POVERTY, PROPERTY AND PLACE

A GEOGRAPHIC ANALYSIS OF POVERTY AFTER HOUSING COSTS IN AUSTRALIA

RANDOLPH, B., LIU, E., AND BRADBURY, B.

A Report for the ACOSS-UNSW Poverty and Inequality Partnership by the City Futures Research Centre and the Social Policy Research Centre at UNSW Acknowledgements

The authors would like to acknowledge the following for their assistance in the research for and preparation of this report. It is much appreciated:

At UNSW: Melissa Wong (SPRC) and Jin Zhu (CFRC)

At ACOSS: Jacqueline Phillips, Peter Davidson, and Penny Dorsch.

All errors of omission or commission are solely the authors.

ISSN: 1326 7124

ISBN: 978-0-85871-015-3

Poverty, Property and Place: A geographic analysis of poverty after housing costs in Australia is published by City Futures Research Centre & the Social Policy Research Centre on behalf of the ACOSS/UNSW Poverty and Inequality Partnership. Find out more at http://povertyandinequality.acoss.org.au

© Australian Council of Social Service and University of New South Wales

This publication is copyright. Apart from fair dealing for the purpose of private study, research, criticism or review, as permitted under the Copyright Act, no part may be reproduced by any process without written permission. Enquiries should be directed to the Publications Officer, Australian Council of Social Service. Copies are available from the address above.

All images including cover © Austockphoto. All images are representative only.

2 Poverty, Property and Place ACOSS Partners

B B & A MILLER FOUNDATION

DAVID MORAWETZ’S SOCIAL JUSTICE FUND

HART LINE AND RAETTVISA SUB-FUNDS OF

3 Glossary

ABS Australian Bureau of Statistics

ACOSS Australian Council of Social Service

CFRC City Futures Research Centre

GFC Global Financial Crisis

Organisation for Economic Co-operation and OECD Development

PAHC Poverty after housing costs are removed

50% of median household after (or before) housing Poverty line costs income

Measure of the average depth of poverty for those Poverty gap living below the poverty line

SA Statistical Area

SIH Survey of Income and Housing

SPRC Social Policy Research Centre

UNSW University of New South Wales

4 Poverty, Property and Place Contents

GLOSSARY 4

EXECUTIVE SUMMARY 7

KEY FINDINGS 8

INTRODUCTION 11

1. MEASURING POVERTY: BEFORE AND AFTER HOUSING COSTS 13

2. POVERTY AFTER HOUSING COSTS IN AUSTRALIA IN 2015-16 15

2.1 Variations in PAHC by tenure 15

2.2 Variations in PAHC across Australia 16

2.3 Regional variations in PAHC at a finer geographic level 17

3. TRENDS IN POVERTY AFTER HOUSING COSTS 29

3.1 Incomes and housing costs 29

3.2 Trends in PAHC 31

3.3 Variations across geographic regions 35

4. THE ROLE OF HOUSING TENURE IN GENERATION POVERTY 37

4.1 PAHC amongst home buyers 37

4.2 PAHC amongst private renters 38

4.3 PAHC amongst home owners 39

5. THE IMPACT OF HOUSING COSTS ON POVERTY RATES 44

6. CONCLUSIONS 57

APPENDIX 1: PREVIOUS RESEARCH 60

APPENDIX 2: AN EXPLANATORY NOTE ON 61 TERMS AND RESEARCH METHODS

REFERENCES 65

5 Figures

Figure 1: Poverty after housing costs by Figure 21 Percentage point change in SA2 areas, Australia, 2015-16 20 poverty before and after housing costs, 52 Perth capital city area, 2015-16 Figure 2: Poverty after housing costs by SA2 areas, Sydney metropolitan area, 21 Figure 22 Percentage point change in 2015-16 poverty before and after housing costs, 53 Hobart capital city area, 2015-16 Figure 3: Poverty after housing costs by SA2 areas, Melbourne metropolitan area, 22 Figure 23 Percentage point change in 2015-16 poverty before and after housing costs, 54 Figure 4: Poverty after housing costs by Darwin metropolitan area, 2015-16 SA2 areas, Brisbane metropolitan area, 23 2015-16 Figure 24 Percentage point change in Figure 5: Poverty after housing costs by poverty before and after housing costs, 55 SA2 areas, Adelaide metropolitan area, 24 Canberra metropolitan area, 2015-16 2015-16 Figure 6: Poverty after housing costs by SA2 areas, Perth metropolitan area, 2015- 25 16 Tables Figure 7: Poverty after housing costs by Table 1: Before and after housing poverty 14 SA2 areas, Hobart metropolitan area, 26 lines, Australia, 2015-16 2015-16 Table 2: Poverty rates before and after 16 Figure 8: Poverty after housing costs by housing costs by tenure, 2015-16 SA2 areas, Canberra metropolitan area, 27 2015-16 Table 3: Poverty after housing costs by state/territory and regions, Australia, 17 Figure 9: Poverty after housing costs by 2015-16 SA2 areas, Darwin metropolitan area, 28 2015-16 Table 4: Twenty SA2 areas with highest Figure 10: Housing costs as a proportion levels of poverty after housing costs, 16 of gross household income, Australia 31 Australia, 2015-16 1994-95 to 2017-18 Table 5: Changes to household income, housing costs and residual income after Figure 11: Comparison of poverty rates 19 measured before- and after-housing housing costs, Australia, 1995-96 to 2015- costs, Australia 1999-00 to 2015-16 (50% 32 16 of median income poverty lines, 1999- Table 6: Changes in rates of poverty after 2017) housing cost by tenure, Australia, 1995-96 34 Figure 12: Change in distribution of to 2015-16 weekly housing costs of SIH participants, 33 Table 7: Changes to level of poverty after Australia, 1995-96 to 2015-16 housing costs by state/territory, Australia, 35 Figure 13: Poverty after housing costs by 1995-96 to 2015-16 SA2 areas, home buyers, Australia, 2015- 41 16 Table 8: Changes to level of poverty after housing costs by regions, Australia, 1995- 36 Figure 14: Poverty after housing costs by 96 to 2015-16 SA2 areas, private renters, Australia, 2015- 42 16 Table 9: Percentage point changes to level of poverty after housing costs by state/ 36 Figure 15: Poverty after housing costs by territory and regions, Australia, 1995-96 SA2 areas, home owners, Australia, 2015- 43 to 2015-16 16 Table 10: Top 20 SA2s by percentage of Figure 16: Percentage point change in home buyer households in poverty after 38 poverty before and after housing costs, 47 housing costs 2015-16 Australia, 2015-16 Table 11: Top 20 SA2s by percentage of Figure 17: Percentage point change in private renter households in poverty after 39 poverty before and after housing costs, 48 housing costs 2015-16 Sydney capital city area, 2015-16 Table 12: Top 20 SA2s by percentage of Figure 18: Percentage point change in poverty before and after housing costs, 49 outright owner households in poverty 40 Melbourne capital city area, 2015-16 after housing costs 2015-16 Figure 19 Percentage point change in Table 13: Twenty SA2 areas with the largest positive percentage point change poverty before and after housing costs, 50 44 Brisbane capital city area, 2015-16 between before and after housing costs poverty, Australia, 2015-16 Figure 20 Percentage point change in Table 14: Twenty SA2 areas with the poverty before and after housing costs, 51 largest positive percentage point change 45 Adelaide capital city area, 2015-16 between before and after housing costs poverty, Australia, 2015-16 6 Poverty, Property and Place Executive Summary

This report presents the facts about the geography of poverty after housing costs (PAHC) in Australia. Taking housing costs into account provides a more accurate picture of material poverty by estimating the incomes that households have left to spend on their living expenses after the cost of their housing has been met and comparing these to overall poverty standards. The analysis reveals the impact of the housing market on the ability of low-income households to maintain a decent standard of living. A key finding is that the vast majority of households living in poverty after their housing costs are accounted for live in private housing. The analysis combines data from the 2015-16 edition of the Survey of Household Expenditure, Income and Housing with data from the 2016 Census of Population and Housing to estimate the distribution of incomes and housing costs across Australian regions. Estimates were calculated at the ABS’s Statistical Area 2 level of geography (approximately 10,000 population). People can experience poverty after housing costs either because their incomes are low, or because their housing costs are high relative to these incomes. Patterns of PAHC across Australia reflect both these factors. Interactive maps of this analysis can be found online at http:// povertyandinequality.acoss.org.au/maps and it is recommended that this report is read in conjunction with these visualisations to aid interpretation.

