Cao Hoc Quan Tri Kinh Doanh ISB Mbus2010 Phan Thanh Si

8/12/2019 Cao Hoc Quan Tri Kinh Doanh ISB Mbus2010 Phan Thanh Si

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UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

--------------------

PHAN THANH SI

KEY FACTORS AFFECTING HOUSE

PURCHASE DECISION OF

CUSTOMERS IN VIETNAM

ID: 60340102

MASTER OF BUSINESS (Honours)

SUPERVISOR: DINH THAI HOANG, Ph.D.

Ho Chi Minh City - Year 2012





iii

ABSTRACT

The main purpose of the study is to investigate the effecting of key factors on housing

purchase decision of customers in Vietnam. First, a model which is proposed basedon analyzing of previous literature. Then the model is tested on a pilot test which is

conducted of a small real estate professional group and another group of 15

respondents, and on a larger survey of 263 samples. The study finds out a strong

positive relationship between top two factors, including “living space”, “distance”

and customers’ housing purchase decision. The three weaker positive relationship

factors are “feature”, “finance” and “environment” to housing decision makers. It is

also found that there is no difference in decision making of customers according to

different demographics consisting of gender, age, marital status, monthly income and

education level.

Key works: real estate, purchase factors, Vietnam



iv

TABLE OF CONTENTS

ACKNOWLEGEMENTS ........................................................................................ ii

ABSTRACT ...........................................................................................................iiiLIST OF TABLES ................................................................................................ vii

LIST OF FIGURES .............................................................................................. viii

ABBREVATIONS ............................................................................................... viii

CHAPTER 1. INTRODUCTION ....................................................................... 1

1.1. BACKGROUND ........................................................................................... 1

1.2. RESEARCH PROBLEMS & RESEARCH QUESTIONS ............................. 1

1.3. RESEARCH PURPOSE ................................................................................ 3

1.4. SCOPE OF THE RESEARCH ...................................................................... 3

1.5. RESEARCH STRUCTURES ........................................................................ 3

CHAPTER 2. LITERATURE REVIEW ........................................................... 4

2.1. LITERATURE REVIEW .............................................................................. 4

2.1.1. Feature ....................................................................................................... 4

2.1.2. Living space ............................................................................................... 4

2.1.3. Finance ....................................................................................................... 5

2.1.4. Distance ..................................................................................................... 5

2.1.5. Environment ............................................................................................... 5

2.1.6. Purchase decision ....................................................................................... 6

2.1.7. Demography ............................................................................................... 6

2.2. CONCEPTUAL FRAMEWORK .................................................................. 7

CHAPTER 3. RESEARCH METHODOLOGY ............................................... 83.1. RESEARCH PROCESS ................................................................................ 8

3.2. SAMPLE SIZE ............................................................................................ 11

3.3. MEASUREMENT SCALE ......................................................................... 11

3.3.1. Measurement scale ................................................................................... 11

3.3.2. Pilot test ................................................................................................... 11

3.4. MAIN SURVEY ......................................................................................... 15



v

3.5. DATA ANALYSIS METHOD .................................................................... 15

3.5.1. Reliability measure ................................................................................... 15

3.5.2. Validity measure by EFA (Exploratory Factor Analysis) .......................... 16

3.5.3. Multiple regression analysis ..................................................................... 16

CHAPTER 4. DATA ANALYSIS & RESULTS ............................................. 18

4.1. PREPARATION DATA .............................................................................. 18

4.1.1. Editing...................................................................................................... 18

4.1.2. Coding...................................................................................................... 18

4.2. DESCRIPTIVE DATA ................................................................................ 21

4.3. ASSESSMENT MEASUREMENT SCALE ................................................ 234.3.1. Cronbach’s Alpha ..................................................................................... 23

4.3.2. Exploratory Factor Analysis (EFA) .......................................................... 26

4.3.2.1. Assessment of data ................................................................................ 26

4.3.2.2. Defining number of extracted factors .................................................... 27

4.4. HYPOTHESES TESTING BY MULTIPLE REGRESSION ....................... 30

4.4.1. Checking assumption of Multiple Regression ........................................... 30

4.4.1.1. Sample size ........................................................................................... 30

4.4.1.2. Assessment multicollinearity of independent variables .......................... 30

4.4.1.3. Normality, linearity, homoscedasticity & outliers.................................. 30

4.4.2. Evaluating the model ................................................................................ 31

4.4.3. Evaluating the independent of variables ................................................... 31

4.4.4. Checking hypotheses of model ................................................................. 32

4.4.5. Analysis effect of control variables by Multiple Regression ..................... 34

CHAPTER 5. CONCLUSIONS AND IMPLICATIONS ................................ 35

5.1. RESEARCH OVERVIEW .......................................................................... 35

5.2. RESEACH FINDINGS ............................................................................... 35

5.3. MANAGERIAL IMPLICATIONS .............................................................. 36

5.4. RESEARCH LIMITATIONS & DIRECTIONS FOR FUTURE RESEARCH ......... 37



vi

REFERENCES .................................................................................................... 38

Appendix 1: The first draft of the questionnaire ..................................................... 42

Appendix 2: The English questionnaire ................................................................. 45

Appendix 3: The Vietnamese questionnaire ........................................................... 49



vii

LIST OF TABLES

Table 3.1: Main factors affecting customers’ housing purchase decision ............... 13

Table 4.1: Codebook of questionnaire items .......................................................... 18

Table 4.2: Characteristics of respondents ............................................................... 22

Table 4.3: Cronbach’s Alpha test results ................................................................ 25

Table 4.4: EFA results ........................................................................................... 28

Table 4.5: Correlations among variables ................................................................ 29

Table 4.6: Coefficient table of MLR ...................................................................... 32

Table 4.7: Hypotheses results ................................................................................ 33

Table 4.8: Descriptive statistics ............................................................................. 54Table 4.9: Cronbach’s Alpha with full items for each constructs ........................... 54

Table 4.10: KMO and Bartlett’s test ...................................................................... 56

Table 4.11: Total variance explained ..................................................................... 56

Table 4.12: Correlation among variables (Partial only) .......................................... 57

Table 4.13: Factor Matrix ...................................................................................... 59

Table 4.14: Factor Correlation Matrix ................................................................... 60

Table 4.15: Model summary .................................................................................. 60

Table 4.16: Anova ................................................................................................. 60

Table 4.17: Casewise diagnostics .......................................................................... 60

Table 4.18: Residuals statistics .............................................................................. 61

Table 4.19: Cofficients of MLR including Sex_Render ......................................... 63

Table 4.20: Cofficients of MLR including Marital_Render .................................... 64

Table 4.21: Cofficients of MLR including Education_Render ............................... 64Table 4.22: Cofficients of MLR including Age_Render ......................................... 64

Table 4.23: Cofficients of MLR including Career_Render ..................................... 65

Table 4.24: Cofficients of MLR including Income_Render ................................... 65



viii

LIST OF FIGURES

Figure 2.1: Conceptual framework .......................................................................... 7

Figure 3.1: Research process ................................................................................. 10

Figure 4.1: Scree plot ............................................................................................ 58

Figure 4.2: Regression standardized residual ......................................................... 62

Figure 4.3: Normal P-P plot ................................................................................... 62

Figure 4.4: Scatterplot ........................................................................................... 63

ABBREVATIONS

EFA : Exploratory Factor Analysis

GSO : Vietnam Government Statistics Office

HCMC : Ho Chi Minh City

Mil. : Million

MLR : Multiple Linear Regression

UEH : University of Economic



1

CHAPTER 1. INTRODUCTION

1.1. BACKGROUND

As universal population levels continue to rise, the housing shortage in many

developing countries has reached critical levels (Morel, 2001, p. 1119). Real estate

is one of the most important things to citizens, so “the house purchase decision of

them can change their life” (Wells, 1993). The house purchase decisions are

different from other business decisions due to “the innate, durable and long-term

characteristics of real estate”. It is a highly differentiated product with “each

specific site unique and fixed in location” (Kinnard, 1968).In Vietnam, it is known as the third largest population in South East Asia and ranked

the 14th largest in the world in terms of total population. Its population estimated of

89 million in 2010 (GSO, 2011). The annual average growth population of Vietnam

from 2000 to 2010 was approximately 1.03 million people per year or 1.2% annual

growth. Particularly, one of the top economic centers of Vietnam is Ho Chi Minh

City which has around 7.2 million people as in April 2009, but its actual population

is likely to be significantly higher because of unrecorded migration from rural areas.

