Residential Preferences of the Elderly Population:
Age, Class, and Geographical Context
Eva Andersson
Dept of Human Geography, Stockholm University, 106 91 Stockholm, Sweden
eva.andersson@humangeo.su.se
Marianne Abramsson
NISAL, Dept of Social and Welfare Studies, Linköping University, 601 74 Norrköping, Sweden
marianne.abramsson@isv.liu.se
Bo Malmberg
Dept of Human Geography, Stockholm University, 106 91 Stockholm, Sweden
bo.malmberg@humangeo.su.se
Abstract
In this paper we analyzed how residential preferences in the 55+ population was related to age, socio-economic background, and geographical context. The share of the 55+ is expected to increase substantially in the coming decades. With increasing age, the risk of disability increase. Analyzing the residential preferences and residential strategies of the 55+ population will increase our understanding of how population ageing will affect the housing market and the extent to which there is a risk for a mis-match between the housing needs and wants of an ageing population and the ability to find adequate housing. The theoretical perspective used is based on the assumption that residential preferences are strongly linked to age, socio-economic status and geographical context. The importance of age will show in changes in health status, physical fitness, and cognitive ability. Socio-economic status changes like retirement, influences the values of individuals and the opportunities that are available to them. Geographical context is also assumed to play an important role. Residential preferences often influence where people live (sorting), and valuations are influenced or re-enforced by the social norms that prevail in the area where one resides (contextual effects). We used an empirical survey, SHIELD, directed to a stratified sample of the 55+ population in Sweden with questions about residential status, residential preferences, and residential plans. With respect to geographical context, our approach was based on a methodological innovation to circumvent potential pitfalls caused by the modifiable areal unit problem. First results show that decreasing with age are preferences for closeness to forest and land, that the dwelling has a garden, is owned, that the family can come and stay, that there are room for social events, and that hobbies can be practiced. Preferences increasing with age are that the dwelling is located in an area where I feel at home, is designed for independence, and for disability, has an elevator, and that it is one-storied. Importantly, elderly 55+ is not a homogenous group concerning housing preferences. A challenging result is however that class and income are not determinants for housing preferences in old age in the same way as age and geographical context. Finally, geographical context is of importance for elderly’s’ housing preferences, e.g. garden is preferred in middle class area types but not in elite and marginal area types.
2 WORKSHOP: WS-04: Housing & Living Conditions of Ageing Populations
Residential Preferences of the Elderly Population: Age, Class and Geographical Context
Introduction
In this paper we analyze how residential preferences in the 55+ population are related to age, socio-economic background, and geographical context. The residential preferences of this population group are interesting for several reasons. On the one hand, the population share of the 55+ is expected to increase substantially in the coming decades due to increasing life expectancy and the ageing of the baby-boom cohorts. On the other hand, with increasing age, the risk of disabilities increases and this will make it difficult for individuals to maintain their quality of life if their dwellings are not well-adapted for disability. This implies that the need for housing market adaptation will increase in the coming years. Analyzing the residential preferences and residential strategies of the 55+ population will increase our understanding of how the challenge of population ageing will affect the housing market and the extent to which there is a risk for a mis-match between the housing needs and preferences of the ageing population and their ability to find adequate housing.
To further add to the research on preferences and supply of housing for elderly, which is mainly based on qualitative research, we will analyze the preferences linked to age, socio-economic status and geographical context. Are there differences in preferences between different age groups supporting the debate on limiting specific housing for certain ages? And most importantly for different structures of local housing markets; are there differences in preferences between municipality types and geographical contexts?
The theoretical approach we will use is constructed around a life-course perspective with the assumption that residential preferences are strongly linked to 1) age, 2) socio-economic status and 3) geographical context. Firstly the importance of age is based on the fact that people above 55 are experiencing relatively significant changes in their health status, physical fitness, cognitive ability, and social position (Huber & Skidmore 2003). There is a discussion whether older people today will be pro-active or not in changing housing to accommodate to these changes and prepare for old age (Pope and Kang 2010). Socio-economic status, in turn, influences both what values individuals hold and the opportunities that are available to them. There is a development towards specific housing for specific groups where housing companies and developers try to find ‘good costumers’ for their business. Finally, geographical context can be assumed to play an important role because values are influenced or re-enforced by the social norms that prevail in the area where one resides. On an even larger scale there is evidence that older people move according to
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the housing stock on the local housing market (Abramsson & Andersson forthcoming). Therefore it is of great importance to compare the preferences of elderly and the housing market’s supply of housing.
Empirically, our analysis of the influence of age and socio-economic status on residential preferences followed a standard approach. We have used an empirical survey (SHIELD) directed to a stratified sample of the 55+ population in Sweden, with questions about residential status, residential preferences, and residential plans, and these answers will be related to the respondents’ age and socio-economic status (compare Ytrehus 2005). With respect to geographical context, though, our approach will be based on a different methodological design to circumvent potential pitfalls caused by the modifiable areal unit problem (Openshaw 1984). Essentially this approach consists of classifying residential areas using multi-scalar context measures that have been computed for individualized neighborhoods with fixed population size.
For a survey like SHIELD there can be several reasons for finding effects from the above mentioned age, socio-economic status and geographical context. As such we are in this study particularly interested in the geographical effects on preferences, since such earlier research for elderly is largely lacking. Therefore we measure the preferences as formed locally for the main part of this paper. We do however admit that there are, like in most contextual effects research, criticism to be made to this declaration. A second explanation for preferences that look like they are formed in a geographical context, is sorting into locations. That is, preferences govern mobility decisions. Like most surveys we also anticipate the cognitive dissonance to be at work, that is, people adjust to where they live and justify their choices as they are already made (Niedomysl &Malmberg 2009).
