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Note: Excludes pilot survey participants.

Table 14 Note: Excludes pilot survey participants.

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Overcrowded accommodation is on the rise Homelessness services user data suggest that recent increases in overall homelessness have been substantially driven by rising numbers of (primarily) women in need of accommodation or other help due to domestic violence, due to the ‘housing crisis’ or due to ‘housing affordability stress’.
Overcrowded accommodation is on the rise Homelessness services user data suggest that recent increases in overall homelessness have been substantially driven by rising numbers of (primarily) women in need of accommodation or other help due to domestic violence, due to the ‘housing crisis’ or due to ‘housing affordability stress’.
Figure 1.1 Recent change in homelessness services expenditure and social housing capital investment - indexed Source: Productivity Commission Report on Government Services 2018. Programs associated with the 2008 policy impetus such as A Place to Call Home and Street to Home successfully enabled many people to exit homelessness and achieve sustainable affordable housing. Latterly, though, efforts to achieve lasting progress have been hampered by the disappearance of clear policy ambition at the national level — as embodied in the lack of response to calls for federal government re-commitment to the Rudd homelessness reduction target or something like it (O'Neill 2013; Cooper 2015).
Figure 1.1 Recent change in homelessness services expenditure and social housing capital investment - indexed Source: Productivity Commission Report on Government Services 2018. Programs associated with the 2008 policy impetus such as A Place to Call Home and Street to Home successfully enabled many people to exit homelessness and achieve sustainable affordable housing. Latterly, though, efforts to achieve lasting progress have been hampered by the disappearance of clear policy ambition at the national level — as embodied in the lack of response to calls for federal government re-commitment to the Rudd homelessness reduction target or something like it (O'Neill 2013; Cooper 2015).
Policy inaction on housing affordability is responsible for increasing homelessness.
Policy inaction on housing affordability is responsible for increasing homelessness.
Figure 2.1: Measures of economic performance 2005-17 Source: ABS Catalogue no 5206.0 Australian National Accounts: National Income, Expenditure and Product. Table 1 Key National Accounts Aggregates. GDP: Gross Domestic Product Series ID A2302459A. GDP-PC: Gross Domestic Product Per Capita Series ID A2302460K. Real GDI: Real Gross Domestic Income, Series ID A2302463T (2014-15 Prices). Real NNDI- PC Real Net National Disposable Income Per Capita Series ID A2302466xX (2014-15 Prices). [Accessed 17/08/2017]
Figure 2.1: Measures of economic performance 2005-17 Source: ABS Catalogue no 5206.0 Australian National Accounts: National Income, Expenditure and Product. Table 1 Key National Accounts Aggregates. GDP: Gross Domestic Product Series ID A2302459A. GDP-PC: Gross Domestic Product Per Capita Series ID A2302460K. Real GDI: Real Gross Domestic Income, Series ID A2302463T (2014-15 Prices). Real NNDI- PC Real Net National Disposable Income Per Capita Series ID A2302466xX (2014-15 Prices). [Accessed 17/08/2017]
Source: ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original September 2017. Employment to Population Ratio Persons Trend Series ID A84423138V Labour Force Participation Rate Persons Trend Series ID A84423135L The economy is the major source of income for most people but it is also the engine that drives the job opportunities that provide access to the incomes that affect household material living standards.‘ It is therefore important to look beyond changes in overall economic growth to also examine how the labour market is performing. The key indicators here are the labour force participation rate and the employment to population ratio (shown in Figure 2.2) and the unemployment and long-term rates and numbers affected (Figures 2.3 and 2.4).
Source: ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original September 2017. Employment to Population Ratio Persons Trend Series ID A84423138V Labour Force Participation Rate Persons Trend Series ID A84423135L The economy is the major source of income for most people but it is also the engine that drives the job opportunities that provide access to the incomes that affect household material living standards.‘ It is therefore important to look beyond changes in overall economic growth to also examine how the labour market is performing. The key indicators here are the labour force participation rate and the employment to population ratio (shown in Figure 2.2) and the unemployment and long-term rates and numbers affected (Figures 2.3 and 2.4).
Figure 2.3: Unemployment rates, 2005-17 Sources: ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original. Unemployment Rate: Trend Series IDA84423134K [Accessed 14/11/17]; and 6291.0.55.001 Labour Force, Australia, Detailed - Electronic Delivery Table 14b. Unemployed persons by Duration of job search and Sex - Trend Seasonally adjusted and Original Series ID - A83903912R: the long-term unemployment rate is calculated as the number of persons unemployed for 52 weeks or more divided by the number of persons in the labour force for that period. ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original. Trend series ID, A84423131C [Accessed 15/11/17]
Figure 2.3: Unemployment rates, 2005-17 Sources: ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original. Unemployment Rate: Trend Series IDA84423134K [Accessed 14/11/17]; and 6291.0.55.001 Labour Force, Australia, Detailed - Electronic Delivery Table 14b. Unemployed persons by Duration of job search and Sex - Trend Seasonally adjusted and Original Series ID - A83903912R: the long-term unemployment rate is calculated as the number of persons unemployed for 52 weeks or more divided by the number of persons in the labour force for that period. ABS Catalogue No. 6202.0 Labour Force Australia Table 1 Labour Force Status by Sex Australia, Trend, Seasonally adjusted and Original. Trend series ID, A84423131C [Accessed 15/11/17]
Table 2.1: Gross labour market flows between May and June 2017 (thousands of persons) Source: ABS, The Labour Force Australia June Quarter 2017 (Catalogue 6202.0): Table 17
Table 2.1: Gross labour market flows between May and June 2017 (thousands of persons) Source: ABS, The Labour Force Australia June Quarter 2017 (Catalogue 6202.0): Table 17
Figure 2.4: Numbers of jobless families and dependents in jobless families, 2005-16 Source: Series 1 ABS Catalogue No. 6224.0.55.001 Labour Force, Australia: Labour Force Status and Other Characteristics of Families, June 2012, Table 4. Series 2: ABS Catalogue No. 6224.0.55.001 Labour Force, Australia: Labour Force Status and Other Characteristics of Families, June 2016, Table 4.1.