The real estate market in Vietnam has significantly changed during from the 1990s

to now and it might be seen as three times fever and declining prices in the last 20

years. Up to the end of 2012, the large real estate outstanding loans and a big

number of inventories created a serious crisis. However, according to the Deputy

Minister of Construction Nguyen Tran Nam, he emphasized that “people’s housing

demand is very large and solvency is high, but the real estate market lacked ofinformation”.

1.2. RESEARCH PROBLEMS & RESEARCH QUESTIONS

In general, the real estate in Vietnam has got many difficulties in making effort to

satisfy customer demands. According to incomplete statistics of the Ministry of

Construction surveyed in 44 provinces up to August 30th, 2012, the country now



2

had 16,469 unsold apartments, in which HCMC was 10,108 unsold apartments and

total number of inventories of low buildings was 4,116, in which HCMC was 1,131

ones (Anh, 2012).

Therefore, the Prime Minister stressed that the solution to rescue real estate market

should be included in the Resolution of the Government. The main reasons of the

crisis were the real estate market supply did not meet customer demands, the

investors lacked of exact information of customer and real estate market conditions.

“There are two main fields of customer research are how customers go about

making decisions and how decisions should be made. In addition, “creating truevalue for customer and customer notion focused approach” is confirmed (Edwards

& Fasolo, 2001). It is found that “customer decision making is one of the most

important areas of customer behavior and it requires gathering a lot of regarding

information” (Bettman et al ., 1998 & Simonson et al ., 2001).

There have been many published academic research about customer house purchase

with variety of both developed and developing countries. However, “the national

and cultural characteristics play a very significant role in house purchase decision,

that mean finding which is applied in specific context may not extend to another

context” (Opoku & Abdul-Muhmin, 2010).

The real estate in Vietnam has got specific characteristics to which connected

customer demands closely. In recent years, researchers, domestic and foreign

companies attracted to real estate field in Vietnam with a number of research works.

However, there has been not enough research into the way customers makingdecision to buy real estate as well as which major factors have got relationship with

customer decision.

Consequently, in the term of real estate purchase decision of customers, the research

questions of the thesis are raised as two following questions:



3

What are the key factors affecting the house purchase decision of

customers in Vietnam?

How is impact of these factors on house purchase decision of customers

evaluated in Vietnamese context?

Understanding relationship between main factors affecting customer house purchase

decision is an important role for both real estate developers and enterprises to

satisfy customers’ demand and to have available strategies in the real estate field.

1.3. RESEARCH PURPOSE

Based on the research questions, the main purpose of this thesis is to identify what

factors have impact on house purchase dicision of customers and examine how

these factors influence their decision of buying house in Vietnam.

1.4. SCOPE OF THE RESEARCH

The research is conducted in Ho Chi Minh City with the respondents who are the

postgraduates and students of UEH with various careers, as well as customers of a

small book-coffee. The timeframe of research lasts from the middle of September tothe end of October in 2012.

1.5. RESEARCH STRUCTURES

The research is divided into five chapters. The first chapter introduces about

background, research problems, research questions, research purpose, scope of

research and research structures. The second chapter covers literature review of the

previous research and shows hypotheses, as well as the conceptual framework of the

research. The third chapter presents the research process, sampling size,

measurement scale, main survey, and data analysis method. The fourth chapter

concentrates on preparation data, descriptive data, assessment measurement scale

and hypotheses testing. Finally, the fifth chapter points out research overview,

research findings, managerial implications, research limitations and directions for

future research.



4

CHAPTER 2. LITERATURE REVIEW

This chapter presents overview of previous literatures relating to housing purchase

decision making of customers. Also, a conceptual framework is built up and relative

hypotheses of research are raised.

2.1. LITERATURE REVIEW

2.1.1. Feature

Firstly, “features” of the building structure itself is an important determinant of a

household choice of residence (Quigley, as cited in Haddad, 2011, p. 234). Also, it

is confirmed that “feature” has significant effects on customers’ house purchase

decision making (Sengul et al., 2010, p. 214). The “feature” of house includes

“design”, “house size” and “quality of building” determinants relating to decision

making to buy a house of an individual (Adair et al., 1996; Daly et al., 2003; Sengul

et al., 2010, p.218; Opoku & Abdul-Muhmin, 2010). As a result,

H1. There is a positive impact of house features on customers’ house purchase

decision.

2.1.2. Living space

Secondly, “private living space” is one of most important factors affecting to

“consumer housing decision”. Living space consists of “size of living room”, “size

of kitchen”, “quantity of bathrooms” and “quantity of bedrooms” (Opoku & Abdul-

Muhmin, 2010, p.219). In addition, it is accepted that there is relationship between

the “space customer” and customers’ purchase making process (Graaskamp, 1981).Accordingly,

H2. There is a positive impact of living space on customers’ house purchase

decision.



5

2.1.3. Finance

Thirdly, “financial” status is much significant to customer house choice (Hinkle and

Combs, 1987, p.375; Kaynak & Stevenson, as cited in Sengul et al ., 2010, p.220).

The “financial” element of real estate requires access to a relative large amount of

“capital” and as well as “borrowing costs” (Xiao & Tan, 2007, p. 865). In addition,

“financial” status bases on combination of “house price”, “mortgage loans”,

“income” and “payment term” (Opoku & Abdul-Muhmin, 2010; Yongzhou, 2009,

p.17). Haddad et al. (2011) finds out the “economic” factor which is consisted of

five variables, such as “income”, “interest rate”, “area”, “conversion” and “taxes”.Moreover, Adair et al.(1996, p.24) and Daly et al. (2003, p.306) group “interest

rate”, “maximum mortgage”, “maximum monthly payment”, and “length of time

payment” into “financial” factor. Consequently,

H3. There is a positive impact of financial status on customers’ house purchase

decision.

2.1.4. Distance

Fourthly, one of the most important factors affecting individual “decision” making

to buy a house is “location” factor (Kaynak & Stevenson, as cited in Sengul et al.,

2010, p.219). The “residential location” has an influence on “people’s housing

choice” (Zabel & Kiel, as cited in Opoku & Abdul-Muhmin, 2010, p.220). Distance

to choose house can be affected by “width of adjacent” and “location to school”

(Opoku & Abdul-Muhmin, 2010). Moreover, “distance to central business”,“distance to school” and “distance to work” are considered (Adair et al., 1996,

p.23 ). In addition, “access to recreational facilities” and “access to main roads” are

proposed (Iman et al., 2012, p.30) . Hence,

H4. There is a positive impact of distance on customers’ house purchase decision.

2.1.5. Environment





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difference in real estate buying decisions to “age” and “gender”, and not to

“educational levels” and “marital status” (Haddad et al., 2011). Correspondingly, in

this study, “gender” and “age” characteristics are considered as control variables sothat investigate whether effect of those demography variables on housing purchase

decision making of customers or not.