With the proliferation of neighborhood-effect studies during the last decade, the question of how to correctly measure context has become a central concern (Kawachi and Berkman 2003; Cummins et al. 2007; Spielman and Yoo 2009; Kwan 2012; Sampson 2012; Matthews and Yang 2013). As stated above this paper applies an approach to contextual measurement that uses individually defined, scalable neighborhoods (Östh, Malmberg, and Andersson forthcoming 2014), and factor analysis to produce a nuanced and composite description of geographical contexts.
Although contextual-effect studies typically focus on economic, behavioral, or health outcomes, there are also a number of studies that analyze context effects on attitudes. Studies have analyzed how context influences attitudes towards health or other social and ethnic groups, civic values, and political preferences (Stein, Post, and Rinden 2000; Macallister et al. 2001; Cummins et al. 2005; Galster and Santiago 2006; Pettigrew and Tropp 2006; Pettigrew et al. 2007; Bowyer
2009). It is often acknowledged only in passing, however, that the findings of these studies can
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clearly demonstrated by Spielman and Yoo (2009) who show that a mis-specification of geographical context can lead to underestimation of the effects.
To address the combined problem of different fields of influence and the possibility that the relevant geographical context varies between fields of influence, this paper uses factors extracted from a broad set of contextual variables computed for individually defined, scalable neighborhoods to estimate contextual influences on older people’s housing preferences and choice.
A starting point for our approach to contextual measurement is the introduction, in the early 2000s, of bespoke neighborhoods that measure contexts using statistical aggregates for the 100, 500, 2500, or 10,000 nearest neighbors of each individual (Macallister et al. 2001; Bolster et al. 2007). In epidemiological research the same approach, applied to mental health data, has been called “spatially adaptive filtering” (Chaix et al. 2005). Later, this method has been applied to segregation measurement (Malmberg, Andersson, and Östh 2011). In addition to being individually defined, using customized neighborhoods implies that context measures are assigned an explicit scale based on the number of nearest neighbors included in the aggregate - alternative ways of constructing scalable neighborhoods are discussed by Reardon (2008) and Spielman and Logan, (2013). Scalability, however, also creates a problem since each scale level will generate a distinct contextual value for every socio-economic indicator that is included in the analysis. Thus, the number of contextual values assigned to each location can become very large.
To handle the number of contextual variables that results from allowing neighborhood scale to vary we employ factor analysis. Contextual values for different neighborhood scales will be correlated and factor analysis can be used to compress the original contextual variables to a smaller set of orthogonal factors that will summarize the spatial structure of the socio-economic environment. Factor analysis is a well-known method for producing summary measures of the urban environment but, to our knowledge, it has not been previously applied to a set of contextual values that are aggregates for differently scaled, bespoke neighborhoods. The advantage of our approach is that the resulting factors can be seen as representing not only different types of socio-economic influence but also the geographical structure of the socio-socio-economic environment. We would, therefore, argue that our approach presents one possible way to address what Kwan (2012) has called the problem of uncertain geographic context. The idea is that if factors extracted from scaled contextual variables give a good representation of the geographical context, then the inclusion of these factors in a regression analysis will generate estimates that indicate what contextual dimensions are most relevant for the outcome under study.
Following this introduction is a section on housing choice and preferences among older people. Next data and methods are presented together with the approach of multi-scalar measures of
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composite contexts. Then there’s the results section on what influences housing preferences and regression estimates. Finally there is a concluding discussion.
Housing choice and preferences among older people
In general the housing choice of older people reveals the preference of remaining where they are, as the major proportion of older people are stayers (Abramsson and Andersson 2012; Abramsson et al. 2012; Ytrehus 2012). Ownership is the most common type of tenure in Sweden, in particular among older people, but other tenures are not considered subordinate (Andersson et al. 2007) but rather a good housing choice depending on life-stage and life style. Rental housing is not generally stigmatized and the rights of tenants are comparatively strong as is security of tenure. As a result, the different types of tenures are attractive to all groups (Andersson 2008). Among older Canadians, Ostrovsky found a higher transition rate, from single-family housing to apartments, than that for moves in the reverse direction. This indicates a shifting preference towards apartments in old age, although limited in scope (Ostrovsky, 2004). Elderly’s satisfaction with apartment housing was shown by James (2008). Swedish studies similarly indicate a growing interest among the elderly in moving to more comfortable housing involving less maintenance (Abramsson et al., 2008). Such moves can be seen also in Norway but only evidently after the age of 80 (Andersen 2005).
For Norway, Ytrehus (2005) on the other hand, show that most older people express a wish to remain in a current large dwelling in order to have room for their own activities and family life. They were prepared to live less comfortably in order to maintain the space as they argued that they might be unhappier in a smaller dwelling with too little room. A majority preferred to own their dwelling also in future (Ytrehus 2005). The youngest age groups, in their 50s and 60s, anticipate that they will have to live in a smaller dwelling in future.
