INTRODUCTION
BACKGROUND
As global population levels rise, many developing countries face a critical housing shortage (Morel, 2001, p 1119) Real estate holds significant importance for citizens, as the decision to purchase a home can profoundly impact their lives (Wells, 1993) Unlike other business decisions, house purchases are influenced by the inherent, long-term nature of real estate, making it a highly differentiated product where each location is unique and immovable (Kinnard, 1968).
Vietnam is the third most populous country in Southeast Asia and ranks 14th globally in total population, with an estimated population of over 98 million people.
From 2000 to 2010, Vietnam's population grew by an average of 1.03 million people annually, reflecting a growth rate of 1.2% As of April 2009, Ho Chi Minh City, one of Vietnam's key economic centers, had an official population of approximately 7.2 million; however, the actual figure is likely much higher due to unrecorded migration from rural regions.
The Vietnamese real estate market has undergone significant transformations since the 1990s, experiencing cycles of rapid growth and declining prices over the past two decades By the end of 2012, the market faced a serious crisis due to substantial outstanding loans and a high volume of unsold properties Despite these challenges, Deputy Minister of Construction Nguyen Tran Nam highlighted a strong demand for housing and high solvency among buyers, pointing out that the market suffers from a lack of information.
RESEARCH PROBLEMS & RESEARCH QUESTIONS
The Vietnamese real estate market is currently facing significant challenges in meeting customer demands As of August 30, 2012, the Ministry of Construction reported that there were 16,469 unsold apartments across 44 provinces, with Ho Chi Minh City accounting for 10,108 of these Additionally, there were 4,116 unsold low-rise buildings nationwide, including 1,131 in Ho Chi Minh City (Anh, 2012).
The Prime Minister emphasized the necessity of incorporating solutions to revitalize the real estate market within the Government's Resolution Key factors contributing to the crisis include a mismatch between real estate supply and customer demands, as well as investors' lack of accurate information regarding customer needs and market conditions.
Customer research primarily focuses on understanding the decision-making processes of consumers and identifying the optimal ways to facilitate those decisions Furthermore, it emphasizes the importance of delivering genuine value to customers through a customer-centric approach.
Customer decision-making is a crucial aspect of consumer behavior, necessitating the collection of extensive information to understand the process effectively (Bettman et al., 1998; Simonson et al., 2001).
Numerous academic studies have explored customer house purchasing behaviors across both developed and developing nations Notably, national and cultural characteristics significantly influence these purchasing decisions, indicating that findings from one context may not be applicable to another (Opoku & Abdul-Muhmin, 2010).
Vietnam's real estate market possesses unique characteristics that closely align with customer demands In recent years, both domestic and international researchers and companies have shown increased interest in this sector, conducting numerous studies However, there remains a lack of comprehensive research focused on understanding how customers make real estate purchasing decisions and the key factors influencing these choices.
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?
RESEARCH PURPOSE
This thesis aims to identify the key factors influencing customers' house purchase decisions in Vietnam and to examine how these factors impact their choices when buying a home.
SCOPE OF THE RESEARCH
The research, conducted in Ho Chi Minh City, involved postgraduates and students from UEH across various fields, along with customers of a small book-coffee shop This study took place from mid-September to the end of October 2012.
RESEARCH STRUCTURES
This research is structured into five chapters: the first chapter outlines the background, research problems, questions, purpose, scope, and structure; the second chapter presents a literature review, hypotheses, and the conceptual framework; the third chapter details the research process, including sampling size, measurement scales, main survey, and data analysis methods; the fourth chapter focuses on data preparation, descriptive statistics, measurement scale assessment, and hypothesis testing; and the fifth chapter summarizes the research overview, findings, managerial implications, limitations, and suggestions for future research.
LITERATURE REVIEW
LITERATURE REVIEW
The features of a building structure play a crucial role in influencing a household's choice of residence, as highlighted by Quigley (Haddad, 2011, p 234) Additionally, Sengul et al (2010, p 214) confirm that these features significantly impact customers' decisions when purchasing a home Key aspects of a house's features include various attributes that cater to potential buyers' preferences and needs.
Key factors influencing an individual's decision to purchase a house include design, house size, and building quality (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
Secondly, “private living space” is one of most important factors affecting to
Consumer housing decisions are influenced by various factors, including the size of the living room, kitchen, and the number of bathrooms and bedrooms (Opoku & Abdul-Muhmin, 2010) Research indicates a significant relationship between the available space in a home and the purchasing behavior of customers (Graaskamp, 1981).