Figure 2.4: Numbers of jobless families and dependents in jobless families, 2005-16 Source: Series 1 ABS Catalogue No. 6224.0.55.001 Labour Force, Australia: Labour Force Status and Other Characteristics of Families, June 2012, Table 4. Series 2: ABS Catalogue No. 6224.0.55.001 Labour Force, Australia: Labour Force Status and Other Characteristics of Families, June 2016, Table 4.1.
Figure 2.5: Young people (aged 15-24 years) not attending full-time education and unemployed or not in the labour force, 2005-17 (thousands) Source: ABS catalogue no. 6202.0 Labour Force, Australia July 2017, Table 15 Labour force status for 15-24 year olds by educational attendance (full-time) and Sex. Persons Sum of series ID A84424348K and A84424350W. [Accessed on 31 August 2017] Trendline based on 12 period moving average. Figure 2.5 shows trends in the numbers of young people (aged 15-24 years) who are not attending full-time education and are either unemployed or not in the labour force®. The overall trend in NEET numbers has been upwards, although this mainly reflects a rise following the GFC [between 2008-09) that has not been reversed. In June 2017, there were around 334,000 young people in this category, divided equally between males and females, which is still above the 2010 level. Not included in Figure 2.5, but of significance for the key findings of this report, is the disproportionate rate at which Indigenous young people were not in employment, education and training. The 2018 Closing the Gap Report shows that 42% of Indigenous 15-24 year olds were not in education, employment, or training (Commonwealth of Australia 2018). This rate is higher than for non- Indigenous Australians, and these rates of exclusion ‘deteriorated somewhat over the past decade’ (Commonwealth of Australia 2018: 80). Throughout the report we make reference to the links between the disproportionate rates of Indigenous homelessness and other key areas of Indigenous exclusion. We present the disproportionate rates of Indigenous disadvantage to substantiate the assertion in Chapters 4 and 5 that disproportionate rates of Indigenous homelessness are inseparable to their high rates of unemployment, exclusion from education and training, mandated engagement with the child protection system, family violence, and engagement with the criminal justice system, including incarceration. Reflecting the model of causation adopted in this report, the high rates of Indigenous disadvantage interact with the housing market and drive the disproportionate extent to which Indigenous people experience homelessness in Australia compared to non-Indigenous Australians.
Figure 2.5: Young people (aged 15-24 years) not attending full-time education and unemployed or not in the labour force, 2005-17 (thousands) Source: ABS catalogue no. 6202.0 Labour Force, Australia July 2017, Table 15 Labour force status for 15-24 year olds by educational attendance (full-time) and Sex. Persons Sum of series ID A84424348K and A84424350W. [Accessed on 31 August 2017] Trendline based on 12 period moving average. Figure 2.5 shows trends in the numbers of young people (aged 15-24 years) who are not attending full-time education and are either unemployed or not in the labour force®. The overall trend in NEET numbers has been upwards, although this mainly reflects a rise following the GFC [between 2008-09) that has not been reversed. In June 2017, there were around 334,000 young people in this category, divided equally between males and females, which is still above the 2010 level. Not included in Figure 2.5, but of significance for the key findings of this report, is the disproportionate rate at which Indigenous young people were not in employment, education and training. The 2018 Closing the Gap Report shows that 42% of Indigenous 15-24 year olds were not in education, employment, or training (Commonwealth of Australia 2018). This rate is higher than for non- Indigenous Australians, and these rates of exclusion ‘deteriorated somewhat over the past decade’ (Commonwealth of Australia 2018: 80). Throughout the report we make reference to the links between the disproportionate rates of Indigenous homelessness and other key areas of Indigenous exclusion. We present the disproportionate rates of Indigenous disadvantage to substantiate the assertion in Chapters 4 and 5 that disproportionate rates of Indigenous homelessness are inseparable to their high rates of unemployment, exclusion from education and training, mandated engagement with the child protection system, family violence, and engagement with the criminal justice system, including incarceration. Reflecting the model of causation adopted in this report, the high rates of Indigenous disadvantage interact with the housing market and drive the disproportionate extent to which Indigenous people experience homelessness in Australia compared to non-Indigenous Australians.
Figure 2.6: Trends in poverty after housing costs, 2003-04 to 2013-14 (persons) Highlighting the role that housing costs play in income poverty estimates, Table 2.2 compares poverty before housing costs (BHC) and after housing costs (AHC)"°. It is clear that housing costs are an important contributor to poverty, leading to an increase in the overall poverty rate of 3.3 percentage points, to 13.3%. The increase is particularly stark for children, indicating that housing cost is a major issue for many families [in contrast to many older people who own their homes outright and thus incur relatively low housing costs). The numbers in poverty indicate that housing costs cause an additional 613,000 people to fall below the poverty line, of whom almost 229,000 are children.
Figure 2.6: Trends in poverty after housing costs, 2003-04 to 2013-14 (persons) Highlighting the role that housing costs play in income poverty estimates, Table 2.2 compares poverty before housing costs (BHC) and after housing costs (AHC)"°. It is clear that housing costs are an important contributor to poverty, leading to an increase in the overall poverty rate of 3.3 percentage points, to 13.3%. The increase is particularly stark for children, indicating that housing cost is a major issue for many families [in contrast to many older people who own their homes outright and thus incur relatively low housing costs). The numbers in poverty indicate that housing costs cause an additional 613,000 people to fall below the poverty line, of whom almost 229,000 are children.
Source: SPRC poverty estimates, prepared for ACOSS; see text.
