Machine Learning Approaches to Residential Property Valuation

Abstract

This project applies machine learning — multiple regression, random forest and gradient boosting — to residential property valuation, comparing model predictions with valuations from traditional comparable methods. Using a dataset of property characteristics and transaction prices, it evaluates accuracy and discusses what automated valuation means for estate surveyors. The study is organised into five chapters covering the introduction, review of related literature, system analysis and design, implementation and testing, and the summary, conclusion and recommendations.

Table of Contents

Chapter One: Introduction

1.1 Background to the Study

This project applies machine learning — multiple regression, random forest and gradient boosting — to residential property valuation, comparing model predictions with valuations from traditional comparable methods. Using a dataset of property characteristics and transaction prices, it evaluates accuracy and discusses what automated valuation means for estate surveyors.

1.2 Statement of the Problem

Compares machine learning models with traditional methods for residential property valuation. Existing approaches remain limited in reach, accuracy and ease of use, which creates the gap this study addresses.

1.3 Aim and Objectives

The aim of this study is to machine learning approaches to residential property valuation. The specific objectives are to:

1. review existing literature and systems related to the study;
2. design and build the system using Python, scikit-learn, Pandas;
3. evaluate the results against the stated problem;
4. make recommendations for practice and further research.

1.4 Scope of the Study

Introduction: background to automated property valuation, statement of the problem (traditional valuation is slow, subjective and inconsistent across valuers), aim and objectives, research questions, scope, significance of the study, and definition of terms.