Life course events, such as retirement, the loss of a partner and declining health, influence mobility rates as well as the destination of moves (Litwak & Longino 1987). Upon retirement the reasons for moving are most often different to moves conducted later on in life or in relation to declining health. Among the latter, smaller dwellings are preferred, in rental tenure that are more easily maintained these dwellings might also be preferred in relation to the loss of a partner (Abramsson et al 2012; Litwak & Longino 1987; Chevan 1995). Residential mobility among the young old, on the other hand, can be expected to result from a preference to change housing area, housing type or housing tenure. In Norway a move to an apartment is more commonly considered by owner occupiers with higher incomes compared to low income owners (Ytrehus & Fyhn 2006). The former group is more likely to be able to afford a better quality apartment.
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Recent research show increased residential mobility rates of the 1940s age cohort in Sweden when compared to older age groups at the same age (Andersson and Abramsson 2012) and may indicate a change in residential mobility behaviour in which mobility in old age becomes more frequent. Increased longevity may result in changing plans for life in retirement as more years are spent in the third age, a time in life that follows family up-bringing and working life while the individual remains in good health. These years can be planned for new social and recreational activities (Laslett 1996; Warnes 1992, p 181).
Most moves among the population in general are local moves, most often the same municipality is preferred and frequently also the same housing area (Abramsson et al. 2012; Fransson and Borgegård 2002; Ytrehus 2007) and this is particularly true among older movers. Local moves are most often housing related, and a result of a desire to change housing, such as housing type, tenure, size or environment (Myers 1990). When moving within a local housing market the individual’s social networks are not lost.
In a Swedish study mobility rates among older people (65+) in different types of municipalities were shown to be largely similar with the exception of the metropolitan cities where residential mobility levels are slightly lower and in the suburbs of major cities where they are higher than in other types of municipalities (Abramsson et al 2012). The choice of tenure when moving from owner occupation showed some interesting differences. In the more urbanized municipalities a larger proportion of older people moved to tenant cooperative apartments whereas in municipalities with a rural character moves into rental tenure was more frequent (Abramsson et al 2012). This is assumedly a result of the housing market supply (Abramsson & Andersson, forthcoming) but may not necessarily be a result of the wish of the older individuals themselves. Data and method
To estimate contextual effects on housing preferences we employ a logistic regression approach using survey answers as dependent variables, with individual background variables and contextual variables as explanatory variables.
In SHIELD, Survey of Housing Intentions among the ELDerly in Sweden survey data on housing preferences were collected in the spring of 2013. The survey was sent out to 4000 individuals aged 55 years and older. Previous Swedish studies have mostly focused on the young old but here the oldest respondent was 103. The survey was stratified on age and on municipality type for the purpose of analyzing age groups as well as geographical differences in attitudes among older people in Sweden. The response rate was 60.7 per cent or 2400 respondents.
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To the survey data register data from Statistics Sweden was added. The register data consists of information on the respondent’s sex, year of birth, country of birth, year of immigration,
educational level, household income, disposable income, municipality and local SAMS-area1.
Table 1 below shows the slightly modified variables used in this study.
Table 1. Composition of respondents.
Respondents' background n= 2400 Share within variable
Share respondents in SHIELD Sex Women 1326 55.3 59.9 Men 1074 44.8 61.8 Age, 10-‐years 55-‐64 564 23.5 56.5 65-‐74 685 28.5 68.4 75-‐84 628 26.2 63.9 85 and older 523 21.8 53.8 Municipality type 1. Metropolitan cities 403 16.8
2. Suburbs of metropolitan cities 410 17.1
3. Major cities 415 17.3
4. Suburbs of major cities 426 17.8
5. Industrial and commuting 107 16.0
6. Low density and tourism 141 15.0
Country of birth
Swedish born 2143 89.3 61.8
Non Swedish born 257 10.7 52.6
Educational level
Lower secondary school 853 35 52.6
Secondary school 956 40 63.6
University degree 591 25 71.2
Income
Disposable income per person,
percentiles 2396
Marital status
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Married 1269 52.9 66.8
Unmarried 241 10.0 51.7
Divorced 399 16.6 58.2
Widow/Widower 491 20.5 54.5
The classification of municipalities, made by the Swedish Association of Local Authorities and Regions (SALAR) label the Swedish municipalities according to type of municipality, that is 1) metropolitan cities; 2) suburbs of metropolitan cities; 3) major cities; 4) suburbs of major cities; 5) commuter municipalities; 6) tourism municipalities; 7) goods producing municipalities, 8) rural municipalities, 9) municipalities in densely populated regions and 10) municipalities in sparsely populated regions. In this study we joined commuter, goods producing and densely populated region municipalities into one type (no. 5). Also tourism, rural and sparsely populated region municipalities were joined (no. 6). The new names to municipality type 5, industrial and commuting and 6, low density and tourism are found in Table 1.
In the regression analysis we use the responses to Question 32 in the survey, that is 21 different alternatives, as outcome variables, see Table 2.
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Table 2. Question and response alternatives used as dependent variables.
Survey question Response alternatives %
Q32 What is most important in the dwelling for you? Choice of seven most important properties in the dwelling.
That the dwelling is one floor 1176 49,0
That the dwelling has an elevator if more than a second floor 868 36,2 That the dwelling is design for disability 407 17,0
That the dwelling is easily maintained 866 36,1
That the dwelling is design in a way that I/we can manage ourselves
1090 45,4 That the dwelling makes it possible for me/us to practice hobbies 397 16,5 That there is room for social events like parties, dinners and
meetings etc.