7 Key Findings

1. Overall, 13.2% of Australian households were estimated to be experiencing after housing cost poverty in 2015-16. This compared with a figure of 8.9% for the before housing cost rate. Note that the figures for poverty in this report are based on those for 2015-16. The latest total poverty figures can be found in Poverty in Australia 2020 Part 1.

2. If we apply the 2015-16 result nationally, this indicates that 1,215,000 Australian households (3.05 million people) were living in poverty after their housing costs were taken into account at the time of the 2016 Census.

3. Rates of PAHC varied by tenure: renters in the private sector (20.5%) and public sector (48.8%) were much more likely than home buyers (9.0%) and home owners (7.7%) to experience after housing cost poverty.

4. Scaled up to national 2016 Census counts, these estimates were equivalent to 452,000, 173,000, 258,000 and 201,000 households respectively across the country. In other words, 84% of all households experiencing poverty after housing costs lived in private housing.

5. Overall, PAHC rates were marginally lower in capital regions compared with non-metropolitan regions, with the exception of South Australia.

6. Wide variations were evident across Australia. Large swathes of the Northern Territory, northern West Australia, the York Peninsula, Tasmania and the more remote areas in South Australia and NSW recorded high levels of PAHC. Other clusters were concentrated in some regional areas including southern Queensland, northern NSW and country Victoria.

7. In addition, high rates of PAHC were also evident in significant suburban census tracts in the major cities, such as Carlton (Melbourne), Elizabeth (Adelaide), Bridgewater (Hobart) and Lakemba (Sydney).

8. Housing costs to income ratios had increased markedly for those households on lowest quintile incomes since 2011-12, while the ratio fell for those in the highest income cohorts.

9. Nationally, while the rate of before housing cost poverty fell since 2009- 10, the after housing cost poverty stayed steady. In other words, the rapid escalation in housing costs after the Global Financial Crisis (GFC) effectively nullified the improvement in the level of income related poverty over this period.

10. Increases in PAHC between 1995-96 and 2015-16 were most severe for public renters and home owners (largely older people), both housing tenures with high proportions of households reliant on benefits and pensions. On the other hand, PAHC rates fell slightly over this time for both home buyers, possibly reflecting the falling cost of mortgage finance, and private

8 Poverty, Property and Place renters, reflecting a move ‘up-market’ as more moderate and higher income households moved into this rapidly expanding sector.

11. Overall PAHC rates increased in metropolitan areas but not in regional and rural areas.

12. A more detailed geographical analysis of the impact of housing costs in the private market by tenure highlighted a range of locations where poverty rates were particularly significant. While remoteness implied higher levels of PAHC for all tenures, elsewhere, higher PAHC rates had different impacts for each the three private sector tenures:

• For home buyers, the highest PAHC rates were found in a range of middle and outer metro suburbs, particularly in Brisbane, Sydney and Melbourne.

• For private renters, middle and outer suburbs also featured strongly, as well as a range of coastal and rural ‘life-style’ communities where pressure on housing may have pushed up rents.

• For home owners, remote and regional centres, especially those associated with Indigenous communities, were prominent as well as some lower income middle suburban and metro hinterland locations.

13. The impact of housing costs is best exposed in the difference between before and after housing cost poverty rates. Areas with major differences between these two measures, indicating where household incomes struggled to meet housing costs, included a band of coastal and rural hinterland locations stretching from Rockhampton through NSW and into central Victoria as well as in the rural hinterlands of Adelaide and Perth. But the greatest differences were all metropolitan, both in inner city and more suburban locations. Some of the largest differences in these two rates were concentrated in and around the Gold Coast/Tweed Heads and the middle suburbs of Sydney. Here, high housing costs compared with low incomes were clearly driving increased levels of poverty.

9 10 Poverty, Property and Place Introduction

This report builds on the ACOSS and UNSW Sydney Poverty in Australia report series, which look in detail at the incidence of poverty at the national level among different types of households. This report extends the findings of these reports and presents a more detailed analysis of the impact of housing costs on the level of poverty experienced by low-income households. It also extends this to a detailed geographical analysis of poverty after housing costs across Australia as a whole and at the local level and explores the role of housing tenure in generating poverty. This report presents the facts about the incidence and geography of poverty after housing costs in Australia in 2015-16. This is a measure of what households have left to spend on their living expenses after the cost of their housing has been met. The analysis reveals the impact of the housing market and housing costs on the ability of low-income households to maintain a basic standard of living. To do this, we combined data from the 2015-16 edition of the Survey of Household Expenditure, Income and Housing (henceforth ‘SIH’; ABS 2017) with data from the 2016 Census of Population and Housing to estimate the distribution of incomes minus housing costs across Australia. Traditional measures of housing affordability such as housing stress or the ‘30:40 rule’ (e.g. Nepal et al. 2010) largely rely on rent-to-income ratios or measures of mortgage affordability. The PAHC measure instead allows a focus on poverty as the key issue. It also deals with the absolute amount of money left to a household once basic housing costs (rent or mortgage) are met (also known as ‘residual income’), thereby exposing the real position that low-income households face in terms of trading-off having a roof over their heads and in meeting the basic costs of living (e.g. Burke et al. 2011). This is important, because as Davidson et al. (2020) highlighted, poverty and housing unaffordability are strongly linked. Moreover, Australian and international evidence shows that both poverty and housing unaffordability affect low-income families more than other households (e.g. Bangham & Judge 2019; Luffman 2006; Yates 2016). Appendix 1 presents a review of some of the more recent literature on poverty and housing affordability. Importantly, the patterns of income and housing unaffordability are not even across regions. This unevenness is contributed to by different rates of economic productivity, (de)industrialisation, political priorities, employment and service accessibility (e.g. Clarke 2019). It also plays out differently in urban, suburban, regional and rural contexts and by different cohorts (e.g. Pawson et al. 2012; Randolph & Holloway 2005). Housing markets and submarkets also function very differently within and across places (e.g. Forrest and Yip 2011). Of particular concern in this report is the role of the private housing market in generating poverty. High poverty rates among public housing tenants are well recognised (e.g. Vinson and Rawsthorne 2015) and arise from the restriction of access to the most disadvantaged individuals and families. Their poverty is thus driven primarily by their very low incomes rather than housing costs (which

11 are capped at around at 25% of income). Consequently, the main focus in this report is on the outcomes for those in the three private sector tenures – home owners, home buyers and private renters. This report therefore sets out to investigate how PAHC is experienced differently across Australia. But it also aims to shed light on the places where variations in housing costs, most notably in the private housing market where cost pressures have been escalating in recent years, have had a compounding effect on the level of poverty.

12 Poverty, Property and Place 1. Measuring poverty: before and after housing costs

The Survey of Income and Housing (SIH), conducted by the Australian Bureau of Statistics (ABS) since 1994-95, is a bi-annual1 survey that collects information on sources of income, amounts received, household net worth, housing, household characteristics and personal characteristics. It is the most comprehensive survey that collects this information. Other surveys and the five-yearly Australian Census of Population and Housing collect income data in ranges and do not have the set of detailed questions on income components included in the SIH. The SIH employs a stratified sampling technique collecting information about usual residents of private dwellings in urban and rural areas of Australia (excluding remote areas).2 Together with post-survey weightings, the results are considered representative of the Australian population. Results from three waves of the SIH – 1995-96, 2005-06 and 2015-16 – are used here to show changes over time. We have also used the SIH 2015-163 and income data from the 2016 Census as the basis of a microsimulation exercise (for a detailed explanation of this method, see Appendix 2), to produce small area estimates at a fine geographic (Statistical Area 42 , SA2) scale. While the most recent 2017-18 SIH was released as this report was being written, we have taken the 2015-16 SIH as being closest to the 2016 Census date to allow broadly comparable datasets to be combined in the microsimulation modelling. Similarly, although a more recent Poverty in Australia report has been released, it was decided to use the earlier report which is based on 2015-16 data. As in the Poverty in Australia 2018 report (Davidson et al. 2018), households that reported zero or negative income or who have income from their own unincorporated business were excluded from this analysis. SA2s with a total population of fewer than 250 residents were also excluded from the geographic results because data coverage is too low for detailed sub-group analysis.5 For the purposes of this report, a household is considered to be experiencing poverty if their income is at or below 50% of median disposable income6 (minus housing costs for the after-housing line). The poverty lines included in the Poverty in Australia 2018 report (Davidson et al. 2018) are used in calculating the level of poverty after housing costs and reproduced below as Table 1 for reference. Our main focus is on poverty after housing costs, but we also present before housing poverty estimates based on disposable income, with no allowance for housing costs.