H2 There is a positive impact of living space on customers’ house purchase decision
Financial status plays a crucial role in customers' decisions when selecting a home, as highlighted by research from Hinkle and Combs (1987) and Kaynak & Stevenson (as cited in Sengul et al., 2010) The financial aspect of real estate necessitates access to a substantial amount of resources.
“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 terms play a crucial role in economic assessments (Opoku & Abdul-Muhmin, 2010; Yongzhou, 2009) According to Haddad et al (2011), the economic factor encompasses five key variables: income, interest rate, area, conversion, and taxes Additionally, Adair et al (1996) and Daly et al (2003) categorize interest rate, maximum mortgage, maximum monthly payment, and payment duration as essential components of the financial factor.
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.,
Residential location significantly impacts people's housing choices, as highlighted by Zabel and Kiel (2010) Factors such as the width of adjacent roads and proximity to schools can influence the distance individuals are willing to travel to select a home Additionally, the distance to central business areas also plays a crucial role in housing decisions.
“distance to school” and “distance to work” are considered (Adair et al., 1996, p.23) In addition, “access to recreational facilities” and “access to main roads” are proposed (Iman et al., 2012, p.30) Hence,
H4 There is a positive impact of distance on customers’ house purchase decision 2.1.5 Environment
Fifthly, “environment” including “neighborhood”, “area attractiveness”, “view”,
Noise levels and general security are key factors influencing a household's decision on residential choice (Adair, 1996, p.23) The environment significantly impacts housing buyers (Tajima, as cited in Opoku & Abdul-Muhmin, 2010, p.224), a sentiment echoed by Morel et al (2001, p.1119) Notably, the quality of the neighborhood plays a crucial role in the purchasing decisions of customers (Gabriel & Rosenthal, 1989, p.240).
H5 There is a positive impact of local environment on customers’ house purchase decision
Customer behavior has become a significant area of research in recent decades, highlighting a notable transition from rational decision-making factors to psychological and social influences This shift underscores the importance of understanding the connection between consumer intentions and their purchasing decisions.
“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 are internal factors that influence customer decision-making, including aspects such as gender, age, educational status, marital status, career, number of family members and children, and type of residence.
Demographic characteristics such as age, education, income level, and gender significantly influence customer purchase intentions Research indicates that gender notably impacts financial aspects of housing decisions Additionally, studies confirm that age and gender play crucial roles in real estate buying decisions, highlighting their importance in understanding consumer behavior.
This study examines the impact of demographic variables, specifically "gender" and "age," on housing purchase decision-making, while also considering the influences of "educational levels" and "marital status" (Haddad et al., 2011) as control variables.
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
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 incorporating measurement scales from prior research was created and subsequently shared with two real estate professionals and three management officers for feedback Following their responses, a discussion was held to refine the questionnaire The primary objective of this pilot phase was to enhance and clarify the measurement scales.
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
A total of 263 questionnaires were distributed, primarily targeting postgraduate students enrolled in master's programs or those pursuing a second business certification at the University of Economics Additionally, 24 individuals from diverse career backgrounds received questionnaires at a book café in Ho Chi Minh City Ultimately, 239 responses were collected, with 230 being valid for analysis.
Step 5: Edit, code and adjust missing data before testing reliable and validity of data
To prepare the data for analysis, it was edited, coded, and adjusted for missing values The reliability of the measuring instrument was assessed using Cronbach’s alpha, with a threshold set above 7 (Hair et al., 2010) Furthermore, the validity of the measuring instrument was evaluated by determining the number of extracted factors based on an Eigenvalue greater than 1 and the slope changes observed 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
To ensure the reliability and validity of the variables, Cronbach’s Alpha and Exploratory Factor Analysis (EFA) were utilized, followed by multiple regression analysis to test the model and hypotheses A sufficient sample size was essential for conducting a robust 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
The minimum required sample size for the study is 170, while the actual number of respondents collected from the questionnaire survey is 230, thus satisfying the necessary criteria for the analysis.
