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Commute travel and its effect on housing tenure choice of males and females living in the urban and rural areas of Bangalore city in India
Journal of Transport Geography 45 (2015) 62–69
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Commute travel and its effect on housing tenure choice of males and females living in the urban and rural areas of Bangalore city in India M. Manoj a, Ashish Verma a,⇑, M. Navyatha b a b
Department of Civil Engineering, Indian Institute of Science (IISc), Bangalore 560012, Karnataka, India Department of Civil Engineering, National Institute of Technology (NIT), Tiruchirappalli 620015, India
a r t i c l e
i n f o
Article history: Received 18 November 2014 Revised 25 April 2015 Accepted 4 May 2015
Keywords: Commute travel Developing country Gender Housing tenure India Location
a b s t r a c t This study attempts to identify various factors influencing individual’s choice of housing tenure with emphasis on the effect of commute trips on that choice. It also focuses on the sensitivity to various factors affecting the housing tenure choice between males and females of urban and rural areas. The study is based on both exploratory analysis and estimation of statistical models using a Household Travel Survey data collected for Bangalore Metropolitan Region in year 2010. The results indicate the role of both the land use attributes and commute travel characteristics on tenure choice and the behavioural difference between males and females. The sensitivity to various attributes is also observed to be varying between the individuals living in the urban and rural areas of the city. The finding that private mode use for commuting governs the housing tenure choice of individuals suggests that promotion of urban-transport policies such as Transit Oriented Development may be an effective strategy to curb the issues related to energy consumption and emission. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Housing related decisions – residential mobility, tenure choice, location, and dwelling type – are components of long-term lifestyle and mobility decisions (Ben-Akiva et al., 1996) that affect activity-travel patterns, emissions and energy consumption of households (Chen et al., 2013), and provide a detailed understanding of urban structure (Bhat et al., 2002) and social well-being (Levin et al., 2014; Arthurson, 2013). Housing decisions of individuals are of interest to transportation planners as such decisions are influencing (e.g., Chen and McKnight (2007)) and are influenced (e.g., Guo and Bhat (2007), Sermons and Koppelman (2001)) by the activity-travel behaviour of individuals/households. Housing tenure – often described as the choice between owning and renting a house – is also a topic of interest in travel behaviour and urban studies literature. Housing tenure choice is observed to influence the travel behaviour of individuals/households. Kim (1994) noted that males living in rented houses in the Los Angeles Metropolitan Area were travelling shorter distance than females belonging to rented houses. The author also observed that the effect (association and magnitude) of renting a house on females’ commute distance varied with respect ⇑ Corresponding author. E-mail addresses: [email protected] (M. Manoj), [email protected] (A. Verma), [email protected] (M. Navyatha). http://dx.doi.org/10.1016/j.jtrangeo.2015.05.001 0966-6923/Ó 2015 Elsevier Ltd. All rights reserved.
to their household context. White (1986), in their analysis from New York City, found that male workers who owned a house had longer commuting journeys than male renters did, and there was no significant difference between the commute durations of female commuters belonging to owned and rented houses. Alkay (2011) observed that the commute times of homeowners and renters were approximately same in the Istanbul Metropolitan Area. Plaut (2006), using an American Housing Survey data set, investigated the inter-relationships between the spousal commuting decisions of individuals belonging to owned and rented houses. It was found that for both housing tenures commute travel by men and women was complementary in nature. Paleti et al. (2013) developed an integrated econometric model for modelling the long-term, medium-term, and short-term activity-travel choices of employed individuals in the US. They observed that homeowners had a higher propensity to locate in less dense neighbourhoods, had longer commute trip lengths, and had a higher tendency to own vehicles. Using the American Housing Survey panel data from the US, Crane (2007) observed that male tenants travelled shorter distance on personal modes than female tenants. Sanit et al. (2013) developed a MNL model for jointly modelling the residential location, workplace location, and travel mode choice of multi-earner households in Bangkok after the introduction of the urban railway. It was found that homeowners had a less preference for residing in locations that were far from the railway line and that incurred a high cost for travel.
M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Housing tenure choice is also influenced by the travel behaviour of individuals/households. Elder and Zumpano (1991) observed that families with head of households commuting on public transport were more likely to rent a home in the US. Chen et al. (2013) analysed residential choices as a joint decision of tenure (own or rent) and housing types (single family unit or multifamily unit). It was observed that household’s time allocation to various activities (shopping, social, recreational, etc.), captured through factor analysis were influencing the joint choice of tenure and housing types. Apart from the time allocation variables, lifecycle stage, socio-demographic characteristic such as vehicle ownership, and land use attributes were also influencing the residential choices. Waddell (1996), in their multi-dimensional residential choice analysis, noted the effect of commute distance on residential choice decisions in Hawaii. It was found that renters had a higher propensity to choose locations that minimised their commute distance. Factors such as price, tax rate, and rent to value ratio . (Mills, 1990), and household socio-demographics such as household size, and presence of children. (Bhat et al., 2002) are found to have influences on housing tenure decision. Overall, a brief summary of the past studies linking housing tenure and travel behaviour indicates that the effects of tenure choice on the travel behaviour of individuals/households are mixed. Commute travel is found to be influenced by and is influencing the housing tenure choice perhaps due to the regularity of work travel. The present work attempts to identify various factors influencing the housing tenure decisions of individuals with emphasis on the effect of commute trips on that decision. It also focuses on the sensitivity to various factors affecting the housing tenure choice between males and females of urban and rural areas. To the authors’ best knowledge no studies have contributed in this research direction from an Indian city. The main objectives of the study include: (i) To summarise the commute travel behaviour of males and females living in owned and rented houses in urban and rural areas, and (ii) To identify various factors influencing the housing tenure choice of males and females living in urban and rural areas Even though residential choices are household decisions, case study of Prashker et al. (2008) from Israel shows that such choices (in their analysis, home location) can be analysed at individual level, and the sensitivity to various attributes – such as commute distance – can vary between genders. The rest of the paper is structured as follows. Next section introduces the study area and data source. Section 3 provides a brief summary of the commute travel behaviour of males and females. Fourth section presents the estimation results of the statistical models that explore the impacts of various factors on the housing tenure choice of individuals. Final section summarises the important findings of the study. 2. Study area and data source The research presented in this paper is a case study from Bangalore Metropolitan Region (BMR) in India. Bangalore has a population of 8 million. Bangalore followed a concentric pattern of spatial development (Revised City Development Plan Bangalore, 2009). However the growth of the city has been unplanned and has been characterised by ‘residential leap frogging and infilling’ with respect to the locations of major industries, academic institutions and transport routes (Sastry, 2008). The data are from a comprehensive Household Travel Survey (HTS) conducted by the Bangalore Metropolitan Region Development Authority (BMRDA) in year 2010 as a part of
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Comprehensive Traffic and Transportation Study (CTTS). A sample (households) size of 2% of the total households in the city was found to be a representative of the study area. Fig. 1 shows the map of the study area. BMR was divided into urban and rural areas in the CTTS. Furthermore, the rural areas in the city were divided into ten coarse traffic-analysis zones in the CTTS. The HTS data was supplemented with land use characteristics of zones (population and employment density, and land use type indicator) and transport network information. However, the HTS data has a few limitations due to the exclusion of non-work travel information of individuals and due to the lack of details regarding residential mobility, house tax and rent information, etc. Further details about the HTS data are available in Bangalore Metropolitan Region Development Authority (2010). The HTS database was subjected to ‘screening and cleaning’ to indentify and to remove incomplete observations. Four sub-samples were developed from the remaining sample of observations – samples for urban males, urban females, rural males, and rural females. The analyses presented in the upcoming sections are based on these subsamples.1
3. Commute travel behaviour of house owners and renters This section concentrates on the commute travel behaviour of males and females living in owned and rented houses in the urban and rural areas of Bangalore city. However, it was felt appropriate to briefly discuss about the personal and household socio-demographics of the individuals. Table 1 summarises the socio-demographic characteristics of the individuals belonging to the urban area of the city, while Table 2 provides a summary of various attributes of the individuals in the rural area. Table 1 suggests that there are several noticeable differences across the individuals. The table indicates that household level car ownership is high amongst females (when they are primary workers); whereas, the household level bicycle ownership is higher for males living in rented houses (both differences are significant at 5% level). Female house-renters have the highest share of unmarried individuals. The share of unmarried individuals in the female house-renters group is significantly (at 5% level) higher than that in female house-owners group and male house-renters group. The share of self-employed individuals is the highest in house-renters groups. For both males and females, the share of self-employed individuals in the house-renters group is significantly (at 5% level) higher than that in house-owners group. Male house-renters have the highest percentage of individuals who possess a license to drive (significantly higher (at 5% level) than that in male house-owners and female house-owners groups). Further, female house-renters have the highest share of individuals whose work varies from day-to-day. This share is significantly (5% level) higher than that in female house-owners group. Other difference amongst the groups with respect to various socio-demographic and socio-economic attributes can be easily comprehended from the summary provided in Table 1. Comparing with the urban sample, the rural households (Table 2) appear to be of small sizes (size differences are significant at 5% level). Bicycle ownership share indicates that it is approximately similar between (at 5% level) males living in owned and rented houses, whereas it is significantly higher amongst male house-renters in the urban sample. Furthermore, bicycle ownership in the male house-owners/renters group is significantly higher 1 The samples are of primary workers, where a primary worker (in case of multi-worker households) is an individual who has the longest work duration amongst all workers in a household, or the older individual if all of them have same work durations (Based on Banerjee (2006)); the lone worker in one-worker households is also designated as a primary worker.
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Fig. 1. Map of the study area (synthesised from metropolitan region development authority (2010)).
(at 5% level) than that in the female house-owners/renters group (Table 2). Amongst all individuals, females living in rented houses have the highest share of unmarried individuals (Table 2). The share of unmarried individuals in the female house-renters group is significantly higher (at 5% level) than that in male house-renters group and female house-owners group. In case of female commuters, the share of self-employed individuals in the house-owners group is comparatively greater (at 5% level) than that amongst the house-renting females and house-owning males in the rural area. The percentage of individuals working in offices is apparently different (but not significant at 5% level) between female house-owners and female house-renters, while it is nearly same for the females in the urban area. Comparing with the urban sample, a higher share of individuals (especially male house-renters) in the rural sample work in farmland or agriculture related activities (differences between urban and rural areas in this case are significant at 5% level). Furthermore, in the rural area, the share of individuals working in farmland in the male house-renters group is significantly (at 5% level) higher than that in male house-owners and female house-renters groups. Table 2 also shows the differences amongst the groups with respect to various other household and personal socio-demographic attributes. Table 3 summarises the (commute) travel behaviour of males and females living in the urban and rural areas of the city. A note is due here about the different types of modes appearing in the table. ‘Auto-rickshaw’ is a motorised three-wheeler mode, which usually serves as a taxi (or IPT). Cab is a four-wheeler mode (car/van) that serves as taxi and is owned and operated by companies/agencies. ‘Taxi’ in the table is also a four-wheeler (car/van) taxi mode, but are owned and operated by individuals. Two-wheeler in the table represents motorised two-wheeler (motorcycle or motorbike).
