European Journal of Housing Policy Vol. 6, No. 1, 77–99, April 2006
Mass Housing: User Satisfaction in Housing and its Environment in Istanbul, Turkey ¨ ¨ ¨ OMER LUTFI KELLEKC˙I & LALE BERKOZ Istanbul Technical University, Faculty of Architecture, Department of Urban and Regional Planning, Turkey
ABSTRACT Individuals’ views of residential areas and the physical and social features of the environment are influenced by their individual characteristics, life quality and other requirements. Research on user satisfaction in housing and environment has become a dynamic and complex field of study. In this study, in order to assess the factors that improve satisfaction with housing and environmental quality, both the concepts of housing and its environment, and the subject of housing and environmental quality satisfaction have been investigated. Linear regression analyses have been made to assess whether there are any distinctions between the factors generally influential in satisfaction with a residential area according to demographic and socio-economic structures of users, and to determine the distinctions, if any, between the factors that affect satisfaction and values. Taking into consideration all of the characteristics that determine housing and environmental quality satisfaction as a result of these analyses, new insights into this subject have been gained by identifying these factor groups as the determinants of user satisfaction in housing and environmental quality. At the end of the regression analyses applied to these factor groups, it has been ascertained that the factors increasing levels of satisfaction vary according to the demographic and socio-economic structural differences of the users. The findings of this study bear similarity with the findings of previous studies made on this subject. KEY WORDS: Housing environment, housing satisfaction, housing and environmental quality
Introduction The Istanbul metropolitan area is Turkey’s principal metropolitan agglomeration with a population of slightly more than 10 million, 13 per cent of Turkey’s population. The city has been expanding rapidly since the 1950s due to rural-urban migration. A number of problems have accompanied this growth, including an infrastructure lag, the expansion of squatter settlements, an acute shortage of housing and a low level of Correspondence Address: Lale Berk¨oz, Istanbul Technical University, Faculty of Architecture, Department of Urban and Regional Planning, Ta¸skı¸sla 34437, Istanbul, Turkey. Email:
[email protected] C 2006 Taylor & Francis ISSN 1461-6718 Print/1473-3629 Online 06/010077–23 DOI: 10.1080/14616710600587654
78 O.L. Kellekcı and L. Berk¨oz services. The rapid expansion has affected the quality of life in different districts of Istanbul. An increase in dwelling and environmental quality satisfaction improves people’s quality of life, thus directly affecting people’s satisfaction in their lives. This study has established the necessity that the factors affecting dwelling and environmental quality satisfaction should be taken into account during the planning process in order to increase user satisfaction.
Literature Review Satisfaction in the residential environment reflects people’s responses to the area they live in. The term ‘environment’ is related not only to the physical components consisting of the housing and neighbourhood, but also to social and economic conditions. If appropriate techniques are used in data collection and analyses, it is possible to measure physical, social and administrative factors that determine the level of user satisfaction in the housing area. This information can be used not only for establishing ways in which improvements could be made (Francescato, 1997). In a behavioural sense, user satisfaction in housing should be defined as a dependent attitude toward a residential environment. As Rosenberg and Hovland have suggested, when different components of attitude (informational, emotional and behavioural) are considered, some researchers prefer a definition of emotional components for defining user satisfaction in housing, while others prefer perception-based definitions (Amerigo, 2002). In the definitions to which an emotional component is significant, user satisfaction in housing means reflecting the sentiments of satisfaction and happiness to the housing place which also creates these feelings (Gold, 1980; Weidemann & Anderson, 1985). In the definitions to which an informational component is significant, user satisfaction in housing is constituted by the corresponding factors between the current conditions of the users and the standards they expect and demand (Campbell et al., 1976; Marans & Rodgers, 1975; Wiesenfeld, 1992). In the informational approach, Bardo & Hughey (1984), Canter & Rees (1982), Morrissy & Handal (1981) suggested that if the gap between demands and needs decreases, housing area user satisfaction increases. Studies in this literature have revealed that researchers have used the following categories variables to investigate satisfaction models by applying various statistical techniques: 1. Variables of housing users’ demographic features. 2. Variables of physical residential environment. 3. Variables demonstrating housing users’ evaluation of the following elements: perception, neighbourhood conditions, administration, social relationships between neighbours, safety, accessibility and the appearance of a residential environment.
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As one of the first examples, Francescato et al.’s (1974) model demonstrated that user satisfaction in housing is composed of objective-individual and objectivephysical features as well as user expectations from the housing environment. Later Galster and Hesser (1981) developed their own model of housing user satisfaction by using path analysis. Their model has been defined by summarizing the relationship between objective-independent and subjective-interventional variables. In Marans and Spreckelmeyer’s (1981) conceptual model the objective features of the physical environment are used to identify the correlation between housing user satisfaction and behaviour. Their model not only shows that housing satisfaction is influenced by perceptions and evaluations of objective environmental features, but also that user behaviour is affected by environmental satisfaction. Weidemann and Anderson (1985) have developed a more comprehensive model, which is based on the relation between the emotional responses of people and their behaviour. Their aim is to compensate for the lack of sufficient evidence of the direct relation between emotional responses and behaviour. They have used the behaviour model that combines both parts. In this way, they have been able to establish a comprehensive model of housing user satisfaction. In their model, housing user satisfaction is conceived in terms of users’ emotional responses to the physical and social environment of the housing. These responses reflect positive or negative user attitudes toward the environment in which they live. Another result of Weidemann and Anderson’s model suggests that user satisfaction in a housing area does not trigger people’s decision to move from that place. Rather, moving between housings areas is directly related to people’s intention to do so. This result suggests that people