MEASUREMENT SCALE
To effectively measure concepts, it was essential to select appropriate scales for various variables The independent variables utilized a five-point Likert scale, ranging from "totally unimportant" (1) to "very important" (5) Similarly, the dependent variable was assessed using the same scale, with responses from "strongly disagree" (1) to "strongly agree" (5) Additionally, a pilot test was conducted to validate these measurements.
To ensure effective logistics for the upcoming data collection, a pilot test was conducted with a small group comprising two real estate professionals from Sacomreal and three management officers from Hoa Binh Corporation This group brought extensive knowledge and years of experience in the real estate sector, allowing for valuable insights into the questionnaire's effectiveness.
The pilot test's purpose was communicated to participants, along with the necessary questionnaires and documents Following this, a discussion took place to determine which sections to remove or add The findings are detailed 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”
The analysis of distance factors reveals that the item "house on a main bus route" should be removed, as it has not garnered customer interest Additionally, the specific criterion of "distance from the house to shopping centre" is deemed unnecessary, as it primarily appeals to female customers Furthermore, the recommendation is to eliminate the "location away from industrial areas" item, as customers tend to overlook it.
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”
After refining the initial questionnaire, it was distributed to a small group of fifteen colleagues to identify any unclear sections All participants comprehended the questions and the response process effectively, confirming the questionnaire's clarity Consequently, this final version was utilized for a larger survey that followed.
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
A questionnaire survey was conducted among the ISB-Mbus class and four economic night classes at UEH located at 59C Nguyen Dinh Chieu Street, as well as three additional economic night classes on Nguyen Tri Phuong Street The survey also included participants from a small PNC book coffee shop on Nguyen Oanh Street The data collection took place in mid-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 collecting data, the initial step is data preparation, which involves editing, coding, and entering the data to ensure its accuracy and identify any errors or omissions for correction Subsequently, the data is classified into groups or categories based on common demographic characteristics.
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
To assess the reliability of each scale for the specific sample and evaluate their internal consistency, Cronbach’s Alpha coefficient was utilized, with a threshold of 7 indicating acceptable reliability (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)
To assess the validity and relationships among variables for identifying underlying factors, Exploratory Factor Analysis (EFA) was conducted using the oblique Promax method It is essential to meet specific requirements for EFA, as outlined by Pallant (2011).
To ensure robust statistical analysis, a minimum sample size of 170 cases is required, calculated as five observations per item for a conceptual model with 34 items With an actual sample size of 230, this study exceeds the necessary threshold, thereby fulfilling the sample size requirements for reliable results.
- 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”,
In order to evaluate the impact of independent variables such as "living space," "finance," "distance," and "environment" on the dependent variable "decision," a multiple regression analysis was performed This analysis necessitated the fulfillment of specific conditions to ensure valid results.
- 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
Multiple regression analysis was employed to test hypotheses and investigate the relationship between five independent variables and one dependent variable Additionally, the analysis considered the influence of control variables on the dependent variable The generalized equation used for this analysis was based on the framework established by Donald and Pamela (2006).
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 collecting 239 responses, a thorough review revealed 3 blank sheets, 2 partially completed forms, 1 missing general information, and 3 instances of respondents predominantly selecting numbers 1, 3, or 4 Consequently, the final count of valid cases was 230, each assigned a reference number for easy identification Importantly, there were no missing data for the independent and dependent variables.
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
The survey included 230 respondents, with males comprising two-thirds of the total A significant majority, 83%, identified as single Educationally, 61.3% had completed university, while 31.7% were pursuing postgraduate studies in master's programs The age of respondents ranged from 18 to 35 years, accounting for 99.1% of the total Most participants were officers, making up 87.8% of the sample, and the majority earned less than 15 million per month, representing 89.6% of respondents In contrast, only 3.9% of respondents were managers or business owners earning at least 15 million per month.
A recent survey revealed that 73.6% of respondents preferred single houses, while 21.6% favored apartments Additionally, 87.3% found house prices below 20 million VND per square meter to be suitable, and 84.3% selected small to medium-sized homes, specifically those under 100 square meters.
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
To assess the appropriateness of a measurement scale, it is essential to evaluate its reliability and validity Reliability is measured using Cronbach’s Alpha, while validity is assessed through Exploratory Factor Analysis (EFA).
