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Housing Implications of Social,Spatial and Structural ChangeJudith YatesPublished online: 14 Jul 2010.

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Housing Studies, Vol. 17, No. 4, 581–618, 2002

Housing Implications of Social, Spatial andStructural Change

JUDITH YATES

School of Economics and Political Science, University of Sydney, New South Wales, Australia

[Paper �rst received July 2001; in �nal form December 2001]

ABSTRACT Over the past few decades Australia, like a number of other countries, hasexperienced a signi�cant polarisation of household incomes as a result of social,demographic and economic changes. At the same time there has been a signi�cant declinein home purchase rates amongst younger households. The paper uses micro data from the1986 and 1996 censuses to explore some of the spatial and socio-economic implicationsof this change in housing tenure. It raises the question whether these changes are bothrelated and interrelated. It suggests that tenure might be yet another factor thatcontributes to a process of social and spatial polarisation.

KEY WORDS: home ownership, tenure choice, income polarisation, spatialpolarisation

Introduction

Over the past few decades, Australia, like a number of other countries, hasexperienced signi�cant social, demographic and economic change as a result ofeconomic restructuring.

Social and demographic change has resulted in smaller households. Demo-graphic change has arisen from an ageing population. The effect of this onhousehold size and structure has been compounded by social change re�ectedin declining marriage rates, declining fertility, increasing in cohabitation, in-creasing divorce and separation and a rise in the proportion of single person andlone parent households (McKay, 1997). Economic change associated with econ-omic restructuring has resulted in spatially concentrated patterns of job loss andexpansion and increasing mismatches of employment skills and opportunities(O’Connor & Stimson, 1996). Economic change associated with labour marketreform has resulted in increased casualisation of employment, a growth inpart-time rather than full-time jobs, increased earnings disparities and increasingdisparities in the unemployment rates of skilled and unskilled workers. Anoverview of these changes in Australia can be found in Richardson (1999). Theextent of these changes has varied across regions but, in Australia, it has beenconsiderably greater in non-metropolitan regions than it has in metropolitanregions (Productivity Commission, 1998). A useful overview of some of the

0267-3037 Print/1466-1810 On-line/02/040581–38 Ó 2002 Taylor & Francis LtdDOI: 10.1080/0267303022014436 7

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582 Judith Yates

factors which have contributed to these changes at a regional level can be foundin Baum et al. (1999).

The combined effect of economic and socio-demographic change has resultedin a signi�cant polarisation of household income and increasing social andspatial differentiation on a range of indicators. Home ownership is one suchindicator. Badcock (1997) provides an excellent overview of the Australianliterature on spatial polarisation and spatial inequality. Useful additional ma-terial can be found in Murphy & Watson (1994) and in Walmsley & Weinand(1997). In a series of in�uential papers Gregory and Hunter have shown therehave been distinct spatial variations in the extent of this change in householdincome (Gregory, 1993, 1996; Gregory & Hunter, 1995, 1996; Hunter & Gregory,1996). More recently, Lloyd et al. (2000) have documented the extent of thesedifferences.

As in a number of other countries, the effects of change have been aggravatedby the withdrawal of the welfare state (Murie, 1998; Murie & Musterd, 1996,Musterd & Ostendorf, 1998). In Australia, home ownership rather than socialrental housing has been seen as one cornerstone of welfare state policies (Castles,1998; Winter & Stone, 1998). Over the past few decades, however, the with-drawal of the welfare state in Australia has also been associated with awithdrawal of explicit government support for home ownership (Bourassa et al.,1995; Yates, 1997). Housing outcomes increasingly have been left to the marketand, for young, newly formed households, will re�ect the social and economicchanges that have taken place. Increasingly, home ownership may be limited tothe realm of the already advantaged. If so, it may contribute to existinginequalities.

Concerns with socio-tenurial polarisation arise because home ownership isseen as contributing to social advantage. In the ABS socio-economic indexes forareas (SEIFAs), for example, households owning or purchasing a dwelling areone of the key factors contributing to both urban and rural relative advantage,along with the proportion of high-income families, high status occupations,skilled households. Conversely, indices of relative disadvantage are based onlow income, low skill, unemployment and the proportion of households inpublic housing.

The possibility that housing and home ownership may be seen as a contribu-tor to social and spatial polarisation is not new. In response to disparateoutcomes in housing markets, Winter & Stone (1998, p. 5), for example, havesuggested the “contemporary process of income polarisation in Australia will inthe long term result in a polarised housing market which will, in turn, reinforcethe polarisation emerging from the labour market”. Their conclusion is based onwork undertaken by Badcock (1994). Hamnett (1994) has provided a similarargument for the UK.

The analysis in this paper is based on the presumption that home ownershipmay be seen as one indicator on which social and spatial polarisation isobserved. This is predicated on the possibility that there may be a cumulativeeffect of disadvantage as a result of the interaction of housing and labourmarkets. Growth regions that have bene�ted from economic restructuringare likely to have strong employment opportunities and strong housingmarkets with high house prices. Affordable housing is more likely to befound in depressed regions. Housing market constraints are likely to encouragelow-income households with a strong preference for home ownership to locate

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Social, Spatial and Structural Change 583

in low cost housing markets. In high cost housing markets, housing constraintsare likely to be re�ected in reduced home ownership rates even though stronghousing markets are more likely to reinforce the economic bene�ts associatedwith home ownership. Any reduced access to home ownership in such marketstherefore will limit access to the social and economic bene�ts associated with it.Low cost housing markets, on the other hand, may exhibit fewer of the economicadvantages conventionally associated with home ownership. They may alsoprovide more limited employment opportunities for households with the resultthat the circumstances that led to households locating in these markets may beentrenched.

As a means of determining the extent to which social and spatial polarisationis likely to be reinforced by such trends, this paper provides an empiricalassessment of the impact of the effect of social, demographic and economicchange on tenure outcomes at an individual household level. It focuses, inparticular, on the spatial variations in these outcomes. Individual householddata from the 1986 and 1996 censuses are used to explore the changes in housingoutcomes which took place from the mid-1980s to mid-1990s amongst house-holds in the critical 25 to 44-year-old age bracket.

The next section provides an overview of some of literature relevant to thequestions to be addressed and develops the broad framework that provides therationale for the approach taken in this paper. This is followed by a descriptiveoverview of the social, demographic and economic changes that have takenplace in Australia between 1986 and 1996 for the 25–44-year-old age group.There is then an overview of the changes in home ownership rates which tookplace between 1986 and 1996. The next section outlines and presents the resultsof the estimation procedures employed to identify different factors contributingto these changes. An overview of the results is presented and the paperconcludes by relating these to the issues raised in the earlier sections.

Framework and Related Literature

The strong spatial impacts of the effects of social and economic restructuringraise a number of questions as to how the social and economic system adjuststo change, but ultimately the interactions between housing and labour marketsare critical. Three potential mechanisms will be considered here: adjustment toshocks through labour market change, housing market change and householdchange.

Labour Market Issues

Regional variations in the impact of economic restructuring have resulted inregional disparities in unemployment rates and in ‘hot’ and ‘cold’ housingmarkets. Stimson et al. (1998), for example, use census data for 1986 and 1996 toprovide an overview of the extent to which there were spatial variations inAustralia in both population and employment growth and decline. Similarissues have been covered for the US by Dieleman et al. (2000). Baum et al. (1999)provide a more detailed analysis of Australian census data to identify the factorsthat have contributed to what they call community opportunity and vulner-ability as a result of the economic restructuring that took place between 1986 and1996. They highlight the diversity of experience within and between various

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584 Judith Yates

regions in Australia but point to the importance of labour market engagementand household income as fundamental factors in contributing to differencesbetween regions.

Labour mobility is presumed to play an important role in bringing about bothhousing market and labour market adjustment in light of regional variations.Groenewold (1997), in his study of interstate migration �ows, suggests that, inAustralia at least, such inter-regional equilibrating forces are slow and do notserve to equalise regional unemployment rates. The Industry Commission (1993)argue that changes in the participation rate are considerably more important inthe adjustment process than is inter-regional migration. A similar conclusionwas reached by Decressin & Fatas (1995) for European labour markets. Debelle& Vickery (1998) suggest that inter-regional migration is important in Australiabut that there are signi�cant barriers to mobility which limit the extent ofmigration. These they attribute to lack of information about interstate jobopportunities and to adjustment costs associated with housing. One barrier notconsidered is that of household structure. A household with at least one personin the workforce and one person unemployed is likely to be less mobile than ahousehold with no persons employed.

Housing Market Issues

A number of housing market constraints have been considered to impinge uponlabour mobility. Only those related to house prices and housing tenure will bediscussed here.

Discussion about interactions between housing and labour markets and on theability of households to move from areas of declining employment opportuni-ties, in general, were generated by observations of inter-regional differences inlevels and rates of change in house prices. This has been a theme of both UK andUS literature.

Literature on the determinants of house prices suggests that the fundamentalfactors that underpin any long run upward pressure on housing demand areincome and demographic structure. Useful overviews of UK literature can befound in Meen (2001) and Meen & Andrew (1998a) and of US literature inChinloy (1996), Cho (1996) and Di Pasquale & Wheaton (1994). At a regionallevel, Muellbauer & Murphy (1997) have focused on uneven spatial impacts ofeconomic restructuring in the UK to show how these can lead to uneven impactson regional house prices when combined with different supply constraints indifferent housing markets. Holmans (1990) points to a widening differential inregional house prices between the north and south associated with a markedcontrast in their economic fortunes.

A concern with the employment impacts of widening house price differentialshas been a focus of some of the US-based spatial mismatch and gentri�cationliterature concerned with trends within cities. Holzer (1991) provides a usefulearly review of this. Later reviews can be found in Ihlanfeldt & Sjoquist (1998)and Kain (1992). Within metropolitan areas, long commuting times and highcommuting costs impose signi�cant barriers to employment opportunities.Across regional boundaries, these constraints are likely to be insurmountable.

Both Cameron & Muellbauer (1998) for the UK and Gabriel et al. (1992) for theUS have provided evidence to support the claim that inter-regional migration toregions with more favourable labour market conditions is constrained by high

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Social, Spatial and Structural Change 585

relative dwelling prices. Meen & Andrew (1998b) suggest differences in houseprices, therefore, cause segmentation in labour markets and Meen (2000, p. 18)suggests this might generate a “dynamic spiral of decline”.

The second housing market constraint on mobility is tenure. In the UKcontext, early work by Gordon (1990) and Hughes & McCormick (1990) sug-gested council housing has imposed constraints on labour market adjustment.Munro (1990), however, pointed out that because housing tenure is closelyrelated to income, it may be dif�cult to isolate effects of each.

Henley (1998), building on Henley et al. (1994), has revived arguments aboutthe impact of tenure. In this work, home ownership has been perceived as animpediment to labour market adjustment. In the recession environment of theUK in the early 1990s, Henley found strong evidence of negative impact ofnegative equity and argues this exacerbated housing market related rigidities inthe job matching process. Van Leuvensteijn & Koning (2000) similarly havesuggested that, for the Netherlands, housing equity, or lack of it, is an importantfactor explaining a positive relationship between home ownership and unem-ployment. This is consistent with Boheim & Taylor (2000) who show mortgageholders have low levels of labour market and residential mobility in the UK.

Similar concerns with the impact of home ownership on mobility arise in theUS literature. Chan (1996, 2001) provides an overview of US-based studies thatshow that borrowers with high loan to value ratios, or low equity in theirdwellings, face a ‘lock-in’ effect and, as a result, have strong constraints onmobility.

