Description
Study Paper on Identifying Key Factors Affecting Purchase Decision of Residential Apartments
Real estate prices in The City Beautiful' Chandigarh have been shooting up to the extent that it is near impossible for salaried class to buy a home in Chandigarh. Major reasons for such unprecedented increase in prices are high demand and low supply. Reason being, expansion is on the outskirts of the Chandigarh, which includes Greater Mohali, Mullanpur, Zirakpur and Panchkula.
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
International Journal of Applied Services Marketing Perspectives © Pezzottaite Journals. 493 | P a g e
IDENTIFYING KEY FACTORS AFFECTING PURCHASE DECISION OF RESIDENTIAL
APARTMENTS: AN EXPLORATORY STUDY IN PERIPHERIES OF CHANDIGARH
Dr. Tejinderpal Singh
13
ABSTRACT
The present paper aims to identify the key factors affecting the decision of customers to buy residential apartments in the
outskirts of Chandigarh. For study purpose, a sample of 200 salaried class persons was taken by using purposive sampling
technique from tri-city i.e., Chandigarh, Mohali and Panchkula. The sample consisted of those respondents who either bought
an apartment in the last one year or were planning to buy it in the coming one year. Respondents were asked to give their
opinion about 19 listed variables on Five-point Likert Scale. By using Exploratory Factor Analysis, six factors were extracted
which explained 73.916 per cent of total variance. The extracted factors were ?Basic Amenities‘ (15.087%), ?Recreational and
Leisure‘ (14.953%), ?Layout‘ (11.570%), ?Financials‘ (11.077%), ?Proximity‘ (10.735%) and ?Connectivity‘ (10.493%)
factors. On the basis of mean scores, it was further found that ?Basic Amenities‘(4.4812), is the most important factor while
selecting a apartment followed by ?Financials‘ (4.0500), ?Connectivity‘ (3.8470), ?Layout‘ (3.0312), ?Proximity‘ (2.8838) and
Recreational and Leisure (2.8362) factors.
Demographics analysis showed that there was no significant effect of gender, marital status and age on the importance
assigned to various factors by the respondents. However, ?type of job‘ had significant effect on importance assigned to factors
like ?Basic Amenities‘ and ?Connectivity‘. Further, income had significant effect on importance assigned to factors like ?Basic
Amenities‘, ?Financials‘ and ?Recreational & Leisure‘. In the end, study suggested that real estate marketers should give due
importance to these factors while offering apartments in the market.
KEYWORDS
Buying Behaviour, Chandigarh, Residential Apartments, Marketing, Real Estate etc.
I NTRODUCTI ON
Real estate prices in ?The City Beautiful‘ Chandigarh have been shooting up to the extent that it is near impossible for salaried
class to buy a home in Chandigarh. Major reasons for such unprecedented increase in prices are high demand and low supply.
Reason being, expansion is on the outskirts of the Chandigarh, which includes Greater Mohali, Mullanpur, Zirakpur and
Panchkula. Now, Major real estate players such as Silver City Group of Companies, Ansals, Gulmohar, DLF, Parsvnath
Developers, Omaxe Construction Ltd, Unitech, TDI, MGF-Emaar have already started their project in this area. Their products
portfolio includes residential plots, commercial property, duplex villas, independent floors and apartments. Although, independent
apartment system is new to the region but demand for this product is rising day by day because of scarcity of housing in
Chand?garh. Customers have the wide choice to select the best suitable apartment for them because of the intense competition and
additional supply in the market. Therefore, it is important for the real estate marketers to understand the behaviour of prospective
buyers and to identify the influencing factors, which affect the choice of customers. Therefore, in this background the present
study aims to identify the key factors affecting the decision of customers to buy residential apartments in outskirts of Chandigarh.
PROBLEM STATEMENT
The research problem has been identified as ?Identifying Key Factors Affecting Purchase Decision of Residential Apartments: An
Exploratory Study in Peripheries of Chandigarh‘.
NEED AND SI GNI FI CANCE OF THE STUDY
The knowledge of different factors affecting the buying behaviour and buying preferences of the consumers will provide to
builders and developers to launch their residential apartment schemes and to understand the insight of buying behaviour. Hence,
they will be able to launch their housing schemes better and effectively.
OBJ ECTI VES OF STUDY
The present study has been designed to identify the key factors affecting purchase decision of buying residential apartments in
peripheries of Chandigarh and, thereafter, to make of customers‘ opinion about factors identified.
13
Assistant Professor, University Business School, Panjab University, Punjab, India, [email protected]
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
International Journal of Applied Services Marketing Perspectives © Pezzottaite Journals. 494 | P a g e
REVI EW OF LI TERATURE
Few studies are available on buying behaviour towards buying of residential apartments.
Beamish et.al (2001) [1] explored influences on housing choice and proposed a conceptual framework that examined the
influence of lifestyle as an intervening factor in housing choice. Influences on housing choice included age, family type, family
size, stage in the life cycle, social class, income, occupation, education and value.
Zhaohui (2003) [2], from the perspective of the consumer residential real estate market, investigated the basic characteristics of
the homebuyers, homebuyers purchasing preferences and influencing factors.
Leishman et al. (2004) [3] gave a detailed examination of new-built housing buyers‘ housing needs and preference and analyzed
on the basis of the physical, location and quality characteristics of housing actually constructed by house builders. Study, further,
examined the relative importance of physical property, locational, neighborhood and price factors to consumers in the housing
choice process.
Shi Lin (2005) [4]
determined the housing preferences and priorities among residents in different socio demographic and
socioeconomic groups in Stellenbosch and explored a functional formula by which the price of housing in Stellenbosch could be
predicted. As per the findings of the study, dwelling related attributes were found to be more important than neighboring and
location-related attributes.
Gupta et al. (2006) [5]
captured the impact of environmental, structural and location variables on housing prices prevailing in the
city and found ?proximity to water body‘ fetches the highest 18.9 per cent of the total value in Navi Mumbai, and garden
proximity fetches the highest 13.2 per cent in Central suburb. The capitalization of land was also observed proximate to water and
greenery.
Ariyawansa (2007) [6] in his study aimed to provide a scientific insight into the consumer behavior of housing market in Sri
Lanka. It was found that consumer preference mainly depends upon the housing transaction, appreciation in the future,
complementary products and services like water and electricity supply.
