Trang 1 VIETNAM NATIONAL UNIVERSITY, HO CHI MINH CITYUNIVERSITY OF ECONOMICS AND LAWFinal ReportTopic: INVESTIGATE THE REAL ESTATE FACTORSTHAT AFFECT PURCHASE DECISIONS BYCUSTOMER GROUPS
VIETNAM NATIONAL UNIVERSITY, HO CHI MINH CITY UNIVERSITY OF ECONOMICS AND LAW Final Report Topic: INVESTIGATE THE REAL ESTATE FACTORS THAT AFFECT PURCHASE DECISIONS BY CUSTOMER GROUP Subject: Data mining Class: 231TO5301 Lecturer: PhD Nguyễn Đình Hiển Group 2 Membership performed: Full name Student’s ID Đoàn Quỳnh Anh K214130930 Trần Thị Như Nguyệt K214132035 Mai Ý Như K214132038 Đỗ Thị Thùy Linh K224131629 HCMC, 27/12/2023 TABLE OF CONTENTS Introduction 3 CHAPTER 1: OVERVIEW 3 1.1: Reasons for choosing 3 1.2 Factors affecting purchase decisions by customer groups 3 CHAPTER 2: THEORY - METHODS of DATA MINING 6 2.1 Data Classification 6 2.1.1 Definition 6 2.1.2 How does classification technique work? 7 2.1.3 The basic techniques for data classification such as 8 2.2 Data association 13 2.2.1 Itemsets 13 2.2.2 Value and Applications .13 2.2.3 Support 13 2.2.4 Confidence 14 2.2.5 Lift .14 2.2.6 Significance in association rule mining: 15 CHAPTER 3 PRACTICE 15 3.1 Biometric characteristics of each customer group 15 3.1.1 Group 1 - Singles, Mainly Purchasing Real Estate for Residence: 15 3.1.2 Group 2 - Young Families, Newly Married, Investing for the Future:15 3.1.3 Group 3 - Elderly Families with Children, Purchasing Real Estate for Savings or Investment: 16 3.1.4 Group 4 - Affluent Families, Purchasing Multiple Real Estates for Investment: 16 3.1.5 Group 5 - Professional Real Estate Investors 16 3.2 Characteristics of Customers' Real Estate Preferences .17 3.2.1 Purpose of Real Estate Acquisition 17 3.2.2 Type of Real Estate Desired for Purchase 18 3.2.3 Property Size .19 3.3 Financial Structures 20 3.4 Financial Characteristics of Customers .23 3.4.1 Planned Budget for Home Purchase by Customers with Monthly Income of 20-30 Million VND 23 3.4.2 Planned Budget for Home Purchase of Customers with Monthly Incomes of 50-70 Million VND 24 3.4.3 Planned Budget for Home Purchase of Customers with Monthly Incomes Above 70 Million VND 25 3.5 The Most Important Factors in Deciding to Buy a Home 25 3.5.1 Apartment Size: Comfort and Flexibility 26 3.5.2 Surrounding Infrastructure: The Foundation of Social Living 26 3.5.3 Proximity to Schools or Workplace: Time-Saving, Lifestyle Optimization .27 3.5.4 Preferences of Demographic Groups: 28 Conclusion 29 Reference 30 INTRODUCTION In recent years, the real estate market in Vietnam has experienced significant growth and attracted a wide range of customers The real estate market is influenced by various factors that play a crucial role in customers’ purchase decisions Understanding these factors can help real estate professionals and investors make informed decisions and tailor their strategies to target specific customer groups This study aims to investigate the real estate factors that affect purchase decisions by customer groups using data classification and data association methods CHAPTER 1: OVERVIEW 1.1: Reasons for choosing In recent years, the Vietnamese real estate market has experienced significant growth and transformation This growth has attracted a diverse range of customers, including first-time home buyers, investors, and families The factors that influence purchase decisions by these customer groups can vary, and it is essential to understand these factors to better serve the Vietnamese real estate market In this report, we will investigate the real estate factors that affect purchase decisions by customer groups in Vietnam 1.2 Factors affecting purchase decisions by customer groups Suitable Apartment Size: One of the primary considerations for customers when purchasing real estate is the size of the apartment Different customer groups have varying requirements based on their family size, lifestyle, and future plans For example, young professionals or small families may prefer smaller apartments that are more affordable and easier to maintain On the other hand, larger families may seek spacious apartments with multiple bedrooms to accommodate their needs Surrounding Infrastructure: The availability of surrounding infrastructure plays a crucial role in purchase decisions Customers consider factors such as proximity to recreational facilities, waste management facilities, green spaces, and public amenities Living near parks, playgrounds, and other recreational areas is particularly desirable for families with children Access to quality schools, workplaces, markets, supermarkets, and shopping centers also influences purchase decisions Proximity to Schools and Workplaces: The location of a property in relation to schools and workplaces is an important consideration for customers Families with school-going children prefer properties that are close to reputable educational institutions Similarly, professionals seek properties that offer convenient access to their workplaces, reducing commuting time and enhancing work-life balance Accessibility to Markets and Commercial Centers: The proximity to markets, supermarkets, and commercial centers is another crucial factor influencing purchase decisions Customers prefer properties located near vibrant markets or commercial hubs where they can easily access essential goods and services This convenience adds value to the property and enhances the overall living experience Community Environment: The community environment surrounding a property significantly impacts purchase decisions Customers consider factors such as the quality of neighbors, safety, and overall community atmosphere A well-maintained and secure neighborhood with a strong sense of community can greatly influence the decision to purchase a property Proximity to Main Roads and Highways: Easy access to main roads and highways is an essential factor for many customers Properties located near major transportation routes provide convenience in terms of commuting and accessibility to other parts of the city or region This factor is particularly important for individuals who rely on public transportation or frequently travel by car Attractive Interior Design: The interior design of a property is an influential factor in purchase decisions, especially for those seeking aesthetic appeal Customers often prefer well-designed apartments with modern finishes, functional layouts, and high-quality materials The visual attractiveness and overall ambiance of the interior space can significantly impact a customer’s decision to purchase a property Competitive Pricing: The price of a property compared to the market value is a critical factor influencing customer decisions Customers are more likely to choose properties that offer good value for money or are reasonably priced