INTRODUCTION
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 14 th 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 of information”.
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 30 th , 2012, the country now 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 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)
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 making decision 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:
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.
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.
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 to the end of October in 2012.
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.
LITERATURE REVIEW
LITERATURE REVIEW
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
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
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
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
Fifthly, “environment” including “neighborhood”, “area attractiveness”, “view”,
“noise from around districts” and “general security” 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) Particluarly, “neighbourhood” quality is paid intention highly to house purchase decision making of customer (Gabriel & Rosenthal, 1989, p.240)
H5 There is a positive impact of local environment on customers’ house 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)
“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, career, 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.
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)
RESEARCH METHODOLOGY
RESEARCH PROCESS
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 the second 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
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
Sampling Design Type, purpose, time frame, scope, environment
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):
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.
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)
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 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
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 and related 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 “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
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
House price X3.1 Adair et al (1996), Daly et al (2003), Kaynak &
Tevenson (1982), Haddad et al (2011), Opoku
Adair et al (1996), Daly et al (2003), Haddad et al
(2011), Opoku & Abdul- Muhmin (2010), Ratchatakulpat (2009), Sengul et al (2010), Xiao
Distance to the central business district X4.6
Access to the main street X4.7
Adair et al (1996), Daly et al (2003), Haddad et al
Gender X6.1 Adair et al (1996), Daly et al (2003), Haddad et al
(2011), Mateja (2009), Ratchatakulpat (2009), Sengul et al (2010), Xiao
Planning to buy a new house X7.1 Ajzen (1991), Han & Kim,
(2011) Making effort to buy a new house X7.2
MAIN SURVEY
The questionnaire survey was conducted at the ISB-Mbus class and four of the economic night classes of UEH in 59C Nguyen Dinh Chieu Street Besides, three of the economic night classes of UEH in Nguyen Tri Phuong Street were also delivered the questionnaires The last surveyed place was a small PNC book coffee in Nguyen Oanh Street Timeframe to survey was from the middle of September,
2012 to at the end of October, 2012
There were 263 hand-delivered questionnaires, only 239 respondents gave feedback immediately, but quantity of available respondents was 230.
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
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)
An important person affecting house purchase decision
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,
- 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,
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
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:
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
DATA ANALYSIS & RESULTS
PREPARATION DATA
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
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
1 Feature House size Fea01 Record respondents’ numbers
Kitchen Size Liv01 Record numbers
13 Finance House Price Fin01 Record numbers
Distance Width of adjacent street
25 Distance to the central Dis06 business district
26 Access to the main street
33 Decision Plan to buy a new house
34 Making effort to buy a new house
35 An important person to make decision to buy a new house
Gender Sex01 Creating dummy variables
37 Age Age01 Creating dummy variable
38 Marital Mar01 Creating dummy variable
39 Income Inc01 Creating dummy variable
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 career 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 house was chosen most with 73.6 percent rate, the second choice of type of house was apartment with 21.6 percent rate The house price which was less than 20 mil./m 2 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
1 = “less than or equal 14 mil.”