Household Issues

A third potential mechanism of adjustment is through household structure. Thepossibility that headship rates are affected by housing market circumstances andthat headship rates cannot be treated as exogenous to the housing market haslong been recognised in tenure choice literature (e.g. Borsch-Supan & Pitkin,1988). The role of house prices and/or housing costs in affecting the housing andhousehold formation decisions of young adults has been explicitly taken intoaccount in work by Haurin et al., (1994) for the US and Bourassa et al. (1994) forAustralia. For the UK, Ermisch (1999) has shown that regional relative houseprices signi�cantly retard household formation, particularly for lower-incomeindividuals. Earlier work by DiSalvo & Ermisch (1997) highlights the impact ofregional unemployment rates and regional relative house prices on householdformation and tenure decisions.

These studies raise important simultaneity issues and introduce a spatialdimension at least implicitly to the extent they consider regional differences inthe price variables incorporated into the analysis or to the extent that employ-ment opportunities vary regionally. Their results clearly indicate the way inwhich socio-economic structures of households can be affected by the interactionof household formation and tenure choice. They provide less insight into theway in which these outcomes may affect regional differences in householdstructure.

Synthesis

The literature reviewed above suggests that housing markets are likely to have

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586 Judith Yates

different impacts on households at different stages in their life cycle. Newly-formed households with income and wealth constraints may be constrained byhigh housing prices from locating in those labour markets where employmentopportunities are greatest. To the extent that such households are in�uenced intheir decisions by an underlying preference for home ownership over renting,these constraints are likely to be even more pressing.

Thus, as Meen (2000) has argued, this raises the possibility of a dynamicinteraction between housing and labour markets that contributes to economicpolarisation over time. Low-income owner occupiers are likely to be less mobilethan higher-income households because they are likely to be faced with bindingcredit market constraints. As a result they are less able to move in response tochanging labour market conditions, particularly if these regions are locations ofhigh house price growth or regions where there is a shortage of affordablehousing. They are also more likely to be locked into locations of poor qualitybecause of declining relative house prices. As a result, they can be ‘trapped’ indeclining areas. Although private renters are more mobile than home owners,similar arguments can be employed for regions where there are relative short-ages of low rent stock.

The three broad factors identi�ed above provide the focus for the primaryissue of concern in this paper; namely, the role of home ownership in contribut-ing to the trends identi�ed. Figure 1 provides a diagrammatic summary of someof the connections between location, household structure and housing marketsoutlined above. Restructuring has a direct effect on regional economies and thesocio-economic characteristics of households and, through this, on regionalhousing markets. Changes in the socio-demographic structure of householdsand different constraints in different housing markets affect tenure preferencesbut also housing opportunities. Tenure preferences may in�uence locationchoices and may affect household structure decisions.

Figure 1. Interactions between spatial, socio-economic and housing change.Note: Heavy lines 5 factors identi�ed in the literature; dotted lines 5 potential

impacts.

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Social, Spatial and Structural Change 587

The Role of Home Ownership

The �nal link in the potential that these processes have for creating a cumulativeimpact on advantage or disadvantage and so contributing to a process ofpolarisation arises from the social and economic bene�ts and costs associatedwith home ownership. Numerous authors have raised concerns about the wayin which the social implications of inequality can interact with other factors toreinforce economic inequality. Much of the formal analysis of these processes inrelation to home ownership has arisen from US-based literature. In part this maybe attributable to the greater emphasis place on the role of home ownership andthe private sector in meeting housing needs there. In part it may be attributableto the observed declines in home ownership amongst young families between1980 and the mid 1990s.

Rohe et al. (2000) and McCarthy et al. (2001), for example, provide a com-panion set of papers that review an extensive US-based literature on these socialand economic bene�ts and costs. From their review of studies on the socialbene�ts, Rohe et al. suggest that there is suf�cient evidence to suggest that homeownership has positive social impacts arising from neighbourhood stability,civic involvement, higher residential and life satisfaction as well as improvedpsychological and physical health. However, they also argue that, forlower-income households, these bene�ts are often offset by social costs associ-ated with reduced residential mobility and negative impacts of the same factorscontributing to bene�ts for many. In other words, home ownership does notnecessarily confer bene�ts on all and many of the observed bene�ts may, in fact,be attributable to the effects of higher earnings or higher education (Aaronsen,1999). The perceived bene�ts are argued to arise from social externalities andcommunity interaction effects (Galster & Killen, 1995; Hoff & Sen, 2000) derivedfrom investment in social capital (DiPasquale & Glaeser, 1999; Glaeser et al.,2001). Others have pointed to the advantages that children of home ownersderive from their parents’ tenure status (Boehm & Schlottman, 1999; Green &White, 1977). Similar arguments have been presented in Australia by Troy (1991)who relied on literature from the 1930s to support his claims and are beginningto be documented in the current round of AHURI research. (The current statusof these projects can be found at http://www.ahuri.edu.au. AHURI is theacronym for the Australian Housing and Urban Research Institute.)

For privileged home owners, these social bene�ts are supplemented byeconomic bene�ts. McCarthy et al. (2001) and much of the literature they reviewconcentrate on the tax breaks and wealth accumulation associated with homeownership. These vary with the institutional arrangement under which housingis taxed. In Australia they bene�t most those with considerable equity in theirhomes, higher income owners and those with appreciating property. Capitalgains, however, are not guaranteed. They are affected by both time and place ofpurchase. The changing fortunes of home owners in Australia and the sensitivityof the returns to home ownership to the time of purchase have been analysed indetail by a number of authors. As Badcock & Beer (2000), Burbidge (2000) and,earlier, Maher (1994) and Badcock (1994) have shown for Australia, there aresigni�cant disparities in the capital gains associated with housing within themajor cities as well as between them. In general, however, the largest gains haveaccrued to the higher socio-economic groups.

Several authors from the UK, whilst not questioning the bene�ts that home

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588 Judith Yates

ownership has for high income and high wealth households, likewise havequestioned the presumption that home ownership provides economic bene�tsfor all. Over a decade ago, Doling et al. (1988) expressed concern about growingindebtedness and Forrest et al. (1990) cautioned against assuming that homeownership automatically conferred bene�ts on all those who undertook it. Morerecently, Maclennan et al. (1997) and Ford & Wilcox (1998) have pointed to thedif�culties faced by marginal home purchasers when an economic downturnresulted in widespread decreases in housing prices and signi�cant negativeequity for numerous households. Many of these marginal purchasers boughtinto declining regions or into locations where capital losses were highly likely.

For those who do bene�t, however, in the longer run, home ownership doesact as a form of forced saving. In Australia, where dwelling assets were valuedat more than $1500 billion in 2000, housing is the most signi�cant component ofhousehold net wealth (Commonwealth of Australia, 2001). Its share in total netprivate sector wealth in Australia increased from 45.7 per cent in 1960 to 53 percent in 2000 and owner-occupied housing accounts for 90 per cent of this nethousing wealth (Baekgaard, 1998).

One further economic bene�t that home ownership confers on those house-holds who have shared in this increase in housing wealth is that it reduces afterhousing poverty once retirement is reached. Numerous studies have docu-mented this contribution in the Australian context. Examples are Bradbury et al.(1986), King (1998), King et al. (1999), Landt (1998), Landt & Bray (1997) andPercival (1998). This protection that home ownership provides for households inretirement is consistent with the argument that home ownership in Australia,along with occupational superannuation, has been seen as a “cornerstone of thewelfare state” (Winter & Stone, 1998, p. 3). Castles has developed this argumentover a number of years (Castles, 1985, 1997, 1998). It is of considerable import-ance in countries where the social housing sector does not provide an alternativeto home ownership.

The evidence on home ownership suggests it has many social and economicbene�ts. However, these bene�ts are not enjoyed by all. They vary spatiallyand are unlikely to be associated with declining regions. They may not beenjoyed by low-income households or those with limited equity. They can beoffset by costs associated with reduced residential and labour mobility. Thebene�ts of home ownership are likely to depend on the socio-economic charac-teristics of the households who gain access and on the economic opportunitiesprovided by the regions in which they are located. Housing markets, therefore,have the potential to reinforce structural inequalities arising from economic andsocial change and to contribute to the processes of cumulative advantage ordisadvantage.

The processes that underlie the links between tenure and social and economicadvantage and those that link households and housing are complex and areaffected by numerous factors not considered here. The remainder of this paperdoes not attempt to describe the processes of change that have the potential tobring about different outcomes in different housing markets. It has more limitedaims. First, it provides an overview of the spatial variations that have takenplace in household structure in Australia and an indication of how these haveaffected home ownership outcomes amongst households most likely to beaffected by economic and social restructuring. Second, it provides some insightsinto outcomes arising from the interactions illustrated in Figure 1.

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Social, Spatial and Structural Change 589

The Changing Socio-demographic and Economic Characteristics of AustralianHouseholds in the 25–44-year-old Age Group

The results presented in the following sections have been derived from specialrequest matrix tabulations from the 1986 and 1996 censuses. Use of the censusas a data source provides a rich source of socio-demographic household data ata spatially disaggregated level. Reliance on special request tabulations allowsmore complex interactions to be considered than is possible from published orelectronically available census products. An added advantage is that the Tablesprovided are based on full count data, so that there are no problems withsampling errors. Use of these data, however, does provide some problems. Costconstraints limit the number of variables that can be considered and there arepotential problems of bias if missing data are ignored or simple pro-rataadjustments are employed. The latter problem has been overcome in the resultspresented by use of conditional mean imputation procedures to correct formissing or incomplete data.

Choice of the 25–44-year-old Age Group

The results presented focus on spatial variations in household characteristics andhome ownership outcomes for households in the 25–44-year-old age group. Thisspeci�c age group has been chosen in order to abstract from the complicationsof life cycle effects and from the impact of an ageing population. A 20-year rangehas been selected as being suf�ciently broad to capture the impact of the majorchanges which have contributed to demographic uncertainties which have takenplace whilst abstracting from the effects of demographic certainties. Changesidenti�ed are unlikely to re�ect the impact of a deferral or delay of criticalhousehold and family formation decisions.

The 25–44-year-old age group is an age group for whom labour forceattachment is the norm and for whom unemployment, retirement or retrench-ment is far more likely to be involuntary than voluntary. It is above the agerange for which remaining in the parental home is a generally acceptablehousing option. It is the age range in which those households who marry and/or have children are most likely �rst to do so, even when there has been adeferral of these decisions. It is the age range in which those households whoultimately become home owners are most likely �rst to do so. Of the householdswho have ultimately attained home ownership in Australia in the post-warperiod, 75 per cent did so before they were 41 years old (Winter & Stone, 1999,p. 47).

Choice of Regions

The housing outcomes for households in this age group are examined at aspatial level which disaggregates Australia into 15 regions based on a metropoli-tan/non-metropolitan split for each of the states and territories in Australia.Table A1 in the Appendix provides an indication both of these regions and ofthe share of households in each region.

The spatial level of aggregation employed has been chosen to be large enoughto ensure that distance alone is suf�cient to de�ne non-overlapping housing orlabour markets (although the size of the regions would suggest there may be

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590 Judith Yates

distinct sub-markets within these regions). It has the added advantage that asigni�cant amount of data and analysis of the data is available at this level ofaggregation. It is the greatest level of disaggregation for which systematic dataon house prices are available on an Australia wide basis.

There are weaknesses in working with the degree of aggregation employedhere. The type of structural change that has taken place means there is consider-able variation within each of the regions chosen. This is particularly so withinthe non-metropolitan regions, which cover areas as disparate as depressedremote rural communities with declining populations and booming coastalresort towns with rapidly growing populations (Baum et al., 1999). Paris (1994)argues that the effect of economic restructuring and demographic change inAustralia has resulted in a breakdown of the pattern of metropolitan dominancere�ected in a state-based metropolitan/non-metropolitan split. Beer & Maude(1995) provide support for this claim and evidence both of the changing role ofregional cities and of differences in these changes.

