ABSTRACT
Accurately
predicting house prices is a critical challenge within the dynamic and complex
Nigerian real estate market. Traditional valuation methods, often reliant on
manual appraisals and subjective judgment, struggle with the market's rapid
changes, data scarcity, and heterogeneity, leading to financial risks for
buyers, sellers, and investors. This study addresses this problem by designing
and implementing a data-driven house price prediction system using machine
learning techniques.
The
research begins by analyzing key factors influencing property values in
Nigeria, including structural attributes, location, and amenities. A
comprehensive methodology is then employed, involving data collection,
preprocessing, feature engineering, and model training. The core of the system
leverages a Random Forest Regressor, with comparative analysis performed
against Linear Regression, Ridge Regression, and Support Vector Machine
(SVM) algorithms. The model is trained on a dataset of Nigerian property
listings to identify complex, non-linear relationships between housing features
and market prices.
Performance
evaluation using metrics such as R-squared (R²), Mean Absolute Error (MAE), and
Root Mean Squared Error (RMSE) demonstrates the model's effectiveness in
generating accurate price forecasts. The study concludes that machine learning
models, particularly ensemble methods like Random Forest, offer a more
objective, scalable, and adaptable alternative to traditional valuation
approaches. This system provides a valuable framework for stakeholders to make
informed decisions, thereby promoting transparency and efficiency in the
Nigerian real estate market. The project's design, including UML diagrams and
system specifications, outlines a practical and deployable solution for
automated property valuation.
TABLE OF CONTENTS
CONTENTS
CERTIFICATION……………………………………………………………………………….ii
DEDICATION…………………………………………………………………………………..iii
ACKNOWLEDGEMENTS………………………………………………………………………iv
ABSTRACT………………………………………………………………………………………v
TABLE OF CONTENT…………………………………………………………………………..vi
LIST OF
FIGURES……………………………………………………………………………...vii
CHAPTER ONE: INTRODUCTION
1.1 INTRODUCTION………………………………………..……………………..………..1
1.2 STATEMENT OF
PROBLEM………………………………………………….………..3
1.3 AIM AND
OBJECTIVES…………………………………………………………...……3
1.4 JUSTIFICATION OF
STUDY………………………………………………...………….4
1.5 SCOPE OF THE
STUDY……………………………………………………….....……..4
1.6 DEFINITION OF
TERMS…………………………………………………………….….5
CHAPTER TWO: LITERATURE REVIEW
2.1 INTRODUCTION………………………………………..………………………..….…..7
2.1.1 ORIGIN AND EVOLUTION OF HOUSE PRICE
PREDICTION SYSTEMS……….…9
2.2 MACHINE
LEARNING……………….………………………..……………….............11
2.2.1 The Role of Machine Learning in House Price
Prediction……………………………....11
2.2.2 Machine Learning Techniques Used in House
Price Prediction…………………….......12
2.2.2.1 Supervised Learning
Models.....…………………..…………..…………………….......12
2.2.2.2
Unsupervised Learning
Models………………..….………………................................13
2.2.2.3 Deep
Learning Techniques ……………………..….………………................................14
2.2.3 Advantages of Machine Learning over
Traditional Valuation Methods………...……….15
2.3 CHALLENGES OF TRADIIONAL APPROACHES TO
HOUSE PRICE PREDICTION SYSTEM IN NIGERIA……………………………………………………………...……..……16
2.4 RELATED WORKS ON HOUSE PRICE
PREDCITION…………………………..…..19
CHAPTER THREE: SYSTEM INVESTIGATION AND ANALYSIS
3.1 PROBLEM
DEFINITION…………………………………………………..….…..……25
3.2 PROPOSED
METHODOLOGY………………………….………………..………..…..25
3.2.1 ALGORITHM………………………………………………………………………...….27
