Phuong
Viet Le-Hoang
Industrial
University of Ho Chi Minh City, Vietnam
Ho Chi Minh City Open University,
Vietnam
E-mail: lehoangvietphuong@iuh.edu.vn
Yen
Truong Thi Ho
Industrial
University of Ho Chi Minh City, Viet
Nam
E-mail: truongyen1220@gmail.com
Danh
Xuan Luu
Industrial
University of Ho Chi Minh City, Viet
Nam
E-mail: luuxuandanh@iuh.edu.vn
Truc
Thanh Thi Le
Industrial
University of Ho Chi Minh City, Viet
Nam
E-mail: lethithanhtruc@iuh.edu.vn
Submission: 7/15/2019
Revision: 9/18/2019
Accept: 10/2/2019
ABSTRACT
The purpose of this research is to identify and measure the factors affecting the intention to buy apartments of customers in Ho Chi Minh City, Vietnam. The survey carried out with the participation of 200 customers. The authors explore five factors which affect customer's apartment purchase intention include location, features, brand, finance, and subjective norm. The result from Exploratory Factor Analysis (EFA) shows that location, features, finance, and subjective norm have a significant effect on the intention to buy customers' apartments. In which, location in Ho Chi Minh City context is the most influential factor, so, it strongly confirm the research of Adair et al. (1996), Clark et al. (2006), Daly et al. (2003), Kaynak and Stevenson (2007), Opoku and AbdulMuhmin (2010), Sengul et al. (2010), Tu and Goldfinch (1996), Xiao and Tan (2007) and Wang and Li (2006). The study also proposes some recommendations to increase the attractiveness of the apartment. What is more, developers, marketers, real estate policymakers can use the results of this research to understand the needs of customers better and satisfy customers.
Keywords: Location, features; brand; finance; subjective norm: purchase intention
1.
INTRODUCTION
People
have many places to go, but the only one to come back is home. Living and
working in peace and contentment, owning a home is always the desire of
everyone. For many households, owning a home is not only for a place of
residence but also a valuable asset for households (GEERTMAN, 2003).
Residential income has improved, living standards have increased markedly; the
demand for housing of the people has increased. As the global population
continues to increase, the shortage of housing in many developing countries has
reached a critical level (MOREL, 2001).
In
big cities such as Ho Chi Minh City and Hanoi, the increase in the density of
population and the limiting of the land fund leads to increasing real estate
prices. So this is more and more difficult to buy the house. Real estate is one
of the most important things for people, so buying a home can change their
lives (WELLS, 1993).
Moreover,
to choose a place to live so that the customer can buy a comfortable house at a
reasonable price. The apartment is the solution to this complex urban problem.
Because the apartment has many features, the apartment is becoming the trend of
many people who need to settle down and real estate investors. In the issues raised for sustainable
development, the apartment is a specific product, leading the HCMC housing
market. How to create the best product which meets customer's demand in this
increasingly competitive market is the first problem to be solved.
According
to Haddad et al. (2011), to maintain a competitive market, real estate
marketers must keep in mind that buying behavior will be considered carefully.
Buying an apartment is one of the most important economic decisions, and it
requires collecting much information regarding features, quality, facilities,
design, and prices and its environment (HADDAD et al., 2011; ZADKARIM; EMARI,
2011; KIEFER, 2007).
Accordingly,
with the viewpoint of "He who sees through life and death will meet most
success," so real estate developers must understand the psychology of
buyers, know what they need, what they want, and their aspirations. At the same
time, market researchers, as well as real estate brokers, need to find out
which factors affect the buying intention to offer products that best suit the
needs of customers.
2.
LITERATURE REVIEW AND HYPOTHESES
DEVELOPMENT
2.1.
Theoretical background
·
Condominiums and apartment
According to Article 3, Housing Law 2014, condominiums
are houses with two
or
more floors, many apartments, walkways, standard stairs, private ownership,
joint ownership, and lower construction systems shared floors for households,
individuals and organizations, including condominiums built for residential
purposes and condos established for the purpose of mixed-use for residential
and business objectives. Notably,
an apartment is a living unit, for a household, in an apartment building.
·
Intention
According to Krueger (1993), to come to any behavior, the
individual must feel the problem before doing so. That feeling plays a decisive
role in making or not doing. Intention represents the level of commitment to
behavior that will be implemented in the future. Through various studies, it is
thought that intention is a premise of an intended behavior (KRUEGER et al.,
2000) and intentions are the best predictions for performance behavior (LUTHJE;
FRANKE, 2004).