might continue to live in the same housing area even though they are not satisfied with it. The physical and social components have clearly been presented in their model. Moore (1997) proposed four levels of theoretical construction for organizing and integrating studies of the residential environment: conceptual orientations, frameworks, models and theories. Amerigo & Aragon´es (1997) presented a theoretical and methodological approach to the study of residential satisfaction, and gave a general view of the relationships established between people and their residential environment. Amerigo (2002) presented a framework for a psychological approach to the study of residential satisfaction. Amerigo’s model is based on the subjective user evaluations about the objective housing area environment defined in terms of its physical and social features. Individual user characteristics depict every user specifically in their housing area, thus making them take satisfaction in the housing area on different levels. As a result of this emotional condition, individuals contribute to the equilibrium in their environment through some spontaneous behaviour. Garling and Friman (2002) noted that residential satisfaction is a natural criterion by which to judge the success of residential choice. Residential dissatisfaction may be an important reason for moving out of a particular area. They presented a psychological conceptualization of residential choice. Kamp et al. (2003) presented a multidisciplinary conceptual framework of environment quality and quality of life for
80 O.L. Kellekcı and L. Berk¨oz the advancement of urban development, environment quality and human well-being. A study in Turkey of residential satisfaction of migrants has remained a neglected subject for a long time. However, there are a few studies on this subject by Suher et al. (1991), Potter (1993), D¨okmeci et al. (1994) and T¨urko˘glu (1997). Suher et al. (1991) conducted a survey of squatter areas of Istanbul in 1990. Another area was researched by Potter (1993), where he measured migrants’ perception of rural residence and urban squatters in Ankara, Turkey. The transformation of slum areas, squatter settlements and middle-class neighbourhoods in Istanbul was investigated by D¨okmeci et al. (1994). Taking the resident perception as a point of reference, T¨urko˘glu evaluated planned and squatter environments in Istanbul (1997). Aims and Methods The aims of this article are: 1. To test the individual, social and physical features of user satisfaction in housing and environmental quality. 2. To determine the different factor groups which influence user satisfaction in housing and environmental quality. 3. Take into account the demographic and socio-economic structural differences among the users of mass housing areas, to establish the relative importance of the factors generally affecting user satisfaction in housing and environmental quality. The dependent variable of the study is satisfaction with the dwelling and quality of the environment; therefore characteristics of the household members, characteristics related to the dwelling, accessibility, features of the dwelling environment, security, neighbour relationships and the appearance of the dwelling environment present the independent variables of the study. The dependent variable of the survey in this research is to measure the housing user satisfaction in housing and environmental quality and to determine the factors increasing the level of satisfaction. To this end, questions relating to the following independent variables have been posed to the heads of the households: the characteristics of household members (the size of the household, gender, age, education, the number of people working, profession, income group, ownership of durable consumer goods, and ownership of vehicles); features related to the housing (when the household moved in, ownership, type, size, the number of inhabitants, the previous neighbourhood, the previous housing type); accessibility (to places of work, the centre where daily needs are met, shopping centres, the city centre, sports facilities, walking areas, refreshment areas, car parks, health institutions, educational institutions, entertainment areas, recreational areas, public transport stops, and contact to close relatives and friends); characteristics of the housing environment (lighting, maintenance of open areas, maintenance of green areas, traffic density, user density,
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Table 1. Characteristics of selected mass housing areas Distance from Emin¨on¨u centre (km) 12 15 20 20 25 35 40 40 40
Selected mass housing areas
Population
Ata¸sehir Atak¨oy Ba¸sak¸sehir Halkalı Bah¸ce¸sehir Bizimkent Mimaroba Sinanoba Kipta¸s
80,000 75,000 54,000 180,000 60,000 16,000 12,000 16,000 9,300
Total area Density Number of (ha) (person/ha) questionnaires 450 377 232.5 920 470 45.3 45 75.6 14.3
225 200 230 195 130 350 270 200 650
64 60 44 143 48 13 10 13 6
District Kadık¨oy Bakırk¨oy K¨uc¸ u¨ k¸cekmece K¨uc¸ u¨ k¸cekmece Avcılar B¨uy¨uk¸cekmece B¨uy¨uk¸cekmece B¨uy¨uk¸cekmece Pendik
building density, housing environment facilities); security (fire, natural disasters, traffic accidents, robbery, murder); neighbour relationships (neighbours of similar social background, acquaintance with people nearby, privacy, charity among neighbours); and the appearance of the housing environment (monotony/variety, social status and economic value). In order to specify the determinants of user satisfaction in housing and environmental quality, samples have been selected from the mass housing areas (constructed by the National Housing Authority, Emlakbank and the Municipality of Istanbul Metropolitan Area) with a population of over 5,000 inhabitants. These mass housing areas: Ata¸sehir, Atak¨oy, Ba¸sak¸sehir, Halkalı, Bah¸ce¸sehir, Bizimkent, Mimaroba, Sinanoba, Kipta¸s-Pendik are situated in zones 10–15 km, 15–20 km, 20–25 km and 25+km from Eminonu centre, and are located in non-core areas of Istanbul, in the peripheral districts (Figure 1). In selecting these samples a quota has been applied to reflect the relative population of each housing estate. A total of 401 surveys were conducted by personal interviews with the heads of households (Table 1). The respondents’ profile and the quality of residence are summarized in the appendix. Factor analysis was used in order to analyse the interrelations between the variables, to explain the common elements underlying them, and to reduce the number of elements (factors) with the minimum level of data loss in related information. Subjects of this analysis, bearing a high degree of correlation, include the level of convenience related to accessibility to function areas, user opinions on the environmental features of the inhabited dwelling, the level of user satisfaction degree relating to various environmental facilities, security level of the inhabited environment, neighbour relationships in the residential area, and appearance of the housing environment. In the questionnaire, among factor analysis techniques the ‘Factor Processing Technique’ has been applied to 13 variables relating to accessibility to various function areas in the housing area, 6 variables indicating opinions about the features of the housing
Figure 1. Survey areas in ˙Istanbul metropolitan area.
