Citation: Masoumi, H.; Aslam, A.B.;
Rana, I.A.; Ahmad, M.; Naeem, N.
Relationship of Residential Location
Choice with Commute Travels and
Socioeconomics in the Small Towns
of South Asia: The Case of Hafizabad,
Pakistan. Sustainability 2022,14, 3163.
https://doi.org/10.3390/su14063163
Academic Editors: Linchuan Yang,
Yuanyuan Guo, Wenxiang Li,
Yaoming Zhou and Jixiang Liu
Received: 13 January 2022
Accepted: 4 March 2022
Published: 8 March 2022
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sustainability
Article
Relationship of Residential Location Choice with Commute
Travels and Socioeconomics in the Small Towns of South Asia:
The Case of Hafizabad, Pakistan
Houshmand Masoumi 1,2,* , Atif Bilal Aslam 3, Irfan Ahmad Rana 4, Muhammad Ahmad 3
and Nida Naeem 3,4
1Center for Technology and Society, Technische Universität Berlin, 10623 Berlin, Germany
2Department of Transport and Supply Chain Management, College of Business and Economics,
University of Johannesburg, Johannesburg 2006, South Africa
3Department of City and Regional Planning, University of Engineering and Technology,
[email protected] (N.N.)
4Department of Urban and Regional Planning, School of Civil and Environmental Engineering,
*Correspondence: [email protected]
Abstract:
The existing literature of emerging markets fails to provide evidence to clarify if people
choose their residential location based on commuting to work or other socioeconomic or household
factors. The present paper seeks to provide such evidence in South Asia using the case study of
a small city in Pakistan. This exploratory study was facilitated by primary data collected from
365 adults in Hafizabad, Pakistan, using face-to-face interviews in 2018. Two research questions were
answered: (1) with what socioeconomic or mobility-related variables are the residential self-selections
correlated? (2) how strong is the possible association of commuting to work to residential location
choices compared to other factors, including social, economic, and family-related issues? The results
of Chi-square tests and Proportional Reduction in Error analyses show that the three variables of
neighborhood place, gender, and housing tenure type are associated with residential location choices.
These findings are partly in line with studies on high-income countries, but gender and housing
tenure are more specific to developing countries. Moreover, results of a Binary Logistic model show
that marital status and house ownership of other household members define whether people choose
their living place based on commuting rather than other socioeconomic and household issues. The
finding of the latter variable contrasts with behaviors in high-income countries, whereas the former
variable has some similarities. These findings highlight some contextual differences between house
location selection in South Asia and other regions.
Keywords:
residential location choice; urban transportation planning; commuting; housing; Pakistan
1. Introduction
The correlations between residential location choices of the inhabitants of urban areas
in high-income countries with different behaviors such as urban mobility choices are
important for urban transportation researchers because they can influence the correlations
between urban travel behaviors and the built environment. In other words, if residential
self-selections meaningfully affect the mobility behaviors such as mode choice and travel
distances, including commuting lengths, then it would be difficult to claim that the built
environment can influence mobility behaviors and decisions. This may be true, particularly
in relation to selecting residential places near the workplace to shorten the commuting
distance. It is possible to hypothesize the commuting preferences and attributes of such
people. Therefore, it is important to understand residential location choices which define
their commuting characteristics.
Sustainability 2022,14, 3163. https://doi.org/10.3390/su14063163 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 3163 2 of 15
The relationship between residential self-selection and travel behavior is complex,
and the built environment plays a significant role in determining it [
1
–
3
]. Other important
determinants that explain the relationship between residential choices and travel behavior
are life choices in other relevant domains such as health and environment [
4
,
5
], decision-
making arrangement at the household level, and commuting distance [
6
]. Cao and Yang
(2017) found that the built environment has a significant effect on the commuting patterns
even after controlling the effect of the residential self-selection [
7
]. However, the above
evidence on the correlations between residential self-selections and commuting is mostly
related to high-income countries. A very large part of the world’s regions, including
emerging markets and developing countries, represent a small proportion of the evidence.