2.2. CONCEPTUAL FRAMEWORK

A conceptual framework which is proposed to show the relationship between five

independent variables consisting of “feature”, “living space”, “financial status”,

“distance” and “environment” and one dependent variable, namely “house purchase

decision”. It also shows the effecting of demography including “gender”, “age”,

“marital status”, “income” and “education” as control variables on the dependent

variable. The conceptual framework is shown as the model (see Figure 2.1).

Figure 2.1: Conceptual framework

FEATURE

LIVING SPACE

FINANCIALSTATUS

DISTANCE

HOUSE

PURCHASE

DECISION

- Gender

- Age

- Marital

- Income

- Education

- Career

Control

H1+

H2+

H +

H3+

ENVIRONMENTH +



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CHAPTER 3. RESEARCH METHODOLOGY

This chapter showed all steps of the research process, the minimum sample size,

measurement scale, main survey and data analysis method.

3.1. RESEARCH PROCESS

The research process was summarized as following steps.

Step 1: Define the research problems, research questions and research purposes.

Step 2: Review the literature background from the previous research, then a

conceptual model was set up and hypotheses were proposed.Step 3: Made and revise the draft questionnaire.

A draft questionnaire with the measurement scales based on the previous research

was set up. Next, the draft questionnaire was delivered to 02 real estate

professionals, 03 management officers to respond, and a discussion about the draft

questionnaire was carried out later. The aim of the pilot phase was to modify and

clear the measure scale.After that, the revised questionnaires were delivered to another small group of 15

persons to test about clear understanding of the questionnaire. Finally, a main

survey was conducted with 263 receivers.

Step 4: Conduct the main survey and collect data within 4 weeks.

The questionnaires were directly sent to 263 persons. The main respondents were

postgraduates of master programs or students who have been studying to get thesecond business certification in the University of Economic. Besides, a small group

about 24 persons with a wide variety of careers was also delivered questionnaires at

a book coffee in Ho Chi Minh City. Finally, there were 239 respondents giving their

feedbacks, but 230 cases were available only.

Step 5: Edit, code and adjust missing data before testing reliable and validity of

data.



9

In order to prepare the data to analysis, data were edited, coded and adjusted for

missing data. Next, reliability of measuring instrument was analyzed by calculation

Cronbach’s alpha which was required above .7 (Hair et al., 2010). In addition,validity of measuring instrument was evaluated due to define the number extracted

factors based on the Eigenvalue value over than 1 and changing of the slope in the

Scree plot (Hair et al., 1998; Tabachnick & Fidell, 2001).

Step 6: Test the hypotheses of research and define relationship of factors in model

through the Multiple linear regression analysis.

The Multiple linear regression analysis was applied to evaluate the relationship between five independent variables, including “feature”, “living space”, “finance”,

“distance” and “environment” and one dependent variable, namely “decision”.

Moreover, defining whether there was any significant contributory of control

variables consisting of “gender”, “age”, “marital”, “income”, “education” and

“career” on customers’ housing purchase decision was also analyzed by the multiple

linear regression. All steps were illustrated by the following Figure 3.1.



10

Figure 3.1: Research

rocess

Research Problems

Theoretical Framework

- Variables identified & labeled- Hypotheses generated

Research Proposal

Research Design

Pilot testing

Data Analysis & Interpretation

Deduction

- Hypotheses substantiated?

- Research questions answered?

Report Writing

Report Presentation

Data Collection & Preparation

Data CollectionDesign

SamplingDesign

Type, purpose, time frame, scope, environment

Instrument Development



11

3.2. SAMPLE SIZE

The reliable and validity of variables were tested by using Cronbach’s Alpha and

EFA, after that the multiple regression was applied to test model and hypotheses.

First of all, the sample size was required to have enough quantity for the analysis.

The minimum sample size was 100 and not less than five times of items (Hair et al.

2010), thus:

n > 100 and n = 5k (where k is the number of items).

Thus, the minimum sample size was 5x34 = 170 samples.

In addition, based on five independent factors of the conceptual model, the multiple

regression analysis required sample size at least (Tabachnick & Fidell, 2007):

50 + (8xm) = 50 + (80x5) = 90 samples.

Where m: is the number of independent factors of the model.

Consequently, the minimum sample size should be 170. Based on the actual

collection data, the quantity of available respondents from the questionnaire survey

estimated 230, so that samples met the requirements above.

3.3. MEASUREMENT SCALE

3.3.1. Measurement scale

In order to operate concepts, it was necessary to measure them in some manners, so

different variables were required to choose an appropriate scale. The independent

variables were applied interval scale with five - point of Likert scale consisting of

totally unimportant (1), unimportant (2), neutral (3), important (4), very important(5); beside, the dependent variable was applied the same measure consisting of

strongly disagree (1), disagree (2), neutral (3), agree (4) and strongly agree (5).

3.3.2. Pilot test

In order to test logistics of the questionnaires prior collection data on large cover, a

pilot test was carried out with a small group consisting of two real estate



12

professionals of Sacomreal and three management officers of Hoa Binh

Corporation. All of them had much knowledge and many experience years in the

real estate field.

Firstly, the aim of the pilot test was explained to all of them; moreover, the

questionnaires and relative documents were also sent to them. After that, a

discussion with them was conducted to define which parts would be deleted or

which parts would be added. The results were presented in Appendix 01.

For items of the “house feature” factor, the item “type of finishing” and “quality of

finishing” should be deleted because their content was inside the content of“construction quality”.

While all items of “private living size” factor were agreed, the item “tax” of

“Finance” factor should be changed into “the registration fee”.

For “distance” factors, the “house on a main bus route” item should be deleted

because this item was not paid attention by customers. The “distance from the house

to shopping centre” item was also proposed to delete because it was too specific andrelated to female only. In addition, the group recommended that customers had got

tendency to ignore the “location away from industrial areas” item so this item

should be removed.

For “environment” factor, its “the attractiveness of the area” item had got the same

meaning of “view” item, so “the attractiveness of the area” should be deleted.

The last “decision” factor, it should change “I will want to buy a new house” into “Iwill make my effort to buy a new house”.

Finally, after adjusting the first questionnaire table, a small sample size of fifteen

convenient colleagues was delivered the questionnaires to recognize whether any

parts of its unclear to understand or misunderstand. However all of them understood

meaning of questionnaires quite well and knew the way to answer, so the



13

questionnaire was the last version to carry out in the massive areas. After that, a

main survey was conducted.

From above discussion above, a summary table of main factors affecting customer’

housing decision making is presented as following Table 3.1.

Table 3.1: Main factors affecting customers’ housing purchase decision

No. F a c t o r

Variables Code Reference

1

F e a t u r e o f H o u s e

House size X1.1

Adair et al. (1996), Daly et

al. (2003), Kaynak &

Tevenson (1982), Haddad

et al. (2011), Opoku &

Abdul-Muhmin (2010),Ratchatakulpat (2009),

Sengul et al. (2010), Xiao

& Tan (2007)

House’s legal status X1.2

Interior design X1.3

External design X1.4

Construction quality X1.5

Construction duration X1.6

Type of house X1.7

2

P r i v a t e L i v i n g S p a c e

Kitchen size X2.1

Quantity of bedroom X2.2

Quantity of bathroom X2.3

Living room size X2.4

Storey of house X2.5

3

F i n a n c i a l s t a t u s

House price X3.1 Adair et al. (1996), Daly et

al. (2003), Kaynak &

Tevenson (1982), Haddad

et al. (2011), Opoku

(2010), Ratchatakulpat

Maximum mortgage X3.2

Maximum monthly

repaymentX3.3

Interest rate X3.4



14

Income X3.5 (2009), Sengul et al.

(2010), Xiao & Tan (2007)Payment duration X3.6

The registration fee X3.7

4

D i s t a n c e

Width of adjacent street X4.1 Adair et al. (1996), Daly et

al. (2003), Haddad et al.