3.3 WORKING
PRINCIPLES………………………………………………..……………...31
3.3.1 DATASET
COLLECTION……………………………………………………...……….31
3.3.2 DATA
PREPROCESSING……………………………………………………………….31
3.3.3 MODEL
TRAINING………………………………………………………...…………..31
3.3.4 RESULT
GENERATION……………………………………………………….……….31
3.4 UML
DIAGRAMS……………………………………………………………….……...32
3.4.1 USE CASE
DIAGRAMS……………………………………………………….……….32
3.4.2 SEQUENCE
DIAGRAM……………………………………………….………..………33
3.4.3 DATAFLOW DIAGRAM……………………………………………….……….……..33
3.5 SYSTEM
REQUIREMENT…………………………………………………….…….…34
3.5.1 SOFTWARE
REQUIREMENTS……………………………………………..….…..….34
3.5.2 HARDWRE
REQUIREMENTS……………………………………………………..….34
CHAPTER FOUR: SYSTEM DESIGN AND IMPLEMENTATION
4.1 SYSTEM
DESIGN…………………………………………………………………..…..35
4.1.1 OUTPUT
DESIGN……………………………………………………………………....35
a)
OUTPUTS TO BE GENERATED...........…………………………………………....35
b)
SCREEN FORMS OF OUTPUT…………………………………………….……....35
c)
FILES USED TO PRODUCE REPORT……………………..……………..………..35
4.1.2 INPUT
DESIGN……………………………………………………………………...….36
a)
LIST OF INPUT ITEMS REQUIRED……………….…….......…………………….36
b)
DATA CAPTURE SCREEN FORMS FOR INPUT…………………..……………..36
c)
METHOD USED TO RETAIN INPUTS……………………….……………………36
4.1.3 PROCESS DESIGN……………………………………………………………………..36
a)
LIST ALL PROGRAMMING ACTIVITIES NECESSARY…………………..…….36
b)
PROGRAM MODULES
TO BE DEVELOPED…………………………...………..37
c)
VIRTUAL TABLE OF CONTENT………………………………………....………..37
4.1.4 STORAGE DESIGN……………………………………………………….……………38
a)
DESCRIPTION OF THE STORAGE USED………………………………………..38
b)
DESCRIPTION OF THE KEY FILES USED………………………………....……38
4.1.5 DESIGN SUMMARY…………………………………………………………..………38
a) SYSTEM FLOWCHART………………………………………………...…………..38
a) HIERARCHICAL INPUT PROCESSING OUTPUT (HIPO)
CHART……………..39
4.2 SYSTEM
IMPLEMENTATION………………………………………………………....40
4.2.1 PROGRAM DEVELOPMENT
ACTIVITY……………………………………….…….40
a)
PROGRAMMING LANGUAGE USED………………………………………….....40
b)
ENVIRONMENT USED FOR DEVELOPMENT………………………………..…40
c)
SOURCE CODE………………………………………………………………...……40
4.2.2 PROGRAM TESTING……………………………………………………..……………40
a)
CODING PROBLEMS ENCOUNTERED……………………………………..……40
b)
USE OF SAMPLE DATA……………………………………………………………41
4.2.3 SYSTEM
DEVELOPMENT…………………………………………………………....41
a) SYSTEM REQUIREMENT…………………………………………………….……41
b) TASKS PRIOR TO
IMPLEMENTATION……………………………………….…..41
c) USER TRAINING………………..…………………………………………………..41
d) CHANGING OVER………………………………………………………………….41
4.3 SYSTEM
DOCUMENTATION…………………………………………………………41
4.3.1 FUNCTIONS OF PROGRAM MODULES……………………………………………..41
4.3.2 USER’S MANUAL……………………………………………………………………...42
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 SUMMARY……………………………………………………………………………..43
5.2 CONCLUSION………………………………………………………………………….43
5.3 RECOMMENDATION…………………………………………………………………44
REFERENCES
APPENDICES
(a)
PROGRAM
FLOWCHART
(b)
PROGRAM
LISTING
(c)
TEST
DATA
(d)
SAMPLE
OUTPUT
CHAPTER ONE
INTRODUCTION
1.1 Background of Study
The financial commitment involved
in buying or selling a property is often one of the most significant decisions
individuals, families, or investors will make (Kauko et al., 2021). The value
assigned to a residential property, commonly referred to as its price, is
influenced by a myriad of factors, making its accurate estimation a complex yet
crucial task. House price prediction is the process of estimating the future or
current market value of a property using various analytical techniques and data
sources. This field has gained substantial attention due to its wide-ranging
implications for homeowners, potential buyers, real estate investors, financial
institutions, and policymakers (Bency et al., 2020).