‘Bus (Public)’ represents the standard buses operated by the public transport authority, Bangalore Metropolitan Transport Corporation (BMTC). Minibus (BMTC) is the medium/mini size operated by BMTC. ‘Bus (Private)’ denotes the standard/medium/mini buses operated by private agencies. Finally, ‘other’ mode category includes unconventional modes used for passenger transport such as carts, tractor with trailers, LCVs, mini/large trucks. The table reveals that, for the urban sample, female house-owners have the highest share of walkers (differences are significant at 5% level) compared to the other groups, while the female house-renters group have a higher share of individuals commuting on car/van (significantly higher than that in female house-owners group). Shares of individuals depending on bus are higher amongst male and female house-owners groups. In general, males travel longer distance than females, and house renters travel longer distance than house owners do. Further, with respect to the commute distance, female house-renters travel significantly longer distance (at 5% level) than the female house-owners do. Subsequently, their commute durations are longer than that for the females living in owned houses (the difference is significant at 5% level). Table 3 also provides a summary of the commute characteristics of the individuals in the rural area. Compared to the urban sample, share of walkers is significantly (at 5% level) higher in the rural sample. Share of individuals depending on two-wheeler is higher (significant at 5% level) amongst males living in owned houses compared to the males belonging to rented houses, whereas the proportion of individuals depending on two-wheelers is approximately same amongst female house-renters and house-owners groups in the rural sample. The average work duration of individuals in the rural sample appears to be (on average 1 h) more than that for the individuals in the urban area (differences are
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69 Table 1 Summary of socio-demographic attributes of males and females in the urban area.1 Attributes
Males Owners
Table 2 Summary of socio-demographic attributes of males and females in the rural area.1
Females Renters
Owners
Attributes
Females
Owners
Renters
Owners
Renters
3.61 1.85 33.85% 59.69% 6.46%d
Household characteristics Household size Avg. no of employed persons Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
2.58 1.07 42.07%a,c 57.44%a,c 0.49%a,c
2.77 1.08 32.67% 66.09% 1.24%
2.92 1.42 25.38%b 70.64%b 3.97%
3.28 1.70 72.59%d 27.41%d 0%
4.82% 95.18%
4.10% 95.90%
Housing type Apartment/flat house Independent house
1.52%a 98.48%a
2.36% 97.64%
3.35% 96.65%
6.54% 93.46%
5.86%d 79.25%d 12.39%d 2.50%d
17.74% 70.97% 8.47% 2.82%
13.82% 75.55% 6.83% 3.80%
Vehicle ownership Car Two-wheeler Bicycle Other vehicles
1.74%c 78.02% 19.71%c 0.53%a,c
1.11% 78.25% 19.34% 1.30%
7.3% 82.9% 7.3%b 2.5%
5.84%d 88.28% 0%d 5.88%d
37.91 51.55%a,c 44.28%a 46.29%a,c 9.43%a,c
34.05 57.27%d 35.42% 62.69%d 1.89%
34.96 29.52% 46.78%b 53.22% 0%b
30.89 35.63% 40.15% 58.20% 1.65%
Personal characteristics Avg. age Own driving license Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
33.98 28.28%a,c 38.99%a,c 60.62%a 0.39%c
31.34 19.04% 49.11% 50.63% 0.26%
35.98 19.17%b 30.56% 63.34% 6.10%
30.79 8.51%d 39.50% 57.77% 2.73%d
Marital status Married individuals Unmarried & other
96.53%a,c 3.47%a,c
89.64% 10.36%
86.64%b 13.36%b
78.49%d 21.51%d
Marital status Married individuals Unmarried & other
95.69%a 4.31%a
86.41% 13.59%
86.49%c 13.51%c
75.47%d 24.53%d
Educational qualification Illiterate Educated up to HSE Diploma & above
4.03%c 49.63%a 46.33%a,c
5.15% 44.02% 50.83%
7.23% 52.76%b 40.01%
6.04% 38.75%d 45.21%d
Educational qualification Illiterate Educated up to HSE Diploma & above
14.47%a,c 50.32%a,c 35.21%a
6.51% 65.01% 28.48%
36.26%b 20.66%b 43.08%b
12.50% 60.14% 27.36%
Occupation status Employed fulltime Employed part-time Daily wages Self-employed
48.57%a 3.17%a,c 24.5%c 23.76%a,c
43.75% 2.06%d 25.63% 28.56%
44.89% 6.68% 33.40%b 15.03%b
45.70% 7.11% 18.10%d 29.09%
Occupation status Employed fulltime Employed part-time Daily wages Self employed
28.19%a 6.33%a 31.88%a 38.60%a,c
22.72% 0.42%d 44.87%d 31.99%
27.56% 8.34% 27.68% 66.10%b
26.49% 3.13% 26.10% 44.28%
Employment sector Government job Private job
5.92%a 94.08%a
2.43% 97.57%
7.03%b 92.97%b
3.95%d 96.05%d
Employment sector Government job Private job
2.52%a 97.48%a
5.74% 94.26%
2.78% 97.22%
6.45% 93.55%
Job type Work in factory Work in office Work in shops/restaurants Work in educational institutions Work in construction sites Work in farmland/agriculture Work varies from day-to-day Work in other sectors Sample size
4.96% 19.84%a 12.81%c 9.06%a 4.24%a,c 0.82%a,c 9.94%a 36.33%a,c 1824
4.18%d 15.46% 11.27%d 0.88% 2.64% 0.34%d 14.93% 50.30% 5763
6.58% 15.57% 6.17% 8.29%b 0.76% 0% 10.69%b 51.94% 295
8.91% 15.76% 7.5% 1.65%d 0.28%d 0% 15.77% 49.13% 757
Job type Work in factory Work in office Work in shops/restaurants Work in educational institutions Work in construction sites Work in farmland/agriculture Work varies from day-to-day Work in other sectors Sample size
2.55%c 12.18%a,c 12.29%a 8.71%a,c 1.94% 6.85%a 25.57%c 29.91%a 4010
2.57% 17.30% 25.62% 0.71% 1.19% 10.28% 25.00% 17.33%d 1395
26.40%b 20.15% 10.30% 15.15% 2.21% 3.68% 6.62% 15.49%c 143
7.56%d 17.44% 12.21%d 9.63%d 3.78% 0%d 8.14%d 39.24%b 57
Household characteristics Household size Avg. no of employed persons Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
3.53 1.42 24.45%a,c 69.41%a 6.14%a,c
3.10 1.34 32.55% 63.15% 3.90%
3.74 1.82 35.71% 64.29% 0%b
Housing type Apartment/flat house Independent house
5.18% 94.82%
4.22% 95.78%
Vehicle ownership Car Two-wheeler Bicycle Other vehicles
9.60%a,c 79.90%c 9.10%a 1.40%a
Personal characteristics Avg. age Own driving license Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
Renters
Males
*
*
Income in ‘INR’ (Indian Rupees). a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
Income in ‘INR’ (Indian Rupees). a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
significant at 5% level). Noteworthy, males in the rural area travel shorter distance than the males in the urban area, while female house-owners in the rural area travel significantly longer distance than the female house-owners in the urban area, and rural females in rented houses travel shorter distance than the female house-renters in the urban area. The difference between commute distances of individuals in the urban and the rural areas are significant at 5% level. Furthermore, female house-renters travel shorter distance (but difference is insignificant at 5% level) than the females living in owned houses, whereas, as noted before, females living in the rented houses in the urban area travel significantly longer distance than the females living in owned houses. Table 4 presents the ‘mode-wise’ travel details of males and females. For the urban area, the trend indicates that, compared to male house-renters, male house-owners travel longer distance
on foot and bicycle, and travel short distance on other modes (differences are significant at 5% level). Female house-owners appear to travel longer distance on foot, and shorter distances on cab, two-wheeler, car, and bus than female house-renters do (differences are significant at 5% level), and, male house-renters travel longer distances than female house-renters on most of the modes. The travel behaviour trend in the rural area is, in general, comparable with that in the urban area. The summaries presented above further help compare the Indian context with the developed world. For example, Kim (1994) noted that males living in rented houses in the Los Angeles Metropolitan Area were travelling shorter distance than females belonging to rented houses, whereas in Bangalore urban area (Table 3) male house-renters travel longer distance than female house-renters (difference is significant at 5% level).