Due to the close relationship between mobility behaviors and decisions, on the one hand,
and culture and climate, on the other hand, it can be hypothesized that context can have an
undeniable role; however, because there is limited empirical evidence, it often cannot be
claimed that several issues in urban planning and mobility planning can only be concluded
based on evidence related to high-income countries. As a result, such conclusions are not
valid to be the basis of mobility planning in emerging markets.
The present paper aims to understand the relationship between socioeconomic condi-
tions and mobility patterns with residential location choices and preferences. It hypothe-
sizes that the residential location choices in the developing countries are less affected by
the commuting to work pattern as compared to the developed countries. Moreover, the
correlates of some of the household-related variables such as household size are different
in developing countries as compared to the same factors in high-income countries. These
hypothetical differences have roots in the cultural differences and lifestyles of people in
South Asia. To test these hypotheses, the small cities of the South Asian region are focused,
exemplified by the city of Hafizabad, Pakistan, as a case study.
The paper continues with a short literature review on the correlates of residential
self-selection. Then, the methods applied for testing the hypotheses of this study and its
case study area, Hafizabad, Pakistan, are introduced. Then, the findings of the general
correlations of residential location choices and different socioeconomic and mobility-related
factors are presented. Finally, the findings of the South Asian context are compared with
those of the existing literature, the majority of which come from high-income countries.
2. Correlates of Residential Location Choices
Travel behavior studies recognize residential location choices or self-selection as an
integral part of understanding land use and transportation interactions [
8
]. It is being
extensively used for launching relevant policy interventions for a sustainable transport
system [
9
]. The public choices in choosing a residential location are primarily based on their
travel options and priorities [
10
]. Numerous variables have been used in past studies to
understand the correlations and determinants of residential location choices. Schirmer et al.
(2014) classified these location variables within the categories of the built environment,
socioeconomic environment, points of interest, and accessibility [
3
]. Frenkel et al. (2013)
found socioeconomics, commuting time, and housing affordability as the primary factors
of the residential location choice [
11
]. Orvin and Fatmi (2021) identified life-cycle events,
accessibility, and socio-demographics as the key factors in determining the residential
location choice [
12
]. Morency and Verreault (2020) found that a well-considered residential
location choice can considerably reduce the commuting distances and as a result could also
cause increasing walking, cycling, and public transport trips [
13
]. In some of the studies
conducted in the developed world, social interactions and neighbors with similar socioeco-
nomic backgrounds have also been a significant factor in residential location choices [
14
].
Other important determinants of residential location choices have been reported as the
quality of schools [
15
], accessibility to services and jobs [
16
,
17
], mobility attitudes, the built
environment [18–22], and the affordability and neighborhood characteristics [23,24].
Although numerous studies have been done in developed countries, travel behavior
varies among different populations and regions due to socioeconomic conditions, hous-
Sustainability 2022,14, 3163 3 of 15
ing types, norms, and attitudes [
25
]. Therefore, it is imperative to understand residential
location choices for developing countries, specifically the South Asian region. There are
limited studies on residential choices and self-selection in developing countries. Masoumi
(2019) observed that residential location choices play a vital role in mode choice selection
in Tehran, Istanbul, and Cairo [
26
]. In one of his recent studies for the same case study
areas, Masoumi (2021) identified neighborhood characteristics, accessibility, commuting
distance, public transit trips, and individual characteristics such as age and driving license
that affect the residential location choice [
27
]. Masoumi (2013) also found a significant role
of socioeconomics in determining the residential location choices in Tehran, Iran [
28
]. In
another study conducted in Alexandria, Masoumi et al. (2021) identified neighborhood
characteristics, availability of transportation modes, and affordability as the strongest deter-
minants of the residential location choices [
29
]. Ibrahim (2017) also found the availability
of transportation modes as the leading determinant of the residential location choices in
Alexandria [
30
]. Albayrak et al. (2019) argued that housing affordability and travel behavior
shapes the housing choices of the residents of the mono-centered city. In contrast, the
situation in a poly-centered city like Istanbul is complex. Several factors such as individual
preferences, job location, accessibility, and sociocultural factors determine housing loca-
tion choices [
31
]. Salihoglu and Turkoglu (2019) also highlighted various factors such as
housing and neighborhood characteristics, accessibility, and residential satisfaction that
affect residential location preferences in Istanbul [32]. Ghazali et al. (2020) studied residen-
tial location choices in the city of Elmina, Malaysia, by conceiving a broader frame of the
migration-related push-pull-mooring model. The study concluded that pull factors such as
affordability and socioeconomic factors are responsible for residential location choices at
the destination places. Certain push factors, such as the origin place, dissatisfaction, and
high housing costs, also play a significant role in residential location choices [
33
]. Jiang
and Zhang (2021) found that neighborhood characteristics, housing price, accessibility to
transportation, and entertainment places are important determinants of location choices
for housing purchase in Anyue County, China [
34
]. Aung and Vichiensan (2019) identified
housing characteristics, neighborhood quality, commuting time, and ethnicity as significant
factors affecting the residential location preferences in Myanmar [
35
]. Many other studies in
the developing world have found similar determinants of residential location choices such as
accessibility and travel behavior [
7
,
36
], neighborhood and socioeconomic characteristics [
37
],
affordability and security [38], convenience and comfort [39], and religious factors [40].