233 9,7 That the family can come and stay in the dwelling 1098 45,8 That one can have pets in the dwelling 448 18,7
That I/we own the dwelling 817 34,0
That the dwelling has a nice view 712 29,7
That the dwelling has a balcony/terrace 1513 63,0
That the dwelling has a private garden 726 30,3 That there are good possibilities for parking close to the dwelling 805 33,5 That the dwelling is located in an area where I feel at home 938 39,1 That the dwelling is located close to the family 452 18,8 That the dwelling is located close to forest and land 669 27,9 That the dwelling is located close to city life/environment 236 9,8 That the dwelling is located close to one or more grocery shops 1073 44,7 That the dwelling is situated in an area with large supply of
service and culture
383 16,0 That the dwelling is located close to public transport 1078 44,9
Geographical context
In addition to individual level background variables (age, sex, education, income) and municipality type the regression analyze will also estimate the effect on housing preferences of the type of areas that the respondent live in. The area types used for this analysis are based on a cluster analysis of variables describing the socio-economic composition of the geographic environment of residential locations in Sweden. Ten area types have been identified: Prime Elite Suburban elite, Major city elite, Single mother neighborhoods, Middle class, Low middle class, White unemployed, White marginal and Marginal non-elite, see Figure 6. The approach used to construct these clusters is described in more detail below.
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Using multi-scalar measures of context to construct geographical clusters
A common approach for measuring the effect of geographic context is to analyze how individual -level attitudes and behavior varies between residential areas that have been classified on the basis of their population composition. The classification can be done in different ways but multivariate cluster analysis tends to be the preferred tool. What is not always acknowledged is that variables that have been computed as aggregates for fixed geographical sub-divisions such as census tracts or output areas can be strongly influenced by the way the boundaries of these areas have been drawn. Moreover, using aggregates for fixed geographical sub-divisions to classify residential areas implies an assumption that only conditions within the sub-division matters for individual level outcomes. This is a very restrictive assumption for how geographical context can play a role, and it has recently been demonstrated that it can lead to seriously biased estimates. Using aggregates for individualized scalable neighborhoods to measure context has been proposed as method for circumventing this problem. Such individualized neighborhoods can be constructed by expanding a circular buffer around different residential locations until the population encircled by the buffer corresponds to a selected population threshold. When this threshold is reached, one can compute aggregate statistics on selected socio-economic variables for the encircled population. By varying the population threshold, contextual measures computed in this way can be designed to focus only the closest neighbors or on larger number of neighbors. In the present study, we allow for multiple scales of ecological influence by varying k, the number of nearest neighbors that are included in the computation of population, from 12 to 12 800 in successive doublings of the population thresholds. The computation was carried out using a new software, Equipop, developed by John Östh in order to address the modifiable areal unit problem (MAUP) in segregation measurement (Östh, Malmberg, and Andersson 2011). Equipop requires that the input data are geo-coded to a high level of detail. In this case data from the Population Labor Market Chorology database (PLACE) of Uppsala University has been used. PLACE contains register-based, individual level information for the population in Sweden from 1990 to 2010 with geocodes of the residential location in 100 meter squares. 8 different socio-demographic indicators were extracted to use as input for EquiPop. On the individual level these indicators are: (1) Being unemployed, (2) Having a tertiary education (3) Being a single mother (4) Belonging to the top-ten percent of the income earners (5) Arrived in Sweden during the last five years (6) Having received social allowance during the year (7) Without employment during the entire year, and (8) Country of birth in Asia, Africa, or Latin America. Before being imputed these data were aggregated to 100 meter squares based on the geo-coordinates.
8 socio-demographic indicators and 11 different scale levels (k = 12, 25, 50, 100, 200, 400, 800, 1600, 3200, 6400, and 12800) result in 88 different measures of neighborhood context that can be used to classify residential areas using cluster analysis. However, since many of these variables will be highly correlated we have subjected the contextual indicators to a factor analysis that
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compresses the 88 original indicators to 15 orthogonal factors before proceeding with the cluster analysis. The factor analysis was based on correlations and the number of factors was selected based on them having eigenvalues higher than one. The factor analysis was based on correlations and the number of factors was selected based on them having eigenvalues higher than one. The factors were rotated using the varimax method.
Figure 1 illustrates the results of the factor analysis. The panels in this figure show the loading of the different factors (columns) for each indicator (rows). The loadings are indicated by a graph showing the values for different k-levels. Every panel also has a reference line indicating the zero-level. The first factor in Figure 1 (left most column) represents an elite context with high values for tertiary education and top ten percent income. The second factor represents top-ten near because of high values for top ten percent income for lower k-levels (closer neighbors). The fourth factor indicates a high share of newly arrived immigrants and visible minorities (VM). The fifth factor signals marginal groups in adjacent areas with high values for many marginal groups but only for relatively large k-values (distant neighbors). The sixth factor highlights a high share of visible minorities but not newly arrived. The tenth factor represent white non-elite context with high levels of unemployment and low values for top ten percent income. The seventh factor has high loadings for social benefits. The ninth factor has high loadings for single mothers. The eleventh factor has high loadings for non-employed. This can indicate either low employment shares in the working age population or a high share retirees (65+). The interpretation of the remaining factors can be done from the graphs.
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Figure 1. Description of 15 factors and their loadings.
As a result of the factor analysis every populated 100 meter square in Sweden can be assigned factor scores for each of the 15 contextual factors. Together these factor scores can be seen as determining the position of different geographical location in a socio-demographic contextual space. Geographic context in this representation does not only consist of the demographic composition of a residential area. Instead, context is a composite feature that includes characteristics of both the closest environment and of more extended local contexts.
