Housing Stress Today: Estimates for Statistical Local...

National Centre for Social and Economic Modelling

• University of Canberra •

Housing Stress Today: Estimates for Statistical Local Areas in 2005

Ben Phillips, Shih-Foong Chin and Ann Harding

Australian Consortium for Social and Political Research Incorporated Conference, Sydney,

10-13 December 2006

About NATSEM

The National Centre for Social and Economic Modelling was established on 1 January 1993, and supports its activities through research grants, commissioned research and longer term contracts

for model maintenance and development with the federal departments of Families, Community Services and Indigenous Affairs, Employment and Workplace Relations, Treasury, and

Education, Science and Training.

NATSEM aims to be a key contributor to social and economic policy debate and analysis by developing models of the highest quality, undertaking independent and impartial research, and

supplying valued consultancy services.

Policy changes often have to be made without sufficient information about either the current environment or the

consequences of change. NATSEM specialises in analysing data and producing models so that decision makers have the best

possible quantitative information on which to base their decisions.

NATSEM has an international reputation as a centre of excellence for analysing microdata and constructing microsimulation

models. Such data and models commence with the records of real (but unidentifiable) Australians. Analysis typically begins by looking at either the characteristics or the impact of a policy

change on an individual household, building up to the bigger picture by looking at many individual cases through the use of

large datasets.

It must be emphasised that NATSEM does not have views on policy. All opinions are the authors’ own and are not necessarily

shared by NATSEM.

Director: Ann Harding

© NATSEM, University of Canberra 2006

National Centre for Social and Economic Modelling University of Canberra ACT 2601 Australia

170 Haydon Drive Bruce ACT 2617

Phone + 61 2 6201 2780 Fax + 61 2 6201 2751 Email [email protected] Website www.natsem.canberra.edu.au

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Abstract This paper presents estimates of housing stress for Statistical Local Areas (SLA) in Victoria, Queensland and the ACT in 2005. The estimates were created by synthesising small-area microdata for measuring housing stress. The technique involves the reweighting of a national ABS sample survey to Census benchmarks for each small area at the SLA level. The reweighting process converts the set of national household weights obtained from the sample survey into sets of household weights for small areas (one set per SLA). This paper defines a household in housing stress as being one that is in the bottom 40 per cent of equivalent household disposable income and whose net spending on housing after subtracting any rent assistance received is more than 30 per cent of their income (i.e. a ‘net’ rather than ‘gross’ housing stress measure). Housing stress was found to be more prevalent in the urban areas – especially in the capital cities, followed by other urban centres (especially the fast-growing regions on the eastern seaboard).

About the authors Ben Phillips is a Senior Research Fellow at NATSEM and Dr Shih-Foong Chin was a Senior Research Fellow at NATSEM, both working on regional modelling. Ann Harding is Professor of Applied Economics and Social Policy at the University of Canberra and inaugural Director of NATSEM.

Acknowledgments The earlier stages of this study were part of the ‘Regional Dimensions’ Linkage Project funded by the Australian Research Council (Project No LP0349152), the NSW Premier’s Department, the Queensland Department of Premier and Cabinet, the Queensland Treasury, the ACT Chief Minister’s Department, the Victorian Department of Sustainability and Environment, the Australian Bureau of Statistics, and NATSEM. The authors would like to thank these partners for their support. The authors would also like to gratefully acknowledge the technical support provided by our international partner investigator – Dr Paul Williamson from the University of Liverpool (UK) - and the efforts of the many past and present NATSEM staff who have contributed to this project (including Anthea Bill, Marcus Blake, Susan Day, Anthony King, Stephen Leicester, Rachel Lloyd, Justine McNamara, Tony Melhuish, Ben Phillips, and Elizabeth Taylor).The final stages of this study, including the production of the SLA level estimates, the preparation of this paper and its presentation at the ACSPRI conference, were funded under an

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ARC Discovery Grant (DP664429 – ‘Opportunity and Disadvantage: Differences in Wellbeing Among Australia’s Adults and Children at a Small Area Level’).

General caveat NATSEM research findings are generally based on estimated characteristics of the population. Such estimates are usually derived from the application of microsimulation modelling techniques to microdata based on sample surveys.

These estimates may be different from the actual characteristics of the population because of sampling and nonsampling errors in the microdata and because of the assumptions underlying the modelling techniques.

The microdata do not contain any information that enables identification of the individuals or families to which they refer.

Keywords

spatial microsimulation, housing affordability: JEL categories – simulation methods; personal income, wealth and their distribution; government expenditures and welfare programs; public administration; general regional economics

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Contents Abstract iii

About the authors iii

Acknowledgments iii

General caveat iv

Keywords iv

1 Introduction 1

2 Housing stress 2 2.1 Overview 2 2.2 Definitions 3

3 Methodology 4 3.1 Overview 4 3.2 Create small-area estimates 6 3.3 Validation 8

4 Results 9 4.1 Extent of housing stress 9 4.2 Where is housing stress located? 10 4.3 Mapping housing stress 15

5 Conclusion 21

Appendix - Benchmark classes used in reweighting 23

References 26

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1 Introduction Housing affordability is a major public policy concern, as many Australians are finding housing increasingly less affordable to rent or buy. However, almost all policy debates on housing affordability to date have been confined to the national or state level (Wood et al. 2005, Harding et al. 2004). This is simply because the requisite small-area data to measure housing affordability have not previously been readily available. This paper shows how this data gap may be filled by the use of a novel, spatial microsimulation technique developed by NATSEM. The technique synthesises small-area housing affordability data, which can then be used in the construction of spatial microsimulation models for supporting policy decisions. NATSEM has also developed a similar model in conjunction with the Australian Housing and Urban Research Institute (AHURI). This model (Housemod) has a strong emphasis on projections and regional microsimulation and has been described in Kelly et al (2005). Housemod is reliant on the 2004 tax and social security system. The modelling used in this research is for 2005 and incorporates a number of significant revisions especially to the rates of taxation faced by individuals that have occurred since 20041.

