INTERNATIONAL UNIVERSITYSCHOOL OF BUSINESS QUANTITATIVE METHODS FOR BUSINESS Analyzing Determinants of Property Values in Ho Chi Minh City: Utilizing Regression Analysis Group: 2 Class:
Trang 1INTERNATIONAL UNIVERSITY
SCHOOL OF BUSINESS
QUANTITATIVE METHODS FOR BUSINESS
Analyzing Determinants of Property Values in Ho Chi Minh City: Utilizing Regression Analysis
Group: 2 Class: S1-2023-2024/ Monday morning
Student Name and ID Responsibility in
4 Trịnh Lê Anh Thư
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5 Nguyễn Phan Thái Bình
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CONTENTS
I Background 3
1 Aim and Issue Statement: 3
2 Data Collection and Analytical Approach: 3
3. Key Findings: 3
4 Practical Applications of the Study: 4
II Comprehensive Data Collection and Methodological Framework 6
1 Data Source: 6
2 Detailed Data Description: 7
3 Methodological Rigor: 8
III Findings and discussion 10
1 Table 1 Variable details 10
2 Table 2 13
3 Discuss the main findings and insights derived from the analysis for each topic 16
IV Comprehensive Conclusion 19
Trang 3I Background
The housing market plays a pivotal role in every economy, and it is essentialfor various stakeholders, such as policymakers, investors, and homeowners, tocomprehend the elements that impact housing prices This study seeks to analyze thedeterminants of housing prices in Ho Chi Minh City and assess the present state ofhousing prices through regression analysis
1 Aim and Issue Statement:
This research aims to discover and scrutinize the primary factors influencinghousing prices in Ho Chi Minh City Utilizing regression analysis, our goal is toquantify the connections between housing prices and a specific set of explanatoryvariables Furthermore, the study aims to assess the present condition of housingprices in the city and extract practical implications for management, investment, andother pertinent domains
2 Data Collection and Analytical Approach:
In pursuit of our objective, we assembled an extensive dataset comprisingdetails on housing prices and diverse factors with potential influence This datasetencompasses variables such as location, size, number of bedrooms, proximity toamenities, and socio-economic indicators
Our methodology involved the application of multiple regression analysis toexplore the connections between housing prices (the dependent variable) and theexplanatory variables This modeling approach enables us to gauge the influence ofeach variable on housing prices while considering other pertinent factors Throughregression analysis, we can pinpoint the noteworthy determinants of housing pricesand quantify their respective impacts
Trang 43 Significant Insights from Analysis:
Our investigation into housing prices in Ho Chi Minh City uncovered crucialfactors that play a role in determining property values Here are the key findings inmore detail:
a Location Matters:
Proximity to central business districts and popular neighborhoods positivelyimpacts housing prices Properties situated in these sought-after areas tend tocommand higher values, reflecting the influence of location on the real estate market
b Property Features and Size:
Larger homes with more bedrooms tend to have higher price tags This suggeststhat the size and characteristics of a property play a substantial role in influencing itsmarket value, with buyers often willing to pay more for increased space andadditional bedrooms
c Amenities Boost Property Value:
Homes located near essential amenities such as schools, hospitals, and publictransportation demonstrate higher price levels This finding underscores theimportance of accessibility and convenience in determining the attractiveness andvalue of residential properties
d Socio-economic Impact:
Various socio-economic indicators, including GDP per capita, employmentrates, and population density, were identified as influential factors affecting housing
Trang 5prices The economic well-being of an area, employment opportunities, andpopulation density all contribute to the dynamics of the real estate market.