Litman (2011) [7]
investigated consumer housing location preferences and their relationship to smart growth. It examined claims
that most households prefer sprawl-location housing and so was harmed by smart growth policies. This analysis indicated that
smart growth tends to benefit consumers in numerous ways.
The review of literature reveals that in India, there is dearth of studies on understanding the consumer behaviour towards buying
of residential apartment. Moreover, no such study has been conducted to study the market in Chandigarh and surrounding areas.
Therefore, the present study will contribute to the domain of existing knowledge too.
RESEARCH METHODOLOGY
Research methodology for the present paper has been discussed as under:
Research Design
The purpose of the study was to identify the key factors affecting purchase decision of buying residential apartments in the
outskirts of Chandigarh. Hence, the exploratory research design has been used.
Population and Target Population
The population for the study consisted of residents of Tri city i.e. Chandigarh, Mohali and Panchkula. Target population has been
defined as ?salaried individual working in government or private sector, residing either in government or rented accommodation
and were planning to buy an apartment in coming one year or have bought an apartment in the last one year.
Sample Size and Sampling Technique
In the present study, purposive sampling technique has been used, as ready sampling frame was not available. However, efforts
have been made to collect the data from different walks of people. Initially, questionnaires were distributed to 300 individuals and
only 221 questionnaire returned. Out of 221 questionnaires, only 200 questionnaires were found valid. Hence, findings of the
study are based on opinion of 200 respondents.
Instruments Design and Data Collection
For collection of data, a structured questionnaire was used. Before drafting of final questionnaire, unstructured interviews were
carried out with property dealers and prospective buyers to list the various variables, which customers generally consider before
buying of an apartment. Help from previous studies were also sought to expand the list of factors. Finally, a list of 19 variables
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was prepared after removing of 7 variables, which were similar to other variables. The questionnaire consisted of two parts. In the
first part, questions were on demographics. In the second part, opinion of respondents was sought on listed variables, using five
point Likert scale ranging from ?Most Important‘ to ?Most Unimportant‘. Before collection of data questionnaire was pretested on
20 customers. ?Survey method‘ was used to collect the data from respondents. Data was collected during the month of January –
February 2013. The ?sampling unit‘ for the study was individuals.
Statistical Tools and Data Analysis
The collected data was analyzed with the help of SPSS16. Exploratory Factor Analysis (EFA) was used to identify the various
factors from the list of variables. Inferential statistics like ANOVA and t-test were also used to see the effect of demographics on
importance given to various factors. To check the normality of data Kolmogorov-Smirnov test was used and it was found that
collected data was normally distributed (Z =.947 Asymp. Sig. 331).
Demographic Profile of Respondents
Table 1 shows that sample consisted of 61.5 per cent Male and 38.5 per cent Female respondents.
Table-1: Demographic Profile of Respondents
N=200
Demographics Frequency Percentage Demographics Frequency Percentage
Gender
Male
Female
123
77
61.5
38.5
Marital
status
Married
Unmarried
84
116
42.0
58.0
Age Group Less 30
30 -40
40 -50
More than 50
39
79
59
23
19.5
39.5
29.5
11.5
Income (Rs.) 1-5lac
5-10lac
10-15lac
>15lac
59
93
33
15
29.5
46.5
16.5
7.5
Type of Job
Government
Private
95
105
47.5
52.5
Sources: Primary Data
Fifty eight per cent of respondents were unmarried and 42.0 per cent were married. According to type of job, 52.5 per cent of
respondents were from private sector and 47.5 per cent were from government sector. Income-wise, 46.5 per cent of respondents
belong to ?5-10 lac‘ category followed by ?1-5lac‘ (29.5%), ?10-15lac‘ (16.5%) and ?More than 50 lac‘ (11.00%). Table further
shows that 39.5 per cent of respondents belonged to age group ?30-40‘years followed be age groups ‘40-50‘years (29.5%), ?less
than30‘years (19.5%) and ?more than 50‘ years (11.5%).
RESULT AND FI NDI NGS
Identification of Factors
The prime objective of the study was to identify the factors influencing the purchase decision of apartment in the peripheries of
Chandigarh. Therefore, respondents were asked to indicate their opinion regarding the importance of each variable in their
purchase decision of apartments. An Exploratory Factor Analysis (EFA) was applied to club the 19 variables into meaningful
factors. Prior to the extraction of the factors, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of
Sphericity were applied to assess the suitability of the respondent data for factor analysis.(Table:2). The calculated KMO value
was .689 and Bartlett‘s Test of Sphericity was found significant (P<.065).
Table-2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity
.689
Approx. Chi-Square df sig
3494.027 190 .000
Sources: Primary Data
The KMO index ranges from 0 to 1, with 0.50 considered suitable for factor analysis. [8] The Bartlett's Test of Sphericity should
be significant (p<.05) for factor analysis to be suitable. [8] Thus the present data set satisfied these two conditions to apply EFA.
Factors were extracted by using Principal components analysis (PCA). Six-factor having Eigen Values more than 1 were extracted
which explained the 73.916 per cent variance (Table3). Rotation of factor was done by using ?Varimax with Kaiser
Normalization‘ rotation method. Based on the computations as represented in the Rotated Component Matrix (Table 1), the six
factors were identified i.e. ?Basic Amenities‘ (15.087%, 4.464), ?Recreational and Leisure‘ (14.953%, 2.840), ?Layout‘ (11.570%,
2.606), ?Financials‘ (11.077%, 2.026), ?Proximity‘ (10.735%, 1.624) and ?Connectivity‘ (10.493%, 1.222). Each variable was
retained a factor having loading more than 0.45.