Source: SPRC poverty estimates, prepared for ACOSS; see text.
Source: ACOSS (2016) Table 4, supplemented by SPRC calculations Notes:1. Percentages of individuals in households below poverty lines are set at 50% of median BHC and AHC income, 2007-08 basis. 2. Identified groups are not mutually exclusive.
Source: ACOSS (2016) Table 4, supplemented by SPRC calculations Notes:1. Percentages of individuals in households below poverty lines are set at 50% of median BHC and AHC income, 2007-08 basis. 2. Identified groups are not mutually exclusive.
Source: HILDA survey as reported in Saunders and Wilkins (2016) Tables 8.2 and 8.3. Most of the groups with a high incidence of material deprivation also experience above-average poverty rates (see Appendix 1). However, the overlap between income poverty and material deprivation varies. For example, 8% of those who experience no deprivation are in relative (income) poverty, while 79% of those deprived of at least two items are not in income poverty. This evidence indicates that poverty and deprivation are different and suggests that both should be taken into account when assessing people's overall status and, in the current context, determining whether or not they are at risk of homelessness.
Source: HILDA survey as reported in Saunders and Wilkins (2016) Tables 8.2 and 8.3. Most of the groups with a high incidence of material deprivation also experience above-average poverty rates (see Appendix 1). However, the overlap between income poverty and material deprivation varies. For example, 8% of those who experience no deprivation are in relative (income) poverty, while 79% of those deprived of at least two items are not in income poverty. This evidence indicates that poverty and deprivation are different and suggests that both should be taken into account when assessing people's overall status and, in the current context, determining whether or not they are at risk of homelessness.
Figure 2.9: Households with very low levels of gross income and net wealth 2003-04-2015-16 Source: ABS, Household Income and Wealth, Australia, 2015-16, Catalogue No. 6523.0: Tables 1.3 & 2.3. Notes from ABS: (a) Estimates presented for 2007-08 onwards are not directly comparable with estimates for previous cycles due to the improvements made to measuring income introduced in the 2007-08 cycle. Estimates for 2003-04 and 2005-06 have been recompiled to reflect the new measures of income, however not all components introduced in 2007-08 are available for earlier cycles.
Figure 2.9: Households with very low levels of gross income and net wealth 2003-04-2015-16 Source: ABS, Household Income and Wealth, Australia, 2015-16, Catalogue No. 6523.0: Tables 1.3 & 2.3. Notes from ABS: (a) Estimates presented for 2007-08 onwards are not directly comparable with estimates for previous cycles due to the improvements made to measuring income introduced in the 2007-08 cycle. Estimates for 2003-04 and 2005-06 have been recompiled to reflect the new measures of income, however not all components introduced in 2007-08 are available for earlier cycles.
Figure 2.10: Number of recipients of selected income support payments, 2005 -16 Source: 2005-13 DSS (2013) Statistical Paper No 12 Income support customers: a statistical overview 2013. Table 1: Summary of income support recipients by payment type, 2003 to 2013. DSS Demographics June 2014, June 2015, and June 2016 from totals column in table: payment recipients by payment type, by state and territory and by sex. Accessed [21/08/17].
Figure 2.10: Number of recipients of selected income support payments, 2005 -16 Source: 2005-13 DSS (2013) Statistical Paper No 12 Income support customers: a statistical overview 2013. Table 1: Summary of income support recipients by payment type, 2003 to 2013. DSS Demographics June 2014, June 2015, and June 2016 from totals column in table: payment recipients by payment type, by state and territory and by sex. Accessed [21/08/17].
Figure 2.11: Jobseekers not reported for non-compliance in previous 12 months, 2010-17 Source: Job seeker compliance data, Department of Employment, data.gov.au website. Table: “Number of Compliance Reports Submitted per job seeker over past 12 months [as at end of quarter)”. Non-compliance reports include Participation Reports, Non-attendance Reports and Provider Appointment Reports.
Figure 2.11: Jobseekers not reported for non-compliance in previous 12 months, 2010-17 Source: Job seeker compliance data, Department of Employment, data.gov.au website. Table: “Number of Compliance Reports Submitted per job seeker over past 12 months [as at end of quarter)”. Non-compliance reports include Participation Reports, Non-attendance Reports and Provider Appointment Reports.
Figure 2.12: Number of quarterly payment suspensions, 2012-17 Source: Job seeker compliance data, Department of Employment, data.gov.au website. March 2012 to March 2017. Table: Income support payment suspensions for non-attendance at appointments/ activities. Notes: Shift from Job Services Australia to jobactive on 1 July, 2015. Numbers refer to suspensions rather than persons. In March 2012: ‘This table includes all participation payment suspensions as a result of non-attendance at JSA and DES provider appointments applied under the new compliance arrangements introduced from 1 July, 2011’. In March 2017: ‘This table is a count of all participation payment suspensions applied as a result of providers submitting: (1) non-attendance reports for non-attendance at a provider appointment and (2) participation reports for non-attendance at a third-party appointment (such as an initial appointment with a Work for the Dole host or Skills for Employment and Education provider) or disengagement from an activity. Where an income support payment suspension has started it will continue until it is lifted, which is usually when a job seeker attends the appointment.
Figure 2.12: Number of quarterly payment suspensions, 2012-17 Source: Job seeker compliance data, Department of Employment, data.gov.au website. March 2012 to March 2017. Table: Income support payment suspensions for non-attendance at appointments/ activities. Notes: Shift from Job Services Australia to jobactive on 1 July, 2015. Numbers refer to suspensions rather than persons. In March 2012: ‘This table includes all participation payment suspensions as a result of non-attendance at JSA and DES provider appointments applied under the new compliance arrangements introduced from 1 July, 2011’. In March 2017: ‘This table is a count of all participation payment suspensions applied as a result of providers submitting: (1) non-attendance reports for non-attendance at a provider appointment and (2) participation reports for non-attendance at a third-party appointment (such as an initial appointment with a Work for the Dole host or Skills for Employment and Education provider) or disengagement from an activity. Where an income support payment suspension has started it will continue until it is lifted, which is usually when a job seeker attends the appointment.
