ArticlePDF Available

Abstract and Figures

Residential self-selection in developing countries and its relation to urban transportation are understudied and not fully understood. This knowledge gap is even greater in the case of small cities in the developing world. This study takes Hafizabad, Pakistan as a case study with the objective of providing data for future quantitative analyses about residential location choices in small cities on the Indian subcontinent. A sample of 365 residents was interviewed from four neighbourhoods with a combined population of 19,042. This 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 results show that the most important factors influencing residents' decisions about moving are availability of utilities/services and affordable prices. Factors related to transportation, accessibility, and social issues, such as proximity to work and relatives, come next. The role of transportation in residential location choices in Hafizabad is less important in comparison to high-income countries. This finding shows how urban form can shape residents' travel behaviour and suggests that small cities are more compact and walkable because about 40% of job-related trips are made by walking. The results of this study will help inform relevant government organizations about how to effectively devise policies for small cities because policies grafted from large metropolises might not work well at a smaller scale.
Content may be subject to copyright.
Urbani izziv, volume 30, no. 1, 2019
115
UDC: 355.67:314.1(549.1)
DOI: 10.5379/urbani-izziv-en-2019-30-01-004
Received: 19 Dec. 2018
Accepted: 25 March 2019
Atif Bilal ASLAM
Houshmand E. MASOUMI
Nida NAEEM
Mohammad AHMAD
Residential location choices and the role
of mobility, socioeconomics, and land use
in Hazabad, Pakistan
Residential self-selection in developing countries and its
relation to urban transportation are understudied and
not fully understood. is knowledge gap is even greater
in the case of small cities in the developing world. is
study takes Hazabad, Pakistan as a case study with the
objective of providing data for future quantitative anal-
yses about residential location choices in small cities on
the Indian subcontinent. A sample of365 residents was
interviewed from four neighbourhoods with a com-
bined population of19,042. is resulted in individual
and household response rates of1.92% and12.65% and
condence levels of±5.08% and±4.79% for individual
and household questions. e results show that the most
important factors inuencing residents’ decisions about
moving are availability of utilities/services and aorda-
ble prices. Factors related to transportation, accessibility,
and social issues, such as proximity to work and relatives,
come next. e role of transportation in residential loca-
tion choices in Hazabad is less important in comparison
to high-income countries. is nding shows how urban
form can shape residents’ travel behaviour and suggests
that small cities are more compact and walkable because
about40% of job-related trips are made by walking. e
results of this study will help inform relevant government
organizations about how to eectively devise policies for
small cities because policies graed from large metropo-
lises might not work well at a smaller scale.
Keywords: residential self-selection, urban transporta-
tion, human perceptions, Pakistan
Urbani izziv, volume 30, no. 1, 2019
116
1 Introduction
Over the past two decades, residential location choices have in-
creasingly attracted the attention of urban transport research-
ers studying urban travel behaviour and land-use interactions.
Scholars are interested in whether people choose to live in a
neighbourhood where they can easily commute or access their
non-work destinations or whether they choose where to live
due to other factors such as mobility needs, the eects of the
built environment, their perceptions and lifestyles, and soci-
oeconomics.
According to the gures released by the Government of
Pakistan (2017), the population of Pakistan has increased
to 207.8 million from 132.4 million in 1998 (a 57% in-
crease), showing an annual average growth rate of2.40 dur-
ing the inter-census period (1998–2017). Although there
was a decline in annual growth from the previous inter-cen-
sus period (1981–1998), the urban share of the population
has increased to 36.38% (2017) from 32.52% (1998). is
shows a growing phenomenon of urbanization and a trend
of population concentration in urban centres. e existing
urban housing stock is under growing pressure due to this,
which has resulted in urban sprawl. Uncontrolled urbanization
is becoming a major challenge for local planning agencies in
Pakistan (Ahmad & Anjum, 2012). A number of develop-
ment plans have been prepared by local planning agencies to
control the situation; however, they have failed to meet their
objectives. Hameed and Nadeem (2008) critically reviewed
the urban master planning processes in Pakistan and found
several reasons for unsuccessful implementation of these plans.
One of the many reasons for failure in plan implementation
was greater reliance on secondary data, minimum primary
data collection, and inadequate public participation. Given
this, many of the master plan proposals related to the housing
sector(e.g.,identifying future residential growth areas) do not
meet people’s aspirations and needs, thus hampering successful
implementation.
Other than basic quantitative housing indicators, housing cen-
sus data in Pakistan do not provide any insight into people’s
choices and preferences when choosing where to live. Further,
there are not enough data for studying the role of transporta-
tion and other related factors in residential location choices in
Pakistan(and other countries of the Global South). Many past
studies on this topic have found a signicant relationship be-
tween the built environment and urban travel behaviour. How-
ever, less well understood is the eect of residential self-selec-
tion on the relation between land use and transportation(Cao
etal., 2009). Primary data become more necessary when one
sees how rare ndings for Pakistan and the developing world
are. is situation provides a rationale to collect primary data
on this topic in the developing world.
As a result, this study was conducted with the objective of
providing reliable primary data to carry out empirical analyses
on residential self-selection in small Pakistani cities. It is based
on the overall hypothesis of this study: that the perceptions
and behaviours of people in a “small city” in Pakistan will not
be comparable to cities with a similar size in North Ameri-
ca, Europe, Australia, and so on. In other words, the decisive
cause of the behavioural dierence is the context, not the size.
However, inside the Pakistani or South Asian context, city
size may be the reasons for behavioural mismatches. Because
of dierences in socioeconomics and lifestyles in large and
medium-sized cities compared to small cities, these mismatches
might be large. ese behavioural disparities between dierent
city sizes can be larger in the developing world compared to
high-income countries(this needs to be tested and can serve
as a hypothesis for other studies). is study addresses the lack
of appropriate primary data suitable for investigating location
choices, not only in small Pakistani cities, but also in cities
of other sizes.
e rst section of this article features an introduction, a prob-
lem statement, and study objectives. e next section reviews
past studies conducted on related topics in various contexts,
mainly in developed countries and countries of the Middle
East and North Africa and the Indian subcontinent. e next
section outlines the research methodology by presenting the
research questions and hypothesis, case-study area prole,
study variables, and data collection and analysis methods.
Findings derived from the collected data in two broad sets
of categorical and continuous variables are presented in the
following section. e last two sections provide topic-specic
discussion and the conclusions of the study.
2 Previous studies
Residential location choices are a part of self-selections, which
are people’s tendencies to make decisions about where to live,
travel, life, and so on based on their needs, preferences, and
attitudes. is has been the topic of empirical studies based
on primary data collected in several countries, including the
Netherlands (Van der Vlist et al., 2002; Zondag& Pieters,
2005; Ettema& Nieuwenhuis, 2017), Germany(Heldt etal.,
2016), the UK(Kim etal., 2005), the United States(Schwa-
nen& Mokhtarian, 2004; Bayoh etal., 2006; Waddell etal.,
2007; Cao etal., 2010; Pinjari etal., 2011; Sener etal., 2011;
Wang etal., 2011; Patacchini& Arduini, 2016), Canada(Fat-
mi etal., 2017), Japan(Ge& Hokao, 2006; Zhang etal., 2014;
Yu et al., 2017), Ireland (Vega & Reynolds-Feighan, 2009;
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Urbani izziv, volume 30, no. 1, 2019
117
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Humphreys & Ahern, 2017), Italy(Chiarazzo etal., 2014),
France(Palma etal., 2005; Buczkowska& Lapparent, 2014),
Denmark(Næss, 2009), and Belgium(van Acker etal., 2014;
Vos& Witlox, 2016). ese studies range from literature re-
views(Van der Vlist etal., 2002) to numerical analysis using
national(Zondag& Pieters, 2005) and city- or regional-level
census databases(Wang etal., 2011;Vega& Reynolds-Feighan,
2009; Pinjari etal., 2011; Sener etal., 2011; Buczkowska&
Lapparent, 2014; Heldt etal., 2016) and mathematical mod-
elling using primary data (Schwanen & Mokhtarian, 2004;
Kim et al., 2005; Bayoh et al., 2006; Ge & Hokao, 2006;
Næss, 2009; Chiarazzo etal., 2014; van Acker etal., 2014;
Zhang etal., 2014; Patacchini& Arduini, 2016; Vos& Witlox,
2016; Fatmi etal., 2017; Humphreys& Ahern, 2017; Yu etal.,
2017), as well as statistical analysis of data produced by simu-
lators(Palma etal., 2005). Most studies were conducted with
mathematical modelling using primary data. Geographically,
most of the case-study areas have been located in the United
States. Some studies have also been conducted on the topic
in emerging and developing countries, such as China(Biying
etal., 2012; Næss, 2013; Wu et al., 2013; Yang etal., 2013;
Wang etal., 2016, 2018; Zhuge etal., 2016), Korea(Jun etal.,
2013; Yi& Lee, 2014; Park& Kim, 2016), ailand(Choo-
charukul etal., 2008), Vietnam(Tran etal., 2016), Chile(Bal-
bontin etal., 2015), and Israel(Frenkel etal., 2013).
e share of residential location choice studies for the Indi-
an subcontinent, the Middle East, and North Africa is small.