Such variations within the chosen regions are likely to obscure differencesbetween them because of a statistical regression to the mean. Thus, any differ-ences observed at the metropolitan/non-metropolitan level of aggregation em-ployed in this paper are likely to be even greater for an analysis undertaken ata more disaggregated level

Characteristics and Changing Characteristics of 25–44-year-old Households

Between 1986 and 1996 there was a 17.3 per cent increase in the number ofhouseholds in the 25–44-year-old age group. Household growth amongstyounger households in the intercensal period 1986 to 1996 was greater innon-metropolitan regions (19.6 per cent) than in metropolitan regions (16.0 percent). Regional growth rates and regional shares are shown in Table A1 in theAppendix.

Household Type

At an Australia-wide level, single-person households and lone-parenthouseholds each grew almost four times faster than all households in the25–44-year-old age group. This growth is disproportionately a non-metropolitanphenomenon and is associated more with the states that have had lower rates ofhousehold growth than it is with the higher growth states.

The numbers of younger couples with children grew only marginally, withthis slow rate of growth being more pronounced in metropolitan regions. Innon-metropolitan regions the incidence of young couple households with chil-dren fell from 63 per cent to 55 per cent. In metropolitan regions it fell from 55per cent to 48 per cent. In many such regions there were fewer such householdsin 1996 than there were in 1986. These declines have been primarily offset by agrowth in single person households. The incidence of couple households hasremained relatively unchanged. Any relative growth of younger couple onlyhouseholds is predominantly, but not entirely, a metropolitan phenomenon.Data on the incidence of these different household types are provided in TableA2 in the Appendix.

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Social, Spatial and Structural Change 591

Employment Patterns

This changing structure of households is associated with a change in the patternof households according to number of persons employed. These changes can beobserved against an overall unemployment rate of 7.4 per cent in 1986 and 8 percent in 1996. The growth in single- and lone-parent households has contributedto almost a doubling of the number of households where there was no personemployed. Despite the relative decline of couple households (with or withoutchildren), the proportion of households where there were at least two personsemployed was sustained.

The differential fortunes of the states are re�ected in a greater variation in thegrowth of households with none, one or two persons employed than is observedin the aggregate data. In general, slower growing states have considerablyhigher growth in numbers of households with no person employed. In otherwords, the incidence of work poor households has increased in these regions.Overall, between 1986 and 1996 the incidence of 25–44-year-old households withno person employed increased from 12 per cent to 14 per cent in metropolitanregions and from 16 per cent to 18 per cent in non-metropolitan regions.

In the metropolitan areas of the faster growing states, but also in non-metropolitan New South Wales, there has been a considerable increase in thedisparity between work poor and work rich households. In the non-metropolitanregions in these states, however, there has been a relatively lower increase inwork poor households. Data on the incidence of the number employed areprovided in Table A3 in the Appendix.

Income Characteristics

These patterns of household change and growth in the number employed arere�ected in the differential growth of household income. Table 1 provides asummary of gross household income ($1996 per week) for households in the 15metropolitan and non-metropolitan regions identi�ed for this study and for theaggregate of all metropolitan and non-metropolitan regions. These income dataare provided for households in the 25–44-year-old age group and for allhouseholds, for purposes of comparison.

Overall, mean household income for the 25–44-year-old age group decreasedby 1.6 per cent in real terms between 1986 and 1996. This downward shift is are�ection of the increased numbers of small households as well as the increasein those with no person employed. A ‘shift-share’ analysis shows that, had therebeen no change in household structure between 1986 and 1996, average house-hold income for 25–44-year olds in Australia would have increased to $923 perweek instead of decreasing to $887 if average income for each household typewere at its 1996 level. For all households, it would have been more or lessunchanged at $803 per week. Household income is lower in all non-metropolitanregions than it is in the same state metropolitan region but the relative differen-tials have changed over a time as a result of the patterns of change identi�edabove.

Socio-economic changes amongst households in the 25–44-year-old-age grouphave the potential to considerably impinge upon housing choices made by thesehouseholds.

Table A4 in the Appendix gives the incidence of households in the 25–44-year-

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592 Judith Yates

Table 1. Average gross household income, 1986 and 1996 ($1996 per week)

All households Households aged 25–44

1986 1996 growth 1986 1996 growth

Sydney 874 886 1.5 972 998 2.6NSW non-metro 702 667 2 5.0 804 790 2 1.7Melbourne 869 828 2 4.7 966 931 2 3.7Vic non-metro 715 653 2 8.6 819 767 2 6.4Brisbane 815 827 1.4 893 907 1.6Qld non-metro 726 721 2 0.6 801 813 1.5Adelaide 775 711 2 8.3 888 815 2 8.2SA non-metro 671 629 2 6.2 765 735 2 3.9Perth 815 795 2 2.4 909 897 2 1.3WA non-metro 779 776 2 0.5 877 893 1.9Hobart 785 714 2 9.0 881 825 2 6.4Tas non-metro 699 625 2 10.6 789 732 2 7.3Darwin 1013 976 2 3.6 1060 1012 2 4.6NT non-metro 928 920 2 0.9 999 960 2 3.8ACT 1106 1001 2 9.5 1164 1067 2 8.3

Metro 856 835 2 2.4 951 937 2 1.4Non-metro 716 689 2 3.9 811 797 2 1.7

Australia 802 776 2 3.2 902 887 2 1.6

Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

old age group in each income category in each region. Table A5 gives the growthof the numbers of 25–44-year-old households in each income category relative tothe overall regional growth of the number of households. These data show apolarisation of household income that is most noticeable in metropolitan andhigh growth regions. In the low growth regions, the growth in the number oflow-income households dominates.

Changes in household composition, in employment structure and in house-hold incomes can all be expected to affect home ownership rates. An overviewof the changes that occurred in the home ownership rates of 25–44-year-oldhouseholds between 1986 and 1996 is the focus of the following section. Thesechanges, however, must also be seen in light of the changing housing marketconstraints that households face.

Changing Housing Market Constraints and Home Ownership Rates

As argued in the second section, regional disparities in house prices (as anindicator of general housing market conditions) provide one of the majorconstraints on inter-regional mobility at any point of time. Over time, the impactof changes in real house prices will depend on the interaction of these changeswith changes in household income and the prevailing �nancial conditions.Because the emphasis in this paper is on spatial variations in outcomes, institu-tional factors affecting access to home ownership are not considered. In Aus-tralia, these factors, such as the provision of housing �nance, operate at a stateor national level. They do not vary at the metropolitan/non-metropolitan levelof disaggregation considered in this paper. The differential impact they have ondifferent types of households is, however, of interest and will be one of the

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Social, Spatial and Structural Change 593

factors that contribute to the outcomes observed. An overview of institutionalchanges and the impact of these on access to home ownership over much of theperiod covered by this paper can be found in Yates (1994).

Housing Market Constraints

Figure 2 illustrates regional differences in levels and trends in median houseprices as indicated by the readily available quarterly data on established houseprices reported in the HIA/CBA Housing Reports. The data in Figure 2 clearlyshow a difference between Sydney house prices and those elsewhere. Sydneyprices are higher and are more volatile. However, the data also clearly show anupward trend in real house prices in most regions with real prices lower in 1996than 1986 only in the non-metropolitan regions of Victoria and South Australia.In general, real house prices have increased by 1 per cent per annum, at least inmetropolitan areas and have been relatively stable in most non-metropolitanregions. Data on prices over a longer period of time, reinforce the conclusionthat real house prices have increased in all metropolitan regions in Australia butsuggest the relativities between Sydney and other capitals is not quite aspronounced as that implied by Figure 2 (Bourassa & Hendershott, 1995). Theseincreases need to be seen against the decline in real household incomes over theperiod as reported in Table 1. Whilst distributional data on dwelling prices overtime are not readily available, analysis of rent data suggests that an increasingdispersion in household incomes has not been met with a matching increase inthe dispersion of dwelling rents. More importantly, a growth in the number oflow-income households has been associated with a loss of low rent stock (Yates& Wulff, 2000). A similar outcome for dwelling prices would suggest thatproblems of affordability or access are likely to be more severe for lower-incomehouseholds than those indicated by use of median price data.

Although the differences between median house prices in Sydney and allother regions are a dominant characteristic of the data presented here,the increasing differences between house prices in the metropolitan and non-metropolitan regions are also noteworthy. Metropolitan prices, on average, weresome 10 to 20 per cent higher than their non-metropolitan counterparts in 1986.By 1996, they were 20 to 30 per cent higher. These differentials were even higherfor NSW and Victoria.

Increases in real house prices over a period when real household incomeshave been steady or falling have been one of the explanations provided for thedeclining home ownership rate amongst younger households (Mudd et al., 1999;Yates, 2000). Numerous other factors, of course, do affect tenure choice althoughmany of these will not vary spatially or over time to the same extent as do houseprices and the household characteristics considered here. Factors, such asethnicity, education, occupation, gender, wealth, etc. that vary at an individuallevel and �nancial and labour market conditions that vary at a national level arepresumed to be of secondary importance at the broad spatial level of analysisbeing considered in this paper.

One factor worth mentioning, however, is the role played by the relative priceof rental versus owner-occupied housing. Wood & Watson (1999) have shownthat, for investors, user costs are higher for low valued properties or propertiesin non-metropolitan areas. This could suggest the possibility of a relative pricebias towards ownership in low value or non-metropolitan regions compared

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594 Judith Yates

Figure 2. Established house prices, Australian regions: 1986–96 ($1996). (a)Metropolitan prices. (b) Non-metropolitan prices. Source: HIA/CBA Housing

Reports, various years, CPI adjusted.

with high value or metropolitan regions. In the presence of capital marketconstraints on access to home ownership, this effect would reinforce declines inhome ownership in high value markets and reinforce increases in low valuemarkets. This result, however, needs to be set alongside the results of theconventional tax arbitrage model of tenure choice that yields a tax bias in favourof ownership for high-income households regardless of the underlying propertyvalue. Wood (2001) provides an overview of this arbitrage literature andcombines the potentially competing results of this literature with those of Wood& Watson. He shows that the institutional arrangements surrounding theprovision of rental and owner-occupied housing in Australia are such thathouseholds in all income categories �nd renting �nancially unattractiveregardless of dwelling values. They will rent only when capital market imperfec-tions constrain them from owning or when direct rent subsidies encourage them

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Table 2. Home ownership rates* by region, 1986 and 1996

All households 25–44 year old households

change change1986 1996 86–96 1986 1996 86–96

Sydney 67.0 63.6 2 3.4 61.3 54.0 2 7.4NSW non-metro 66.8 66.4 2 0.4 61.9 57.5 2 4.4Melbourne 72.9 69.8 2 3.0 69.3 63.1 2 6.1Vic non-metro 72.8 71.2 2 1.6 68.9 65.2 2 3.8Brisbane 70.7 65.0 2 5.7 68.6 59.0 2 9.6Qld non-metro 61.1 61.2 0.2 56.0 52.5 2 3.5Adelaide 70.3 67.1 2 3.2 69.5 63.5 2 6.0SA non-metro 64.4 67.3 2.9 59.6 61.9 2.3Perth 70.5 68.5 2 2.0 69.5 65.1 2 4.4WA non-metro 53.4 58.7 5.3 48.1 51.7 3.6Hobart 70.7 67.2 2 3.6 69.0 62.6 2 6.4Tas non-metro 71.0 69.3 2 1.7 67.9 65.5 2 2.4Darwin 38.4 45.3 6.9 40.4 41.7 1.3NT non-metro 27.9 32.6 4.6 28.8 30.6 1.8ACT 65.8 63.8 2 2.0 66.1 58.7 2 7.4

Metro 69.7 66.4 2 3.3 66.2 59.6 2 6.7Non-metro 65.4 65.2 2 0.2 60.5 57.3 2 3.2

Australia 68.1 66.0 2 2.2 64.2 58.7 2 5.5

Notes: *Rates are lower than commonly published data as not stated cases are included inthe base.Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

to do so. Direct rent subsidies in Australia are limited to social securityrecipients and are less valuable in high than low rent markets. In the 25–44-year-old age group, generally speaking recipients are either unemployed or arelow-income households with dependent children. For other households, Wood’sresults lend support to the argument that capital market constraints associatedwith spatial differences in house prices can be used to explain different out-comes in relation to home ownership for otherwise similar households.