What we propose in this paper is that these factors can be used to classify residential areas into categories that capture important dimensions in the spatial variation of geographical context. This is achieved by computing residential-area averages for the 15 factors and, then, to use cluster analysis of these averages to group residential areas into classes with varying contextual characteristics. Although this can appear to be almost equivalent to a cluster analysis based on characteristics of the residential area population it is not. Consider for example a residential area where no one has a tertiary education but with neighboring areas that have a high percentage of highly educated. The population composition of these neighboring areas will affect the averages of Equipop-based contextual measures but not measures that are based only on the population
g Elite Top ten
near Top ten adjac VM, new, near Margi nal adjac VM Social benef it Margi nal medi Singl e near White non-elite Non-emp Singl e adjac Misc 2 Terti ary near Misc 1 Top 10 Tertia ry No n Emp Si nge l Moth Unem p So cia l Ben New Arriv Visib e l Min K 10 1000 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 -0.10.3 0.7 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 10 1000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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composition of the residential area in focus. This implies that our proposed approach will not be as strongly influenced by MAUP considerations as the traditional approach. This increases the probability that contextual effects on residential preferences will be successfully captured by a regression design that uses residential-area categories in an explanatory role.
Using Ward's clustering method we initially grouped the SAMS areas into 20 different clusters. However, in order to avoid having cluster with comparatively few SAMS-areas, the number of clusters was reduced to 10 by merging smaller cluster into larger ones. The principle used was that clusters identified as being too small were merged with the cluster having the most similar mean factor scores (minimum Euclidian distance between cluster centroids).
In Figure 2, the mean factor scores for the resulting ten contextual clusters are presented. The names given to the clusters are based on the profile of these mean factor scores and on the municipality type where the cluster is found, see Figure 3.
Figure 2. Clusters explained by factors.
The Prime Elite clusters have high values for the Elite, the Top Ten Near, and Top Ten Adjacent factor. The Sub Urban Elite cluster has positive but smaller values for the same factors and a
p
Cluster means
Elite Top ten near Top ten adja VM, new, near Mar gina l dj VM Social ben # Mar gina l d Sing le near Whit e non Non -emp Sing le adja Misc 2 Terti ary near Misc 1 Factor Prime Elite Sub urban elite Major city elite Single mother Middle class Low middle class White unemp White marginal White older Marginal non-elite New C lus ter formul a 1 1 1 1 1 1 1 1 1 1
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negative value for the White Non-Elite factor. The Major City Elite cluster has a high value for the Elite factor but negative for the Top Ten Near and Top Ten adjacent. This cluster also has a high value for the Marginal Group Nearby factor. The Single Mother cluster has high values for the Single Mother factor, the Marginal Nearby and VM factor, as well as for the Non-Employed factor. The characteristic of the Middle-Class cluster is factor scores close to the mean on all the factors. This cluster, thus, represents areas in which the composition is close to the Swedish average. The Low Middle-Class cluster is similar but it has a higher value on the Non-Employed factor. The White Unemployed cluster is also similar to the Middle-Class cluster but has high values for the White Elite factor. The White Older cluster has high values for the Non-Employed and Single Mother Factor. The Marginal Non-Elite has high values for factors with high loadings for visible minorities, social allowances, and new arrivals. The White Marginal cluster has positive but lower values for the same factors. See Figure 2.
Figure 3 Municipality type and clusters.
0.00 0.25 0.50 0.75 1.00
Metropolitan Metropolitan suburb
s
Major cities
Major cites suburban
Low
density and
tourism Industrial and commuting
K 2011
Prime Elite Sub urban elite Major city elite Single mother Middle class Low middle class White unemp White marginal White older Marginal non-elite
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Figure 4 presents a map of the geographical location of the different clusters which helps explain the results of the preferences in different clusters/area types. A close up of mid-Sweden and six major Swedish cities are provided in the appendix.
Figure 4. Map of clusters, Sweden SAMS-areas with enlargement of Stockholm, Malmö and Gothenburg.
Results: Influences on Housing Preferences
The estimation results for the 21 logit models are presented in Table 3, Figure 5, and Figure 6. Table 3, first, shows the likelihood ratio Chi-Square values for the explanatory variables for each response to the question What is most important in the dwelling for you? (Q32). The Chi-Square values indicate the strength of the effect of the explanatory variable and, can be used to calculate significance levels. In the table significance levels below 0.001% are marked in yellow and levels below 1% in pink. Figure 5 presents graphically the point estimates for the different levels of the explanatory variables, and Figure 6 presents a close up for the effects of different clusters on responses.
0 110 220 Km 0 25 50 Km
Stockholm area Gothenburg area
Scania area Sweden Samsclusters No data Cluster Marginal non-elite White older White marginal White unemp
Low middle class Middle class Single mother Major city elite Sub urban elite Prime Elite
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The first results concerning age groups show that questions concerning the interior of the dwelling, alternative answers 1 to 9, all have significant and important ChiSquare estimates, Table 3. The necessity to divide elderly into age groups with different preferences is strengthened. Secondly the contextual effects on housing preferences among elderly cannot be ignored. There are surprisingly many significant survey responses in the Area type column in Table 3 The significant response results concern the location and environment of the dwelling (Q32, alternative 10-13, 17-21). This is in a way inherent in that responses reflect present housing (cognitive dissonance) among the elderly but nevertheless show significant results.