A study by Munshi (2016) observed that residential location choice is important to be
considered for determining mode choice in Rajkot, India [
41
]. Pandya and Maind (2017)
found distance to the central business district, housing affordability, and family income
to be significant factors that affect residential location choice in the Mumbai Metropolitan
Region [
42
]. Aslam et al. (2019) conducted a study on a similar topic in the same small city of
Hafizabad, Pakistan, and, through descriptive analysis, found affordability and availability
of utility services to be the leading factors of residential location choices [
43
]. De and
Vupru (2017) found socioeconomics, accessibility to the workplace, and amenity facilities
to be important factors in determining housing location choice and the rental values of the
residents of a small city of Dimapur Town in Nagaland, India [
44
]. Digambar et al. (2010)
found housing ownership and housing type to be significant factors affecting the residential
location choices of high- and middle-income groups in Nagpur, India [
45
]. Rehman and
Jamil (2021) reported commuting cost and housing rent to be the determinants of residential
location choice in the twin cities of Rawalpindi and Islamabad [
46
]. Some other studies
have also revealed the importance of socioeconomics in shaping housing location choices in
the South Asian region [
47
]. For example, Choudhury and Ayaz (2015) found the quality of
educational institutions and house rents as the leading determinants of residential location
choices in Bangladesh [
48
]. Shawal and Ferdous (2014) did a similar study with workers
of garment factories in Dhaka, Bangladesh. They found a range of factors, including
socioeconomics, affordability, accessibility to services, and commuting distance, which
affected residential location choices [
49
]. Thus, it is imperative to understand the residential
location choices in other South Asian cities for improving land-use transportation dynamics.
Sustainability 2022,14, 3163 4 of 15
3. Materials and Methods
Based on the literature review and the knowledge gaps, the current study seeks to
answer the following research questions: (1) with what socioeconomic or mobility-related
variables are the residential self-selections correlated? (2) how strong is the possible as-
sociation of commuting to work to residential location choices compared to other factors,
including social, economic, and family-related issues? This study hypothesizes that unlike
some studies conducted in Western countries, residential location choices in South Asian
countries are less influenced by commuting to work. Thus, it is easier to study the corre-
lations between urban travel behaviors and the built environment in that context. This is
because if the hypothesis is tested to be true, residential location choices in the South Asian
context would work more as a constant than a variable to cause changes in other domains,
most importantly, the travel behavior and the characteristics of the built environment.
A small city of Hafizabad located in the upper central Punjab region of Pakistan
was chosen to conduct this study as the monocentric character of the city offered some
advantages for reliably concluding this study with a smaller sample size. The population
of Hafizabad, according to the 2017 Census, has been reported as 245,784. Furthermore,
there were 37,270 housing units in Hafizabad with a household size of 6.6 persons—slightly
higher than the national average of 6.5 [
50
]. Despite being a small city, it is well connected
with other urban places in the surroundings. Gujranwala, the fifth largest city in Pakistan
with a population of 2.03 million [
50
], is located only around 55 km away in the East,
enabling traveling between these two cities [
51
]. The urban fabric of the city consisted of
many layers dating back to the Mughal dynasty, followed by the British empire, which
exercised Victorian architecture during the colonial times. Since the independence of
Pakistan in 1947, post-partitioned time urban layers have also been added to the urban
landscape of Hafizabad.