Changes in Home Ownership Rates

Table 2 provides a spatially disaggregated overview of the variations in thedeclines in home ownership between 1986 and 1996 as a result of the combinedeffect of the changes that have taken place. At an Australia-wide level, the homeownership rate for those in the 25–44-year-old age bracket in 1996 was someseven percentage points lower than the home ownership rate for the populationas a whole. In small part this arises because of later entry into home ownership.Survey data, however, indicates that less than 11 per cent of �rst-time buyers areolder than 44 (ABS, 1993) and there are signs this is declining (ABS, 1998). Inlarger part, it re�ects the lower incidence of home ownership in each age bracketin the 1990s compared with the same age bracket in the 1980s. Table 2 shows thisexplicitly for households in the 25–44-year-old age bracket for whom there wasa 5.5 percentage point decline in home ownership between 1986 and 1996.

These declines have not been uniform across all regions. On average, the

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declines in home ownership were twice as great in metropolitan regions as theywere in non-metropolitan regions. This outcome is consistent with the possibilitythat households with a high predilection for home ownership have shifted toregions where it remains relatively more affordable. Differences in the extentand pattern of change in home ownership at a spatial level amongst a clearlyde�ned demographic group, however, raise questions about whether theremight be any socio-economic trends which can explain, or at least partly explain,the observed outcomes. The changes in household income and in householdstructure described in the previous section are two obvious explanations.

Spatial Differences in Home Ownership Rates by Household Income

Disaggregation of the data by income shows that, for households in the critical25–44-year-old age group, national home ownership rates have declined acrossthe whole income spectrum with declines being greatest amongst those in thelower part of the income distribution. These results are presented in Table A6.

Table A6 also shows marked differences in the changes in home ownershipbetween metropolitan and non-metropolitan households for any given level ofincome. The income component of these results is presented more graphically inFigure 3, which shows, additionally, the split in home ownership rates betweenoutright owners and home purchasers (that is, those with a mortgage). Tables or�gures used to present the results of data disaggregated by household character-istics are limited to a metropolitan/non-metropolitan split for ease of presen-tation. The following section reports on disaggregation at a 15-region level. Thedecline in home ownership rates is greatest amongst households in the lower-income groups in all regions. In non-metropolitan regions, against the generaltrend, home ownership rates amongst higher income households increased.

Different outcomes in non-metropolitan compared with metropolitan regionscan be explained by differences in house prices in each of these broadly de�nedregions. Although not the primary focus of the discussion in this paper, theincreases in outright ownership amongst households in the 25–44-year-old agegroup suggest a changing pattern in wealth distribution between 1986 and 1996.Increases in outright ownership have been most signi�cant in households inmetropolitan rather than non-metropolitan regions.

Spatial Differences in Home Ownership Rates by Household Structure

Traditionally, home ownership rates have been highly correlated with age andhousehold structure as well with income. For any income and age category,couples and couples with children have a higher propensity for home ownershipthan have single persons and sole parents. This can be seen for households in the25–44-year-old age group in Table A6. Combined with the different patterns ofchange in the socio-economic characteristics of households in metropolitan andnon-metropolitan regions, this would be suf�cient to explain differences inspatially de�ned aggregate home ownership rates.

Winter & Stone (1999), however, have provided strong evidence which sug-gests that traditional relationships no longer hold as housing careers havebecome increasingly disconnected from other associated life events, such asmarriage and children. Over the past few decades the household group whichhas exhibited the greatest propensity to increase its home ownership rates has

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Social, Spatial and Structural Change 597

Figure 3. Changes in home ownership rates by income 1986–96. Households aged25–44. (a) Metropolitan. (b) Non-metropolitan. Source: ABS Special Request Matrix,

Census of Population and Housing, 1986 and 1996.

been single persons. This may re�ect the increased independence of women overthis time period. Supplementary evidence from ABS surveys supports thispresumption given a 15.7 per cent decline in the proportion of income unitsunder 35 who were �rst-time buyers but a 24 per cent growth in the number ofyoung female headed income units who were �rst-time buyers (ABS, 1988b,Table 24; 1999, p. 154). It may re�ect the impact of increased divorce andseparation and property settlement of what had been a home-owning couple.The increases in home ownership for low- to moderate-income, single-person

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598 Judith Yates

Figure 4. Changes in home ownership rates by household type 1986–96.Households aged 25–44. (a) Metropolitan. (b) Non-metropolitan. Source: ABS

Special Request Matrix, Census of Population and Housing, 1986 and 1996.

households provides some element of support for this explanation. Homeownership rates for single-person households, however, are still considerablylower than for couple households in the same age group because far more ofthem have low household incomes.

The differences in the home ownership rates presented in Table A6 areillustrated in Figure 4. This �gure clearly shows that declines in home ownershiprates have been most signi�cant for all households with children, regardless ofincome level and have been discernibly greater in metropolitan compared withnon-metropolitan regions. Again this highlights the constraints higher costhousing markets impose upon tenure choice and the greater impact theseconstraints can have on those households for whom home ownership tradition-

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ally has been the preferred tenure but for whom there are many competingdemands made upon their incomes.

Decomposition of Changes in Home Ownership Rates

The previous two sections have provided a descriptive overview of some of thecomplex changes that have taken place in Australia the socio-economic structureof younger households and of spatial differences in the impact of these changeson housing tenure at a metropolitan and non-metropolitan level.

These changes raise a number of questions. One is the extent to whichchanging socio-economic household structure and the related polarisation ofhousehold income has affected tenure outcomes. A second is the extent to whichhousing market constraints and tenure preferences have contributed to theobserved changes in household structure and household income.

Neither has a clear cut a priori answer. Increased polarisation of householdincome has resulted in an increased proportion of households at both the topand the bottom of the income distribution. In both instances, these are house-holds for whom tenure choices are less marginal than for middle-incomehouseholds. Capacity to pay generally does not constrain high-income house-holds in their choice of tenure. Low-income households generally have nochoice. The observed polarisation of income, however, has been associated witha disproportionate increase in the numbers of small households with lower (butincreasing) ownership propensities. It has also been associated with a generaldownward shift in average real household income. Because of the changeswhich have taken place, it is probable that changed socio-economic structure hascontributed signi�cantly to the observed declines in home ownership amongsthouseholds in the 25–44-year-old age group.

There are factors, of course, that could alleviate these pressures. One is achange in wealth, whether associated with household dissolution or withinheritance. A number of authors have focused on the importance of accumu-lated wealth in in�uencing home ownership. Boehm (1993), for example, focusesspeci�cally on the impact that employment history has on wealth accumulation.Haurin et al. (1996) see wealth as being endogenous to the home ownershipdecision. These are outside the scope of this paper. Also outside the scope of thispaper is consideration of the impact of housing markets on household forma-tion, household structure and household income. Some of these issues werediscussed in the second section in relation to the endogeneity of householdformation and tenure choice, although most discussion of these has focused ona younger age group than considered here. Related issues arise in relation to theendogeneity of household income and tenure choice with the possibility that apreference for (mortgage �nanced) home ownership may in�uence participationrates, the number of employed persons in any household and, hence, income.

An indication of the contribution made to the overall decline in homeownership rates by changes in the socio-economic structure can be obtainedthrough decomposition techniques that show what home ownership would havebeen had employment, income and household structure all remain unchanged.

Modelling Approach

This section sets up a simple model that enables the impact of changes in the

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600 Judith Yates

socio-economic structure of households over time and space to be isolated fromother factors contributing to declines in home ownership. It is based on acomparative static analysis and suffers from all the constraints imposed by suchan approach. Its advantage, however, is that it allows the complex interactionsoutlined above to be taken into account and to be considered at a moredisaggregated spatial level than generally employed above. The model em-ployed makes no attempt to provide a behavioural explanation of tenureoutcomes. Nor does it attempt to articulate the interdependencies betweenhousehold formation, tenure choice, labour market decisions and location choice.

Because of data constraints, the technique used relies solely on discrete orcategorical variables. Use of categorical data, however, has the advantage ofavoiding the dif�culty of superimposing a speci�c functional relationship on thedata and allows the relationship between the key variables to vary over time andspace. This is particularly advantageous given the presumption that changingsocio-economic structures may be associated with changing tenure relationships.The analysis is based on estimating home ownership probabilities for house-holds in the 25–44-year-old age group in each of the 15 regions described inTable A1. These probabilities are assumed to vary by the six household typesshown in Table A2, the three employment outcomes shown in Table A3, the �veincome categories shown in Table A4 and a household size dummy (describedbelow).

Estimation of home ownership probabilities based on these variables providesa way of summarising the interdependencies between them and has the addedadvantage of eliminating or at least reducing the impact of idiosyncratic datathat can affect observed outcomes (Wachter & Megbolugbe, 1992). Constrainingthe analysis to a particular life-cycle group reduces dif�culties that arise fromthe use of current rather than permanent income.

The prime modelling tool employed is that of decomposition analysis associ-ated with logistic regression techniques. These techniques were developedinitially for use in labour market analyses by Blinder (1973, 1976). They havebeen employed in a number of housing studies concerned with the changingimpact on home ownership of race (for example, Wachter & Megbolugbe, 1992),gender (for example, Haurin & Kamara, 1992), marital status (for example,Bourassa, 1994) and education (for example, Gyourko & Linneman, 1997).

A vast array of literature has employed logistic regression techniques to modeltenure choice, either as an independent decision or as one which interacts withsome of the factors outlined above. The limited data on which this paper reliesdo not allow for such ambitious aims. Instead, logistic regression techniques areemployed as an alternative to the multi-layered cross tabulations which wouldbe required to record the complex interactions in the data employed.

Model Speci�cation and Data

The descriptive results presented in the previous section show that tenureoutcomes vary by age and by life-stage, economic capacity and by housingopportunities or constraints. In the regressions used here to estimate homeownership, household type, household size, income and employment status andhousehold size are presumed to determine economic capacity. Household size,income and employment status are as presented in the descriptive sectionsabove. Household size is represented by a dummy variable for large households,

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Social, Spatial and Structural Change 601

de�ned as those who need more than two bedrooms on the basis of standardoccupancy criteria. They are limited to couples or sole parents with at least twochildren and to group or multiple family households with at least three resi-dents. Larger households, and particularly lower-income households, may �ndtheir size requirements impose additional constraints to those imposed by theircapacity to pay. This may have an independent impact on the ability to accesshousing via home ownership. The household size dummy variable is designedto incorporate such effects.

In addition to these key variables, the model estimated includes terms whichallow household type, number employed and household size to interact with theincome variable. This allows for the constraints imposed by income to vary withhousehold structure (and so addresses one weakness of using gross income). Italso allows for the possibility that a given level of income is less secure the morepersons needed to earn it. All of the equations have been estimated with (small)single person, low-income households with no person employed as the basecase. In all, this speci�cation yields 44 explanatory variables in total in additionto the variables implicit in the choice of the base case.

Housing opportunities and constraints are presumed to be affected predomi-nantly by the structure of dwelling prices and, hence by location. The impact oflocation and the role it plays in affecting house price relativities is taken intoaccount by separately analysing outcomes within each of the 15 regionsidenti�ed in Tables 1 and 2. One factor that has been shown to affect tenurechoice and which may affect it differentially over time and space, but which isnot modelled explicitly because of data constraints, is the relative price of rentalversus owner-occupied housing. This can vary between households (for examplebecause of the interaction of the income tax system with housing choices) oracross housing markets (for example, because of different speeds of adjustmentto disequilibrium in rental and owner-occupied markets). The impact of the �rstof these will be absorbed into the impact that income has on decisions under-taken as long as suf�cient �exibility is incorporated into functional speci�cationsto allow for this. A fully �exible functional form is employed by the use ofcategorical variables. The impact of the second is taken into account by treatingeach region as a separate housing market and examining change within thatregion.