82 O.L. Kellekcı and L. Berk¨oz
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environment, 18 variables relating to facilities, 6 variables indicating levels of security, 7 variables revealing neighbour relationships, and 5 variables indicating opinions about the appearance. In the first stage a correlation matrix was determined for all the variables, and the pairwise method was used for incorrect responses. The Kaiser–Meyer–Olkin (KMO) measurement is an index value used to analyse the suitability of the sample group to factor analysis.1 When a ‘principle Component’ analysis of the data was executed, it was found that five of the variables were at threshold levels of Eigen values while the remaining were in excess of the value of 1. Application of the KMO and the Bartlett Test of Sphericity has indicated that the factor analysis results are reliable. The KMO values of samples suggest that the factor analysis results may be accepted with confidence. As mentioned above, one of the objectives of this study is to determine how the factors specifying user satisfaction in housing and environmental quality present changes according to the demographic and socio-economic differences among the users. Finally, factor analysis has been applied to the variables of housing and environmental quality satisfaction. At the end of the factor analysis, which provides the factor groups determining housing and environmental quality satisfaction, regression analyses have been made by using the highest factor scores. Evaluation of the Findings As a result of factor analysis, factor groups that increase the level of user satisfaction in housing and environmental quality have been specified. The elements influencing these factor groups include accessibility to various function areas in the residential area, environmental features of the housing, satisfaction in the various facilities in the inhabited environment, environmental security, neighbour relationships, and the appearance of the housing environment. Accessibility also has an important influence on the level of user satisfaction in housing and environmental quality. In order of importance, the factor groups of this criterion are centrality and accessibility to educational institutions, open areas, health institutions and public transportation, respectively (Table 2). Parallel results were reached by T¨urko˘glu (1997). According to the level of importance respectively, maintenance of the environment and the density of building and traffic are the two factors revealing the opinions of housing area users about the criteria of their housing environmental features (Table 3). A well cared for housing environment creates a positive image, decreasing users’ complaints about the housing area and increasing environmental quality. As a result, housing and environmental quality satisfaction is improved. This result is parallel with the results of the studies by Becker (1974), Galster and Hesser (1981). In the subject of environmental quality variants, five factor groups, according to the level of importance, have been specified to include satisfaction in recreation
84 O.L. Kellekcı and L. Berk¨oz Table 2. Factor dimensions related to accessibility to function areas in the housing area
Factors
Factor loading
1 Factor: centrality V58 Accessibility to shopping centre V59 Accessibility to city centre V56 Accessibility to work V70 Accessibility to places of entertainment V57 Accessibility to the market where daily needs are obtained 2 Factor: accessibility to education institutions V67 Accessibility to elementary schools V68 Accessibility to high schools 3 Factor: accessibility to open areas V64 Accessibility to parking areas V62 Accessibility to walking areas V61 Accessibility to sports centres 4 Factor: accessibility to health institutions V65 Accessibility to local clinics V66 Accessibility to hospital 5 Factor: accessibility to public transport V72 Accessibility to public transport stops Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. KMO: 0.80
Eigen value
Explained variance (per cent)
3.739
21.6
1.429
12.2
1.249
11.8
1.139
11.2
.912
8.4
.775 .772 .705 .654 .642 .742 .740 .824 .627 .538 .861 .619 .927
Table 3. Factor groups related to the features of inhabited residence environment
Factors 1 Factor: maintenance of the environment V76 In this environment maintenance of open areas is adequate V77 In this environment maintenance of green areas is adequate V75 In this environment night lighting is adequate 2 Factor: building and traffic density V79 This housing area is small with respect to its population V80 The buildings are too close to mine V78 In this housing area traffic density (motor vehicles) is high Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. KMO: 0.67
Explained Factor Eigen variance loading value (per cent) 2.585
34.1
1.420
32.7
.899 .839 .694 .859 .809 .725
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Table 4. Factor groups related to satisfaction in various facilities in the residence environment
Factors 1 Factor: satisfaction in recreational areas V87 Satisfaction in walking areas V88 Satisfaction in relaxation areas V86 Satisfaction in sports centres V84 Satisfaction in green areas V85 Satisfaction in children’s playgrounds 2 Factor: satisfaction in centrality V99 Satisfaction in accessibility to city centre V93 Satisfaction in accessibility to entertainment places V96 Satisfaction in shopping facilities 3 Factor: satisfaction in social structure and physical features of the settlement V98 Satisfaction in social and neighbourhood relationships V97 Satisfaction in substructure (water, electricity, natural gas, telephone, cable TV) V92 Satisfaction in social activities V100 Satisfaction in the scenery 4 Factor: Satisfaction in transportation and accessibility V81 Satisfaction in pedestrian paths V82 Satisfaction in traffic roads V89 Satisfaction in parking areas V94 Satisfaction in public transport 5 Factor: Satisfaction in social facilities V90 Satisfaction in health institutions V91 Satisfaction in education institutions Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser NormaliZation. KMO: 0.76
Factor loading
Eigen value
Explained variance (per cent)
4.224
14.1
2.128
13.0
1.466
12.9
1.367
10.5
1.255
7.5
.827 .796 .723 .493 .459 .832 .748 .745 .817 .815 .735 .577 .655 .645 .629 .455 .744 .668
areas, satisfaction in centrality, satisfaction in the social structure (physical characteristics of the settlement), satisfaction in transportation and accessibility, and satisfaction with social facilities (Table 4). A high level of satisfaction is related to a planned settlement and the facilities provided for the community. In a planned settlement, recreation areas, centrality, socio-physical characteristics of the settlement, transport and accessibility, social facilities, playgrounds for children, cultural and recreational activities, and security have a positive impact on satisfaction in housing and environmental quality. These results are parallel with the findings of the research by Michelson (1977), Savasdisara (1988) and Amerigo & Aragon´es (1990). Two factor groups demonstrating the residents’ views about the security of their environment have been specified to reflect structural and environmental security
86 O.L. Kellekcı and L. Berk¨oz Table 5. Factor groups related to environmental safety
Factors 1 Factor: housing’s structural and environmental safety V102 Housing area’s protection against fire V104 Housing area’s safety against traffic accidents V103 Housing area’s safety against natural disasters (earthquake, flood, etc.) V107 Family’s general safety in the housing area 2 Factor: life and property safety V106 Housing area’s safety against murder V105 Housing area’s safety against robbery Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalzation. KMO: 0.74