Figure 2.13: Number of short-term financial penalties, serious failures and unemployment non-payment periods (UNPPs), 2009-17 Source: Job Seeker Compliance Data, Department of Employment, data.gov.au website: September 2009 to March 2017. Tables: Sanctions for serious failures and financial penalties, connection failures, income support payment suspensions and CCAs by gender. Non-payment periods are the author's calculation of the total NPPs minus serious failures where the financial penalty is waived for compliance activity or financial hardship. Note: ‘Serious failures’ are for refusing to accept or commence a Suitable job, and for persistent non-compliance following a Comprehensive Compliance Assessment. Unemployment non-payment periods (UNPPs) are for voluntarily leaving employment or unemployment due to misconduct and are usually for 8 weeks, but may be 12 weeks if a person has received relocation assistance. Short-term financial penalties are for reconnection and no show no pay penalties and the length depends on the delay in attending required appointments or activities. Changes to compliance requirements and penalty framework occurred in July 2009, July 2011, January 2015, July 2015. There was a shift from Job Network to Job Services Australia on July 2009 then to jobactive on 1 July, 2015. The numbers refer to penalties rather than persons.
Figure 2.13: Number of short-term financial penalties, serious failures and unemployment non-payment periods (UNPPs), 2009-17 Source: Job Seeker Compliance Data, Department of Employment, data.gov.au website: September 2009 to March 2017. Tables: Sanctions for serious failures and financial penalties, connection failures, income support payment suspensions and CCAs by gender. Non-payment periods are the author's calculation of the total NPPs minus serious failures where the financial penalty is waived for compliance activity or financial hardship. Note: ‘Serious failures’ are for refusing to accept or commence a Suitable job, and for persistent non-compliance following a Comprehensive Compliance Assessment. Unemployment non-payment periods (UNPPs) are for voluntarily leaving employment or unemployment due to misconduct and are usually for 8 weeks, but may be 12 weeks if a person has received relocation assistance. Short-term financial penalties are for reconnection and no show no pay penalties and the length depends on the delay in attending required appointments or activities. Changes to compliance requirements and penalty framework occurred in July 2009, July 2011, January 2015, July 2015. There was a shift from Job Network to Job Services Australia on July 2009 then to jobactive on 1 July, 2015. The numbers refer to penalties rather than persons.
Sources: Australian Institute of Health and Welfare (AIHW) (2013). The health of Australia’s prisoners 2012. Cat. no. PHE 170. Canberra: AIHW. Table 2.1 p10, 26, 28. Australian Institute of Health and Welfare (AIHW) (2015). The health of Australia’s prisoners 2015. Cat. no. PHE 207. Canberra: AIHW. Table 2.1 p, 12, 26, 29. All other things being equal, rising prisoner numbers mean rising rates of prisoner discharge. AIHW data confirms such a trend, as shown in Table 2.3. These statistics reveal that 31% of the 51,000 prisoners released in 2014 expected to be homeless on release. While this was a lower percentage than the equivalent figure for 2012, the rising number of released prisoners means the likely number of homeless ex-prisoners was also rising over this period.
Sources: Australian Institute of Health and Welfare (AIHW) (2013). The health of Australia’s prisoners 2012. Cat. no. PHE 170. Canberra: AIHW. Table 2.1 p10, 26, 28. Australian Institute of Health and Welfare (AIHW) (2015). The health of Australia’s prisoners 2015. Cat. no. PHE 207. Canberra: AIHW. Table 2.1 p, 12, 26, 29. All other things being equal, rising prisoner numbers mean rising rates of prisoner discharge. AIHW data confirms such a trend, as shown in Table 2.3. These statistics reveal that 31% of the 51,000 prisoners released in 2014 expected to be homeless on release. While this was a lower percentage than the equivalent figure for 2012, the rising number of released prisoners means the likely number of homeless ex-prisoners was also rising over this period.
Figure 2.14: Children aged 15-17 discharged from Out of Home Care, 2005-06 to 2015-16 Sources: Australian Institute of Health and Welfare publications Child Protection Australia 2005-6 to 2015- 16. Note: Data for 2006-7 are missing numbers for Queensland so are below the true national figure.
Figure 2.14: Children aged 15-17 discharged from Out of Home Care, 2005-06 to 2015-16 Sources: Australian Institute of Health and Welfare publications Child Protection Australia 2005-6 to 2015- 16. Note: Data for 2006-7 are missing numbers for Queensland so are below the true national figure.
Sources: NSW Bureau of Crime Statistics Analysis and Research (2017); Victoria Crime Statistics Agency (2017).
Sources: NSW Bureau of Crime Statistics Analysis and Research (2017); Victoria Crime Statistics Agency (2017).
(a) Australia-wide (eight capital cities, weighted average]
(a) Australia-wide (eight capital cities, weighted average]
Source: ABS 2017e: Table 1. (b}] Selected capital cities tt should also be acknowledged that the variation in house price trends shown in Figure 2.16(b) will have been equally marked across regional Australia — especially in terms of the contrast between some coastal resort areas (for example in northern NSW and south-east Queensland) and some inland settlements which have experienced a p00m and then a slump associated with the vicissitudes of the mining industry.
Source: ABS 2017e: Table 1. (b}] Selected capital cities tt should also be acknowledged that the variation in house price trends shown in Figure 2.16(b) will have been equally marked across regional Australia — especially in terms of the contrast between some coastal resort areas (for example in northern NSW and south-east Queensland) and some inland settlements which have experienced a p00m and then a slump associated with the vicissitudes of the mining industry.