1 The bi-annual schedule commenced in 2003-04. Previously the survey was conducted annually between 1994-95 and 1997-98 as well as in 1999-00, 2000-01 and 2002-03. The 2015-16 edition was conducted in conjunction with the Household Expenditure Survey. 2 Our simulation, however, is able to produce remote area estimates for 2015-16 by drawing on the Census data from these regions. 3 The 2015-16 SIH was used rather than the more recent 2017-18 SIH to allow synchronisation with 2016 Census data for the microsimulation exercise. 4 Statistical Area 2 (SA2) is a medium-sized general purpose areal unit created by the Australian Bureau of Statistics for statistical measurements. Across the 2,310 SA2s Australia-wide, they have an average population of about 10,000 persons, roughly resembling a medium-sized suburb. 5 We also do not show results for the separate housing tenures where there are less than 50 residents in that tenure. 6 Disposable income is all regular income minus income tax payable. See Davidson et al (2018).

13 Table 1: Before and after housing poverty lines, Australia 2015-16

50% of median income, 50% of median income, before housing costs after housing costs

Lone person $432.73 $353.29

Couple only $649.10 $529.93

Sole parent, 2 children $692.37 $565.26

Couple, 2 children $908.74 $741.90

Source: Davidson et al. (2018: 19)

Note: The before and after housing poverty lines for Australia in 2017-18 can be found at: http:// povertyandinequality.acoss.org.au/poverty/poverty-lines-by-family-type/

14 Poverty, Property and Place 2. Poverty after housing costs in Australia in 2015-16

Both household income and housing costs influence the amount of income left after housing costs are taken into consideration. In 2015-16, the median weekly household income of all households7 that participated in the SIH was $1,298.00 across Australia (Table 2). This median varied across the different tenure types, with public renters having the lowest median of $522.85 and those buying their homes with mortgages having the highest median of $2,110.51. In 2015- 16, the median weekly housing cost across Australia was $165.00. However, this median varied significantly across the different tenures, with households that fully-owned their residence enjoying the lowest median housing costs ($43.04 per week), while home buyers ($294.62) and private renters ($349.83) paid significantly more. How did the impact of these two factors play out in poverty rates once housing costs were accounted for? Overall, in 2015-16, the ACOSS/UNSW Sydney 2018 modelling estimated that 13.2% of Australian households were estimated to be experiencing poverty after housing cost are considered. This compared with 8.9% of households for the before housing cost poverty rate. If grossed up to reflect national household numbers at the time of the 2016 Census, this indicates that 1,215,000 Australian households (or 3.05 million people) were living in poverty after their housing costs were taken into account.

2.1 Variations in PAHC by tenure Given the variability of both income and housing costs across different tenures, the prevalence of poverty after housing costs also varied greatly. Specifically, while home buyers with a mortgage (9.0%) and home owners (7.7%) recorded lower rates of PAHC compared to the overall average, those who rented, whether in the private rental sector (20.5%) or in the public housing system (48.8%), were far more likely to experience PAHC (Table 2). For those households where their housing tenure was stated, this scales up to 258,000 home buyers, 201,000 home owners, 452,000 private renters and 173,000 public tenants8. In other words, 84% of all households experiencing poverty after housing costs live in private market housing and, of these households, most are in the private rental sector.

7 Figures not adjusted for household size. 8 These totals exclude households where the tenure was not stated or was classified as ‘Other tenure type’.

15 Table 2: Poverty rates before and after housing costs by tenure, 2015-16

Home Home Private Public Total owner buyer renter9 renter (16,941) (5,923) (5,574) (2,672) (861)

Median weekly $1,298.00 $879.70 $2,110.51 $1,363.73 $522.85 income

Median weekly $165.00 $43.04 $294.62 $349.83 $125.00 housing cost

Poverty after 13.2% 7.7% 9.0% 20.5% 48.8% housing costs

Total n in brackets refers to total number of households, in ‘000s. Source: SIH 2015-16 The overall poverty rate in 2017-18 was 13.6% (see Poverty in Australia 2020: Part 1, Overview)

2.2 Variations in PAHC across Australia While differences in PAHC were observed across different tenures and household types, variations were also observed spatially across Australia. This is due to the geographic interplay of varying levels of income and housing costs, factors that are influenced by job availability, socio-demographic characteristics and local housing market conditions, among others, such that areas with similar levels of PAHC may reflect different drivers. This section explores these geographic variations in more detail. Across Australia overall, there was a lower level of PAHC in metropolitan (Greater Capital City) areas than in non-metropolitan (Rest of state) areas (12.8% compared with 13.8%; Table 3). This pattern generally held across the states (Territory data was undifferentiated spatially) with the exception of South Australia, where the capital city area recorded a higher level of PAHC than the rest of the state. Indeed, Adelaide had the highest level of PAHC among all greater capital city areas, most likely a reflection of lower average incomes rather than high housing costs. The higher percentages of poverty after housing costs in non-capital city NSW and Victoria are also due to lower household incomes in these areas, but especially compounded in non- metropolitan NSW where the median weekly housing costs ($169.90) were higher than the national median ($153.30).

9 Includes those who rent via a real estate agent, from a parent/family member, as well as those who rent from another private individual who does not live in the same dwelling.

16 Poverty, Property and Place Table 3: Poverty after housing costs by state/territory and regions, Australia, 2015-16

Greater Capital Rest of state Total City area % (N) % (N) % (N) 12.7% 14.6% 13.3% NSW (2,112) (853) (2,965)

12.6% 15.1% 13.2% VIC (2,432) (1,055) (3,487)

11.9% 13.8% 12.9% QLD (1,456) (1,092) (2,548)

15.1% 13.4% 14.7% SA (1,597) (1,047) (2,644)

13.6% 13.9% 13.7% WA (1,367) (995) (2,362)

10.9% 12.0% 11.5% TAS (678) (1,057) (1,735)

8.5% 8.5% ACT / NT ------(1,200) (1,200)

12.8% 13.8% 13.2% Australia (9,642) (7,299) (16,941)

Total n in brackets. Source: SIH 2015-16

2.3 Regional variations in PAHC at a finer geographic level Spatial variations in the prevalence of PAHC are not limited to broad differences between metropolitan and non-metropolitan areas, however. In fact, much greater variations are observed at more disaggregated levels. This section reviews the results at the SA2 level as used in our microsimulation. Figure 1 shows the percentage of households in each SA2 across Australia that were estimated to be experiencing PAHC at the time of the 2015-16 SIH. With a quintile-based distribution (i.e. each range band representing approximately 20% of SA2s), the red and orange hues represent higher percentages of households experiencing PAHC in each SA2 and yellow and green hues represent lower percentages. Table 5 lists the 20 SA2s with the highest levels of PAHC in descending order in 2015-16 while Figures 2-9 show the variation in levels of PAHC across each state/territory capital city. For a more detailed view, please see the interactive maps available at http://povertyandinequality.acoss. org.au/maps.

17 Taken together, Figures 1 to 9 and Table 4 illustrate a range of contrasting outcomes. At the scale of the country as a whole, much of the remote centre of the continent scores highly on PAHC. We see large swathes of the Northern Territory, eastern West Australia, the York Peninsula, Tasmania and the more remote areas in South Australia and NSW with high levels of PAHC. Other clusters were concentrated in some regional areas including southern Queensland, northern NSW and country Victoria. Notable exceptions with relatively low PAHC rates are areas associated with mineral extracting and exporting industries where local incomes are elevated by high employment levels, such as the Pilbara, West Australia, and around Mackay in Queensland. The major cities and their immediate hinterlands show relatively lower rates of PAHC at this scale. However, the detailed data shows greater pattern of variation, especially within the major city regions. Table 5, which lists the twenty SA2s with the highest rates of poverty after housing costs, highlights the range of contrasting localities where PAHC levels are most pronounced. The remote communities in Queensland and the Northern Territory contrast to the urban areas of Carlton (Melbourne), Elizabeth (Adelaide) and Lakemba (Sydney). Clearly, different drivers are behind these comparable results, with the interaction of incomes and housing costs playing out differently in these differing locations. The remote areas are more likely related to very low incomes while the urban areas are more likely to reflect areas of both lower income and higher housing costs, particularly for both private renters and home buyers. All eight capital cities (Figures 2 to 9) have clusters of high PAHC. Many of the outer suburban SA2s with high levels of PAHC, such as in southwestern Sydney and northern Adelaide, include large public housing estates, although these suburbs also contain significant (and often larger) numbers of low income private tenants and owners. This tenure-based variation will be explored in more detail in the Section 4.