Table 3. L-R ChiSquare, significant <0.001 in pink and yellow.
Less pronounced in Table 3 (fewer significant, colored ChiSquare estimates), nonetheless our results also show significant results in differences in housing preferences among elderly men and women. Gender, thus, is another important aspect to consider in the planning of housing for elderly. Also some significant results are found from municipality type for the preferences of elderly. Finally and somewhat surprising when compared to earlier research is the little significance found for housing preferences for differently educated groups and income groups. Normally this is referred to in the ‘right to choose’ debate of care and planning for elderly; that
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preferences are different and that the supply of housing must differ in both profile and quality to meet demands of e.g. well-educated middle class in the future.
As stated in the introduction we will analyze the preferences linked to age, socio-economic status and geographical context. Are there differences in preferences between different age groups supporting the debate on limiting specific housing for certain ages? And most importantly, differences in preferences for different structures of local housing markets; are there differences in preferences between municipality types and geographical contexts/area types?
The above overall description of variables (age, geographical context, sex, municipality type, education and income) of importance for housing preferences will be elaborated on in further detail below. We will show for instance the difference between age groups’ preferences etc. Age groups will start the section of results to be continued with geographical context, sex, municipality type, education and income as important determinates of housing preferences among the elderly.
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Figure 5. Effect of age, sex education, income, municipality type, and cluster type on responses to the question "What is the most important in the dwelling for you".
Q32$What$is$most$important$
in$the$dwelling$for$you?$$ Age$ Sex$ Education$ Disp$Income$ percentile$ Municipality$ $type$$ Area$Type$ $
That$the$dwelling$is$one$floor$ ! That$the$dwelling$has$an$elevator$if$ more$than$a$second$floor$ That$the$dwelling$is$design$for$ disability$ That$the$dwelling$is$easily$maintained$ That$the$dwelling$is$design$in$a$way$ that$I/we$can$manage$ourselves$ That$the$dwelling$makes$it$possible$ for$me/us$to$practice$hobbies$ That$there$is$room$for$social$events$ like$parties,$dinners$and$meetings$ etc.$ That$the$family$can$come$and$stay$in$ the$dwelling$ That$one$can$have$pets$in$the$ dwelling$ That$I/we$own$the$dwelling$ That$the$dwelling$has$a$nice$view$ That$the$dwelling$has$a$ balcony/terrace$ That$the$dwelling$has$a$private$ garden$ That$there$are$good$possibilities$for$ parking$close$to$the$dwelling$ That$the$dwelling$is$located$in$an$ area$where$I$feel$at$home$ That$the$dwelling$is$located$close$to$ the$family$ That$the$dwelling$is$located$close$to$ forest$and$land$ That$the$dwelling$is$located$close$to$ city$life/environment$ That$the$dwelling$is$located$close$to$ one$or$more$grocery$shops$ That$the$dwelling$is$situated$in$an$ area$with$large$supply$of$service$and$ culture$ That$the$dwelling$is$located$close$to$ public$transport$
$ ! ! 55#64! 65#74! 75#84! 85#105! ! ! ! Female! ! Male! ! Primary! Secondar
y! Tert ia ry ! ! ! 10! 25! 50! 75! 90! ! Metr o! Me tr o!Sub ! Majo r!c it! Majo r!Sub! Low !den s.! Ind !!& !C om ! ! Prime!elite! Sub!ur b! Maj!c itty !! Sing!moth! Mid!cl ass!
Low!middle! W!unemp! Wh!marg! W!o
lder ! Mar g!no n! !!! ! ! ! -1 -0.5 0 0.5 -1 -0.5 0 0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -1 -0.5 0 0.5 Dat a -1 -0.5 0 0.5 Dat a -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 Dat a -0.4 -1.5 -0.4 -1.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 Dat a -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 n Q -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -1 -0.5 0 0.5 Dat a -1 -0.5 0 0.5 Dat a -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1 -0.5 0 0.5 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -1.5 -1 -0.5 0 0.5 1 -0.4 -0.2 0 0.2 0.4 Dat a -0.4 -0.2 0 0.2 0.4 Dat a -0.4 -0.2 0 0.2 0.4 Dat a -0.4 -0.2 0 0.2 0.4 Dat a -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -0.4 -0.2 0 0.2 0.4 -1 -0.5 0 0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 -1 -0.5 0 0.5 Dat a 1.5 -1.5 -1 -0.5 0 0.5 1 -0.4 -0.2 0 0.2 0.4 -1 -0.4 -0.3 -0.2 -1 -1.5 -1 -0.4 -0.2 -0.5 0 0.5 -0.1 0 0.1 0.2 0.3 -0.5 0 0.5 -0.5 0 0.5 1 0 0.2 0.4
19
Age effects on preferences
The results for outcomes about location and environment show age effects in several alternatives Figure 5. Public transport in the vicinity is less important with increasing age except for the youngest 65 to 75 years, also decreasing with age is preferences for closeness to forest and land, that the dwelling has a garden, that we/I own the dwelling, that one can have pets, that the family can come and stay, that there are room for social events like parties, dinners and meetings, that we/I can practice hobbies and that the dwelling is easily maintained (first column, Age, Figure 5). Preferences increasing with age are, that the dwelling is located in an area where I feel at home, that the dwelling is designed in a way that we can manage ourselves (except for the oldest, 85+), that the dwelling is designed for disability, that the dwelling (if higher than on the second floor) has an elevator, and finally increasingly important with age is that the dwelling has one floor (first column, Age, Figure 5).