This report presents the simulated results on housing stress for 2005. Earlier preliminary results, for 2001, have been reported by Chin and Harding (2006b) using revised data, and by Taylor et al. (2004) using preliminary data. The 2005 simulation results reported here have incorporated several improvements since the 2004 study was published. Firstly, the benchmarks have been revised so that the estimated household numbers for small-areas now exactly match the census household counts . Secondly, a key input dataset has been substantially upgraded so that the core process known as ‘reweighting’ is now greatly improved. Thirdly, the definition for housing stress has been refined, to consider ‘net’ housing stress. Finally, we have “aged” the 2001 estimates to 2005 levels.

Other related publications describing the methodology of this project include Chin et al. (2006) and Chin and Harding (2006a), while Chin et al. (2005) uses the same spatial microsimulation approach to simulate the regional impact of income tax cuts.

1 Housemod is currently in the process of being updated to more recent survey data and will

also be updated to include recent changes to the tax and social security system.

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2 Housing stress

2.1 Overview

In the literature on housing affordability, ‘housing stress’ is used interchangeably with terms like ‘housing affordability’, ‘affordable housing’, and ‘housing need’. Reporting to the Australian Housing and Urban Research Institute (AHURI), the National Affordability Project defined ‘housing stress‘ as a situation which ‘denotes the negative impacts for households with insufficient income to secure adequate housing …’ (NAHP 2004). Thus, housing affordability is generally measured in terms of housing costs relative to the income of the household, family, or income unit.

There are two broad approaches to measuring housing affordability: the so-called ‘ratio’ and ‘residual’ approaches. The ‘ratio’ approach simply examines what proportion of income is spent on housing. The common benchmark used to denote unaffordability is 30 per cent or more of household income spent on housing. The ‘residual’ approach, however, examines both housing and non-housing costs. It is concerned as much with a household’s capacity to pay for housing as its capacity to maintain an adequate standard of living after housing costs are met. In this study, we adopt the ‘ratio’ approach, with a threshold of 30 per cent. This approach is more widely used and has the advantage of being simple to compute and to interpret.

Within the ‘ratio’ approach, there are still many different variations in the ways the various parameters are measured. On the income side, both gross income and ‘after-income-tax’ disposable income have been used, with or without adjustment for household size (i.e. equivalisation), and including households of all income levels within scope or just those in the bottom two quintiles (i.e. 40 per cent) of the household income distribution. On the cost side, rent and mortgage interest payments are commonly included, but rates, insurance and other maintenance costs may or may not be included. There is also the issue of how housing subsidies – commonly in the form of Commonwealth rent assistance paid to eligible persons renting in the private market – should be treated. Some studies have included them in household income, others have subtracted them from housing costs, while others have them removed from both.

All the above issues have been thoroughly reviewed by Gabriel and Yates (2005). Among their observations, the following were noted by us in particular for the definition of housing stress used in this study:

(1) Use of gross income (instead of disposable income) in calculating housing cost (relative to income) tends to underestimate housing stress; and

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(2) Inclusion of rent assistance in the definition of income tends to overestimate housing stress.

2.2 Definitions

This study defines a household as being in housing stress when the household:

• is in the bottom two quintiles (40 per cent) of equivalent2 disposable household income distribution (in the respective state or territory); and

• has a housing cost (rent plus mortgage minus rent assistance) that represents 30 per cent or more of its disposable income (exclusive of rent assistance) 3.

In the above definition, all inputs were inflated to 2005 dollars. Also, rent assistance refers to the Commonwealth Rent Assistance (CRA) income supplement paid to eligible income support recipients.

Note that in the above definition, CRA has been excluded from the income of a household and has been explicitly taken into account when calculating the housing costs faced by a household. This is referred to in the housing literature as a ‘net’ housing affordability measure. An alternative approach is the so-called ‘gross’ approach, which instead adds CRA to household income. Thus, the ‘net’ approach treats rent assistance as a direct offset to housing costs, whereas the ‘gross’ approach treats it as an addition to income. The ‘gross’ approach has been shown to produce higher estimates of housing stress compared to the ‘net’ approach (Landt and Bray, 1997, p 11).

2 Equivalent income is calculated using the modified OECD equivalence scale, which is widely used nationally and internationally. OECD-equivalent household income is calculated by dividing the total disposable household income (including transfer incomes) by an equivalence factor, which takes account of household size and composition. This factor gives a value of one to the first adult, 0.5 to the second and subsequent adults, and 0.3 to each dependent child (aged 0 – 14 years). A single-person household will have a factor of 1. A couple-family with two children will have a factor of 2.1.

3 This is referred to in the housing literature as a net housing affordability measure (see page 11 in Landt and Bray, 1997).

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3 Methodology

3.1 Overview

This study uses a novel spatial microsimulation technique developed at NATSEM called spatial MSM. It involves the synthesis of small-area microdata and the use of these data to simulate the regional impact of public policies. Increasingly, spatial microsimulation is being used to simulate the regional impact of public policies, to formulate regional-based policies, and to provide a cheaper alternative for generating small-area data to the more expensive option of conducting regional surveys (Ballas et al. 2005).

Microdata generally consist of thousands of records about individual persons, families, or households. They are generally available in the form of a confidentialised unit record file (CURF) from an ABS national survey; from the one percent Household Sample File from the ABS Housing and Population Census; from non-ABS sources such as the Household, Income and Labour Dynamics in Australia (HILDA) Survey (http://www.melbourneinstitute.com/hilda/); or from administrative data (e.g. Thurecht et al. 2003). Microdata usually consist of a dataset of unidentifiable individuals or households with their associated characteristics such as age, sex, marital status, household type, etc. Sample survey microdata often lack a geographical indicator – and if one is present, it is often only at the broad level of the state or territory (in order to protect the privacy of survey respondents). In this sense, national sample surveys typically do not supply the small-area microdata needed for spatial microsimulation. Therefore, small-area microdata need to be synthesised — and the technique used in this study to achieve this is called ‘reweighting’.