18 Poverty, Property and Place Table 4: Twenty SA2 areas with highest levels of poverty after housing costs, Australia, 2015-16

SA2 State/Territory Aurukun QLD Carlton VIC Tiwi Islands NT Yarrabah QLD East Arnhem NT Palm Island QLD Melbourne VIC Ravenswood TAS Kowanyama - Pormpuraaw QLD Lakemba NSW West Arnhem NT Elizabeth SA Fairfield NSW Torres Strait Islands QLD Wiley Park NSW Smithfield - Elizabeth North SA Roebuck WA Elsey NT East Pilbara WA Sandover - Plenty NT

Source: Simulated data based on SIH 2015-16 and 2016 Census

19 20 Figure 1: Poverty after housing costs by SA2 areas, Australia, 2015-16 Poverty, Property andPlace

Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 2: Poverty after housing costs by SA2 areas, Sydney metropolitan area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

21 22 Figure 3: Poverty after housing costs by SA2 areas, Melbourne metropolitan area, 2015-16 Poverty, Property andPlace

Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 3: Poverty after housing costs by SA2 areas, Melbourne metropolitan area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 4:Poverty after housingcosts by SA2 areas, Brisbanemetropolitan area, 2015-16 Source: Simulated data basedonSIH2015-16 and2016 Census 23 Figure 5: Poverty after housing costs by SA2 areas, Adelaide metropolitan area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

24 Poverty, Property and Place Figure 6: Poverty after housing costs by SA2 areas, Perth metropolitan area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

25 26 Figure 7: Poverty after housing costs by SA2 areas, Hobart metropolitan area, 2015-16 Poverty, Property andPlace

Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 7: Poverty after housing costs by SA2 areas, Hobart metropolitan area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

Figure 8: Poverty after housing costs by SA2 areas, Canberra metropolitan area, 2015-16 27

Source: Simulated data based on SIH 2015-16 and 2016 Census 28 Figure 9: Poverty after housing costs by SA2 areas, Darwin metropolitan area, 2015-16 Poverty, Property andPlace

Source: Simulated data based on SIH 2015-16 and 2016 Census 3. Trends in poverty after housing costs

PAHC clearly varies between different population groups and by location. But how has the level of PAHC changed over time? This section reviews the trends over the 20 years between 1995-96 and 2015-16 at the national and state level.10 As the above analysis has shown, while household income is the primary predictor of household poverty, the impact of housing costs on poverty levels is not insignificant, especially in key geographical areas. Moreover, PAHC changes over time and these trends can be tracked using past SIH datasets. It is instructive to use a longer period to make these comparisons, especially given the short-term fluctuations in housing markets and costs as well as changes in incomes that impact shorter term outcomes. Given that income and housing costs both impact the rate of PAHC, we begin by looking at trends in these two factors.

3.1 Incomes and housing costs211 Over the two decades to 2015-16, median weekly household income doubled from $809 to $1,955 in real terms (Table 5). But over the same period, median weekly housing costs increased by four-and-a half times (from $67 to $306). These changes in income and housing costs are highlighted by the increasing absolute gap between those on very low incomes and those with high incomes. Table 6 shows the changing range in weekly household income, housing costs and residual incomes for the lowest (Q1), median and highest (Q5) income quintiles between 1995-96 and 2015-16. The data show that while Q1 income levels actually increased at a faster rate than Q5 incomes (155% compared to 146%), housing cost increases were proportionally similar (193% and 198% respectively). However, median incomes only increased by 142% in this period while housing costs increased by 356%. Once these incomes are adjusted for housing costs, the resulting residual incomes increased by 146% for Q1 incomes and 139% for those at Q5 but only 133% for those on median incomes. So those in the middle of the income range did worse in a relative sense over the 20 years as their incomes failed to keep up with their housing costs more than either those at Q1 or Q5 levels. However, housing costs have nevertheless impacted disproportionately on lower income households over the last two decades. Figure 10 shows the percentage of household income accounted for by housing costs for Australia as a whole between 1994-95 to 2017-18. As this clearly illustrates, costs to income ratios have increased markedly for those households on lowest quintile incomes, especially after 2011-12, while the percentage has also risen for those on low and moderate incomes. In contrast, the cost to income ratios for the higher income quintiles rose slightly to peak around 2003-04 to 2007-08 and then have fallen away to stand at the same level they were at 25 years ago. Housing cost inflation during the housing boom years has therefore had a substantial impact on the households with lowest incomes, but not for those on higher incomes.

10 Data from the 2017-18 SIH are included in some charts where available. 11 The income data in this section is unequivalised and not adjusted for inflation. The housing data is also not adjusted for inflation.

29 In relation to poverty, the impact of increasing cost burdens on those on lowest incomes can be severe. As Saunders (2017, p753) has noted, “for those with low income …. high housing costs can make the difference between just getting by and being driven below the poverty line”. It is worth noting that a high proportion of households recorded in the lowest income band will be aged pensioners living in homes they own outright. The cost- to-income ratio for non-pensioners in this income band, including the significant proportion who are renters, will therefore be significantly higher than these figures indicate.

Table 5: Changes to household income, housing costs, and residual income after housing costs, Australia, 1995-96 to 2015-16

Weekly Household Income Q1 Median Q5

1995-96 $377.00 $809.00 $1,378.00

2005-06 $619.88 $1,313.00 $2,178.00

2015-16 $962.80 $1,954.60 $3,385.00

Change $585.80 $1,145.60 $2,007.00

% 155.4 141.6 145.6

Weekly Housing Costs Q1 Median Q5

1995-96 $19.00 $67.00 $178.00

2005-06 $30.00 $122.00 $261.00

2015-16 $55.62 $305.59 $530.39

Change $36.62 $238.59 $352.39

% 192.7 356.1 198.0

Residual Income after Costs Q1 Median Q5

1995-96 $300.00 $687.00 $1,241.00

2005-06 $504.88 $1,140.33 $1,995.00

2015-16 $737.46 $1,599.23 $2,964.36

Change $437.46 $912.23 $1,723.36

% 145.8 132.8 138.9

Note: Dollar amounts not equivalised or adjusted for inflation. Source: SIH 1995-96, 2005-06, 2015-16

30 Poverty, Property and Place Figure 10: Housing costs as a proportion of gross household income, Australia 1994-95 to 2017-18

3.2 Trends in PAHC So, what happened to poverty levels over these two decades? Overall, there was an increase in poverty after housing costs from 12.0% in 1995-96 to 13.2% in 2015-16. The trend has fluctuated but the pattern is clear. Figure 11 shows trends in poverty rates before and after housing costs are considered at the national level between 1999-00 and 2017-18. Specifically, it shows that the rate of poverty after housing costs (blue line) has persistently remained above that of the rate of poverty before housing costs (red line) over this period. This is to be expected12, but the important feature of this chart is that while the gap between before and after housing costs poverty was narrowing before 2007-8, it has since widened, especially since 2009-10. At five percentage points, the gap between these two rates is the widest since 1990. Over the past decade, then, the rate of poverty before housing costs has steadily declined while poverty after housing costs has remained steady.

12 Because housing costs are a higher share of income for low-income households than for households near the median.

31 Figure 11: Comparison of poverty rates measured before- and after-housing costs, Australia 1999-00 to 2015-16 (50% of median income poverty lines, 1999-2017)

Note: Excludes self-employed households and those with non-positive incomes. Source: SIH various years.

The impact of rising housing costs is further illustrated in Figure 12, which shows that the proportion of SIH participants with very low housing costs (typically home owners and social housing tenants) decreased significantly across the three surveys analysed. At the same time, more households had housing costs across the middle cost ranges between $200 to $500 per week in 2015-16 than previously. For those on low incomes, then, this cost squeeze has proved problematic.

32 Poverty, Property and Place Figure 12: Change in distribution of weekly housing costs of SIH participants, Australia, 1995-96 to 2015-16

Note: $ not equivalised or adjusted for inflation. Source: SIH 1995-96, 2005-06, 2015-16 33 Again, this increase is not equally distributed. This is most noticeable when the data is disaggregated by housing tenure (Table 6). There was an 18.1 percentage point increase in poverty among public renters, from 30.7% in 1995- 96 to 48.8% in 2015-16. This may reflect a structural shift over this time in the composition of public housing tenants towards with a much greater reliance on social security and aged pensions, which almost nine in ten (86%) public renters now rely on as their main source of income. Home owners also saw the level of poverty after housing costs increase, from 4.6% to 7.7%. While a small proportion overall, the extent of this increase suggests a minority of such owners, the great majority of whom are retired with half (48%) reliant on state pension payments, may be facing additional housing costs. These may relate to strata levies or other service charges faced by elderly households in strata and retirement accommodation.