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Table-3: Factors Affecting Purchase Decision of Apartments
N=200
Factor Factors Name Variables
Eigen
Values
Total
Variance (%)
Factor
Loading
1 Basic Amenities
Electricity backup
4.464
15.087
.969
Water supply .958
Sewerage system .963
Car parking .556
2
Recreational
and Leisure
Close to gym
2.840
14.953
.969
Park facing .958
Near to Community hall .963
3 Layout
Floor of the apartment
2.606 11.570
.964
Number of rooms / bedrooms .551
Servant room .962
4 Financials
Price
2.026 11.077
.465
Booking amount .783
EMI .825
5 Proximity
Proximity to own office
1.624
10.735
.829
Proximity to spouse office .829
Proximity to children's school .706
6 Connectivity
Access to market
1.222 10.493
.818
Access to public transportation .711
Connectivity to main road .683
Total variance Explained : 73.916 %
Sources: Primary Data
Table: 3 shows that four variables were loaded on Factor 1. All these four variables are related with basic amenities without which
a place cannot be considered viable for living purpose. The four variables loaded on ?1
st
Factor‘ were ?Electricity backup‘ (.969),
?Water supply‘ (.958) ?Sewerage system‘ (.963) and ?Car parking‘ (.556). This Factor was labeled as ?Basic Amenities‘. Three
variable loaded on the ?2
nd
Factor‘ were related to opinion of respondents towards Recreational and Leisure facilities near
apartments which included ?Close to gym‘ (.969), ?Park Facing‘ (.958) and ?Near to Community hall‘ (.963). This Factor was
named as ?Recreational and Leisure‘. Three variables loaded on ?3
rd
Factor‘ were related with the general ?Layout‘ and floor of
the apartments. The ?3
rd
Factor‘ was loaded on by variables; ?Floor of the apartment‘ (.964), Number of rooms/bedrooms (.551),
Servant room (.962). Third factor was labeled as ?Layout‘. Three variables loaded on ?4
th
Factor‘ were Price (.465), Booking
amount (.783) and EMI (.825). It clearly shows that these variables are related with Financials. Hence, this factor was named as
?Financials‘. Items identified for ?5
th
Factor‘ were ?Proximity to own office‘ (.829), ?Proximity to spouse office‘ (.829) and
?Proximity to children's school‘ (.706). This factor was labeled as ?Proximity‘. Similarly, three items were loaded on ?6
th
Factor‘
which was named as ?Connectivity‘. The four items loaded on this factor were ?Access to market‘ (.818), ?Access to public
transportation‘ (.711) and ?Connectivity‘ to main road‘ (.683).
Importance Assigned to Factors
To identify the order of importance of each factor, mean scores were calculated for each factor (Table 4). Firstly, factor scores for
each respondent were calculated by summing raw scores corresponding to all variables loading on a factor and divided by number
of variables. Thereafter, mean was calculates for each factor by dividing number of respondents.
Table-4: Importance Assigned to Factors
N=200
Factors
Basic
Amenities
Financials Connectivity Layout Proximity
Recreational
and Leisure
Mean Score 4.4812 4.0500 3.8470 3.0312 2.8838 2.8362
Standard
Deviation(±)
.66259 .67576 .70603 .57858 .60144 .57786
Sources: Primary Data
Table 4 shows that on the basis of mean scores, it was found that ?Basic Amenities‘(4.4812), is the most important factor while
selecting an apartment followed by ?Financials‘ (4.0500), ?Connectivity‘ (3.8470), ?Layout‘ (3.0312), ?Proximity‘ (2.8838) and
?Recreational and Leisure‘ (2.8362) factors.
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Demographics- Wise Importance Assigned to Factors
The second objective of the study was to analyze the identified factors according to demographics. The effect of selected
demographics such as Gender, Marital status, Type of Job (Table 5), Age and Income (Table 6) on importance given to factors
identified has been analyzed in this part of the study.
Table-5: Gender-wise and Marital status- wise and Type of Job wise importance assigned to Factors
N=200
Factors
Gender t-statistic Marital Status t-statistic Type of Job t-statistic
M
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F
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Basic Amenities 4.4553 4.0305 .700 485 4.5625 4.4224 1.480 140 4.2921 4.6524 3.861 000*
Financials 4.0812 4.5227 .515 .607 4.1101 4.0065 1.071 .285 4.1184 3.9881 1.388 .167
Connectivity 3.8740 3.8039 .682 .496 3.9155 3.7974 1.168 .244 3.7411 3.9429 2.034 .043*
Layout 2.9858 3.1039 1.408 .161 3.0238 3.0366 .154 .877 3.0000 3.0595 .726 .469
Proximity 2.8923 2.8701 .253 .801 2.8274 2.9246 1.129 .260 2.9184 2.8524 .775 .439
Recreational and Leisure 2.8333 2.8409 .090 .928 2.8065 2.8578 .618 .538 2.7842 2.8833 1.213 .227
Note: *Significant at .05
Sources: Primary Data
Table 5 shows that gender-wise, male respondents gave more importance to factors like ?Basic Amenities‘, ?Connectivity‘ and
?Proximity‘ as compared to female respondents. On the other hand female respondents gave more importance to factors such as
?Financials‘, ?Layout‘ and ?Recreational and Leisure‘ as compare to male respondents. However, t-test showed that there was no
significant effect of gender on importance given to all the factors ; ?Basic Amenities‘ [t(198) = .700, p > .005], ?Financials‘
[t(198) = .515, p > .005] ?Connectivity‘ ,[t(198) = .682, p > .005], ?Layout‘ [t(198) = 1.408, p > .005], ?Proximity‘ [t(198) = .253,
p > .005] ?Recreational and Leisure‘, [t(198) = .090, p > .005]. Marital status wise, married respondents assigned more importance
to factors like ?Basic Amenities‘ ?Financials‘ and ?Connectivity‘ as compare to unmarried respondents. Similarly, unmarried
respondents assigned more importance to rest of the factors like ?Layout‘, ?Proximity‘ and ?Recreational and Leisure‘. However,
t-test showed that there was no significant effect of marital on importance given to all the factors i.e. ?Basic Amenities‘ [t(198) =
1.480, p > .005], ?Financials‘ [t(198) =1.071 , p > .005] ?Connectivity‘ [t(198) = 1.168, p > .005], ?Layout‘ [t(198) = .154, p >
.005], ?Proximity‘ [t(198) = 1.129, p > .005] and ?Recreational and Leisure‘, [t(198) = .618, p > .005]. Further, type of job-wise,
respondents in private job assign more importance to factors like ?Basic Amenities‘, ?Connectivity‘, ?Layout‘ and ?Recreational
and Leisure‘ as compared to respondents in government job. It was further found that respondents in government job gave more
importance to factors like‘ ?Layout‘‘ and ?Proximity‘. T-test showed that there was significant effect of type of job on importance
given to the factors like Basic Amenities [t (198) = 3.861, p < .005] and ?Connectivity‘ [t (198) = 2.034, p < .005]. On the other
hand, there was no significant effect of type of job on importance given to the factors like ?Financials‘ [t (198) =1.388, p > .005]
?Layout‘ [t (198) = .726, p > .005], ?Proximity‘ [t (198) = .775, p > .005] and ?Recreational and Leisure‘, [t (198) = 1.213, p >
.005]. Table 6 shows that among given age categories , respondents belonging to age category ?40-50‘ has given maximum
importance to factor ?Basic Amenities‘(4.5466) followed by age categories ?>50‘(4.5217), ?<30‘(4.4423) and ?30-40‘(4.4399).