(2011), Opoku & Abdul-

Muhmin (2010),

Ratchatakulpat (2009),


& Tan (2007)

Distance to work X4.2

Distance to market X4.3

Distance to school X4.4

Distance to recreation centre X4.5

Distance to the central business district

X4.6

Access to the main street X4.7

5

E n v i r o n m e n t

Neighbour’s condition X5.1 Adair et al. (1996), Daly et


(2011), Opoku & Abdul-

Muhmin (2010),Ratchatakulpat (2009)

General security X5.2

View X5.3

Noise X5.4

Pollution X5.5

Nearby traffic X5.6

6

D e m

o g r a p h i c

Gender X6.1 Adair et al. (1996), Daly et


(2011), Mateja (2009),

Ratchatakulpat (2009),


& Tan (2007)

Age X6.2

Marital X6.3

Income X6.4

Education X6.5

Career X6.6

7

D e c i s i o n

Planning to buy a new house X7.1 Ajzen (1991), Han & Kim,

(2010), Kunshan & Yiman,

(2011)

Making effort to buy a new

houseX7.2





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3.5.2. Validity measure by EFA (Exploratory Factor Analysis)

In order to evaluate the validity and the correlation among variables to identify

underlying factors or define number of extracted factors, EFA was applied with the

oblique approach using the Promax method. However, some requirements of EFA

should be satisfied (Pallant, 2011):

- The minimum of sample size should be at least 100 and rate of observations per

items of models should be five cases for each of the items, so that meant the

minimum required sample size should be at least 5m = 5x34 = 170 cases (where m:

quantity of items from the conceptual model). The actual sample size was 230, bigger than 170 so it met the requirement.

- The correlations of r of the correlation matrix should show at least .3.

- Kaiser-Meyor-Olkin (KMO) test must be equal or above .6 (Tabachnick & Fidell,

2007).

- Barllett’s test of sphericity should have significant less than 5%.

- In order to extract factors, the eigenvalue of factors must be greater than 1 (Kaiser,

1956).

3.5.3. Multiple regression analysis

To explore the relationship between independent variables, consisting of “features”,

“living space”, “finance”, “distance” and “environment”, and dependent variable,

namely “decision” as well as to evaluate the importance of those independent

variables in the framework model, the multiple regression analysis was conducted.

The multiple regression analysis required that some following conditions should be

satisfied:

- The minimum sample size based on the formula:

n > 50 + 8m = 50 + 8x5 = 90 samples, where m: number of independent variables

in the conceptual model



17

The actual quantity of cases was 230, so this condition was satisfied.

- The multicollinearity did not exist, so r value, the correlated score was less than .9.

- The collinearity test on variables was via two values “tolerance” and “VIF”,

particularly the VIF should not be less than .1, or above 10.

- The Normal probability plot (P-P) was required with most of the scores

concentrated in the centre (along the 0 point).

- The presence detection of outliers was considered from the Scatterplot.

The multiple regression was used to test hypotheses, to explore the relationship between five INDEPENDENT VARIABLEs and one dependent variable, and to

consider whether control variables supported or not to dependent variable. The

generalized equation (Donald & Pamela, 2006) was:

Y = o + 1X1 + 2X2 + 2X2 + … +nXn +

Where:

o = a constant, the value of Y when all X values are zero

1 = the slope of the regression surface (the represents the regression coefficient

associated with each Xi)

= an error term, normally distributed about a mean of 0



18

CHAPTER 4. DATA ANALYSIS & RESULTS

This chapter presented data preparation with editing, coding, and data entry from

raw data to correct errors. Then data were described through frequency tables about

the general information. Using Cronbach’s alpha to test the reliability of variables

and EFA to test their validity, then the multiple regression was run to explore the

relationship between independent variables and dependent variable, and to test

hypotheses.

4.1. PREPARATION DATA

4.1.1. Editing

After collection 239 cases from respondents, all cases were checked first. There

were 03 cases of blank sheets, 02 cases of filling in half of I part only, 01 case of no

filling in the general information part, and 03 cases of filling almost choosing

number 1 or 3 or 4. The last available numbers of cases was 230, and each of all

cases was marked a reference number on it to find easily. Others did not have any

cases of missing data for contend of INDEPENDENT VARIABLEs and dependentvariable.

4.1.2. Coding

Answers were assigned numbers of symbols so that the responses were grouped into

a limited number of categories (see Table 4.1).

Table 4.1: Codebook of questionnaire items

No. Factors Code Description

Varia-

ble

name

Coding/

Creating Dummy

1 Feature House size Fea01 Record respondents’ numbers

2 House’s Legal Status Fea02

3 Interior Design Fea03



19

4 External Design Fea04

5 Construction Quality Fea05

6 Construction Duration Fea06

7 Type of House Fea07

8

Private

Living

Space

Kitchen Size Liv01 Record numbers

9 Quantity of Bedroom Liv02

10 Quantity of Bathroom Liv0311 Living room size Liv04

12 Storey of House Liv05

13 Finance House Price Fin01 Record numbers

14 Maximum mortgage Fin02

15Maximum monthly

repayment

Fin03

16 Interest rate Fin04

17 Income Fin05

18 Payment duration Fin06

19 The registration fee Fin07

20Distance Width of adjacent

street

Dis01 Record numbers

21 Distance to work Dis02

22 Distance to market Dis03

23 Distance to school Dis04

24Distance to recreation

centre

Dis05

25 Distance to the central Dis06



20

business district

26

Access to the main

street

Dis07

27Environ-

ment

Neighbour’s

condition

Env01 Record numbers

28 General security Env02

29 View Env03

30 Noise Env04

31 Pollution Env0532 Nearby Traffic Env06

33 Decision Plan to buy a new

house

Dec01 Record numbers

34 Making effort to buy

a new house

Dec02

35 An important person

to make decision to

buy a new house

Dec03

36 Demogra-phy

Gender Sex01 Creating dummy variables

1= Females

0 = Males

37 Age Age01 Creating dummy variable1 = “less than 35”; 0 = “above

35”

38 Marital Mar01 Creating dummy variable

1 = “Single”; 0 = “Married”

39 Income Inc01 Creating dummy variable





22

Table 4.2: Characteristics of respondents

Characteristics Frequency % Cumulative %

Gender 230

- Male 76 33.0 33.0

- Female 154 67.0 100.0

Marital status 230

- Single without children 191 83.0 83.0

- Single with children 2 .9 83.9

- Married without children 16 7.0 90.9- Married with children 20 8.7 99.6

- Divorce 1 .4 100.0

Education 230

- High school 2 .9 .9

- Colleague 14 6.1 7.0

- University 141 61.3 68.3

- Postgraduate 73 31.7 100.0

Age 230

- <=25 year olds 111 48.3 48.3

- 26-35 year olds 117 50.9 99.1

- 36-45 year olds 2 .9 100.0

Career 230

- Officer 202 87.8 87.8- Management/ Manager 9 3.9 91.7

- Others 19 8.3 100.0

Income 229

- Less than 5 mil./month 70 30.4 30.4

- 5 - 9 mil./month 105 45.7 76.1



23

Characteristics Frequency % Cumulative %

- 10 - 14 mil./month 31 13.5 89.6

- 15 -24 mil./month 18 7.8 97.4

- 25 - 40 mil./month 3 1.3 98.7

- more than 40 mil./month 3 1.3 100.0

House Size Choosing 229

- less than 50 m2 22 9.6 9.6

- 51-75 m2 93 40.6 50.2

- 76-100 m2 78 34.1 84.3

- 101-150 m2 24 10.5 94.8

- more than 150 m2 12 5.2 100.0

House Price choosing 229

- Less than 20 mil./m2 200 87.3 87.3

- 21-32 mil./m2 21 9.2 96.5

- more than 32 mil./m2 8 3.5 100.0

House type choosing 227

- Apartment 49 21.6 21.6

- Single house 167 73.6 95.2

- Villa 11 4.8 100.0

4.3. ASSESSMENT MEASUREMENT SCALE

In order to evaluate appropriation of a measurement scale, the scale should be

checked its reliability and validity. The reliability was tested by Cronbach’s Alpha

and the validity was tested by EFA.

