Key factors influencing on apartment purchase decision of customers in ho chi minh city

LIST OF TABLE Table 3.1: Main factors affecting customers’ housing purchase decision .... The main reasons of the crisis were the real estate market supply did not meet customer demands,

RESEARCH PROJECT

(BMBR5103)

KEY FACTORS INFLUENCING ON APARTMENT PURCHASE DECISION OF CUSTOMERS IN HO CHI MINH CITY

ADVISOR’S NAME & TITLE : A Prof PHAN DINH NGUYEN

September 2014

Page

ADVISOR’S ASSESSMENT

Advisor’s signature

ACKNOWLEDGEMENTS

First of all, I would like to express my sincere thanks to my thesis coach – A Prof Phan Dinh Nguyen for dedicated deep guidance, precious advice and teaching me data analysis guidance in his precious time

I also would like to thank you all my colleagues and friends of HUTECH class for their valuable contributions to give comments and suggestion to revise the questionnaire survey

Warmly thanks to my classmates, friends and all anonymous participants, and especially my family for their supports and inspiring me to complete the MBA course

Sep 2014

Pham Thi Ngoc Van

ABSTRACT

The speed of growing population in a recent decade together with the economic development, the demand of housing is more and more increasing while the price of ownership is big issue at Vietnam Condominium is becoming the convenient living model for people stay in big cities such as: Ha Noi, HCM and Da Nang where the demand of living and working of immigration increasing The beneficial view of condominium is the residents can enjoy convenient facilities from their own apartment to opening space with greenery system anywhere

The demand of condominium in big cities is certain and especially at Ho Chi Minh City where the research will pay attention real demand for middle income people The researcher will identify factors or determinants influence on the demand of buying apartments at HCM City

The study aims to identify the key factors affecting the decision of customers to buy residential apartments in HCMC For study purpose, a sample of 300 salaried class persons was taken by using purposive sampling technique The sample consisted of those respondents who either bought an apartment or are planning to buy Respondents were asked to give their opinion about listed variables on Five-point Likert Scale By using Exploratory Factor Analysis, six factors were extracted which explained 73.916 per cent of total variance