1
1
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Table 3 Travel behaviour of males and females in the urban and rural areas.*,1 Attributes
Males
Females
Owners
Renters
Owners
Renters
Urban area Trip mode share Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others Avg. commute duration (min) Avg. work duration (h) Avg. no of stages Avg. commute distance (km) Avg. travel fare (INR) Works in the ‘home zone’ Sample size
9.57%a,c 3.53%a,c 0.09% 4.04%a 1.16%a,c 39.43%a,c 3.27%a 4.83%a,c 12.74%a 0.56% 20.21%a,c 0.36% 0.21% 13.09 (20.63)a 8.05 (11.32)a 1.09 (0.42) 6.18 (25.52)a,c 13.62 (59.33) 58.87%a 1824
6.60% 2.31% 0.11% 1.84% 1.94% 64.94% 6.62% 2.91% 2.40% 0.36% 9.21% 0.44% 0.32% 21.38 (41.55) 9.38 (8.37)d 1.11 (0.51) 7.96 (16.52)d 12.94 (27.74)d 47.04% 5763
16.57%b 1.10% 0.12% 2.04% 2.75% 46.90% 5.22% 10.05% 8.84% 0.79% 5.62% 0% 0% 12.72 (22.84)b 7.44 (4.35)b 1.10 (0.43)b 1.53 (9.51)b 13.20 (53.80)b 56.95% 295
11.10%d 1.47% 0% 2.21% 5.54%d 47.39%d 10.98%d 5.17%d 9.13%d 0.92%d 6.09%d 0% 0% 24.98 (82.90) 8.38 (5.09) 1.18 (0.55) 5.07 (6.39) 6.67 (27.73) 51.92%d 757
Rural area Trip mode share Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others Avg. commute duration (min) Avg. work duration (h) Avg. no of stages Avg. commute distance (km) Avg. travel fare (INR) Works in the ‘home zone’ Sample size
35.67% 8.85%a,c 0.16%a 0.60% 0.82% 43.66%a 3.58%a,c 0.58%c 2.96%a 1.90%a,c 0.83%a 0.06% 0.33%a,c 26.83 (22.88)a,c 9.09 (7.53)a,c 1.1 (0.49)a 5.21 (6.82)a 6.43 (26.27) 53.67%a,c 4010
33.19% 5.53% 0.75% 0.42% 0.65% 39.92% 1.28% 0.84% 8.33% 6.78% 2.3% 0% 0% 22.90 (18.88) 10.22 (13.10) 1.07 (0.39) 4.33 (5.37) 5.71 (28.5) 49.47% 1395
37.19%b 0.74% 0.74% 0.74% 0% 38.52% 9.74%b 2.22% 4.19%b 2.96%d 0.74% 0% 2.22%b 23.29 (20.95) 8.36 (1.43)b 1.09 (0.43) 4.98 (7.51) 5.44 (21.65) 66.14% 143
15.37%d 0.95% 0% 1.90% 0% 41.29% 0% 3.81%d 18.71%d 0% 0% 0% 18.57%d 21.75 (18.57) 9.28 (2.29)d 0.98 (0.34) 3.86 (4.96) 4.56 (25.3) 59.65% 57
*
Standard deviation in brackets. a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters. 1
Furthermore, the commute travel time of house owners and renters (in general) is significantly different in the Bangalore sample (urban area), while it was nearly same in the Istanbul Metropolitan Area (Alkay, 2011). 4. Housing tenure choice of males and females In this section, binary logit models are estimated for investigating the effects of various attributes on the housing tenure choice of males and females living in the urban and rural areas of the city. Table 5 presents the estimation results of the tenure choice models. The dependent variable is the probability of renting a house. The data did not provide enough support to estimate models with exogenous variables (e.g., commute distance) interacted with each other (e.g., with socio-demographics). Hence, all variables appear in linear fashion. The table indicates that the probability of renting a house decreases with increase in an individual’s age (estimates are significant at 5% level). Amongst the four groups, age has its highest impact on the housing tenure choice of rural females. It may be implying that the possibility of changing a job and finding
an appropriate housing location may be very limited in the rural area, and hence, rural females may be deciding upon their job and residential choice on a long-term basis, which may eventually lead to less inclination towards renting a house as they become older. The effects of private sector employment on tenure choice are different for urban and rural areas. The positive effects for the urban area (significant at 10% level) may be an indication of individuals’ propensity to change jobs frequently due to a variety of employment opportunities in the area, which in turn may be leading to less preference for owning a home and settling at a particular location. Rural area may not have job opportunities compared to the extent with the urban area, and hence individuals may not be changing their jobs frequently, and may be deciding upon their residential choices on a long-term basis, leading to less inclination towards renting (estimates are significant at 5% level). The effect of individual’s income on the propensity to rent a house is mixed (parameter estimates are significant at 5% level). Highly paid individuals in the rural area are less likely to rent a house, an intuitive finding (see Bhat et al. (2002) for positive effect of income on owning a house). The positive effect in case of the urban
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69 Table 4 Mode-wise commute details of males and females in the urban and rural areas.1 Trip mode
Males
Females
Owners
Renters
Owners
Renters
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Urban area Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus* (BMTC) Train Others
24.21 (11.55) 20.12 (10.52) 20.00 (15.33) 20.02 (20.54) 20.53 (15.56) 19.40 (13.00) 14.20 (4.20) 12.05 (13.20) 20.51 (22.51) 48 (6.17) 10.07 (22.30) – –
2.26a,c (4.38) 4.14a,c (4.48) 5.60a (0.57) 2.54 (4.00) 2.50a,c (6.81) 3.40a,c (3.90) 2.73a,c (7.31) 5.02a (6.90) 5.84a,c (4.59) 7.50a (25.21) 2 .00 (4.10) – –
14.85 16.60 35.00 12.52 78.84 26.24 28.71 19.61 35.95 13.99 26.40 35.57 90.00
(19.81) (15.33) (18.02) (12.83) (33.60) (30.72) (26.75) (24.78) (33.20) (22.37) (15.27) (6.26) (70.34)
1.75 (2.64) 2.10 (12.14) 12.14 (12.32) 3.15 (3.72) 11.74 (24.26) 8.03 (6.05) 9.00 (8.63) 8.87 (7.89) 9.73 (6.26) 10.46 (5.94) 7.45 (5.22) 12.70 (11.11) 20.40 (14.15)
22.07 12.35 – 17.10 18.50 21.55 19.43 18.26 30.13 15.00 33.07 – –
3.37b (2.17) 2.60 (3.32) – 1.73b (1.66) 4.82b (7.00) 5.65b (6.02) 5.89b (7.46) 5.75 (5.41) 8.97 (7.90) 4.00b (3.20) 3.96 (3.97) – –
17.30 (5.19) 30 (15.15) 40 (11.21) 22.30 (12.35) 35.92 (34.12) 27.33 (20.13) 29.30 (26.75) 45.78 (30.20) 32.40 (22.59) 30.00 (12.69) 23.15 (12.35) – –
2.93 (5.49) 3.28 (1.63) 20 (18.12) 4.53 (2.56) 7.5d (5.14) 8.32 (6.86) 7.28 (6.11) 6.74d (4.49) 9.44 (7.36) 6.12d (5.41) 4.84d (3.81) – –
Rural area Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others
21.80 (19.60) 19.75 (11.80) 15.00 (7.07) 18.57 (7.94) 58.18 (35.37) 33.89 (24.53) 64.52 (45.24) 35.95 (26.20) 41.54 (33.49) 25.90 (23.35) 11.46 (17.18) 15 (1.45) –
2.89 (3.44) 2.99 (1.77) 4.5c (0.70) 2.03 (1.04) 10.18 (6.64) 8.92 (6.15) 14.09c (13.14) 11.52c (13.15) 11.11c (11.13) 10.09c (9.82) 14.60c (11.93) 12.50 (4.08) –
18.59 19.30 10.00 15.83 42.00 28.95 50.00 22.50 28.88 26.92 16.88 – –
(15.50) (11.06) (1.34) (9.17) (32.20) (21.39) (40.72) (17.07) (17.18) (29.22) (12.51)
2.18 (3.74) 2.86 (1.55) 5.00 (.98) 1.83 (0.75) 8.00a (6.20) 8.50d (5.51) 8.00a (5.71) 19.00a,d (17.38) 7.51a (5.65) 8.61a,d (7.80) 8.63a (5.28) – –
18.90 (16.46) 7.55 (1.21) 20.00 (1.36) 30.00 (2.21) – 31.09 (18.74) 37.50 (10.60) 30.00 (12.32) 38.18 (41.18) 21.66 (19.40) 30.00 (2.11) – –
2.68 (4.48) 2.25 (1.16) 3.00 (1.22) 2.00 (0.56) – 7.63 (4.21) 8.00 (2.62) 4.68 (7.07) 13.09 (18.06) 16.67 (6.05) 4.00 (1.28) – –
15.89 15.04 – 30.00 – 30.31 – 30.00 30.00 20.00 – – –
2.11 (1.86) 2.11 (9.08) – 2.00 (1.56) – 6.87 (3.24) – 6.00b (0.34) 8.22b (10.25) 10.00b (0.25) – – –
(9.30) (8.45) (23.17) (25.07) (22.39) (25.60) (26.87) (34.37) (15.00) (17.97)
(8.34) (2.34) (1.34) (19.62) (1.09) (34.64) (0.34)
TT – Travel time (min.); TD – Travel distance (km); Standard deviation in brackets. * Bangalore Metropolitan Transport Corporation. 1 a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
area may be implying that the property tax and other investments needed for owning a house can be higher in the urban area than that for the rural area, and hence, for comparable income levels between urban and rural areas, an urban dweller may more likely to rent a house. Further, the urban region also includes a significant share of migrants (Sastry, 2008) who work in highly paid job sectors (such as IT, Biotechnology and defence), and may not be considering the urban area as their permanent residential location, and hence may be renting houses. For both the urban and rural areas, females exhibit higher sensitivity to income than males. Part-time employees and self-employed individuals in the rural area are less likely to rent a house (estimates are significant at 5% level). Individuals with such employments may be interested in houses where they can run their business as well (cattle farm and milk production, part-time tuition class at home, etc.), and since the appropriate houses may not be available for renting, individuals may have less preference for renting than building and owning one. It appears that employment type does not significantly (insignificant at 10% level) explain the tenure choice of urban commuters. The parameters estimates pertaining to educational attainment are significant at 5% level. Rural individuals with higher educational qualification in are less likely to rent a house – perhaps suggesting that such individuals may be in highly paid job, which may eventually be leading to owning a house. The differences in the contexts between urban and rural areas in terms of migration level, and property tax and other housing related investments may be the reasons behind the positive association of education attainment on renting a house in the urban area. The sensitivity to education attainment is much higher for the individuals in the urban area than that for the individuals in the rural area. Individuals working in educational institutions have a less
propensity to rent a house compared to the individuals working in other sectors (coefficients in the rural models are significant only at 10% level). This may be due to the more permanent nature of jobs in the education sector (for example, teachers are less likely to be transferred from one place to another) than in other sectors. Owning a license to drive a vehicle decreases a rural individual’s propensity to rent a house, whereas urban dwellers are more likely to rent a house if they possess a license to drive. Rural inhabitants might be considering the rural area as their permanent residential location choice; hence, possession of a driving license, which is a component of long-term mobility choice, might be directly influencing the long-term residential choice through owning a house than renting. On the other hand, given the job opportunities in the urban area, driving license owners (who may be holding vehicles) may be changing their jobs and/or residential locations occasionally (due to mobility offered by private vehicles, if they own) and may be considering residential choices on a short-term basis (coefficients in the urban models are significant at 5% level). The sensitivity to this variable is much higher for the rural individuals than that for the urban individuals. Married individuals in the rural area are less likely to rent a house than other individuals are, and the sensitivity to marital status is much higher amongst rural females where a change in marital status is highly important. The higher sensitivity to marital status in the rural area may also be due to the effect of socio-cultural norms of the society – which emphasise married females to be well settled – the effect of which can be more visible in rural areas than urban areas. The partial effects of marital status on the housing tenure choice of urban individuals are very limited (insignificant at even 10% level). Duration at work is observed to have a positive association with an individual’s propensity to rent a house (estimates are significant
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Table 5 Estimation results of the tenure choice models.*,# Attributes
Constant Age (years) Employed in private sector Individual’s income > INR 5000 Employed part-time Self-employed Qualified above diploma Work in educational institutions Own a license to drive Married individuals Work duration (min) Commute mode is private Commute distance (km) Works in ‘home zone’ Children’s distance to school (km) Employment/population Other land use Sample size Adjusted rho-square # *
Urban Males
Females
Males
Females
0.748 (3.62) 0.004 ( 3.12) 0.161 (1.80) 0.126 (2.05) 0.0006 (1.60) 0.0001 (1.51) 0.348 (4.93) 0.516 ( 3.64) 0.311 (7.78) 0.050 (1.41) 0.005 (2.27) 0.101 (1.73) 0.024 (3.23) 0.081 (2.45) 0.015 ( 1.83) 0.0732 (2.00) 0.098 (1.82) 7587 0.338
0.538 (2.92) 0.009 ( 2.42) 0.174 (1.69) 0.217 (3.57) 0.0004 (1.20) 0.0008 (1.79) 0.232 (2.13) 0.301 ( 2.11) 0.211 (2.65) 0.062 (1.54) 0.004 (3.01) 0.162 (2.60) 0.053 (2.92) 0.133 (3.20) 0.047 ( 1.75) 0.222 (3.16) 0.086 (1.72) 1052 0.268
4.06 (8.50) 0.021 ( 7.35) 0.545 ( 4.35) 0.098 ( 2.13) 0.350 ( 10.25) 0.643 ( 6.71) 0.089 (3.26) 0.110 ( 1.89) 0.602 ( 3.42) 0.157 (1.74) 0.001 (4.38) 0.246 ( 2.76) 0.038 (3.33) 0.657 ( 8.26) 0.029 (1.66) 5.67 ( 6.56) 0.114 (1.75) 5405 0.231
7.62 (2.88) 0.045 ( 2.20) 1.49 ( 3.20) 0.114 (3.03) 0.210 ( 4.21) 0.201 ( 2.31) 0.062 (1.95) 0.096 ( 1.91) 0.340 ( 1.89) 1.07 (2.73) 0.005 (2.60) 1.05 ( 1.89) 0.074 (1.98) 0.721 ( 2.68) 0.052 (1.88) 16.20 ( 3.12) 0.153 (1.67) 200 0.250
t-Statistic in brackets. Significant at 5% level if t-statistic P 1.96 and Significant at 10% level if t-statistic P 1.65.