compared to similar options in the market Developers who offer competitive pricing strategies often attract more potential buyers Financing Options: The availability of financing options, such as installment plans or flexible payment schedules, can greatly influence purchase decisions Many customers prefer properties that offer convenient payment options, allowing them to manage their finances effectively while acquiring their desired real estate assets Legal Assurance: Ensuring legal compliance and proper documentation is crucial for customers when making real estate purchases Customers want assurance that the property they are buying has clear legal ownership and all necessary permits and approvals from relevant authorities Trustworthy developers who prioritize legal compliance can instill confidence in potential buyers Reputation of Developer and Construction Contractor: The reputation of the developer and construction contractor involved in a real estate project can significantly impact purchase decisions Customers often consider the track record and credibility of the developer and contractor before investing in a property A reputable developer with a history of delivering high-quality projects can attract more customers Document continues below Discover more fdraotma :mining Trường Đại học… 17 documents Go to course Bài tập lấy điểm giữa kỳ và quá trình 1 None Outline Datamining - classification… 3 None Chuyển đổi số 27 Chuyển 100% (2) đổi số và… AI Application - hay lắm coi đuy 13 Chuyển 100% (1) đổi số và… Trading HUB 3 36 Xác suất 96% (28) thống kê File giáo trình bản pdf HSK 2 100% (11) 8 Giáo trình chủ nghĩ… CHAPTER 2: THEORY - METHODS OF DATA MINING In today’s world, we are surrounded by a massive amount of data from everything we do – from buying stuff online to using social media Data mining is like a super detective that helps us make sense of all this information It is a way to dig through this huge pile of data and find important things like patterns, trends, and useful insights Talking about data mining is important because it is how businesses and experts make smarter decisions It is like a treasure hunt where we search for valuable information hiding in all that data Understanding data mining helps us figure out how to use this information to improve things, like making better products, offering more personalized services, and even predicting things that might happen in the future It is a way to make sense of all the chaos in the data world and turn it into something really useful Two of a lot of prominent methodologies that stand out in this domain are Classification and Association Classification techniques are akin to the intuitive sorting hat of the data world, where data points are categorized into predefined classes or groups based on their features Conversely, Association methods delve into the inherent structures within data, autonomously grouping similar entities without predefined labels Both Classification and Association mining play pivotal roles in various industries by enabling decision-making, pattern recognition, and prediction While classification focuses on labeling and prediction, association mining uncovers hidden relationships, collectively enriching our understanding of complex datasets and enhancing decision-making processes across diverse domains 2.1 Data Classification 2.1.1 Definition Classification is a form of data analysis that extracts models describing important data classes Such models, called classifiers, predict categorical (discrete, unordered) class labels To divide bank loan applications into safe and dangerous categories, for instance, we may create a classification model Our comprehension of the facts as a whole may improve with the use of such analysis Researchers in machine learning, statistics, and pattern recognition have put forth many classification techniques Given a minimal amount of data, the majority of algorithms are memory-resident Based on earlier research, data mining algorithms have been developed recently that can handle enormous volumes of disk-resident data using scalable categorization and prediction methods There are many uses for classification, such as in production, target marketing, fraud detection, performance prediction, and medicine 2.1.2 How does classification technique work? a) In the first step: Building the Classification Model In this initial phase, the focus is on constructing a robust classification model leveraging historical or previous data This stage encompasses several key actions: Data Collection and Preparation: Gathering a comprehensive dataset that includes both input features and their corresponding labeled classes or categories This data is preprocessed to handle missing values, normalize features, and ensure its suitability for model training Model Training: Utilizing various classification algorithms such as Decision Trees, Support Vector Machines (SVM), Logistic Regression, or Neural Networks, among others The selected algorithm learns from the provided dataset, establishing patterns and relationships between input features and their associated classes Evaluation: Assessing the trained model's performance using validation techniques like cross-validation or by splitting the dataset into training and testing subsets This step ensures that the model has learned effectively without overfitting to the training data b) In the second step: Model Validation and Application The second step involves evaluating the model's accuracy and readiness for real- world application: Accuracy Assessment: Checking the model's performance metrics, such as accuracy, precision, recall, or F1-score, to determine its effectiveness in correctly classifying instances This assessment ensures that the model generalizes well to new, unseen data Application to New Data: If the model demonstrates acceptable accuracy and reliability during evaluation, it is deployed to classify new or incoming data The validated model becomes a predictive tool, assigning classes or categories to new instances based on the learned patterns from the training data This two-step approach ensures a systematic and thorough process in developing, evaluating, and deploying classification models It emphasizes the importance of not only constructing an accurate model but also validating its performance before utilizing it for real-time classification tasks, thereby ensuring reliable decision- making based on data-driven insights 2.1.3 The basic techniques for data classification such as a) Decision tree classifiers Flow Chart Structure: Decision tree classifiers create a tree-like structure resembling a flowchart This hierarchical representation begins with a root node, branches out into intermediate nodes, and concludes with leaf nodes