Refer to the Case processing summary of all variables of five concepts, the number samples of each concepts was valid with 230 available cases
The analysis of Reliability Statistics presented in Tables 4.3 and 4.9 indicates that all Cronbach’s Alpha values for the concepts exceeded 7, following the removal of items with “Corrected item-Total correlation” values below 3.
In the analysis of the "feature" concept, the items "Fea06_Construction duration" and "Fea07_Type of house" exhibited low Corrected item-Total correlation values of 253 and 08, respectively This indicates that they do not significantly contribute to measuring the "feature" concept Consequently, removing these items led to an improvement in the Cronbach’s Alpha value, which increased from 748 to 830, suggesting enhanced reliability of the remaining items.
For “private living space” concept, the “Corrected item - Total correlation” value of
The "Liv04_Living room size" item had a low score of 371, while the "Liv05_Storey of house" item scored only 012 Given that all other items contributed positively to the concept of "living space," it is recommended to remove these two items Following this adjustment, the Cronbach’s Alpha value for "private living space" improved from 619 to 739, indicating a more acceptable level of reliability, although it remains moderate.
For “finance” concept, the “Corrected item - Total correlation” value of
The "Fin07_Registration Fee" was removed due to its low value of 013, which is less than 0.3 Additionally, the "Fin02_Max Mortgage" item was also deleted because its total correlation value was only 36 In contrast, the Cronbach’s Alpha for "private living space" improved significantly from 726 to 865, indicating a strong reliability.
For “distance” concept, the “Corrected item - Total correlation” value of
The "Dis06_Business Distance" and "Dis07_Main Access" items, with values of 139 and 202 respectively, are below the acceptable threshold of 3 and should be removed Additionally, the "Corrected item - Total correlation" value for "Dis05_Recreation Distance" is 377, indicating it should also be deleted The Cronbach’s Alpha value further supports these deletions.
“Distance” concept was increased from 765 to 890
For “environment” concept, the “Corrected item - Total correlation” value of
The "Env03_View" and "Env06_Nearby Traffic" items recorded values of 284 and 272, respectively, both below the threshold of 3 These findings suggest that the perceptions of "Nearby Traffic" and "View" may be influenced by factors such as "Noise," "Pollution," and "Neighbour Condition."
“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 involves three key steps: first, assessing the appropriateness of the data for factor analysis; second, extracting the factors; and third, rotating and interpreting the factors (Pallant, 2011).
To assess the suitability of the data, a sample size of 230 cases was utilized, as detailed in Chapter 3 This sample size meets the minimum requirement necessary for testing the strength of the relationships among the variables (refer to Table 4.12).
Factorability of the correlation matrix
To enhance the total explained variance, it is essential to eliminate low communality values from the pattern matrix After removing certain low communality variables and conducting the analysis again, the results of the Exploratory Factor Analysis (EFA) are displayed in Table 4.4 Additionally, Table 4.5 shows that several correlation coefficients between the variables exceed 3, indicating significant relationships among them.
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
Table 4.11 indicates that the first six components have eigenvalues greater than 1, with values of 6.335, 3.099, 2.860, 2.259, 1.647, and 1.166 These components collectively account for 64.26% of the total variance, surpassing the 50% threshold, making this result suitable for analysis.
The Scree Plot (Figure 4.1) indicated a significant break between components 2 and 3, with components 1 and 2 collectively accounting for 36.64% of the variance, surpassing the remaining four components Additionally, a minor break was observed after component 6, confirming that six factors were extracted in total.
According to the Factor Matrix Table 4.13, the first component accounted for the majority of the items, while the second, third, and fourth components exhibited similar loading patterns, and the last two components showed the least loading.
In the Pattern Matrix Table 4.4, all items demonstrated significant loadings above 4 across six components Specifically, component 1 included five items, component 2 contained four items, component 3 was represented by five items, and component 4 had three items.
4, four items loading on component 5, and four items loading on component 6
The analysis of the correlation table reveals that there were 230 valid samples, indicating sufficient data for evaluation The positive correlation values suggest that as one variable changes, the others tend to follow in the same direction Notably, all Pearson correlation coefficients were below 0.7, which falls within the acceptable range of -1 to 1.
HYPOTHESES TESTING BY MULTIPLE REGRESSION
4.4.1 Checking assumption of Multiple Regression
The study included 230 respondents, surpassing the minimum required sample size of 90 for multiple regression analysis, as outlined in Chapter 3, Item 3.2 This confirms that the necessary sample size for conducting multiple regression was successfully achieved.