Geographical context, area types
In this section we will refer to the map in Figure 4 and its colored pattern but also to Figure 6 for a quicker overview of how respondents in different geographical contexts/clusters responded. Prime elite (202 respondents): The cluster type of Prime elite areas is visible as purple areas. The areas are characterized by having a higher proportion of people with tertiary education and high shares of people with top incomes. These elite concentrations are found in the metropolitan cities and in major cities, as well as in attractive coastal locations. These are areas that have typically experienced favorable development in the last 20 years with population gains and employment growth. Our logistic regression on housing preferences as outcomes shows interesting results for elderly in Prime elite areas, Figure 6. The respondents stated that the most important in the dwelling was the location close to a large supply of service and culture, that the dwelling should be located close to city life/city environment and that it should have a nice view. If compared to the map of Prime elite cluster locations these preferences seemed to be able to be fulfilled there. Elderly living in Prime elite areas also preferred to have room for social events, hobbies and that the dwelling should have an elevator. These preferences were also by large possible to fulfill in Prime elite areas. The preferences with positive estimates for Prime elite clusters can be considered indicators for higher classes in the Swedish society. Also the negative estimates, that is, the preferences not important for elderly in Prime elite clusters can be seen as indicators of higher classes. Negative estimates were found for importance of a garden, and that the dwelling is located close to family and that the family can come and stay in the dwelling.
Weak elite suburban (205 respondents): This cluster is mainly found in the sub-urban parts of metropolitan Stockholm and metropolitan Göteborg, but there are also some such areas in Western Skane and in some parts of the major cities. Individuals in this cluster value having a garden and having a dwelling that include room for hobbies. Less stress is given to having and
20
urban environment and good communications. A dwelling designed for disabilities and having an elevator is also of less importance. Instead people living in these areas think that having a dwelling on one floor only is a plus.
Weak elite, major city (255 respondents): This cluster is well represented in Göteborg and Malmö but less well represented in Stockholm. It is also very well-represented in major cities such as Uppsala, Västerås, Örebro, Lund, Linköping etc. Individuals in this cluster put great stress on living in an urban environment but little stress on living near nature, owning their own dwelling, having a dwelling suited for hobbies and pets. Instead they stress the importance of having an elevator if more than a second floor and they also prefer an area with large supply of service and culture.
Middle class (427 respondents): Areas belonging to the middle class cluster tend to be found in rural areas under urban influence. This cluster is, for example, well represented in the non-urban areas north and west of Stockholm, east of Göteborg, and in western Skane. It is also found in the rural areas around Linköping and Jönköping, in central Gotland, around Falun and Borlänge; around Örebro, north of Östersund, and in the coastal areas of north Sweden. Individuals in these areas express a strong preference for having a garden and a preference for having access to forest and nature. They also have a tendency to stress owning their dwelling, having a dwelling good for pets and where family can stay. Little emphasis is given to living in an urban environment, good communications, availability of grocery stores, or having a terrace or elevator.
Low middle class (380 respondents): Low middle class areas tend to be located at larger distances from major urban centers, in border areas between more central regions and in areas strongly specialized in agriculture. Examples include the most peripheral parts of the Stockholm region, the most peripheral parts of the Skane region, and the most peripheral part of the Göteborg region between Lake Vänern and the Norwegian border. Examples also can be found in the inland parts of northern Sweden between the costal and mountain areas. The residential preferences of the low middle class areas are similar to those of the middle class cluster.
Single Mother (247 respondents): The Single mother cluster is mostly represented in the metropolitan areas and in the major cities. Individuals living in these areas tend strongly tend not to stress the importance of having a garden. Neither is it important to have room for social events or room for having family members staying. Likewise, being close to forest and owning one's own dwelling is not emphasized. What is important is instead to have a balcony or terrace, having an elevator, good communications, an urban environment and, to some extent, having a great view.
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Figure 6. Important traits (preferences) for elderly living in different clusters.
White older (287 respondents): Areas belonging to this cluster tend to be located in smaller Swedish towns in different parts of Sweden. Individuals living in these areas value an urban environment and availability of grocery stores but not closeness to nature and forest.
White marginal (152 respondents): Areas in this cluster tend to lie close to white unemployment cluster areas. To live in these areas is not associated with any significant patterns in residential preferences.
The white unemployment (140 respondents) cluster demonstrate high levels of unemployment in the more sparsely populated, forested areas of northern and north-western Sweden, as well as in areas that are distant from the metropolitan areas and other major cities. Preferences with negative estimates for elderly in these contexts are that the dwelling should be located close to public transportation, service and culture, grocery shops and close to city life/ city environment. According to our mapping these preferences correspondingly seem difficult to fulfill at least in some of the white unemployment areas. The less importance of the very same preferences is contrasting those by elderly in Prime elite areas, see Figure 6. However, positive estimates were
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found for preferences for closeness to forest and land, that I feel at home in the area, that the dwelling has a garden, that we/I own the dwelling and that the dwelling makes it possible for me/us to practice hobbies.
Marginal non-elite (104 respondents): Figure 5 shows that the marginal non-elite cluster is associated with metropolitan areas and with Sweden’s larger cities but also with some major cities. This macro-level segregation is associated with the establishment of large housing estates during the 1960s and 1970s that today have an overrepresentation of visible minorities, newly arrived immigrants, and social allowance recipients. Preferences found in these areas are that the dwelling should be located close to public transportation, grocery shops and close to city life/ city environment, see Figure 6.