In one of his studies, Fishbein
and Ajzen (1975) analyzed more clearly the intentions
with its manifestations. The intention involves four different components:
behavior, goal (target) - subject matter to target, a situation that the
behavior is performing, time but behavior ongoing (FISHBEIN; AJZEN, 1975).
According to Ajzen (1991), the intention is a
motivational factor, which motivates an individual to be willing to perform the
behavior. When people have a strong intention to engage in a specific behavior,
they are more likely to perform that behavior.
The intention of action defined by Ajzen
(2002) is human action directly affected by attitude, subjective norms, and
behavior control awareness. The stronger these beliefs, the higher the
intention of human action. In it, the attitude is "an individual's
assessment of the results of an act.
Regarding the intention to buy, Kotler (1991) argued
that, in the evaluation phase of the purchase plan, consumers scored different
brands and formed the intention to buy. Dodds et al.
(1991) indicated that the intention to buy represents the ability of consumers
to buy a particular product. Long and Ching (2010) conclude that the intention
to buy represents what an individual wants to buy in the future.
The intention to buy is "what we think we will
buy" (SAMIN et al., 2012). It can also be defined as an active decision
that shows the behavior of the individual according to the product (SAMIN et
al., 2012).
2.2.
Research model and hypothesis
Based on the results of
previous studies on the intention of customer behavior and the actual situation
in the study area, this study proposes five factors affecting the intention to
buy apartments in Ho Chi Minh City: Location (LOC), Features (FE), Brand (BR), Finance
(FIN), Subjective Norm (SN).
Figure 1: Proposed research model of the authors
Findings
from past studies, the location is as one of the most critical factors
affecting the individual's decision making in purchasing a house (ADAIR et al.,
1996; DALY et al., 2003; KAYNAK; STEVENSON, 2007; SENGUL et al. 2010; XIAO;
TAN, 2007). Importantly, location is closely related to distance from various
points of interest. Some of the various
points of interest to be considered by house buyers are the distance to the
central business district, distance to school, and distance to work and
distance to retailer outlets (ADAIR et al., 1996; CLARK et al., 2006; OPOKU;
ABDULMUHMIN, 2010; TU; GOLDFINCH, 1996; WANG; LI 2006).
In
Malaysia, studies also found that locational attributes appeared to support
previous studies' findings whereby location was considered an essential
consideration for house buyers (RAZAK et al., 2013; TAN, 2011). In this study,
distance is defined as the strategic location of the house from several
essential points, such as business area, school.
·
H1:
The location has a positive effect on buying apartment intention of the
customer.
House
features include house design, building quality, interior and exterior designs,
or finishing which these features are expected to influence an individual's
house purchase decision (ADAIR et al., 1996; DALY et al.. 2003; SENGUL;
YASEMIN; EDA, 2010; OPUKU; ABDUL-MUHMIN,
2010). Several scholars found that these house features are essential factors
in determining consumers' choice and purchase of a house (EL-NACHAR, 2011;
HADDAD; JUDEH; HADDAD, 2011; SENGUL et al., 2010). Hence, this present study
refers house features as internal house attributes such as quality of the
building, the design, as well as interior and exterior design; which are
essential for a consumer when they select and purchase a house.
·
H2:
Features has a positive effect on buying apartment intention of the customer.
Kotler
and Armstrong (2001) define marketing as the science and art of discovering,
creating, and providing value to meet the needs of the target market with
profits. Marketing identifies unfulfilled needs and desires. It identifies,
measures, and quantifies the size of the identified market and profit
potential. It identifies precisely which segment the company has the best
ability to serve, and it designs and promotes appropriate products and
services. In this study, focus on advertising factors, reputation, and reputation
of developers (HADDAD et al., 2011). According to Foi
(2007), the research presented on brand awareness is an aspect that affects
customer satisfaction. The more popular the brand, the higher the level of
awareness, the more likely it is to affect customers' intentions.
·
H3:
Brand has a positive effect on buying apartment intention of the customer.
Past
researchers defined financial status concerning house buying a combination of
house price, mortgage loans, income, and terms of repayment (OPOKU; ABDUL-MUHMIN,
2010; ZHOU, 2009). In other words, this definition refers to mortgage
availability, terms of purchase, house price, assessment value of the property,
the opportunity for quick appreciation, and waiting period (HADDAD et al.,
2011). Remarkably, several past studies found that the financials of the house
has much influence on how consumers make their house choice (ADAIR et al.,
1996; DALY et al., 2003; KAYNAK;
STEVENSON, 2007; SENGUL et al. 2010; XIAO;
TAN, 2007). In the Malaysian context,
the study by Razak, Ibrahim, Hoo,
Osman, and Alias (2013) confirmed that financial consideration, especially
house price, has a strong influence on house purchase intention.
