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Rahman, 2009; Rahman et al., 2010a). At small area levels, the estimated data are typically unavailable from another source. Accordingly, some researchers have suggested re-aggregating the small area estimates up to larger levels, where reliable data are available to compare the results (Ballas and Clarke, 2001; Kelly, 2004), while others have attempted to use alternative methods to determine the accuracy of their model estimates (Hynes et al., 2006; Edwards and Clarke, 2009). Discussions about various validation methods used by researchers are outlined in detail in other studies (i.e. in Rahman, 2011; Rahman et al., 2013; and Rahman and Harding, 2014). This section offers a new validation tool for testing the accuracy of SLA level housing stress estimates in Australia which are produced by the microsimulation modelling technology.

Absolute Standardised Residual Estimate (ASRE) Analysis

–  –  –

to make a decision about the accuracy. The mathematical formulae for

the ASRE use the following standard notations:

ˆ Yij is an observed household total in the j th data at the i th small area;

–  –  –

AEMSE is the Average Empirical Mean Square Error (see for example, Gomez-Rubio et al., 2008 and Rahman, 2011).

The decision criterion for this validation technique is: 1) when the value of ASRE is close to zero or less than 2 for a SLA then the synthetic household estimate is acceptable (i.e. the performance of the model estimate is good); and 2) when the ASRE value is at least 2, then it is usually considered as a large error (Field, 2000) suggesting that unexplained errors exist in the model estimates and/or the microsimulated datasets.

Results from the ASRE Analysis

Results of ASRE analysis for overall households in housing stress confirm that for 1 205 SLAs out of 1 278 (94.3 percent) in Australia, the model determined very accurate housing stress estimates (Figure 6).

There are 73 SLAs that have an ASRE measure of at least 2, and many of these SLAs are located in the capital cities and coastal centres such as Wollongong, Newcastle, Coffs Harbour, Tweed Heads, Gold Coast, Hervey Bay, Mackay etc. For instance, a few SLAs in Ipswich show a Spatial Analysis of Housing Stress Estimation Within 475 Australia with Statistical Validation high value of ASRE, which indicates that the model has produced statistically non-significant housing stress estimates in this area. In particular the SLA: Ipswich (C) – Central shows an ASRE value of 5.6, which is much bigger than 2. So, for this small area, the estimate of housing stress is not statistically accurate using the ASRE measure.

Ipswich is one of the fastest growing regions in Brisbane and the population characteristics are quite different to the Australian average. In particular, a significantly large number of working population families (about 60 percent) are Technicians & trades workers, Community & personal service workers, Clerical & administrative workers, and Labourers, who tend to have lower incomes (ABS, 2007). But the housing costs in this area are relatively high. The supply of housing in this area is also inadequate with growing housing demand for increasing populations. As a result, the model simulates significantly high estimates of housing stress for the region by considering the micro-level attributes.

–  –  –

Figure 6. ASRE Analysis for the Estimates of Total Households in Housing Stress.

Source: the Authors.

To get an idea of why a non-significant value of ASRE arises for some of these small area estimates, we may check detailed micro-level results for an SLA (such as Petermann-Simpson in Alice Springs, NT) along with its geographic characteristics. For the Petermann-Simpson SLA, the ASRE value of 8.5 has revealed that the model overestimated the housing 476 Rahman and Harding stress for overall households. It is noted that Petermann-Simpson is one of the functional economic and strategic growing areas in rural central Australia (ABS, 2007; Rahman, 2011). Economic growth in this SLA results from the flow-on effects of providing regional support services to major national projects such as tourism, culture and heritages conservation, mining development, defence construction, forestry and horticultural trials, and a transport and logistics hub servicing the central Australia railway. However, residential land release and housing supply is not consistently adequate in this remote area with its growing population. High demands for housing increase the house price and rents in the area that increase noticeably the money allocated to housing for lower income households and perhaps skew the estimate of housing stress. Sharply increasing housing costs (the average annual change for 2008-09 is estimated as 27 percent) for a large group of low income households (having median weekly income of 961 AUD) residing in Petermann-Simpson has influence over a high rate of housing stress.