40 Education Edu01 Creating dummy variable
1 = “not yet graduated university”; 0 = “graduated university”
41 Career Car01 Creating dummy variable
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
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 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 items above
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
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Fin06_Payment Duration 17.04 9.614 506 878 DISTANCE: Alpha= 890
Exploratory factor analysis was carried out through three steps consisting of the 1 st step was evaluation the suitability of data for factor analysis, the 2 nd step was factor extraction, and the 3 rd 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
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 repeating the 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
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 was changed, 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
Feature Finance Space Distance Environ ment
In the Normal Probability Plot (P-P) of the Regression Standardised Residual, most of 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
HYPOTHESES TESTING BY MULTIPLE REGRESSION
4.4.1.1 Sample size The 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 VIF values 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 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
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 contribution to 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
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
Next hypothesis H3, the relationship between “finance” and “house purchase decision” was also positive based on the correlation zero - order value of 150, but the significant value of “finance” was 234, this value was bigger than 05 Thus,
“finance” did not contribute to explain “house purchase decision”
For hypothesis H4, “distance” had got the very positive affecting on “decision” with the correlation zero-order value of 566 Besides, the significant was less than 05 and t value was high 8.597, they were shown that “distance” made the strongest contribution to predict the “decision” Along with “living space”, “distance” explained for all three remaining factors to predict “house purchase decision”
The last hypothesis H5, “environment” had positive effect on “decision” with the correlation zero - order value was 233, but “environment” did not support to explain “house purchase decision” because the significant value was 425, above 05 and t value was minus 799
H1 There is a positive impact of house features on house purchase decision
.003 167 951 Positive impact, no support for explaining dependent variable because the significant value was above 05
H2 There is a positive impact of private living space status on house purchase decision
.186 329 002 Positive impact, strong support for explaining dependent variable
H3 There is a positive impact of finance status
.066 150 234 Positive impact, no support for explaining 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
.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 purchase decision with different classified demographics (Sex, Marital, Education, Age, Career and Income) of customers.
CONCLUSIONS AND IMPLICATIONS
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.
RESEACH FINDINGS
The multiple regression of the study shows that “house feature”, “finance”, “living space”, “distance” and “environment” make a positive impact on “house purchase decision” 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 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,
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, adjacent Jordan country (Opoku & Abdul-Muhmin, 2010, p.222).
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 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.
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
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Appendix 1: The first draft of the questionnaire
Is the an important element to a house purchase decision?
Comments of the first pilot test
Area of usable floor of the house 100% - The item “finishing”
(3&4) should be deleted because its content was inside the content of
9 Type of house (Town house, apartment, villa) 100%
Size of kitchen 100% - Agreed with all items of the “private living size”
15 Finance House price 100% - It should be added item
“the registration fee’ based on “tax” item
Is the an important element to a house purchase decision?
Comments of the first pilot test
22 Distance The width of a street adjacent the house 100% - Suggested to delete item
“House on a main bus route” In addition, the
“shopping centre, item 28” was proposed to delete because it was interested by female only
- They suggested to remove the industrial distance (item 30)
23 Distance from the house to work 100%
24 Distance from the house to a market 80%
25 Distance from the house to a school 80%
26 Distance from the house to a recreation center 100%
27 House on a main bus route 20%
28 Distance from the house to shopping centre 40%
29 Distance from the house to a business centre 60%
30 Location away from industrial areas 20%
31 Access to the main street 80%
32 Location close to own family 40%
Neighbourhood 100% - “The attractiveness of the area” as the same “view”
Is the an important element to a house purchase decision?
Comments of the first pilot test
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 house 100%
43 I am an important person affecting house purchase decision of my family
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
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
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
No Is the an important element to a house purchase decision? Important level
11 The width of a street adjacent the house 1 2 3 4 5
12 Access to the main street 1 2 3 4 5
13 Distance from the house to work 1 2 3 4 5
14 Distance from the house to a business centre 1 2 3 4 5
24 Type of house (Town house, apartment, villa) 1 2 3 4 5
29 Distance from the house to a market 1 2 3 4 5
30 Distance from the house to a school 1 2 3 4 5
31 Distance from the house to a recreation center 1 2 3 4 5
32 I have got a plan to buy a new house 1 2 3 4 5
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
34 I am an important person affecting house purchase decision of my family 1 2 3 4 5
Please stick a cross (x) into the appropriate blanks
2 Marital Single without children ( ) Married with children ( )
Business owner/ Manager Other (Please specify): …………
10-14 million VND ( ) Above 40 million VND ( )