Table-6: Age-Wise and Income-Wise Importance Assigned to Factors
N=200
Factors
Age Categories(Years)
ANOVA
Statistic
Income Categories (Lac)
ANOVA
Statistic
<30 30-40 40-50 >50 F-value sig. 1-5 5-10 10-15 >15 F-value Sig
Basic Amenities 4.4423 4.4399 4.5466 4.5217 .364 .77 4.2288 4.5269 4.7045 4.7000 5.092 .00*
Financials 4.1603 4.0506 4.0508 3.8587 .960 .41 4.2331 4.0806 3.9091 3.4500 6.410 .00*
Connectivity 3.9231 3.8785 3.7492 3.8609 .580 .62 3.8593 3.7742 3.9606 4.0000 .853 .46
Layout 3.0577 2.9747 3.0424 3.1522 .617 .60 3.0466 2.9731 3.1894 2.9833 1.186 .31
Proximity 2.9615 2.8734 2.9195 2.6957 1.045 .37 2.9407 2.8548 2.9697 2.6500 1.232 .29
Recreational and Leisure 2.6795 2.8354 2.9068 2.9239 1.436 .23 2.8517 2.7204 3.0152 3.1000 3.481 .01
Note: *Significant at .05
Sources: Primary Data
On the other hand respondents belonging to age category ?<30‘ gave maximum importance to ?Financials‘ (4.16030),
?Connectivity‘ (3.9231) and ?Proximity‘ (2.9615) amongst given age categories. Similarly, respondents belonging to age category
?>50‘ gave importance to factors; ?Layout‘ (3.1522) and ?Recreational and Leisure‘ (2.9239). However, ANOVA results showed
that in case of all factors, there was no significant impact of age on importance given to factors; Basic Amenities[F(199) = .364, p
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
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> .005], ?Financials‘ [F(199) = .960, p > .005] ?Connectivity‘ ,[ F(199) = .580, p > .005], ?Layout‘ [ F(199) = .617, p > .005],
?Proximity‘ [F(199) = 1.045, p > .005] ?Recreational and Leisure‘, [F(199) = 1.436, p > .005]. It was further found that among
given income categories, respondents belonging to category ‘10-15‘ have given maximum importance to ?Basic Amenities‘
(4.7045) followed by income categories ?>15‘ (4.7000), ?5-10‘ (4.5269), ?1-5‘ (4.2288).
It seems that with the rise in income, more importance has been assigned to this factor. Further, ANOVA results showed that there
was significant impact of income on the importance assigned to the factor ?Basic Amenities‘ [F (199) = 5.092, p < .005. Similarly,
respondents belonging to income category ?1-5‘ have give maximum importance to the factor ?Financials‘(4.2331) followed by
categories ?5-10‘(4.0806). ‘10-15‘ (3.9091), and >15‘ (3.4500). It is evident that respondents having low income had given more
importance to ?Financials‘. Further, ANOVA results showed that there was significant impact of income on the importance
assigned to the factor ?Financials‘ [F (199) = 6.410, p < .005. Table further shows that maximum importance has been given to
factors like ?Layout‘ ?Proximity‘ ?Recreational and Leisure‘ by the respondents from income category ?10-15‘ amongst given
income categories. The factor ?Connectivity‘ has been assigned maximum importance by the respondents from income category
?>50‘. ANOVA results showed there was no significant effect of income on the importance given to factors; ?Connectivity‘, [F
(199) = .853, p > .005], ?Layout‘ [F (199) = 1.186, p > .005], ?Proximity‘ [F (199) = 1.232, p > .005]. However, there was
significant effect of income on the importance given to factors ?Recreational and Leisure‘, [F (199) = 3.481, p < .005].
DI SCUSSI ON, MARKET I MPLI CATI ONS AND CONCLUSI ON
The present study was conducted to explore the preferences assigned to various factors by the customers when it comes to buying
of an apartment. The factor ?Basic amenities‘ has been identified as the most important factor, which influences the choice of
customers. It clearly shows that the first thing which prospective buyer looks for, are basic amenities like water supply, electricity,
sewerage system etc., which are essential to start with living. ?Financial Factors‘, ?Connectivity‘ Factors‘ and ?Layout Factors‘ are
other primary factors which influence the choice of the customers. Therefore, these factors seek proper attention from the builder.
Factors like ?Proximity‘ and ?Recreational and Leisure‘ seem to be secondary factor. However, these factors cannot be ignored at
all by the builders. The study has significant implications for the real estate marketers too. When, it comes to the offering of
product to the prospective buyer marketers should focus on these identified factors according to the order of preference as found
in the study. Marketers should prominently communicate features of their projects based on identified factors. Factors like
?Proximity‘ and ?Recreational & Leisure‘ seem to be secondary. However, may be highlighted depending upon the segment of
consumers. Demographics analysis showed that ?type of job‘ had significant effect on importance assigned to factors like ?Basic
Amenities‘ and ?Connectivity‘. Further, income had significant effect on importance assigned to factors like ?Basic Amenities‘,
?Financials‘ and ?Recreational & Leisure‘. Therefore, real estate marketer should pay special attention towards these factors
especially when dealing with customers from such categories. In the end, it is concluded that there is an ample scope for the future
research in the domain. The scope of present study is limited to the tri-city, which may be extended to other areas like NCR,
Mumbai etc., and comparisons may be made to see the differences in the buying behavior of customers belonging to these
respective areas
.