This paper has empirically examined the statistical local area level housing stress estimates across Australia using a synthetically simulated micro-dataset and analysed the results. It has also demonstrated a new method for validating the results of small area housing stress statistics.

According to our findings housing stress estimate is greatest within several-hotspot areas in Australia. One of the key findings using outputs from the spatial microsimulation model was that in 2011 around one in ten Australian households were experiencing housing stress, with large numbers of these households residing in the east coast states of New South Wales, Victoria and Queensland. When looking at housing stress at a higher geographic disaggregation, findings from the model outputs have revealed that households experiencing housing stress were mostly residents of the Sydney, Melbourne, Brisbane, Perth, Adelaide, Gold Coast, Hunter, Illawarra, Mid-North Coast statistical divisions, along with some other statistical divisions located across the coastal centres of New South Wales and Queensland. The Canberra, Hobart and Darwin statistical divisions all have relatively low housing stress levels.

Breaking the geographic classifications down to a finer level, we find greater heterogeneity in housing stress estimates, but still the households are concentrated in these main locations or spots. Areas with a high proportion of households living in housing stress were those concentrated in the outer fringes of capital cities along the east coast of Australia. Of Spatial Analysis of Housing Stress Estimation Within 477 Australia with Statistical Validation particular interest was Newcastle, which has the largest estimated number of households (20 990) in housing stress among all of the statistical subdivisions in Australia. More explicitly, the range of estimated numbers of housing stress was from 1 886 households for Newcastle (C) Outer West to 2 826 households for Newcastle (C) - Inner City among the nine SLAs in this statistical subdivision. Although the estimated number is the highest for Newcastle, the percentage estimate (about 11.4 percent) was relatively lower than in many hotspot SSDs within the capital and non-capital cities. Some other non-capital coastal cities - such as Wollongong, Richmond-Tweed, Hastings and Clarence etc in New South Wales and Gold Coast, Sunshine Coasts, Wide Bay-Burnett and Cairns City in Queensland - have spatial subdivisions with much higher rates of housing stress. In addition, many statistical subdivisions within capital cities have also demonstrated large estimated figures. Basically, these regional subdivisions are located in the greater western and northern regions of Sydney, in the western, inner, eastern middle, southern and northern outer regions of Melbourne, in the north-west, south-east and Logan City regions of Brisbane, in the north, east and south-east metropolitan regions of Perth, as well as in the northern, southern, western and eastern regions of Adelaide.

Breaking the geographic scale down even further to one of the smallest and administratively helpful areas – the SLA - we can really see which small areas are suffering the most from housing stress. Findings have demonstrated that a large number of SLAs in the New South Wales coastal cities, including Sydney, had the highest numbers of households in housing stress. Most of the SLAs in Melbourne, Adelaide, and Hobart also had significantly high estimates. Moreover, the rapidly growing mining areas around inland locations in different states have resulted in many SLAs with relatively higher estimates of housing stress. This could be because of a significant lack in the supply of housing within these quickly growing mining areas, which in turn creates a high demand of housing and then increasing housing costs for mainly low and middle income households. In contrast, significantly large numbers of SLAs in Brisbane, Canberra and Darwin have much lower numbers of households in housing stress. This is probably because these SLAs are not only small in size but also have relatively smaller household populations. The results of the percentage estimates reveal somewhat opposite results to the number count estimates: that is, many small SLAs with few households show high percentages of households in housing stress, but there are actually only a few households in stress in these locations. Nonetheless, 478 Rahman and Harding various SLAs in different capital cities indeed confirm significantly large values in housing stress for both number counts as well as percentages.

The validation tool outlined in this paper is the ASRE analysis, where an ASRE for the SLA level housing stress estimate has been calculated and then analysed using a standard cut-off criteria for making a decision.

Results have demonstrated statistically accurate estimates for a very high number of SLAs (about 94.3 percent). There are a number of SLAs with statistically insignificant values of ASRE, and most of them are geographically located in the capital cities, including Melbourne, Brisbane, Canberra and Darwin, as well as major coastal centres in the Eastern part of Australia. Additionally, findings suggest that the proposed validation tools can not only check the statistical validity of an SLA level estimate, but can also identify and describe the possible features of the SLAs that may have insignificant results. The SLAs with ASRE values significantly bigger than 2 demonstrate inaccurate housing stress estimates for the respective SLAs. In such a case researchers would undertake further analysis of these micro-level data for these SLAs, along with their geographic attributes.