Factor loading
Eigen value
Explained variance (per cent)
2.258
31.2
1.062
24.2
.732 .701 .650 .627 .796 .610
of the housing, as well as life and property security (Table 5). In this research the findings related to housing’s structural and environmental safety and life and property security show parallels with studies by Jacobs (1961), Newman (1972), Weidemann and Anderson (1982), Perkins (1987), Marans (1979), Francescato et al. (1979), Lawton (1980), Anderson et al. (1983) and Cook (1988). According to the level of importance, three factor groups related to residents’ views about their neighbour relationships contain neighbour relationships, social homogeneity and distance neighbour relationships (Table 6). To provide satisfaction in housing area, neighbour relationships and the importance of their quality are parallel with the research by Galster (1981), Lansing et al. (1970), Deutschman (1972), Marans and Rodgers (1975). The finding that as a result of social homogeneity social unity increases user satisfaction has similarities with the findings of the research of Rent and Rent (1978). Regarding the appearance of the housing environment and its economic value, according to the level of importance, the specified factor groups include the harmony between physical appearance of the mass housing area and the status of the users (Table 7). Research in this area has found that there is a significant correlation between user satisfaction in housing and residential environment, and perception of the physical quality of that environment. Likewise, Allport and Vernan (1931), Gurin et al. (1960), Dalkey (1972), Francescato et al. (1974, 1979), and Hourihan (1984) have also stressed the importance of the physical condition of the residential environment while users evaluate their satisfaction with the residential environment. A study by Enosh et al. (1984), has demonstrated that responses related to the appearance of residential environment (beauty, attraction, cleanliness) have direct and indirect
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Table 6. Factor groups related to neighbour relationships Explained Factor Eigen variance loading value (per cent)
Factors 1 Factor: neighbour relationships V112 Satisfaction in neighbour relationships V114 General satisfaction in neighbours in the housing area V113 Satisfaction in social relationships 2 Factor: social homogeneity V108 Similarity among inhabitants of the housing area in terms of income level, education, and origin V109 Acquaintance with many people in the building and neighbourhood 3 Factor: distanced neighbour relationships V111 Receiving help from neighbours when necessary V110 Sufficient privacy from the neighbours nearby Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. KMO: 0.72
2.829
40
1.295
16.8
1.017
16.7
.967 .957 .924 .877 .630 .735 .586
influences on user satisfaction in the environment they live in. Jirovec et al. (1985) have also reached the same conclusion. Taking into account all of the characteristics that determine housing and environmental quality satisfaction, new perspectives on this subject have been found by obtaining these factor groups as the determinants of user satisfaction in housing and environmental quality. Table 7. Factor groups related to residence environment and economic value
Factors
Explained Factor Eigen variance loading value (per cent)
1 Factor: physical appearance of housing estate area 1.635 V116 This housing estate area has an interesting appearance .801 V115 In this housing estate area monotony is prevalent; −.663 buildings and constructions are all the same. V117 This housing estate area looks beautiful. .656 2 Factor: propriety to user status 1.065 V118 This housing estate area reflects my income level .807 and career. V120 In general my housing is a good future investment in terms .701 of the area it is situated in Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. KMO: 0.61
30.7
23.3
88 O.L. Kellekcı and L. Berk¨oz This study aims to establish whether there are any differences between the factors influencing housing and environmental quality satisfaction for mass housing users from different demographic and socio-economic strata. In the case of any differences among the effect values of these satisfaction factors, linear regression analyses have been made using the stepwise regression technique. The dependent variable in these regression analyses is ‘the overall satisfaction in the housing area (Satisf) (v124)’. The following are the independent variables, respectively, which have obtained the highest factor scores at the end of the factor analysis: for accessibility centrality (Centr), for environmental features maintenance of the environment (Enmaintance), for environmental quality variables satisfaction in recreational areas (Recre), for security structural-environmental security of the housing (Secrty), for neighbour relationships neighbour relationships (Neigrelation), and for housing’s environmental and physical appearance physical appearance (Appear). In the regression analyses, the maximum value of accuracy should be 0.05. By using the independent variables, R2 values illustrate the percentage of the correlations which can be presented in the database constituted in this study. Coefficient values demonstrate the positive (+) or negative (–) effects these factors generally have on user satisfaction in the mass housing area, and the strength of each factor. At the end of the regression analyses which have used ‘the overall satisfaction in the housing area (v124)’ as the dependent variable, 4 factor groups have been determined, explaining 39 per cent of the variance pertaining to the analysed variables (Table 8). In mass housing areas, the most influential factor group increasing the overall satisfaction is Centrality. Satisfaction in recreational areas, maintenance of the environment, and the physical appearance of the mass housing are the other factor groups (Table 8). The following equation of regression formulated as a result of the regression analysis: Satisf = .338∗ Centr + .192∗ Recre + .177∗ Enmaintance + .066∗ Appear According to the regression analysis carried out with household heads aged 0– 29, satisfaction in recreational areas and centrality are the 2 respective factor groups increasing overall housing satisfaction. These two factor groups represent 54 per cent of the variance pertaining to the variables in the regression analysis. Below is the equation of regression formulated as a result of the regression analysis: Satisf = .447∗ Recre + .267∗ Centr According to the regression analysis carried out with household heads aged 30– 59, centrality, maintenance of the environment and satisfaction in recreational areas are the 3 respective factor groups increasing overall housing satisfaction. These 3 factor groups signify 36 per cent of the variance pertaining to the variables in the
0–29 years Coefficient (t)
30–59 years Coefficient (t)
Household head’s age Higher Coefficient (t)
Middle and lower middle Coefficient (t)
Primary education-high school Coefficient (t) Higher education Coefficient (t)
Household head’s education level
Tenant Coefficient (t)
Ownership Landowner Coefficient (t)
∗∗∗ , 99 per cent Confidence level; ∗∗ , 95 per cent Confidence level; n.a, non-availability; Centr, Centrality; Enmaintance, Maintenance of the environment; Recre, Satisfaction in recreation areas; Secrty, Structural and environmental security of the dwelling; Neigrelation, Neighbour relationships; Appear, Appearance of dwelling environment.