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Study Paper on Identifying Key Factors Affecting Purchase Decision of Residential Apartments
Real estate prices in The City Beautiful' Chandigarh have been shooting up to the extent that it is near impossible for salaried class to buy a home in Chandigarh. Major reasons for such unprecedented increase in prices are high demand and low supply. Reason being, expansion is on the outskirts of the Chandigarh, which includes Greater Mohali, Mullanpur, Zirakpur and Panchkula.
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
International Journal of Applied Services Marketing Perspectives © Pezzottaite Journals. 493 | P a g e
IDENTIFYING KEY FACTORS AFFECTING PURCHASE DECISION OF RESIDENTIAL
APARTMENTS: AN EXPLORATORY STUDY IN PERIPHERIES OF CHANDIGARH
Dr. Tejinderpal Singh
13
ABSTRACT
The present paper aims to identify the key factors affecting the decision of customers to buy residential apartments in the
outskirts of Chandigarh. For study purpose, a sample of 200 salaried class persons was taken by using purposive sampling
technique from tri-city i.e., Chandigarh, Mohali and Panchkula. The sample consisted of those respondents who either bought
an apartment in the last one year or were planning to buy it in the coming one year. Respondents were asked to give their
opinion about 19 listed variables on Five-point Likert Scale. By using Exploratory Factor Analysis, six factors were extracted
which explained 73.916 per cent of total variance. The extracted factors were ?Basic Amenities‘ (15.087%), ?Recreational and
Leisure‘ (14.953%), ?Layout‘ (11.570%), ?Financials‘ (11.077%), ?Proximity‘ (10.735%) and ?Connectivity‘ (10.493%)
factors. On the basis of mean scores, it was further found that ?Basic Amenities‘(4.4812), is the most important factor while
selecting a apartment followed by ?Financials‘ (4.0500), ?Connectivity‘ (3.8470), ?Layout‘ (3.0312), ?Proximity‘ (2.8838) and
Recreational and Leisure (2.8362) factors.
Demographics analysis showed that there was no significant effect of gender, marital status and age on the importance
assigned to various factors by the respondents. However, ?type of job‘ had significant effect on importance assigned to factors
like ?Basic Amenities‘ and ?Connectivity‘. Further, income had significant effect on importance assigned to factors like ?Basic
Amenities‘, ?Financials‘ and ?Recreational & Leisure‘. In the end, study suggested that real estate marketers should give due
importance to these factors while offering apartments in the market.
KEYWORDS
Buying Behaviour, Chandigarh, Residential Apartments, Marketing, Real Estate etc.
I NTRODUCTI ON
Real estate prices in ?The City Beautiful‘ Chandigarh have been shooting up to the extent that it is near impossible for salaried
class to buy a home in Chandigarh. Major reasons for such unprecedented increase in prices are high demand and low supply.
Reason being, expansion is on the outskirts of the Chandigarh, which includes Greater Mohali, Mullanpur, Zirakpur and
Panchkula. Now, Major real estate players such as Silver City Group of Companies, Ansals, Gulmohar, DLF, Parsvnath
Developers, Omaxe Construction Ltd, Unitech, TDI, MGF-Emaar have already started their project in this area. Their products
portfolio includes residential plots, commercial property, duplex villas, independent floors and apartments. Although, independent
apartment system is new to the region but demand for this product is rising day by day because of scarcity of housing in
Chand?garh. Customers have the wide choice to select the best suitable apartment for them because of the intense competition and
additional supply in the market. Therefore, it is important for the real estate marketers to understand the behaviour of prospective
buyers and to identify the influencing factors, which affect the choice of customers. Therefore, in this background the present
study aims to identify the key factors affecting the decision of customers to buy residential apartments in outskirts of Chandigarh.
PROBLEM STATEMENT
The research problem has been identified as ?Identifying Key Factors Affecting Purchase Decision of Residential Apartments: An
Exploratory Study in Peripheries of Chandigarh‘.
NEED AND SI GNI FI CANCE OF THE STUDY
The knowledge of different factors affecting the buying behaviour and buying preferences of the consumers will provide to
builders and developers to launch their residential apartment schemes and to understand the insight of buying behaviour. Hence,
they will be able to launch their housing schemes better and effectively.
OBJ ECTI VES OF STUDY
The present study has been designed to identify the key factors affecting purchase decision of buying residential apartments in
peripheries of Chandigarh and, thereafter, to make of customers‘ opinion about factors identified.
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Assistant Professor, University Business School, Panjab University, Punjab, India, [email protected]
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
International Journal of Applied Services Marketing Perspectives © Pezzottaite Journals. 494 | P a g e
REVI EW OF LI TERATURE
Few studies are available on buying behaviour towards buying of residential apartments.
Beamish et.al (2001) [1] explored influences on housing choice and proposed a conceptual framework that examined the
influence of lifestyle as an intervening factor in housing choice. Influences on housing choice included age, family type, family
size, stage in the life cycle, social class, income, occupation, education and value.
Zhaohui (2003) [2], from the perspective of the consumer residential real estate market, investigated the basic characteristics of
the homebuyers, homebuyers purchasing preferences and influencing factors.
Leishman et al. (2004) [3] gave a detailed examination of new-built housing buyers‘ housing needs and preference and analyzed
on the basis of the physical, location and quality characteristics of housing actually constructed by house builders. Study, further,
examined the relative importance of physical property, locational, neighborhood and price factors to consumers in the housing
choice process.
Shi Lin (2005) [4]
determined the housing preferences and priorities among residents in different socio demographic and
socioeconomic groups in Stellenbosch and explored a functional formula by which the price of housing in Stellenbosch could be
predicted. As per the findings of the study, dwelling related attributes were found to be more important than neighboring and
location-related attributes.
Gupta et al. (2006) [5]
captured the impact of environmental, structural and location variables on housing prices prevailing in the
city and found ?proximity to water body‘ fetches the highest 18.9 per cent of the total value in Navi Mumbai, and garden
proximity fetches the highest 13.2 per cent in Central suburb. The capitalization of land was also observed proximate to water and
greenery.
Ariyawansa (2007) [6] in his study aimed to provide a scientific insight into the consumer behavior of housing market in Sri
Lanka. It was found that consumer preference mainly depends upon the housing transaction, appreciation in the future,
complementary products and services like water and electricity supply.