The logistic regressions estimated provide an indication of the contributionmade to the estimated probability of home ownership by each of the keyvariables outlined above. By replacing the actual values of the variables withtheir values as they were in a different time period or in a different location, itis possible to estimate a hypothetical probability of home ownership. This can betaken as an indication of what home ownership would have been had theobserved changed in endowments not taken place. The difference between theactual and the hypothetical results provides an indication of the extent to whichthe changing socio-economic structure contributed to the observed declines inhome ownership rates. The remaining difference, the residual, is explained bychanges in all the other factors that impinge upon tenure choice. In this paper,the focus has been on housing market constraints re�ected in house prices.

The impact of changes in the socio-economic structure of households in thecritical household formation age range (25 to 44 years) can be viewed from botha temporal and spatial perspective. Both are considered below. In the former,the impact of changes in socio-economic characteristics (or endowments)

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602 Judith Yates

Table 3. Decomposition of changes in home ownership rates overtime, households aged 25–44

Estimated total Due to change inchange Endowments residual

(% points) (% points) (% points)

Metropolitan:Sydney 2 8.7 2 1.7 2 7.0Melbourne 2 6.6 2 3.7 2 2.9Brisbane 2 10.6 2 2.4 2 8.2Adelaide 2 6.2 2 5.3 2 0.9Perth 2 4.5 2 2.3 2 2.1Hobart 2 6.3 2 4.3 2 2.0Darwin 1.8 2 1.2 3.0ACT 2 7.8 2 5.6 2 2.2

Non-metropolitan:NSW non-metro 2 4.6 2 2.5 2 2.0Vic non-metro 2 3.4 2 3.7 0.3Qld non-metro 2 3.8 2 1.4 2 2.4SA non-metro 2.7 2 2.8 5.5WA non-metro 4.0 2 0.7 4.7Tas non-metro 2 1.7 2 3.9 2.2NT non-metro 1.8 1.4 0.4

between 1986 and 1996 are considered for each of the 15 metropolitan andnon-metropolitan regions in Australia. In the latter, the impact of differences inendowments between the metropolitan and metropolitan regions in each of thestates and territories in Australia are considered for each of the two censusyears.

The coef�cients and diagnostics for the logistic equations estimated for each ofthe 15 regions in 1986 and 1996 are available from the author. The results are notpresented here because the use of categorical variables and the presence ofinteraction terms means what little intuition might be derived from them is lost.

Decomposition of Changes in Home Ownership Rates over Time

Table 3 provides a summary of the results of the estimation procedure outlinedfor changes over time in each region. These results are estimates of the observedchanges reported in Table 2. The �rst column shows the changes in homeownership rates between 1986 and 1996 as estimated by the logistic equationsundertaken for each region. Columns two and three provide the results ofdecomposing the estimated change in home ownership rates into that due tochanges in the socio-economic structure of 25–44-year-old households (theirendowments) and that due to changes in other factors (the residual component).

If the estimated probabilities are given by P0 5 B09 X0 and P1 5 B1 9 X1, where0,1 re�ect either time or space and where X is the vector of regressors, and Bthe estimated coef�cients, then the change in probabilities can be decomposedeither as P0 2 P1 5 B0 9 (X0 2 X1) 1 X1 9 (B0 2 B1) with X0 as the base or asP1 2 P0 5 B19 (X1 2 X0) 1 X09 (B1 2 B0) with X1 as the base. In both cases, the �rstcomponent provides the endowment effect. For both temporal and spatial

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Social, Spatial and Structural Change 603

decompositions, the average of the results with X0 and X1 as the base has beenreported.

The persistent negative effect in column two highlights the impact of thesocio-economic changes that took place in the population between 1986 and1996. In all but one region, the increase in the proportion of smaller households,the rise in the proportion with no person employed and the consequent increasein the proportion of lower-income households have contributed to a decline inhome ownership. This contribution varies from a negative 5.6 per cent in theACT (where mean household income declined by more than in any otherregion), to a negative 0.7 per cent in non-metropolitan Western Australia (wheremean household income for households in the age group considered increased).Mean household income also increased in Sydney, Brisbane and non-metropoli-tan Queensland. In each of these regions, however, this increase was associatedwith a strong polarisation of income. The negative net endowment effect onaggregate home ownership rates in each of these regions, despite a householdrestructuring which has resulted in higher average household income, suggestsa greater sensitivity of home ownership to changes in income at the lower endof the income spectrum than at the top.

Increased mean income arising from higher real incomes at the top end of theincome distribution can serve to reinforce this negative net endowment effect onhome ownership rates through an upward pressure on real house prices. Anyupward pressure on real house prices is likely to add to the access constraintsfaced by those at the lower end of the income distribution. Because of this,increased income polarisation can further limit housing opportunities of those inthe lower-income groups. Gyourko (1998) makes a similar point. He relates theproblems of affordability faced by those whose wages have eroded with global-isation to two factors. The �rst is the upward pressure on house prices associ-ated with increased demand from those with increased household income; thesecond is the failure of the market to produce low quality, affordable homes.

The residual effect shown in column three of Table 3 incorporates the impactof changes in housing market constraints and changes in any other factorsaffecting home ownership. Key changes illustrated above are those arising fromincreased real house prices. Others identi�ed are changes in the relative price ofowning and renting or changes in preferences. This residual effect is negative inall metropolitan regions and positive in all non-metropolitan regions other thanthe high cost regions of NSW and Queensland.

The differences in these residual effects are closely related to levels of andtrends in median dwelling prices in the different regions shown in Figure 2. Thelargest residual contributions to the change in home ownership rates occur inSydney and Brisbane. After Sydney, Brisbane (along with Perth) had the highestrate of growth of real house prices over the period under consideration. Thishigher relative growth in house prices, in turn, can be attributed to higherhousehold growth (in Brisbane and Perth) compared with all other regions andto higher income growth (in Sydney and Brisbane).

The temporal decomposition of the change in home ownership rates presentedin Table 3 shows that socio-economic change amongst 25–44-year-old house-holds has systematically contributed to, but by no means is the sole explanationof, declining home ownership rates between 1986 and 1996 in most regions ofAustralia. In all but some non-metropolitan regions, other factors have con-tributed as much or more to the observed decline. These residual effects have a

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greater negative value in the regions where real house prices have increasedmost and a positive value in regions where real house prices have been stagnant.This suggests that affordability (along with the relative price effects identi�ed byWood & Watson, 1999) has contributed more than preference change to exacer-bating declines in home ownership. Attributing these residual effect outcomes topreference changes would require preferences to vary with access constraintsand to vary systematically across regions.

Spatial Decomposition of Changes in Home Ownership Rates

The second question to be addressed relates to the possibility that restructuringhas been associated with different patterns of socio-economic change amongsthouseholds between metropolitan and non-metropolitan regions and that thishas contributed to the differences in the changes in home ownership ratesbetween these regions. Closely related to this is the question of the extent towhich changes in home ownership re�ect an increasing division between ‘thecity and the bush’.

Table 4 presents the results of a similar decomposition exercise to thatreported in Table 3. In this case, the focus is on the differences between themetropolitan and non-metropolitan regions within each state in each of 1986 and1996.

In 1986, positive endowment effects arose in NSW, Queensland, Tasmania andthe Northern Territory. This suggests the socio-economic and demographicstructure of households in the metropolitan regions in these states meant theywould have been more likely to gain access to home ownership had they facedthe same preferences and same housing market constraints as households innon-metropolitan regions. In Victoria, South Australia and Western Australia,

Table 4. Decomposition of changes in home ownership rates within regions,households aged 25–44

Estimated total Due to change inchange Endowments residual

(% points) (% points) (% points)

1986:NSW metro/non-metro 0.0 1.3 2 1.3Victoria metro/non-metro 1.8 2 0.3 2.0Queensland metro/non-metro 15.1 1.7 13.4South Australia metro/non-metro 12.5 2 1.4 13.9Western Australia metro/non-metro 24.9 2 0.8 25.6Tasmanian metro/non-metro 2.8 0.3 2.5Northern Territory metro/non-metro 11.0 2.9 8.1

1996:NSW metro/non-metro 2 4.1 3.2 2 7.4Victoria metro/non-metro 2 1.5 1.5 2 3.0Queensland metro/non-metro 8.3 1.5 6.7South Australia metro/non-metro 3.6 2 1.8 5.4Western Australia metro/non-metro 16.4 2 2.2 18.6Tasmanian metro/non-metro 2 1.8 1.4 2 3.2Northern Territory metro/non-metro 11.0 1.0 10.0

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however, the reverse was true. However, between 1986 and 1996, the endow-ment effects in most states increased with the result that they were positive inall but the small states of the SA and Western Australia. This suggests there hasbeen an increased spatial disparity in the underlying factors affecting capacityfor home ownership between households in metropolitan and non-metropolitanhouseholds in most states. If they had faced the same housing market constraintsas their non-metropolitan counterparts, metropolitan households in 1996 gener-ally would have been more likely to be home owners. Illustrative of why this isso is the greater decline in average household income in the non-metropolitancompared with the metropolitan regions, at least in the larger states.

The results of systematic changes in the endowment effects between 1986 and1996 clearly suggest that the socio-economic changes amongst households havebeen very different between metropolitan and non-metropolitan regions in eachstate. They signal the possibility that there may be a strong interaction betweenhousing market constraints (for example, as re�ected in differential dwellingprices) and the socio-economic structure of households within any region.

In all states other than NSW, residual effects were positive in 1986, suggestingthat, in general, households with given endowments were both more able andmore willing to undertake home ownership in the metropolitan regions of eachstate. To the extent that preferences are unlikely to vary systematically acrossregions this suggests that the metropolitan/non-metropolitan differences inhouse prices in 1986 generally were not suf�cient to have a negative impact onhome ownership in regions other than NSW.

Between 1986 and 1996, however, was a decrease in the residual effect in allregions other than the Northern Territory, with these effects becoming morenegative or less positive. In NSW and Victoria, which account for almost 50 percent of households in Australia, these effects were negative which suggests that,by 1996, households with given endowments in metropolitan regions were lessable or willing to gain access to home ownership than their non-metropolitancounterparts. Whilst this may re�ect a differential change in the underlyingpreferences between city and country of households with given characteristics,it is more likely to re�ect the increasing price differential between metropolitanand non-metropolitan regions illustrated in Figure 2. The positive effects of achanging economic and socio-demographic structure of households in metro-politan regions (re�ected in an increase in the endowment effect) has not beensuf�cient to offset the negative impact of the factors affecting the tenure choicesmade by these households. These negative factors represent the combined effectof reduced affordability associated with the increase in relative prices in metro-politan compared with non-metropolitan regions and all other factors affectingtenure choice.

Summary

The analysis undertaken above has focused on the differential socio-economicchanges in younger households that took place between 1986 and 1996 in themetropolitan and non-metropolitan regions of the various states in Australia.This enabled the impact of these changes on home ownership rates to beseparated out from the impact of other factors affecting tenure outcomes.

The results from the decomposition over time showed that the householdrestructuring that took place explains a considerable amount of the decline in

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home ownership over the decade being considered. However, it also showedthat there were increasing constraints on access to home ownership in allmetropolitan regions (and in some non-metropolitan regions). Households arebeing excluded from home ownership by the changes in their socio-economicstructure, and they are also increasingly being excluded by housing marketconstraints, particularly in metropolitan regions.

The results from the decomposition over space focused on the differentialchanges in the socio-economic structure of households in metropolitan andnon-metropolitan regions. They highlighted an increasing spatial disparityin their respective abilities to access home ownership. Changes in thesocio-economic structure of non-metropolitan households between 1986 and1996 have resulted in a relative reduction in their potential to access homeownership in any housing market compared with metropolitan households.Household structures are becoming increasingly spatially disparate.