at 5% level). Individuals with longer work duration may be balancing their daily activity-travel schedule by moving closer to the work location, and it is possible that such individuals will not be able to find preferable residential units near work location, which may eventually be contributing to renting a house (all else being equal). Chen et al. (2013) did not find any significant effect of work duration on tenure choice in their analysis from the US. The effect of commute mode choice on housing tenure choice is mixed. Urban populace has a high tendency to rent a house if they travel on private mode, whereas rural individuals have a less propensity to rent a house if their commute mode is private.2 Given the employment and residential opportunities in the urban area, private mode use may be a medium, which helps urban dwellers in exploring new residential locations (and jobs) on a short-term basis, and this may be leading to renting a house. In case of rural area, where employment opportunities are comparatively lower than that in the urban area, individuals may be planning their job participation and commute mode on a long-term basis, which may be contributing to the less inclination towards renting a house. The coefficients on the variable commute mode are significant at 10% level (and below). The effect of commute distance indicates that individuals with longer commutes are less likely to rent a house in the rural area (all coefficients are significant at 5% level). Rural area may predominantly be having agricultural and other traditional jobs, and housing locations preferable to the individuals may not be available near the employment sites, which may lead to longer commutes and because of this, individuals may be planning their residential choices on a long-term basis, eventually leading to less inclination towards renting a house. On the other hand, for the urban populace, the positive effect may be indicating the effect of efficient transportation system that helps to reach preferable housing locations much conveniently than in the rural area. Comparatively, females have a higher sensitivity to commute distance than males, and rural individuals have a higher sensitivity to distance than urban individuals do. Being employed in the home zone of an individual decreases their propensity to rent a house perhaps indicating their long-term preference for living closer to work location and hence a less tendency to rent a house 2
Rural
Vehicle ownership and use may actually be endogenous with housing tenure choice. However, vehicle ownership has been treated as an exogenous variable in this study.
(estimates are significant at 5% level). The negative effect of an individual’s children’s commute distance on their housing tenure choice indicate that the lesser the distance the more is the tendency to rent a house (estimates are significant at 10% level). Although it seems counter intuitive, the result may be indicating that affordable and preferable housing may not be available near schools, and hence, individuals may prefer to rent a house. In general, females have a higher sensitivity to this variable perhaps due to the need to shoulder the responsibilities related to children (e.g., taking children to a practice session in their school), and hence they may be interested in reducing overall daily travel distance. This may lead females to move closer to school location, and rent a house, all else being equal. Individuals living in zones with high employment to population ratio have a lesser tendency to rent a house relative to ‘own’ (all coefficients are significant at 5% level). Such zones may be having different types of jobs and other opportunities available at higher intensities, and under such conditions, individuals may be less interested in renting a house than owning. Finally, compared to the individuals living in zones with residential land use, commuters living in zones characterised by other land use types are more likely to rent a home (estimates are significant at 10% level). Zones with other land use (e.g., industrial, agricultural) may not be having the facilities needed for the day-to-day life of individuals (e.g., playgrounds, community centres), and hence individuals may have high propensities to rent a house than owning one. 5. Summary and conclusions In this study, an attempt has been made to investigate the housing tenure choice decisions of male and female commuters living in the urban and rural areas of Bangalore city in India. The data set used in this study is from a household travel survey conducted in year 2010. The study was performed through an exploratory analysis of travel behaviour, and estimation of binary logit models of tenure choice. Important findings from the overall analysis are as follows: (i) In general, females travel shorter distance than males; house-renters travel longer distance than house-owners in the urban area, and house-owners travel longer distance than house-renters in the rural area.
M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
(ii) Employment type and marital status do not appear to be a significant predictor of housing tenure choice of individuals in the urban area, whereas in the rural area, married individuals, those having part-time jobs, and those who are self-employed are less likely to rent a house. (iii) Urban individuals commuting on private modes are more likely to rent a house, whereas rural individuals are less likely to rent a house if they travel on private vehicle. (iv) Urban commuters with longer commutes are more likely to rent a house, whereas rural commuters have comparatively less preference for renting a house with increase in their commute distances; further, the lesser is the commute distance of an individual’ children the more is their propensity to rent a house. (v) Individuals also consider land use type and employment to population ratio in the residential zone for deciding upon their housing tenure. Overall, the study provided insights into the individuals’ decision regarding housing tenure and the role of commute travel on that decision. Viewing from a broad perspective, the effect of commute mode choice on tenure selection is important in the context of urban-transport policies. Basing the discussion on the empirical findings – which show that housing related decisions are conditional on employment related decisions (e.g., Prashker et al. (2008)) – the result has implication for energy consumption and emission. For a fixed work location, an individual’s residential choice in the urban and rural areas is governed by private mode use for commuting. In order to alleviate the ill effects of private mode use, urban-transport planners may focus on public transport oriented urban-transport strategies (e.g., Transit Oriented Development). Creating more employment in the residential zones may help individuals to find jobs in the same zone and along with this, promotion of effective sustainable transport policies (e.g., emission tax) further help control ill effects of private mode use. This may be extremely relevant when Bangalore is projected to become ‘Greater Bangalore’ by increasing its municipal (urban) area by including rural areas and by generating new employments in the rural areas (Sastry, 2008). The present study can be extended in many ways. Inclusion of non-work travel behaviour in the tenure choice analysis is one possible avenue for the research. Though individual-level analysis is relevant in its own case, household-level analysis would further help investigate the effect of interaction amongst individuals on tenure choice. Estimation of models with data sets having information related to house rent, tax information, etc. would enrich the models estimated in this study. Future models can also include ‘neither owning nor renting’ as another tenure choice alternative as about 2.6% of the households in the nation belong to such category (Census of India, 2011). Development of causal models would be another avenue of research where one could investigate the interaction between housing decisions, commute behaviour and vehicle ownership of individuals/households. Analysis of the influences of residential preferences and housing choices on the travel patterns of individuals of Indian cities can be another front of further research.
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Acknowledgement The authors wish to acknowledge the support of Urban Mass Transit Company in arranging the HTS data of Bangalore city for the study. References Alkay, E., 2011. In depth analysis of the home to work travel pattern in the istanbul metropolitan area. In: ERSA Conference Papers (No. ersa11p371). European Regional Science Association. Arthurson, K., 2013. Mixed tenure communities and the effects on neighbourhood reputation and stigma: residents’ experiences from within. Cities 35, 432–438. Banerjee, A., 2006. Understanding Activity Engagement and Time use Patterns in a Developing Country Context. Doctoral dissertation, University of South Florida. Bangalore Metropolitan Region Development Authority, 2010. Comprehensive Traffic and Transport Plan for Bangalore. Final Report. Ben-Akiva, M., Bowman, J.L., Gopinath, D., 1996. Travel demand model system for the information era. Transportation 23 (3), 241–266. Bhat, C.R., Srinivasan, S., Guo, J.Y., 2002. Activity-Based Travel-Demand Analysis for Metropolitan Areas in Texas: Data Sources, Sample Formation, and Estimation Results (No. FHWA/TX-03/4080-3). Census of India, 2011. HLO Indicators. Available at: http://censusindia.gov.in/ 2011census/hlo/Data_sheet/India/HLO_Indicators.pdf (accessed 26.02.15). Chen, C., McKnight, C.E., 2007. Does the built environment make a difference? Additional evidence from the daily activity and travel behaviour of homemakers living in New York City and suburbs. J. Transp. Geogr. 15 (5), 380–395. Chen, R.B., Gehrke, S.R., Jang, Y., Liu, J.H., Clifton, K.J., 2013. Exploring residential tenure and housing type decisions and household activity engagement. Transp. Res. Rec. 2344, 68–78. Crane, R., 2007. Is there a quiet revolution in women’s travel? Revisiting the gender gap in commuting. J. Am. Plan. Assoc. 73 (3), 298–316. Elder, H.W., Zumpano, L.V., 1991. Tenure choice, housing demand and residential location. J. Real Estate Res. 6 (3), 341–356. Guo, J.Y., Bhat, C.R., 2007. Operationalising the concept of neighbourhood: application to residential location choice analysis. J. Transp. Geogr. 15 (1), 31– 45. Kim, S., 1994. Gender Differences in Commuting: an Empirical Study of the Greater Los Angeles Metropolitan Area. University of California Transportation Centre. Levin, I., Arthurson, K., Ziersch, A., 2014. Social mix and the role of design: competing interests in the carlton public housing estate redevelopment, Melbourne. Cities 40, 23–31. Mills, E.S., 1990. Housing tenure choice. J. Real Estate Finance Econ. 3 (4), 323–331. Paleti, R., Bhat, C.R., Pendyala, R.M., 2013. An integrated model of residential location, work location, vehicle ownership, and commute tour characteristics. Transp. Res. Rec. 2382, 162–172. Plaut, P.O., 2006. The intra-household choices regarding commuting and housing. Transp. Res. Part A: Policy Practice 40 (7), 561–571. Prashker, J., Shiftan, Y., Hershkovitch-Sarusi, P., 2008. Residential choice location, gender and the commute trip to work in Tel Aviv. J. Transp. Geogr. 16 (5), 332– 341. Revised City Development Plan Bangalore, 2009. Urban Infrastructure and Governance. Volume-1. Sanit, P., Nakamura, F., Tanaka, S., Wang, R., 2013. Location and mode choice decision mechanism analysis of multi-worker household in Bangkok, Thailand. J. Eastern Asia Soc. Transp. Stud. 10, 1243–1257. Sastry, G.S., 2008. Emerging Development Issues of Greater Bangalore. Institute for Social and Economic Change. Sermons, M.W., Koppelman, F.S., 2001. Representing the differences between female and male commute behaviour in residential location choice models. J. Transp. Geogr. 9 (2), 101–110. Waddell, P., 1996. Accessibility and residential location: the interaction of workplace, residential mobility, tenure, and location of choices. In: Proceedings of the Lincoln Land Institute TRED Conference. White, M.J., 1986. Sex differences in urban commuting patterns. Am. Econ. Rev. 76 (2), 368–372.