Predicting house prices accurately
is a critical aspect of real estate market analysis and economic forecasting.
It involves identifying and quantifying the impact of various property-specific
attributes (e.g., size, number of rooms, condition) and external market
dynamics (e.g., interest rates, economic growth, location, neighborhood
characteristics) on property values (Bokhari & Geltner, 2020; Zhang et al.,
2023). Understanding these dynamics is essential for making informed decisions
in the property market. Historically, property valuation relied heavily on
manual appraisals and comparative market analysis, which, while valuable, can
be subjective and time-consuming (McCluskey et al., 2020).
A House Price Prediction System
(HPPS) is an advanced analytical framework that utilizes data-driven
techniques, statistical models, and machine learning algorithms to forecast the
market value of properties (Wen et al., 2021). Property value fluctuation, in
real estate terminology, refers to the changes in the monetary worth of a
property over a given period. This fluctuation can be attributed to various
reasons, including changes in local amenities, infrastructure development,
macroeconomic trends, shifts in buyer preferences, or even changes in socio-political
stability (Liu et al., 2020). It is a crucial metric that directly impacts
investment returns, lending risks, wealth assessment, and overall economic
stability (Kang et al., 2023).
The ability to predict and
understand house price movements has become a focal point in various sectors,
particularly for those involved in real estate investment, urban planning, and
financial services. Inaccurate property valuations can lead to suboptimal
investment choices, increased economic risk for lenders, and inefficient urban
development. Consequently, developing robust and accurate house price
prediction models has become a top priority for stakeholders aiming to navigate
the complexities of the real estate market and improve decision-making (Wen et
al., 2021). For instance, prospective buyers often seek to understand if a
listed price is fair, while sellers aim to price their property competitively
yet profitably.
A significant issue that is
frequently related to the current real estate market cycle is price volatility
and uncertainty. During periods of rapid economic change or market speculation,
the predictability of house prices can decrease, posing challenges for all
market participants. However, even in more stable market conditions, achieving
precise valuations remains a complex task due to the unique nature of each
property and the multitude of interacting price determinants (Bency et al.,
2020; Zhang et al., 2023).
The factors influencing house
prices can be broadly categorized. Some are intrinsic to the property itself,
such as size, age, condition, and number of bedrooms/bathrooms. Others are
extrinsic, such as location (neighborhood, proximity to amenities, school
districts), prevailing economic conditions (interest rates, inflation,
employment rates), and broader market sentiment. (Bokhari & Geltner, 2020;
Zhang et al., 2023) To address the challenge of accurate valuation, real estate
analysts and data scientists must identify the most influential factors and
model their complex interactions. Machine learning algorithms like decision
trees, linear regression, K-Nearest Neighbors (KNN), and ensemble methods are
increasingly used to attain this goal (Park & Bae, 2022; Wang et al., 2023).
The best innovative features and modeling techniques for predicting property
values should be the main focus of this research work. For this, relevant
property data will be collected and analyzed, and based on that analysis,
several well-known machine-learning methods will be utilized and evaluated.
Property valuations will consider factors such as location, square footage,
number of bedrooms and bathrooms, age of the property, local market trends, and
comparable sales data.
1.2 Statement of the Problem
Conventional approaches may rely on
limited comparable sales or overly simplistic models, which may not adequately
reflect the diverse factors influencing property prices, such as unique
property characteristics, micro-market trends, and broader economic influences.
Additionally, many existing valuation models struggle to adapt to rapidly
changing market conditions or incorporate real-time data, making them less
effective in dynamic environments.
1.3 Aim and Objectives of the Study
Aim
This
project aims to design and implement an effective house price prediction system
using machine learning techniques to accurately estimate property values and
enable stakeholders to make informed decisions in the real estate market.
Objectives
The
specific objectives of this project include:
- To
analyze factors influencing house prices: Identify and evaluate key
determinants affecting property values, including structural attributes,
locational characteristics, and market conditions.