Table 6: Changes in rates of poverty after housing cost by tenure, Australia, 1995-96 to 2015-16

Home Home Private Public All owner buyer renter renter

1995-96 12.0% 4.6% 11.2% 22.8% 30.7%

2005-06 12.7% 5.3% 10.1% 20.6% 38.5%

2015-16 13.2% 7.7% 9.0% 20.5% 48.8%

Change* +1.2% +3.1% -1.2% -2.3% +18.1%

* Percentage point change 1995-96 to 2015-16

Source: SIH 1995-96, 2005-06, 2015-16

At the national level, however, there have been gradually declining levels of PAHC among both home buyers (-1.2%) and private renters (-2.3%) between 1995-96 and 2015-16. The former may well reflect the effect of falling interest rates on mortgage repayment levels over this period. The fall in renters facing PAHC is likely to be accounted for by the structural shift towards more higher income earners renting their homes in the last two decades as the sector has grown significantly in central city locations (Hulse and Yates 2017), with the median (non-equivalised) residual income for private renters after housing costs increasing from $478.50 in 1995-96 to $933.52 in 2015-16. But it is noticeable that the fall in the proportion of private renters in PAHC occurred almost entirely in the decade after 1995-96 but has hardly changed since then.

34 Poverty, Property and Place 3.3 Variations across geographic regions Regionally, increases in the level of PAHC were observed across all states, though the magnitude of change varied (Table 7). There were notable increases in PAHC in Victoria (+2.8 percentage points), South Australia (+2.8 percentage points) and Tasmania (+2.3 percentage points), while increases in all other states were more moderate (<1 percentage point). Only in the ACT and NT did the incidence of PAHC decline over the full period (-0.6 percentage point), though this was after a sharp decline between 1995-96 and 2005-06 (-3.0 percentage points) followed by a subsequent increase to 2015-16 (+2.4 percentage points).

Table 7: Changes to level of poverty after housing costs by state/territory, Australia, 1995-96 to 2015-16

ACT / NSW VIC QLD SA WA TAS NT

1995-96 13.2% 10.4% 12.6% 11.9% 12.7% 9.3% 9.1%

2005-06 13.2% 13.8% 11.8% 13.3% 10.9% 11.0% 6.1%

2015-16 13.3% 13.2% 12.9% 14.7% 13.7% 11.5% 8.5%

Change* +0.1% +2.8% +0.3% +2.8% +1.0% +2.3% -0.6%

* Percentage point change 1995-96 to 2015-16

Source: SIH 1995-96, 2005-06, 2015-16

Change in PAHC also varied across metropolitan and non-metropolitan areas, with increases in poverty in metropolitan areas (+1.6 percentage points) outpacing those observed in non-metropolitan areas where there was no change (Table 8). When looked at together (Table 10), the percentage point increases in PAHC were most acute in Hobart (+3.7 percentage points), Adelaide (+3.5 percentage points) and Perth (+2.9 percentage points). Perhaps surprisingly, Sydney was the only capital city area that experienced a decline in PAHC over the two decades, though this decline is only very marginal (-0.1 percentage point). In non-metropolitan areas, there was a sharp increase in regional Victoria (+3.4 percentage points), the highest across all regional areas. Increases in PAHC in all other regional areas were more moderate (around 1 percentage point or less) with the exception of regional Queensland, which declined moderately (-0.6 percentage points), and in regional WA, which declined sharply (-4.1 percentage points). The latter is most likely a reflection of the mining boom in the early 2010s and the increase in income levels in these regions as a result.

35 Table 8: Changes to level of poverty after housing costs by regions, Australia, 1995-96 to 2015-16

Greater capital Rest of state city area

1995-96 11.2% 13.8%

2005-06 12.4% 13.5%

2015-16 12.8% 13.8%

Change* +1.6% No change

* Percentage point change 1995-96 to 2015-16

Source: SIH 1995-96, 2005-06, 2015-16

Table 9: Percentage point changes to level of poverty after housing costs by state/territory and regions, Australia, 1995-96 to 2015-16

Greater Capital Rest of state Total City Area

NSW -0.1% +0.6% +0.2%

VIC +2.6% +3.4% +2.8%

QLD +1.3% -0.6% +0.2%

SA +3.5% +0.3% +2.8%

WA +2.9% -4.1% +1.0%

TAS +3.7% +1.3% +2.3%

ACT / NT N/A -0.6% -0.6%

Source: SIH 1995-96, 2015-16

36 Poverty, Property and Place 4. The role of housing tenure in generating poverty

As we alluded to above, the role that housing tenure plays in influencing the level of PAHC across various geographies is likely to be significant given the vastly different local housing market conditions across Australia within the various main housing tenures. In this section, we look at this in more detail. As noted in the introduction, the pressure of housing costs on poverty is likely to be most pronounced in the private market where rents and prices vary significantly by locality. Yet there has been relatively little focus on how the housing market directly relates to the geography of poverty. This section therefore focuses on the difference in PAHC for home owners, home buyers and private renters. For a more detailed view of the data discussed here please see the interactive maps available at http://povertyandinequality.acoss.org.au/ maps

4.1 PAHC amongst home buyers Figure 13 shows the rates of poverty after housing costs experienced by home buyers (as a percentage of all home buyers) in each SA2 area across Australia in 2015-16. As in the previous figures, red indicates the highest level of poverty after housing costs among owner-occupying households by quintiles and green indicates the lowest level. At this scale, two main patterns can be seen. A group of largely remote areas in Central Western Australia, much of the Northern Territory and the Far North of Queensland form one broad grouping. Many of these areas are characterised by remote Aboriginal or Torres Strait Islander communities that have limited resources to draw on to support a mortgage, including access to employment. The second broad grouping of LGAs with higher PAHC amongst homebuyers is distributed in a broad sweep along the south east coastal and rural areas of Australia, from Bundaberg into northern NSW and again through country into southern South Australia. A similar grouping can be found in south west Western Australia. It is likely that the driver of PAHC in these areas is low household incomes among home buyers rather than high housing costs per se. Table 10 confirms that some of the highest rates of PAHC among home buyers are found in rural and regional areas (Dorrigo in NSW, Kilkivan in Queensland and Ravenswood, Tasmania), reflecting the pattern of low incomes and limited employment opportunities described above. But they are also found in a range of middle and outer suburban neighbourhoods such as Campbellfield – Coolaroo in Melbourne’s outer north, Cabramatta –Lansvale in Sydney middle west and Smithfield – Elizabeth North in northern Adelaide. These SA2s were too small to be picked out in the larger maps. Poverty rates here are likely to be explained by buyers facing high mortgage repayment costs in relatively expensive metropolitan housing markets. While these may not be households on very lowest incomes, many nevertheless face a daunting financial commitment to buy in these areas. The non-metro exceptions in Table 11 are remote locations, for example in the Kimberly and South Australia, possibly related to resource-related employment.

37 Table 10: Top 20 SA2s by percentage of home buyer households in poverty after housing costs 2015-16

SA2 State/Territory Tiwi Islands NT Meadow Heights VIC Campbellfield - Coolaroo VIC Gin Gin QLD Ravenswood TAS Dorrigo NSW Smithfield - Elizabeth North SA Central Highlands TAS Cabramatta - Lansvale NSW Elizabeth SA Redland Islands QLD Guildford - South Granville NSW Coober Pedy SA Kilkivan QLD Maryborough Region VIC Ashcroft - Busby - Miller NSW Lurnea - Cartwright NSW Fairfield - East NSW Halls Creek WA St Helens - Scamander TAS

Note: % of all home buyers in the same SA2 only. SA2s with fewer than 50 home buyers excluded.

Source: Simulated data based on SIH 2015-16 and 2016 Census

4.2 PAHC amongst private renters For private renters, the highest rates of PAHC are again found in both a wide range of remote and rural areas as well as the inner and middle ring suburbs of capital cities. The resource-rich regions in central and north Australia feature again, but also a belt of rural areas across south east regional Australia (Figure 14). Table 12 illustrates this latter point, where SA2s with highest PAHC rates for private renters are represented by a range of coastal and urban hinterland and rural life-style districts such as coastal Queensland, parts of Tasmania and East Gippsland in Victoria. In these regional areas, rents may be high due to relative lack of competition (e.g. Akbar et al. 2017) and more recently the impacts of short-term holiday lettings contributing to an undersupply of rental housing (e.g. Parkinson et al. 2020). In the metro areas private rental poverty hot-spots do not coincide with the most expensive rental markets in the inner city but are located in middle and more distant suburban locations. Here, low incomes relative to high metropolitan rent levels severely stretch the capacity of households to meet rental payments, especially those on income support payments. These are the urban locations where housing stress is highest and most entrenched.