When the ABS issues the microdata files from its national sample surveys, it attaches a ‘weight’ to the record of every household within the sample. For example, the weight attached to the first household within this sample file represents the number of comparable households within Australia that the ABS believes are similar to that particular household. These weights are the mechanism used to ‘gross up’ from the sample survey results to estimates for the whole of Australia. In the spatialMSM project, NATSEM has replaced the national weights calculated by the ABS with new small-area weights derived from the Census.

Figure 1 outlines the overall process of spatial microsimulation used in this study. It involves two major steps:

Step one creates household weights for small-areas using the reweighting technique. It involves six sub-steps numbered 1.1 to 1.6 in Figure 1. It culminates in the creation of 2005 household weights, by Statistical Local Area (SLA) (output #2). Step two

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applies the SLA household weights thus created to the selected output variables generated by NATSEM’s STINMOD4 model, to create the 2005 small-area estimates of housing stress for each SLA (output #4). This step involves two sub-steps numbered 2.1 and 2.2 in Figure 1.

In the following discussion, only sub-step 1.6 and sub-steps 2.1 and 2.2 will be discussed in detail. The other sub-steps are discussed in detail in NATSEM Technical Paper no. 33 (Chin and Harding, 2006a).

Figure 1 Overall process for the creation of housing stress estimates, by SLA, for 2005

1.1Select variables for

matching survey andCensus data

1.4Prepare 2001 Census data:

cleansing and balancing

1.3Prepare 1998/99 HES data:

recoding anduprating via STINMOD

1.5Run GREGWT:

reweightingalgorithm

2.1Run STINMOD on 2005

base population

Output #12001 Householdweights, by SLA

Output #42005 estimates of

housing stress, by SLA

Output #3selected 2005 STINMOD

output variables,by household

2.2Apply 2005 household weights

(for each SLA) to 2005STINOMD output variables

Measureconvergence

Determine output requirementsfor small-area estimates

1.2Construct benchmarks

Output #22005 Householdweights, by SLA

1.6Upscale

householdweights

4 NATSEM's static model – STINMOD – simulates the impact of major federal government cash transfers, income tax and the Medicare levy on individuals and families in Australia. The model is used by the Australian Treasury, the Department of Family Community Services and Indigenous Affairs, and other government agencies, in policy formulation. The STINMOD manual, which describes STINMOD and all the government programs simulated within it, is available from www.natsem.canberra.edu.au/products/STINMOD/stinmod%2005b%20user%20guide.pdf.

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3.2 Create small-area estimates Steps 1.1 to 1.5 in Figure 1 depict the steps in developing the 2001 household weights by SLA. A major step forward in this work is the development of a projections facility to the household estimates. In developing estimates for 2005 there are broadly two steps:

1) update the 2001 population to 2005 by up-scaling the weights; and 2) developing the 2005 estimates by combining these weights with the unit

records contained in STINMOD’s 2005 base population. In order to estimate 2005 housing stress, the 2001 synthetic household weights, by SLA, were scaled up to 2005. This was achieved by first calculating the population growth for each SLA between 2001 and 2005, and then applying these growth rates to the 2001 household weights to obtain the 2005 household weights, by SLA. This simple approach assumes that the population growth in a small-area is a reasonable proxy for the growth in household numbers in the same area. The second step in the spatial microsimulation process is the creation of the 2005 estimates of housing stress, by SLA. Two sub-steps are involved:

• Run the STINMOD Model to produce a 2005 ‘base population’ dataset

containing the necessary variables for calculating housing stress (2.1); • Merge the 2005 household weights (by SLA) produced in Step 1 onto the

above dataset, to produce the 2005 estimates of housing stress, by SLA.

These sub-steps, numbered as per Figure 1, are explained below.

• Run STINMOD (2.1)

STINMOD comes in two versions. The standard version is currently based on the ABS Survey of Income and Housing Costs (SIHC) for 1999-00 and 2000-01. The other version is based on the ABS Household Expenditure Survey 1998-99 (HES). This latter version was used in this study. The HES version of STINMOD, like the HES file itself, contains the same 6,892 households.

In this study, STINMOD was run using the following parameters: the ‘base year’ population was as at June 2005; the tax scale was that for 2005-2006 (as announced in the May 2005 Australian Budget and effective from 1 July 2005); private income, rent, mortgage, and rent assistance were uprated to June 2005, using the appropriate uprating factors constructed from official ABS CPI and Average Weekly Earnings figures, as explained in Chin et al. (2006).

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It is important to note that while personal incomes and housing costs have been updated to 2005 levels, this has been done at the state/territory level rather than at the SLA level (because of the lack of trend data for each SLA between 2001 and 2005). For example, we have not been able to update private rental paid in 2001 for a specific SLA to 2005 levels, as we do not have SLA-level information on trends in housing costs between the two years.

• Apply 2005 weights (2.2) The 2005 household weights (by SLA) produced in Step 1 were merged onto the above STINMOD output dataset to produce, for every SLA, a dataset containing the selected output variables and the household weights, by household. For each SLA, the above output variables (by household) are aggregated to SLA totals in the following manner:

• For each categorical output variable (e.g. ‘Labour force status’), the household weights of each of the categories (e.g. ‘Employed’, ‘Unemployed’) are summed to produce the SLA total (e.g. summing all household weights in the SLA for the ‘Employed’); and

• For each numerical output variable (e.g. ‘Taxable income’), the value of the output variable is first multiplied by the household weight and the weighted value is then summed to produce SLA totals.

The end result is a dataset containing the necessary output variables for calculating housing stress, by SLA.

