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«SPATIAL ANALYSIS OF HOUSING STRESS ESTIMATION IN AUSTRALIA WITH STATISTICAL VALIDATION Azizur Rahman Lecturer, School of Computing and Mathematics, ...»

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452 Australasian Journal of Regional Studies, Vol. 20, No. 3, 2014

SPATIAL ANALYSIS OF HOUSING STRESS

ESTIMATION IN AUSTRALIA WITH

STATISTICAL VALIDATION

Azizur Rahman

Lecturer, School of Computing and Mathematics, Charles Sturt University,

Wagga Wagga, NSW, 2678, Australia. Email: azrahman@csu.edu.au

Adjunct Associate Professor, University of Canberra, Canberra, ACT 2601, Australia. Email: azizur.rahman@canberra.edu.au Ann Harding Professor, National Centre for Social and Economic Modelling (NATSEM), University of Canberra, Canberra, ACT, 2601, Australia.

Email: ann.harding@natsem.canberra.edu.au ABSTRACT: A large number of Australian households are experiencing housing stress. Decision makers at the national and regional levels need reliable small area statistics on housing stress, to most efficiently and fairly target assistance and policy design. This paper studies small area housing stress estimation in Australia and examines various distributive scenarios of the estimates through spatial analysis of a synthetically microsimulated data. Results reveal that one in every nine households in Australia is experiencing housing stress, with private renter households being most greatly affected. About twothirds of Australian households with housing stress reside in the eight major capital cities, principally in Sydney and Melbourne. The statistical local area level estimates of housing stress are much lower in Canberra, compared to the other major cities. Scenarios of the spatial analysis identify small area level hotspots for housing stress across Australia. A new approach for validating the results of microsimulated data produced by the microsimulation modelling technology reveals statistically accurate housing stress estimation for about 94.3 percent of small areas.

KEY WORDS: ASRE analysis; housing stress estimates; microsimulation modelling; spatial microdata; statistical local area; validation technique.

ACKNOWLEDGEMENTS: This paper utilises the methodological research that has been undertaken as part of the PhD of Dr Rahman, based on the three prestigious scholarships: an E-IPRS from the Commonwealth of Australia, the ACT – Land Development Agency Postgraduate Research Scholarship from the Government of Australian Capital Territory (ACT) and the Australian Housing and Urban Research Institute (AHURI) and the NATSEM Top-Up Scholarship from the University of Canberra (UC). Special thanks are due to Robert Tanton Spatial Analysis of Housing Stress Estimation Within 453 Australia with Statistical Validation and Shuangzhe Liu at UC, and Mark Morrison, Kenneth Russell and peoples involved in the CSIRO workshop at CSU in Australia for their valuable comments and stimulus.

1. INTRODUCTION Housing stress has emerged as a widely discussed public policy issue among politicians, academics and policy makers in Australia. With the unprecedented growth in housing prices - and rents - throughout the past decade, many Australians are increasingly finding housing unaffordable (Rahman, 2011; Yates, 2011). Between 1995 and 2005, real house prices in Australia increased by more than 6 percent per year, with an average annual increase of almost 15 percent from 2001 to 2003 (Yates, 2011).

This was well above the average annual increase in the 20 years to 1995 of just 1.1 percent and the 50-year average (from 1960 to 2010) of 2.5 percent per year. These data are illustrated in Figure 1 and contrast with the significantly slower growth in Gross Domestic Product (GDP) per capita and average earnings over much of the period. A significant increase of the real house prices is marked from 2001 onwards.

Figure 1. Real House Prices, GDP Per Capita and Earnings.

Source: Yates, (2011).

454 Rahman and Harding Compared with other economically advanced nations, Australia is often reported as having experienced relatively rapid growth in real house prices over the past 20 years or so (Tumbarello and Wang, 2010). Just over the five year period from 2000 to 2004, Australia had the third highest rate of house price inflation among Organisation for Economic Co-operation and Development (OECD) member countries, ranking behind only Britain and Spain (Productivity Commission, 2004; The Economist, 2011). Moreover, a recent report of the Australian Bureau of Statistics (ABS) shows that established house prices increased by an average of 33 percent between 2002-03 and 2006-07 (ABS, 2008).

Within this time period house rents have also increased rapidly. For instance, within only a 12 month period ending in August 2007, house rents increased in Perth by 36.4 percent, Melbourne by 23.4 percent, Australian Capital Territory by 22.7 percent, Sydney by 18.8 percent, and Brisbane by 13.5 percent (Pearson, 2007). So, housing stress has become an important financial challenge for households, especially for low and middle income groups and an important public policy concern for the national, state and local governments.

About 1.7 million people in this country are in housing stress (Sandel and Wright, 2006).

Households with relatively low income and housing costs greater than a certain proportion of household income (for instance, more than or equal to 30 percent) are typically defined as being in housing stress (Rahman, 2009). The concept may also be extended to describe inadequate housing for a proportion of the population. Most of the policy debates on housing stress to date have been confined to the national or state level (Wood et al., 2005; Harding et al., 2004; Nepal et al., 2010; Rahman, 2011; Flood, 2012). This is largely due to the ready availability of data at this coarse geographic level in the sample survey files available from the ABS. However, methodological advances in spatial microsimulation modelling mean that it is now possible to generate synthetic spatial micro-population data (Rahman et al., 2010a).