Sources: 1. Victoria State Government Rental Report series http://www.dhs.vic.gov.au/about- the-department/documents-and-resources/research,-data-and-statistics/current-rental- report. 2. ABS Cat 6401 Consumer Prices Index http://www.abs.gov.au/AUSSTATS/abs@.nsf/ DetailsPage/6401.0Mar%202017?0penDocument Figure 2.17: Private rent trends, 2011-17 - Victoria
Sources: 1. Victoria State Government Rental Report series http://www.dhs.vic.gov.au/about- the-department/documents-and-resources/research,-data-and-statistics/current-rental- report. 2. ABS Cat 6401 Consumer Prices Index http://www.abs.gov.au/AUSSTATS/abs@.nsf/ DetailsPage/6401.0Mar%202017?0penDocument Figure 2.17: Private rent trends, 2011-17 - Victoria
Figure 2.18: Private rent trends, 2011-17 - New South Wales Sources: 1. NSW Govt Rent and Sales Report series http://www.housing.nsw.gov.au/about-us/ reports-plans-and-papers/rent-and-sales-reports. 2. ABS Cat 6401 Consumer Prices Index http:// www.abs.gov.au/AUSSTATS/abs(@.nsf/DetailsPage/6401.0Mar%202017?0penDocument Note: Data relates to rents for all two-bed properties with newly lodged rental bonds in the March quarter of each year.
Figure 2.18: Private rent trends, 2011-17 - New South Wales Sources: 1. NSW Govt Rent and Sales Report series http://www.housing.nsw.gov.au/about-us/ reports-plans-and-papers/rent-and-sales-reports. 2. ABS Cat 6401 Consumer Prices Index http:// www.abs.gov.au/AUSSTATS/abs(@.nsf/DetailsPage/6401.0Mar%202017?0penDocument Note: Data relates to rents for all two-bed properties with newly lodged rental bonds in the March quarter of each year.
Figure 2.19: Rents as a percentage of incomes, 2011 and 2016 Source: ABS Census 2011 and 2016 - statistics extracted via ABS Quickstats portal. Note: Median weekly rents as shown here incorporate both private sector and social sector tenancies.
Figure 2.19: Rents as a percentage of incomes, 2011 and 2016 Source: ABS Census 2011 and 2016 - statistics extracted via ABS Quickstats portal. Note: Median weekly rents as shown here incorporate both private sector and social sector tenancies.
Figure 2.20: Private rental vacancy rates, selected capital cities, 2012-1¢ Source: Productivity Commission Report on Government Services 2017 Table G.A10 (original data from Real Estate Institute of Australia).
Figure 2.20: Private rental vacancy rates, selected capital cities, 2012-1¢ Source: Productivity Commission Report on Government Services 2017 Table G.A10 (original data from Real Estate Institute of Australia).
Source: ABS Survey of Housing Occupancy and Costs, Cat 4130.0
Source: ABS Survey of Housing Occupancy and Costs, Cat 4130.0
Table 2.4: Private market rents in Greater Sydney: relationship between quartile and median rents Sources: 2006 and 2016 — NSW Family and Community Services special tabulation; 2017 NSW Family and Community Services Rent and Sales Report.
Table 2.4: Private market rents in Greater Sydney: relationship between quartile and median rents Sources: 2006 and 2016 — NSW Family and Community Services special tabulation; 2017 NSW Family and Community Services Rent and Sales Report.
Figure 3.1: Thinking about the past five years, would you say there have been any significant changes to the welfare benefits system and/or Centrelink practice that have impacted on homelessness? Source: Online survey of homelessness service provider agencies (N=70). In elaborating the survey responses summarised in Figure 3.1, research informants highlighted three specific changes that had negatively affected homelessness in recent years:
Figure 3.1: Thinking about the past five years, would you say there have been any significant changes to the welfare benefits system and/or Centrelink practice that have impacted on homelessness? Source: Online survey of homelessness service provider agencies (N=70). In elaborating the survey responses summarised in Figure 3.1, research informants highlighted three specific changes that had negatively affected homelessness in recent years:
Figure 3.2: State and territory recent policy reforms and initiatives perceived as having a bearing on homelessness Source: Online survey of homelessness service provider agencies. (N=70)
Figure 3.2: State and territory recent policy reforms and initiatives perceived as having a bearing on homelessness Source: Online survey of homelessness service provider agencies. (N=70)
Figure 3.3: Relative change in population and social housing provision, 2012-17 Sources: Productivity Commission Report on Government Services 2018 (Table 18A.3); ABS Cat 3101.0.
Figure 3.3: Relative change in population and social housing provision, 2012-17 Sources: Productivity Commission Report on Government Services 2018 (Table 18A.3); ABS Cat 3101.0.