Points to note

The approach outlined above for producing the 2005 housing stress estimates did not capture any other regional changes between 2001 and 2005 apart from population growth within the SLAs as estimated by the ABS. For example, in the intervening years, interstate migration might have led to changes in the characteristics of the households living within small-areas, or jobs growth might have led to a fall in the unemployment rate within a particular SLA, but these types of changes have not been reflected in our estimates.

Accordingly, the 2005 estimates are more meaningful for showing the relative differences between regions, rather than for their absolute values. In other words, the estimates can essentially be thought of as replicating the 2001 patterns of housing stress, with some rudimentary efforts to update them to 2005 in the absence of the SLA-level data that would allow more comprehensive updating.

An iterative algorithm is used to reweight the household weights to the census benchmarks, when the algorithm fails to meet these benchmarks the SLA is said to be

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non-convergent. All results relate only to SLAs that converged. The details of the convergence results can be found in Chin and Harding, 2006b.

3.3 Validation

Validation refers to the comparison of our synthetic estimates of housing stress against similar results obtained from an independent source in order to assess the reliability of the estimates produced. Validation of the 2005 housing stress estimates was not conducted because of the lack of any suitable external data to validate against. However, in a related work (Chin and Harding, 2006b), we reported the validation of our 2001 housing stress estimates against commissioned ABS data. But in this earlier work, in order to compare like-for-like and match the available 2001 census data, the 2001 housing stress measure was defined using gross (instead of disposable) income, and rent assistance was included (instead of excluded) in income. (This was because only gross – not disposable – income was available in the Census and the rent assistance received by a household could not be isolated in the Census.) Note that validations were done for convergent SLAs only (because the results of non-convergent SLAs were unreliable as previously explained). Similar definitional and time differences exist between Housemod and this work, implying that meaningful comparisons/validation would be difficult.

Table 1 (reproduced from Chin and Harding, 2006b) shows the validation results at the state level, for the 2001 housing stress estimates. Overall, our estimates compared very well with the ABS estimates, with less than a one percentage point difference in each of the four states and territory. However, it should be noted that the ABS estimates still could not be expected to precisely match ours because they only included private households which had responded completely to the census questions on household income, whereas our estimates were based on all households in private dwellings. Therefore, the ABS validation data only covered a subset of the entire household population, and were found to have about 12 per cent less households than ours (with ours matching the Census counts exactly).

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Table 1 ABS and NATSEM estimated number of households in housing stressa, 2001

NATSEM estimates ABS estimates Households Households in

housing stress Households Households in

housing stress

Percentage point

difference Sate/

Territory no. (‘000) no. (‘000) % no. (‘000) no. (‘000) % %

VIC 1,663 147 8.8 1,463 119 8.2 0.6 QLD 1,270 147 11.6 1,120 120 10.7 0.9

ACT 109 7 6.8 98 6 6.0 0.8

All three 3,042 296 9.7 2,681 245 9.1 0.6 a Convergent SLAs only Source: Chin and Harding (2006b, p. 14)

At the SLA-level, when the estimated counts of households in stress produced by NATSEM was plotted against the ABS estimates for each of the convergent SLAs, very high correlation (with a correlation coefficient value of over 0.98) was observed in all the four jurisdictions. These and other results have also been reported in Chin and Harding (2006b).

In brief, validation results at both the state/territory level and the SLA level show that the reweighting process created very credible estimates for 2001. On this basis, we then felt able to scale-up the 2001 weights to 2005 and use the new weights to create the 2005 housing stress estimates, with some degree of confidence that the results will be credible.

4 Results

4.1 Extent of housing stress

Housing stress in 2005 for the two Eastern Australian states and one territory included in this study is estimated to be about 7 per cent. The ACT has the lowest proportion (5.5%), followed by Victoria (6.4%) and Queensland (7.8%) (Table 2). The respective shares of households in housing stress across the four jurisdictions closely reflect their respective shares of households. These estimates of housing stress are lower than those found in the earlier research of Chin et al (2006) where the rate of housing stress Australia-wide was 2.8 percentage points higher at 9.8 per cent. There are a number of reasons for the different housing stress estimates between the 2001 estimates of Chin et al (2006) and these estimates. As discussed earlier, the earlier numbers were based on a ‘gross’ measure of housing stress, whereas these estimates are based on the ‘net’ measure. There will also be differences related to the different base years of each estimate.

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Table 2 Households in housing stressa, 2005

Total number of households Households in housing stress State/territory no. (‘000) no. (‘000) %1

Victoria 1,797 115 6.4Queensland 1,373 107 7.8ACT 114 6 5.5All three 3,284 228 6.9a Convergent SLAs only Note:1. Percentage figures derived from pre-rounded data. Source: NATSEM

Table 3 shows that across all the three states and territory, about 37 per cent of all SLAs have a higher proportion of households in housing stress than the respective state or territory average. The figure is highest in Queensland (39%), and lowest in Victoria (34%). To some extent this may be explained by the different geographical classifications in each state. The ACT and Queensland both have relatively small SLAs relative to Victoria. Larger SLAs may tend to average out areas with high and low levels of housing stress. The results may also be driven by most SLAs in Queensland and the ACT being in the capital city, where housing stress levels tend to be higher. Where these SLAs with high rates of housing stress are located will be discussed in Section 4.2, and will be mapped in Section 4.3.

Table 3 SLAs with a higher percentage of households in housing stress than state/territory average, 2005

Convergent SLAs Convergent SLAs with a higher than average proportion of households in stress1 State/territory

no. no. % Victoria 196 66 34 Queensland 441 171 39 ACT 89 32 36 All three 726 269 37

Note: 1. State/territory averages are: 6.4% (VIC), 7.8% (Qld), 5.5% (ACT). Source: NATSEM

4.2 Where is housing stress located?

The top ten SLAs from each of the three states and territory, ranked by the number and proportion of households in housing stress, are listed in Tables 4 to 6. So these tables show where there are numerically a lot of households in housing stress and where the proportion of households in housing stress is high.