4.3.1. Cronbach’s Alpha

Refer to the Case processing summary of all variables of five concepts, the number

samples of each concepts was valid with 230 available cases.



24

Based on the Reliability Statistics Table 4.3 and Table 4.9, all Cronbach’s Alpha

values of all concepts were above .7 after deleting items that the “Corrected item-

Total correlation” values of them were less than .3.

For “feature” concept, both “Fea06_Construction duration” item and “Fea07_Type

of house”, their “Corrected item-Total correlation” values were .253 and .08. For

meaning consideration, the “feature” concept could be measured by remaining

items, so both of them should be deleted. After they were removed, the Cronbach’s

Alpha value increased from .748 to .830.

For “private living space” concept, the “Corrected item - Total correlation” value of“Liv04_Living room size” item was low with .371, of “Liv05_Storey of house”

item was .012. All other items ensured the content of “living space”, so those two

items should be deleted and the Cronbach’s Alpha value of “private living space”

increased from .619 to .739, this value was not high, but it could be acceptable.

For “finance” concept, the “Corrected item - Total correlation” value of

“Fin07_Registration Fee” item was .013, less than 0.3, so this item was deleted.

Also, “Corrected item - Total correlation” value of “Fin02_Max Mortgage” item

was quite low .36, it should be also deleted. The Cronbach’s Alpha value of “private

living space” increased from .726 to .865, this value was so quite good.

For “distance” concept, the “Corrected item - Total correlation” value of

“Dis06_Business Distance” item and “Dis07_Main Access” which were .139 and

.202. Those values were too low compared with .3, so they should be deleted.

Beside, the “Corrected item - Total correlation” value of “Dis05_Recreation

Distance” was .377, it also should be deleted. The Cronbach’s Alpha value of

“Distance” concept was increased from .765 to .890.

For “environment” concept, the “Corrected item - Total correlation” value of

“Env03_View” item and “Env06_Nearby traffic” which were .284 and .272. Those

values were less than .3 and the “Env06_Nearby Traffic” and “View” could be



25

explained by characteristics of “Noise”, “Pollution”, “Neighbour Condition”, and

“Security” of the environment, so they were removed. The Cronbach’s Alpha value

of “environment” concept was increased from .767 to .846 after deleting two itemsabove.

Finally, “decision” concept had got all the “Corrected item - Total correlation”

value of all items were above .4, and its Cronbach’s Alpha value was .816, those

values were quite good.

Table 4.3: Cronbach’s Alpha test results

Variables

Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Cronbach's

Alpha if

Item Deleted

FEATURE: Alpha= .830

Fea01_House Size 15.39 3.933 .791 .744

Fea02_House's Legal Status 16.97 4.536 .484 .848

Fea03_Interior Design 16.09 4.927 .601 .805

Fea04_External Design 15.53 5.325 .458 .838

Fea05_Construction Quality 16.29 4.094 .874 .724

LIVING SPACE: Alpha= .739

Liv01_Kitchen Size 9.89 3.774 .524 .659

Liv02_Bedroom Quantity 9.85 3.882 .586 .630

Liv03_Bathroom Quantity 10.42 3.398 .583 .622FINANCE: Alpha= .865

Fin01_House Price 16.38 9.476 .654 .846

Fin03_Max Repayment 17.15 7.463 .781 .812

Fin04_Interest rate 16.93 7.737 .790 .808

Fin05_Income 16.52 8.862 .734 .827



26

Fin06_Payment Duration 17.04 9.614 .506 .878

DISTANCE: Alpha= .890

Dis01_Adjacent Street 11.00 6.677 .638 .900Dis02_Work Distance 10.87 4.830 .759 .876

Dis03_Market Distance 11.28 5.774 .878 .818

Dis04_School Distance 11.33 5.793 .825 .835

ENVIRONMENT: Alpha= .846

Env01_Neighbour Condition 12.50 3.168 .727 .796

Env02_Security 12.35 3.573 .622 .840

Env04_Noise 12.14 2.289 .754 .786

Env05_Pollution 11.83 2.598 .735 .783

DECISION: Alpha= .816

Dec01_Plan to buy 6.33 1.691 .794 .617

Dec02_Try to buy 6.07 3.013 .461 .928

Dec03_Important Person 6.26 2.141 .819 .598

4.3.2. Exploratory Factor Analysis (EFA)

Exploratory factor analysis was carried out through three steps consisting of the 1st

step was evaluation the suitability of data for factor analysis, the 2 nd step was factor

extraction, and the 3rd step was factor rotation and interpretation (Pallant, 2011).

4.3.2.1. Assessment of data

Sample size

Sample size and the strength of the relationship among the variables were required

to test suitability of data (see Table 4.12). The sample size was 230 available cases

and shown detail in chapter 3 that this requirement was met the minimum required

sample size.



27

Factorability of the correlation matrix

From the pattern matrix, low communality values should be removed to increase the

total explained variance. Removing some low communality variables and repeatingthe same analysis, the result of EFA was presented in the Table 4.4. From the partial

Correlation Matrix Table 4.5, some of the correlation coefficients between variables

each others were above .3.

The KMO value was .779, exceeding value of .6 and Bartlett’s test of Sphericity

value was .000, that means less than the statistically significant at p < .05 (see Table

4.10).

Therefore, the condition about the factorability of the correlation matrix was

appropriated with assumptions of EFA.

4.3.2.2. Defining number of extracted factors

From Total variance explained value in Table 4.11, there were first six components

with eigenvalues above 1 including following values: 6.335; 3.099; 2.860; 2.259;

1.647 and 1.166. And those components explained total 64.26% of the variance,

exceeding than 50% explained total, so this value was appropriate.In addition, the Scree Plot Figure 4.1 showed that there was hard break from

components 2 and 3, and both of components 1 and 2 xplained 36.64% than four

remaining components. However, there was slight break after component 6, so the

number of extracted factors was six.

From the Factor Matrix Table 4.13, the first component presented most of the items

loaded on it while the second three components loaded quite the same, and the final

two components loaded the least.

In the Pattern Matrix Table 4.4, all items loading on six components were above .4.

Besides, there were five items loading on component 1, four items loading on

component 2, five items loading on component 3, three items loading on component

4, four items loading on component 5, and four items loading on component 6.



28

Table 4.4: EFA results



29

From the Correlation Table 4.5, the information about samples was enough with N

equal 230 available cases. Also, the direction of the relationship between the

variables, the correlations values were positive, that means one variable waschanged, the others would change follow the same direction. All the Pearson

Correlation values were less than .7 compared with the permitted range from -1 to

1, and should less than .7 (Pallant, 2011). Moreover, the significant level almost less

than .05, except the significant of correlation between Finance variable and Feature

variable, along with Finance variable and LivSpace variable, so all variables had got

a quite strong correlation and supported to explain together.