Apart from some notable exceptions, such as studies carried
out in India (Schwanen & Mokhtarian, 2003; Molugar-
am& Rao, 2005; Srinivasan, 2005; Lall etal., 2006), Bang-
ladesh(Choudhury& Ayaz, 2015), Iran(Masoumi, 2013), and
Egypt(Ibrahim, 2017), a limited number of studies have been
undertaken to present a better picture of self-selections in the
countries of these vast regions. Studies related to Pakistan are
almost non-existent in this literature. Given more frequent in-
Table1: Methodological considerations of similar past studies (source: authors).
Study Sample size Response
rate
Case-study areas Response
ratio
Data collection
method
Ahmad, 1992
6,275 households selected
through quasi-random sam-
pling
Twenty-six zones of Karachi
based on socioeconomic and
neighbourhood characteristics
Not available
City-wide socio-
economic survey
in 1987–1988
Ahmad, 1993
6,275 households selected
through quasi-random sam-
pling
Twenty-six zones of Karachi
based on socioeconomic and
neighbourhood characteristics
0.38% (city)
City-wide socio-
economic survey
in 1987–1988
Cao et al., 2006a 6,000 randomly selected
households 22.8% (1,368)
Six middle-income neighbour-
hoods belonging to three
different periods in Austin, TX
4.64%
Self-administered
mailed survey
in 1995
Cao et al., 2006b 8,000 (6,746 valid) households
randomly selected from a com-
mercially maintained database
24.9% (1,682)
Eight neighbourhoods of
varying characteristics belong-
ing to two different periods in
northern California
1.74%
Two rounds of
self-administered
mailed survey
in 2003
Frank et al., 2007
Two sub-samples: 2,088 (2,056
valid) and 1,466 (1,455 valid)
households selected from the
SMARTRAQ study
30.4% The thirteen-county Atlanta
region Not available
Computer-aided
telephone interview
in 2001 and 2002
Handy & Clifton,
2001 6,000 respondents and 75
interview participants 22.8% (1,368)
Six middle-income neighbour-
hoods belonging to three
different periods in Austin, TX
4.64%
Self-administered
mailed survey
in 1995 and a focus
group in 1997
Ibrahim, 2017 224 households Seven residential districts of
Alexandria 0.01% (city) Field survey
Kitamura et al.,
1997
5,472 randomly selected
households 17.6% (963)
Five study sites, each compris-
ing approximately a square
mile in the San Francisco Bay
area
Not available Self-administered
mailed survey
Painter, 1996 496 randomly selected
pedestrians Three similar streets and a
footpath in London Not available On-street pedestrian
survey in 1992
Mokhtarian, 2003 8,000 households
25.0% (2000:
1,358 valid
workers)
Three neighbourhoods in the
San Francisco Bay area Not available Self-administered
mailed survey
Urbani izziv, volume 30, no. 1, 2019
118
vestigations of self-selections in India, rough generalization of
ndings can be made, but, due to some key dierences(mainly
related to religious beliefs), independent studies of the Paki-
stani context seem necessary. e ndings of one of the rare
studies on Pakistan were published in1992 by Ahmad, who
studied a sample of6,275 households in Karachi using data
from a city-wide socioeconomic survey conducted in 1987
and1988. By analysing the data, she concluded that ethnicity
is important in determining households’ location choices and
mobility. Ahmad also found relations between these consider-
ations with urban sprawl and outward growth of Karachi. An-
other study was conducted by Connor(1989), who found that
ethnographic ties, political involvement, and lack of political
activity motivated residential association. ese two Pakistani
studies were conducted many years ago and had only a weak
or no relation to urban travel behaviour.
A review of past studies shows that the topic has mainly been
explored through quantitative methods with probabilistic
sampling techniques as the main method for sampling and
recruiting the respondents. Many such studies also used some
samples already available– census data, previous studies, or
any other maintained databases – to determine how large the
sample size should be and to identify the target population.
Neighbourhoods or residential districts remained the main
unit of analysis for conducting many of these studies at dif-
ferent locations. e key considerations for the selection of
these neighbourhoods were primarily similarities or dierences
in socioeconomic characteristics and the time period when
these neighbourhoods were developed or inhabited. Two main
data-collection approaches were employed: direct interviews
through eld surveys and self-administered mailed surveys.
e limitation with the self-administered mailed survey is
the low response rate, as is also evident from the review of
such studies, because none of them could achieve a response
rate of more than33% (see Table1). In turn, the response
ratio(the sample coverage of the overall neighbourhood/city
population in terms of percentage) ranged from a low of1.74%
to a high of4.64%. Table1 summarizes the methodology of
some past studies.
3 Methodology
is study assumes that residential self-selections are con-
text-sensitive; that is, people in Pakistan choose where to live
dierently compared to other contexts. ese dierences are
very much connected to cultural issues (religion, local life-
styles, and mentality), socioeconomics(exemplied by people’s
dierent approaches to earning money), social classes and the
connection to space, and geography(such as climate). It is as-
sumed that these phenomena can motivate dierent approach-
es to residential location choices in small cities in the Pakistani
context compared to the contexts in Western or high-income
countries. is assumption is based on the hypothesis that the
interrelations between urban space and urban travel behav-
iour(in this case, commuting to one’s place of work or study)
are highly context-specic, and so policymaking for urban
mobility cannot be based on studies or concepts rooted in
high-income countries, but must be based on local studies. is
study presents data that provide a basis for in-depth analysis
in future studies. e main question is how Pakistanis choose
where to live in small cities.
3.1 Case-study areas
Life in large urban centres is a multitude of many complex
processes, making it very dicult to create reliable ndings out
of empirical studies conducted in urban settings. Conducting
such studies in large urban centres requires great investment
of eort and resources to produce reliable conclusions. In
comparison, smaller cities oer opportunities to reliably an-
alyse less complex urban life through the established research
frame. Accounting for many of the underlying factors is easier
than in large urban centres. Given this fact, the small town of
Hazabad, located in the upper central Punjab region with a
population of 245,784(2017), was chosen to carry out this
study(see Figure1). According to the latest census(Govern-
ment of Pakistan, 2017), Hazabad has37,270 housing units
with an average household size of6.6 persons – almost the
same as the national average, at6.5.
e large city nearest Hazabad is Gujranwala (popula-
tion2.0million in2017), located55km to the east. Hazabad
has a strong link with Gujranwala and there is a commuting
pattern between these two cities, although on a limited scale.
e Hazabad district is well known for its rice and cotton
textile industries and, as the district capital, Hazabad also
oers job opportunities to its surrounding population, which
commutes daily to the city centre(Naeem& Ahmad, 2018).
e other large cities near Hazabad are Lahore (popula-
tion11.1million in2017), located102km to the southeast,
and Faisalabad(population3.2million in2017), located106
km to the southwest and with which there are socioeconomic
links.
Although the history of the region where Hazabad is locat-
ed reaches as far back as 327BC, to Alexander the Great’s
invasion of Punjab(Government of the Punjab, 2018), Haz-
abad itself was founded by the city’s namesake, Haz Meerak,
a companion of Mughal Emperor Akbar I(1542–1605). e
older central part of the city thus has some visible features
reecting an urban layer of Mughal architecture dating back
to the sixteenth century. Aer the fall of the Mughal Empire,
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Urbani izziv, volume 30, no. 1, 2019
119
Figure1: a) Location of Hafizabad in Pakistan (source: Google Maps, 2019); b) Location of Hafizabad in the regional context (source: Google
Maps, 2019).
a
b
Figure2: Various urban layers of Hafizabad (photo: Anwaar ul Haq).
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Urbani izziv, volume 30, no. 1, 2019
120
the entire Indian subcontinent operated under British colo-
nial rule until 1947. During that period, British rule made
an indelible impression on the regions urban fabric through
Victorian architecture. e same is the case with Hazabad,
where vestiges of the colonial urban layer can be seen in the
central parts of the city. is urban layer is part of the pre-par-
tition(i.e., pre-1947) built environment of Hazabad. Aer
Pakistans independence in1947, much of the Hindu and Sikh
population of the city migrated to India, and, in turn, many
Muslims from India settled in Hazabad. is demographic
transition helped transform the urban landscape of the city,
thus giving rise to an urban layer of the post-partition peri-
od(from independence until the late1990s). Pakistans2001
National Housing Policy declared housing a priority sector,
resulting in a real estate boom. Small cities like Hazabad
were no exception. ey also faced the consequences of ur-
ban sprawl in the form of housing developments outside of
municipal borders, although it was less intense than in larger
cities. is gave birth to the third urban layer: newer planned
developments and gated communities. e various urban lay-
ers of Hazabad are shown in Figure2.