Domestic and family violence has been a pathway into homelessness for many women and children. .. very often those women wont stay in the home long term... for a range of reasons. If there is a mortgage on the home she won't be able to afford it ... What we are finding that [Safe at Home] is really great for, she might stay at home for six months or a year, and that buys her time. She can be supported through brokerage programs to cover costs. But it buys her time to work through what to do next in life ... This is very different from having to flee in the middle of the night. (Victorian stakeholder) nitiatives that require the perpetrator of violence to eave the home while providing safety measures to keep victim survivors in the house are also playing a role in preventing homelessness (although potentially not or the perpetrator, see below). These Safe at Home measures were described by research participants as significant, not only for averting an immediate entry into homelessness for victim survivors needing to escape violence, but also in enabling women to think through and organise their next moves:
Domestic and family violence has been a pathway into homelessness for many women and children. .. very often those women wont stay in the home long term... for a range of reasons. If there is a mortgage on the home she won't be able to afford it ... What we are finding that [Safe at Home] is really great for, she might stay at home for six months or a year, and that buys her time. She can be supported through brokerage programs to cover costs. But it buys her time to work through what to do next in life ... This is very different from having to flee in the middle of the night. (Victorian stakeholder) nitiatives that require the perpetrator of violence to eave the home while providing safety measures to keep victim survivors in the house are also playing a role in preventing homelessness (although potentially not or the perpetrator, see below). These Safe at Home measures were described by research participants as significant, not only for averting an immediate entry into homelessness for victim survivors needing to escape violence, but also in enabling women to think through and organise their next moves:
Source: ABS Census. Notes: 1. ABS category: ‘Persons living in improvised dwellings, tents, or sleeping out’ treated as proxy for ‘rough sleepers’. 2. ACT excluded on account of small absolute numbers - albeit a large percentage increase over the period: from 28 to 54 people. Figure 5.1: Change in enumerated rough sleepers, 2011-16
Source: ABS Census. Notes: 1. ABS category: ‘Persons living in improvised dwellings, tents, or sleeping out’ treated as proxy for ‘rough sleepers’. 2. ACT excluded on account of small absolute numbers - albeit a large percentage increase over the period: from 28 to 54 people. Figure 5.1: Change in enumerated rough sleepers, 2011-16
Figure 5.2: Incidence of recent rough sleeping among assisted service users - indexed trend, 2012-17 *including use of ‘non-conventional accommodation’ Note: ‘recent rough sleeper’ figures adjusted for non-response on relevant question Source: AIHW. Notes: 1. ‘Recent rough sleeper’ figures adjusted for non-response on relevant question. 2. 2011-12 data unpublished
Figure 5.2: Incidence of recent rough sleeping among assisted service users - indexed trend, 2012-17 *including use of ‘non-conventional accommodation’ Note: ‘recent rough sleeper’ figures adjusted for non-response on relevant question Source: AIHW. Notes: 1. ‘Recent rough sleeper’ figures adjusted for non-response on relevant question. 2. 2011-12 data unpublished
Figure 5.3: Changing incidence of rough sleeping in selected cities, 2011-18: street count data Source: collated by authors from municipal street count statistics. Notes: 1. Sydney data collated from August street counts. 2. Melbourne data collected via two-yearly counts — intermediate years interpolated. 3. Adelaide data from August/September counts, 2015 interpolated. 4. No 2017 count ir Melbourne; no August/September 2017 count in Adelaide. 5. Parramatta figure for 2017 interpolated
Figure 5.3: Changing incidence of rough sleeping in selected cities, 2011-18: street count data Source: collated by authors from municipal street count statistics. Notes: 1. Sydney data collated from August street counts. 2. Melbourne data collected via two-yearly counts — intermediate years interpolated. 3. Adelaide data from August/September counts, 2015 interpolated. 4. No 2017 count ir Melbourne; no August/September 2017 count in Adelaide. 5. Parramatta figure for 2017 interpolated
Source: ABS Census. Notes: 1. ABS category: ‘Persons living in improvised dwellings, tents, or sleeping out’ treated as proxy for ‘rough sleepers’; 2. Indigenous and non-Indigenous numbers adjusted to account to differential non-response in 2011 and 2016 - non-respondents allocatec pro rata to identified Indigenous and non-Indigenous persons for each year.
Source: ABS Census. Notes: 1. ABS category: ‘Persons living in improvised dwellings, tents, or sleeping out’ treated as proxy for ‘rough sleepers’; 2. Indigenous and non-Indigenous numbers adjusted to account to differential non-response in 2011 and 2016 - non-respondents allocatec pro rata to identified Indigenous and non-Indigenous persons for each year.
Sources: AIHW Specialist Homelessness Services statistics and ABS Census As indicated by both sets of statistics, AIHW and ABS, NSW saw the most rapid increase in homelessness in the period 2011-16 — by 42% and 37%, respectively. To a certain extent, this reflects the population growth in that state. However, even taking that into account, the NSW ABS-calibrated homelessness rate per 10,000 population rose by 27% which is far above the comparable figures recorded in every other jurisdiction (ABS 2018a).
Sources: AIHW Specialist Homelessness Services statistics and ABS Census As indicated by both sets of statistics, AIHW and ABS, NSW saw the most rapid increase in homelessness in the period 2011-16 — by 42% and 37%, respectively. To a certain extent, this reflects the population growth in that state. However, even taking that into account, the NSW ABS-calibrated homelessness rate per 10,000 population rose by 27% which is far above the comparable figures recorded in every other jurisdiction (ABS 2018a).
Figure 5.7: ABS Census homelessness operational groups: Percentage change 2011-16
Figure 5.7: ABS Census homelessness operational groups: Percentage change 2011-16
Figure 5.6: ABS Census homelessness operational groups: Australia-wic numbers 2011 and 2016
Figure 5.6: ABS Census homelessness operational groups: Australia-wic numbers 2011 and 2016
5.4.3 Marginal housing ‘Other overcrowding’ is one of three categories identified by ABS as equating to ‘marginal housing’. The other two involve people living in: In the five years to 2016, the ‘other crowded’ category expanded by 33% (see Figure 5.8), and over the past decade by 88% (59% even after allowing for population increase). It must be emphasised that even ‘other crowding’ as defined here represents what, for most Australians, would be considered extreme deprivation of personal residential space. Placing all of the above within a broader setting, ABS survey data shows that households living in homes with a shortage of at least one bedroom increased in number from 3.2% of the overall household population in 2009-10 to 3.6% in 2015-16 (Housing Occupancy and Costs - ABS Catalogue no. 4130)
5.4.3 Marginal housing ‘Other overcrowding’ is one of three categories identified by ABS as equating to ‘marginal housing’. The other two involve people living in: In the five years to 2016, the ‘other crowded’ category expanded by 33% (see Figure 5.8), and over the past decade by 88% (59% even after allowing for population increase). It must be emphasised that even ‘other crowding’ as defined here represents what, for most Australians, would be considered extreme deprivation of personal residential space. Placing all of the above within a broader setting, ABS survey data shows that households living in homes with a shortage of at least one bedroom increased in number from 3.2% of the overall household population in 2009-10 to 3.6% in 2015-16 (Housing Occupancy and Costs - ABS Catalogue no. 4130)
Source: ABS Census. Note: Indigenous and non-Indigenous numbers adjusted to account to differential non-response in 2011 and 2016 - non-respondents allocated pro rata to identified Indigenous and non-Indigenous persons for each year.