An overview of the results presented in this paper and an indication of theirimplications is given the �nal section below.

Overview and Conclusions

Overview

Between 1986 and 1996 social change in Australia resulted in signi�cant changesin the household composition of the 25–44-year-old age group. The mostsigni�cant of these was the growth in single-person and single-parent house-holds and a decline in the proportion of couple households with children. As aresult of these changes in household structure, average household income for25–44-year-old households declined between 1986 and 1996.

The general decline in household income that occurred between 1986 and 1996was shown to be associated with a signi�cant polarisation of household income.This was observed in both metropolitan and non-metropolitan regions but wasmost noticeable in metropolitan and high growth regions. Likewise, changes inthe socio-economic structure of young households were not uniform across themetropolitan and non-metropolitan regions of Australia nor were they uniformacross the various states. Partly as a result of differential changes in householdstructure (but only partly as a result of this), household income for younghouseholds in non-metropolitan regions was lower than for their metropolitancounterparts. Economic change associated with structural readjustment andlower employment opportunities has also contributed to lower incomes forhouseholds in non-metropolitan regions. Thus, there also has been a spatialpolarisation of income between households in metropolitan and non-metropoli-tan regions.

The empirical part of this paper focused on changes in home ownershipoutcomes for households in the 25–44-year-old age group in light of these social,spatial and structural changes. It pointed to widespread declines in homeownership rates amongst these households. The analysis showed that bothchanges in household composition and changes in household income explainedmuch of the observed decline in their home ownership rates between 1986 and1996 but that both socio-economic change and housing market constraintsindependently contributed to declining home ownership rates.

Within the 25–44-year-old age group under consideration, home ownership

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rates generally declined most for households with children, yet these arehouseholds for whom many of the social bene�ts attributed to home ownershipare perceived to be the most pronounced. Overall declines in home ownershiphave been greater in metropolitan regions, where the economic gains from homeownership (as re�ected by real capital gains) have been higher and lower innon-metropolitan regions where the economic gains have been lower.

The paper began by raising the possibility that housing outcomes, as re�ectedin home ownership rates, contribute to a process of social and spatial polaris-ation that has been observed over the past few decades. The observed outcomessignalled spatially varying changes in the structure of households in the forma-tive 25–44-year-old age range. They suggest that many young householdsincreasingly are being excluded from home ownership in high-cost housingmarkets. These effects are particularly strong amongst lower-income householdswith children for whom home ownership has been a preferred tenure and onethat has conferred social, if not economic, advantages. One possibility raised atthe start of the paper was that a strong preference for home ownership mighthave contributed to the observed differences in household structure in thedifferent regions.

Conclusions

The framework outlined at the start of the paper and the data analysed in thispaper suggest that social and economic restructuring has had a dual impact indepressing home ownership. First, social changes that have contributed to agrowth of smaller households have combined with economic changes whichhave resulted in a disproportionate growth in the number of households with noperson employed to increase the numbers of low-income households. These arehouseholds who, traditionally, have been excluded from home ownershipthrough economic constraints and who continue to be so excluded.

At the same time, at the opposite extreme, social and economic changes havealso resulted in a disproportionate increase in the number of two-earner andhigh-income households. In regions where the impact of income polarisation hasbeen most noticeable, house prices are highest or have increased most. Theimpact of these regional trends in house prices has served to place additionalconstraints on the ability of lower-income households to access home ownership.Thus, any tendency for income to polarise, regardless of whether this resultsfrom social change or economic change, will have negative impacts on homeownership (and consequent greater pressures on private or social rental mar-kets).

An underlying presumption of this paper has been that home ownership, onbalance, provides most, but not all, households with positive net social andeconomic bene�ts. Households less likely to derive positive net economicbene�ts are low-income households and households in regions with weakerhousing markets. In broad terms (with obvious sub-market exceptions),weaker housing markets can be equated with non-metropolitan rather thanmetropolitan housing markets.

One concern with the increased disparity between dwelling prices in metro-politan compared with non-metropolitan regions is that younger householdswith a strong predilection towards home ownership may be encouraged torelocate to non-metropolitan regions in order to attain home ownership. To the

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extent that these regions offer lower employment opportunities and lowereconomic bene�ts from home ownership, these households will be increasinglylocked out of the gains associated with economic restructuring.

In the Australian case examined in this paper, there is evidence to suggest thatan increasing number of low and moderate income households are beingexcluded from home ownership in the high cost metropolitan areas. It is theseareas where, historically at least, home ownership has conferred the greatesteconomic advantages on those who do gain access to it. In the time periodexamined in this paper, there is also an indication that those low and moderateincome households who have gained access to home ownership have done so bylocating in regions where the bene�ts of structural change have yet to beobserved. Such outcomes mean that home ownership can add an additionalspatial dimension to the processes of economic and social polarisation that havebeen emerging since the mid-1980s.

Acknowledgements

Earlier versions of this paper were presented at the ENHR Research Conferenceon Housing in the 21st Century: Fragmentation and Reorientation, 26–30 June2000, Gavle, Sweden and at the SPRC National Social Policy Conference:Competing Visions, 4–6 July 2001, University of NSW, Sydney. The material wasproduced with funding from the Commonwealth of Australia and the AustralianStates and Territories. AHURI Ltd gratefully acknowledges the �nancial andother support it has received from the Commonwealth, State and Territorygovernments, without which this work would not have been possible. Thecomments of Gavin Wood and Ken Gibb on earlier drafts of this paper aregratefully acknowledged as are those of three anonymous referees. The usualcaveat applies.

Correspondence

Judith Yates, School of Economics and Political Science, University of Sydney,NSW 2006, Australia. Email: j.yates@econ.usyd.edu.au

References

Australian Bureau of Statistics (1988a) 1988 Housing Survey: Housing Costs and Occupancy, Cat. No.4130.0 (Canberra, Australian Bureau of Statistics).

Australian Bureau of Statistics (1988b) Income Distribution Survey, Cat. No. 6523.0 (Canberra,Australian Bureau of Statistics).

Australian Bureau of Statistics (1993) First Home Buyers, 1988–1990, Cat. No. 4137.0 (Canberra,Australian Bureau of Statistics).

Australian Bureau of Statistics (1998) Housing Costs and Occupancy, 1997–98, Cat. No. 4130.0(Canberra, Australian Bureau of Statistics).

Australian Bureau of Statistics (1999) Australian Social Trends, 1999, Cat. No. 4102.0 (Canberra,Australian Bureau of Statistics).

Aaronson, D. (1999) A note on the bene�ts of home ownership, Journal of Urban Economics, 47(3),pp. 356–369.

Badcock, B. (1994) ‘Snakes or ladders?’ The housing market and wealth distribution in Australia,International Journal of Urban and Regional Research, 18(4), pp. 609–627.

Badcock, B. (1997) Recently observed polarising tendencies and Australian cities, Australian Geograph-ical Studies, 35(3), pp. 243–259.

Badcock, B. & Beer, A. (2000) Home Truths (Melbourne, Melbourne University Press).

Dow

nloa

ded

by [

Flor

ida

Atla

ntic

Uni

vers

ity]

at 1

9:44

14

Nov

embe

r 20

14

Social, Spatial and Structural Change 609

Baekgaard, H. (1998) The distribution of household wealth in Australia: 1986 and 1993. Discussion PaperNo. 34 (NATSEM, University of Canberra). http://www.natsem.canberra.edu.au/pubs/tps/tp14/tp14.html.

Baum, S., Stimson, R., O’Connor, K., Mullins, P. & Davis, R. (1999) Community Opportunity andVulnerability in Australia’s Cities and Towns (Brisbane, Australian Housing and Urban ResearchInstitute, University of Queensland Press).

Beer, A. & Maude, A. (1995) Regional cities in the Australian urban system, Urban Policy and Research,13(3), pp. 135–148.

Blinder, A. (1973) Wage discrimination: reduced form and structure estimates, Journal of HumanResources, 8, pp. 438–455.

Blinder, A. (1976) On dogmatism in human capital theory, Journal of Human Resources, 11(1), pp. 8–22.Boehm, T. (1993) Income, wealth accumulation and �rst-time homeownership: an intertemporal

analysis, Journal of Housing Economics, 3(1), pp. 16–30.Boehm, T. & Schlottmann, A. (1999) Does home ownership by parents have an economic impact on

their children? Journal of Housing Economics, 8(3), pp. 217–232.Boheim, R. & Taylor, M. (2000) Residential mobility, housing tenure and the labour market in Britain.

Discussion Paper 99/35 (Colchester, Institute for Labour Market Research, University of Essex).http://www.essex.ac.uk/ilr/discussion/ILRdps35.pdfdiscuss.htm

Borsch-Supan, A. & Pitkin, J. (1988) On discrete choice models of housing demand, Journal of UrbanEconomics, 24, pp. 153–172.

Bourassa, S. (1994) Gender, marital status and home ownership in Australia, Journal of HousingEconomics, 3(3), pp. 220–239.

Bourassa, S. & Hendershott, P. (1995) Australian capital city real house prices, 1979–1993, AustralianEconomic Review, 95(3), pp. 16–26.

Bourassa, S., Haurin, D. & J., Hendershott, P. (1994) independent living and home ownership; ananalysis of Australian youth, Australian Economic Review, 94(3), pp. 29–44.

Bourassa, S., Greig, A. & Troy, P. (1995) the limits of housing policy: home ownership in Australia,Housing Studies, 10(1), pp. 83–104.

Bradbury, B., Rossiter, C. & Vipond, J. (1986) Poverty before and after housing. Reports and ProceedingsNo. 56 (Sydney, Social Policy Research Centre, University of New South Wales).

Burbidge, A. (2000) Capital gains, homeownership and economic inequality, Housing Studies, 15(2),pp. 259–280.

Cameron, G. & Muellbauer, J. (1998) the housing market and regional commuting and migrationchoices, Scottish Journal of Political Economy, 45(4), pp. 420–446.

Castles, F. (1985) The Working Class and Welfare (Sydney, Allen and Unwin).Castles, F. (1997) The institutional design of the Australian welfare state, International Social Security

Review, 50(2), pp. 25–41.Castles, F. (1998) The really big trade-off: home ownership and the welfare state in the new world

and the old, Acta Politica, Spring, 33, pp. 5–19.Chan, S. (1996) Residential mobility and mortgages, Regional Science and Urban Economics, 26,

pp. 287–311.Chan, S. (2001) Spatial lock-in: do falling house prices constrain residential mobility? Journal of Urban

Economics, 49(3), pp. 567–586.Chinloy, P. (1996) Real estate cycles: theory and empirical evidence, Journal of Housing Research, 7(2),

pp. 173–191.Cho, M. (1996) House price dynamics: a survey of theoretical and empirical issues, Journal of Housing

Research, 7(2), pp. 145–172.Commonwealth of Australia (2001) Australian net private wealth, Economic Roundup, Centenary

Edition (Canberra, Treasury). Ausinfo, http://www.treasury.gov.au/.Debelle, S. & Vickery, J. (1998) Labour Market Adjustment: Evidence on Interstate Mobility. Discussion

paper 9801 (Sydney, Reserve Bank of Australia). (http://www.rba.gov.au/RePEc/rba/rbardp/rdp9801.pdf, as at May 2000).

Decressin, J. & Fatas, A. (1995) Regional labor market dynamics in Europe, European Economic Review,39(9), pp. 1627–1655.

Dieleman, F., Clark, W. & Duerloo, M. (2000) The geography of residential turnover in twenty-sevenlarge US metropolitan housing markets, 1985–1995, Urban Studies, 37(2), pp. 223–245.

DiPasquale, D. & Wheaton, W. (1994) housing market dynamics and the future of housing prices,Journal of Urban Economics, 35(1), pp. 1–27.

DiPasquale, D. & Glaeser, E. (1999) Incentives and social capital: are homeowners better citizens?Journal of Urban Economics, 45(2), pp. 354–384.