The top SLAs are entirely located in the capital cities (recognised by having ‘05’ in the second and third digit of the SLA code) in Victoria (Table 4) and the ACT (Table 6). Outside the capital cities, SLAs with the highest numbers of households in housing stress are mainly located in cities other than the capitals, such as Mackay, Rockhampton, Bundaberg in Queensland and the major population centres up and

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down the coast from Brisbane such as Hervey Bay, Southport, Labrador, and Maroochy (Table 5). To some extent, these results can be explained by the relative size being larger in the larger cities.

In terms of proportions of households in housing stress the picture is quite similar, with most SLAs in housing stress being those located in the capital cities or other large cities.

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Table 4 SLAs with the highest estimated number of households in housing stress in VIC, and their selected profiles, 2005

Households in stress


Top 10 SLAs (number) no. % of SLA Top 10 SLAs (proportion) No. % of SLA

Glen Eira (C) – Caulfield 205652311 2,597 8.4%

Melbourne (C) -

Remainder 205054608 2,188 14.9%

Port Phillip (C) - St Kilda 205055901 2,491 10.8% Port Phillip (C) - St Kilda 205055901 2,491 10.8%

Frankston (C) – West 205852174 2,244 7.5%

Moreland (C) -

Brunswick 205255251 1,814 10.6%

Darebin (C) – Preston 205301892 2,242 7.6%

Stonnington (C) -

Prahran 205056351 2,131 10.4%

Maribyrnong (C) 205104330 2,191 9.3% Yarra (C) - North 205057351 1,721 9.4%

Melbourne (C) - Remainder 205054608 2,188 14.9% Darebin (C) - Northcote 205301891 1,802 9.4%

Stonnington (C) - Prahran 205056351 2,131 10.4% Maribyrnong (C) 205104330 2,191 9.3%

Yarra Ranges (S) - South-West 205607455 2,109 5.4% Gr. Bendigo (C) - Central 235052621 664 8.9%

Moonee Valley (C) - Essendon 205105063 2,106 7.9%

Gr. Dandenong (C) -

Dandenong 205752671 1,693 8.8%

Brimbank (C) - Sunshine 205101182 2,064 7.5%

Boroondara (C) -

Hawthorn 205451113 1,221 8.8%

Vic Average 6.4

Note: 1. PRVR – private renting, PUBR – public renting, SOLE – sole parent household, LONE – lone-person household, GOV – main source of household income from government, NILF – reference person of household not in labour force. Source: NATSEM

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Table 5 SLAs with the highest estimated number of households in housing stress in Queensland, and their selected profiles, 2005




Mackay (C) - Pt A 340054762 1785 7.3% City - Remainder 305051146 229 29.3%

Ipswich (C) - Central 305253962 1625 6.5% Coolangatta 310053527 296 21.1%

Rockhampton (C) 330056350 1542 7.3% St Lucia 305051506 672 20.6%

Bundaberg (C) 315051810 1404 8.2% Surfers Paradise 310053587 761 17.1%

Hervey Bay (C) - Pt A 315073751 1397 8.6% Broadbeach 310053513 208 16.5%

Southport 310053585 1281 13.5% Bilinga 310053512 84 16.4%

Labrador 310053553 1105 15.5% Spring Hill 305051528 158 16.1%

Ipswich (C) - East 305253965 1092 7.3% Cairns (C) - City 350052066 212 16.0%

Maroochy (S) - Buderim 310154902 1056 7.1% South Brisbane 305051525 153 16.0%

Guanaba-Currumbin Valley 310053542 1041 9.2% Kangaroo Point 305051304 424 15.6%

QLD Average 7.8% 7.8%

Note: 1. PRVR – private renting, PUBR – public renting, SOLE – sole parent household, LONE – lone-person household, GOV – main source of household income from government, NILF – reference person of household not in labour force. Source: NATSEM

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Table 6 SLAs with the highest estimated number of households in housing stress in the ACT, and their selected profiles, 2005




Kambah 805254869 241 4.3% Braddon 805050639 160 13.1%

Ngunnawal 805406249 213 6.2%

Belconnen Town

Centre 805100459 164 13.0%

Kowen 805055049 190 7.8% Reid 805057209 77 11.8%

Ainslie 805050189 170 8.6% Oaks Estate 805356309 16 11.5%

Lyneham 805055229 169 9.5% Turner 805058289 131 11.0%

Belconnen Town Centre 805100459 164 13.0% Lyons 805155319 106 10.5%

Braddon 805050639 160 13.1% Lyneham 805055229 169 9.5%

Narrabundah 805356219 154 7.0% Remainder of ACT 810059009 9 9.5%

Gordon 805253289 154 5.7% Charnwood 805101179 106 9.1%

Wanniassa 805258379 138 4.7% Symonston 805357929 13 9.0%

ACT Average 5.5% 5.5%

Note: 1. PRVR – private renting, PUBR – public renting, SOLE – sole parent household, LONE – lone-person household, GOV – main source of household income from government, NILF – reference person of household not in labour force. Source:

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4.3 Mapping housing stress

Overview

A series of maps in Figures 2 to 8 show where housing stress is located among the SLAs in Victoria, Queensland and the ACT. The SLAs in each state or territory were divided into four groups, using the Jenk’s natural break algorithm, based on percentage of households in housing stress. The algorithm minimises the within-class difference and maximises the between-class difference.

In the maps, only convergent SLAs are shown. Non-convergent SLAs show up as blank regions. The SLAs labelled in the maps are mainly those in the top two groups of households with the highest housing stress. Many of these SLAs have their profiles described in Tables 4 to 6. Other SLAs not in the two groups may also be labelled if they are included in Tables 4 to 6. It should also be pointed out that 2001 SLAs have been used to map the 2005 results. Although this is not ideal, it can be tolerated because only a small number of SLAs have had changes to their boundaries between 2001 and 2005.