Contents

ADVISOR’S ASSESSMENT i

ACKNOWLEDGEMENTS ii

ABSTRACT iii

LIST OF TABLE vii

LIST OF FIGURE ix

LIST OF ABBREVIATIONS x

CHAPTER 1 INTRODUCTION 1

2.1 BACKGROUND 1

2.2 PROBLEM STATEMENT 3

2.3 RESEARCH OBJECTIVES 4

2.4 RESEARCH QUESTIONS 5

2.5 SCOPE OF STUDY 6

2.6 METHODOLOGY 6

2.7 CHAPTER OVERVIEW 7

CHAPTER 2 LITERATURE REVIEW 8

2.1 APARTMENT OVERVIEW 8

2.2 LITERATURE REVIEW 11

2.2.1 Feature 11

2.2.2 Finance 12

2.2.3 Distance 12

2.2.4 Facilities 13

2.2.5 Environment 13

2.2.6 Purchase decision 14

2.3 CONCEPTUAL FRAMEWORK 14

CHAPTER 3 RESEARCH METHODOLOGY 16

3.1 RESEARCH PROCESS 16

3.2 SAMPLE SIZE 19

3.3 MEASUREMENT SCALE 19

3.3.1 Measurement scale 19

3.3.2 Pilot test 19

3.4 MAIN SURVEY 22

3.5 DATA ANALYSIS METHOD 23

3.5.1 Reliability measure 23

3.5.2 Validity measure by EFA (Exploratory Factor Analysis) 23

3.5.3 Multiple regression analysis 24

CHAPTER 4 DATA ANALYSIS AND RESULTS 26

4.1 PREPARATION 26

4.1.1 Editing 26

4.1.2 Coding 26

4.2 DESCRIPTIVE DATA 29

4.3 ASSESSMENT MEASUREMENT SCALE 32

4.3.1 Cronbach’s Alpha 32

4.3.2 Exploratory Factor Analysis (EFA) 37

4.4 HYPOTHESES TESTING BY MULTIPLE REGRESSION 42

4.4.1 Checking assumption of Multiple Regression 42

4.4.2 Evaluating the model 43

4.4.3 Evaluating the independent of variables 43

4.4.4 Checking hypotheses of model 44

4.4.5 Analysis effect of control variables by Multiple Regression 46

5.2 RESEARCH FINDINGS 47

5.3 MANAGERIAL IMPLICATION 48

5.4 RESEARCH LIMITATIONS & DIRECTIONS FOR FUTURE RESEARCH HYPOTHESIS 49

REFERENCES 50

Appendix 1: Vietnamese questionnaire 64

Appendix 1: English questionnaire 69

LIST OF TABLE

Table 3.1: Main factors affecting customers’ housing purchase decision 21

Table 4.1: Codebook of questionnaire items 26

Table 4.2: Characteristics of respondents 29

Table 4.3: Cronbach’s Alpha test results 34

Table 4.4: EFA results 39

Table 4.5: Correlations among variables 41

Table 4.6: Coefficient table of MLR 44

Table 4.7: Hypotheses results 45

Table 4.8: Descriptive statistics 74

Table 4.9: Cronbach’s Alpha with full items for each constructs 75

Table 4.10: KMO and Bartlett’s test 76

Table 4.11: Total variance explained 77

Table 4.12: Correlation among variables (Partial only) 78

Table 4.13: Factor Matrix 79

Table 4.14: Factor Correlation Matrix 80

Table 4.15: Model summary 80

Table 4.16: Anova 80

Table 4.17: Casewise diagnostics 81

Table 4.18: Residuals statistics 81

Table 4.19: Coefficients of MLR including Gender_Render 85

Table 4.20: Coefficients of MLR including Marital_Render 85

Table 4.22: Coefficients of MLR including Age_Render 86 Table 4.23: Coefficients of MLR including Occupation_Render 87 Table 4.24: Coefficients of MLR including Income_Render 87

LIST OF FIGURE

Figure 2.1 : Research Model 15

Figure 3.1: Research process 18

Figure 4.1: Scree plot 78

Figure 4.2: Regression standardized residual 82

Figure 4.3: Normal P-P plot 83

Figure 4.4: Scatterplot 84

LIST OF ABBREVIATIONS

ANOVA Analysis Of Variance

CFA: Confirmatory Factor Analysis

EFA: Exploratory Factor Analysis

SPSS: Statistical Package for Social Scientists EFA Exploratory Factor Analysis

GSO Vietnam Government Statistics Office HCMC Ho Chi Minh City

Mil Million

MLR Multiple Linear Regression

CHAPTER 1 INTRODUCTION

Vietnamese idiom says: “An cư lạc nghiệp” means that everybody wish to desire a stable dwelling place aimed to reside peacefully in order to be a foundation to build long term occupation This wishing is completely satisfactory that each of us (you and me) works hard to achieve it

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)

Ho Chi Minh City, the primary economic hub for Vietnam, had a population of around 7.9 million as at December 2013 Ho Chi Minh is a biggest city of Vietnam where people choose to live, work and reside Therefore, demand of housing is increasing significant while the current price of house ownership is extremely high in spite of critical declines in real estate in recent years And choosing apartment to reside is emerged as a practical need of people

First, thank for the economic growth and rapid urbanization and demand of housing spending has been increasingly drastic in recent years In Vietnam, the residential market in urban areas is expanding widely Expanding urban land supply is critical for cities in which Vietnam requires rapidly growing urban economies with supporting infrastructure and physical structure

In 2013, the Vietnamese economy recorded an annual growth rate of 5.42 boasting an

GDP per capital of Vietnam has been growing steadily since 2000 GDP per capita has improved steadily and in line with GDP growth since 2000 The graph below shows the average national GDP; however, the actual GDP in cities such as Ho Chi Minh City and Hanoi is significantly higher (Grant Thornton, 2014)

Inflation in Vietnam averaged 7.33% from 1996 until 2013 While inflation peaked at 28.3% in August 2008, followed by inflation of 18.58% in 2011, the Vietnamese government successfully controlled the inflation at 6.6% in 2013, the lowest rate in a decade thanks to its monetary tightening policy Grant Thornton (2014)

From positive signals of economic, the real estate has been started rebounding with many projects deployed in which segment of apartment for residence is emerged sharpen while the demand of people especially are young people is increasing

This thesis examines housing preference of people live and work (especially urban

residents) in Ho Chi Minh City According to the report of “Vietnam Housing Sector

Forecast to 2015” indicates the housing requirement is rising with trend of nuclear

families has created immense demand expected to grow around 6% during 2012-2015 The condominiums in new urban areas are preferred by many young people because the types of condominiums offer various advantages over traditional housing types such as: lower price, good infrastructure with various facilities and services which are suitable for the needs The demand of young people for condominiums has been increasing massively in recent few years