Understanding these key findings provides valuable insights for individualsinvolved in property management, investment decisions, and urban planning.Recognizing the impact of location, property features, amenities, and socio-economicfactors is essential for making informed decisions in the dynamic and multifacetedrealm of the housing market in Ho Chi Minh City
4 Practical Applications of the Study:
The findings of this research carry tangible implications for a variety ofstakeholders, offering actionable insights that can guide decision-making acrossdifferent sectors
a City Management and Policymakers:
For those involved in city management and policy formulation, acomprehensive understanding of the core factors impacting housing prices is pivotal.This knowledge serves as a foundation for crafting effective urban development andhousing policies, addressing challenges such as affordable housing, urban expansion,and the prioritization of infrastructure projects
b Real Estate Investors and Developers:
Real estate investors and developers can leverage the study's insights to makeinformed decisions The findings provide valuable information on potentialinvestment opportunities and current market trends, enabling stakeholders to optimizetheir choices regarding property location, size, and amenities for maximum returns oninvestment
c Homeowners and Buyers:
Trang 6Homeowners and prospective buyers can benefit from understanding thedeterminants of housing prices This awareness empowers them to make well-informed decisions during property transactions, aiding in the evaluation of aproperty's value Armed with this knowledge, buyers can identify promisinginvestment opportunities and negotiate more effectively.
In essence, the practical applications extend across urban planning, investmentstrategies, and individual property transactions By incorporating the insights derivedfrom this study, stakeholders can contribute to more informed, strategic, andsuccessful decision-making within the ever-evolving landscape of the housing market
in Ho Chi Minh City
5 Concluding Remarks:
Concluding the study, the application of regression analysis provedinstrumental in unraveling the intricacies of housing price determinants in Ho ChiMinh City The comprehensive examination highlighted the multifaceted influences
of factors like location, property characteristics, amenities, and socio-economicindicators on the housing market
The findings underscore the pivotal role of location, with proximity to centralbusiness districts and desirable neighborhoods emerging as significant drivers ofhousing prices Additionally, the study shed light on the impact of property features,revealing that larger-sized homes with more bedrooms tend to command higherprices The proximity to amenities, such as schools, hospitals, and publictransportation, was identified as another key determinant influencing housing prices.Moreover, socio-economic indicators such as GDP per capita, employment rates, andpopulation density were recognized as crucial factors shaping the housing landscape
Trang 7The practical implications of these findings are substantial, resonating acrossdiverse sectors For city management and policymakers, these insights provide aroadmap for the formulation of effective urban development and housing policies.Real estate investors and developers can leverage this knowledge to strategicallyidentify investment opportunities and align their decisions with prevailing markettrends Likewise, homeowners and prospective buyers stand to benefit by makinginformed choices in property transactions, armed with a nuanced understanding of thefactors influencing housing prices.
In essence, a comprehensive grasp of these underlying determinants empowersstakeholders to contribute to the sustainable growth of the housing sector in Ho ChiMinh City The study underscores the importance of informed decision-making,emphasizing that a nuanced understanding of the intricate interplay between variousfactors is essential for fostering a resilient and thriving housing market in the city
II Comprehensive Data Collection and Methodological Framework
1 Data Source:
The cornerstone of this study lies in the acquisition of a meticulously curatedprimary data source that stands as a testament to both its utmost importance andunwavering credibility This comprehensive dataset has been thoughtfully assembledfrom an array of reliable sources, including governmental databases, esteemed realestate websites, and meticulously conducted market research reports The selection ofthese sources has been intentional, and tailored to capture the nuances of Ho ChiMinh City's vibrant urban landscape, ensuring the accuracy and robustness of theinformation embedded within the dataset
This dataset, representing an extensive collection of housing transactions withinthe city, holds noteworthy size and scope It goes beyond mere volume; each data
Trang 8point has been meticulously selected to create a representative sample, allowing us todelve into the intricate dynamics of the housing market in Ho Chi Minh City.The richness of the data extends beyond the quantitative realm, encompassing aplethora of detailed information This dataset doesn't just offer precise and up-to-datehousing prices; it unfolds a narrative through a comprehensive set of locationattributes, property features, economic indicators, and various other relevantvariables This holistic approach ensures that the dataset is a true reflection of themulti-dimensional nature of the housing market, providing a nuanced foundation forour analysis.