·
H4:
Finance has a positive effect on buying apartment intention of the customer.
Subjective
norms are the standard belief of a personal belief that is influenced by others
like family members who think whether an individual should perform a particular
behavior (RIVIS; SHEERAN, 2003). Usually, an individual will perceive the
pressures placed on them whether to perform the behavior or not (AJZEN, 1991; HAN;
KIM, 2010). Many studies show that the reference group has a strong positive
influence on purchase intention (PANTHURA, 2011; NUMRAKTRAKUL et al., 2012; RAZAK
et al., 2013). Songkakoon et al. (2014) believe that
children and spouses are the main parties that will change their intention to
buy home-related decisions in their Thai culture. Al-Nahdi
et al. (2015) also find that there is a positive impact between subjective
norms on the intention to buy real estate in Jeddah and similar cases in
Malaysia (MDRAZAK et al., 2013).
·
H5:
Subjective norm has a positive effect on buying apartment intention of the
customer.
Generally,
the intent is a sign that a person is willing to perform a specific behavior,
and it is considered a premise of immediate behavior (SHEN, 2009). The
intention is an indication of a person's willingness to perform the behavior,
and it is an immediate antecedent of behavior (NAHDI; HABIB; ALBODOUR, 2015).
The intention is that the dependent variable predicted by an independent
variable is attitude (AJZEN; FISHBEIN, 1980; AJZEN, 1991; TAYLOR; TODD, 1995;
HAN; KIM, 2010). Therefore, in the case of apartment purchasing, the intention
to purchase is an antecedent of a purchase decision (NUMRAKTRAKUL; NGARMYARN;
PANICHPATHOM, 2012; PHUNGWONG, 2010).
Table 1: Variables in the research model
No. |
Items |
Variables |
LOCATION |
||
1 |
LOC1 |
Nearby working place |
2 |
LOC2 |
Nearby school |
3 |
LOC3 |
Nearby shopping mall |
4 |
LOC4 |
Nearby downtown |
5 |
LOC5 |
Nearby high way |
6 |
LOC6 |
Peaceful living environment |
FEATURES |
||
7 |
FE1 |
Good design |
8 |
FE2 |
Beautiful view |
9 |
FE3 |
Appropriate size |
10 |
FE4 |
High quality |
BRAND |
||
11 |
BR1 |
Broad advertising |
12 |
BR2 |
Credibility |
13 |
BR3 |
Reputation |
FINANCE |
||
14 |
FIN1 |
House Price |
15 |
FIN2 |
Monthly income |
16 |
FIN3 |
Loan Repayment Duration |
17 |
FIN4 |
Monthly Repayment |
SUBJECTIVE NORM |
||
18 |
SN1 |
My family thinks that I should buy a house |
19 |
SN2 |
I will buy the house my family advise me to buy |
20 |
SN3 |
Before I make a decision, I always collect house information from
family and friends. |
BUYING INTENTION |
||
21 |
BI1 |
I want to buy the house |
22 |
BI2 |
I will try to buy housing frequently in the future |
23 |
BI3 |
I plan to buy the house |
24 |
BI4 |
I intend to buy the house in the future |
25 |
BI5 |
I will continue to buy the house in the future |
3.
METHODOLOGY
Based
on the research model, refer to the research profiles from different sources to
establish a survey questionnaire that clarifies the factors affecting the
intention of buying customers' apartments. The authors collect customer
information through survey questionnaires delivered directly and Google form
tool for online survey.
The
complete questionnaire consists of two parts. Part one is a demographic survey
of eight questions. Part two is the factors affecting the intention to buy a
client's apartment with five factors that combine scales such as a nominal
scale and Likert with five levels: (1) strongly disagree, (2) disagree, (3)
neutral, (4) agree, (5) strongly agree to measure values. The study was carried
out with 25 variables. To reach the minimum number of samples, the sample size
must be at least 125 elements (= 5 * 25 observed variables).
So
choose 200 as the number of research samples for the report. The analytical
data were collected by non-probability sampling method according to the convenient
sampling method in Ho Chi Minh City in the period from February 2019 to May
2019. Study to use the method of measuring scales with Cronbach's Alpha
coefficients, exploratory factor analysis (EFA), Pearson analysis, and multivariate regression
analysis.