One of the most significant economic decisions of everybody is buying a house oneself The need of dwelling is practical and more increasingly when the population is growing and choosing big cities for permanent residency

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 2013, 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 of information”

This thesis focuses on factors which influence on people's purchase decision toward apartments / condominiums in HCM City

The problem here is demand of choosing condominium for residence of people in HCM City has been emerged obviously and there are many projects of apartment and condominium have also been developing with multi-diversified segments by both local and international project investors, developers in a recent decade

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

In fact, the demand and the supply do not meet each other due to having some undefined problems relating to buying decision of people as follow:

- In Vietnam, in spite of having basic legal framework for classifying apartments

of Department of Construction such as: regulatory and Decree 121/2013/CP (effective from 30 November 2013, replaced for Decree 23/2009/CP) but in practice, definition

of apartments is usually stated unmethodical way by project ownership investor These investors seem to offer many kinds of apartment with multifarious names are labeled as: middle cost apartment, high-class apartment, Penthouse apartment, … etc to launch

on the market with unreasonably ridiculous prices

- There are many ambiguous concepts in definition of apartments in Vietnam and also unclearly founded criteria to classify as well as assess apartment in HCM City as particular

- The quality of apartment as well as infrastructure and facilities in HCM location are big questions need to be concerned

Some matters relating to legal procedure such as: certificate of apartment ownership, term of ownership

“There are two main fields of customer research are how customers go about making decisions and how decisions should be made In addition, “creating true value 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)

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 making decision

to buy real estate as well as which major factors have got relationship with customer decision

In practice, beside of problems raised above, there are many limitations that the thesis would study after defining and collecting opinions and contribution through research methodology conduct This research is supported to capture the key factors influence

on demand of people in choosing apartment for dwelling in HCM City

 To determine factors affect purchasing demand of residential apartment in HCMC

 To study which critical factors to impact on buying decision of apartment

 To suggest solutions to match demand of people and supply of residential apartments located in HCMC

The objective of thesis aims to conduct a survey on housing preference (condominiums /apartments) of people in Ho Chi Minh City and examine difficulties, challenges in choosing apartment for long term residence

The thesis aims to understand buying behavior of people in HCM City by studying their decision making process and type of buying behavior

This thesis will conduct studying any influence both positive and negative that may impact on purchasing decision of apartment

From investigation, survey and hypothesis to analyze each factors as well as stimulants

to describe factual situation toward residential apartment Once identifying and defining pattern of residential apartment demand corresponding to each segment, investors, planners, developers in real estate industry could use to the information to adapt in their company strategy to match with factual market needs

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

1.5 SCOPE OF STUDY

The study scope should be surveyed in apartment segment at HCM City where is considered a typical representative is given its population profile, excellent demographic statistical data in general The research only focuses on key factors which influence to demand of residential apartment

Target respondents for this thesis should be people who are living and working in HCMC

The real estate is a broad topic requires further deeply research with consuming significant time and efforts This thesis is relatively provided merely firsthand information to assess real demand and determinants impact on residential apartment buying decision

Research methodology is an important part of the whole process of research to determine how the research should be undertaken

In order to achieve the objective, both primary data and secondary data are collected to use in the thesis:

The primary data is gained by distributing questionnaire on residential apartment purchasing decision to study key factors influence on The questionnaire consists of 3 parts: socio-demographic; apartment preference pattern; apartment purchasing intention; factors influence on buying decision

The secondary data is collected from a wide variety of reliable sources including academic books, articles, newspapers, magazines, journals, publications, reports of real estate companies, official websites Data related to apartment market in HCM City, definition and theory of apartment preference pattern; apartment purchasing intention; decision making process and previous finding will be utilized in the thesis

 By analyzing results of the primary data and compiling the secondary data

 The research method refers to techniques and procedures

 The research methodology refers to research designs and data collection via questionnaires

The inductive approach should be applied for this research to draw results from data collected and analysis and empirical observation

This thesis consists of chapters including:

Charter 1: Introduction This chapter is to present the general introduction of

residential apartment demand and overview of Vietnamese real estate industry with apartment segment particularly The problem statement and objectives of study are described in this chapter in details

Charter 2: Literature Review The conceptual framework of key factors of apartment

preference pattern; apartment purchasing intention, decision making process are described and the previous model/research to support for proposal hypothesis and research model for this study