,338 ,267 ,355 ,420 ,252 ,313 ,394 ,332 ,354 (10,548)∗∗∗ (4,155)∗∗∗ (8,792)∗∗∗ (9,820)∗∗∗ (5,303)∗∗∗ (7,323)∗∗∗ (7,918)∗∗∗ (8,458)∗∗∗ (6,479)∗∗∗ Enmaintance ,177 n.a ,215 ,174 ,176 ,193 ,157 ,276 (5,185)∗∗∗ (5,506)∗∗∗ (4,509)∗∗∗ (2,730)∗∗ (3,363)∗∗∗ (3,620)∗∗∗ (6,479)∗∗∗ Recre ,192 ,447 ,154 105 ,272 ,220 ,123 ,262 ,136 (5,887)∗∗∗ (6,921)∗∗∗ (3,795)∗∗∗ (2,395)∗∗ (5,626)∗∗∗ (5,421)∗∗∗ (2,116)∗∗ (6,573)∗∗∗ (2,637)∗∗ Secrty n.a n.a n.a n.a n.a n.a n.a n.a n.a Neigrelation n.a n.a n.a n.a n.a n.a n.a n.a n.a Appear ,066 n.a n.a ,105 n.a n.a ,135 ,113 n.a ∗∗ ∗∗ ∗∗ ∗∗ (2,019) (2,496) (2,354) (2,848) R2 = ,394 R2 = ,350 R2 = ,540 R2 = ,355 R2 = ,466 R2 = ,356 R2 = ,466 R2 = ,366 R2 = ,482 (F = 64,356∗∗∗ ) (F = 46,973∗∗∗ ) (F = 49,799∗∗∗ ) (F = 45,223∗∗∗ ) (F = 34,017∗∗∗) (F = 42,231∗∗∗ ) (F = 34,212∗∗∗ ) (F = 50,865∗∗∗ ) (F = 39,955∗∗∗ )
Centr
Factors
Overall satisfaction in housing area Coefficient (t)
Income group
Table 8. Demographic and socio-economic characteristics, according to the results of the regression analysis
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90 O.L. Kellekcı and L. Berk¨oz analysis. Below is the equation of regression formulated as a result of the regression analysis: Satisf = .355∗ Centr + .215∗ Enmaintance + .154∗ Recre According to the regression analysis carried out with household members from higher income groups, centrality is the most influential among the 4 factor groups which affect overall housing satisfaction. It explains 47 per cent of the variance pertaining to the variables. Other influential factor groups are respectively maintenance of the environment, physical appearance of the mass housing area, and satisfaction in recreational areas (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .420∗ Centr + .174∗ Enmaintance + .105∗ Appear + .105∗ Recre Regression analysis carried out with household members from the middle and lower-middle income groups established that the 3 factor groups which influence user satisfaction in housing and environmental quality are satisfaction in recreational areas, centrality and maintenance of the environment. These 3 factor groups represent 36 per cent of the variance pertaining to the variables of the analysis (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .272∗ Recre + .252∗ Centr + 176∗ Enmaintance According to the regression analysis carried out with household heads who have no higher education, centrality, satisfaction in recreational areas and maintenance of the environment are the 3 factor groups influencing the overall user satisfaction in mass housing area and environment quality. These 3 factor groups reflect 35 per cent of the variance pertaining to the variables in the analysis (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .313∗ Centr + .220∗ Recre + .193∗ Enmaintance According to the regression analysis carried out with household heads who have received higher education, the 4 factor groups increasing the overall user satisfaction in mass housing area and environment quality are centrality, maintenance of the environment, physical appearance of the mass housing area, and satisfaction in recreational areas. These factor groups represent 47 per cent of the variance pertaining to the variables in the regression analysis (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .394∗ Centr + .157∗ Enmaintance + .135∗ Appear + .123∗ Recre
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With the regression analysis applied to the home-owners, centrality, satisfaction in recreational areas, and physical appearance of the mass housing are the 3 factor groups increasing overall housing satisfaction. These groups represent 37 per cent of the variance pertaining to the variables in the regression analysis (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .332∗ Centr + .262∗ Recre + .113∗ Appear In line with the regression analysis of tenant users, the 3 factor groups increasing the overall housing satisfaction are respectively centrality, maintenance of the environment, and satisfaction in recreational areas. These 3 factor groups reveal 48 per cent of the variance pertaining to the variables in the analysis (Table 8). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .354∗ Centr + .276∗ Enmaintance + .136∗ Recre In terms of housing features, according to the regression analysis of the housing users whose housing size is less than 65 m2 , satisfaction in recreational areas and centrality constitute the 2 factor groups increasing the overall housing satisfaction. These factor groups constitute 58 per cent of the variance pertaining to the variables in the regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .345∗ Recre + .280∗ Centr According to the regression analysis of the users whose housing size is between 66– 100 m2 , the 2 factor groups increasing the overall housing satisfaction are centrality and satisfaction in recreational areas. The factor groups here encompass 45 per cent of the variance pertaining to the variables in the analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .388∗ Centr + .247∗ Recre According to the regression analysis applied to the users who live in housing of 101–149 m2 , the 4 factor groups increasing the overall housing satisfaction are centrality, satisfaction in recreational areas, maintenance of the environment, and structural-environmental security of the housing. These groups describe 66 per cent of the variance pertaining to the variables in the analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .295∗ Centr + .241∗ Recre + .232∗ Enmaintance + .116∗ Secrty
Factors
n.a
,247 (3, 678)∗∗∗ n.a
,388 (6,579) ∗∗∗ n.a
,295 (5, 906)∗∗∗ ,232 (5, 003)∗∗∗ ,241 (4, 815)∗∗∗ ,116 (2, 476)∗∗ n.a
101–149 m2 Coefficient (t)
n.a
n.a
,423 (5, 156)∗∗∗ ,243 (2, 350)∗∗ n.a
150–165 m2 Coefficient (t)
n.a
,324 (8, 124)∗∗∗ ,170 (4, 212)∗∗∗ ,279 (5, 967)∗∗∗ n.a
1st–5th floors Coefficient (t)
n.a
n.a
,204 (2, 531)∗∗ ,330 (5, 172)∗∗∗ n.a
6th–10th floors Coefficient (t)
n.a
n.a
,524 (7, 079)∗∗∗ ,220 (2, 307)∗∗ n.a
11th floor and above Coefficient (t)
n.a
,344 (9, 643)∗∗∗ ,190 (5, 395)∗∗∗ ,213 (5, 675)∗∗∗ n.a
10–20 km Coefficient (t)
n.a
n.a
,254 (3, 902)∗∗∗ n.a
21–30 km Coefficient (t)
Distance to the centre
n.a
n.a
,312 (2, 492)∗∗ n.a
30+km Coefficient (t)
,261 n.a (2, 200)∗∗ n.a n.a n.a n.a n.a n.a n.a n.a ,317 ,257 (4, 211)∗∗∗ (2, 365)∗∗ R2 = ,452 R2 = ,657 R2 = ,340 R2 = ,404 R2 = ,243 R2 = ,526 R2 = ,434 R2 = ,520 R2 = ,238 R2 = ,584 (F = 16,681∗∗∗ ) (F = 35,415∗∗∗ ) (F = 38,526∗∗∗ ) (F = 16,503∗∗∗ ) (F = 48,736∗∗∗ ) (F = 18,098∗∗∗ ) (F = 34,373∗∗∗ ) (F = 78,487∗∗∗ ) (F = 15,882∗∗∗ ) (F = 6,087∗∗∗ )
n.a
,345 (3,995)∗∗∗ n.a
,280 (3,357)∗∗ n.a
66–100 m2 Coefficient (t)
Floor of the housing
Satisfaction in recreation areas; Secrty, Structural and environmental security of the dwelling; Neigrelation, Neighbour relationships; Appear, Appearance of dwelling environment.