4.4.1.2 Assessment multicollinearity of independent variables
The analysis of the Coefficient Table 4.6 revealed high tolerance indicators ranging from 782 to 943, exceeding the minimum requirement of 10 Additionally, the Variance Inflation Factor (VIF) values, which are the inverse of the tolerance values, were all below the threshold of 2, indicating no issues with multicollinearity among the independent variables The maximum value recorded was 34.23, while the mean of the outliers was 4.98, suggesting that the presence of outliers is acceptable Therefore, all assumptions for multiple regression analysis were satisfied.
The Model Summary indicates an R Square value of 356, signifying that 35.6 percent of the variance in the Decision dependent variable is explained by the model Additionally, the Anova Table shows a significance level of 000, indicating that the model is statistically significant with p < 0005.
According to Table 4.6, the standardized coefficients labeled Beta indicate that various independent variables have been normalized for comparison in their contribution to the dependent variable, "decision." The variable "distance" emerged as the most significant factor, with the highest Beta value of 522, followed by "living space" at 186 In contrast, the contributions of "feature," "finance," and "environment" were minimal, suggesting that their effects are largely accounted for 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
The variable "distance" significantly influenced the prediction of the dependent variable, while the variables "feature," "finance," and "environment" did not contribute meaningfully to predicting the dependent variable "decision."
Table 4.6: Coefficient table of MLR
The analysis of hypothesis H1 revealed a positive correlation of 167 between "feature" and the "house purchase decision." However, the significance value for "features" was 951, exceeding the 05 threshold, and the Beta value was 003, indicating that "feature" does not significantly predict the "decision" dependent variable.
The analysis of hypothesis H2 revealed a significant relationship between the independent variable "living space" and the dependent variable "decision," with a zero-order correlation value of 329 and a significance level of 002, indicating a strong contribution Furthermore, the Beta value for "living space" was 186, reinforcing its predictive power regarding the "decision" dependent variable.
The analysis of hypothesis H3 indicates a positive relationship between "finance" and the "house purchase decision," with a zero-order correlation value of 0.150 However, the significance level for "finance" was found to be 0.234, which exceeds the 0.05 threshold, suggesting that the relationship is not statistically significant.
“finance” did not contribute to explain “house purchase decision”
Hypothesis H4 indicates that "distance" has a strong positive effect on "decision," with a zero-order correlation value of 566 The significance level is below 05, and the t value is notably high at 8.597, demonstrating that "distance" is a key predictor of "decision." Additionally, alongside "living space," "distance" accounts for all three remaining factors influencing the "house purchase decision."
The hypothesis H5 indicated that "environment" had a positive correlation of 0.233 with "decision." However, it failed to significantly explain "house purchase decision," as evidenced by a significance value of 0.425, which exceeds the 0.05 threshold, and a t-value of -0.799.
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 coding demographic variables such as gender, age, marital status, income, and education, we conducted a multiple linear regression analysis This analysis aimed to identify the impact of control variables on the dependent variable, Decision.
The analysis of Tables 4.19 to 4.24 indicates that the significant values of multiple linear regression (MLR) for control variables—such as sex, marital status, education, age, career, and income—were all greater than 05 This suggests that there are no significant differences in house purchase decisions among customers based on these demographic classifications.
CONCLUSIONS AND IMPLICATIONS
RESEARCH OVERVIEW
The research identified crucial factors that real estate agents, companies, and investors should prioritize to understand the areas influencing customers' housing purchase decisions.
Based on prior research, key variables were identified and categorized into groups, leading to the development of a framework model that illustrates the relationships among five independent variables: "feature," "finance," "living space," "distance," and others.
“environment” and one “decision” dependent variable
The study commenced with a pilot test to refine the questionnaire and ensure clarity in its revised form Following this, data analysis was conducted to assess reliability through Cronbach’s Alpha and validity via Exploratory Factor Analysis The research concluded with model and hypotheses testing using multiple regression, while also examining the impact of demographic factors on the dependent variables.