38 Poverty, Property and Place Table 11: Top 20 SA2s by percentage of private renter households in poverty after housing costs 2015-16

SA2 State/Territory East Pilbara WA Ravenswood TAS Carlton VIC Bridgewater - Gagebrook TAS Cooloola QLD Fairfield NSW Elizabeth SA Mudgee Region - East NSW Forster-Tuncurry Region NSW Roebuck WA Redland Islands QLD Lakemba NSW Orbost VIC Cygnet TAS Invermay TAS Esk QLD Meadow Heights VIC Granville QLD Smithfield - Elizabeth North SA Logan Central QLD

Note: % of all private renter households in the same SA2 only. SA2s with fewer than 50 private renters excluded.

Source: Simulated data based on SIH 2015-16 and 2016 Census

4.3 PAHC amongst home owners Figure 13 shows how widespread poverty after housing costs are for home owners. While the overall proportion and numbers of this group experiencing poverty may be low, the prevalence of those who do is widely spread across rural and remote Australia. While housing costs for these home owners may be low, their incomes, often limited by pension payments, are also correspondingly low (see Table 2). At this scale, the metropolitan regions appear less impacted by poverty for home owners. This observation is reflected in the list of those SA2s with the highest PAHC rates. As Table 12 illustrates, remote areas, especially those in the Northern Territory, are heavily represented in the locations which accommodate the highest proportions of this tenure group in poverty once housing costs are accounted. Many of these locations have a high proportion of Aboriginal residents, which may help explain the incidence of PAHC in these cases. Others in the list illustrate contrasting areas where home owner poverty is high – for example rural locations in Victoria and Tasmania as well inner urban locations. These become more apparent in the online maps where concentration of home owner poverty can be seen in the middle and outer suburbs of the larger metro areas. Some of the PAHC experienced among this group may be explained by service charges in strata units. 39 Table 12: Top 20 SA2s by percentage of outright owner households in poverty after housing costs 2015-16

SA2 State/Territory Tiwi Islands NT Melbourne VIC French Island VIC Sandover – Plenty NT Elsey NT Northern Peninsula QLD West Arnhem NT Carlton VIC Tanami NT Coober Pedy SA Wacol QLD Yuendumu – Anmatjere NT Mowbray TAS Walgett - Lightning Ridge NSW Victoria River NT Barkly NT Gulf NT Daly NT Kowanyama – Pormpuraaw QLD Inverell Region – East NSW

Note: % of all outright owner households in same SA2 only. SA2s with fewer than 50 home owners excluded.

Source: Simulated data based on SIH 2015-16 and 2016 Census

40 Poverty, Property and Place Figure 13: Poverty after housing costs by SA2 areas, home buyers, Australia, 2015-16 41 Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 14: Poverty after housing costs by SA2 areas, private renters, Australia, 2015-16 42 Poverty, Property andPlace

Note: Percentage of households in same SA2 of same tenure Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 15: Poverty after housing costs by SA2 areas, home owners, Australia, 2015-16 43 5. The impact of housing costs on poverty rates

One way of identifying the impact housing costs have on poverty is by contrasting before and after housing cost poverty rates. Where poverty increases most when housing costs are factored in will identify those places where housing makes a real difference in aggravating poverty levels. Figure 16 shows the percentage point change when the level of poverty of each SA2 across Australia before and after housing costs is compared while Figures 17 to 24 present data from the eight state and territory capitals. Tables 13 and 14 list the 20 SA2s with the largest positive and negative changes in before and after housing cost poverty rates respectively. In Figure 15, areas in yellow, orange and red are SA2s where poverty levels increased once housing costs were considered. In these areas it is the high cost of housing compared to incomes that compound the prevalence of income poverty. A swathe of rural and urban hinterland areas running down south east Australia, and around Adelaide and Perth recorded moderate increases in poverty rates on this measure. But the locations where poverty levels increased significantly after housing costs were accounted for (6 percentage points or more) are all in or adjacent to metropolitan and major regional centres. They include both older middle metro suburbs as well as more peripheral satellite and outer suburb locations. The impact of higher housing costs on poverty rates is therefore largely felt in the metropolitan areas and their immediate hinterlands. Table 13: Twenty SA2 areas with the largest positive percentage point change between before and after housing costs poverty, Australia, 2015-16

SA2 State/Territory Melbourne VIC Southport - North QLD Pimpama QLD Carlton VIC Biggera Waters QLD Surfers Paradise QLD Labrador QLD Merrimac QLD Chullora NSW Lakemba NSW Wiley Park NSW Fairfield NSW Beenleigh QLD Liverpool NSW Coomera QLD

44 Poverty, Property and Place Caboolture - South QLD St Lucia QLD Bankstown - North NSW Caloundra - Kings Beach QLD Coolangatta QLD

Source: Simulated data based on SIH 2015-16 and 2016 Census Perhaps the standout from Table 13 is the number of SA2s recording significant increases in poverty levels once housing costs are considered in a distinct cluster of eleven suburbs from Caboolture north of Brisbane through the Gold Coast to northern NSW. The inner areas of Melbourne (Melbourne and Carlton) and solid cluster in the middle suburbs of Sydney (Chullora, Lakemba, Wiley Park, Fairfield, Liverpool and Bankstown) also stand out – places with some of the highest prices and rents relative to incomes in the country. Areas in green in Figure 16 denote places where poverty levels decreased after housing costs were considered. This can occur when housing costs are lower than average.13 As Table 14 confirms, these areas are mainly in regional and remote areas where housing costs may be low, but income is very low. Table 14: Twenty SA2 areas with the largest positive percentage point change between before and after housing costs poverty, Australia, 2015-16

SA2 State/Territory Yuendumu – Anmatjere NT West Arnhem NT Sandover - Plenty NT Gulf NT East Arnhem NT Halls Creek WA Barkly NT Tanami NT APY Lands SA Tiwi Islands NT Victoria River NT Elsey NT Kowanyama - Pormpuraaw QLD Aurukun QLD Far West NSW East Pilbara WA

13 The PAHC poverty line is normally lower than the PBHC line. To decide if a household is in poverty before housing costs, their household disposable income is compared with the average before housing (BH) cost poverty line (set at half the median of household disposable income, adjusted for family size). Similarly, to decide whether a household is in poverty after housing costs, their residual income (i.e. income minus housing costs) is compared with the average after housing (AH) costs poverty line (set at half the median of disposable income minus housing costs). Consider someone whose income is just below the BH line, but who has zero housing costs. They are poor on a BH basis, but may not be poor on an AH basis because the AH poverty line is lower than the BH line. So while overall, the AH poverty rate is higher than the BH rate for most households, our definition means that those household types with housing costs that are low or zero will tend to have a lower AH poverty rate. For example, the elderly often have a lower AH than BH rate because many are outright owners. Similarly, people in remote areas have relatively low housing costs, and so there will be many people who are in poverty on a BH basis, but not in poverty on an AH basis. Of course this does not mean that they are not experiencing financial stress or deprivation 45 Outback SA Croydon - Etheridge QLD Derby - West Kimberley WA Daly NT

Source: Simulated data based on SIH 2015-16 and 2016 Census

46 Poverty, Property and Place Figure 16: Percentage point change in poverty before and after housing costs, Australia, 2015-16 47 Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 17: Percentage point change in poverty before and after housing costs, Sydney capital city area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

48 Poverty, Property and Place Figure 18: Percentage point change in poverty before and after housing costs, Melbourne capital city area, 2015-16 49 Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 19 Percentage point change in poverty before and after housing costs, Brisbane capital city area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

50 Poverty, Property and Place Figure 20 Percentage point change in poverty before and after housing costs, Adelaide capital city area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

51 Figure 21 Percentage point change in poverty before and after housing costs, Perth capital city area, 2015-16

Source: Simulated data based on SIH 2015-16 and 2016 Census

52 Poverty, Property and Place Figure 22 Percentage point change in poverty before and after housing costs, Hobart capital city area, 2015-16 53 Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 23 Percentage point change in poverty before and after housing costs, Darwin metropolitan area, 2015-16 54 Poverty, Property andPlace

Source: Simulated data based on SIH 2015-16 and 2016 Census Figure 24 Percentage point change in poverty before and after housing costs, Canberra metropolitan area, 2015-16