4.
DATA ANALYSIS AND RESULTS
4.1.
Data description:
Table 2: Data
description
Frequency |
Percent |
Cum. Percent |
|||
Gender |
Men |
99 |
49.5 |
49.5 |
|
Women |
101 |
50.5 |
100 |
||
Age |
Under 25 years old |
33 |
16.5 |
16.5 |
|
25-35 years old |
100 |
50 |
66.5 |
||
36-45 years old |
47 |
23.5 |
90 |
||
Over 46 years old |
20 |
10 |
100 |
||
Marital status |
Single |
79 |
39.5 |
39.5 |
|
Intended |
45 |
22.5 |
62 |
||
Married |
76 |
38 |
100 |
||
Education background |
High school |
18 |
9 |
9 |
|
College degree |
26 |
13 |
22 |
||
Bachelor degree |
128 |
64 |
86 |
||
Master degree |
11 |
5.5 |
91.5 |
||
PhD/Dr degree |
2 |
1 |
92.5 |
||
Others |
15 |
7.5 |
100 |
||
Occupation |
Worker |
7 |
3.5 |
3.5 |
|
Officer |
53 |
26.5 |
30 |
||
State employees |
11 |
5.5 |
35.5 |
||
Private enterprise |
21 |
10.5 |
46 |
||
Business |
37 |
18.5 |
64.5 |
||
Other |
71 |
35.5 |
100 |
||
EXPERIENCE |
Under one year |
59 |
29.5 |
29.5 |
|
One - three years |
60 |
30 |
59.5 |
||
Three to five years |
30 |
15 |
74.5 |
||
Over five years |
51 |
25.5 |
100 |
||
INCOME |
Under five million VND |
28 |
14 |
14 |
|
6-10 million VND |
62 |
31 |
45 |
||
11-20 million VND |
64 |
32 |
77 |
||
21-30 million VND |
25 |
12.5 |
89.5 |
||
Over 31 million VND |
21 |
10.5 |
100 |
||
PURPOSE |
Living |
138 |
69 |
69 |
|
For lease |
26 |
13 |
82 |
||
Investment (re-sale) |
33 |
16.5 |
98.5 |
||
Others |
3 |
1.5 |
100 |
||
Gender: In the 200 survey questionnaires, 99 men
accounted for 49.5%, and 101 women accounted for 50.5%. The ratio of men and
women is similar, which is consistent with practical observations.
Age:
The results show that 33 customers who are under 25 years old (accounting for
16.5%) are mostly young people who like modernity and comfort. Customers from
25-35 years old have 100 people (accounting for the highest percentage of 50%).
This age group is mostly grown up with the demand for buying housing. Customers
from 36-45 years old with 47 people (accounting for 23.5%) are a group of
people with stable incomes, who intend to buy apartments to live or invest.
There are 20 customers aged 46 and older (accounting for 10%) who are
middle-aged people who intend to stay, invest, or buy to make property.
Marital
status: There are 79 unmarried customers (accounting for 39.5%), with attention
to the type of apartments for the young and modern. Forty-five customers are
about to get married and intend to buy an apartment to prepare for marriage and
build a home. There are 76 customers out of 200 survey results that are married
(accounting for 38%). These families are those who wish to own a home or intend
to buy an apartment as a profitable investment channel.
Education:
The survey showed that 18 educated clients are high school students (9%), there
are 26 college-level clients (accounting for 13%), 128 customers have
university degrees (accounting for 64% have The highest rate), there are 11
masters (accounting for 5.5%). 2 customers have a doctorate (accounting for
1%), and another level is 15 (accounting for 7.5%). In the research sample, it
shows that the proportion of customers with university and postgraduate degrees
is quite high, it can be assessed that this is a highly educated and
knowledgeable customer in society.
Occupation:
Through the process of surveying and analyzing data, seven customers are
workers and accounted for 3.5%. It is the occupation group with the lowest
percentage of the surveyed occupational groups. Besides, there are 53 office
workers and accounted for 26.5%. Public servants, private enterprises, and
other careers such as doctors, technicians, freelancers are accounted for 5.5%,
10.5%, and 35.5% respectively.