Table2: The four case-study neighbourhoods selected.
No. Neighbourhood Period Grid type Population
(2018)
Gross
area (ha)
Net area (ha) Gross population
density (per ha)
Net population
density (per ha)
1Gali Haji Miraj Deen pre 1947 Organic 3,584 5.5 4.9 649.11 730.54
2 Sharifpura pre 1947 Organic 3,298 31.5 27.4 104.64 120.26
3Nawab Colony 1947–2000 Semi-grid 4,299 8.9 6.8 484.88 636.74
4Hassan Town post 2000 Full-grid 7,861 22.7 20.1 346.73 514.64
Table3: Urban characteristics of the selected case-study neighbourhoods.
No. Neighbourhood Links Nodes Link-node ratio Intersection
density (nodes/ha)
Facilities Per capita
facilities
Retail
Health
Religious
Total
1Gali Haji Miraj Deen 66 59 1.12 10.73 43 1 – 44 0.012
2 Sharifpura 190 168 1.13 5.33 119 119 0.036
3Nawab Colony 44 35 1.26 3.93 66 – 1 67 0.016
4Hassan Town 83 45 1.84 1.98 72 – 1 73 0.009
Figure3: Location of the neighbourhoods studied in Hafizabad (illustration: authors).
N
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Urbani izziv, volume 30, no. 1, 2019
121
N N
NN
Figure4: Land-use functions of the selected case-study neighbourhoods (illustration: authors).
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Urbani izziv, volume 30, no. 1, 2019
122
Identifying various urban layers helped in selecting case-study
neighbourhoods based on dierences in the built environment.
Dierences in the urban character also reect some dierences
in the socioeconomic status of their residents. e city main-
ly grew to the south to accommodate newer developments,
including planned housing schemes. Four neighbourhoods in
Hazabad were selected for detailed investigation based on the
main criterion of a distinct urban form. Two of them belong to
older central parts of the city, one in the northern part having
a semi-grid form, and one chosen from the new developments
in the southern part of the city. e urban characteristics and
details of the facilities available in these four neighbourhoods
are provided in Tables2 and3, and their location within the
city is shown in Figure3. e urban form and available facil-
ities in each neighbourhood are shown in Figure4.
3.2 Data, variables, and analysis
e survey was divided into three parts: household and so-
cioeconomic information, current dwelling unit characteris-
tics, and housing demand characteristics. It included sixteen
questions, some of which consisted of more than one ques-
tion or conditional questions. e questionnaire contained
six individual questions(age, sex, marital status, employment,
commuting time, and commuting mode), and ten household
questions covering vehicle ownership, type of housing, reason
for choosing the place, date of moving, main reason for mov-
ing, owning another housing unit, number of owned housing
units, vacant/occupied housing, tenure type, unit price, unit
rent, search for new housing, neighbourhood preference, main
reason for choosing where to live, and preferred tenure type.
Except for the number of housing units owned, all of the var-
iables are categorical. e questionnaires were completed by
the interviewers while talking to the respondents.
e survey provides exploratory data for the case-study
neighbourhoods with a high level of precision. e sample
sizes and the estimated condence intervals were based on
Cochran’s (1963) formulation. ese gures were calculated
separately for individual and household questions. e house-
hold condence intervals were calculated using the average
household size of the city(6.6, based on the2017 census re-
sult). As a result,1.9% of the19,042 residents in the overall
population(N) were interviewed. Regarding household ques-
tions, the survey collected data for about12.7% of the city’s
households. is provides a condence interval of±5.1% for
individual questions and±4.8% for household questions. e
condence intervals for neighbourhoods are given in Table4,
which summarizes the sample size for each question, based on
the general sample of365 respondents.
e analysis included frequency reports related to categori-
cal variables and descriptive statistics reports related to the
number of housing units owned(the only continuous varia-
ble). is was done aer validating and correcting the results,
which was primarily done for the Nawab Colony data, which
contained some problematic input that was removed from the
sample.
4 Results
A summary of the collective ndings regarding categorical
indicators for the overall sample of the four neighbourhoods
is presented below. Among the respondents,45% were thir-
ty-six to forty-ve years old, with 84% male. Balancing the
number of male and female respondents proved to be dicult
due to cultural considerations. Similar to the high share of
middle-aged respondents,82% of the survey participants were
married and78% were employed full-time.
Consistent with several other surveys conducted in neigh-
bouring countries and nearby regions, household vehicle
ownership was targeted instead of individual ownership. In
such cultures, it is more likely that household members use
vehicles together. In this vein,56% of the households owned
one motorbike,22% had no car, and only5% had one car. A
large percentage of the responding households(87%) owned
self-built houses, whereas only12% lived in rented housing.
As expected for the case of a small Pakistani city,41% of the
households(such as young couples or similar) lived in their
family’s houses. Living on family property was the most im-
portant reason for choosing the current place of living. Living
in a family property is followed by two weaker reasons:16%
of the households chose their current house because it was
located in a nice neighbourhood, and14% found the current
house aordable. A large share of the respondents (73%)
reached their place of work or education in less than thirty
minutes. Walking and riding a motorbike are the dominant
commuting mode, each making up40% of the responses. More
than one-third of the responding households had moved at
the time of the survey. is share of the sample is the focus
of this article for studying motives for moving and self-selec-
tions. e most frequently cited time of the last move was
between two and ten years ago (42%). e living unit type
or neighbourhood was the most important reason for15%
of the respondents, considering that65% did not answer this
question because they had not moved before. Transportation
was a motive for moving for almost none of the households
surveyed(0.27%). Only18% of the households in the sample
owned a second living unit, about half of which were occupied.
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Urbani izziv, volume 30, no. 1, 2019
123
Only11% of the sample rented a living unit, and all the others
lived in their own house. More than half of these houses cost
between PKR 1.5 and3 million. Of the11% that rented a
house,68% paid less than one-third of their income for rent.
In the future,28% intend to search for a new house. About
half of the sample is interested in continuing to live in their
current neighbourhood in the future, whereas45% will look
for housing in another neighbourhood in Hazabad and the
remaining5% are interested in leaving Hazabad and living in
another city, most likely Lahore. Having good services and util-
ities(neighbourhood amenities) is the most important reason
for moving for23% of respondents, followed by a reasonable
price for20%. Transportation(proximity to work) is a reason
for17% of the households. Finally,91% would prefer to move
into their own house in the future.
e above information is related to the overall sample of re-
spondents from all four neighbourhoods. To understand the
role of dierent urban forms and environments on respond-
ents’ choices in each of the four neighbourhoods, the outputs
of the categorical variables were analysed separately for each
case site and also presented in graphs. Selected graphs are pre-
sented in Figure6. Hassan Town had the youngest respond-
ents(44%), whereas Gali Haji Miraj Deen and Sharifpura were
older, with25% of the respondents aged forty-six or older.
Women had the largest share in Hassan Town(32%), and the
smallest share of unmarried respondents came from Nawab
Colony(28%). Gali Haji Miraj Deen had the largest share of
full-time employees (84%), the largest share of people with
one motorbike (62%), and the highest car ownership rate.
Self-built houses are found equally frequently in Gali Haji
Miraj Deen, Sharifpura, and Hassan Town (86–88%), and
the highest share of house-renters came from Gali Haji Miraj
Deen(10%). However, living on family property is the dom-
inant form of housing in the overall sample. Aordability,
proximity to work, and living in a nice neighbourhood are
the most important reasons for respondents living in their cur-
rent house. e sociocultural status of this subset is slightly
dierent from the entire sample. Hassan Town had the shortest
commuting distances, with79% of respondents reporting that
their trips take less than half an hour. is gure falls to67%
for Nawab Colony. Nawab Colony also has the lowest walking
mode(28%). Half of the households in Hassan Town have al-
ready moved, compared to27% in Sharifpura. More than half
of the responding households in Nawab Colony moved with-
in the past two years, whereas40% in Gali Haji Miraj Deen
moved over ten years ago. Transportation reasons were impor-
tant for only1.5% in Nawab Colony and were not a reason
for moving in the other areas surveyed. Possession of another
living unit is seen most oen in Nawab Colony(28%). At88
to90%, house ownership is almost equal in the four neigh-
bourhoods. According to the self-reported ndings concerning
house prices in this survey, the cheapest houses are found in
Hassan Town(29%), whereas the most expensive houses are in
Nawab Colony(16%). In Hassan Town,91% of respondents
spend less than one-third of their income on rent. e highest
share of households surveyed intending to move is in Sharif-
pura(32%). For the overall sample,41 to54% of responding
households intend to remain in their neighbourhood in the
future. Respondents in Hassan Town showed the least interest
in moving out of the neighbourhood(37%). For respondents
interested in moving to another city, Lahore is more likely to
be selected compared to more distant cities. In Gali Haji Miraj
Deen, the most important reason for deciding where to live in
the future is the availability of services and utilities(31%). In
Sharifpura, the main motives are aordability(22%), followed
by availability of services and utilities(21%), and proximity to
social relations and relatives(20%). Aordability(30%) and
proximity to work(29%) are by far the most inuential reasons
in Hassan Town. Finally, respondents in Nawab Colony look
for services and utilities(29%) and a quiet environment(23%)
more than other issues. Between85 and99% of the respond-
ents of the four neighbourhoods would like to own a house
when they move in the future.