Dow

nloa

ded

by [

Flor

ida

Atla

ntic

Uni

vers

ity]

at 1

9:44

14

Nov

embe

r 20

14

610 Judith Yates

Di Salvo, P. & Ermisch, J. (1997) Analysis of the dynamics of housing tenure choice in Britain, Journalof Urban Economics, 42(1), pp. 1–17.

Doling, J., Ford, J. & Stafford, B. (Eds) (1988) The Property Owing Democracy (Aldershot, Gower).Ermisch, J. (1999) Prices, parents, and young people’s household formation, Journal of Urban

Economics, 45(1), pp. 47–71.Ford, J. & Wilcox, S. (1998) Owner occupation, employment and welfare: the impact of changing

relationships on sustainable home ownership, Housing Studies, 13(5), pp. 623–638.Forrest, R. Murie, A. & Williams, P. (1990) Home Ownership: Differentiation and Fragmentation (London,

Unwin Hyman).Gabriel, S., Shack-Marquez, J. & Wascher, W. (1992) regional house-price dispersion and interre-

gional migration, Journal of Housing Economics, 2(3), pp. 235–256.Galster, G. & Killen, S. (1995) The geography of metropolitan opportunity: a reconnaissance and

conceptual framework, Housing Policy Debate, 6(1), pp. 7–43.Glaeser, E., Laibson, D. & Sacerdote, B. (2001) The economic approach to social capital. Harvard Institute

of Economic Research Paper No. 1916. http://post.economics.harvard.edu/hier/2001papers/2001list.html.

Gordon, I. (1990) Housing and labour market constraints on migration across the north-south divide,in: J. Ermisch (Ed.) Housing and the National Economy (Aldershot, Avebury, National Institute ofEconomic and Social Research).

Green, R. & White, M. (1997) Measuring the bene�ts of home ownership: effects on children, Journalof Urban Economics, 41, pp. 441–461.

Gregory, R. (1993) Aspects of Australian and US living standards: the disappointing decades1970–1990, Economic Record, 69(204), pp. 61–76.

Gregory, R. (1996) Growing locational disadvantage in Australian cities. The 1995 Shann MemorialLecture. Discussion paper (Perth, University of Western Australia).

Gregory, R. & Hunter, B. (1995) The macro economy and the growth of ghettos and urban poverty inAustralia. Mimeo. Address to the National Press Club, 26 April.

Gregory, R. & Hunter, B. (1996) An exploration of the relationship between changing inequality ofindividual, household and regional inequality in Australian cities, Urban Policy and Research, 14(3),pp. 171–182.

Groenewold, N. (1997) Does migration equalise regional unemployment rates? Evidence fromAustralia, Papers in Regional Science, 76(1), pp. 1–20.

Gyourko, J. (1998) The changing strength of socioeconomic factors affecting homeownership in theUnited States: 1960–1990, Scottish Journal of Political Economy, 45(4), pp. 466–490.

Gyourko, J. & Linneman, P. (1997) The changing in�uences of education, income, family structure,and race on homeownership by age over time, Journal of Housing Research, 8(1), pp. 1–26.

Hamnett, C. (1994) Social polarization in global cities: theory and evidence, Urban Studies, 31(3),pp. 401–424.

Haurin, D. & Kamara, D. (1992) the home ownership decision of female-headed households, Journalof Housing Economics, 2(4), pp. 293–309.

Haurin D., Hendershott, P. & Kim, D. (1994) Housing decisions of American youth, Journal of UrbanEconomics, 35, pp. 28–45.

Haurin, D. & Hendershott, P. & Wachter, S. (1996) wealth accumulation and housing choices ofyoung households: an exploratory investigation, Journal of Housing Research, 7(1), pp. 33–57.

Henley, A. (1998) Residential mobility, housing wealth and the labour market, Economic Journal, 108,pp. 414–427.

Henley, A., Disney, R. & Carruth, A. (1994) Job tenure and asset holdings, Economic Journal, 104,pp. 338–349.

Hoff, K. & Sen, A. (2000) Home-ownership, community interactions, and segregation. World Bankdiscussion paper. http://www.worldbank.org/research/pdf�les/hoff/hoff%20sen.pdf.

Holmans, A. (1990) House prices: changes through time at national and sub-national level. WorkingPaper No. 110 (London, Department of the Environment).

Holzer, H. (1991) The spatial mismatch hypothesis: what has the evidence shown? Urban Studies, 28,pp. 105–122.

Hughes, G. & McCormick, B. (1990) Housing and labour market mobility, in J. Ermisch (Ed.) Housingand the National Economy (Aldershot, Avebury, National Institute of Economic and Social Re-search).

Hunter, B. & Gregory, R. (1996) An exploration of the relationship between changing inequality ofindividual, household and regional inequality in Australian cities, Urban Policy and Research, 14(3),pp. 171–182.

Dow

nloa

ded

by [

Flor

ida

Atla

ntic

Uni

vers

ity]

at 1

9:44

14

Nov

embe

r 20

14

Social, Spatial and Structural Change 611

Ihlanfeldt, K. & Sjoquist, D. (1998) The spatial mismatch hypothesis: a review of recent studies andtheir implications for welfare reform, Housing Policy Debate, 9(4), pp. 849–892.

Industry Commission (1993) Impediments to Regional Adjustment (Canberra, Australian GovernmentPublishing Service) (as quoted in Debelle & Vickery, 1998).

Kain, J. (1992) The spatial mismatch hypothesis: three decades later, Housing Policy Debate, 3(2),pp. 371–460.

King, A. (1998) Income poverty since the early 1970s, in: R. Fincher & J. Nieuwenhuysen (Eds)Australian Poverty: Then and Now (Melbourne, Melbourne University Press).

King, A., Baekgaard, H. & Harding, A. (1999) Australian retirement incomes. Discussion Paper No. 43(Canberrra, NATSEM, University of Canberra). http://www.natsem.canberra.edu.au/pubs/dps/dp43/dp43.html.

Landt, J. (1998) Housing affordability of low-income households in Australia, 1981–82 to 1994–95. Paperpresented at the 27th Conference of Economists, Sydney, 28 September–1 October.

Landt, J. & Bray, R. (1997) Alternative approaches to measuring rental housing affordability in Australia.Discussion Paper No. 16 (Canberra, NATSEM, University of Canberra). http://www.natsem.canberra.edu.au/pubs/dps/dp16/dp16.html.

Lloyd, R., Harding, A. & Hellwig, O. (2000) Regional Divide? A Study of Incomes in Regional Australia.NATSEM Discussion Paper No 51 (Canberra, University of Canberra).

McCarthy, G., van Zandt, S. & Rohe, W. (2001) The economic bene�ts and costs of homeownership.Working Paper No. 01–02 (Arlington, Research Institute for Housing America).

Maclennan, D., Gibb, K, Stephens, M. & Meen, G. (1997) Sustainable Owner Occupation and theEconomy (York, Joseph Rowntree Foundation).

McKay, H. (1997) Generations: Baby Boomers, their Parents and their Children (Sydney, Macmillan).Maher, C. (1994) Housing prices and geographical scale: Australian cities in the 1980s, Urban Studies,

31(1), pp. 5–27.Meen, G. (1996) Explaining house prices in the nineties: the impact of labour market change. Discussion

Paper No. 121 (Reading, University of Reading, Department of Economics).Meen, G. (1998) Modelling sustainable home ownership: demographics or economics? Urban Studies,

35(11), pp. 1919–1934.Meen, G. (2000) Economic polarisation in London and South East England. Paper presented at the ENHR

2000 Conference, Gavle, Sweden, 26–30 June.Meen, G. (2001) Modelling Spatial Housing Markets (Boston, Kluwer Academic Publishers).Meen, G. & Andrew, M. (1998a) Modelling Regional House Prices: A Review of the Literature. A Report

prepared for and published by the Department of the Environment, Transport and the Regions.(Norwich, HMSO).

Meen, G. & Andrew, M. (1998b) On the aggregate housing market implications of labour marketchange, Scottish Journal of Political Economy, 45(4), pp. 393–419.

Mudd, W., Habtemariam, T. & Bray, R. (1999) Some issues in home ownership, in: J. Yates & M.Wulff (Eds) Australia’s Housing Choices (Brisbane, University of Queensland Press).

Muellbauer, J. & Murphy, A. (1997) Booms and busts in the UK housing market, Economic Journal,107(445), pp. 1701–1727.

Munro, M. (1990) Comment, in: J. Ermisch (Ed.) Housing and the National Economy (Aldershot,Avebury, National Institute of Economic and Social Research).

Murie, A. (1998) Segregation, exclusion and housing in the divided city, in: S. Musterd & W.Ostendorf (Eds) Urban Segregation and the Welfare State (London, Routledge and Kegan Paul).

Murie, A. & Musterd, S. (1996) Social segregation, housing tenure and social change in Dutch citiesin the late 1980s, Urban Studies, 33(3), pp. 495–526.

Murphy, P. & Watson, S. (1994) Social polarization and Australian cities, International Journal of Urbanand Regional Research, 18(4), pp. 573–590.

Musterd, S. & Ostendorf, W. (1998) Segregation, polarisation and social exclusion in metropolitanareas, in: S. Musterd & W. Ostendorf (Eds) Urban Segregation and the Welfare State (London,Routledge and Kegan Paul).

O’Connor, K. & Stimson, R. (1996) Convergence and divergence of demographic and economictrends, in: P. Newton & M. Bell (Eds) (1996) Population Shift, Mobility and Change in Australia(Canberra, Australian Government Publishing Service).

Paris, C. (1994) New patterns of urban and regional development in Australia: demographicrestructuring and economic change, International Journal of Urban and Regional Research, 18(4),pp. 555–572.

Percival, R. (1998) Changing housing expenditure, tenure trends and household incomes in Australia,1975–76 to 1997. Discussion Paper No. 28 (Canberra, NATSEM, University of Canberra).

Dow

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ded

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ida

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ntic

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ity]

at 1

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14

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612 Judith Yates

Productivity Commission (1998) Aspects of Structural Change in Australia. Research Report (Canberra,Ausinfo). http://www.pc.gov.au/research/otherres/strctchg/index.html, as at May 2000.

Richardson, S. (Ed.) (1999) Reshaping the Labour Market—Regulation, Ef�ciency and Equality in Australia(Cambridge, Cambridge University Press).

Rohe, W., van Zandt, S. & McCarthy, G. (2000) The social bene�ts and costs of homeownership. WorkingPaper No. 00–01 (Arlington, Research Institute for Housing America).

Stimson, R., Shuaib, F. & O’Connor, K. (1998) Population and Employment in Australia, Regional HotSpots and Cold Spots (Brisbane, AHURI, University of Queensland Press).

Troy, P. (1991) The bene�ts of owner occupation. Urban Research Program Working Paper No. 29(Canberra, Australian National University).

van Leuvensteijn, M & Koning, P. (2000) The effect of home-ownership on labour mobility in theNetherlands: Oswald’s thesis revisited. Research Memorandum No. 173 (The Hague, CPB Nether-lands Bureau for Economic Policy Analysis). Paper presented at AREUEA mid-year meeting onHousing and the Economy, Washington, DC, 29–31 May, 2001, url:http://www.cpb.nl/eng/pub/onderzoek/173.

Wachter, S. & Megbolugbe, I. (1992) Racial and ethnic disparities in homeownership, Housing PolicyDebate, 3(2), pp. 333–370.

Walmsley, D. & Weinand, H. (1997) Is Australia becoming more unequal? Australian Geographer,28(1), pp. 69–88.

Winter, I. & Stone, W. (1998) Social polarisation and housing careers. Working Paper No.13 (Melbourne,Australian Institute of Family Studies).

Winter, I. & Stone, W. (1999) Home ownership: off course?, in: J. Yates & M. Wulff (Eds) Australia’sHousing Choices (Brisbane, University of Queensland Press).