Working
seniority: Of the 200 people surveyed, 59 clients had a working year of less
than one year (accounting for 29.5%). Customers in this group have just started
work; their income has not been stable. There are 60 clients with working age
from 1 to under three years (accounting for 30%). 30 senior clients are working
from 3 to under five years (accounting for 15%). Moreover, 51 customers have
worked for more than five years (25.5%). IT is a group of customers who have
stabilized their jobs and tend to stick with their jobs and stable income.
Income:
There are 28 customers with incomes below 5 million (accounting for 14%), there
are 62 customers with incomes between 6-10 million (accounting for 31%), there
are 64 customers with income from 11-20 million (32%) — customers with income
from 21-30 million account for 12.5% with 25 customers. Finally, customers have
income from 31 million 21 guests (accounting for 10.5%). For a property of
significant value like home, customers need to have a stable income to own, and
pay for the services and utilities every month to meet the needs of daily life.
Purpose
of buying apartments: From the research shows, 138 customers intend to buy
apartments to stay (accounting for the highest rate of 69%). It can be seen
that the surveyed clients have a high demand for accommodation and desire to
choose an apartment to build a family. Twenty-six customers intend to buy for
rent (accounting for 13%). Thirty-three customers buy for resale (accounting for
16.5%) and three customers who buy to make assets, make long-term money keeping
the channel, and have high-profit potential, easily change the purpose of use.
4.2.
Reliability test: Cronbach’s Alpha
Table 3: Results of
testing the reliability of the scale
Reliability
Statistics |
||||
Factors |
The number of variables |
Cronbach's
Alpha |
Corrected
Item-Total Correlation |
Note |
LOC |
6 |
0.782 |
>= 0.451 |
Reject LOC5 (0.244) |
FE |
4 |
0.807 |
>= 0.565 |
|
BR |
3 |
0.812 |
>= 0.630 |
|
FIN |
4 |
0.823 |
>= 0.601 |
|
SN |
3 |
0.664 |
>= 0.444 |
|
BI |
5 |
0.808 |
>= 0.504 |
|
In
the obtained results, the variable LOC5 (Location nearby high way) has the
total variable correlation coefficient of 0.244 <0.3, and if this type of
variable is found, the Cronbach’s Alpha coefficient will increase from 0.753 to
0.782, so this variable type. Test results after the variable type show that
the variables have a correlation coefficient of greater than 0.3 and Cronbach’s
Alpha coefficients if the variables are smaller than the Cronbach’s Alpha
coefficients, so they are statistically significant.
4.3.
Exploratory Factor Analysis (EFA)
Table 4: Result of
exploratory factor analysis
|
Component Matrix |
||||||
Factors |
Variables |
Component |
|||||
1 |
2 |
3 |
4 |
5 |
|||
FEATURES (FE) |
Good design (FE1) |
0.818 |
|
|
|
|
|
Beautiful view (FE2) |
0.764 |
|
|
|
|
||
High quality (FE4) |
0.733 |
|
|
|
|
||
Appropriate size (FE3) |
0.655 |
|
|
|
|
||
Nearby shopping mall (LOC3) |
|
0.771 |
|
|
|
||
Nearby working place (LOC1) |
|
0.734 |
|
|
|
||
Nearby school (LOC2) |
|
0.733 |
|
|
|
||
Nearby downtown (LOC4) |
|
0.697 |
|
|
|
||
Peaceful living environment (LOC6) |
|
0.655 |
|
|
|
||
Loan Repayment Duration (FIN3) |
|
|
0.875 |
|
|
||
Monthly Repayment (FIN4) |
|
|
0.871 |
|
|
||
Monthly income (FIN2) |
|
|
0.628 |
|
|
||
House Price (FIN1) |
|
|
0.595 |
|
|
||
BRAND (BR) |
Reputation (BR3) |
|
|
|
0.858 |
|
|
Broad advertising (BR1) |
|
|
|
0.847 |
|
||
Credibility (BR2) |
|
|
|
0.823 |
|
||
SUBJECTIVE NORM (SN) |
I will buy the house my family advise me to buy (SN2) |
|
|
|
|
0.819 |
|
My family thinks that I should buy a house (SN1) |
|
|
|
|
0.752 |
||
Before I make a decision, I always collect house information from
family and friends. (SN3) |
|
|
|
|
0.681 |
||
KMO (Kaiser-Meyer-Olkin) |
0.787 |
||||||
Barlett’s: Sig |
0.000 |
||||||
Eigenvalues |
1.565 |
||||||
Cumulative (%) |
65.35 |
||||||
The
EFA analysis results show that the KMO coefficient (Kaiser-Meyer-Olkin) is
0.787> 0.5, showing that factor analysis is appropriate. Barlett’s test: Sig
= 0.000 <0.05 shows that variables in factor analysis are correlated with
each other in the overall. The Eigenvalues value = 1.565> 1 represents the
variation part explained by each factor, the factor that draws the most
meaningful information. The total variance extracted value: 65.35% indicates
that five factors explain 65.35% variation of variables in the data, the model
is appropriate — factor loading of all variable is greater than 0.5, indicating
a correlation between variables for representative factors.