∗∗∗ , 99 per cent Confidence level; ∗∗ , 95 per cent Confidence level; n.a, non-availability; Centr, Centrality; Enmaintance, Maintenance of the environment; Recre,
Appear
Neigrelation
Secrty
Recre
Enmaintance
Centr
65 m2 and less Coefficient (t)
Housing size
Table 9. The results of the regression analysis according to the housing features.
92 O.L. Kellekcı and L. Berk¨oz
Mass Housing in Istanbul
93
According to the regression analysis applied to the users living in housing of 150– 165 m2 , the 2 factor groups increasing the overall housing satisfaction are centrality and maintenance of the environment. These factor groups comprise 34 per cent of the variance pertaining to the variables in the regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .423∗ Centr + .243∗ Enmaintance Regression analysis applied to the housing users who live on the 1st–5th floors, the 3 factor groups increasing the overall housing satisfaction are composed of centrality, satisfaction in recreational areas, and maintenance of the environment. These 3 groups explain 40 per cent of the variance pertaining to the variables in this regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .424∗ Centr + .279∗ Recre + .170∗ Enmaintance The regression analysis applied to the housing users who live on the 6th–10th floors indicate the 2 factors increasing the overall housing satisfaction include maintenance of the environment and centrality. These 2 groups explain 24 per cent of the variance pertaining to the variables in this regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .330∗ Enmaintance + .204∗ Centr Similarly, according to the regression analysis of the users who live on the 11th floor and above, centrality and maintenance of the environment are the 2 factors increasing overall housing satisfaction. These 2 groups represent 53 per cent of the variance pertaining to the variables in this regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .524∗ Centr + .220∗ Enmaintance According to the regression analysis of the housing users who live 10–20 km from the city centre, the 3 factor groups increasing the overall housing satisfaction are centrality, satisfaction in recreational areas, and maintenance of the environment. These factor groups present 43 per cent of the variance pertaining to the variables in this analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .344∗ Centr + .213∗ Recre + .190∗ Enmaintance In line with the regression analysis of the users who live in mass housing areas 21–30 km away from the city centre, the 3 factor groups increasing overall housing
94 O.L. Kellekcı and L. Berk¨oz satisfaction are physical appearance of the mass housing area, neighbour relationships, and centrality. These factor groups represent 52 per cent of the variance pertaining to the variables in this analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .317∗ Appear + .261∗ Neigrelation + .254∗ Centr According to the regression analysis applied to the users living in mass housing areas 30+km away from the city centre, centrality and physical appearance of the mass housing area constitute the 2 factor groups increasing overall housing satisfaction. These factors signify 24 per cent of the variance pertaining to the variables in this regression analysis (Table 9). Below is the equation of regression formulated as a result of the regression analysis: Satisf = .312∗ Centr + .257∗ Appear Conclusions As a result of factor analyses assessing user satisfaction in housing and environmental quality, the most significant factors increasing the level of satisfaction have been determined as follows: centrality in the area of accessibility, maintenance of the environment in inhabited environmental features, satisfaction with the recreation areas for environmental quality variants, structural-environmental security of the housing in the area of security, good neighbour relationships within the category of neighbour relationships, and physical appearance in the area of housing environment and physical appearances. Consequently, all these have disclosed that centrality, maintenance of the environment, satisfaction in the recreation areas, structural-environmental security of the housing, neighbour relationships, and physical appearance are the most influential factors for increasing user satisfaction in housing and environmental quality in mass housing areas in the Istanbul Metropolitan Area. On completion of the regression analyses applied to these factor groups, it can be observed that the factors increasing the level of satisfaction vary according to the demographic and socio-economic structural differences of the users. The findings of this study bear similarity with the findings of previous studies made on the subject of housing and environmental quality satisfaction. Linear regression analyses have been made to assess whether there are any distinctions between the factors generally influential in dwelling area satisfaction according to the demographic and socio-economic structures of users, and to determine these distinctions, if any, between the factors that affect satisfaction and effect values. As a result of implemented linear regression analyses, factor groups that affect satisfaction and their levels of importance have been determined. In the regression analyses, in which factor scores have been used, it has been assessed that housing and environmental quality satisfaction shows distinctions among
Mass Housing in Istanbul
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importance levels of factor groups influential in the issues of accessibility, environmental features of the housing, satisfaction with various amenities, environmental security, neighbour relationships, and the appearance of the housing, according to user demographic and socio-economic structures. While satisfaction in recreational areas and centrality are the factor groups increasing the overall satisfaction of household heads between 0–29 years of age, the household heads of the age group 30–59 add maintenance of the environment to the above factors. The regression analyses on overall satisfaction have been applied among the families from different income groups. Accordingly, for middle and lower-middle income groups, centrality, satisfaction in recreational areas, and maintenance of the environment are the important factors affecting the overall satisfaction in housing and environmental quality. When higher income groups are concerned, the physical appearance of the mass housing area has also been specified to be an influential factor. The regression analyses between the overall satisfaction of mass housing users and their education levels indicate that centrality, maintenance of the environment, and satisfaction in recreational areas are the important factors influencing the overall satisfaction of the users not having received a higher education. With users who have received a higher education, in addition to the factor groups above, the physical appearance of the mass housing area has been determined to be another factor increasing user satisfaction. According to the regression analyses between ownership and overall satisfaction, centrality, satisfaction with recreational areas, and physical appearance