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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo
Commute travel and its effect on housing tenure choice of males and females living in the urban and rural areas of Bangalore city in India M. Manoj a, Ashish Verma a,⇑, M. Navyatha b a b
Department of Civil Engineering, Indian Institute of Science (IISc), Bangalore 560012, Karnataka, India Department of Civil Engineering, National Institute of Technology (NIT), Tiruchirappalli 620015, India
a r t i c l e
i n f o
Article history: Received 18 November 2014 Revised 25 April 2015 Accepted 4 May 2015
Keywords: Commute travel Developing country Gender Housing tenure India Location
a b s t r a c t This study attempts to identify various factors influencing individual’s choice of housing tenure with emphasis on the effect of commute trips on that choice. It also focuses on the sensitivity to various factors affecting the housing tenure choice between males and females of urban and rural areas. The study is based on both exploratory analysis and estimation of statistical models using a Household Travel Survey data collected for Bangalore Metropolitan Region in year 2010. The results indicate the role of both the land use attributes and commute travel characteristics on tenure choice and the behavioural difference between males and females. The sensitivity to various attributes is also observed to be varying between the individuals living in the urban and rural areas of the city. The finding that private mode use for commuting governs the housing tenure choice of individuals suggests that promotion of urban-transport policies such as Transit Oriented Development may be an effective strategy to curb the issues related to energy consumption and emission. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Housing related decisions – residential mobility, tenure choice, location, and dwelling type – are components of long-term lifestyle and mobility decisions (Ben-Akiva et al., 1996) that affect activity-travel patterns, emissions and energy consumption of households (Chen et al., 2013), and provide a detailed understanding of urban structure (Bhat et al., 2002) and social well-being (Levin et al., 2014; Arthurson, 2013). Housing decisions of individuals are of interest to transportation planners as such decisions are influencing (e.g., Chen and McKnight (2007)) and are influenced (e.g., Guo and Bhat (2007), Sermons and Koppelman (2001)) by the activity-travel behaviour of individuals/households. Housing tenure – often described as the choice between owning and renting a house – is also a topic of interest in travel behaviour and urban studies literature. Housing tenure choice is observed to influence the travel behaviour of individuals/households. Kim (1994) noted that males living in rented houses in the Los Angeles Metropolitan Area were travelling shorter distance than females belonging to rented houses. The author also observed that the effect (association and magnitude) of renting a house on females’ commute distance varied with respect ⇑ Corresponding author. E-mail addresses: [email protected] (M. Manoj), [email protected] (A. Verma), [email protected] (M. Navyatha). http://dx.doi.org/10.1016/j.jtrangeo.2015.05.001 0966-6923/Ó 2015 Elsevier Ltd. All rights reserved.
to their household context. White (1986), in their analysis from New York City, found that male workers who owned a house had longer commuting journeys than male renters did, and there was no significant difference between the commute durations of female commuters belonging to owned and rented houses. Alkay (2011) observed that the commute times of homeowners and renters were approximately same in the Istanbul Metropolitan Area. Plaut (2006), using an American Housing Survey data set, investigated the inter-relationships between the spousal commuting decisions of individuals belonging to owned and rented houses. It was found that for both housing tenures commute travel by men and women was complementary in nature. Paleti et al. (2013) developed an integrated econometric model for modelling the long-term, medium-term, and short-term activity-travel choices of employed individuals in the US. They observed that homeowners had a higher propensity to locate in less dense neighbourhoods, had longer commute trip lengths, and had a higher tendency to own vehicles. Using the American Housing Survey panel data from the US, Crane (2007) observed that male tenants travelled shorter distance on personal modes than female tenants. Sanit et al. (2013) developed a MNL model for jointly modelling the residential location, workplace location, and travel mode choice of multi-earner households in Bangkok after the introduction of the urban railway. It was found that homeowners had a less preference for residing in locations that were far from the railway line and that incurred a high cost for travel.
M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Housing tenure choice is also influenced by the travel behaviour of individuals/households. Elder and Zumpano (1991) observed that families with head of households commuting on public transport were more likely to rent a home in the US. Chen et al. (2013) analysed residential choices as a joint decision of tenure (own or rent) and housing types (single family unit or multifamily unit). It was observed that household’s time allocation to various activities (shopping, social, recreational, etc.), captured through factor analysis were influencing the joint choice of tenure and housing types. Apart from the time allocation variables, lifecycle stage, socio-demographic characteristic such as vehicle ownership, and land use attributes were also influencing the residential choices. Waddell (1996), in their multi-dimensional residential choice analysis, noted the effect of commute distance on residential choice decisions in Hawaii. It was found that renters had a higher propensity to choose locations that minimised their commute distance. Factors such as price, tax rate, and rent to value ratio . (Mills, 1990), and household socio-demographics such as household size, and presence of children. (Bhat et al., 2002) are found to have influences on housing tenure decision. Overall, a brief summary of the past studies linking housing tenure and travel behaviour indicates that the effects of tenure choice on the travel behaviour of individuals/households are mixed. Commute travel is found to be influenced by and is influencing the housing tenure choice perhaps due to the regularity of work travel. The present work attempts to identify various factors influencing the housing tenure decisions of individuals with emphasis on the effect of commute trips on that decision. It also focuses on the sensitivity to various factors affecting the housing tenure choice between males and females of urban and rural areas. To the authors’ best knowledge no studies have contributed in this research direction from an Indian city. The main objectives of the study include: (i) To summarise the commute travel behaviour of males and females living in owned and rented houses in urban and rural areas, and (ii) To identify various factors influencing the housing tenure choice of males and females living in urban and rural areas Even though residential choices are household decisions, case study of Prashker et al. (2008) from Israel shows that such choices (in their analysis, home location) can be analysed at individual level, and the sensitivity to various attributes – such as commute distance – can vary between genders. The rest of the paper is structured as follows. Next section introduces the study area and data source. Section 3 provides a brief summary of the commute travel behaviour of males and females. Fourth section presents the estimation results of the statistical models that explore the impacts of various factors on the housing tenure choice of individuals. Final section summarises the important findings of the study. 2. Study area and data source The research presented in this paper is a case study from Bangalore Metropolitan Region (BMR) in India. Bangalore has a population of 8 million. Bangalore followed a concentric pattern of spatial development (Revised City Development Plan Bangalore, 2009). However the growth of the city has been unplanned and has been characterised by ‘residential leap frogging and infilling’ with respect to the locations of major industries, academic institutions and transport routes (Sastry, 2008). The data are from a comprehensive Household Travel Survey (HTS) conducted by the Bangalore Metropolitan Region Development Authority (BMRDA) in year 2010 as a part of
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Comprehensive Traffic and Transportation Study (CTTS). A sample (households) size of 2% of the total households in the city was found to be a representative of the study area. Fig. 1 shows the map of the study area. BMR was divided into urban and rural areas in the CTTS. Furthermore, the rural areas in the city were divided into ten coarse traffic-analysis zones in the CTTS. The HTS data was supplemented with land use characteristics of zones (population and employment density, and land use type indicator) and transport network information. However, the HTS data has a few limitations due to the exclusion of non-work travel information of individuals and due to the lack of details regarding residential mobility, house tax and rent information, etc. Further details about the HTS data are available in Bangalore Metropolitan Region Development Authority (2010). The HTS database was subjected to ‘screening and cleaning’ to indentify and to remove incomplete observations. Four sub-samples were developed from the remaining sample of observations – samples for urban males, urban females, rural males, and rural females. The analyses presented in the upcoming sections are based on these subsamples.1
3. Commute travel behaviour of house owners and renters This section concentrates on the commute travel behaviour of males and females living in owned and rented houses in the urban and rural areas of Bangalore city. However, it was felt appropriate to briefly discuss about the personal and household socio-demographics of the individuals. Table 1 summarises the socio-demographic characteristics of the individuals belonging to the urban area of the city, while Table 2 provides a summary of various attributes of the individuals in the rural area. Table 1 suggests that there are several noticeable differences across the individuals. The table indicates that household level car ownership is high amongst females (when they are primary workers); whereas, the household level bicycle ownership is higher for males living in rented houses (both differences are significant at 5% level). Female house-renters have the highest share of unmarried individuals. The share of unmarried individuals in the female house-renters group is significantly (at 5% level) higher than that in female house-owners group and male house-renters group. The share of self-employed individuals is the highest in house-renters groups. For both males and females, the share of self-employed individuals in the house-renters group is significantly (at 5% level) higher than that in house-owners group. Male house-renters have the highest percentage of individuals who possess a license to drive (significantly higher (at 5% level) than that in male house-owners and female house-owners groups). Further, female house-renters have the highest share of individuals whose work varies from day-to-day. This share is significantly (5% level) higher than that in female house-owners group. Other difference amongst the groups with respect to various socio-demographic and socio-economic attributes can be easily comprehended from the summary provided in Table 1. Comparing with the urban sample, the rural households (Table 2) appear to be of small sizes (size differences are significant at 5% level). Bicycle ownership share indicates that it is approximately similar between (at 5% level) males living in owned and rented houses, whereas it is significantly higher amongst male house-renters in the urban sample. Furthermore, bicycle ownership in the male house-owners/renters group is significantly higher 1 The samples are of primary workers, where a primary worker (in case of multi-worker households) is an individual who has the longest work duration amongst all workers in a household, or the older individual if all of them have same work durations (Based on Banerjee (2006)); the lone worker in one-worker households is also designated as a primary worker.