4.4.
Regression analysis
Table 5: The Pearson
analysis
Correlations |
|||||||
|
BI |
LOC |
FE |
BR |
FIN |
SN |
|
BI |
Pearson coefficient |
1 |
.653** |
.404** |
.110 |
.409** |
.431** |
Sig.
(2-tailed) |
|
.000 |
.000 |
.120 |
.000 |
.000 |
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
LOC |
Pearson coefficient |
.653** |
1 |
.337** |
.056 |
.314** |
.250** |
Sig.
(2-tailed) |
.000 |
|
.000 |
.430 |
.000 |
.000 |
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
FE |
Pearson coefficient |
.404** |
.337** |
1 |
.166* |
.463** |
.222** |
Sig.
(2-tailed) |
.000 |
.000 |
|
.019 |
.000 |
.002 |
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
BR |
Pearson coefficient |
.110 |
.056 |
.166* |
1 |
.057 |
.110 |
Sig.
(2-tailed) |
.120 |
.430 |
.019 |
|
.425 |
.122 |
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
FIN |
Pearson coefficient |
.409** |
.314** |
.463** |
.057 |
1 |
.219** |
Sig.
(2-tailed) |
.000 |
.000 |
.000 |
.425 |
|
.002 |
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
SN |
Pearson coefficient |
.431** |
.250** |
.222** |
.110 |
.219** |
1 |
Sig.
(2-tailed) |
.000 |
.000 |
.002 |
.122 |
.002 |
|
|
N |
200 |
200 |
200 |
200 |
200 |
200 |
|
**.
Correlation is significant at the 0.01 level (2-tailed). |
|||||||
*.
Correlation is significant at the 0.05 level (2-tailed). |
Before
conducting the regression analysis, the study used the Pearson correlation
coefficient to quantify the degree of rigidity of the linear relationship
between variables. The results of the correlation analysis with Pearson show
that: Sig value between the brand and buying intention factor is 0.120 greater
than 0.05, so it is not statistically significant, in other words, there is no
correlation between the two variables.
Sig
coefficients of the variables location, finance, features, and subjective norm
are less than 0.05 and the correlation coefficients of the variables are
positive, so these factors have positively correlated with the buying intention
variable. In particular, the most influential factor to the variable buying
intention is the location factor (r = 0.653), the factor with the lowest
correlation to buying intention is the factor features (r = 0.404).
From the results table, most Sig coefficients between
independent variables are less than 0.05, so the multicollinearity phenomenon
should likely be checked when regression analysis. The adjusted R2 coefficient
is 53.2%, indicating that five independent variables affect 53.2% of the
variation of the dependent variable, the remaining 42.8% is due to out-of-model
variables and random errors.
Durbin-Watson has a variable value between 0 and 4. The
result of looking at the Durbin Watson table with k '= 5, n = 200, The result
is dL = 1.623 and dU = 1,725, dU <1,950 <4 - dU, respectively 1,725
<1,950 <4 - 1,725 (= 2,275). Thus, there is no self-correlation
phenomenon in the linear regression model. The research model satisfies the
conditions of assessment and verification of suitability for research results
because the sig value of F test is 0.000 <0.05. Thus, the linear regression
model is suitable.
Table 6: Results of
regression analysis
Coefficientsa |
||||||||
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
Collinearity
Statistics |
|||
B |
Std. Error |
Beta |
Tolerance |
VIF |
||||
1 |
(Constant) |
-.072 |
.288 |
|
-.249 |
.803 |
|
|
LOC |
.504 |
.053 |
.509 |
9.552 |
.000 |
.830 |
1.205 |
|
FE |
.105 |
.056 |
.107 |
1.872 |
.063 |
.722 |
1.386 |
|
FIN |
.134 |
.052 |
.144 |
2.569 |
.011 |
.749 |
1.336 |
|
SN
|
.250 |
.052 |
.246 |
4.807 |
.000 |
.901 |
1.110 |
|
BR |
.023 |
.039 |
.029 |
.588 |
.557 |
.966 |
1.035 |
|
Adjusted R
Square |
0.532 |
|||||||
Durbin-Watson |
1.950 |
|||||||
Sig |
0.000 |
Sig coefficient: With the reliability of 95%, the sig
coefficient of regression of the independent variables such as location,
finance, and the subjective norm is smaller than 0.05, so these independent
variables are statistical significance and able to explain the dependent
variable. The Sig coefficient of the features (or apartment characteristics) is
0.063, higher than 0.05, but the features variable is still statistical
significance with 90% confidence level. Besides, the Sig coefficient of a brand
variable is 0.577 > 0.05, and in the Pearson analysis, so the brand variable
does not affect the intention to buy the apartment of the customer.