Charter 3: Research Methodology This chapter describes the research methodology

and to develop the process study, measurement scale, sampling plan and the method of analysis and testing hypothesis

Charter 4: Data analysis & Results This chapter presents the result of research such as

statistical analysis and discussion

Chapter 5: Conclusions & Implications This chapter presents the main findings and

managerial implications Besides, the limitations and further studies are also shown

CHAPTER 2 LITERATURE REVIEW

This chapter presents overview of previous literatures relating to apartment purchase decision making of customers Also, a conceptual framework is built up and relative hypotheses of research are raised

According to David R Henderson, the concise of encyclopedia of economic (David, 2002), the law of demand is built almost the whole edifice of economics The law of demand states that when the price of a good rises, the amount demanded falls, and when the price falls, the amount demanded rises However, the real estate industry does not always apply this law conclusively The effective housing demand is the amount of housing for which the population is willing and able to pay The effective housing demand for private housing is volatile and has been affected in the past by the market supply and market allocation mechanism There are many factors impact on demand and decision of buyer of apartments such as: price, financing supports, household income, features of apartment, legal framework, developer, contractor, management… etc

Potential buyers have been divided into two subgroups: residents who buy and sell houses for personal use, and speculators and property developers who make money by selling and buying property (Roehner, 1999)

Residents’ choice to migrate from one city to another depends on the employment, income and political and social environments (Dieleman et al 2000) have demonstrated that population growth and employment growth seem to create differences in the rate of turnover and the differences in price levers Many researchers claim that income is an important determinant of housing demand

There are many speculators like to get involved in buying apartments “off-the-plan” as they can see a quick profit is there to be made (Peter, 2013) These speculators like to buy off-the-plan is that they hope the property will be worth more when it is completed

but they seem to forget a basic economic principle; price is a function of supply and demand In scenario of oversupply of residential apartments in HCMC and the price of

an item is dependent on the demand for that item but in fact, the demand for apartment does not meet match with the supply due to price has been pushed up very high by these speculators

The population density in HCMC could be considered highest with the average density

in the central at 27,000 persons per km2 when comparing with density cities in the world, namely Singapore and Hong Kong where high-rise apartment buildings are ubiquitous The density in Hong Kong Island stands at 16,000 persons per km2 (2008) (Census & Hong Kong., 2009), and the density in Singapore is 7100 persons per km2 (2010) (Department of Statistics, 2010) (Jieming Zhu, 2011)

Another factor influences on demand of buying apartment is financing support from bank and government Credit constraints in the form of down payment requirements significantly affect housing consumption for many buyers Bank loans are usually required by most households to finance their purchases The credit or financial market typically cannot lend on the basis of the borrower’s expected future income prospects Therefore, current income and current financial assets then become important indicators of a borrower’s means of repaying a loan The availability of housing loans and government subsidies will influence consumers’ choice of whether or not to buy a home (Omar and Ruddock, 2002) The demand for housing also depends on the mortgage rates and the general level of consumer confidence (Tutor2u, 2003)

The study of RNCOS (Business Consultancy Services) on Vietnam Housing Sector Forecast to 2015, one of issues relating to housing demand of Vietnamese is affordability The report studies the affordability scenario of Vietnam and found that house price to household income ratio is much higher than the neighboring regions,

Young people live in big cities such as Ha Noi, Da Nang and HCM have great demand

of housing based on various experiences o life milestones (adulthood, education and graduation stages, job seeking, and occupation path, leaving parental home, job opportunities, marriage and children) These various experiences somehow influence

on demands of housing types, attributes and services associated into

But the price of residential condominium in HCMC is still quite high and people tends

to purchase apartments depends on the level of disposable income (Bible&Hsieh (2001); Brandy&Parsons (2002)) High level of disposable income will increase the ability of spending and especially in purchase of housing is a major goal of most people

Firstly, price is one of important determinants of a household choice and buying decision However, unlike other consumption goods, the housing market is unique because it manifests the characteristics of durability, heterogeneity, and spatial fixity Thus, to model this differentiation effectively, the second approach of the hedonic price model has been introduced The hedonic price model posits that goods are typically sold as a package of inherent attributes (Rosen, 1974) Therefore, the price of one house relative to another will differ with the additional unit of the different attributes inherent in one house relative to another house The relative price of a house

is then the summation of all its marginal or implicit prices estimated through the regression analysis The market price of a housing unit’s bundle of inherent attributes, such as: location, structure and neighborhood or environment attributes (Freeman, 1979)