RESEACH FINDINGS
The study's multiple regression analysis indicates that factors such as "house features," "finance," "living space," "distance," and "environment" positively influence the "house purchase decision." A total of 21 independent variables were identified as significant in shaping customers' real estate purchasing choices Notably, "house features" do not include outdoor spaces like gardens, a limitation attributed to land scarcity in Ho Chi Minh City, Vietnam Additionally, while the research aligns with Adair et al (1996) regarding the "environment," it diverges in aspects like "slope/topography of the land" and "wooded area/tree coverage," which are less emphasized in Vietnam This discrepancy is further highlighted by the World Economic Forum's 2012 report, noting that many Vietnamese environmental indicators fall below average.
The study reveals that a significant 87.9% of respondents prefer houses priced below 20 million per square meter, while only 9.1% are interested in properties ranging from 21 to 32 million per square meter This indicates a substantial demand in the low to medium-income market, warranting focused attention Additionally, the primary preference among customers is for single houses, with 72.6% favoring this option, followed by apartments at 21.3% These findings align with previous research on consumer preferences for smaller homes in Saudi Arabia (Opoku & Abdul-Muhmin).
The study's results indicate that demographic factors such as gender, marital status, education, age, career, and income do not significantly influence customer decision-making Unlike Haddad (2011), who found notable differences in customer choices based on gender and age, this research reveals no variations across different demographic groups in multi-group analyses.
Research indicates that 99.1 percent of customers under the age of 36 are prevalent in the study, highlighting a significant demographic trend Additionally, there is a pronounced gender segregation in Saudi Arabia, which is closely related to the cultural context of neighboring Jordan (Opoku & Abdul-Muhmin, 2010, p 222).
MANAGERIAL IMPLICATIONS
Understanding real estate consumers and their household decision-making processes is crucial for predicting trends in the market, benefiting both real estate management teams and investors This study highlights the practical implications for individuals and organizations in formulating effective marketing and investment strategies, particularly targeting low to medium-income segments that show a strong preference for small houses over apartments.
Research indicates that there is a significant demand among low- to medium-income citizens for adequate housing, highlighting the need for government initiatives to develop targeted housing programs Focusing on these demographics will ensure that housing solutions effectively address their specific needs and challenges.
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
The research data was collected from a limited demographic of postgraduates and students at UEH in Ho Chi Minh City To enhance the study's validity, it is recommended to conduct surveys across various regions in Vietnam with a larger and more diverse population Expanding the age range of respondents to include those over 36 would provide valuable insights for comparisons across different age groups Additionally, exploring the decision-making factors influencing couples would yield interesting findings.
Future research should focus on the specific factors influencing customer preferences for different types of properties, including apartments, villas, commercial buildings, and offices Additionally, there is a need for more studies on real estate agents and companies to better understand their requirements for renting offices, commercial spaces, or villas.
In multiple regression analysis, previous research suggested that the independent variables of "feature," "finance," and "environment" contribute to explaining the dependent variable of house purchasing decisions However, the beta values and significance levels indicate that these factors do not significantly impact the main dependent variable This finding should be considered in 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
Amid the current economic crisis, Vietnamese enterprises are grappling with significant challenges, particularly in the real estate sector, which has experienced a drastic decline in transaction volumes.
Understanding the key factors that influence customers' home buying decisions is essential for both individual real estate businesses and the broader real estate industry.
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
When making a house purchase decision, it's essential to evaluate each variable thoughtfully Based on your knowledge and experience, assess the significance of each factor and identify which one you believe is the most influential by circling it.
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
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 bối cảnh nền kinh tế suy thoái, doanh nghiệp Việt Nam đang gặp phải nhiều khó khăn và thách thức Ngành bất động sản là một trong những lĩnh vực chịu tác động nặng nề nhất, với sự sụt giảm mạnh mẽ trong số lượng giao dịch.
Để hỗ trợ các doanh nghiệp bất động sản và các ngành liên quan, việc xác định chính xác các yếu tố ảnh hưởng đến quyết định mua nhà của khách hàng là rất quan trọng và hữu ích.
Chúng tôi rất trân trọng nếu Anh/ Chị có thể dành ít thời gian để hoàn thành bảng câu hỏi dưới đây Mọi thông tin cung cấp sẽ được bảo 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) hãy xem xét cẩn thận từng yếu tố và dựa trên hiểu biết cũng như kinh nghiệm của mình, đánh giá mức độ quan trọng của từng yếu tố ảnh hưởng đến quyết định mua nhà bằng cách khoanh tròn câu trả lời phù 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 4.19: 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