Table4: Neighbourhood-level sample characteristics and overall sample.
Neighbourhood
Census district
Projected population
Number of households*
Number of subjects
interviewed
Neighbourhood-le-
vel validated sample
size (n)
Response ratio for indi-
vidual variables (%)
Response ratio for hou-
sehold variables (%)
Confidence interval for
individual variables (%)
Confidence interval for
household variables (%)
Hassan Town 12 7,861 1,191 100 100 1.27 8.40 9.74 9.38
Sharifpura 10 3,298 500 100 100 3.03 20.00 9.65 8.77
Gali HajiMiraj Deen 6 3,584 543 100 100 2.79 18.42 9.66 8.86
Nawab Colony 5 4,299 651 98 65 1.51 9.98 12.06 11.54
Total sample 19,042 2,885 398 365 1.92 12.65 5.08 4.79
Note: *Calculated by the average household size of Hafizabad (6.6).
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Urbani izziv, volume 30, no. 1, 2019
124
Average house area Valid Missing Total
NPercent NPercent NPercent
350 95.9% 15 4.1% 365 100.0%
Descriptives
Mean Statistic 122.33 Std. deviation Statistic 50.183
Std. Error 2.682 Std. error
95% confidence interval
for mean
Lower Bound Statistic 117.06 Minimum Statistic 20
Upper Bound 127.61 Std. error
5% trimmed mean Statistic 118.49 Maximum Statistic 379
Std. Error Std. error -
Median Statistic 126.00 Range Statistic 359
Std. Error Std. error
Variance Statistic 2518.302 Interquartile range Statistic 37
Std. Error Std. error
Kurtosis Statistic 3.989 Skewness Statistic 1.428
Std. Error 0.260 Std. error 0.130
Tests of normality
Category Kolmogorov–Smirnov Shapiro–Wilk
Statistic df p-value Statistic df p-value
0.242 350 < 0.001 0.875 350 < 0.001
Figure5: Descriptive statistics and normality test for the area of the respondents’ housing (source: authors).
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
e survey included only one continuous variable: the area of
housing owned by each household. e descriptive statistics
related to this question are presented in Figure5. Out of365
respondents, 350 answered this question. e areas range
from20m² to379m², with a mean of122.3m² and a stand-
ard error of only2.68m². e large range of359m² is due to
some outliers at the upper end of the range. e results of the
Kolmogorov–Smirnov and Shapiro–Wilk tests of normality
yielded p-values of less than0.001, indicating non-normality.
5 Discussion
Like many previous studies on this topic, this study employed
quantitative methods to generate ndings on the residential
location choices of Hazabad residents. In line with previous
studies, the neighbourhood was selected as the unit of anal-
ysis. e sampling frame for conducting this study was the
census data for the neighbourhoods. Similar to the majority
of previous studies on the same topic, the methodological
consideration of selecting four neighbourhoods in Hazabad
was based on dierences in urban character and dierent pe-
riods they belong to. Because the literature review indicated
a low response rate for indirect data collection methods(i.e.,
self-administered mailed surveys), direct interviewing through
eld surveys was selected as the data collection method. e
response ratio of1.92% for this study is within the range re-
ported by past studies conducted in the developed world. e
discussion presented shows that the chosen methodology is in
line with many previous studies on similar topics in developed
country contexts. is indicates the reliability of the ndings
of this study.
e ndings reveal that, for the majority of the respondents
that own their self-built houses and have lived there for more
than two years, their main reason for choosing their current
housing location is the family’s property. e high homeown-
ership rate in Pakistan stands in sharp contrast with the sit-
uation in the developed world. An increased ratio of home
ownership reduces overall housing mobility; consequently,
the majority of respondents consider their family’s property
to be the main deciding criterion for determining where to
live. Owning houses reects socioeconomic status within Pa-
kistani society, which does not provide much motivation to
rent housing units. e joint family system as a dominant living
style of many of the households in developing countries may
also foster the importance of family property as the leading
criterion for residential location choice. ese ndings are not
in line with the results of studies conducted in the developed
world.
Proximity to the workplace did not turn out to be one of the
leading reasons for current residential location of the respond-
ents. is could be a reection of the small size of the city,
where most jobs are not as distant from homes as is sometimes
the case in larger cities. Around three-fourths of the respond-
Urbani izziv, volume 30, no. 1, 2019
125
Figure6: Selected graphs presenting the frequencies of responses for variables analysed for case-study neighbourhoods (source: authors).
ents require less than thirty minutes to reach their jobs, and
the preferred mode of travel for around40% of respondents is
walking. is shows a good job-housing balance and could be
representative of other smaller cities in Pakistan, which gener-
ally grew organically with fewer planning controls. is also
provides a clue for why proximity to work is not among the
leading factors for residential location choice in a small city.
is is further strengthened by the nding that transportation
was not a motivating factor for any of the households that had
moved in the past. Again, such ndings contrast with ndings
from studies conducted in the developed world. In another
study by the authors(forthcoming) in a large urban centre of
Lahore, the average commuting distance to work for a residen-
tial neighbourhood was found to be8.4km; investigating the
factors for residential location choices in that sample might
yield dierent results. e insignicance of transportation or
proximity to work in inuencing residential location choice in
smaller cities suggests that a massive push toward transit-ori-
ented development might not be a wise strategy for smaller
cities in the developing world.
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Present status of housing units
Actual price of house on real estate market
In search of new house?
< PKR 1.5 milion
Type of housing tenure
If renting, you pay:
Preferred neighbourhood
Less than 1/3 of your income
100
80
60
40
20
0
10
8
6
4
2
0
100
80
60
40
20
0
100
80
60
40
20
0
60
50
40
30
20
10
0
80
60
40
20
0
No answer Owned
Vacant Occupied Rent
PKR 1.5–3.0 milion PKR 3.0–4.5 milion > PKR 4.5 milion Between 1/3 and 1/2 of your income
Yes No
Same New Other old
neighbourhood
in Hafizabad
Outside
Hafizabad
Count
CountCount
CountCountCount
Urbani izziv, volume 30, no. 1, 2019
126
Another important insight from this study is the intent of
out-migration. Although the share of current residents in-
tending to move out of the city is only5%, the tendency of
out-migrating to larger cities(especially Lahore) is a notable
nding. Although the reasons for such intentions are un-
known, relevant municipal government departments should
try to devise policies to help curb the urbanization of larger
cities in Pakistan. e city of Lahore is already saturated in
terms of size and population, and the continuous addition of
population through in-migration from surrounding small cit-
ies will aggravate the situation by further overburdening the
existing infrastructure.
e main limitation of this study is that only around one-third
of the respondents moved in the past and were thus able to
provide the main deciding factor behind deciding where to
live. is was due to the high homeownership rate in Pakistan.
For all other respondents, the questions inquired about their
intentions to move in the near future and their anticipated
deciding factors for deciding where to live. However, only one-
fourth of the respondents reported that they were searching
for a new home. Furthermore, because the responses to such
questions related to an uncertain future time, they might vary
at the time of actual moving. is shortcoming of the collected
data was addressed by asking respondents about their current
intentions to move, and therefore this limitation will not have
any signicant impact on the reliability of the ndings.
6 Conclusion
e methodology of this study was carefully designed in line
with the methodological considerations of many previous
studies conducted in the developed world. Due to the high
home-ownership rate in Pakistan, a family’s current housing
was the leading deciding factor when deciding where to live.
Access to transportation facilities or proximity to jobs were
not leading factors in deciding where to live. is could be
a manifestation of the small size of Hazabad, where the
study was conducted. It also reects the fact that small Pa-
kistani cities are more compact, are denser, and have a good
job-housing balance compared to large cities. ese ndings
allow relevant policy-oriented circles to better devise urban
and transportation policies to achieve the objectives of tran-
sit-oriented development, address low-income housing issues,
and manage the urbanization of large cities. is study shows
that transportation factors are insignicant in deciding where
to live in a small city with a population of245,784(2017).