of the mass housing area are the factor groups increasing housing and environmental satisfaction of the users who own the housing. On the other hand, centrality, maintenance of the environment, and satisfaction in recreational areas have been determined to be the factor groups influencing tenant user satisfaction in housing and environmental quality. The regression analyses on demographic and socio-economic features show that structural-environmental security of the housing and neighbour relationships do not affect user satisfaction in housing and environmental quality. The regression analyses of the size of the housing and overall satisfaction reveal the following findings: a) Centrality and satisfaction in recreational areas are the factor groups influencing housing and environmental quality satisfaction of users who live in maximum 100 m2 housings. b) Centrality, maintenance of the environment, satisfaction in recreational areas, and structural-environmental security of the housing are the influential factor groups for the housings between 101–149 m2 . c) Centrality and maintenance of the environment are the determinant factor groups for the housings between 150–165 m2 . The regression analyses of the floor of the housing and the overall satisfaction reveal the following findings: a) Centrality, satisfaction in recreational areas, and maintenance of the environment are the factor groups that determine housing and environmental quality satisfaction of the users living on 0–5th floors. b) When the
96 O.L. Kellekcı and L. Berk¨oz users living on the 6th and above are considered, maintenance of the environment has been found to be determinant in the overall satisfaction, whereas satisfaction in recreational areas does not have any influence. The regression analyses between housing’s distance to the city centre and overall satisfaction present the following findings: a) Centrality, satisfaction in recreational areas, and maintenance of the environment are the factor groups increasing the overall housing and environmental satisfaction of the users living in mass housing areas 10– 20 km away from the city centre. b) Physical appearance of the mass housing area, neighbour relationships and centrality are the determinant factors for the users living 21–30 km away from the centre. c) When users living 30+km away from the city centre are considered, centrality and physical appearance of the environment are the factor groups increasing the overall satisfaction. Provided that housing area planners, designers and producers carry out their work considering the distinctions among the demographic and socio-economic characteristics of housing users, this study has revealed the possibility for improving the level of user satisfaction in housing and environmental quality. Note 1. The KMO tests suitability by comparing the rate of significance between the observed correlation coefficient and the partial correlation coefficient. If the KMO value is 0.90, the sample has an ‘excellent’ factor analysis suitability rating. If the value is 0.80, the sample is rated as ‘highly suitable’. A 0.70 rating determines ‘suitable’, while a rating of 0.50 and below signifies that the sample is ‘unsuitable’ for factor analysis (Norusis, 1992). The sample group in this case has a KMO value of approximately 0.81 and, therefore, tests as ‘highly suitable’ for factor analysis.
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Canter, D. & Rees, K. (1982) A multivariate model of housing satisfaction, International Review of Applied Psychology, 31, pp. 185–208. Cook, C. C. (1988) Components of neighborhood satisfaction: responses from urban and suburban singleparent women, Environment and Behavior, 20(2), pp. 115–149. Dalkey, N. C. (1972) Studies in the Quality of Life. MA: Delphi and Decision Making (Lexington, MA: Lexington Books). Deutschman, H. D. (1972) The residential location decision: study of residential mobility, Socio-economic Planning Sciences, 6, pp. 349–364. D¨okmeci, V., Yurekli, H., Cagdas, G., Berkoz, L. & Levent, H. (1994) The transformations of neighborhoods in Istanbul. Paper presented at Societies in Transition Conference, Edinburgh, 29–30 June. Enosh, N., Leslau, A. & Shacham, J. (1984) Residential quality assessment: a conceptual modal and empirical test, Social Indicators Research, 14: 453–476. Francescato, G. (1997) Residential Satisfaction, in: W. van Vliet (Ed.) Encyclopedia of Housing (CA:Sage, Monterey). Francescato, G., Weidemann, S., Anderson, J. R. & Chenoweth, R. (1974) Evaluating residents’ satisfaction in housing for low and moderate income families: A multi-method approach, in: D. H. Carson (Ed), Man-Environment interactions: Evaluation and Applications (Washington, DC: EDRA), 5, pp. 285– 296. Francescato, G., Weidemann, S., Anderson, J. R. & Chenoweth, R. (1979) Residents’ Satisfaction in HUDassisted Housing: Design and Management Factors (Washington, DC: Office of Policy Development of Housing and Urban Development). Galster, G. C. & Hesser, G. W. (1981) Residential satisfaction: residential and compositional correlates, Environment and Behavior, 13, pp. 735–758. Garling, T. & Friman, M. (2002) A Psychological Conceptualization of Residential Choice and Satisfaction, in: J. I. Aragon´es, G. Francescato & T. Garling (Eds), Residential Environments: Choice, Satisfaction, and Behavior (Westport: Bergin & Garvey), pp. 55–80. Gold, J. R. (1980) An introduction to behavioral geography (Oxford, UK: University Press). Gurin, G., Veroff, J. & Feld, S. (1960) American View Their Mental Health (New York: Basic Books). Hourihan, K. (1984) Context-dependent models of residential satisfaction: an analysis of housing groups in Cork, Ireland, Environment and Behavior, 16, pp. 369–393. Jacobs, J. (1961) The Death and Life of Great American Cities (New York: Random House, Inc). Jirovec, R., Jirovec, M. M. & Bosse, R. (1985) Residential satisfaction as a function of micro and macro environmental conditions among urban elderly men Research on Aging, 7(4), pp. 601–616. Kamp, I. V., Leidelmeijer, K., Marsman, G. & Hollandaer, A. D. (2003) Urban environmental quality and human well-being towards a conceptual framework and demarcation of Concepts; a literature study, Landscape and Urban Planning, 65, pp. 5–18. Lansing, J. B., Marans, R. W. & Zehner, R. B. (1970) Planned Residential Environment (Ann Arbor, MI: Institute for Social Research). Lawton, M. P. & Yaffe, S. (1980) Victimization and fear of crime in elderly public housing tenants, Journal of Gerontology, 35, pp. 768–779. Marans, R. W. (1979) The determinants of neighborhood quality: an analysis of the 1976 Annual Housing Survey, US Department of Housing and Urban Development, Housing Survey Studies, 3, US Government Printing Office, Washington, DC. Marans, R. W. & Rodgers, S. W. (1975) Toward an understanding of community satisfaction, in: A. Hawley & V. Rock (Eds), Metropolitan America in contemporary perspective (New York: Halstead Press), pp. 299–352. Marans, R. W. & Spreckelmeyer, K. F. (1981). Evaluating Built Environment: A Behavioral Approach (Ann Arbor, MI: University of Michigan). Michelson, W. (1977) Environmental Choice, Human Behavior, and Residential Satisfaction (New York: Oxford University Press).