55 Source: Simulated data based on SIH 2015-16 and 2016 Census 56 Poverty, Property and Place 6. Conclusions

This report aimed to do two things. The first was to establish the impact that housing costs have in compounding income poverty in Australia. The second was to show how the resulting after-housing-cost poverty is distributed across Australia. We have used data from the 2015-16 Survey of Income and Housing (SIH) and the 2016 Census to extrapolate rates of poverty after housing costs (PAHC) to small Census statistical areas (SA2s). As well, the role of housing tenure was also explored in more detail, reflecting the very different costs different tenures place on households with a focus on the private housing market. Overall, one in eight (13.2%) Australian households surveyed in the 2015-16 SIH were estimated to be in poverty when housing costs were deducted from their incomes—equivalent to 1.215 million households (or 3.05 million people). The problem of widespread poverty across much of remote Australia was quite clear from the geographical analysis. In these areas, low incomes alone were the most likely driver of the experiences of poverty. But there were also concentrations of PAHC in a range of other areas: regional towns, coastal hinterlands, and middle and outer suburbs in our main metropolitan areas, usually concentrated into clusters of Census statistical areas as used in our analysis. In other words, poverty is a widespread phenomenon, with no one area or region not represented. The report has also explored the impact housing tenure has on poverty rates. The rates of PAHC varied significantly between housing tenures, with almost half (49%) of public renters experiencing PAHC compared to a fifth (21%) of private renters, one in ten (9%) home buyers and one in twelve (8%) home owners. That almost half a million private renters are estimated to be in poverty after their housing costs are accounted for could be considered a significant policy failure. Similarly, it is perhaps surprising that quarter of a million home buyers struggle to get their incomes above the poverty line after they have paid their housing costs, especially when it is remembered that they have all been assessed at some point as being capable of repaying a mortgage. Even one in twelve outright home owners are so poor they must be considered to be in poverty, despite living in a home that costs relatively little. And it is surely shocking that half of all public renters live in poverty in Australia when they occupy some of the most affordable homes in the country. The trends are not encouraging. While before housing cost poverty was seen to fall in the last decade, when housing costs are factored in poverty rates did not shift. Escalating housing costs—sale prices and rents—during the recent housing boom completely wiped out the positive gains in income poverty due to pension and labour market participation increases. These figures suggest a major failure of Australia’s housing market to provide homes that many households can actually afford to live in without falling into poverty. Locations where these poverty rates are high are also instructive. As we noted above, no region escapes its concentrations of households with high rates of PAHC. Perhaps the extent of remote area PAHC comes as no surprise: the failure to address poverty in these communities is well known. Similarly,

57 the concentration of poverty among public housing tenants is also well documented. But elsewhere, the wash of after housing poverty across many regional areas is less commonly appreciated. Low house prices and rents here are no real compensation for the low incomes many households in these areas live on. Pressure on housing costs in lifestyle locations along our coasts and in attractive rural locations— the so-called ‘sea-change’ and ‘tree-change’ communities—push many low-income households in these areas into poverty. And our major metropolitan regions all have their PAHC concentrations. Some of these are extensive, for example in Melbourne and Sydney. Low-income households in middle and outer suburbs face metropolitan housing costs, both for renters and owners. When we analysed the impact housing costs had on before and after housing cost poverty rates, the places where housing costs impose the greatest burden on household incomes were exposed. While these generally confirm the overall analyses, several areas with the largest pre- and post-housing cost poverty increases stand out spectacularly: for example, a band of suburbs stretching from the Sunshine Coast through the Gold Coast into northern NSW and the middle suburbs of Sydney. But this analysis also highlighted the areas where poverty is simply a function of lack of income, where housing costs play little part: many of the most remote communities fall into this group.14 The geography of poverty and the locational factors—in the form of varying housing costs—that drive variations in poverty are well depicted here. While income will always be the fundamental determinant of poverty (and addressing inadequate income support rates must be a priority in the COVID 19 economic recovery), the role our housing system plays in aggravating poverty for those on low incomes is quite evident from this analysis. Australia’s housing system is clearly causing significant financial harm for many Australian households. It is surely not beyond the wit of our policy makers to devise a housing system that does not work to push people into greater poverty. The current COVID-19 crisis, which has placed the country into a war-time footing, has exposed the fragility of the Australian housing market. Both private renters in the so-called labour market ‘precariat’ and laid off or furloughed home buyers face a very uncertain future. Household debt levels were already at historic levels before the crisis struck (Price, Beckers & La Cava 2019; Yates 2011). The development industry, private landlords and the lenders are clearly concerned (MBA 2020). This crisis offers Australian policy makers the perfect opportunity to look to the long term and instigate reforms that will make the housing system part of the solution to the crisis, not part of the cause. The answers are well known, but largely unheeded (Pawson et al. 2019). A national program of building affordable homes at scale is one. Australia did this to great effect after the Second World War when the country faced an acute housing crisis. We could do it again, supported by the newly minted National Housing Finance Investment Corporation. Adjusting the Commonwealth Rent Assistance system to better reflect real rental costs and regional rent and cost disparities is another. And integrated reform of land and housing taxation across all levels of Government, as presaged in the Henry Review a decade ago (2010), is surely also due for sensible and constructive revisiting.

14 An analysis of the adequacy of and condition of housing in remote communities is beyond the scope of this report, but over-crowding and poor quality dwellings are known to be widespread.

58 Poverty, Property and Place As this report shows, different tenures and places will require different policy mixes to at least make housing a positive help to low income households rather than another headache—there is no ‘one club’ solution. Moreover, the impact of housing related poverty only adds to the drag on the collective economic productive capacity of the country through the chronic impacts on domestic spending as a result of escalating rent and mortgage payments (Maclennan et al. 2019). For the households under consideration here, high housing costs mean even less income to spend on essentials: food, energy, clothes, schooling and health, amongst others. If for no other reason than to boost our economic viability in the post-COVID-19 rebuilding, a national housing policy to address housing related poverty is clearly a priority.

59 Appendices

Appendix 1: Previous research A number of recent studies have examined the issue of poverty in relation to housing affordability. These studies often have specific focus, such as by particular population cohorts, tenure, or within a specific geographic context (e.g. in a particular city). The ACOSS/UNSW Sydney Poverty in Australia report series (e.g. Davidson et al. 2018 and 2020), for example, examined this issue in detail at the national level. Saunders (2017) discussed the associations between housing costs, income poverty and inequality in Australia, like Stephens and van Steen’s (2011) discussion in the contexts of England and the Netherlands, Soaita Gibb and Maclennan (2019) in Scotland, and Social Housing Commission (2018; 2019) and Clarke (2019) in the UK. All note the income remaining after taking housing costs into account as important factors in determining a household’s likelihood of falling into poverty. Stephens and Leishman (2017) took it further by analysing how housing-related poverty changed over time and found strong links with households’ housing pathways (which also differ by tenure), partly an outcome of people moving out of deprived housing conditions (living in crowded situations, in poor quality homes). In contrast, Chomik et al. (2018), Reynolds et al. (2018) and Smith and Hetherington (2016) all focussed on older people. Both Chomik et al. (2018) and Smith and Hetherington (2016) discussed the level of the age pension and its effects on poverty among older Australians, highlighting lone private renters as having a higher propensity to experiencing poverty with housing costs considered. Through a qualitative discussion, Reynolds et al. (2018) reflected on the situations of older women and housing insecurity as an outcome of unaffordability and discrimination. There are similar findings from the UK (Rahman 2019) and more recent studies in Australia (Dawson & Jordan 2020; Leishman & Baker 2019; Ong & Wood 2019), reflecting our changed pathways to retirement (Banks et al. 2010; Boldrin García-Gómez & Jiménez-Martín 2010; Warran 2015) and a continued shift to asset-based welfare (de Vaus et al. 2007) that has led to growing incidences of older people who do not own their homes experiencing poverty. There is more international evidence of the impact of rent and tenure insecurity of private rental on incidences of poverty. For example, Haffner et al. (2014) investigated this in the European Union context through analysis of the Survey of Income and Living Conditions dataset, while Rhodes and Rugg (2018) compared incidences of before and after housing poverty in the English context, similar to Marks’ (2007) earlier discussion in the Australian context, and more recent Australian evidence on tenure security and longer- term household wellbeing (Pawson et al. 2017). The insecurity of tenure, at times worsened by financial precarity as a result of means-tested benefits and sanctioning, was noted as particularly critical. Spatially, SGS Economics and Planning’s postcode level Rental Affordability Index (with National Shelter, Community Sector Banking and the Brotherhood of St Laurence) is updated twice yearly and utilises the 30/40 rule to show rental affordability relative to

60 Poverty, Property and Place city-wide median household income. The PAHC discussed in this report offers a nuanced analysis of the impacts of housing affordability on households. It extends the methodology developed in the Poverty in Australia report series by having a more localised view (relating housing costs to incomes at the local scale) to showcase its spatiality and impacts across different tenure cohorts.