Table 4.5: Correlations among variablesFeature Finance Space Distance Environ

mentDecision

Feature

PearsonCorrelation

1 .118 .303** .216** .144* .156*

Sig. (2-tailed) .075 .000 .001 .029 .018

N 230 230 230 230 230 230

Finance

Pearson

Correlation .118 1 .092 .205**

.175**

.189**

Sig. (2-tailed) .075 .165 .002 .008 .004

N 230 230 230 230 230 230

Liv-Space

PearsonCorrelation

.303** .092 1 .287** .307** .399**

Sig. (2-tailed) .000 .165 .000 .000 .000 N 230 230 230 230 230 230

Distance

PearsonCorrelation

.216** .205** .287** 1 .411** .574**

Sig. (2-tailed) .001 .002 .000 .000 .000 N 230 230 230 230 230 230

Environment

PearsonCorrelation

.144* .175** .307** .411** 1 .214**

Sig. (2-tailed) .029 .008 .000 .000 .001 N 230 230 230 230 230 230

Decision

PearsonCorrelation

.156* .189** .399** .574** .214** 1

Sig. (2-tailed) .018 .004 .000 .000 .001

N 230 230 230 230 230 230



30

4.4.1.3. Normality, linearity, homoscedasticity & outliers

In the Normal Probability Plot (P-P) of the Regression Standardised Residual, mostof scores concentrated in the centre and along the 0 point (see Table 4.3). Also, the

Scatterplot Figure 4.4 showed that almost the presence of outliers were from -2 to

+2, there were only few of outline less than -3.0, so those value were acceptable. In

order to discover which items had got their values exceed permitted range (-3; 3),

the Casewise Diagnostics Table 4.17 was checked and recognized that the case 105

with a standard residual value of minus 3.23 and the case 205 with a standard

residual value of minus 3.37, those values less than minus 3 and fell outside the

normally distributed sample. However, this value was less than 1 percent of cases

to fall outside the range of below minus 3.0 or above 3.0, so the Normality

condition was appropriated.

Outliers were checked through the Mahal. Distance value of Residual Statistics

Table 4.18. This research had got five independent variables, so the critical value

4.4. HYPOTHESES TESTING BY MULTIPLE REGRESSION

4.4.1. Checking assumption of Multiple Regression

4.4.1.1. Sample sizeThe actual cases with 230 available respondents were more than the 90 minimum

required sample size of the multiple regression based on Item 3.2 in Chapter 3, so

the required sample size of multiple regression was available.

4.4.1.2. Assessment multicollinearity of independent variables

From the Coefficient Table 4.6, the tolerance indicators were quite high from .782

to .943 and those values were higher than the required value of .10. Also, the VIFvalues which were inversed of the Tolerance values and required not above 2 were

less than 2, so those VIF values were quite good. Conclusion, the multicollinearity

among independent variables did not violate.



31

was 24.32 (Tabachnick & Fidell, 2007). In this study, although the maximum value

was 34.23, the mean of outliers was 4.98, hence the outliers could be acceptable.

Consequently, all assumptions of the multiple regression were available.

4.4.2. Evaluating the model

From the Model Summary box (see Table 4.15), the R Square value of .356 shown

that there was 35.6 percent of the variance in the Decision dependent variable

explained by the model. From the Anova Table 4.16, the significance of the result

was .000, this means p < .0005, so the significance of the model was available.

4.4.3. Evaluating the independent of variables

Refer to the Cofficient Table 4.6, the Standardised coefficients labeled Beta showed

each of different variables have been converted to the same scale to compare the

contribution to explain “decision” dependent variable of independent variables. The

strongest contribution to explain “decision” was “distance” with the largest Beta

.522, the second strongest contribution was “living space” with the Beta .186. On

the contrary, “feature”, “finance” and “environment” were very small contributionto explain the “decision” dependent variable, they were considered to be explained

by “living space” and “distance”.

Moreover, according to the significant column, the significant values of “living

space” and “distance” were less than .05, while the significant value of “feature”,

“finance” and “environment” were greater than .05. Therefore, “living space” and

“distance” made a significant unique contribution to the prediction of the dependent

variable; otherwise, ‘feature”, “finance” and “environment” did not make a

significant unique contribution to the prediction of ‘decision” dependent variable.



32

Table 4.6: Coefficient table of MLR

4.4.4. Checking hypotheses of model

For hypothesis H1, the relationship between “feature” and “house purchase

decision” was the positive relationship with the “zero-order” correlation value of

positive .167. However, the significant value of “features” was .951, bigger .05 and

its Beta value was .003, so “feature” did not support to predict the “decision”

dependent variable.

Refer to hypothesis H2, there was a strong contribution of the “living space”

independent variable to the “decision” dependent variable because the correlation

zero-order value of .329, and the significant was .002, less than .05. In addition, the

Beta value of “living space” was .186, so it strongly supported to predict “decision”

dependent variable.





34

on house purchase

decision

dependent variable because

the significant value was

above .05

H4 There is a positive

impact of distance on

house purchase decision

.522 .566 .000 Positive impact,

very strong support for

explaining dependent variable

H5 There is a positive

impact of local

environment on house

purchase decision

-

.048

.233 .425 Positive impact,

no support for explaining

dependent variable because

the significant value was

above .05

4.4.5. Analysis effect of control variables by Multiple Regression

After recoding all variables of demography consisting of the gender, age, marital,

income and education, each control variables along with all independent variables

and dependent variable were input to run the multiple linear regression to clarify

which one of control variables would make an impact on the Decision dependent

variable.

According to the result from Table 4.19 to Table 4.24, all significant values of MLR

of control variables including sex_render, marital_render, education_render,

age_render, career_render and income_render were bigger than .05, so these value

reflected that there was not any significant difference in customer house purchasedecision with different classified demographics (Sex, Marital, Education, Age,

Career and Income) of customers.



35

CHAPTER 5. CONCLUSIONS AND IMPLICATIONS

This chapter summarizes the main areas covered in the thesis, presents the key

findings of the research, proposes managerial implication, shows the research

limitation and recommends for the future research.

5.1. RESEARCH OVERVIEW

The research defined the key factors on which the real estate agents, companies or

investors should focus when they expected to know on which regarding areas with

customers’ housing purchase decision making.

Based on the previous research, main variables were chosen and divided into groups

and a framework model was created to express the relationship between five

independent variables consisting of “feature”, “finance”, “living space”, “distance”,

“environment” and one “decision” dependent variable.

The study was begun with using the pilot test to adjust the questionnaire and to

check the clear meaning of the revised questionnaire, continued with data analysis

of reliability by using Cronbach’s Alpha analysis and of validity by using

Exploratory Factor Analysis, and ended with model and hypotheses testing by using

the multiple regression; moreover, the effect of demography on the dependent

variables was considered as well.

5.2. RESEACH FINDINGS

The multiple regression of the study shows that “house feature”, “finance”, “living

space”, “distance” and “environment” make a positive impact on “house purchasedecision”. The research results also shows that there are main 21 items which are

included in independent variables of the model affecting decision making to

purchase real estate of customers. These findings were shown that “house features”

are not consisted of “outdoor space” including “presence of garden” and “size of

garden” of Opoku & Abdul-Muhmin (2010, p.223), that can be explained by

shortage of land in Ho Chi Minh City in Vietnam. Also, “environment” of research



36

results are agreed with “environment” result of Adair et al. (1996, p.22), but there is

different with “slope/topography of the land” and “wooded area/ tree coverage”

from “environment” variables of Adair et al. (1996, p.22). Those differences caused by the tree factor are not paid much attention in Vietnam and many Vietnamese

environmental indicators are below average as announced by the World Economic

Forum in 2012.

The findings of the study show that customer demands with house price less than 20

million per square meter are very much at the rate of 87.9 percent of respondents,

and from 21 to 32 million per square meter are 9.1 percent of respondents. Hence,the low-medium income market is very large and need to be concentrated on it. The

most priority of customers is single house with small size of 72.6 percent, and the

second one is apartment of 21.3 percent. The research findings agree with the result

toward the small house of consumers in Saudi Arabia (Opoku & Abdul-Muhmin,

2010, p.224).