Serving as the bedrock upon which this study is constructed, this robust andextensive dataset instills confidence in the reliability, credibility, and thoroughness ofthe findings and conclusions presented herein By leveraging this invaluable resource,the study aspires to provide not just an analysis but a compelling and persuasivenarrative of the housing market in Ho Chi Minh City, illuminating its intricacies andoffering profound insights for researchers, policymakers, and industry professionals
2 Detailed Data Description:
The dataset employed in this study unfolds a comprehensive and insightfuldepiction of the dynamic housing market in Ho Chi Minh City To unravel theintricacies within, we delve into key descriptive statistics, offering a granularunderstanding of the central tendencies, variability, and overall distribution of thehousing market data
Mean: As a barometer of central tendency, the mean housing price serves as anindicator of the average prevailing in the market It unveils insights into the generalprice level and typical costs associated with housing in Ho Chi Minh City
Trang 9Standard Deviation: This measure of variability sheds light on the extent to whichhousing prices deviate from the average It acts as a compass, guiding ourunderstanding of the diversity and predictability within the market.
Minimum and Maximum: By exploring the minimum and maximum housing prices,
we gain insights into the affordability spectrum and upper limits of the market Thisprovides a vivid picture of the price range prevailing in Ho Chi Minh City
Number of Observations: Reflecting the sample size, the total number of housingtransactions included in the dataset enhances the representativeness of the analysis,offering a robust understanding of housing market dynamics
These descriptive statistics function as a gateway, allowing us to uncover patterns,trends, and relationships within the dataset This deeper understanding enriches theanalysis, contributing to a more nuanced comprehension of the housing market in HoChi Minh City
Location Attributes: Proximity to central business districts, transportation hubs,schools, parks, and shopping centers
Trang 10Property Features: Variables such as property size, number of bedrooms, bathrooms,and additional amenities.
Economic Indicators: Factors such as GDP growth, inflation rate, unemployment rate,and interest rates
The estimation of coefficients through regression analysis quantifies the impact ofthese independent variables on housing prices The significance and magnitude ofthese coefficients offer insights into the relative importance of each factor
Additionally, diagnostic tests have been conducted to validate the regression model,including tests for multicollinearity, heteroscedasticity, and normality of residuals.These meticulous tests serve as safeguards, enhancing the reliability and robustness ofthe results
Leveraging this comprehensive dataset and employing multiple regression analysis,the study aspires to unearth the key factors propelling housing prices in Ho Chi MinhCity The chosen methodology establishes a robust framework for understanding thedynamics of the housing market, contributing to practical implications formanagement, investment decisions, and other relevant stakeholders
This rigorous approach ensures that the study's findings are not only insightful butalso reliable, offering substantial contributions to the understanding of Ho Chi MinhCity's housing market dynamics It is poised to provide a nuanced and detailedaccount, unraveling the complex tapestry of factors that shape the city's real estatelandscape
Trang 12III Findings and discussion
1 Table 1 Variable details
Panel A Interior variables
Variable Description Data type
Sqft of living Square footage of the apartment interior living space
Quantitative, discrete
Sqft of lot Square footage of the land space
Quantitative, discrete
Sqft of above Square footage of the interior housing space above ground
level
Quantitative, discrete
Sqft of basement Square footage of the interior housing space below ground
level
Quantitative, discrete
Number of
bedrooms Number of bedrooms
Quantitative, discrete
Number of
bathrooms Number of bathrooms, full of conveniences
Quantitative, discrete
Number of floors Number of floors
Quantitative, discrete
Panel B Exterior variables Variable Description Data type
Waterfront
presence
A dummy variable for whether the apartment was
overlooking the waterfront or not
Qualitative, binary
View Quality An index from 0 to 4 of how good the view of the property Qualitative, ordinal
Trang 13Panel C Housing variables
Variable Description Data type
An index from 1 to 5 on the current condition: 1 =
Poor- Worn out, 2 = Fair- Badly worn, 3 = Average,
4 = Good, 5= Very Good
Qualitative, ordinal
Average condition The dummy variable, the base one Binary
Grade of
Apartment’s
Design and
Construction
An index from 1 to 13, where 1-3 falls short of
building construction and design, 7 has an average
level of construction and design, and 11-13 has a
high-quality level of construction and design.
Qualitative, ordinal