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Fig. 1. Map of the study area (synthesised from metropolitan region development authority (2010)).
(at 5% level) than that in the female house-owners/renters group (Table 2). Amongst all individuals, females living in rented houses have the highest share of unmarried individuals (Table 2). The share of unmarried individuals in the female house-renters group is significantly higher (at 5% level) than that in male house-renters group and female house-owners group. In case of female commuters, the share of self-employed individuals in the house-owners group is comparatively greater (at 5% level) than that amongst the house-renting females and house-owning males in the rural area. The percentage of individuals working in offices is apparently different (but not significant at 5% level) between female house-owners and female house-renters, while it is nearly same for the females in the urban area. Comparing with the urban sample, a higher share of individuals (especially male house-renters) in the rural sample work in farmland or agriculture related activities (differences between urban and rural areas in this case are significant at 5% level). Furthermore, in the rural area, the share of individuals working in farmland in the male house-renters group is significantly (at 5% level) higher than that in male house-owners and female house-renters groups. Table 2 also shows the differences amongst the groups with respect to various other household and personal socio-demographic attributes. Table 3 summarises the (commute) travel behaviour of males and females living in the urban and rural areas of the city. A note is due here about the different types of modes appearing in the table. ‘Auto-rickshaw’ is a motorised three-wheeler mode, which usually serves as a taxi (or IPT). Cab is a four-wheeler mode (car/van) that serves as taxi and is owned and operated by companies/agencies. ‘Taxi’ in the table is also a four-wheeler (car/van) taxi mode, but are owned and operated by individuals. Two-wheeler in the table represents motorised two-wheeler (motorcycle or motorbike).
‘Bus (Public)’ represents the standard buses operated by the public transport authority, Bangalore Metropolitan Transport Corporation (BMTC). Minibus (BMTC) is the medium/mini size operated by BMTC. ‘Bus (Private)’ denotes the standard/medium/mini buses operated by private agencies. Finally, ‘other’ mode category includes unconventional modes used for passenger transport such as carts, tractor with trailers, LCVs, mini/large trucks. The table reveals that, for the urban sample, female house-owners have the highest share of walkers (differences are significant at 5% level) compared to the other groups, while the female house-renters group have a higher share of individuals commuting on car/van (significantly higher than that in female house-owners group). Shares of individuals depending on bus are higher amongst male and female house-owners groups. In general, males travel longer distance than females, and house renters travel longer distance than house owners do. Further, with respect to the commute distance, female house-renters travel significantly longer distance (at 5% level) than the female house-owners do. Subsequently, their commute durations are longer than that for the females living in owned houses (the difference is significant at 5% level). Table 3 also provides a summary of the commute characteristics of the individuals in the rural area. Compared to the urban sample, share of walkers is significantly (at 5% level) higher in the rural sample. Share of individuals depending on two-wheeler is higher (significant at 5% level) amongst males living in owned houses compared to the males belonging to rented houses, whereas the proportion of individuals depending on two-wheelers is approximately same amongst female house-renters and house-owners groups in the rural sample. The average work duration of individuals in the rural sample appears to be (on average 1 h) more than that for the individuals in the urban area (differences are
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69 Table 1 Summary of socio-demographic attributes of males and females in the urban area.1 Attributes
Males Owners
Table 2 Summary of socio-demographic attributes of males and females in the rural area.1
Females Renters
Owners
Attributes
Females
Owners
Renters
Owners
Renters
3.61 1.85 33.85% 59.69% 6.46%d
Household characteristics Household size Avg. no of employed persons Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
2.58 1.07 42.07%a,c 57.44%a,c 0.49%a,c
2.77 1.08 32.67% 66.09% 1.24%
2.92 1.42 25.38%b 70.64%b 3.97%
3.28 1.70 72.59%d 27.41%d 0%
4.82% 95.18%
4.10% 95.90%
Housing type Apartment/flat house Independent house
1.52%a 98.48%a
2.36% 97.64%
3.35% 96.65%
6.54% 93.46%
5.86%d 79.25%d 12.39%d 2.50%d
17.74% 70.97% 8.47% 2.82%
13.82% 75.55% 6.83% 3.80%
Vehicle ownership Car Two-wheeler Bicycle Other vehicles
1.74%c 78.02% 19.71%c 0.53%a,c
1.11% 78.25% 19.34% 1.30%
7.3% 82.9% 7.3%b 2.5%
5.84%d 88.28% 0%d 5.88%d
37.91 51.55%a,c 44.28%a 46.29%a,c 9.43%a,c
34.05 57.27%d 35.42% 62.69%d 1.89%
34.96 29.52% 46.78%b 53.22% 0%b
30.89 35.63% 40.15% 58.20% 1.65%
Personal characteristics Avg. age Own driving license Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
33.98 28.28%a,c 38.99%a,c 60.62%a 0.39%c
31.34 19.04% 49.11% 50.63% 0.26%
35.98 19.17%b 30.56% 63.34% 6.10%
30.79 8.51%d 39.50% 57.77% 2.73%d
Marital status Married individuals Unmarried & other
96.53%a,c 3.47%a,c
89.64% 10.36%
86.64%b 13.36%b
78.49%d 21.51%d
Marital status Married individuals Unmarried & other
95.69%a 4.31%a
86.41% 13.59%
86.49%c 13.51%c
75.47%d 24.53%d
Educational qualification Illiterate Educated up to HSE Diploma & above
4.03%c 49.63%a 46.33%a,c
5.15% 44.02% 50.83%
7.23% 52.76%b 40.01%
6.04% 38.75%d 45.21%d
Educational qualification Illiterate Educated up to HSE Diploma & above
14.47%a,c 50.32%a,c 35.21%a
6.51% 65.01% 28.48%
36.26%b 20.66%b 43.08%b
12.50% 60.14% 27.36%
Occupation status Employed fulltime Employed part-time Daily wages Self-employed
48.57%a 3.17%a,c 24.5%c 23.76%a,c
43.75% 2.06%d 25.63% 28.56%
44.89% 6.68% 33.40%b 15.03%b
45.70% 7.11% 18.10%d 29.09%
Occupation status Employed fulltime Employed part-time Daily wages Self employed
28.19%a 6.33%a 31.88%a 38.60%a,c
22.72% 0.42%d 44.87%d 31.99%
27.56% 8.34% 27.68% 66.10%b
26.49% 3.13% 26.10% 44.28%
Employment sector Government job Private job
5.92%a 94.08%a
2.43% 97.57%
7.03%b 92.97%b
3.95%d 96.05%d
Employment sector Government job Private job
2.52%a 97.48%a
5.74% 94.26%
2.78% 97.22%
6.45% 93.55%
Job type Work in factory Work in office Work in shops/restaurants Work in educational institutions Work in construction sites Work in farmland/agriculture Work varies from day-to-day Work in other sectors Sample size
4.96% 19.84%a 12.81%c 9.06%a 4.24%a,c 0.82%a,c 9.94%a 36.33%a,c 1824
4.18%d 15.46% 11.27%d 0.88% 2.64% 0.34%d 14.93% 50.30% 5763
6.58% 15.57% 6.17% 8.29%b 0.76% 0% 10.69%b 51.94% 295
8.91% 15.76% 7.5% 1.65%d 0.28%d 0% 15.77% 49.13% 757
Job type Work in factory Work in office Work in shops/restaurants Work in educational institutions Work in construction sites Work in farmland/agriculture Work varies from day-to-day Work in other sectors Sample size
2.55%c 12.18%a,c 12.29%a 8.71%a,c 1.94% 6.85%a 25.57%c 29.91%a 4010
2.57% 17.30% 25.62% 0.71% 1.19% 10.28% 25.00% 17.33%d 1395
26.40%b 20.15% 10.30% 15.15% 2.21% 3.68% 6.62% 15.49%c 143
7.56%d 17.44% 12.21%d 9.63%d 3.78% 0%d 8.14%d 39.24%b 57
Household characteristics Household size Avg. no of employed persons Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
3.53 1.42 24.45%a,c 69.41%a 6.14%a,c
3.10 1.34 32.55% 63.15% 3.90%
3.74 1.82 35.71% 64.29% 0%b
Housing type Apartment/flat house Independent house
5.18% 94.82%
4.22% 95.78%
Vehicle ownership Car Two-wheeler Bicycle Other vehicles
9.60%a,c 79.90%c 9.10%a 1.40%a
Personal characteristics Avg. age Own driving license Monthly income* (1500–5000) Monthly income (5001–20,000) Monthly income (>20,001)
Renters
Males
*
*
Income in ‘INR’ (Indian Rupees). a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
Income in ‘INR’ (Indian Rupees). a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
significant at 5% level). Noteworthy, males in the rural area travel shorter distance than the males in the urban area, while female house-owners in the rural area travel significantly longer distance than the female house-owners in the urban area, and rural females in rented houses travel shorter distance than the female house-renters in the urban area. The difference between commute distances of individuals in the urban and the rural areas are significant at 5% level. Furthermore, female house-renters travel shorter distance (but difference is insignificant at 5% level) than the females living in owned houses, whereas, as noted before, females living in the rented houses in the urban area travel significantly longer distance than the females living in owned houses. Table 4 presents the ‘mode-wise’ travel details of males and females. For the urban area, the trend indicates that, compared to male house-renters, male house-owners travel longer distance
on foot and bicycle, and travel short distance on other modes (differences are significant at 5% level). Female house-owners appear to travel longer distance on foot, and shorter distances on cab, two-wheeler, car, and bus than female house-renters do (differences are significant at 5% level), and, male house-renters travel longer distances than female house-renters on most of the modes. The travel behaviour trend in the rural area is, in general, comparable with that in the urban area. The summaries presented above further help compare the Indian context with the developed world. For example, Kim (1994) noted that males living in rented houses in the Los Angeles Metropolitan Area were travelling shorter distance than females belonging to rented houses, whereas in Bangalore urban area (Table 3) male house-renters travel longer distance than female house-renters (difference is significant at 5% level).