Results

Where is housing stress located? Results from mapping show that housing stress is almost entirely located in urban rather than rural areas. Figures 2 and 4 show that in Victoria and Queensland, SLAs with a lower proportion of households in housing stress (i.e. the two groups with lighter colours), tend to be located in the rural regions.

In contrast, SLAs in the top group of households with the highest proportions of housing stress are mostly found in the capital cities of Melbourne (e.g. Stonnington – Prahran, Port Philip – St Kilda, Fig. 3), Brisbane (e.g. Spring Hill, St Lucia, Fig. 7); and Canberra (e.g. Braddon, Turner, Fig.8). These SLAs appear as the darkest brown regions and are labelled with a blue underline in the maps. Outside the capital cities, they show up entirely in urban centres such as Cairns and Townsville (Fig. 4), and up and down the coast north and south of Brisbane (Fig. 5,6).

The tendency for housing stress to concentrate in urban centres is even more obvious when we examine the next highest group of SLAs with a high proportion of households in stress (dark brown regions with black labels). In the capital cities, these SLAs form a second layer close to the top-group SLAs. In Melbourne, they include Dandenong and Darebin – Preston (Fig 3). In Brisbane, they form an ‘outer cluster’ around the city centre and include SLAs with a high student population such as Dutton Park and Toowong (Fig. 7). In Canberra, they include Narrabundah and O’Conner, both with a high renting population (Fig. 8).

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Outside the capital cities, this second-highest group of SLAs include the major urban centres in Victoria (Fig. 2). In Queensland, they are especially prominent along the coast from Noosa on the Sunshine Coast in the north, through Surfers Paradise, to Gold Coast in the south, which includes urban centres like Palm Beach and Coolangatta (Fig. 5,6). The region from Coffs Harbour to Noosa has experienced a sustained growth in inter-state migration. As a result, housing costs have been driven up and housing affordability down.

The above results are corroborated by other researchers. For instance, data from Yates and Gabriel (2006) show that Melbourne has 76 per cent of the households in housing stress in Victoria. In Queensland, only 45 per cent of the households in housing stress are located in Brisbane. These results are very similar to our findings, namely, in Victoria, relatively few SLAs outside the capital city have high housing stress (Fig. 2), but in Queensland, more SLAs with high housing stress can be seen outside Brisbane (Fig. 4).

Empirically, it makes sense that housing stress is mainly an urban phenomenon. This is because houses are less affordable to rent or to buy in the cities than in the country and, within the city, they are even more unaffordable the closer they are to the city centre. Wood (2005) for instance, plotted the distance from Melbourne CBD against house prices and private rents, and clearly showed an inverse relationship in both cases.

Detailed housing stress results for the SLAs included in this study will be made available on-line and free to interested researchers through the ARCRNSISS website in a project NATSEM is currently collaborating upon with the University of Queensland and others.

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Figure 2 Households in housing stress (%) – Victoria SLAs, 2005

Surf Coast (S) - West

Colac-Otway (S) - South

Alpine (S) - East

E. Gippsland (S) - Bairnsd

Murrindindi (S) - East

Yarra Ranges (S) - Central

Mildura (RC) - Pt A

Swan Hill (RC) - Robinvale

Wodonga (RC)

Gr. Shepparton (C) - Pt A

Hepburn (S) - East

Moorabool (S) - Ballan

Bass Coast (S) Bal

Mornington P'sula (S) - East

Gr. Dandenong (C) Bal

Geelong

Gr. Bendigo (C) - Central

Mount Alexander (S) - C'maine

Campaspe (S) - Echuca

Swan Hill (RC) - Central

Ballarat (C) - Central

Frankston (C) - West

Warrnambool (C)

Melbourne

Percentage of householdsin housing stress

2% - 5%

6% - 7%

8% - 9%

10% - 15%

Figure 3 Households in housing stress (%) – Melbourne selected SLAs, 2005

Yarra Ranges (S) -

Yarra Ranges (S) - North

Mornington P'sula (S) - South

Mornington P'sula (S) - East

Gr. Dandenong (C) Bal

Frankston (C) - West

Maribyrnong (C)Melbourne (C) - Remainder

Yarra (C) - NorthDarebin (C) - Northcote

Moreland (C) - Brunswick

Stonnington (C) - PrahranPort Phillip (C) - St Kilda


2% - 5%

6% - 7%

8% - 9%

10% - 15%

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Figure 4 Households in housing stress (%) – Queensland SLAs, 2005

(Townsville)

Mackay (C) - Pt A

Rockhampton (C)

Bundaberg (C)Hervey Bay (C) - Pt A

Noosa (S) - Noosa-Noosaville

Tiaro (S)

Magnetic Island

LabradorSt Lucia

Bilinga

Cairns (C) - City

Maroochy (S) - Mooloolaba


1% - 6%

7% - 9%

10% - 14%

15% - 29%

Note: SLAs with a blue underline are in the darkest group (highest proportions of households in housing stress)

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Figure 5 Households in housing stress (%) – Queensland selected SLAs north of Brisbane, 2005

Caloundra (C) - Caloundra S.