Litman (2011) [7]
investigated consumer housing location preferences and their relationship to smart growth. It examined claims
that most households prefer sprawl-location housing and so was harmed by smart growth policies. This analysis indicated that
smart growth tends to benefit consumers in numerous ways.
The review of literature reveals that in India, there is dearth of studies on understanding the consumer behaviour towards buying
of residential apartment. Moreover, no such study has been conducted to study the market in Chandigarh and surrounding areas.
Therefore, the present study will contribute to the domain of existing knowledge too.
RESEARCH METHODOLOGY
Research methodology for the present paper has been discussed as under:
Research Design
The purpose of the study was to identify the key factors affecting purchase decision of buying residential apartments in the
outskirts of Chandigarh. Hence, the exploratory research design has been used.
Population and Target Population
The population for the study consisted of residents of Tri city i.e. Chandigarh, Mohali and Panchkula. Target population has been
defined as ?salaried individual working in government or private sector, residing either in government or rented accommodation
and were planning to buy an apartment in coming one year or have bought an apartment in the last one year.
Sample Size and Sampling Technique
In the present study, purposive sampling technique has been used, as ready sampling frame was not available. However, efforts
have been made to collect the data from different walks of people. Initially, questionnaires were distributed to 300 individuals and
only 221 questionnaire returned. Out of 221 questionnaires, only 200 questionnaires were found valid. Hence, findings of the
study are based on opinion of 200 respondents.
Instruments Design and Data Collection
For collection of data, a structured questionnaire was used. Before drafting of final questionnaire, unstructured interviews were
carried out with property dealers and prospective buyers to list the various variables, which customers generally consider before
buying of an apartment. Help from previous studies were also sought to expand the list of factors. Finally, a list of 19 variables
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
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was prepared after removing of 7 variables, which were similar to other variables. The questionnaire consisted of two parts. In the
first part, questions were on demographics. In the second part, opinion of respondents was sought on listed variables, using five
point Likert scale ranging from ?Most Important‘ to ?Most Unimportant‘. Before collection of data questionnaire was pretested on
20 customers. ?Survey method‘ was used to collect the data from respondents. Data was collected during the month of January –
February 2013. The ?sampling unit‘ for the study was individuals.
Statistical Tools and Data Analysis
The collected data was analyzed with the help of SPSS16. Exploratory Factor Analysis (EFA) was used to identify the various
factors from the list of variables. Inferential statistics like ANOVA and t-test were also used to see the effect of demographics on
importance given to various factors. To check the normality of data Kolmogorov-Smirnov test was used and it was found that
collected data was normally distributed (Z =.947 Asymp. Sig. 331).
Demographic Profile of Respondents
Table 1 shows that sample consisted of 61.5 per cent Male and 38.5 per cent Female respondents.
Table-1: Demographic Profile of Respondents
N=200
Demographics Frequency Percentage Demographics Frequency Percentage
Gender
Male
Female
123
77
61.5
38.5
Marital
status
Married
Unmarried
84
116
42.0
58.0
Age Group Less 30
30 -40
40 -50
More than 50
39
79
59
23
19.5
39.5
29.5
11.5
Income (Rs.) 1-5lac
5-10lac
10-15lac
>15lac
59
93
33
15
29.5
46.5
16.5
7.5
Type of Job
Government
Private
95
105
47.5
52.5
Sources: Primary Data
Fifty eight per cent of respondents were unmarried and 42.0 per cent were married. According to type of job, 52.5 per cent of
respondents were from private sector and 47.5 per cent were from government sector. Income-wise, 46.5 per cent of respondents
belong to ?5-10 lac‘ category followed by ?1-5lac‘ (29.5%), ?10-15lac‘ (16.5%) and ?More than 50 lac‘ (11.00%). Table further
shows that 39.5 per cent of respondents belonged to age group ?30-40‘years followed be age groups ‘40-50‘years (29.5%), ?less
than30‘years (19.5%) and ?more than 50‘ years (11.5%).
RESULT AND FI NDI NGS
Identification of Factors
The prime objective of the study was to identify the factors influencing the purchase decision of apartment in the peripheries of
Chandigarh. Therefore, respondents were asked to indicate their opinion regarding the importance of each variable in their
purchase decision of apartments. An Exploratory Factor Analysis (EFA) was applied to club the 19 variables into meaningful
factors. Prior to the extraction of the factors, Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of
Sphericity were applied to assess the suitability of the respondent data for factor analysis.(Table:2). The calculated KMO value
was .689 and Bartlett‘s Test of Sphericity was found significant (P<.065).
Table-2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity
.689
Approx. Chi-Square df sig
3494.027 190 .000
Sources: Primary Data
The KMO index ranges from 0 to 1, with 0.50 considered suitable for factor analysis. [8] The Bartlett's Test of Sphericity should
be significant (p<.05) for factor analysis to be suitable. [8] Thus the present data set satisfied these two conditions to apply EFA.
Factors were extracted by using Principal components analysis (PCA). Six-factor having Eigen Values more than 1 were extracted
which explained the 73.916 per cent variance (Table3). Rotation of factor was done by using ?Varimax with Kaiser
Normalization‘ rotation method. Based on the computations as represented in the Rotated Component Matrix (Table 1), the six
factors were identified i.e. ?Basic Amenities‘ (15.087%, 4.464), ?Recreational and Leisure‘ (14.953%, 2.840), ?Layout‘ (11.570%,
2.606), ?Financials‘ (11.077%, 2.026), ?Proximity‘ (10.735%, 1.624) and ?Connectivity‘ (10.493%, 1.222). Each variable was
retained a factor having loading more than 0.45.