1
1
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Table 3 Travel behaviour of males and females in the urban and rural areas.*,1 Attributes
Males
Females
Owners
Renters
Owners
Renters
Urban area Trip mode share Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others Avg. commute duration (min) Avg. work duration (h) Avg. no of stages Avg. commute distance (km) Avg. travel fare (INR) Works in the ‘home zone’ Sample size
9.57%a,c 3.53%a,c 0.09% 4.04%a 1.16%a,c 39.43%a,c 3.27%a 4.83%a,c 12.74%a 0.56% 20.21%a,c 0.36% 0.21% 13.09 (20.63)a 8.05 (11.32)a 1.09 (0.42) 6.18 (25.52)a,c 13.62 (59.33) 58.87%a 1824
6.60% 2.31% 0.11% 1.84% 1.94% 64.94% 6.62% 2.91% 2.40% 0.36% 9.21% 0.44% 0.32% 21.38 (41.55) 9.38 (8.37)d 1.11 (0.51) 7.96 (16.52)d 12.94 (27.74)d 47.04% 5763
16.57%b 1.10% 0.12% 2.04% 2.75% 46.90% 5.22% 10.05% 8.84% 0.79% 5.62% 0% 0% 12.72 (22.84)b 7.44 (4.35)b 1.10 (0.43)b 1.53 (9.51)b 13.20 (53.80)b 56.95% 295
11.10%d 1.47% 0% 2.21% 5.54%d 47.39%d 10.98%d 5.17%d 9.13%d 0.92%d 6.09%d 0% 0% 24.98 (82.90) 8.38 (5.09) 1.18 (0.55) 5.07 (6.39) 6.67 (27.73) 51.92%d 757
Rural area Trip mode share Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others Avg. commute duration (min) Avg. work duration (h) Avg. no of stages Avg. commute distance (km) Avg. travel fare (INR) Works in the ‘home zone’ Sample size
35.67% 8.85%a,c 0.16%a 0.60% 0.82% 43.66%a 3.58%a,c 0.58%c 2.96%a 1.90%a,c 0.83%a 0.06% 0.33%a,c 26.83 (22.88)a,c 9.09 (7.53)a,c 1.1 (0.49)a 5.21 (6.82)a 6.43 (26.27) 53.67%a,c 4010
33.19% 5.53% 0.75% 0.42% 0.65% 39.92% 1.28% 0.84% 8.33% 6.78% 2.3% 0% 0% 22.90 (18.88) 10.22 (13.10) 1.07 (0.39) 4.33 (5.37) 5.71 (28.5) 49.47% 1395
37.19%b 0.74% 0.74% 0.74% 0% 38.52% 9.74%b 2.22% 4.19%b 2.96%d 0.74% 0% 2.22%b 23.29 (20.95) 8.36 (1.43)b 1.09 (0.43) 4.98 (7.51) 5.44 (21.65) 66.14% 143
15.37%d 0.95% 0% 1.90% 0% 41.29% 0% 3.81%d 18.71%d 0% 0% 0% 18.57%d 21.75 (18.57) 9.28 (2.29)d 0.98 (0.34) 3.86 (4.96) 4.56 (25.3) 59.65% 57
*
Standard deviation in brackets. a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters. 1
Furthermore, the commute travel time of house owners and renters (in general) is significantly different in the Bangalore sample (urban area), while it was nearly same in the Istanbul Metropolitan Area (Alkay, 2011). 4. Housing tenure choice of males and females In this section, binary logit models are estimated for investigating the effects of various attributes on the housing tenure choice of males and females living in the urban and rural areas of the city. Table 5 presents the estimation results of the tenure choice models. The dependent variable is the probability of renting a house. The data did not provide enough support to estimate models with exogenous variables (e.g., commute distance) interacted with each other (e.g., with socio-demographics). Hence, all variables appear in linear fashion. The table indicates that the probability of renting a house decreases with increase in an individual’s age (estimates are significant at 5% level). Amongst the four groups, age has its highest impact on the housing tenure choice of rural females. It may be implying that the possibility of changing a job and finding
an appropriate housing location may be very limited in the rural area, and hence, rural females may be deciding upon their job and residential choice on a long-term basis, which may eventually lead to less inclination towards renting a house as they become older. The effects of private sector employment on tenure choice are different for urban and rural areas. The positive effects for the urban area (significant at 10% level) may be an indication of individuals’ propensity to change jobs frequently due to a variety of employment opportunities in the area, which in turn may be leading to less preference for owning a home and settling at a particular location. Rural area may not have job opportunities compared to the extent with the urban area, and hence individuals may not be changing their jobs frequently, and may be deciding upon their residential choices on a long-term basis, leading to less inclination towards renting (estimates are significant at 5% level). The effect of individual’s income on the propensity to rent a house is mixed (parameter estimates are significant at 5% level). Highly paid individuals in the rural area are less likely to rent a house, an intuitive finding (see Bhat et al. (2002) for positive effect of income on owning a house). The positive effect in case of the urban
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69 Table 4 Mode-wise commute details of males and females in the urban and rural areas.1 Trip mode
Males
Females
Owners
Renters
Owners
Renters
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Avg.TT
Avg.TD
Urban area Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus* (BMTC) Train Others
24.21 (11.55) 20.12 (10.52) 20.00 (15.33) 20.02 (20.54) 20.53 (15.56) 19.40 (13.00) 14.20 (4.20) 12.05 (13.20) 20.51 (22.51) 48 (6.17) 10.07 (22.30) – –
2.26a,c (4.38) 4.14a,c (4.48) 5.60a (0.57) 2.54 (4.00) 2.50a,c (6.81) 3.40a,c (3.90) 2.73a,c (7.31) 5.02a (6.90) 5.84a,c (4.59) 7.50a (25.21) 2 .00 (4.10) – –
14.85 16.60 35.00 12.52 78.84 26.24 28.71 19.61 35.95 13.99 26.40 35.57 90.00
(19.81) (15.33) (18.02) (12.83) (33.60) (30.72) (26.75) (24.78) (33.20) (22.37) (15.27) (6.26) (70.34)
1.75 (2.64) 2.10 (12.14) 12.14 (12.32) 3.15 (3.72) 11.74 (24.26) 8.03 (6.05) 9.00 (8.63) 8.87 (7.89) 9.73 (6.26) 10.46 (5.94) 7.45 (5.22) 12.70 (11.11) 20.40 (14.15)
22.07 12.35 – 17.10 18.50 21.55 19.43 18.26 30.13 15.00 33.07 – –
3.37b (2.17) 2.60 (3.32) – 1.73b (1.66) 4.82b (7.00) 5.65b (6.02) 5.89b (7.46) 5.75 (5.41) 8.97 (7.90) 4.00b (3.20) 3.96 (3.97) – –
17.30 (5.19) 30 (15.15) 40 (11.21) 22.30 (12.35) 35.92 (34.12) 27.33 (20.13) 29.30 (26.75) 45.78 (30.20) 32.40 (22.59) 30.00 (12.69) 23.15 (12.35) – –
2.93 (5.49) 3.28 (1.63) 20 (18.12) 4.53 (2.56) 7.5d (5.14) 8.32 (6.86) 7.28 (6.11) 6.74d (4.49) 9.44 (7.36) 6.12d (5.41) 4.84d (3.81) – –
Rural area Walk Bicycle Taxi Auto-rickshaw Cab Two-wheeler Car/Van Bus (Public) Bus (Company) Bus (Private) Minibus (BMTC) Train Others
21.80 (19.60) 19.75 (11.80) 15.00 (7.07) 18.57 (7.94) 58.18 (35.37) 33.89 (24.53) 64.52 (45.24) 35.95 (26.20) 41.54 (33.49) 25.90 (23.35) 11.46 (17.18) 15 (1.45) –
2.89 (3.44) 2.99 (1.77) 4.5c (0.70) 2.03 (1.04) 10.18 (6.64) 8.92 (6.15) 14.09c (13.14) 11.52c (13.15) 11.11c (11.13) 10.09c (9.82) 14.60c (11.93) 12.50 (4.08) –
18.59 19.30 10.00 15.83 42.00 28.95 50.00 22.50 28.88 26.92 16.88 – –
(15.50) (11.06) (1.34) (9.17) (32.20) (21.39) (40.72) (17.07) (17.18) (29.22) (12.51)
2.18 (3.74) 2.86 (1.55) 5.00 (.98) 1.83 (0.75) 8.00a (6.20) 8.50d (5.51) 8.00a (5.71) 19.00a,d (17.38) 7.51a (5.65) 8.61a,d (7.80) 8.63a (5.28) – –
18.90 (16.46) 7.55 (1.21) 20.00 (1.36) 30.00 (2.21) – 31.09 (18.74) 37.50 (10.60) 30.00 (12.32) 38.18 (41.18) 21.66 (19.40) 30.00 (2.11) – –
2.68 (4.48) 2.25 (1.16) 3.00 (1.22) 2.00 (0.56) – 7.63 (4.21) 8.00 (2.62) 4.68 (7.07) 13.09 (18.06) 16.67 (6.05) 4.00 (1.28) – –
15.89 15.04 – 30.00 – 30.31 – 30.00 30.00 20.00 – – –
2.11 (1.86) 2.11 (9.08) – 2.00 (1.56) – 6.87 (3.24) – 6.00b (0.34) 8.22b (10.25) 10.00b (0.25) – – –
(9.30) (8.45) (23.17) (25.07) (22.39) (25.60) (26.87) (34.37) (15.00) (17.97)
(8.34) (2.34) (1.34) (19.62) (1.09) (34.64) (0.34)
TT – Travel time (min.); TD – Travel distance (km); Standard deviation in brackets. * Bangalore Metropolitan Transport Corporation. 1 a = 5%; a = significant difference between male owners and male renters; b = significant difference between female owners and female renters; c = significance difference between male owners and female owners; d = significance difference between male renters and female renters.