Most importantly marginal non-elite areas are the only ones with high positive estimates for the dwelling to be located close to the family. Clearly not important in this geographical context is to feel at home in the area, possibilities of parking, having a garden, or that the dwelling makes it possible to practice hobbies. These preferences are rather much in accordance with the living opportunities in the brownish/darkest colored areas in Figure 5. Effect of age, sex education, income, municipality type, and cluster type on responses to the question "What is the most important in the dwelling for you".
Gender differences
Eight housing preferences significant for gender were found, see Table 3. The preferences primarily concerned the whereabouts of the dwelling. The most important one was that the dwelling according to women should be located close to the family. Secondly the dwelling should have a balcony or terrace. Women also preferred if the dwelling had an elevator should it be located higher than on the second floor.
Men, on the other hand answered that owning the dwelling was important as well as a location close to forest and land. Also important was having a private garden as well as parking facilities and the possibilities to practice hobbies in the dwelling.
Municipality type
Six housing preferences differed significantly with municipality type, Table 3. An association between the preference of the dwelling to be located close to public transport and the municipality type according to population density/urbanity could be observed. The metropolitan cities, suburbs of metropolitan cities, and major cities were home to respondents answering that public transport nearby was important. Public transport was however not important to those living in municipality types of suburbs of metropolitan cities, industrial and commuting and low density and tourism type municipalities. In the same way elevator if the dwelling was situated
23
higher than second floor was only important to those living in the three most urban municipality types but not in the three less population-dense/ rural municipality types. Of course public transportation and elevator is inherent in urban areas whereas the same preferences would not naturally show among respondents living in rural/sparsely populated areas. Here we can observe the above mentioned sorting according to preferences and/or adjusted preferences probably visible in the municipality significances, that is, a selection effect. An additional sorting according to preferences is that those living in the more rural and sparsely populated municipality types preferred having a garden of one’s own to a higher degree than others. Also connected to the urban – rural divide but with mixed results, were respondents in metropolitan cities preferring the dwelling to be located close to city life or city environment whereas they didn’t prefer forest and land to be close to the dwelling.
Education and Income
The overall analysis of housing preferences showed, as stated above, that education and income did not have significance to a very large degree compared to age and geographical context. We could however observe some associations in that the higher educated the more preferences were found for large supplies of service and cultural facilities in the area, that the dwelling should have room for social events like parties, dinners and meetings, and provide possibilities to practice hobbies. The two latter preferences were also associated with higher incomes. That the dwelling was designed in a way that one can manage without help from others was preferred by those with lower education as well as those with lower incomes. Significant preferences among lower income groups were also the fact that the dwelling is located in an area where the respondent feel at home.
Concluding Discussion
In this paper we have added to the research on preferences and considerations of the supply of housing for elderly. We have analyzed the preferences linked to age, socio-economic status and geographical context. The main questions were if there are differences in preferences between different age groups? and if there are differences in preferences between municipality types and geographical contexts? The findings indicate precisely that age and area type together with municipality type is highly important for preferences held by elderly in our SHIELD survey. Concerning these questions this paper makes three important contributions. First, we have been able to show that neighborhood context plays a fundamental role in housing preferences among elderly Swedes. The strongest effects of geographical context are found for preferences that are related to the type and the location of dwellings rather than the dwelling’s physical design. One suggestion that explains this is that age is the determining factor for the interior of the dwelling in
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terms of needs and preferences regarding for instance disability. This is also in line with the finding in our study that age is rather everywhere putting demand on the interior of the dwelling such as the dwelling in one floor, elevator, design for disability and a dwelling designed in a way that we/I can manage myself/ourselves.
Second, we have demonstrated that a classification of areas based on multi-scalar measures of geographical context provides a powerful tool both for analyzing spatial variation in socio-economic structure and for analyzing how residential preferences vary geographically. Even if the multi-scalar approach is more cumbersome compared to methods that build on aggregate fixed geographical subdivision it leads to strong and useful results. One particular strength of our cluster based approach is that survey data can be used to identify spatial patterns in residential preferences not only for a few selected locations but in a way that allows a generalization to the whole of Sweden.
Third, our analysis shows that variation in residential preferences among the elderly does not follow a simple urban rural dichotomy. For sure, there are contrasts between metropolitan and non-metropolitan areas. But also within these two broad groups there is substantial variation, for example with respect to the preference of access to service and centrality.
Despite the strong and systematic results pointing in the same direction of context being important there are certainly limitations to the result that most preferences are formed locally. As mentioned in the introduction we also admit that preferences govern choice of housing in a way that people can already be sorted according to preferences. However using a method where scale is inherent is in some ways protective to that. Another limitation to the idea off effects from the geographical context is that respondents most likely form justifications for living in a certain dwelling/location and for moves already made.
What we have not, for reasons of data availability, been able to do in this paper is to systematically explore how variations in socio-economic context relate to the difference in housing market structure. Such differences are clearly of large importance for the local socio-demographic composition and access to such information would have provided additional insights into how residential preferences influence and are influenced by local conditions.
Acknowledgement: The Equipop software used for computing contextual variables based on individualized neighborhoods was developed by John Östh, Uppsala University. This research was supported by the Linnaeus Center on Social Policy and Family Dynamics in Europe, SPaDE, Stockholm University and by the Swedish Research Council grant 2009-6299.
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