Noosa (S) - Noosa-Noosaville

Noosa (S) - Sunshine-Peregian

Maroochy (S) - Coastal North

Maroochy (S) - Coastal NorthMaroochy (S) - Nambour

Caboolture (S) - Central

Noosa (S) - Tewantin

Maroochy (S) - Mooloolaba


1% - 6%

7% - 9%

10% - 14%

15% - 29%


Figure 6 Households in housing stress (%) – Queensland selected SLAs south of Brisbane, 2005

Coomera-Cedar Creek

Oxenford

Mermaid Beach

Robina

Carrara-Merrimac

Tugun

Miami

Burleigh Heads

Palm Beach

Currumbin

Stephens

Coombabah

Southport

Biggera WatersArundel

Parkwood

Ernest-Molendinar

Labrador

Bilinga

Surfers Paradise

Coolangatta

Broadbeach

Surfers Paradise


1% - 6%

7% - 9%

10% - 14%15% - 29%


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Figure 7 Households in housing stress (%) – Brisbane selected SLAs, 2005

Ipswich (C) - East

Ipswich (C) - Central

Wacol

Archerfield

Nathan

Windsor

Annerley

Fairfield

Inala

Richlands

New Farm

Kelvin Grove

Toowong

Indooroopilly

East Brisbane

Dutton Park

Taringa

Bowen Hills

Fortitude Valley - Rem

Milton

St Lucia

South Brisbane

Spring Hill

Highgate HillWest End (Brisbane)

Kangaroo Point

City - RemainderCity - Remainder


1% - 6%

7% - 9%

10% - 14%

15% - 29%

Figure 8 Households in housing stress (%) – ACT (Canberra) SLAs, 2005

Kambah

Ngunnawal

Narrabundah

Gordon

O'Connor

Remainder of ACT

Symonston

Lyneham

Ainslie

Watson

Lyons

Turner

Downer

Reid

Braddon

Charnwood

Belconnen Town Centre

Oaks Estate

Belconnen Town Centre


2.1% - 3.9%

4.0% - 5.5%

5.6% - 8.0%

8.1% - 13.1%

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5 Conclusion This paper presents an innovative process developed by NATSEM for creating credible small-area data for public policy research — spatial MSM. The process redresses the lack of publicly available small-area microdata, which has long hampered debates about the impact of public policy at the local level. The process is called spatial microsimulation and uses a core technique called reweighting. Two steps are involved. The first creates synthetic household weights, and the second applies these weights to generate synthetic estimates for small-areas. In effect, the first step creates synthetic households (one set for each small-area) whose profile is made to resemble that of the small-area, for certain selected characteristics (i.e. benchmarks).

The core technique of spatial microsimulation is called reweighting. This technique melds the survey data from the HES survey with the 2001 Census. National household weights, obtained from the HES, are converted (reweighted) into 2001 household weights for SLAs (one set per SLA). These weights are then updated to 2005, and then applied to the selected 2005 outputs obtained from NATSEM’s STINMOD model to produce the housing estimates for 2005, by SLA.

The reweighting technique works well for over 95 per cent of the SLAs in each state or territory. The remaining SLAs are ‘atypical’ and can be excluded from the study. They either have few or no households, or have ‘atypical’ households made up of, for instance, a large student population. There are more such SLAs in the inner cities, and in the ACT.

In this study, spatial microsimulation has been used to examine housing stress in 2005 for three states and territories — Victoria, Queensland and the ACT. A household in housing stress is defined as one which is in the bottom 40 per cent of the equivalent disposable income distribution (within the respective state or territory) and which faces housing costs that equal or exceed 30 per cent of its disposable income. In this definition, rental assistance has been excluded from both the household income and the housing costs.

To test the reliability of reweighting, we draw on the results from related work on 2001 housing stress. Small-area estimates were first generated and were tested against 2001 estimates sourced from the ABS. Both sets of results agreed well, both at the state and the SLA level.

This study estimates that about 7 per cent of all households in the three jurisdictions are in housing stress. This figure is highest in Queensland (7.8 per cent) and lowest in the ACT (5.5 per cent). Geographically, housing stress features prominently as an urban phenomenon. High levels of housing stress are observed, firstly in the city

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centres, followed by the areas radiating from them, and followed by major urban centres outside the capital cities.

These results match well with what we know empirically —namely, housing is less affordable in the capital cities (and more so closer to the CBD) than in the country. Housing is also less affordable in fast-growing urban centres where rental demand is high.

It is important to remember that there are no readily available, alternative sources of small-area housing stress data for 2005. This is in fact the very reason why we synthesised them in the first place. The close match between our synthetic estimates of housing stress and our empirical knowledge provides strong evidence of the reliability of the synthetic estimates for this application — and points to the potential of future applications using spatial microsimulation techniques to inform debate about the local impact of public policy change.

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Appendix - Benchmark classes used in reweighting

Benchmark XCP table Benchmark classes

1. All household types

XU46 TENURE TYPE AND LANDLORD TYPE BY WEEKLY HOUSEHOLD INCOME BY HOUSEHOLD TYPE (Occupied private dwellings containing family, group or lone person households)

Total number of households Total: 1 class (Not fully specified. The benchmark class ‘Non-private dwellings’ was excluded)

2. Age by Labour force status by Sex

XU13 AGE BY LABOUR FORCE STATUS (FULL-TIME/PART-TIME) BY SEX (Persons aged 15 years and over (excluding overseas visitors))

Male, 0 to 14, Not applicable Male, 15 to 24, Employed full-time Male, 15 to 24, Employed part-time Male, 15 to 24, Unemployed Male, 15 to 24, Not in labour force Male, 25 to 54, Employed full-time Male, 25 to 54, Employed part-time Male, 25 to 54, Unemployed Male, 25 to 54, Not in labour force Male, 55 to 64, Employed full-time Male, 55 to 64, Employed part-time Male, 55 to 64, Unemployed Male, 55 to 64, Not in labour force Male, 65+, Employed full-time Male, 65+, Employed part-time Male, 65+, Not in labour force

Female – repeated as above

Total: 32 classes1 (Fully specified)

3. Tenure type by weekly household rent

XU44 LANDLORD TYPE BY WEEKLY RENT (Occupied private dwellings being rented)

Public rent, $0-$99 Public rent, $100+ Private rent, $0-$99 Private rent, $100-$199 Private rent, $200-$299 Private rent, $300-$399 Private rent, $400+

Total: 7 classes (Not fully-specified. Excludes ‘other tenure type’)