7 How much size is your own house or a new house which you are going to buy?
8 How much price is your own house or a new house which you are going to buy?
< 4 mil VND/m 2 ( ) 15-20 mil VND/m 2 ( ) 33-38 mil VND/m 2 ( ) 5-8 mil VND/m 2 ( ) 21-26 mil VND/m 2 ( ) 39-45 mil VND/m 2 ( ) 9-14 mil VND/m 2 ( ) 27-32 mil VND/m 2 ( ) ≥ 45 mil VND/m 2 ( )
9 Type of house is your own house or a new house which you are going to buy?
Single town house ( ) Twin villa ( )
10 If you own a house, which year did you buy it?
Thank you very much for your help
Trong giai đoạn nền kinh tế đang suy thoái hiện nay, tình hình hoạt động kinh doanh của các doanh nghiệp Việt Nam đang đối mặt với nhiều khó khăn và thách thức lớn
Một trong những ngành bị ảnh hưởng nặng nề nhất là ngành bất động sản với số lượng giao dịch giảm mạnh
Nhằm đánh giá đúng các yếu tố chính ảnh hưởng đến quyết định mua nhà của khách hàng là điều cần thiết và hữu ích cho các doanh nghiệp kinh doanh bất động sản nói riêng và cho cả các doanh nghiệp liên quan đến bất động sản nói chung
Rất mong Anh/ Chị dành chút thời gian quý báu để trả lời trong bảng câu hỏi bên dưới Tất cả thông tin trong bảng câu hỏi này được giữ bí mật và chỉ được sử dụng cho mục đích nghiên cứu
Xin chân thành cảm ơn
BẢNG CÂU HỎI PHẦN I: CÁC YẾU TỐ CHÍNH ẢNH HƯỞNG ĐẾN QUYẾT ĐỊNH MUA NHÀ
Xin Anh (Chị) vui lòng xem xét kỹ từng yếu tố, sau đó dựa trên hiểu biết và kinh nghiệm của mình, Anh (Chị) hãy đánh giá mức độ quan trọng của từng yếu tố ảnh hưởng đến quyết định mua nhà của mình bằng cách khoanh tròn câu trả lời mà Anh (Chị) cho là thích hợp nhất
Thang đánh giá từ 1 đến 5:
Hoàn toàn không quan trọng Rất quan trọng (Từ câu 1 đến 31)
Hoàn toàn không đồng ý Đồng ý (Từ câu 32 đến 34)
Yếu tố có quan trọng đến quyết định mua nhà không?
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
4 Thời gian xây dựng ngôi nhà 1 2 3 4 5
5 Kiến trúc bên ngoài ngôi nhà 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
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
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
Yếu tố có quan trọng đến quyết định mua nhà không?
21 Lệ phí trước bạ mua nhà 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
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:
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 ( )
Cao đẳng ( ) Trên đại học ( )
Doanh nhân/ Nhà quản lý Khác (Xin ghi rõ): ………
6 Thu nhập hàng tháng của Anh (Chị)
7 Anh (Chị) đã mua hay dự định mua nhà với kích cỡ nhà ở như thế nào?
8 Anh (Chị) đã mua hay dự định mua nhà với giá nhà trong khoảng nào?
< 4 triệu/m 2 ( ) 15-20 triệu/m 2 ( ) 33-38 triệu/m 2 ( ) 5-8 triệu/m 2 ( ) 21-26 triệu/m 2 ( ) 39-45 triệu/m 2 ( ) 9-14 triệu/m 2 ( ) 27-32 triệu/m 2 ( ) ≥ 45 triệu/m 2 ( )
9 Anh (Chị) đã mua hay dự định mua loại nhà nào?
Chung cư ( ) Biệt thự đơn lập ( )
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:
Xin chân thành cám ơn sự giúp đỡ của Anh (Chị)
Table 4.9: Cronbach’s Alpha with full items for each constructs
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Table 4.10: KMO and Bartlett’s test
Table 4.12: Correlation among variables (Partial only)
Total 155.770 229 a Dependent Variable: Decision b Predictors: (Constant), Environment, Feature, Finance, LivSpace, Distance Table 4.17: Casewise diagnostics
Figure 4.4: Scatterplot Table 4.19: Cofficients of MLR including Sex_Render
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