Wood, G. (2001) Are there tax arbitrage opportunities in private rental housing markets? Journal ofHousing Economics, 10(1), pp. 1–20.

Wood, G. & Watson, R. (1999) Private rental investors costs; why who you are matters, in: J. Yates& M. Wulff (Eds) Australia’s Housing Choices (Brisbane, University of Queensland Press).

Yates, J. (1994) Home ownership and Australia’s housing �nance system, Urban Policy and Research,12(1), pp. 27–39.

Yates, J. (1997) Changing directions in Australian housing policies: the end of muddling through?Housing Studies, 12(2), pp. 265–277.

Yates, J. (2000) Is Australia’s home ownership rate really stable? Urban Studies, 37(2), pp. 319–342.Yates, J. & Wulff, M. (2000) W(h)ither low cost private rental housing? Urban Policy and Research,

18(1), pp. 45–64.

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AppendixTable A1. Regional share of households

All households Households aged 25–44

1986 1996 growth* 1986 1996 growth*(%) (%) (%) (%) (%) (%)

Sydney 21.8 20.5 16.2 21.7 20.8 12.4NSW non-metro 13.0 13.1 23.4 12.4 12.1 14.6Melbourne 18.4 17.6 17.9 18.6 18.1 13.7Vic non-metro 7.3 7.1 19.5 7.0 6.7 11.9Brisbane 7.6 8.3 35.8 7.7 8.5 28.7Qld non-metro 8.8 10.0 40.1 8.6 10.0 35.9Adelaide 6.7 6.4 18.0 6.4 6.1 11.2SA non-metro 2.4 2.2 15.6 2.4 2.2 8.5Perth 6.5 7.2 35.8 6.8 7.3 26.0WA non-metro 2.4 2.5 28.0 2.6 2.8 23.3Hobart 1.2 1.1 18.4 1.2 1.1 11.3Tas non-metro 1.7 1.6 18.1 1.6 1.5 10.4Darwin 0.4 0.4 20.8 0.6 0.5 6.7NT non-metro 0.4 0.4 27.8 0.5 0.5 24.8ACT 1.5 1.6 34.4 1.9 1.9 15.8

Metro 64.0 63.2 21.7 64.9 64.3 16.0Non-metro 36.0 36.8 26.3 35.1 35.7 19.6

Australia 100.0 100.0 23.4 100.0 100.0 17.3

Total no. of households 5 264 621 6 494 608 2 307 196 2 705 920

Notes: *Growth is in number of households in each region, not in regional shares.Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

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614 Judith Yates

Table A2. Incidence of households aged 25–44, by household type in 1986 and 1996

Household category (%)*

Coupleswith Lone

Couples children Singles parents Family Group

Region 1986 1996 1986 1996 1986 1996 1986 1996 1986 1996 1986 1996

Sydney 16.2 17.0 53.5 47.5 12.7 15.6 8.4 10.8 5.2 5.8 4.0 3.3NSW non-metro 12.8 11.6 62.5 55.0 9.4 14.1 9.4 14.5 2.9 3.2 3.0 1.6Melbourne 16.1 16.4 56.3 48.1 12.4 17.1 7.6 10.5 4.3 5.1 3.3 2.8Vic non-metro 12.8 11.9 65.5 56.7 8.9 14.2 8.2 13.2 2.5 2.8 2.1 1.1Brisbane 16.2 16.3 56.4 48.4 10.7 14.9 8.9 12.5 4.2 5.6 3.7 2.4Qld non-metro 14.3 14.4 59.9 52.3 9.5 13.9 8.5 12.9 3.8 4.7 3.9 1.8Adelaide 16.4 15.2 54.4 46.0 12.0 19.4 9.8 13.2 4.1 4.4 3.2 1.8SA non-metro 14.2 12.9 64.2 56.6 9.4 15.6 7.6 11.6 2.2 2.2 2.3 1.1Perth 15.8 15.5 54.5 48.2 11.9 17.4 9.6 12.2 4.4 4.8 3.9 1.9WA non-metro 12.9 13.5 63.3 56.6 8.8 14.1 7.1 11.1 2.7 3.1 5.1 1.6Hobart 14.6 13.3 57.1 49.0 11.2 17.3 10.7 15.0 3.5 3.9 2.9 1.5Tas non-metro 13.9 12.6 63.7 55.7 9.1 15.2 8.8 13.0 2.1 2.4 2.4 1.1Darwin 16.0 17.6 49.0 45.0 13.5 15.4 9.3 12.6 6.4 7.3 5.8 2.0NT non-metro 16.4 16.2 50.2 47.9 13.3 15.9 6.5 8.5 4.8 3.7 8.8 7.7ACT 14.8 15.9 57.0 47.3 11.0 17.8 9.2 12.0 5.1 5.6 3.0 1.4

Metro 16.1 16.3 55.0 47.7 12.2 16.6 8.6 11.4 4.6 5.3 3.6 2.6Non-metro 13.4 12.8 62.5 54.7 9.3 14.3 8.6 13.2 3.0 3.5 3.2 1.6

Australia 15.1 15.0 57.6 50.2 11.2 15.8 8.6 12.1 4.0 4.6 3.5 2.3

Notes: *Because a recoding of families with only independent children between 1986 and 1996, led tothese being recorded as family households in 1986 and lone parents in 1996, the breakdowns withinthe �nal three categories should be treated with caution. However, this is expected to have less effecton 25–44 year old households than it is on older households.Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

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Table A3. Incidence of households aged 25–44, by numberemployed in 1986 and 1996

Number employed (%)

None One Two 1

1986 1996 1986 1996 1986 1996

Sydney 13.2 13.8 40.8 38.2 46.0 48.1NSW non-metro 17.0 19.4 41.9 38.2 41.0 42.3Melbourne 10.9 13.4 41.1 40.6 48.0 46.0Vic non-metro 12.6 17.2 42.0 39.4 45.5 43.4Brisbane 12.4 14.2 43.3 38.8 44.3 47.0Qld non-metro 17.8 17.0 42.4 39.1 39.9 43.9Adelaide 13.2 17.5 40.2 40.3 46.6 42.2SA non-metro 14.5 17.4 38.9 39.5 46.6 43.2Perth 13.8 14.7 43.1 41.8 43.2 43.5WA non-metro 17.4 14.6 41.9 41.4 40.7 44.0Hobart 13.3 17.1 40.8 39.7 45.9 43.2Tas non-metro 15.1 20.1 43.6 40.7 41.3 39.3Darwin 14.3 13.5 38.8 38.1 46.9 48.4NT non-metro 21.6 14.5 36.3 37.4 42.2 48.1ACT 7.8 11.1 38.7 39.6 53.6 49.3

Metro 12.4 14.2 41.3 39.6 46.4 46.2Non-metro 16.2 17.8 41.8 39.1 42.0 43.1

Australia 13.7 15.5 41.5 39.4 44.8 45.1

Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and1996.

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616 Judith Yates

Table A4. Incidence of households aged 25–44, by income category in 1996

Income category ($1996 pw)

Low Low-mod Moderate Mod-high High Total no.Upper boundary , $300 , $500 , $800 , $1200 $1200 1 households

Sydney 8.2 12.8 21.6 26.4 31.0 562 731NSW non-metro 12.2 19.9 27.2 25.8 14.9 327 169Melbourne 8.4 14.4 24.2 27.8 25.2 489 263Vic non-metro 11.7 20.1 30.0 25.7 12.6 179 924Brisbane 8.2 14.9 26.3 28.5 22.1 229 176Qld non-metro 10.2 19.2 28.7 26.1 15.8 269 663Adelaide 12.2 17.4 28.0 25.2 17.3 164 635SA non-metro 13.3 21.1 30.5 24.1 11.1 59 120Perth 9.1 15.4 24.8 28.1 22.6 198 712WA non-metro 9.0 15.6 24.9 29.2 21.3 74 606Hobart 11.8 16.8 27.5 26.0 17.9 29 590Tas non-metro 13.3 21.2 30.5 24.1 10.8 41 626Darwin 6.5 11.5 23.0 27.7 31.3 14 410NT non-metro 7.6 12.8 24.5 28.2 26.8 14 397ACT 7.1 9.6 19.5 28.6 35.2 50 307

Metro 8.8 14.2 24.0 27.2 25.8 1 738 825Non-metro 11.4 19.4 28.2 26.0 15.0 967 096

Australia 9.7 16.1 25.5 26.8 22.0 2 705 920

Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

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Table A5. Growth relative to regional growth of households aged 25–44,1986–1996*

Income category ($1996 pw)

Low Low-mod Moderate Mod-high HighUpper boundary , $300 , $500 , $800 , $1200 $1200 1 growth (%)

Sydney 644 421 2 76 2 81 289 12.4NSW non-metro 459 311 2 51 21 177 14.6Melbourne 882 668 2 28 2 61 140 13.7Vic non-metro 782 519 2 30 2 68 51 11.9Brisbane 346 264 9 49 183 28.7Qld non-metro 221 175 33 73 171 35.9Adelaide 1044 635 2 11 2 123 34 11.2SA non-metro 666 407 2 66 2 58 75 8.5Perth 379 312 11 26 165 26.0WA non-metro 305 223 1 52 193 23.3Hobart 974 563 2 35 2 92 83 11.3Tas non-metro 1054 505 2 92 2 71 27 10.4Darwin 1241 1043 25 2 98 8 6.7NT non-metro 436 240 72 62 64 24.8ACT 934 737 178 4 2 4 15.8

Metro 630 451 2 19 2 31 174 16.0Non-metro 390 271 2 5 32 155 19.6

Australia 520 367 2 13 2 8 165 17.3

Number of 262 008 435 307 690 154 724 208 594 243 2 705 920households

Notes: *Index reported (base 5 100) is ratio of growth in each income category in each region to totalgrowth in that region (as reported in �nal column).Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

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618 Judith Yates

Table A6. Home ownership rates for households aged 25–44, 1986 and 1996

Australia Metropolitan Non-metropolitan

Household type Income 1986 1996 1986 1996 1986 1996

Couples low 45.8 38.9 35.4 32.9 53.6 45.3low-mod 43.6 40.2 38.6 34.4 48.1 47.8mod 47.4 50.9 44.0 46.2 52.3 57.7mod-high 65.2 64.4 66.4 64.9 62.7 63.4high 72.9 68.7 74.8 69.4 65.8 66.0all 63.0 61.0 64.8 61.4 59.1 60.1

Couples with low 64.0 49.5 55.2 43.5 69.2 57.3children low-mod 56.0 53.0 53.0 51.3 58.6 54.9

mod 72.2 66.3 74.5 66.6 69.1 66.0mod-high 78.8 78.4 82.0 79.7 72.5 76.4high 84.5 84.5 87.9 87.1 74.8 77.6all 75.1 72.4 78.6 74.4 69.5 69.2

Singles low 28.2 26.1 24.4 23.4 33.8 30.1low-mod 27.5 35.7 26.0 34.3 30.4 38.2mod 43.0 46.8 44.8 47.7 38.2 44.5mod-high 57.8 56.3 61.9 58.4 43.3 49.5high 57.0 53.6 60.4 55.4 44.2 46.6all 41.0 41.2 42.8 42.2 36.4 39.2

Lone parents low 28.7 23.3 29.6 22.8 27.3 23.9low-mod 38.9 29.1 41.2 29.8 35.0 28.2mod 51.2 46.0 52.9 46.4 47.4 45.3mod-high 58.1 55.2 59.9 56.4 53.3 52.5high 62.5 55.9 64.1 56.7 57.4 53.7all 42.2 35.0 44.7 36.3 37.6 33.0

Group households all 31.4 26.6 32.7 25.9 27.5 28.4Multiple family all 55.7 55.1 57.4 57.0 52.1 49.2households

All households 64.2 58.7 66.2 59.5 60.5 57.3

Source: ABS Special Request Matrix, Census of Population and Housing, 1986 and 1996.

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