affordability analysis. The results are not reported for brevity. Thanks to the referee for highlighting this point.
10 The system GMM results of low-income, medium-income, and high-income regions as well as strata and non-strata housing types are not reported for brevity but they are available from the authors.