- To
develop and implement a house price prediction model: Utilize machine
learning techniques to train and test models that accurately predict house
prices based on the identified factors.
- To
evaluate the performance of different machine learning models: Compare the
accuracy and efficiency of various algorithms in predicting house prices.
- To
propose a data-driven framework for property valuation: Based on
prediction insights, recommend a system that can assist in providing
reliable property value estimates.
1.4 Justification of the Study
The
justification of this study lies in its potential to help various stakeholders
in the real estate ecosystem improve decision-making by leveraging machine
learning techniques for house price prediction. This includes:
- Enhancing
Decision-Making for Buyers and Sellers: By accurately predicting property
values, individuals can make more informed decisions when buying or
selling homes, ensuring fair transactions and optimizing financial
outcomes.
- Reducing
Financial Risk: For mortgage lenders and investors, effective house price
prediction models can help assess risk more accurately, leading to better
lending practices and investment strategies, thereby minimizing potential
losses.
- Improving
Urban Planning and Policy Making: The study can provide insights into
factors driving property values, enabling urban planners and policymakers
to make data-driven decisions regarding infrastructure development, zoning
regulations, and housing policies.
- Advancing
Machine Learning Applications in Real Estate: This research contributes to
the growing field of machine learning by evaluating different algorithms
and their effectiveness in house price prediction, potentially guiding
future research in predictive analytics for the property sector.
- Increased
Market Transparency and Efficiency: Organizations and individuals equipped
with robust prediction tools can contribute to a more transparent and
efficient real estate market by reducing information asymmetry.
1.5 Scope of the Study
This study focuses on the design
and implementation of a house price prediction system using machine learning
techniques (random forest regressor, linear regression, ridge regression and
support vector machine), with specific attention to residential properties. The
study will examine various datasets containing property information, which may
include structural details (e.g., square footage, number of bedrooms/bathrooms,
age, condition), locational attributes (e.g., neighborhood, proximity to
amenities, schools, transport links), and historical transaction data.
Four well-known machine learning
algorithms, decision trees, linear regression (as a more direct counterpart to
logistic regression for this problem), K-Nearest Neighbors (KNN), and Naïve
Bayes (potentially adapted for regression tasks or specific feature analysis) will
be analyzed and compared for their effectiveness in house price prediction.
The project also explores feature selection techniques to identify the most
influential variables and data preprocessing methods, such as handling missing
values, encoding categorical features, and feature scaling, to improve
prediction accuracy and model performance. The geographical scope may be
general or focused on a specific urban/suburban region, depending on data
availability, to develop a model that can be adapted to different localities.
1.6 Definition of Terms
- Decision
Trees: A
machine learning algorithm that predicts the value of a target variable by
learning simple decision rules inferred from the data features, often
represented as a tree structure.
- Feature
Engineering:
The process of selecting, transforming, or creating new features
(variables) from raw data to improve the performance of machine learning
models.
- House
Price Prediction:
The analytical process of estimating the future or current market value of
a residential property based on historical data, property characteristics,
and market trends.
- K-Nearest
Neighbors (KNN):
A machine learning algorithm that predicts the value of a new data point
based on the average value (for regression) or majority class (for
classification) of its 'k' nearest neighbors in the feature space.
- Linear
Regression: A
statistical model used to predict a continuous outcome variable (like
price) based on one or more predictor variables by fitting a linear
equation to observed data.
- Machine
Learning (ML):
A subset of artificial intelligence (AI) that enables computer systems to
learn from data, identify patterns, and make decisions or predictions
without being explicitly programmed for each specific task.
- Predictive
Analytics:
The use of statistical techniques, machine learning, and data mining to
make predictions about future or otherwise unknown events, such as
forecasting house prices.
- Property
Features:
Specific attributes of a property that can influence its value, such as
size (square footage), number of bedrooms and bathrooms, age, condition,
and presence of amenities like garages or gardens.
- Real
Estate Market:
A market where rights in property are bought and sold; it encompasses all
activities related to buying, selling, leasing, and investing in
properties.
- Regression
Analysis: A
statistical process for estimating the relationships between a dependent
variable (e.g., house price) and one or more independent variables (e.g.,
property features).
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