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Table-3: Factors Affecting Purchase Decision of Apartments
N=200
Factor Factors Name Variables
Eigen
Values
Total
Variance (%)
Factor
Loading
1 Basic Amenities
Electricity backup
4.464
15.087
.969
Water supply .958
Sewerage system .963
Car parking .556
2
Recreational
and Leisure
Close to gym
2.840
14.953
.969
Park facing .958
Near to Community hall .963
3 Layout
Floor of the apartment
2.606 11.570
.964
Number of rooms / bedrooms .551
Servant room .962
4 Financials
Price
2.026 11.077
.465
Booking amount .783
EMI .825
5 Proximity
Proximity to own office
1.624
10.735
.829
Proximity to spouse office .829
Proximity to children's school .706
6 Connectivity
Access to market
1.222 10.493
.818
Access to public transportation .711
Connectivity to main road .683
Total variance Explained : 73.916 %
Sources: Primary Data
Table: 3 shows that four variables were loaded on Factor 1. All these four variables are related with basic amenities without which
a place cannot be considered viable for living purpose. The four variables loaded on ?1
st
Factor‘ were ?Electricity backup‘ (.969),
?Water supply‘ (.958) ?Sewerage system‘ (.963) and ?Car parking‘ (.556). This Factor was labeled as ?Basic Amenities‘. Three
variable loaded on the ?2
nd
Factor‘ were related to opinion of respondents towards Recreational and Leisure facilities near
apartments which included ?Close to gym‘ (.969), ?Park Facing‘ (.958) and ?Near to Community hall‘ (.963). This Factor was
named as ?Recreational and Leisure‘. Three variables loaded on ?3
rd
Factor‘ were related with the general ?Layout‘ and floor of
the apartments. The ?3
rd
Factor‘ was loaded on by variables; ?Floor of the apartment‘ (.964), Number of rooms/bedrooms (.551),
Servant room (.962). Third factor was labeled as ?Layout‘. Three variables loaded on ?4
th
Factor‘ were Price (.465), Booking
amount (.783) and EMI (.825). It clearly shows that these variables are related with Financials. Hence, this factor was named as
?Financials‘. Items identified for ?5
th
Factor‘ were ?Proximity to own office‘ (.829), ?Proximity to spouse office‘ (.829) and
?Proximity to children's school‘ (.706). This factor was labeled as ?Proximity‘. Similarly, three items were loaded on ?6
th
Factor‘
which was named as ?Connectivity‘. The four items loaded on this factor were ?Access to market‘ (.818), ?Access to public
transportation‘ (.711) and ?Connectivity‘ to main road‘ (.683).
Importance Assigned to Factors
To identify the order of importance of each factor, mean scores were calculated for each factor (Table 4). Firstly, factor scores for
each respondent were calculated by summing raw scores corresponding to all variables loading on a factor and divided by number
of variables. Thereafter, mean was calculates for each factor by dividing number of respondents.
Table-4: Importance Assigned to Factors
N=200
Factors
Basic
Amenities
Financials Connectivity Layout Proximity
Recreational
and Leisure
Mean Score 4.4812 4.0500 3.8470 3.0312 2.8838 2.8362
Standard
Deviation(±)
.66259 .67576 .70603 .57858 .60144 .57786
Sources: Primary Data
Table 4 shows that on the basis of mean scores, it was found that ?Basic Amenities‘(4.4812), is the most important factor while
selecting an apartment followed by ?Financials‘ (4.0500), ?Connectivity‘ (3.8470), ?Layout‘ (3.0312), ?Proximity‘ (2.8838) and
?Recreational and Leisure‘ (2.8362) factors.
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Demographics- Wise Importance Assigned to Factors
The second objective of the study was to analyze the identified factors according to demographics. The effect of selected
demographics such as Gender, Marital status, Type of Job (Table 5), Age and Income (Table 6) on importance given to factors
identified has been analyzed in this part of the study.
Table-5: Gender-wise and Marital status- wise and Type of Job wise importance assigned to Factors
N=200
Factors
Gender t-statistic Marital Status t-statistic Type of Job t-statistic
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Basic Amenities 4.4553 4.0305 .700 485 4.5625 4.4224 1.480 140 4.2921 4.6524 3.861 000*
Financials 4.0812 4.5227 .515 .607 4.1101 4.0065 1.071 .285 4.1184 3.9881 1.388 .167
Connectivity 3.8740 3.8039 .682 .496 3.9155 3.7974 1.168 .244 3.7411 3.9429 2.034 .043*
Layout 2.9858 3.1039 1.408 .161 3.0238 3.0366 .154 .877 3.0000 3.0595 .726 .469
Proximity 2.8923 2.8701 .253 .801 2.8274 2.9246 1.129 .260 2.9184 2.8524 .775 .439
Recreational and Leisure 2.8333 2.8409 .090 .928 2.8065 2.8578 .618 .538 2.7842 2.8833 1.213 .227
Note: *Significant at .05
Sources: Primary Data
Table 5 shows that gender-wise, male respondents gave more importance to factors like ?Basic Amenities‘, ?Connectivity‘ and
?Proximity‘ as compared to female respondents. On the other hand female respondents gave more importance to factors such as
?Financials‘, ?Layout‘ and ?Recreational and Leisure‘ as compare to male respondents. However, t-test showed that there was no
significant effect of gender on importance given to all the factors ; ?Basic Amenities‘ [t(198) = .700, p > .005], ?Financials‘
[t(198) = .515, p > .005] ?Connectivity‘ ,[t(198) = .682, p > .005], ?Layout‘ [t(198) = 1.408, p > .005], ?Proximity‘ [t(198) = .253,
p > .005] ?Recreational and Leisure‘, [t(198) = .090, p > .005]. Marital status wise, married respondents assigned more importance
to factors like ?Basic Amenities‘ ?Financials‘ and ?Connectivity‘ as compare to unmarried respondents. Similarly, unmarried
respondents assigned more importance to rest of the factors like ?Layout‘, ?Proximity‘ and ?Recreational and Leisure‘. However,
t-test showed that there was no significant effect of marital on importance given to all the factors i.e. ?Basic Amenities‘ [t(198) =
1.480, p > .005], ?Financials‘ [t(198) =1.071 , p > .005] ?Connectivity‘ [t(198) = 1.168, p > .005], ?Layout‘ [t(198) = .154, p >
.005], ?Proximity‘ [t(198) = 1.129, p > .005] and ?Recreational and Leisure‘, [t(198) = .618, p > .005]. Further, type of job-wise,
respondents in private job assign more importance to factors like ?Basic Amenities‘, ?Connectivity‘, ?Layout‘ and ?Recreational
and Leisure‘ as compared to respondents in government job. It was further found that respondents in government job gave more
importance to factors like‘ ?Layout‘‘ and ?Proximity‘. T-test showed that there was significant effect of type of job on importance
given to the factors like Basic Amenities [t (198) = 3.861, p < .005] and ?Connectivity‘ [t (198) = 2.034, p < .005]. On the other
hand, there was no significant effect of type of job on importance given to the factors like ?Financials‘ [t (198) =1.388, p > .005]
?Layout‘ [t (198) = .726, p > .005], ?Proximity‘ [t (198) = .775, p > .005] and ?Recreational and Leisure‘, [t (198) = 1.213, p >
.005]. Table 6 shows that among given age categories , respondents belonging to age category ?40-50‘ has given maximum
importance to factor ?Basic Amenities‘(4.5466) followed by age categories ?>50‘(4.5217), ?<30‘(4.4423) and ?30-40‘(4.4399).