98 O.L. Kellekcı and L. Berk¨oz Moore, G. T. (1997) Towards environment-behavior theories of middle range, in: G. T. Moore & R. W. Marans (Eds), Advances in Environment, Behavior, and Design, pp. 1–40 (New York: Plenum Press). Morrissy, E. & Handal, P. J. (1981) Characteristics of the residential environment scale: reliability and differential relationship to neighborhood satisfaction in divergent neighborhoods, Journal of Community Psychology, 9, pp. 125–132. Newman, O. (1972) Defensible Space (New York: Macmillan). Norusis, M. (1992) SPSS for Windows Professionel Statistics, Release 5 (Chicago: SPSS Inc.). Perkins, N. (1987) Residents’ perception of vandalism, safety, and maintenance at four St Louis low income housing developments, Master’s Thesis, University of Illinois at Urbana-Champaign. Potter, J. J. (1993) The impact of change upon rural-urban migrants in Turkey, Landscape and Urban Planning, 26, pp. 99–114. Rent, G. S. & Rent, C. S. (1978) Low income housing: factors related to residential satisfaction, Environment and Behavior, 10(4), pp. 459–487. Savasdisara, T. (1988) Residents’ satisfaction and neighborhood characteristics in Japanese urban communities, Landscape and Urban Planning, 15, pp. 201–210. Suher, H., Ocakcı, M. & Berk¨oz, L. (1991) “Being Urbanized” of the New Inhabitant in the Metropolitan City of Istanbul, Paper presented at ENHR Housing for the Urban Poor International Congress, ¨ uekren (Eds), Housing for the Urban Poor, Istanbul, 17–20.9.1991, in: G. Sa˘glamer, S¸. ve Oz¨ pp. B034–B048. T¨urko˘glu, H. (1997) Residents’ satisfaction of housing environments: the case of Istanbul, Turkey, Landscape and Urban Planning, 39, pp. 55–67. Weidemann, S. & Anderson, J. R. (1982) Residents’ perception of satisfaction and safety: a basis for change in multifamily housing, Environment and Behavior, 14, pp. 695–724. Weidemannn, S. & Anderson, J. (1985) A conceptual framework for residential satisfaction. In: I. Altmann, & C. Werner (Eds) Home Environments, pp. 153–182 (New York: Plenum Press). Wiesenfeld, E. (1992) Public housing evaluation in Venezuela: A case study, Journal of Environmental Psychology, 12, pp. 213–223.
Appendix A. Respondent’s Profile District
Number of questionnaire
Per cent
Ata¸sehir, Atak¨oy, Ba¸sak¸sehir, Balkalı, Bah¸ce¸sehir, Bizimkent, Mimaroba, Sinanoba, Kipta¸s-Pendik Family Income
401
100
Frequency
Per cent
Low income Middle income High income Household Size
16 173 212 Frequency
4 43.1 52.9 Per cent
1–2 3–4 5–6 Age of Parents
27.2 59.4 13.4 Per cent 22.4 74.6 3
0–29 30–59 60 +
Father Frq.
Per cent
109 238 54 Mother Frq.
73 265 32
19.7 71.6 8.6
83 276 11
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A. Respondent’s Profile (continued) Age of children 1st child. Frq. Per cent 2nd child. Frq. Per cent 3rd child. Frq. Per cent 0–6 7–14 15–17 18+ Family Education
48 84 47 115
16.3 28.6 16 39.1 Father Frq.
Primary school Secondary school High school Vocational school University Occupation
16 6 132 30 185 Father Frq.
Worker Civil servant Tradesman Artisan Housewife Retired Tradesman Lecturer Self-employed House Ownership
17 42 12 19 0 15 56 23 167
Renter Owner Family House Lodging
43 65 23 72 Per cent 4.3 1.6 35.9 8.1 50.1 Per cent 4.6 11.3 3.2 5.1 0.0 4 15 6.2 44.7 Frequency
21.2 3 32 3 11.3 8 35.5 37 Mother Frq.
5.9 5.9 15.7 72.5 Per cent
30 29 150 30 131 Mother Frq.
8.1 7.8 40.6 8.1 35.4 Per cent
9 55 3 10 161 19 9 27 46
2.4 14.9 0.8 2.7 43.6 5.1 2.4 7.3 12.4 Per cent
131 202 66 2
32.6 50.4 16.5 0.5
B. The Quality of Residence Building type
Frequency
Per cent
Single Family houses Multi-Family houses Number of Rooms
8 393 Frequency
2 98 Per cent
1 2 3 4 5+ Building Structure
27 99 199 66 10 Frequency
6.7 24.7 49.6 16.5 2.5 Per cent
Concrete Heating Type
401 Frequency
100 Per cent
401
100
Central heating