Source: ABS Census. Note: Indigenous and non-Indigenous numbers adjusted to account to differential non-response in 2011 and 2016 - non-respondents allocated pro rata to identified Indigenous and non-Indigenous persons for each year.
Figure 5.10: Indexed trend in Indigenous versus non-Indigenous service users assisted, 2011-17 Source: AIHW Specialist Homelessness Services statistics. Drilling down further into the AIHW data it can be seen that the larger proportionate increase in the Indigenous caseload enumerated at the national scale was recorded in all jurisdictions other than Tasmania (see Figure 5.11). In NSW, for example, logged Indigenous service users rose by 80% while non- Indigenous numbers grew by only 43%. In Victoria the respective figures were 62% and 35%.
Figure 5.10: Indexed trend in Indigenous versus non-Indigenous service users assisted, 2011-17 Source: AIHW Specialist Homelessness Services statistics. Drilling down further into the AIHW data it can be seen that the larger proportionate increase in the Indigenous caseload enumerated at the national scale was recorded in all jurisdictions other than Tasmania (see Figure 5.11). In NSW, for example, logged Indigenous service users rose by 80% while non- Indigenous numbers grew by only 43%. In Victoria the respective figures were 62% and 35%.
Figure 5.11: Change in service user caseloads 2011-17: breakdown by Indigenous/non-Indigenous status Source: AIHW Specialist Homelessness Services statistics.
Figure 5.11: Change in service user caseloads 2011-17: breakdown by Indigenous/non-Indigenous status Source: AIHW Specialist Homelessness Services statistics.
Table 5.1: Spatial framework for census analysis of changing homelessnes geography
Table 5.1: Spatial framework for census analysis of changing homelessnes geography
Figure 5.12: Homelessness change 2006-2016 - breakdown by age cohort 5.5 The changing spatial pattern of homelessness
Figure 5.12: Homelessness change 2006-2016 - breakdown by age cohort 5.5 The changing spatial pattern of homelessness
Figure 5.13: Change in the incidence of homelessness 2011-16 by settlement type Source: Based on ABS Census data.
Figure 5.13: Change in the incidence of homelessness 2011-16 by settlement type Source: Based on ABS Census data.
Source: Research team calculations based on ABS Census data. Table 5.2: Percentage change in the incidence of homelessness 2011-16 by settlement type and jurisdiction
Source: Research team calculations based on ABS Census data. Table 5.2: Percentage change in the incidence of homelessness 2011-16 by settlement type and jurisdiction
Source: Research team calculations based on ABS Census data. At the capital city level, Sydney saw the sharpest increase in homelessness in the five years to 2016. As shown in Figure 5.14, Sydney saw a numerical increase of 48% over this period, more than three times the national increase. Although part of this can be attributed to population growth, a 34% increase remains even when this is taken into account. Nevertheless, the rate of homelessness per 10,000 population remains far higher in Darwin than in any other capital city, see Figure 5.15.
Source: Research team calculations based on ABS Census data. At the capital city level, Sydney saw the sharpest increase in homelessness in the five years to 2016. As shown in Figure 5.14, Sydney saw a numerical increase of 48% over this period, more than three times the national increase. Although part of this can be attributed to population growth, a 34% increase remains even when this is taken into account. Nevertheless, the rate of homelessness per 10,000 population remains far higher in Darwin than in any other capital city, see Figure 5.15.
Figure 5.15: Changing rates of capital city homelessness, 2011-16 Source: Research team calculations based on ABS Census data.
Figure 5.15: Changing rates of capital city homelessness, 2011-16 Source: Research team calculations based on ABS Census data.
Source: AIHW Specialist Homelessness Services statistics. Figure 5.16: Assisted homelessness service users in 2016-17: number with identified ‘associated issues’ The number of homelessness service users subject to domestic violence has been rising at a rate substantially above the general rate of service user increase. As shown in Figure 5.17, while the overall SHS caseload expanded by 13% in the two years to 2016-17, the number reporting domestic violence increased by 24%. Growth rates well above the cohort-wide norm were also recorded over this period in relation to older people and to people with mental ill health. Notably, the number of service users logged as ‘young people’ remained virtually static over this period, at around 40,000 applicants annually (see Figure 5.16).
Source: AIHW Specialist Homelessness Services statistics. Figure 5.16: Assisted homelessness service users in 2016-17: number with identified ‘associated issues’ The number of homelessness service users subject to domestic violence has been rising at a rate substantially above the general rate of service user increase. As shown in Figure 5.17, while the overall SHS caseload expanded by 13% in the two years to 2016-17, the number reporting domestic violence increased by 24%. Growth rates well above the cohort-wide norm were also recorded over this period in relation to older people and to people with mental ill health. Notably, the number of service users logged as ‘young people’ remained virtually static over this period, at around 40,000 applicants annually (see Figure 5.16).
Source: AIHW Specialist Homelessness Services statistics. A related variable within the AIHW dataset is the ‘main [stated] reason for seeking assistance’. In 2016-17 domestic violence once again looms large here, with more than a quarter of all service users citing this issue (Figure 5.18). At the same time, comparing Figure 5.16 with Figure 5.18 it is implicit that for a significant number of service users reporting domestic violence as a contributory factor, it was not the ‘main reason’ that prompted them to seek help.