The house price determination is associated with macro (market-related factors) and micro (house-specific factors) How do the researchers link these factors to the price? The first step to this is to establish theoretical background for the pricing theory Thus, the review begins with identifying price determinants The review will then followed

by discussion on the utilization of statistical tool as one of the approaches available to

segregate the contribution of each variable hence enabling the quantification of perceived contribution in the overall price

In this thesis, the micro factors (house-specific factors should be focused replace for macro factors (such as: interest rates, inflation rates, taxes are critical explanations for setting price from developers) The micro factors could be derived from buyers of apartments in evaluating factors such as: structural (physical characteristic), location and life environment attributes (contain of neighborhood) impact on buying decision process

During last economic recession years, there was a phenomenon of bubble real estate industry where the price of apartment segment had pushed up very high and caused customers who have real need could be unaffordable to buy This had been occurring problem of affordability and inequality (Yip, 2008) and major real estate developers, private and public alike to focus on squeezing into higher end market and this had made low income workers out of reach for new flats

The city government increasingly seeks solutions to support middle income and low income group could be affordable to buy apartment (SGGP, 2013) According to HCMC Real Estate Association said many capable Vietnamese businesses have channeled their capital into the luxury housing sector to satisfy demand of high income groups, while affordable housing apartments for low-income people has fallen short The unbalanced supply of accommodation has now left a large unsold inventory

2.2.1 Feature

One of important factors impact on buying decision of apartment is feature attribute (Quiley, 2011) and (Sengul et al 2010) also confirmed that feature has significant effects on customers’ house purchase decision making

The feature of apartment includes designs, apartment size, building quality are key determinants to making decision of buying apartment (Adair et al., 1996; Daly et al., 2003; Sengul et al., 2010, p.218; Opoku & Abdul-Muhmin, 2010)

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

2.2.2 Finance

Each of buying decision made is based on financial capacity and especially in purchasing an apartment, financial attribute significant impact on customer behavior

“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

H2 There is a positive impact of financial status on customers’ house purchase decision

2.2.3 Distance

Thirdly, 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 “a person’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,

H3 There is a positive impact of distance on customers’ house purchase decision 2.2.4 Facilities

“Apartment facilities” is one of most important factors affecting to “consumer housing decision” Facilities consists of “Storey of apartment building”, “Fire prevention & extinguish system and exit system of apartment building”, “Basic facilities must have: ATM stations, branches of banks, convenient stores, mini-marts, pharmacist, clinics, and kindergarten”, “Provision system of electric & water”, “Sewage and drainage system of apartment building” and “Parking place” (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,

H4 There is a positive impact of facility on customers’ house purchase decision 2.2.5 Environment

Fifthly, “environment” including “Community / Neighborhood”, “Living space”,

“Apartment security 24/24”, “View”, “Private” and “Pollution” is stated as one of the determinants of a household’s residential decision (Adair, 1996, p.23) It is confirmed that “environment” has a big influent to housing buyer (Tajima, as cited in Opoku & Abdul-Muhmin, 2010, p.224) and it is agreed by Morel et al (2001, p.1119) Particularly, “neighborhood” quality is paid intention highly to house purchase decision making of customer (Gabriel & Rosenthal, 1989, p.240) Therefore,

H5 There is a positive impact of local environment on customers’ house purchase decision

2.2.6 Purchase decision

Customer behavior is an important research topic for recent decades “There is also a clear shift from rational factors to psychological factors and to social decision factors” (Bargh, 2002) Beside, there is a link between the “intention to purchase” to “decision

to purchase” of customers, especially the decision related to purchase real estate (Ajzen, 1991, p 179; Han & Kim, 2010, p 659; Kunshapn & Yiman, 2011, p.7579)

2.1.1 Demography

“Demographic” characteristics of customers are internal factors related to decision making (Mateja & Irena, 2009) “Demographic” characteristics consist of the individuals in term of “gender, age, educational status, marital status, occupation, the quantity of family members and children, as well as the residence property”

“Demographic” characteristics consist of age (Yalch & Spangenberg, 1990), education (Gattiker et al., 2000), income level (Dawson et al., 1990), gender (Zhang et al., 2007) which are factors influenced on the “purchase intention” of customer