However, the situation in large urban centres could be dif-
ferent and should be investigated more thoroughly. Similar
studies on the large urban centres of Pakistan are needed to
understand the situation more clearly. Furthermore, this study
presents survey results in a descriptive form only, and further
empirical studies are needed to ascertain the relationship of
dierent variables and corroborate this study’s ndings. It is
suggested that similar studies be replicated in other smaller
cities of the developing world with a particular focus on the
younger population, especially young couples or families, be-
cause their decisions will signicantly shape the future course
of urban commuting patterns.
Atif Bilal Aslam, Department of City and Regional Planning, Universi-
ty of Engineering and Technology Lahore, Pakistan
E-mail: atif.aslam@uet.edu.pk
Houshmand E. Masoumi, Centre for Technology and Society, Techni-
cal University of Berlin, Germany
E-mail: masoumi@ztg.tu-berlin.de
Nida Naeem, Department of City and Regional Planning, University
of Engineering and Technology Lahore, Pakistan
E-mail: nida.naeem64@yahoo.com
Mohammad Ahmad, Department of City and Regional Planning, Uni-
versity of Engineering and Technology Lahore, Pakistan
E-mail: ahmadnoul786@gmail.com
References
Ahmad, N. (1992) Choice of location and mobility behaviour of migrant
households in a third world city. Urban Studies, 29(7), pp. 1147–1157.
DOI: 10.1080/00420989220081091
Ahmad, N. (1993) Choice of neighbourhoods by mover
households in Karachi. Urban Studies, 30(7), pp. 1257–1270.
DOI: 10.1080/00420989320081161
Ahmad, N. & Anjum, G. A. (2012) Legal and institutional perplexities
hampering the implementation of urban development plans in Paki-
stan. Cities, 29(4), pp. 271–277. DOI: 10.1016/j.cities.2011.07.006
Balbontin, C., Ortúzar, J. de D. & Swait, J. D. (2015) A joint best–worst
scaling and stated choice model considering observed and unobserved
heterogeneity: An application to residential location choice. Journal of
Choice Modelling, 16, pp. 1–14. DOI: 10.1016/j.jocm.2015.09.002
Bayoh, I., Irwin, E. G. & Haab, T. (2006) Determinants of residential
location choice: How important are local public goods in attracting
homeowners to central city locations? Journal of Regional Science, 46(1),
pp. 97–120. DOI: 10.1111/j.0022-4146.2006.00434.x
Biying, Y., Zhang, J. & Fujiwara, A. (2012) Analysis of the residential
location choice and household energy consumption behavior by incor-
porating multiple self-selection effects. Energy Policy, 46, pp. 319–334.
DOI: 10.1016/j.enpol.2012.03.067
Buczkowska, S. & Lapparent, M. de (2014) Location choices of newly
created establishments: Spatial patterns at the aggregate level. Region-
al Science and Urban Economics, 48, pp. 68–81.
DOI: 10.1016/j.regsciurbeco.2014.05.001
Cao, X., Handy, S. L. & Mokhtarian, P. L. (2006a) The influences of the
built environment and residential self-selection on pedestrian behavior:
Evidence from Austin, TX. Transportation, 33(1), pp. 1–20.
DOI: 10.1007/s11116-005-7027-2
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Urbani izziv, volume 30, no. 1, 2019
127
Cao, X., Mokhtarian, P. L. & Handy, S. L. (2006b) Neighborhood design
and vehicle type choice: Evidence from northern California. Transpor-
tation Research Part D: Transport and Environment, 11(2), pp. 133–145.
DOI: 10.1016/j.trd.2005.10.001
Cao, X., Mokhtarian, P. L. & Handy, S. L. (2009) Examining the
impacts of residential self‐selection on travel behaviour: A fo-
cus on empirical findings. Transport Reviews, 29(3), pp. 359–395.
DOI: 10.1080/01441640802539195
Cao, X., Xu, Z. & Fan, Y. (2010) Exploring the connections among resi-
dential location, self-selection, and driving: Propensity score matching
with multiple treatments. Transportation Research Part A: Policy and
Practice, 44(10), pp. 797–805. DOI: 10.1016/j.tra.2010.07.010
Chiarazzo, V., Coppola, P., Dell’Olio, L., Ibeas, A. & Ottomanelli, M. (2014)
The effects of environmental quality on residential choice location.
Procedia – Social and Behavioral Sciences, 162, pp. 178–187.
DOI: 10.1016/j.sbspro.2014.12.198
Choocharukul, K., Van, H. T. & Fujii, S. (2008) Psychological effects of
travel behavior on preference of residential location choice. Trans-
portation Research Part A: Policy and Practice, 42(1), pp. 116–124.
DOI: 10.1016/j.tra.2007.06.008
Choudhury, C. F. & Ayaz, S. B. (2015) Why live far? – Insights from mod-
eling residential location choice in Bangladesh. Journal of Transport
Geography, 48, pp. 1–9. DOI: 10.1016/j.jtrangeo.2015.08.001
Cochran, W. G. (1963) Sampling techniques. New York, John Wiley and
Sons.
Connor, K. M. (1989) Factors in the residential choices of self-settled
Afghan refugees in Peshawar, Pakistan. International Migration Review,
23(4), pp. 904–932. DOI: 10.1177/019791838902300408
Ettema, D. & Nieuwenhuis, R. (2017) Residential self-selection and
travel behaviour: What are the effects of attitudes, reasons for location
choice and the built environment? Journal of Transport Geography, 59,
pp. 146–155. DOI: 10.1016/j.jtrangeo.2017.01.009
Fatmi, M. R., Chowdhury, S. & Habib, M. A. (2017) Life history-oriented
residential location choice model: A stress-based two-tier panel mod-
eling approach. Transportation Research Part A: Policy and Practice, 104,
pp. 293–307. DOI: 10.1016/j.tra.2017.06.006
Frank, L. D., Saelens, B. E., Powell, K. E. & Chapman, J. E. (2007) Stepping
towards causation: Do built environments or neighborhood and travel
preferences explain physical activity, driving, and obesity? Social Sci-
ence & Medicine, 65(9), pp. 1898–1914.
DOI: 10.1016/j.socscimed.2007.05.053
Frenkel, A., Bendit, E. & Kaplan, S. (2013) Residential location choice
of knowledge-workers: The role of amenities, workplace and lifestyle.
Cities, 35, pp. 33–41. DOI: 10.1016/j.cities.2013.06.005
Ge, J. & Hokao, K. (2006) Research on residential lifestyles in Japanese
cities from the viewpoints of residential preference, residential choice
and residential satisfaction. Landscape and Urban Planning, 78(3),
pp. 165–178. DOI: 10.1016/j.landurbplan.2005.07.004
Google (2019) Map of study area. Available at: https://bit.ly/2XIZbyt
(accessed 17 Feb. 2019)
Government of Pakistan (2017) 6th Population and Housing Cen-
sus – 2017. Islamabad.
Government of the Punjab (2018) Punjab portal. Available at: https://
www.punjab.gov.pk/hafizabad_history (accessed 25 Jun. 2018).
Hameed, R. & Nadeem, O. (2008) Challenges of implementing urban
master plans: The Lahore experience. International Journal of Humanities
and Social Sciences, 2(12), pp. 1297–1304.
Handy, S. L. & Clifton, K. J. (2001) Local shopping as a strategy for
reducing automobile travel. Transportation, 28(4), pp. 317–346.
Heldt, B., Gade, K. & Heinrichs, D. (2016) Determination of attributes
reflecting household preferences in location choice models. Transporta-
tion Research Procedia, 19, pp. 119–134. DOI: 10.1016/j.trpro.2016.12.073
Humphreys, J. & Ahern, A. (2017) Is travel based residential self-selec-
tion a significant influence in modal choice and household location
decisions? Transport Policy, 75, pp. 150–160.
DOI: 10.1016/j.tranpol.2017.04.002
Ibrahim, M. R. (2017) How do people select their residential locations in
Egypt? The case of Alexandria. Cities, 62, pp. 96–106.
DOI: 10.1016/j.cities.2016.12.012
Jun, M.-J., Ha, S.-K. & Jeong, J.-E. (2013) Spatial concentrations of Ko-
rean Chinese and determinants of their residential location choices in
Seoul. Habitat International, 40, pp. 42–50.
DOI: 10.1016/j.habitatint.2013.02.002
Kim, J. H., Pagliara, F. & Preston, J. (2005) The intention to move and
residential location choice behaviour. Urban Studies, 42(9), pp. 1621–
1636. DOI: 10.1080/00420980500185611
Kitamura, R., Mokhtarian, P. L. & Laidet, L. (1997) A micro-analysis of
land use and travel in five neighborhoods in the San Francisco Bay
area. Transportation, 24(2), pp. 125–158.