The
standardized regression coefficients Beta show that the beta coefficients are
greater than 0, indicating that the accepted independent variables have a
positive effect on the customers' decisions. The independent location variable
has the largest Beta coefficient of 0.509, so it has the most influence on the
change of the dependent variable. Most of the customers intend to buy an
apartment because of their convenient location, meeting their daily activities
such as near work, shopping, entertainment. So the location factor has the
greatest impact on the intention of buying customers.
Secondly,
the subjective norm and finance with a beta coefficient of 0.246 and 0.144
respectively effect to the intention of buying customers' apartments. The least
influential factor is features with Beta equals 0.107. The standardized
regression value of the subjective norm variable shows that the reference to
family, friends, and relatives also affect customers' intention to buy.
Moreover, the settlement is an important issue for people, so many customers
need to have references from people around when they intend to buy an
apartment. The standardized regression value of the finance variable shows that
finance affects the intention of buying customers' apartments. Apartments are a
big asset, so the problem of solving financial problems also has a significant
influence on the intention to buy apartments.
The
standardized regression value of the features variable is 0.107, meaning
features affect the intention of purchasing customers. The customers said that
the apartment that they wanted to buy must have a beautiful design, beautiful
view, excellent construction quality, and apartment size suitable for them.
Variance inflation factor (VIF) coefficient of all variables is less than 2, so
it can be concluded that there is no multicollinearity phenomenon between
independent variables.
5.
CONCLUSION
5.1.
Conclusion
The
authors have proposed a research model consisting of five factors based on
precursor studies and practical observations in the process of working in the
real estate industry. After analyzing indicators, sufficiently reliable
factors, EFA factor analysis, regression analysis and elimination of
unsatisfactory variables, this study has identified four factors that affect
the intention to buy that apartment is location, finance, subjective norm, and
features. The brand variable is excluded because in regression analysis with
Sig coefficient = 0.557> 0.05, it rejects the brand hypothesis that has a
positive effect on the intention of buying customers' apartments.
According
to the regression results, the location has the greatest impact on the
intention to buy apartments (Beta = 0.509, Sig = 0.000). In particular, customers
are very interested in the location which close to the workplace, school, city
center, or shopping mall to serve daily activities. Besides, they are also
concerned about the living environment. The surveyed customers do not want
their apartment is located near the highway. The reason is that the density of
large vehicles in Ho Chi Minh City is relatively high, frequent accidents,
large trucks moving at high speed significantly affect the living of people.
The
subjective norm variable has the second effect on the buying intention (Beta =
0.246, Sig = 0.000). Buying a house has a great meaning for every person. Most
of the customers surveyed are customers who buy to settle so they cannot decide
by herself/himself. The surveyed customers agreed that family, friends, and
relatives influenced their intentions. They feel more secure when their choice
supported by their friends and relatives than having no supporting. They also
collect information about apartments from people around when they intend to buy
apartments.
The
findings of this study provide evidence that finance has a significant positive
effect on buying intention (Beta = 0.144, Sig = 0.011). Without financial
preparation and financial balance, buying a house will be difficult. Customers
want to buy an apartment that is suitable for their finance, with many flexible
policies from investors such as original debt grace, interest rate support.
Flexible payment policies will significantly affect the consideration of
apartment selection, from the price of apartments, how much to pay, how well
the payment periods are carefully considered.
Results
of regression analysis with Beta = 0.107 with Sig coefficient of 0.063 <0.1
(90% reliability) shows that apartment features affect the buying apartments
intention. Customers agree that they like apartments with beautiful designs
such as flexible layout, maximum direction to welcome the sun and wind for the
apartment. They also want the apartment to have a beautiful view of sightseeing
and relaxation when at home. Also, the majority of customers believe that the
apartment they buy needs to be a size suitable for finance as well as following
the number of family members. Besides, the construction quality of the apartment
is also a factor that customers care.