area may be implying that the property tax and other investments needed for owning a house can be higher in the urban area than that for the rural area, and hence, for comparable income levels between urban and rural areas, an urban dweller may more likely to rent a house. Further, the urban region also includes a significant share of migrants (Sastry, 2008) who work in highly paid job sectors (such as IT, Biotechnology and defence), and may not be considering the urban area as their permanent residential location, and hence may be renting houses. For both the urban and rural areas, females exhibit higher sensitivity to income than males. Part-time employees and self-employed individuals in the rural area are less likely to rent a house (estimates are significant at 5% level). Individuals with such employments may be interested in houses where they can run their business as well (cattle farm and milk production, part-time tuition class at home, etc.), and since the appropriate houses may not be available for renting, individuals may have less preference for renting than building and owning one. It appears that employment type does not significantly (insignificant at 10% level) explain the tenure choice of urban commuters. The parameters estimates pertaining to educational attainment are significant at 5% level. Rural individuals with higher educational qualification in are less likely to rent a house – perhaps suggesting that such individuals may be in highly paid job, which may eventually be leading to owning a house. The differences in the contexts between urban and rural areas in terms of migration level, and property tax and other housing related investments may be the reasons behind the positive association of education attainment on renting a house in the urban area. The sensitivity to education attainment is much higher for the individuals in the urban area than that for the individuals in the rural area. Individuals working in educational institutions have a less
propensity to rent a house compared to the individuals working in other sectors (coefficients in the rural models are significant only at 10% level). This may be due to the more permanent nature of jobs in the education sector (for example, teachers are less likely to be transferred from one place to another) than in other sectors. Owning a license to drive a vehicle decreases a rural individual’s propensity to rent a house, whereas urban dwellers are more likely to rent a house if they possess a license to drive. Rural inhabitants might be considering the rural area as their permanent residential location choice; hence, possession of a driving license, which is a component of long-term mobility choice, might be directly influencing the long-term residential choice through owning a house than renting. On the other hand, given the job opportunities in the urban area, driving license owners (who may be holding vehicles) may be changing their jobs and/or residential locations occasionally (due to mobility offered by private vehicles, if they own) and may be considering residential choices on a short-term basis (coefficients in the urban models are significant at 5% level). The sensitivity to this variable is much higher for the rural individuals than that for the urban individuals. Married individuals in the rural area are less likely to rent a house than other individuals are, and the sensitivity to marital status is much higher amongst rural females where a change in marital status is highly important. The higher sensitivity to marital status in the rural area may also be due to the effect of socio-cultural norms of the society – which emphasise married females to be well settled – the effect of which can be more visible in rural areas than urban areas. The partial effects of marital status on the housing tenure choice of urban individuals are very limited (insignificant at even 10% level). Duration at work is observed to have a positive association with an individual’s propensity to rent a house (estimates are significant
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M. Manoj et al. / Journal of Transport Geography 45 (2015) 62–69
Table 5 Estimation results of the tenure choice models.*,# Attributes
Constant Age (years) Employed in private sector Individual’s income > INR 5000 Employed part-time Self-employed Qualified above diploma Work in educational institutions Own a license to drive Married individuals Work duration (min) Commute mode is private Commute distance (km) Works in ‘home zone’ Children’s distance to school (km) Employment/population Other land use Sample size Adjusted rho-square # *
Urban Males
Females
Males
Females
0.748 (3.62) 0.004 ( 3.12) 0.161 (1.80) 0.126 (2.05) 0.0006 (1.60) 0.0001 (1.51) 0.348 (4.93) 0.516 ( 3.64) 0.311 (7.78) 0.050 (1.41) 0.005 (2.27) 0.101 (1.73) 0.024 (3.23) 0.081 (2.45) 0.015 ( 1.83) 0.0732 (2.00) 0.098 (1.82) 7587 0.338
0.538 (2.92) 0.009 ( 2.42) 0.174 (1.69) 0.217 (3.57) 0.0004 (1.20) 0.0008 (1.79) 0.232 (2.13) 0.301 ( 2.11) 0.211 (2.65) 0.062 (1.54) 0.004 (3.01) 0.162 (2.60) 0.053 (2.92) 0.133 (3.20) 0.047 ( 1.75) 0.222 (3.16) 0.086 (1.72) 1052 0.268
4.06 (8.50) 0.021 ( 7.35) 0.545 ( 4.35) 0.098 ( 2.13) 0.350 ( 10.25) 0.643 ( 6.71) 0.089 (3.26) 0.110 ( 1.89) 0.602 ( 3.42) 0.157 (1.74) 0.001 (4.38) 0.246 ( 2.76) 0.038 (3.33) 0.657 ( 8.26) 0.029 (1.66) 5.67 ( 6.56) 0.114 (1.75) 5405 0.231
7.62 (2.88) 0.045 ( 2.20) 1.49 ( 3.20) 0.114 (3.03) 0.210 ( 4.21) 0.201 ( 2.31) 0.062 (1.95) 0.096 ( 1.91) 0.340 ( 1.89) 1.07 (2.73) 0.005 (2.60) 1.05 ( 1.89) 0.074 (1.98) 0.721 ( 2.68) 0.052 (1.88) 16.20 ( 3.12) 0.153 (1.67) 200 0.250
t-Statistic in brackets. Significant at 5% level if t-statistic P 1.96 and Significant at 10% level if t-statistic P 1.65.
at 5% level). Individuals with longer work duration may be balancing their daily activity-travel schedule by moving closer to the work location, and it is possible that such individuals will not be able to find preferable residential units near work location, which may eventually be contributing to renting a house (all else being equal). Chen et al. (2013) did not find any significant effect of work duration on tenure choice in their analysis from the US. The effect of commute mode choice on housing tenure choice is mixed. Urban populace has a high tendency to rent a house if they travel on private mode, whereas rural individuals have a less propensity to rent a house if their commute mode is private.2 Given the employment and residential opportunities in the urban area, private mode use may be a medium, which helps urban dwellers in exploring new residential locations (and jobs) on a short-term basis, and this may be leading to renting a house. In case of rural area, where employment opportunities are comparatively lower than that in the urban area, individuals may be planning their job participation and commute mode on a long-term basis, which may be contributing to the less inclination towards renting a house. The coefficients on the variable commute mode are significant at 10% level (and below). The effect of commute distance indicates that individuals with longer commutes are less likely to rent a house in the rural area (all coefficients are significant at 5% level). Rural area may predominantly be having agricultural and other traditional jobs, and housing locations preferable to the individuals may not be available near the employment sites, which may lead to longer commutes and because of this, individuals may be planning their residential choices on a long-term basis, eventually leading to less inclination towards renting a house. On the other hand, for the urban populace, the positive effect may be indicating the effect of efficient transportation system that helps to reach preferable housing locations much conveniently than in the rural area. Comparatively, females have a higher sensitivity to commute distance than males, and rural individuals have a higher sensitivity to distance than urban individuals do. Being employed in the home zone of an individual decreases their propensity to rent a house perhaps indicating their long-term preference for living closer to work location and hence a less tendency to rent a house 2
Rural
Vehicle ownership and use may actually be endogenous with housing tenure choice. However, vehicle ownership has been treated as an exogenous variable in this study.
(estimates are significant at 5% level). The negative effect of an individual’s children’s commute distance on their housing tenure choice indicate that the lesser the distance the more is the tendency to rent a house (estimates are significant at 10% level). Although it seems counter intuitive, the result may be indicating that affordable and preferable housing may not be available near schools, and hence, individuals may prefer to rent a house. In general, females have a higher sensitivity to this variable perhaps due to the need to shoulder the responsibilities related to children (e.g., taking children to a practice session in their school), and hence they may be interested in reducing overall daily travel distance. This may lead females to move closer to school location, and rent a house, all else being equal. Individuals living in zones with high employment to population ratio have a lesser tendency to rent a house relative to ‘own’ (all coefficients are significant at 5% level). Such zones may be having different types of jobs and other opportunities available at higher intensities, and under such conditions, individuals may be less interested in renting a house than owning. Finally, compared to the individuals living in zones with residential land use, commuters living in zones characterised by other land use types are more likely to rent a home (estimates are significant at 10% level). Zones with other land use (e.g., industrial, agricultural) may not be having the facilities needed for the day-to-day life of individuals (e.g., playgrounds, community centres), and hence individuals may have high propensities to rent a house than owning one. 5. Summary and conclusions In this study, an attempt has been made to investigate the housing tenure choice decisions of male and female commuters living in the urban and rural areas of Bangalore city in India. The data set used in this study is from a household travel survey conducted in year 2010. The study was performed through an exploratory analysis of travel behaviour, and estimation of binary logit models of tenure choice. Important findings from the overall analysis are as follows: (i) In general, females travel shorter distance than males; house-renters travel longer distance than house-owners in the urban area, and house-owners travel longer distance than house-renters in the rural area.
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(ii) Employment type and marital status do not appear to be a significant predictor of housing tenure choice of individuals in the urban area, whereas in the rural area, married individuals, those having part-time jobs, and those who are self-employed are less likely to rent a house. (iii) Urban individuals commuting on private modes are more likely to rent a house, whereas rural individuals are less likely to rent a house if they travel on private vehicle. (iv) Urban commuters with longer commutes are more likely to rent a house, whereas rural commuters have comparatively less preference for renting a house with increase in their commute distances; further, the lesser is the commute distance of an individual’ children the more is their propensity to rent a house. (v) Individuals also consider land use type and employment to population ratio in the residential zone for deciding upon their housing tenure. Overall, the study provided insights into the individuals’ decision regarding housing tenure and the role of commute travel on that decision. Viewing from a broad perspective, the effect of commute mode choice on tenure selection is important in the context of urban-transport policies. Basing the discussion on the empirical findings – which show that housing related decisions are conditional on employment related decisions (e.g., Prashker et al. (2008)) – the result has implication for energy consumption and emission. For a fixed work location, an individual’s residential choice in the urban and rural areas is governed by private mode use for commuting. In order to alleviate the ill effects of private mode use, urban-transport planners may focus on public transport oriented urban-transport strategies (e.g., Transit Oriented Development). Creating more employment in the residential zones may help individuals to find jobs in the same zone and along with this, promotion of effective sustainable transport policies (e.g., emission tax) further help control ill effects of private mode use. This may be extremely relevant when Bangalore is projected to become ‘Greater Bangalore’ by increasing its municipal (urban) area by including rural areas and by generating new employments in the rural areas (Sastry, 2008). The present study can be extended in many ways. Inclusion of non-work travel behaviour in the tenure choice analysis is one possible avenue for the research. Though individual-level analysis is relevant in its own case, household-level analysis would further help investigate the effect of interaction amongst individuals on tenure choice. Estimation of models with data sets having information related to house rent, tax information, etc. would enrich the models estimated in this study. Future models can also include ‘neither owning nor renting’ as another tenure choice alternative as about 2.6% of the households in the nation belong to such category (Census of India, 2011). Development of causal models would be another avenue of research where one could investigate the interaction between housing decisions, commute behaviour and vehicle ownership of individuals/households. Analysis of the influences of residential preferences and housing choices on the travel patterns of individuals of Indian cities can be another front of further research.
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