Looking at future research directions, we are currently finalising estimates of SLA level housing stress estimates by tenure types within eight major capital cities in Australia, comparing the estimates of housing stress between the cities as well as looking at different SLAs within a specific major city. In addition, a proposed technique for estimating confidence intervals around the housing stress estimates will also be explored. Finally, using groupings of various housing costs such as 0-10, 10-20, 20-30 percent etc of the households’ income, a new study would estimates the housing stress for different income deciles and then map the estimates within these groups at a chosen spatial scale such as local government area.

Spatial Analysis of Housing Stress Estimation Within 479 Australia with Statistical Validation


ABS (2002). Measuring Australia's Progress. ABS Cat. No. 1370.0, Australian Bureau of Statistics, Canberra.

ABS (2005). Housing occupancy and costs of Australia - 2002-03. ABS Cat. No. 4130.0.55.001, Australian Bureau of Statistics, Canberra.

ABS (2007). The 2006 Census Community Profile Series. Cat. No.

2001.0, Australian Bureau of Statistics, Canberra.

ABS (2008). Year Book Australia. ABS Cat. no. 1301.0, Australian Bureau of Statistics, Canberra.

AIHW (2008). Housing Assistance in Australia. Catalogue no. HOU 173, Australian Institute of Health and Welfare, Canberra.

AIHW (2009). Public Rental Housing 2007–08: Commonwealth state housing agreement national data report. Housing assistance data development series, Catalogue no. HOU 187, Australian Institute of Health and Welfare, Canberra.

Ballas, D. and Clarke, G. P. (2001). Modelling the Local Impacts of National Social Policies: a Spatial Microsimulation Approach.

Environment and Planning C: Government and Policy, 19(4), pp.


Ballas, D., Clarke, G., Dorling, D., Rigby, J. and Wheeler, B. (2006a).

Using geographical Information Systems and Spatial Microsimulation for the Analysis of Health Inequalities. Health Informatics Journal, 12(1), pp. 65-79.

Ballas, D., Clarke, G.P. and Dewhurst, J. (2006b). Modelling the SocioEconomic Impacts of Major Job Loss or Gain at the Local level: A Spatial Microsimulation Framework. Spatial Economic Analysis, 1(1), pp 127-146.

Burke, T., Stone, M.E. and Ralston, L. (2010). Residual Incomes in Australia: Methodology and Exemplification. Positioning Paper, Melbourne: Australian Housing and Urban Research Institute.

Cassells, R., Harding, A., Miranti, R., Tanton, R. and McNamara, J.

(2010). Spatial Microsimulation: Preparation of Sample Survey and Census Data for SpatialMSM/08 and SpatialMSM/09.

Technical Paper No 36, NATSEM, University of Canberra.

Chin, S. F. and Harding, A. (2006). Regional Dimensions: Creating Synthetic Small-Area Microdata and Spatial Microsimulation Models. Online Technical Paper - TP33, NATSEM, University of Canberra.

480 Rahman and Harding Chin, S. F., Harding, A., Lloyd, R., McNamara, J., Phillips, B. and Vu, Q.

N. (2005). Spatial Microsimulation Using Synthetic Small Area Estimates of Income, Tax and Social Security Benefits.

Australasian Journal of Regional Studies, 11(3), pp. 303-335.

Clarke, G. P. (1996). Microsimulation for Urban and Regional Policy Analysis. Pion, London.

Edwards, K. L. and Clarke, G. P. (2009). The Design and Validation of a Spatial Microsimulation Model of Obesogenic Environments for Children in Leeds, UK: SimObesity. Social Science and Medicine, 69(7), pp. 1127-1134.

FaHCSIA (2008). Making housing affordable again. Online version accessed 26 July 2009, http://www.facs.gov.au/internet/facsinternet.nsf/vIA/housing_affor dable/$File/making_housing_affordable_again.pdf.

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