On the other hand, the results of the study has found that the demography consisting

of gender, marital status, education, ages, career and income are not support to

explain dependent variable and no difference of customer decision making in the

different multi-group analysis between gender, age, marital status, income and

education level. The findings of this study are contrary to Haddad (2011, p.234)

where there are significant differences in customer decision due to gender and age.

It is explained by customers of age less than 36 of the research are at the rate of 99.1

percent. In addition, there is a strict “gender” separation in Saudi Arabia, adjacentJordan country (Opoku & Abdul-Muhmin, 2010, p.222).

5.3. MANAGERIAL IMPLICATIONS

The better knowledge of real estate consumer and their household decision making

will lead prediction of decision making in the real estate, it is important role for

both managerial board of real estate enterprises and investors. Also, this study has

got practical implications for individual and decision makers in organizations to



37

decide suitable strategies in marketing or investment to attract, especially segment

of low-medium income customers with priority to small houses than apartments is

very high.

It creates a general picture about customer demands for government to develop right

housing programs. From the research finding, it is suggested to concentrate on

programs for low-medium income citizens due to their huge demands for house.

Moreover, the real estate agencies or enterprises can use the model and list of items

as the checklist to consider during house purchase decision-making process of

customers.

5.4. RESEARCH LIMITATIONS & DIRECTIONS FOR FUTURE

RESEARCH

Data of the research are surveyed in Ho Chi Minh City with a few main

postgraduates and student groups of UEH with limited characteristics of

demography. Therefore, it is recommended to conduct a survey with different areas

in Vietnam with lager target population. It will be useful to extend ages level of

respondents of above than 36 so that comparing with many different age levels will be better. Also, it will be interesting to investigate decision-making of husband and

wife parties solely on which of the factors.

Moreover, it is recommended that future research will concentrate on exploring

detail of customer choosing with each type of houses separately such as apartments,

villas, commercial buildings, officers. Besides, it is concerned to have more

research about real estate officers and companies to meet their demands to rent

officers, commercial buildings or villas.

In the multiple regression analysis, although in the theory of the previous research,

there is contribution to explain dependent variable from the independent variables

of ‘feature”, “finance” and “environment”, but their beta values and significant of

the research showed that they are not contribute to explain the main dependent

variable of house purchasing decision, so this result needs to be noted in the future

research.



38

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Zhang, X., Prybutok, V., & Strutton, D. (2007). Modeling influences on impulse

purchasing behavior during online marketing transation. Journal of

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Research, 19(4), 489-504.



42

Appendix 1: The first draft of the questionnaire

No. Factors

Is the ...

an important element to a

house purchase decision?

%Agreed

Commentsof the first pilot test

1House

Feature

Area of usable floor of the

house100%

- The item “finishing”

(3&4) should be deleted

because its content was

inside the content of

“construction quality”.

2 Status of house legal 100%

3 Type of finishing 20%

4 Quality of finishing 40%

5Interior design and

decoration100%

6 External design 80%

7 Construction quality 100%

8 Construction duration 80%

9Type of house (Town house,

apartment, villa)100%

10 Private

Living

Size

Size of kitchen 100% - Agreed with all items of

the “private living size”.11 Quantity of bedrooms 100%

12 Quantity of bathrooms 80%

13 Size of living room 100%

14 Storey of house 80%

15 Finance House price 100% - It should be added item

“the registration fee’ based

on “tax” item.

16 Maximum mortgage 100%

17 Maximum monthly payable 80%

18 Interest rate 100%

19 Your monthly income 100%

20 Payment duration 100%

21 The registration fee 80%





44

No. Factors

Is the ...

an important element to a

house purchase decision?

%

Agreed

Comments

of the first pilot test

40 Nearby traffic 60%

41 Decision I have got a plan to buy a

new house.100%

- Agreed with all of items.

42 I will make my effort to buy

a new house100%

43 I am an important person

affecting house purchase

decision of my family.

100%



45

Appendix 2: The English questionnaire

Dear Sir/ Madam,

In period of economic crisis at present, the business status of Vietnamese enterprises has

been facing with many difficulties and big challenges. One of departments has been

seriously affected most is the real estate with the quantity of exchanges decreased

seriously.

It is necessary and useful for both business enterprises of real estate partially and relative

real estate companies generally to measure right the key factors affecting house purchase

decision of customers.

You are pleased to give some your worth time to answer the following questionnaire. All

information from this questionnaire will be secret and used for research purpose only.

Thank you.

THE QUESTIONNAIRE

PART 1: THE MAIN FACTORS AFFECTING HOUSE PURCHASE DECISION

Please consider carefully each variable, then basing on your knowledge and experience,you will measure the important rate of each variables affecting your house purchase

decision by circle which one you consider it as the best.

Scale from 1 to 5:

1 2 3 4 5

Totally Unimportant Very Important (sentence 1 to 31 )

Strongly Disagree Strongly Agree (sentence 32 to 34)

No.Is the ...

an important element to a house purchase decision? Important level

1 Area of usable floor of the house 1 2 3 4 5

2 Interior design and decoration 1 2 3 4 5

3 House price 1 2 3 4 5

4 Construction duration 1 2 3 4 5





47

No.Is the ...

an important element to a house purchase decision? Important level

33 I will make my effort to buy a new house 1 2 3 4 5

34I am an important person affecting house purchase decision of my

family.1 2 3 4 5

PART II: GENERAL INFORMATION

Please stick a cross (x) into the appropriate blanks

1. Gender Male:

Female:

2. Marital

Single without children ( ) Married with children ( )

Single with children ( ) Divorced ( )

Married without children ( )

3. Education

Highchool ( ) University ( )

Colleague ( ) Postgraduate ( )

4. Ages

≤ 25: ( ) 26-35: ( ) 36-45: ( ) 46-55: ( ) Above 55 ( )

5. Career

Worker

Officer

Business owner/ Manager Other (Please specify): …………..

6. Your monthly income

< 5 million VND ( ) 15-24 million VND ( )

5-9 million VND ( ) 25-40 million VND ( )







50

TT

Yếu tố....

có quan trọng đến quyết định mua nhà không?

Mức độ

quan trọng

1 Diện tích sử dụng của ngôi nhà 1 2 3 4 5

2 Thiết kế và trang trí bên trong 1 2 3 4 5

3 Giá của ngôi nhà 1 2 3 4 5

4 Thời gian xây dựng ngôi nhà 1 2 3 4 5

5 Kiến trúc bên ngoài ngôi nhà 1 2 3 4 5

6 Diện tích phòng khách 1 2 3 4 5

7 Khả năng thế chấp tối đa 1 2 3 4 5

8 Khả năng thanh toán nợ hàng tháng tối đa 1 2 3 4 5

9 Lãi suất vay 1 2 3 4 5

10 Thu nhập của Anh (Chị) 1 2 3 4 5

11 Chiều rộng của lòng đường gần nhà 1 2 3 4 5

12 Lối đến trục đường chính 1 2 3 4 5

13 Khoảng cách từ chỗ nhà đến nơi làm việc 1 2 3 4 5

14 Khoảng cách từ nhà đến các khu vực kinh doanh trung tâm 1 2 3 4 5

15 Diện tích của nhà bếp 1 2 3 4 5

16 Số lượng phòng ngủ 1 2 3 4 5

17 Hàng xóm xung quanh 1 2 3 4 5

18 Tình hình an ninh 1 2 3 4 5

19 Thời gian thanh toán nợ vay 1 2 3 4 5

20 Tình trạng pháp lý của ngôi nhà 1 2 3 4 5



51

TTYếu tố....

có quan trọng đến quyết định mua nhà không?