Lall, S. V., Suri, A. & Deichmann, U. (2006) Household savings and resi-
dential mobility in informal settlements in Bhopal, India. Urban Studies,
43(7), pp. 1025–1039. DOI: 10.1080/00420980500406744
Masoumi, H. E. (2013) Residential self-selection and its effects on urban
commute travels in Iranian cities compared to US, UK, and Germany.
International Journal of Social Sciences, 7(5), pp. 877–881.
Molugaram, K. & Rao, K. V. (2005) A stated preference residential loca-
tion choice model in Indian context (= Australasian Transport Research
Forum (ATRF) 28). Sydney, Curtin University.
Naeem, N. & Ahmad, M. (2018) Residential location choice behaviour: A
case study of city Hafizabad-Punjab. Lahore, University of Engineering
and Technology.
Næss, P. (2009) Residential self-selection and appropriate control vari-
ables in land use: Travel studies. Transport Reviews, 29(3), pp. 293–324.
DOI: 10.1080/01441640802710812
Næss, P. (2013) Residential location, transport rationales and daily-life
travel behavior: The case of Hangzhou Metropolitan Area, China. Pro-
gress in Planning, 79, pp. 1–50. DOI: 10.1016/j.progress.2012.05.001
Painter, K. (1996) The influence of street lighting improvements on
crime, fear and pedestrian street use, after dark. Landscape and Urban
Planning, 35(2–3), pp. 193–201. DOI: 10.1016/0169-2046(96)00311-8
Palma, A. de, Motamedi, K., Picard, N. & Waddell, P. (2005) A model of
residential location choice with endogenous housing prices and traffic
for the Paris region. European Transport, 31, pp. 67–82.
Park, J. & Kim, K. (2016) The residential location choice of the elderly
in Korea: A multilevel logit model. Journal of Rural Studies, 44, pp. 261–
271. DOI: 10.1016/j.jrurstud.2016.02.009
Patacchini, E. & Arduini, T. (2016) Residential choices of young Ameri-
cans. Journal of Housing Economics, 34, pp. 69–81.
DOI: 10.1016/j.jhe.2016.08.003
Pinjari, A. R., Pendyala, R. M., Bhat, C. R. & Waddell, P. A. (2011) Mode-
ling the choice continuum: An integrated model of residential location,
auto ownership, bicycle ownership, and commute tour mode choice
decisions. Transportation, 38(6), p. 933. DOI: 10.1007/s11116-011-9360-y
Residential location choices and the role of mobility, socioeconomics, and land use in Hafizabad, Pakistan
Urbani izziv, volume 30, no. 1, 2019
128
Schwanen, T. & Mokhtarian, P. L. (2004) The extent and determinants
of dissonance between actual and preferred residential neighborhood
type. Environment and Planning B: Planning and Design, 31(5), pp. 759–
784. DOI: 10.1068/b3039
Schwanen, T. & Mokhtarian, P., L. (eds.) (2003) Does dissonance between
desired and current residential neighbourhood type affect individual travel
behaviour? An empirical assessment from the San Francisco Bay area.
Earlier faculty research. Berkeley, CA, UC Berkeley. Available at: https://
escholarship.org/uc/item/26k8w6xf (accessed 13 Mar. 2019).
Sener, I. N., Pendyala, R. M. & Bhat, C. R. (2011) Accommodating spatial
correlation across choice alternatives in discrete choice models: an
application to modeling residential location choice behavior. Journal of
Transport Geography, 19(2), pp. 294–303.
DOI: 10.1016/j.jtrangeo.2010.03.013
Srinivasan, S. (2005) Influence of residential location on travel behavior
of women in Chennai, India. In: National Research Council (ed.) Re-
search on women’s issues in transportation, report of a conference. Volume
2, technical papers, pp. 4–13. Washington, DC: TRB.
Tran, M. T., Zhang, J., Chikaraishi, M. & Fujiwara, A. (2016) A joint analy-
sis of residential location, work location and commuting mode choices
in Hanoi, Vietnam. Journal of Transport Geography, 54, pp. 181–193.
DOI: 10.1016/j.jtrangeo.2016.06.003
Van Acker, V., Mokhtarian, P. L. & Witlox, F. (2014) Car availability ex-
plained by the structural relationships between lifestyles, residential
location, and underlying residential and travel attitudes. Transport
Policy, 35, pp. 88–99. DOI: 10.1016/j.tranpol.2014.05.006
Van der Vlist, Arno J, Gorter, C., Nijkamp, P. & Rietveld, P. (2002) Resi-
dential mobility and local housing-market differences. Environment and
Planning A, 34(7), pp. 1147–1164. DOI: 10.1068/a34176
Vega, A. & Reynolds-Feighan, A. (2009) A methodological framework for
the study of residential location and travel-to-work mode choice under
central and suburban employment destination patterns. Transportation
Research Part A: Policy and Practice, 43(4), pp. 401–419.
DOI: 10.1016/j.tra.2008.11.011
Vos, J. de & Witlox, F. (2016) Do people live in urban neighbourhoods
because they do not like to travel? Analysing an alternative residential
self-selection hypothesis. Travel Behaviour and Society, 4, pp. 29–39.
DOI: 10.1016/j.tbs.2015.12.002
Waddell, P., Bhat, C., Eluru, N., Wang, L. & Pendyala, R. M. (2007) Mode-
ling interdependence in household residence and workplace choices.
Transportation Research Record: Journal of the Transportation Research
Board, 2003(1), pp. 84–92. DOI: 10.3141/2003-11
Wang, L., Waddell, P. & Outwater, M. L. (2011) Incremental integration
of land use and activity-based travel modeling, Transportation Research
Record: Journal of the Transportation Research Board, 2255(1), pp. 1–10.
DOI: 10.3141/2255-01
Wang, M., Yang, Y., Jin, S., Gu, L. & Zhang, H. (2016) Social and cultural
factors that influence residential location choice of urban senior cit-
izens in China – The case of Chengdu city. Habitat International, 53,
pp. 55–65. DOI: 10.1016/j.habitatint.2015.10.011
Wang, Y., Peng, Z. & Chen, Q. (2018) The choice of residential layout
in urban China: A comparison of transportation and land use in
Changsha (China) and Leeds (UK). Habitat International, 75, pp. 50–58.
DOI: 10.1016/j.habitatint.2018.04.005
Wu, W., Zhang, W. & Dong, G. (2013) Determinant of residential loca-
tion choice in a transitional housing market: Evidence based on micro
survey from Beijing. Habitat International, 39, pp. 16–24.
DOI: 10.1016/j.habitatint.2012.10.008
A. B. ASLAM, H. E. MASOUMI, N. NAEEM, M. AHMAD
Yang, L., Zheng, G. & Zhu, X. (2013) Cross-nested logit model for the
joint choice of residential location, travel mode, and departure time.
Habitat International, 38, pp. 157–166.
DOI: 10.1016/j.habitatint.2012.06.002
Yi, C. & Lee, S. (2014) An empirical analysis of the characteristics of resi-
dential location choice in the rapidly changing Korean housing market.
Cities, 39, pp. 156–163. DOI: 10.1016/j.cities.2014.03.002
Yu, B., Zhang, J. & Li, X. (2017) Dynamic life course analysis on residen-
tial location choice. Transportation Research Part A: Policy and Practice,
104, pp. 281–292. DOI: 10.1016/j.tra.2017.01.009
Zhang, J., Yu, B. & Chikaraishi, M. (2014) Interdependences between
household residential and car ownership behavior: A life history analy-
sis. Journal of Transport Geography, 34, pp. 165–174.
DOI: 10.1016/j.jtrangeo.2013.12.008
Zhuge, C., Shao, C., Gao, J., Dong, C. & Zhang, H. (2016) Agent-based
joint model of residential location choice and real estate price for land
use and transport model. Computers, Environment and Urban Systems,
57, pp. 93–105. DOI: 10.1016/j.compenvurbsys.2016.02.001
Zondag, B. & Pieters, M. (2005) Influence of accessibility on residential
location choice. Transportation Research Record: Journal of the Transpor-
tation Research Board, 1902, pp. 63–70. DOI: 10.3141/1902-08
... 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]. ...
... 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 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]. ...
Article
Full-text available
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.
... This also effects changes in individual self-selections, preferences, and attitudes, e.g. regarding the choice of residential location and household mobility (Aslam et al., 2019). It has been estimated that, overall, in North America, Australia, and New Zealand, the share of households moving annually is about 15 % to 20 % and in Europe is 5 % to 10 % (Knox and Pinch, 2010). ...