Table-6: Age-Wise and Income-Wise Importance Assigned to Factors
N=200
Factors
Age Categories(Years)
ANOVA
Statistic
Income Categories (Lac)
ANOVA
Statistic
<30 30-40 40-50 >50 F-value sig. 1-5 5-10 10-15 >15 F-value Sig
Basic Amenities 4.4423 4.4399 4.5466 4.5217 .364 .77 4.2288 4.5269 4.7045 4.7000 5.092 .00*
Financials 4.1603 4.0506 4.0508 3.8587 .960 .41 4.2331 4.0806 3.9091 3.4500 6.410 .00*
Connectivity 3.9231 3.8785 3.7492 3.8609 .580 .62 3.8593 3.7742 3.9606 4.0000 .853 .46
Layout 3.0577 2.9747 3.0424 3.1522 .617 .60 3.0466 2.9731 3.1894 2.9833 1.186 .31
Proximity 2.9615 2.8734 2.9195 2.6957 1.045 .37 2.9407 2.8548 2.9697 2.6500 1.232 .29
Recreational and Leisure 2.6795 2.8354 2.9068 2.9239 1.436 .23 2.8517 2.7204 3.0152 3.1000 3.481 .01
Note: *Significant at .05
Sources: Primary Data
On the other hand respondents belonging to age category ?<30‘ gave maximum importance to ?Financials‘ (4.16030),
?Connectivity‘ (3.9231) and ?Proximity‘ (2.9615) amongst given age categories. Similarly, respondents belonging to age category
?>50‘ gave importance to factors; ?Layout‘ (3.1522) and ?Recreational and Leisure‘ (2.9239). However, ANOVA results showed
that in case of all factors, there was no significant impact of age on importance given to factors; Basic Amenities[F(199) = .364, p
Volume 2, Number 3, July – September? 2013 ISSN (P):2279-0977, (O):2279-0985
International Journal of Applied Services Marketing Perspectives © Pezzottaite Journals. 498 | P a g e
> .005], ?Financials‘ [F(199) = .960, p > .005] ?Connectivity‘ ,[ F(199) = .580, p > .005], ?Layout‘ [ F(199) = .617, p > .005],
?Proximity‘ [F(199) = 1.045, p > .005] ?Recreational and Leisure‘, [F(199) = 1.436, p > .005]. It was further found that among
given income categories, respondents belonging to category ‘10-15‘ have given maximum importance to ?Basic Amenities‘
(4.7045) followed by income categories ?>15‘ (4.7000), ?5-10‘ (4.5269), ?1-5‘ (4.2288).
It seems that with the rise in income, more importance has been assigned to this factor. Further, ANOVA results showed that there
was significant impact of income on the importance assigned to the factor ?Basic Amenities‘ [F (199) = 5.092, p < .005. Similarly,
respondents belonging to income category ?1-5‘ have give maximum importance to the factor ?Financials‘(4.2331) followed by
categories ?5-10‘(4.0806). ‘10-15‘ (3.9091), and >15‘ (3.4500). It is evident that respondents having low income had given more
importance to ?Financials‘. Further, ANOVA results showed that there was significant impact of income on the importance
assigned to the factor ?Financials‘ [F (199) = 6.410, p < .005. Table further shows that maximum importance has been given to
factors like ?Layout‘ ?Proximity‘ ?Recreational and Leisure‘ by the respondents from income category ?10-15‘ amongst given
income categories. The factor ?Connectivity‘ has been assigned maximum importance by the respondents from income category
?>50‘. ANOVA results showed there was no significant effect of income on the importance given to factors; ?Connectivity‘, [F
(199) = .853, p > .005], ?Layout‘ [F (199) = 1.186, p > .005], ?Proximity‘ [F (199) = 1.232, p > .005]. However, there was
significant effect of income on the importance given to factors ?Recreational and Leisure‘, [F (199) = 3.481, p < .005].
DI SCUSSI ON, MARKET I MPLI CATI ONS AND CONCLUSI ON
The present study was conducted to explore the preferences assigned to various factors by the customers when it comes to buying
of an apartment. The factor ?Basic amenities‘ has been identified as the most important factor, which influences the choice of
customers. It clearly shows that the first thing which prospective buyer looks for, are basic amenities like water supply, electricity,
sewerage system etc., which are essential to start with living. ?Financial Factors‘, ?Connectivity‘ Factors‘ and ?Layout Factors‘ are
other primary factors which influence the choice of the customers. Therefore, these factors seek proper attention from the builder.
Factors like ?Proximity‘ and ?Recreational and Leisure‘ seem to be secondary factor. However, these factors cannot be ignored at
all by the builders. The study has significant implications for the real estate marketers too. When, it comes to the offering of
product to the prospective buyer marketers should focus on these identified factors according to the order of preference as found
in the study. Marketers should prominently communicate features of their projects based on identified factors. Factors like
?Proximity‘ and ?Recreational & Leisure‘ seem to be secondary. However, may be highlighted depending upon the segment of
consumers. Demographics analysis showed that ?type of job‘ had significant effect on importance assigned to factors like ?Basic
Amenities‘ and ?Connectivity‘. Further, income had significant effect on importance assigned to factors like ?Basic Amenities‘,
?Financials‘ and ?Recreational & Leisure‘. Therefore, real estate marketer should pay special attention towards these factors
especially when dealing with customers from such categories. In the end, it is concluded that there is an ample scope for the future
research in the domain. The scope of present study is limited to the tri-city, which may be extended to other areas like NCR,
Mumbai etc., and comparisons may be made to see the differences in the buying behavior of customers belonging to these
respective areas
.
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