Appendix 2. An Explanatory Note on Terms and Research Methods What is after-housing poverty (or poverty after housing costs - PAHC)? After-housing poverty estimates the number of people living in poverty after estimated housing costs have been removed from disposable income. The reason for this is that housing is the largest fixed cost for most households, and those households with lower housing costs can afford a higher standard of living than those on the same income with higher housing costs. Although housing is to some extent an individual choice, it is often driven by other factors such as location and affordability. The poverty line used for this report is the internationally recognised 50% of national median income poverty line, adjusted for family size using the modified OECD equivalence scale. The new, more comprehensive definition of income from the ABS is used for the 2015-16 estimates (including the small area estimates), and the older income definition for the trend estimates. How do we calculate after-housing poverty rates by location? When constructing the analysis, several methods of estimating after-housing poverty rates at a local level for this report were considered. It was decided that spatial microsimulation methodology should be used as it was both flexible and suitable for the data that was available. What data is used for spatial microsimulation? This methodology uses the following data: • Data on household before tax income (in bands), housing costs and household structure taken from the 2016 Census for Statistical Areas Level 2 (SA2); and data on the number of income support recipients in SA2s, • Combined with national-level income data in the 2015-16 Income Survey. The estimates use the Census and income support information to show the variation between regions, and the income survey data to assign disposable income from the income bands in the Census. Where else has this approach been used? The spatial microsimulation approach has previously been used by other Australian researchers in small area estimation applications, particularly the National Centre for Social and Economic Modelling (NATSEM) at the University of Canberra. (Tanton et al. 2011) The approach used in this report is broadly similar to that used by NATSEM. However, there are some differences in the method, and a different set of calibration variables have been used in order to focus on the estimation of small area after-housing poverty. 61 How does this method work? This method uses the calibration approach to statistical estimation. This approach is used in national surveys such as those conducted by the ABS. Ideally, the group of people surveyed provide a miniature of the entire population. However, this is rarely the case. Some groups can be over- represented while others are under-represented. Survey weighting is used to make sure the total of those surveyed will match known information about the population. It is used, for example, when lower survey responses are collected among certain population groups such as young adults. The spatial microsimulation approach uses the same methods. However, instead of calibrating the weights in a national survey to match national population information, the weights are calibrated to make sure that they match the characteristics of each small area. So, a new set of weights or values is constructed for each small area, in order to estimate the characteristics available in the national income survey database. Key requirements There are three main requirements of this method: 1. The calibration variables must be available and comparable in both the national survey data (in this case the ABS Survey of Income and Housing or SIH) and in the small area calibration data (in this case from the Census and income support data). 2. The calibration variables must be strongly correlated with the output variables that we are interested in. For instance, if the only calibration variable was the number of people in different age groups in the small area, reweighting by this would provide a good estimate of small area age distributions, but not of small area income distributions. 3. The calibration variables must not be inconsistent with each other. This can be a problem if one calibration table has missing data, or when calibration data has been randomly adjusted to ensure privacy. For this calculation we use the generalised regression approach developed by Singh and Mohl (1995) and implemented by the ABS in their GREGWT macro.15 (Bell, 2009) This approach adjusts the weights to satisfy all the calibration tables while at the same time being as close to the original weights as possible and without having any negative weights. Providing the assumptions described are met, there are still computational challenges with this reweighting strategy. If the calibration process is very detailed (many cells and/or many tables) it will require some cases in the income survey to have very high weights and others to have zero weights. In the limit, if a combination of characteristics is required in the reweighted data, but this does not exist in the income survey data, the required weight goes to infinity and the estimation process will not converge. For our preferred set of calibration variables, we do find failed convergence for a minority of regions. For these, we then use a simpler calibration approach using fewer variables. Detailed calibration information Our main calibration method uses these tables, which are created for each

15 We use the default GREGWT methods and parameters. 62 Poverty, Property and Place SA2 region in Australia (although they exclude regions with fewer than 250 residents, or 50 residents for the tenure-specific tables). Calibration tables

Data source Calibration tables (P=Person count, H=Household count) ABS Estimated Resident • Estimated resident population of region, June Population (ERP) 2016 (P) estimates16

DSS payment data. • Income support payment received, 6 categories: Payments by 2011 SA217 Age Pension, DSP, Newstart, Parenting Payment (single or partnered), Youth Allowance, Other or none. Last category derived by subtraction from ERP estimate (P).

ABS Census 2016, • Household family composition, 5 categories BY Tablebuilder. Population: Household equivalent income, 10 categories (H) Occupied Private BY Paying rent (yes/no) BY Paying Mortgage Dwellings (yes/no) • Mortgage repayments, 7 categories (H). • Rent payments, 7 categories (H). • Age, 5 categories (P). Calculation details There are several challenges when combining the income survey, Census, and income support data. 1. The income survey scope is for people in private dwellings only. We correspondingly restrict the population for the Census data to people in occupied private dwellings only. 2. Very remote regions are excluded from the income survey scope. The reweighting approach allows us to use Census data for the very remote areas to obtain estimates for all Australia (except very low population locations). However, when we compare the national Census data with the Income Survey data for our alignment adjustment (see below) we exclude the very remote regions. 3. The income survey data coverage is for the usual residents in the dwelling, while the Census data relates to the people in the household on census night. In addition, the SIH top-codes the size of large households. 4. Where people do not answer questions in the income survey, this information has been imputed. In the Census, only age and sex are imputed if missing. We assume that the Census data is missing at random. This is

16 ERP by SA2 and above (ASGS 2016), 2001 onwards. Downloaded: 1 August 2019 from stat.data.abs.gov.au 17 File: 2016-2018 dss-payments-by-2011-statistical-area-2.csv downloaded from https://data.gov.au/data/ dataset/89b93a02-5bff-412a-a842-4ae45a208805. Adjusted to 2016 SA2 geography using ABS concordance table CG_SA2_2016_SA2_2011.xls.

63 likely to be inaccurate as larger households are more likely to have missing data for household income, in particular. 5. The Census variable for equivalent household income was calculated by the ABS by setting personal income to an estimated median of the income range based on income survey data, then aggregating across the household, then grouping back into ranges. We replicate this procedure to produce our grouped income survey household income table. We assume that the 2016 Census income refers to the same time period as the 2015-16 income survey. (The actual Census question is expressed in both weekly and annual terms). 6. Most important, the Census data is self-reported, while the income survey is conducted by trained interviewers, often with many questions used to calculate a single data item such as gross income. Bradbury (2017) finds a good fit for personal income between the two sources, but in general, we would expect to find a higher data quality in the income survey. The net result of these differences is that we cannot expect to find a perfect match between the distribution of the calibration variables in the SIH and the Census or administrative data – even at the whole Australia level. To address this, the first stage of the calculation is an alignment adjustment, where the calibration table is adjusted to match the SIH distribution. This means that the national-level calibration table is compared with the corresponding table from the SIH. The difference between the two distributions at the national level is then subtracted from the distribution table for each small region.18 This approach assumes that the distribution of the variable in the SIH is the best indicator of the variable. It also ensures that, when the calibration is applied at the national level, it produces weights which are identical to those in the source SIH data. We use the ABS Estimated Resident Population as the target number of persons for each region.19 The target number of households is estimated by dividing by an estimate of persons per household in the region, and then applied to calibration tables calculated after the alignment adjustment to obtain calibration targets for the number of people and households with the given characteristics. These calibration targets are then applied using the ABS GREGWT code, which iterates across the SIH weights, adjusting the weights so that they add to the required targets. In 70% of population-weighted SA2 regions, this was achieved using the combination of the tables above. If this was unsuccessful, the family structure variable was moved to a separate table where it did not interact with income and tenure (16% of regions). For the remainder of the regions, the main payment table was removed (10%) and then the tables were entered individually for the remaining regions (4%). None of this last group were included in the ‘top 20’ tenure-specific lists because interaction with tenure is not specifically modelled for these regions. The output from this calculation is a weight vector across the cases in the SIH for each region. Estimates such as poverty rates are then calculated for each region using the region-specific weight vector. 18 In some regions this leads to negative population fractions for some values of the calibration variables – particularly the housing cost variables with many categories. In these cases, the population fraction is set to zero and the difference carried up to the next highest category. 19 The ABS reports the distribution of household size. We assume an average of 6.48 persons per households for households of size 6 or more (based on more detailed tables). 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