... A comprehensive body of literature on residential choice adopts stated-preference approaches and discrete-choice modelling to study this decision process and the corresponding determinants of residential location choice. This includes case studies, e.g. for Burkina Faso (Traoré, 2019), China (Wu, 2004), Colombia (Stokenberga, 2019), Germany (Heldt et al., 2016), Israel (Frenkel et al., 2013, the Netherlands (Ettema and Nieuwenhuis, 2017), Pakistan (Aslam et al., 2019), or the UK (Kim et al., 2005;Walker et al., 2002). McFadden (1978) describes the choice of housing location as a rational, complex decision based on multiple dwelling characteristics such as the number of rooms or types of appliances, as well as location or neighbourhood attributes such as proximity to green spaces and the accessibility to places of work, commerce, education, and transportation. ...
Article
Full-text available
The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.
... This also effects changes in individual self-selections, preferences, and attitudes, e.g. regarding the choice of residential location and household mobility (Aslam et al., 2019). It has been estimated that, overall, in North America, Australia, and New Zealand, the share of households moving annually is about 15 % to 20 % and in Europe is 5 % to 10 % (Knox and Pinch, 2010). ...
... A comprehensive body of literature on residential choice adopts stated-preference approaches and discrete-choice modelling to study this decision process and the corresponding determinants of residential location choice. This includes case studies, e.g. for Burkina Faso (Traoré, 2019), China (Wu, 2004), Colombia (Stokenberga, 2019), Germany (Heldt et al., 2016), Israel (Frenkel et al., 2013, the Netherlands (Ettema and Nieuwenhuis, 2017), Pakistan (Aslam et al., 2019), or the UK (Kim et al., 2005;Walker et al., 2002). McFadden (1978) describes the choice of housing location as a rational, complex decision based on multiple dwelling characteristics such as the number of rooms or types of appliances, as well as location or neighbourhood attributes such as proximity to green spaces and the accessibility to places of work, commerce, education, and transportation. ...
Article
Full-text available
The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.
... Access to public transportation, home ownership, and the number of workers in the household, for example, were not represented by our model. This differs from previous studies that found those factors to be significant [1,3,7,16,21,25,35,39,59]. ...
... The population has decreased most in areas in the vicinity of public transport stops, which leads to the conclusion that public transport is not an important element when choosing a place of residence. This finding agrees with the results of some other studies (Aslam et al., 2019). In municipalities with the most intense settlement dynamics, newer settlements are partly placed in the vicinity of public transport stops; however, a detailed analysis indicates that some either do not have suitable access to public transport stops via the footpath network (e.g., Sončna Pot in Kozina), or that the vicinity of public transport stops is secondary compared to freeway access (e.g., the Grofice housing development in Vransko under construction), judging by the absence of public transport being mentioned on the development's presentation website. ...
Article
Full-text available
This article analyses the accessibility of public transport in Slovenia in terms of the proximity of stops and trip frequency. By combining the Central Population Register with data on the provision of public transport services, geographic information systems were used to calculate the share of the population living within a 500 and 1,000 m radius from stops with a basic number of daily trips. The spatial differences in accessibility were analysed, and the population density data were utilized to identify the main gaps in provision. Moreover, the location of newer settlements was analysed in terms of their integration into the existing public transport network. It was determined that public transport accessibility in the country is relatively adequate within a 1,000 m radius; however, within a 500 m radius, it is adequate only in most urban areas. There are extensive areas without adequate accessibility, which is a consequence of low population density particularly in the countryside, whereas larger gaps in provision appear in suburban areas that have grown outside public transport corridors. The 2004–2020 study period revealed a trend of lower demographic growth than the Slovenian average in areas with the best public transport accessibility, whereas the areas of the greatest population growth and most intense residential construction have been only partly located in the vicinity of the public transport network. This confirms the hypothesis that current strategic spatial planning documents are not followed consistently, and that transport and spatial planning are insufficiently integrated.
... Senior citizens, however, were likely to choose the bus rapid transit (BRT) [14]. Commuters preferred walking for job-related trips in small cities [15]. A high immobility rate was observed among women in Pakistan (55%) as compared to men (4%), and this percentage shows a further decrease if women are married and have children [16]. ...
Article
Full-text available
The COVID-19 outbreak is changing the patterns in travel activity for key destinations. Travel behavior during the pandemic has not been investigated adequately, specifically in developing countries. A sound understanding of travel-mode choice determinants is needed to design interventions to slow down and prevent the spread of the COVID-19 disease. This study explores travel-mode choice determinants for three key destinations, the workplace, market, and hospital, in Islamabad, Pakistan, during the COVID-19 disease outbreak. This study used a primary dataset of 163 observations and applied the multinomial logit (MNL) regression to analyze it. The survey results highlighted that the proportion of public transport mode was marginal for the three key destinations because public transport was closed during the lockdown, except for the metro bus. The streamlined model estimation results implied that the family-size factor had no relationship with the travel-mode choice. Males were most likely to travel to the workplace and market by 2&3 wheelers and least likely to travel by car. Females, unemployed persons, and students are likely to stay at home. Married people were more likely than single people to travel to the workplace and hospital by car. Self-employed people and state officials/public servants were most likely to go to the market by car. People living in towns/rural areas and cities were likely to travel by motorcycle/rickshaw and car, respectively. People living farther than 5 km from the workplace were most likely to travel by car, followed by motorcycle. This study is important for designing strategies to curb the pandemic with sustainable mobility during the lockdown.
... Contexts in the neighboring regions such as South Asia can also be investigated with the aim of examining the topic within the contexts directly located outside of Western or European countries. In the small city of Hafizabad in Pakistan, located in the neighboring region of South Asia, the availability of utility services and affordability are the most decisive factors in the residential location preferences [20]. In the same country, in the cities of Rawalpindi and Islamabad, accessibility to public transportation is correlated with house rent and demand [21]. ...
Article
Full-text available
The determinants of residential location choice have not been investigated in many developing countries. This paper examines this topic, including the influence of urban travels on house location decision-making in the Middle East and North Africa (MENA). Based on 8284 face-to-face interviews in Istanbul, Tehran, and Cairo, the dummy variable of residential location choice, including two categories of mobility reasons and other factors, was modeled by binary probit regression modeling. By means of receiver-operating characteristic analysis, the cutoff value of commuting distance and the time passed from the last relocation was estimated. Finally, the significant difference between the value of these two variables for people with different house location reasons were tested by Mann-Whitney U-test. The results show that the eight variables of shopping-entertainment mode choice in faraway places, frequency of public transit trips, neighborhood attractiveness perception, age, number of driving licenses in household, commuting distance, number of accessed facilities, and the (walkable) accessibility of facilities influence the residential self-selections. People who chose their current home based on mobility commute a daily mean distance of 8596 m and relocated less than 15.5 years ago, while those who chose their home based on other reasons, such as socioeconomics or personal reasons, commute longer and moved to a new house more than 15.5 years ago. This shows how the attitudes of people about residential location have changed in the MENA region, but there are still contextual differences to high-income countries.
Article
The aspects of spatial planning have been aimed at supporting older people to stay healthy and active in their daily lives, as well as to improve their overall quality of life. Older people require accessible and functional venues and social environments that suit their emotional needs and goals. However, there has been limited study on the most significant characteristics of residential settings that impact the well-being of elderly inhabitants. This study aims to provide a comprehensive review of neighborhood residential environment elements and various Quality of Life (QOL) attributes, as well as their interrelationships, to encourage healthy aging. A framework for analyzing neighborhood features was developed as part of this critical analysis through extensive analysis of chosen articles. According to the review, social life is the most influential component of QOL, followed by neighborhood living settings and housing layouts that promote mobility features to participate in physical activities, generating a feeling of community and belonging and leading to a healthy life. Further research should be conducted to investigate the influence of these characteristics on the overall satisfaction level of the elderly in later life.
Article
The aspects of spatial planning have been aimed at supporting older people to stay healthy and active in their daily lives, as well as to improve their overall quality of life. Older people require accessible and functional venues and social environments that suit their emotional needs and goals. However, there has been limited study on the most significant characteristics of residential settings that impact the well-being of elderly inhabitants. This study aims to provide a comprehensive review of neighborhood residential environment elements and various Quality of Life (QOL) attributes, as well as their interrelationships, to encourage healthy aging. A framework for analyzing neighborhood features was developed as part of this critical analysis through extensive analysis of chosen articles. According to the review, social life is the most influential component of QOL, followed by neighborhood living settings and housing layouts that promote mobility features to participate in physical activities, generating a feeling of community and belonging and leading to a healthy life. Further research should be conducted to investigate the influence of these characteristics on the overall satisfaction level of the elderly in later life.