Particularly, “gender” has significantly influence on the financial feature of the house (Sengul et al., 2010, p.214) It is also confirmed that there is a significant 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 so that investigate whether effect of those demography variables on housing purchase decision making of customers or not

“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 : Research Model

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

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 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 the second business certification in the University of Economic Besides, a small group about 24 persons with a wide variety of occupations 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

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”, “finance”, “distance”,

“facilities” 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 “occupation” on customers’ housing purchase decision was also analyzed by the multiple linear regression All steps were illustrated by the following Figure 3.1

Figure 3.1: Research process

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

3.3.1 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 Liker 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

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

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 “I will 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 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

Apartment size/usable area X 1.1 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)

Status of apartment legal X 1.2

Interior design and decoration X 1.3

Apartment price X 2.1 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

Location close to own family X 3.1

Adair et al (1996), Daly et

al (2003), Haddad et al (2011), Opoku & Abdul- Muhmin (2010), Ratchatakulpat (2009), Sengul et al (2010), Xiao

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

Adair et al (1996), Daly et

al (2003), Haddad et al (2011), Opoku & Abdul- Muhmin (2010), Ratchatakulpat (2009)

al (2003), Haddad et al (2011), Mateja (2009), Ratchatakulpat (2009), Sengul et al (2010), Xiao

Planning to buy a new apartment X 7.1

Ajzen (1991), Han & Kim, (2010), Kunshan & Yiman,

(2011)

Making effort to buy a new apartment X 7.2

An important person affecting apartment

There were 263 hand-delivered questionnaires, only 239 respondents gave feedback immediately, but quantity of available respondents was 230

3.5 DATA ANALYSIS METHOD

After data collection, the first step would be data preparation with editing, coding, and data entry to ensure accuracy of data from raw data and to detect errors or omissions to correct Next, data were classified to arrange them into groups or classes of common demographic

Finally, variables would be tested reliable by Cronbach’s alpha, validity by EFA, and hypothesis and model would be tested by multiple regression of SPSS

3.5.1 Reliability measure

In order to check reliability of each of scales with particular sample, as well as consider the internal consistency of the scales, it was necessary to use Cronbach’s Alpha coefficient which should be above 7 (Devellis, 2003)

Also, the corrected item - total correlation values should be at least 3 to ensure each of items was measuring the same from the scale as a whole (Pallant, 2011)

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,

- 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”,

“finance”, “distance”, “facilities” 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

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

- The multi-co linearity did not exist, so r value, the correlated score was less than 9

- The co linearity 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 + 3X3 + … + nXn +

Where:

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

1 ents the regression coefficient associated with each Xi)

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

CHAPTER 4 DATA ANALYSIS AND 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

4.1.1 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 dependent variable

4.1.2 Coding

Answers were assigned numbers of symbols so that the responses were grouped intoa limited number of categories (see Table 4.1)

Table 4.1: Codebook of questionnaire items

7 Type of apartment Fea07

35 An important person affecting apartment

1= Female 0 = Males

Creating dummy variable

1 = “less than 35”; 0 =

“above 35”

Creating dummy variable

1 = “not yet graduated university”; 0 =

“graduated university”

Creating dummy variable

1 = “staff”; 0 =

“management board”

Creating dummy variable

1 = “Single”; 0 =

“Married”

Creating dummy variable

1 = “less than or equal

15 mil.” 0 = “more than 15mil.”

4.2 DESCRIPTIVE DATA

According to Table 4.2, there were 230 available respondents, the male was two thirds

of total of cases and almost respondents were single with percent of 83 percent Also, 61.3 percent respondents graduated university and 31.7 percent postgraduates studying master programs Their ages range from 18 year olds to 35 year olds with 99.1 percent

of total cases Almost all of them were officers with their ages at least 18 years old and less than 36 years old Besides, the main occupation of respondents was officers with 87.8 percent per total of cases, their income was less than 15 million per month with 89.6 percent rate, while the group of managers or owners at least 15 million per month with 3.9 percent rate

Also, the single apartment was chosen most with 73.6 percent rate, the second choice

of type of apartment was with 21.6 percent rate The apartment price which was less than 15 mil./m2 was appropriate with 87.3 percent of cases and the type of small and medium house size of less than 100 square meters was chosen most with 84.3 percent rate

Table 4.2: Characteristics of respondents

Gender

Frequency Percent Valid Percent

Cumulative Percent Valid Female 74 32.2 32.2 32.2

26-35 years old 117 50.9 50.9 99.1