In
the study, customers did not think that they would buy an outspread advertised
apartment. The big promotion of an apartment only shows the ability to
communicate; the distribution system is sound, has not confirmed whether the
apartment is perfect and suitable for their needs or not. The investor plays an
essential role in buying apartments. However, in the mid-end segment, these
customers are more interested in the factors mentioned above. The growth of
investors or their reputation is also one of the bases to make the selling
price of apartments higher than that of smaller investors. Therefore, the image
of the investor has not affected too much the intention of buying customers'
apartments.
5.2.
Recommendation
Many
of the real estate investors often said "location, location,
location" because of the vital role of location in real estate. The areas
near the center will always have higher prices due to the convenience for work,
education, health, or entertainment of the people. However, real estate in the
suburbs of Ho Chi Minh City has been the focus of investors when gradually
completing the roads; transportation is easy and convenient, synchronized
planning and especially the price of apartments in the coastal areas are still
extremely reasonable, towards most people with middle income.
It
is a large potential customer that needs to be fully exploited. Investors
should consider their financial potential, pay attention to the development
potential of the real estate, and have a long-term vision of the future of the
region for accurate judgment and investment. Investors need to choose the
location that best suits their finance and customers, as well as capture the
tendency of customers. Priority should be given to locations such as near
schools, labor-intensive areas, near shopping, entertainment, and secure and
quiet residential areas.
Accordingly,
the location to buy is an apartment located in the suburbs or near the center,
with an ideal radius of 10-12km back or located in the urban embellishment
area, densely populated population such as schools, shopping centers, offices.
Research
shows that customers who intend to buy an apartment are consulted with family,
relatives, and friends. Therefore, developers, as well as counselors, not only
persuade customers to spend money to buy but also to convince as many people as
possible. Increasing the level of brand awareness for customers by building a
brand identity system to raise customer awareness, creating a sense of business
size is prestige and quality. Consultants, as well as product distribution
units, need to build professional, conscientious, and enthusiastic images with
customers, build professional distribution channels, modern facilities to build
the right image.
One
of the other important issues when the customers buy real estate in general and
apartments, in particular, is finance. Customers always consider carefully to
choose an apartment suitable for their ability to pay. Investors need to
research the market carefully to make the price reasonable and competitive. The
payments and the accompanying support loan policy always occupy the great
interest of customers besides the price of apartments.
Customers
want to know how much the initial money I spent, then pay each installment,
should use the payment method of the Investor to receive the incentives or use
the loan policy of the supporting bank. Therefore, to increase the interest of
customers for apartments, customers have more choices, investors should diversify
payment policies. Offer attractive discounts if customers pay quickly, pay
once, 1% / month. It is combined with banks to offer attractive loan policies
with many incentives such as low-interest rates, support grace of principal,
interest.
One
factor to consider is the characteristics of the apartment. For customers who
buy in, apartments they buy will be a long-term place for them and their
families. The Investor should choose excellent and knowledgeable design units
to optimize the apartment use area, arrange flexible space, feng
shui, create more space to expand the excellent view.
In order to have reasonable prices for affordable customers, it is necessary to
design an apartment with a reasonable area but still fully functional for the
daily activities of the family. Investors should also pay attention to
selecting a construction contractor and a reputable monitoring unit to ensure
the quality of the project.
5.3.
Limitation and future research
Due
to limited time and survey condition, the research has some limitation.
Firstly, the surveyed customers are relatively young and belong to some areas,
so they have not accurately reflected the intention of customers in all areas.
Secondly, the topic is only done in one time so the results may be appropriate
to the current research period; the later stages cannot be confirmed. Third,
the study only reused the existing model. Fourth, this study only focused on
investigating five factors affecting the intention to buy condominiums. The
results of the regression analysis show that these variables explain 52.2% of
the fluctuation of the intention of customers. Thus, 47.8% of the variation of
the intention to buy apartment buildings is explained by factors outside the
model, which are factors not mentioned in the proposed research model.
In the next research, the authors will increase the
sample size in the direction of increasing the survey sample rate compared to
the overall. Include a number of other factors that are thought to affect the
intention of buying customers' apartments into the proposed research model via
explore new factors affect customers' intention to buy. Further research
directions can go deeper into the factors affecting the intention to buy
apartments for investment or the intention to buy luxury apartments, town
houses, villas.
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