Article
Full-text available
While it would be desirable to encourage people to live in places that are safer from natural disasters to minimize casualties and property damage, few studies have focused on people’s relative preference for living in such places. The present study has sought to clarify the extent to which Tokyo residents consider safety from natural disaster to be more important than other factors relevant to the choice of residential location, as well as what personal attributes may be correlated with this perception. An online survey was conducted to collect 1554 valid responses from residents in the 23 city wards of Tokyo, Japan, and statistical analysis (a chi-square test and multivariable logistic regression analysis) was then applied to the collected responses. The results demonstrated that, on average, 45.1% of the respondents considered that “safety from natural disasters” was relatively important among twelve such factors related to the selection of a suitable residential location. It was also found that showing a hazard map to Tokyo residents or educating them to take more interest in their health and the surrounding natural environment could be effective to increase the number of people preferring to live in safer places.
Conference Paper
Full-text available
Residential Self-Selection is a theory that states that a significant proportion of people base their choice of residential location on the ability of an area to suit their transportation and modal preferences. This paper seeks to examine if residential self-selection can be observed in the population of the Greater Dublin Area and to examine how travel choices and household location decisions interact. The research is based on data collected through the means of a postal survey issued in April 2014. The research briefly outlines the differences observed in travel behaviour across contrasting land-use areas; with higher use of sustainable modes for residents of denser, mixed-use urban areas with greater public transport options. The link between residency in such areas and particular respondent characteristics is also highlighted with a tendency for residents of these areas to be younger renters, without families, have lower car ownership and shorter occupancies. The paper describes how for a significant proportion of the survey respondents, modal choices were made prior to actual residency. However, the paper also describes that while transport is an important factor in choosing where to live, it is not the primary factor for many residents: whether or not it is the primary factor is dependent on the characteristics of the respondent involved. The key conclusion is that residential self-selection does not occur to such an extent that it is more important than land-use factors in determining modal-split characteristics. However, it remains a significant contributory factor for certain populations when sustainable modes are considered: younger people, renters, without cars or children. The paper outlines implications for policy, which highlight the importance of taking self-selection tendencies into account when preparing land-use-transport policies to reduce car dependency.
Article
Previous research has indicated that mode-specific attitudes can affect travel mode choice through the residential location choice. According to the principle of residential self-selection, people will try to choose a residential neighbourhood that enables them to travel with as high a share as possible of their amount of travel with their preferred mode. In this study, however, we will analyse whether differences in travel distance, travel time and travel satisfaction in urban versus suburban neighbourhoods are due to travel-liking attitudes, the residential location or a combination of both. Results of this study À analysing leisure trips within the city of Ghent (Belgium) À indicate that suburban respondents are, compared to urban respondents, more satisfied with their trips, which are also longer in time and distance. Suburban respondents also have a more positive stance towards travelling, suggesting a possible residential self-selection process. Travel lovers might prefer a residential neighbourhood where travel distances and travel time are relatively high, while people who do not like to travel might prefer to live in a neighbourhood that enables more short-distance and less travel-time intensive trips. This study suggests that especially people who do not like to travel self-select themselves in urban neighbourhoods in order to limit travel distance and travel time. In contrast, respondents with a more positive stance towards travelling are equally distributed in urban and suburban neighbourhoods. Results also indicate that travel distance and travel time are mainly affected by respondents' residential neighbourhood, while travel satisfaction is mainly affected by travel-liking attitudes.
Article
Previous research indicated that settlement behaviors of refugees and rural-to-urban migrants in Third World contexts are influenced by: 1) geographic origins, 2) ethnicity, 3) education level, 4) employment background, 5) political involvement, 6) dates of departure from the homeland and 7) reasons for leaving. This research evaluates the influence of these factors on residential choices of Afghan refugees self-settled in Peshawar, Pakistan. The data indicated that ethnogeographic ties, political involvement, and lack of political activity had been most affective in motivating residential associations and disassociations. Other variables were related to ethnicity, origins and status and, thus, did not influence directly residential choices.
Article
Gated communities with tall and dense residential buildings are common in China's cities due to the limited land resources and highly concentrated populations. Living in a gated community is comfortable, safe and removed from outside pollution and vehicles, but these large enclosed areas tend to block traffic and, therefore, increase drivers' travel distance. Comparing the residential layouts and transportation in Changsha, which is a provincial capital city in Central China, with those in Leeds, which is a city in the UK, this paper discusses the choice of residential layout in Chinese cities based on the characteristics of the residential buildings (tall and densely populated) and reveals that in Chinese cities, the size of the closed residential areas should be controlled to allow more route choices for vehicles. Controlled residential areas should not be open to outside vehicular traffic but can be designed to be semi-open, allowing entry only to pedestrians from outside, to improve the proportion of pedestrian traffic and separate pedestrians from vehicles on the roads. Enclosed areas can be designed to separate people from vehicles, and vehicles should be parked together by building car parks, parking structures or underground garages to reorder traffic, improve the quality of life of the people in the community and promote the efficient use of land.
Article
This paper examines if residential self-selection can be observed in the population of the Greater Dublin Area and analyses the interactions that occur between travel choices and household location decisions. The research is based on data collected through a postal survey issued in April 2014. The paper outlines the differences observed in travel behaviour across contrasting land-use areas; with the findings showing higher use of sustainable modes for residents of denser, mixed-use urban areas with greater public transport options. Travel-based residential self-selection is a contributory factor to modal split characteristics but not to an extent that would invalidate the positive role of land-use measures in promoting sustainable travel. The study found that while transport is an important factor in choosing where to live, it is not the primary factor for all residents and its role is dependent on the characteristics of the respondent involved. The key conclusion is that residential self-selection does not occur to such an extent that it is more important than land-use factors in determining modal-split characteristics. However, it remains a significant contributory factor for certain populations when sustainable modes are considered. The paper highlights the importance of taking self-selection tendencies and housing characteristics into account when creating land-use-transport policies to reduce car dependency and discusses the role of the urban region in producing self-selection behaviour.
Article
From a behavioral viewpoint, people choose where to live based on various factors, including their current situations, past experience, and plans for the future. Some aspects of residential preference might be constant over time, inherited from the initial stage of life, and other parts might be responses to residential biography or other biographical domains like household structure, employment/education, and travel. Capturing these intertemporal dependences needs a life course analysis of residential location choices. However, a serious methodological gap exists between the perceived importance of dynamic life course analyses and quantitative modeling approaches. This study developed a dynamic choice model with cross-sectional and longitudinal heterogeneities as well as discounted utility (called the DU-DCLH model) to describe the decision-making process for residential relocation by incorporating various intertemporal dependences over the life course. Model parameters were estimated using data collected from a life history survey conducted in Japan in 2010. The estimation results firstly confirm the effectiveness of the DU-DCLH model for portraying the dynamics of residential mobility over a life course. Next, it was found that previous experiences dominate decisions on residential location choice and can explain more than 75% of the total variations in choice. It was also revealed that as the mobility age increases, the influence of the past on their choices increases continuously. In contrast, the influence of the present situation is small and almost negligible. Furthermore, the study empirically confirmed not only the influence of time-constant and time-varying preference for residential neighborhoods but also the specific influence of household biography, employment/education biography, and travel biography. This study enriches the existing research by providing a systematic modeling framework incorporating broader behavioral mechanisms for residential location choice over the life course.
Poster
Challenges coming along with changing mobility behaviour patterns require planning decisions to mitigating negative effects. Land-use and transport interaction models can provide valuable decision support for this purpose. But they require tremendous effort in terms of model design as well as data collection and preparation. We introduce a methodology and procedures with the aim to minimize the magnitude of modelling work with particular attention to the selection of model segmentation and model parameters in a structured and efficient way. The methodology combines literature and statistical analyses for model design. The paper outlines the methodology and presents its application to the design of a location choice model for the city of Berlin, Germany. We demonstrate how household types exhibiting specific location patterns and related accessibility parameters can be identified from the literature and how standard deviation maps and correlation analysis can be used to detect these households and test hypotheses. The results suggest that the methodology is capable to identify segmentations and parameters for usage in choice models, such as location decisions, in an efficient way.
Article
Affordable housing unit is still a rare resource in Egypt, despite the active role of the government to reform housing policies. The deficits of affordability and housing supply have been addressed intensively in the framework of the Egyptian housing policies. Indeed, housing prices and market conditions can be among the factors that influence the choices of the people to select certain residential locations. However, there are still no significant impacts that show affordability as the primary factor in the self-selection process of residential locations in Egypt. This complicates housing development scenario in Egypt. This paper highlights various debates concerning some of the current assumptions regarding housing development in Egypt. Focusing on Alexandria, Egypt, the main factors influencing current residential location choices are shown to be the availability of public transportation, followed by living in a good neighborhood and housing affordability.