Source: AIHW Specialist Homelessness Services statistics. A related variable within the AIHW dataset is the ‘main [stated] reason for seeking assistance’. In 2016-17 domestic violence once again looms large here, with more than a quarter of all service users citing this issue (Figure 5.18). At the same time, comparing Figure 5.16 with Figure 5.18 it is implicit that for a significant number of service users reporting domestic violence as a contributory factor, it was not the ‘main reason’ that prompted them to seek help.
Source: AIHW Specialist Homelessness Services statistics
Source: AIHW Specialist Homelessness Services statistics
Figure 5.19: Assisted service users - main reason for seeking assistance: percentage change 2014-15 - 2016-17 Source: AIHW Specialist Homelessness Services statistics.
Figure 5.19: Assisted service users - main reason for seeking assistance: percentage change 2014-15 - 2016-17 Source: AIHW Specialist Homelessness Services statistics.
Appendix 1: Poverty rates before and after housing costs 2013-14 Note: 1. Table shows percentages of individuals in households below poverty lines set at 50% of median BHC and AHC income, 2007-08 basis*' 2. An asterisk (*) indicates that the estimates are based on a small sample size and are unreliable. Source: ACOSS (2016: Table 4) supplemented by tables prepared by SPRC. 31 It should be noted that the groups are identified by a single characteristic of the HRP, but there will be many instances where the identified groups overlap (for example, a single household may be identified as being a sole parent family, unemployed, a public renter and in receipt of a specific social security payment and will thus appear multiple times in the estimates).
Appendix 1: Poverty rates before and after housing costs 2013-14 Note: 1. Table shows percentages of individuals in households below poverty lines set at 50% of median BHC and AHC income, 2007-08 basis*' 2. An asterisk (*) indicates that the estimates are based on a small sample size and are unreliable. Source: ACOSS (2016: Table 4) supplemented by tables prepared by SPRC. 31 It should be noted that the groups are identified by a single characteristic of the HRP, but there will be many instances where the identified groups overlap (for example, a single household may be identified as being a sole parent family, unemployed, a public renter and in receipt of a specific social security payment and will thus appear multiple times in the estimates).
Australian Homelessness Monitor Online Survey of Homelessness Service Providers Appendix 2: Online survey report
Australian Homelessness Monitor Online Survey of Homelessness Service Providers Appendix 2: Online survey report
Table A2.1: Survey coverage and response rate Table A2.2: Distribution of responding organisations compared with national population
Table A2.1: Survey coverage and response rate Table A2.2: Distribution of responding organisations compared with national population
In any survey of this kind consideration must be given to the risk that ‘national’ results could be distorted by a geographically unbalanced cohort of respondents, thus giving rise to a need to ‘weight’ the raw data. However, although survey response rates varied across the country, the representation of respondents in each jurisdiction/grouping relative to total population was contained in a fairly narrow band, see Table A2.2 Col 4. Proceeding from this observation it was decided that there was no strong case for weighting the raw data.
In any survey of this kind consideration must be given to the risk that ‘national’ results could be distorted by a geographically unbalanced cohort of respondents, thus giving rise to a need to ‘weight’ the raw data. However, although survey response rates varied across the country, the representation of respondents in each jurisdiction/grouping relative to total population was contained in a fairly narrow band, see Table A2.2 Col 4. Proceeding from this observation it was decided that there was no strong case for weighting the raw data.
*Organisations could indicate as many area types as applicable. **Not including the four pilot organisations who were not asked the relevant question. Table A2.3 - Responding organisations spatial coverage by area type
*Organisations could indicate as many area types as applicable. **Not including the four pilot organisations who were not asked the relevant question. Table A2.3 - Responding organisations spatial coverage by area type
*Organisations could indicate as many area types as applicable. Table A2.4: Responding organisations by homelessness service type
*Organisations could indicate as many area types as applicable. Table A2.4: Responding organisations by homelessness service type
*Organisations could indicate as many area types as applicable. Table A2.5 - Responding organisations by homelessness service user grout
*Organisations could indicate as many area types as applicable. Table A2.5 - Responding organisations by homelessness service user grout
(a) Breakdown by jurisdiction (%) (b) Breakdown by responding organisation's service coverage (%] Table A2.6: Perceived change in homelessness over past 5 years
(a) Breakdown by jurisdiction (%) (b) Breakdown by responding organisation's service coverage (%] Table A2.6: Perceived change in homelessness over past 5 years
Note: Excludes pilot survey participants and the one organisation reporting that its functions did not include helping people find suitable housing. Table A2.7 - Ease of finding suitable accommodation - change over past five years (%)
Note: Excludes pilot survey participants and the one organisation reporting that its functions did not include helping people find suitable housing. Table A2.7 - Ease of finding suitable accommodation - change over past five years (%)
Problematic developments In terms of problematic social security developments, the most commonly cited issue was the growing rental affordability stress due to inadequate indexation of social security payment rates. As experienced across the country, the past few years have seen benefits failing to keep pace with rent inflation.
Problematic developments In terms of problematic social security developments, the most commonly cited issue was the growing rental affordability stress due to inadequate indexation of social security payment rates. As experienced across the country, the past few years have seen benefits failing to keep pace with rent inflation.
32 Although in a few instances it was clear from their answers to the follow-up question that respondents were mistakenly citing ‘negative’ policy change initiated by the Australian Government Figure A2.2: State and territory recent policy reforms and initiatives perceived as having a bearing on homelessness (N=70)
32 Although in a few instances it was clear from their answers to the follow-up question that respondents were mistakenly citing ‘negative’ policy change initiated by the Australian Government Figure A2.2: State and territory recent policy reforms and initiatives perceived as having a bearing on homelessness (N=70)
Related topics:
Homelessness And Housing ExclusionUrban HomelessYouth homelessnessAboriginal homelessness and housingLGBTQ Youth HomelessnessHomeless Person
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