As in many other countries, substantial spatial differences in socioeconomic growth and wellbeing exist across Australia (Chin et al., 2005; Harding et al., 2006; Stimson et al., 2008). Australian housing programs include subsidising housing costs and rent assistance; mortgage subsidies; and land development planning for housing. All of these policies have had significant impacts on individuals and their living standards, experiences, choices, constraints, decisions and lifestyle preferences (Melhuish et al., 2004; Kelly et al., 2006; Rowley and Ong, 2012; Rahman et al., 2013). In addition, housing acts as a proxy for a host of other factors relevant to economic disadvantage and social Spatial Analysis of Housing Stress Estimation Within 455 Australia with Statistical Validation inequalities at small area levels. Small area level housing stress statistics also vary with the demographic and socioeconomic conditions of households - and with geography (Rahman, 2011). So, there is a keen interest in understanding who is struggling to afford to buy or rent a house and the impact at small geographic area levels.

This paper studies a spatial analysis of the estimation of statistical local area (SLA) level housing stress in Australia. One of the arguments frequently evoked in the literature is that microsimulation modelling technology based small area estimation lacks vigorous tests of statistical reliability for the microsimulated estimates. So this paper also offers a new statistical approach for validating the results of small area housing stress statistics.

2. A REVIEW OF THE LITERATURE

Typically housing stress describes a financial situation of households where the cost of housing – either as rental, or as a mortgage repayment – is considered to be significantly high relative to household income. A range of definitions for describing the situation of housing stress are available in the literature. The following subsections will discuss all methods of measuring housing stress and compare different definitions.

Measures of Housing Stress

Housing stress can be measured by combining two basic quantities - the income and expenditure of a household. A household can be considered under housing stress when it is spending more than an affordable expected proportion of its household income on housing. The affordable expected cut-off point of housing expenditure can vary with the circumstance of households as well as location of dwelling.

As a general rule of thumb, a household spending at least 30 percent of its income on housing can be considered under housing stress (see King, 1994; Landt and Bray, 1997). Some researchers use a different threshold of housing expenditure by restricting the definition to households within different income quintiles. For example, an income threshold of more than 25 percent for housing costs is used by the National Housing Strategy (1991) and Foard et al. (1994). Additionally a commonly used definition of housing stress is specified in Harding et al. (2004), where a threshold of more than 30 percent of housing costs was used, but only for those households having income in the bottom 40 percent (lowest two 456 Rahman and Harding quintiles) of the equivalised income distribution. Another definition restricts the designation of ‘being in housing stress’ to those households spending more than 30 percent of their income on housing and belonging to the bottom 10th to 40th income percentile of the income distribution (ABS 2005). It is noted that any threshold-based definition is an arbitrary slice through a continuum, meaning that small area level estimates of a percentage of households in housing stress would be better treated as estimates of small areas with the greatest percentage of households in housing stress. More explicitly, if an area has a very high percentage of households suffering from housing stress under one of the above definitions, the area probably ranks highly on percentage of households suffering from housing stress however defined.

The residual income approach to housing stress measure looks at what different household types can afford to spend on housing after taking into account the other necessary expenditures of living (Stone et al., 2011).

Although it is an alternative to benchmarking the income and expenditure ratio measures of housing stress commonly used in Australia, this approach requires an operationalised residual income standard that is not only difficult to quantify but also arbitrary according to varying circumstances of households. This means that a household has a housing related financial stress problem if it cannot meet its non-housing related needs at some minimum level of adequacy after paying for housing (Stone, 2006a). The appropriate indicator of the tension between housing costs and incomes is thus the difference between them - the residual income after paying for housing, rather than the ratio of costs to income.

Defining a residual income standard involves use of a socially-defined standard of adequacy for non-housing items. Thus, while the residual income logic has some conceptual broadness, a particular residual income standard is not universal, but socially grounded in space and time (Stone, 2006b; Stone et al., 2011). Issues involved in selecting such a standard for non-housing necessities can be difficult and complex.

Both the ratio approach and the residual income approach suggest that as the housing costs behaviourally tend to make the first claim on disposable income, a household has a housing stress problem if, after paying for housing, it has insufficient (residual) income to meet its nonshelter needs at some normative level of adequacy. The difference between the two approaches is how they define the normative level of adequacy for non-shelter items. The ratio approach defines it as a fraction of income: traditionally 75 percent. More recently 70 percent has been defined as the minimum share of income that must be available after housing costs in order to avoid hardship in meeting non-shelter needs Spatial Analysis of Housing Stress Estimation Within 457 Australia with Statistical Validation (Nepal et al., 2010; Rahman, 2011). By contrast, the residual income approach defines the normative level of adequacy for non-shelter items as a monetary amount that is independent of income but very dependent upon household composition and the non-housing cost of living as a function of time and place (Burke et al., 2010).

Types of Ratio Measures

A rationale for the use of the 30/40 rule based ratio measure is given in this subsection. It is noted that this ratio measure not only provides continuity with traditionally used measures, but also it is simple to apply and easy to understand.

The definitions of housing stress by three ‘rules’-based ratio measures

are as follows:

1) 30-only rule: A household is considered to be in housing stress if it spends more than 30 percent of its disposable or gross income on housing costs;

2) 30/40 rule: A household is considered to be in housing stress if it spends more than 30 percent of its disposable or gross income on housing costs and the household also belongs to the bottom 40 percent of the equivalised disposable income distribution; and 3) 30/(10-40) rule: A household is considered to be in housing stress if it spends more than 30 percent of its disposable or gross income on housing and falls into the bottom 10th to 40th income percentile of the equivalised disposable income distribution.



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