This study is based on a survey undertaken in Hafizabad in 2018, which led to a
validated sample of 365 residents. Cochran’s (1963) formula was applied in determining
the sample size and confidence interval for conducting this study. A two-stage sampling
technique was used where in the first stage, four neighborhoods were selected based on
their distinct land use and built environment characteristics. The calculated sample was
equally distributed among the selected neighborhoods. In the second stage, a probabilistic
random sampling technique was used to complete the sample size for ensuring its repre-
sentativeness for the overall population. The random sampling offered an opportunity
to handle the cases of refusals as the field surveyors moved to the next respondents in
all such cases [
52
]. The survey method was face-to-face interviews in the four case study
neighborhoods of the study, which contained a combined population of 19,042 inhabitants.
The survey resulted in individual and household response rates of 1.92% and 12.65% and
confidence levels of
±
5.08% and
±
4.79% for individual and household questions. The
overall data collection was performed as an exploratory survey. The response rates and
confidence levels of each neighborhood have been summarized in Table 1. The full details
of the data collection have already been published by Aslam et al. (2019) [43].
The most important factors in connection with residential self-selections were selected
to be applied in statistical analyses. The neighborhood was applied to the tests because
it is an indirect index of socioeconomic status. As an example of the difference in the
economic levels of the neighborhoods, house prices can be raised. The cheapest houses are
found in Hassan Town (29%), whereas the most expensive houses are in Nawab Colony
(16%). Personal variables include age, gender, marital status, and employment. For cultural
reasons, gender was only designated as two categories, making up a dummy variable.
Household variables include vehicle ownership (bike and car), type of housing, previous
relocation, time of relocation, house ownership of other members, the present status of
housing, and the actual price of a house. Finally, two variables represent mobility habits:
travel time and mode choice. All of these data were designed as categorical (and binary)
variables, the frequencies shown in Table 2.
Sustainability 2022,14, 3163 5 of 15
Table 1. The survey characteristics [43].
Neighborhoods Projected
Population
Number of
Households
Number of
Interviewed
Subjects
Neighborhood-
Level Validated
Sample Size (n)
Response Ratio
for Individual
Variables (%)
Response Ratio
for Household
Variables (%)
Confidence Interval
for Individual
Variables
Confidence Interval
for Household
Variables (%)
Muhallah Hassan Town 7.861 1.191 100 100 1.27 8.40 9.74 9.38
Muhallah Shareef Pura 3.298 500 100 100 3.03 20.00 9.65 8.77
Gali Haji Miraaj Din 3.584 543 100 100 2.79 18.42 9.66 8.86
Nawab Colony 4.299 651 98 65 1.51 9.98 12.06 11.54
Total 19.042 2.885 398 365 1.92 12.65 5.08 4.79
Table 2. The frequencies of independent and explanatory variables of this study.
Category Sub-Category Frequency Percent Category Sub-Category Frequency Percent
Residential Location Choice
Commuting 61 16.7
Age
Between 21–35 128 35.1
Other Factors 304 83.3 Between 36–45 164 44.9
46 and above 72 19.7
Neighborhood
(Socioeconomic Status)
Gali Haji Miraaj Din 100 27.4
Shareef Pura 100 27.4
Hassan Town 100 27.4
Nawab Colony 65 17.8
Gender
Male 308 84.4
Female 57 15.6
Marital Status
Single 44 12.1
Engaged 14 3.8
Car and Bike Ownership
No car 83 22.7
Married 299 81.9 One car 17 4.7
Widow 7 1.9 No bike 50 13.7
One bike 205 56.2
Two bikes 7 1.9
Employment
Full-time job 286 78.4 More than two bikes 3 0.8
Part-time job 40 11.0
Work at home 24 6.6 Other Types of Housing
(tenure)
Owned 321 87.9
Searching for a job 8 2.2 Rent 44 12.1
Retired 7 1.9
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