Mức độ

quan trọng

21 Lệ phí trước bạ mua nhà 1 2 3 4 5

22 Số lượng phòng tắm 1 2 3 4 5

23 Chất lượng xây dựng 1 2 3 4 5

24 Loại nhà (Nhà phố, nhà chung cư, biệt thự) 1 2 3 4 5

25 Số tầng của ngôi nhà 1 2 3 4 5

26 Cảnh quang xung quanh 1 2 3 4 5

27 Tiếng ồn xung quanh 1 2 3 4 5

28 Ô nhiễm của môi trường xung quanh 1 2 3 4 5

29 Khoảng cách từ nhà đến chợ 1 2 3 4 5

30 Khoảng cách đến trường học 1 2 3 4 5

31 Khoảng cách đến khu vui chơi giải trí 1 2 3 4 5

32 Tôi đang có kế hoạch để mua nhà. 1 2 3 4 5

33 Tôi sẽ cố gắng để mua nhà. 1 2 3 4 5

34 Tôi là người quan trọng để đóng góp vào quyết định mua nhà. 1 2 3 4 5

PHẦN II: THÔNG TIN CHUNG

Xin Anh/ Chị vui lòng đánh dấu vào các ô trống thích hợp:

1. Giới tính Nam: Nữ:

2. Tình trạng hôn nhân

Độc thân, không có con ( ) Đã lập gia đình, đã có con ( )

Độc thân, có con ( ) Đã ly hôn ( )

Đã lập gia đình, chưa có con ( )



52

3. Tình trạng học vấn

Trung học ( ) Đại học ( )

Cao đẳng ( ) Trên đại học ( )

4. Tuổi

≤ 25: ( ) 26-35: ( ) 36-45: ( ) 46-55: ( ) trên 55: ( )

5. Nghề nghiệp

Công nhân Nhân viên

Doanh nhân/ Nhà quản lý Khác (Xin ghi rõ): ……………..

6. Thu nhập hàng tháng của Anh (Chị)

< 5 triệu ( ) 15-24 triệu ( )

5-9 triệu ( ) 25-40 triệu ( )

10-14 triệu ( ) Trên 40 triệu ( )

7. Anh (Chị) đã mua hay dự định mua nhà với kích cỡ nhà ở như thế nào?

< 50 m2 ( ) 101-150 m2 ( )

51-75 m2 ( ) >150 m2 ( )

76-100 m2 ( )

8. Anh (Chị) đã mua hay dự định mua nhà với giá nhà trong khoảng nào?

< 4 triệu/m2 ( ) 15-20 triệu/m2 ( ) 33-38 triệu/m2 ( )

5-8 triệu/m2 ( ) 21-26 triệu/m2 ( ) 39-45 triệu/m2 ( )

9-14 triệu/m2 ( ) 27-32 triệu/m2 ( ) ≥ 45 triệu/m2 ( )

9. Anh (Chị) đã mua hay dự định mua loại nhà nào?

Chung cư ( ) Biệt thự đơn lập ( )



53

Nhà phố đơn lập ( ) Biệt thự song lập ( )

Nhà phố song lập ( ) Loại khác:…………….. ( )

10. Nếu đã mua, Anh (Chị) đã mua nhà năm nào dưới đây:

Trước năm 2000 ( ) 2007-2009 ( )

2001-2003 ( ) 2010-2012 ( )

2004-2006 ( )

Xin chân thành cám ơn sự giúp đỡ của Anh (Chị).

Trân trọng kính chào!



54

Table 4.8: Descriptive statistics

N Minimum Maximum Mean Std.

Deviation

33_Gender 230 0 1 .67 .471

34_Marital 230 1 5 1.43 .981

35_Education 230 1 4 3.24 .598

36_Age 230 1 3 1.53 .518

37_Career 230 2 4 2.20 .574

38_Income 229 1 6 2.08 1.038

39_House Size Choosing 229 1 5 2.63 .986

40_House Price Choosing 229 1 8 3.19 1.434

41_House Type Choosing 227 1 6 2.01 .823

42_House Buying Time 54 1 5 3.93 1.344

Valid N (listwise) 54

Table 4.9: Cronbach’s Alpha with full items for each constructs

Items Scale

Mean if

Item

Deleted

Scale

Variance

if Item

Deleted

Corrected

Item-Total

Correlation

Squared

Multiple

Correlation

Cronbach's

Alpha if

Item Deleted

Fea01_House Size 22.45 7.340 .653 .690 .591

Fea02_House's Legal Status 24.03 7.811 .465 .374 .641

Fea03_Interior Design 23.15 8.319 .557 .511 .632

Fea04_External Design 22.59 8.881 .400 .323 .665

Fea05_Construction Quality 23.35 7.461 .743 .794 .580

Fea06_Construction

Duration23.71 8.171 .253 .096 .712

Fea07_Type of house 23.48 9.037 .080 .032 .767



55

Liv01_Kitchen Size 13.38 4.638 .468 .366 .497

Liv02_Bedroom Quantity 13.34 4.714 .536 .382 .473

Liv03_Bathroom Quantity 13.91 4.249 .520 .340 .461Liv04_Living room Size 12.58 4.856 .371 .188 .549

Liv05_Storey of House 13.09 6.158 .012 .002 .726

Fin01_House Price 23.59 14.391 .587 .588 .734

Fin02_Max Mortgage 24.77 13.975 .360 .174 .782

Fin03_Max Repayment 24.36 11.673 .767 .750 .682

Fin04_Interest rate 24.14 12.356 .714 .708 .698

Fin05_Income 23.73 13.445 .705 .684 .710

Fin06_Pay Duration 24.26 14.357 .489 .295 .748

Fin07_Registration Fee 24.45 17.323 .013 .020 .834

Dis01_Adjacent Street 22.66 12.278 .578 .670 .719

Dis02_Work Distance 22.53 9.831 .700 .859 .680

Dis03_Market Distance 22.93 11.197 .772 .877 .677

Dis04_School Distance 22.98 11.030 .767 .772 .676

Dis05_Recreation Distance 22.05 13.569 .377 .155 .756

Dis06_Business Distance 22.96 14.239 .139 .041 .806

Dis07_Main Access 22.78 13.824 .202 .053 .794

Env01_Neighbour

Condition20.83 5.970 .700 .653 .696

Env02_Security 20.67 6.537 .583 .534 .728

Env03_View 19.91 6.773 .284 .116 .785

Env04_Noise 20.47 4.791 .728 .710 .664

Env05_Pollut ion 20.15 5.306 .677 .649 .685

Env06_Nearby Traffic 20.96 6.300 .272 .114 .807

Dec01_Plan to buy 6.33 1.693 .794 .785 .617

Dec02_Try to buy 6.07 3.013 .461 .213 .928

Dec03_Important person 6.26 2.141 .819 .786 .598



56

Table 4.10: KMO and Bartlett’s test

Table 4.11: Total variance explained



57

Table 4.12: Correlation among variables (Partial only)





59

Table 4.13: Factor Matrix



60

Table 4.14: Factor Correlation Matrix

Table 4.15: Model summary

Table 4.16: Anova

Model Sum of

Squares

df Mean

Square

F Sig.

1

Regression 55.385 5 11.077 24.717 .000 b

Residual 100.385 224 .448

Total 155.770 229

a. Dependent Variable: Decision

b. Predictors: (Constant), Environment, Feature, Finance, LivSpace, Distance

Table 4.17: Casewise diagnostics



61

Table 4.18: Residuals statistics



62

Figure 4.2: Regression standardized residual

Figure 4.3: Normal P-P plot



63

Figure 4.4: Scatterplot

Table 4.19: Cofficients of MLR including Sex_Render



64

Table 4.20: Cofficients of MLR including Marital_Render

Table 4.21: Cofficients of MLR including Education_Render

Table 4.22: Cofficients of MLR including Age_Render



65

Table 4.23: Cofficients of MLR including Career_Render

Table 4.24: Cofficients of MLR including Income_Render

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