4. Tenure type by household type


Fully-owned, family household Fully-owned, lone–person household Fully-owned, group household Being purchased, family household Being purchased, lone–person household Being purchased, group household Rent-private, family household Rent-private, lone–person household Rent-private, group household Rent-public, family household Rent-public, lone–person household Rent-public, group household Other tenure, family household Other tenure, lone–person household Other tenure, group household

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Total: 15 classes (Fully specified)

5. Tenure type by weekly household income


Fully-owned, $0-$1992 Fully-owned, $200-$599 Fully-owned, $600-$999 Fully-owned, $1,500+ Being purchased, $0-$1992 Being purchased, $200-$599 Being purchased, $600-$999 Being purchased, $1,500+ Rent-private, $0-$1992 Rent-private, $200-$599 Rent-private, $600-$999 Rent-private, $1,500+ Rent-public, $0-$1992 Rent-public, $200-$599 Rent-public, $600-$999 Rent-public, $1,500+ Total: 16 classes (Not fully specified. Excludes ‘other tenure type’)

6. Persons in different types of non-private dwelling

XU50 (custom version of XU45) TYPE OF NON-PRIVATE DWELLING (Non-private dwellings and Persons in non-private dwellings)

Hotel, motel, boarding-house Boarding-school, residence hall Homes for the aged Staff quarters Hospitals Children institutions Prisons Other NPD (disabled, homeless, religious,

welfare) Total: 8 classes (Fully specified)

7. Monthly household mortgage by weekly household Income

XU41 MONTHLY HOUSING LOAN REPAYMENT BY WEEKLY HOUSEHOLD INCOME (occupied private dwellings being purchased, includes dwellings being purchased under a rent/buy scheme)

Mortgage $1-$399, income $0-$3992 Mortgage $1-$399, income $400-$799 Mortgage $1-$399, income $800+ Mortgage $400-$799, income $0-$3992 Mortgage $400-$799, income $400-$799 Mortgage $400-$799, income $800+ Mortgage $800-$1,199, income $0-$3992 Mortgage $800-$1,199, income $400-$799 Mortgage $800-$1,199, income $800+ Mortgage $1,200+, income $0-$3992 Mortgage $1,200+, income $400-$799 Mortgage $1,200+, income $800+ Total: 12 classes (Fully specified)

8. Dwelling structure by household family composition

X47 DWELLING STRUCTURE BY HOUSEHOLD TYPE BY FAMILY TYPE (Occupied private dwellings containing family, group or lone person households)

Separate house, couple with kids Separate house, couple without kids Separate house, one-parent family Separate house, lone person household Semi-detached3, couple with kids Semi-detached3, couple without kids Semi-detached3, one-parent family Semi-detached3, lone person household Flat etc4, couple with kids Flat etc4, couple without kids Flat etc4, one-parent family

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Flat etc4, lone person household Total: 12 classes (Not fully specified. Excludes ‘other dwelling type’, ‘group households’, ‘other family’)

9. Household size - number of persons usually resident

XU48 DWELLING STRUCTURE BY NUMBER OF MOTOR VEHICLES BY NUMBER OF PERSONS (USUALLY RESIDENT) (Occupied private dwellings containing family, group and lone person households)

One usual resident Two usual residents Three usual residents Four usual residents Five usual residents Six or more usual residents Usual resident in non-private dwellings Total: 7 classes (Fully specified)

10. Weekly household rental by weekly household income

XU40 WEEKLY RENT BY WEEKLY HOUSEHOLD INCOME (Occupied private dwellings being rented)

Rent $0-$99, income nil or negative Rent $0-$99, income $1-$599 Rent $0-$99, income $600-$1,499 Rent $0-$99, income $1,500+ Rent $100-$199, income nil or negative Rent $100-$199, income $1-$599 Rent $100-$199, income $600-$1,499 Rent $100-$199, income $1,500+ Rent $200-$299, income nil or negative Rent $200-$299, income $1-$599 Rent $200-$299, income $600-$1,499 Rent $200-$299, income $1,500+ Rent $300-$399, income nil or negative Rent $300-$399, income $1-$599 Rent $300-$399, income $600-$1,499 Rent $300-$399, income $1,500+ Rent $400+, income nil or negative Rent $400+, income $1-$599 Rent $400+, income $600-$1499 Rent $400+, income $1,500+

Total: 20 classes (Fully specified)

Note: 1. Not all classes are applicable to all variables (e.g. persons 65 years old or over cannot be ‘unemployed’). 2. Includes negative income. 3. Semi-detached, row- , terrace, or townhouse. 4. Flat, unit or apartment

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Australia, Confidentialised Unit Record File (CURF), ABS Catalogue no. 2005.0, Commonwealth of Australia, Canberra.

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Australian Bureau of Statistics, 2001, Statistical Geography Volume 1, Australian Standard Geographical Classification (ASGC) 2001, ABS Catalogue no. 1216.0, Commonwealth of Australia, Canberra.

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Chin, SF., Harding, A., Lloyd, R., McNamara, J., Phillips, B. and Vu, QN. 2005, ‘Spatial microsimulation using synthetic small-area estimates of income, tax and social security benefits’, Australasian Journal of Regional Studies, vol. 11, no. 3, pp. 303-336. (See also NATSEM Online Conference Paper CP 0516, which contains maps).

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Tranmer, M., Pickles, A., Fieldhouse, E., Elliot, M., Dale A., Brown, M., Martin D., Steel, D., and Gardiner C. 2005, ‘The case for small area microdata’, Journal of the Royal Statistical Society: Series A, vol. 168, no. 1, p. 29.

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Wood, G. 2005, ‘Housing Markets, Structural Change and Transitions into Employment’, Paper presented at the Transitions & Risk: New Direction in Social Policy Conference, Melbourne, 24 February.

Yates, J., Randolph, B. and Holloway, D. 2006, Housing Affordability, Occupation and Location in Australian Cities and Regions, Australian Housing and Urban Research Institute, Melbourne.

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* denotes available from www.natsem.canberra.edu.au

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