Modeling Housing Demand in Jos Metropolis Using Linear Regression

Modeling Housing Demand in Jos Metropolis Using Linear Regression

Abstract

Urbanization in Nigeria has accelerated over the past two decades, creating intense pressure on housing availability and affordability. This study applies linear regression modeling to analyze and predict housing demand in Jos Metropolis. The research specifically investigates the influence of factors such as population growth, household income, construction costs, and rental prices on housing demand. Data were collected from both primary and secondary sources, including surveys and official statistics.

The results revealed that population growth and income level are the most significant determinants of housing demand, while building material costs also play a moderate role. Moreover, the linear regression model achieved a high coefficient of determination (R²), indicating strong explanatory power. Overall, the findings provide a quantitative framework that can guide policymakers and urban planners in making data-driven decisions for sustainable housing development in Jos and similar urban centers across Nigeria.

CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Housing remains one of the most essential human needs and a major determinant of social and economic well-being. In Nigeria, providing adequate housing has become a daunting task due to the persistent gap between demand and supply. Over the years, Jos Metropolis in Plateau State has experienced steady population growth as people migrate in search of better opportunities. Consequently, the demand for housing has outpaced supply, leading to overcrowding, increased rents, and the expansion of informal settlements.

Various socio-economic factors, including population size, income level, and building costs, directly influence the housing market. Furthermore, economic instability, inflation, and urban migration patterns contribute to fluctuations in housing demand. Hence, there is a growing need to develop quantitative models capable of identifying and predicting the major drivers of this demand. Linear regression modeling provides an effective statistical framework to achieve this, as it quantifies the relationships among multiple variables.

By applying linear regression analysis, researchers can evaluate how each factor contributes to housing demand and predict future trends. This approach offers valuable insights for policy formulation, urban planning, and efficient resource allocation. Ultimately, understanding these relationships enables governments and private developers to plan more effectively for the future housing needs of residents in Jos Metropolis.

1.2 Statement of the Problem

Despite numerous housing policies and development initiatives, the housing deficit in Jos continues to widen. Population growth, rising construction costs, and unstable household incomes have made affordable housing increasingly scarce. Unfortunately, most government housing strategies lack data-driven support and are often based on short-term interventions rather than predictive modeling.

The absence of a reliable mathematical framework makes it difficult to anticipate future housing needs accurately. As a result, misallocation of resources, unplanned urban sprawl, and high rent prices persist. Therefore, this study seeks to develop a linear regression model that examines and forecasts housing demand using key socio-economic variables, thereby filling the gap in quantitative housing analysis within Jos Metropolis.

1.3 Objectives of the Study

The primary aim of this research is to model housing demand in Jos Metropolis using linear regression techniques. Specifically, the study seeks to:

1. Identify the major socio-economic factors influencing housing demand.

2. Develop a regression model that relates these factors to housing demand.

3. Estimate model parameters and assess the statistical significance of each variable.

4. Forecast future housing demand based on the regression model.

5. Recommend strategies for effective and sustainable housing policy formulation.

1.4 Research Questions

1. What socio-economic factors significantly influence housing demand in Jos Metropolis?

2. How strong is the relationship between population growth, income level, and housing demand?

3. Can linear regression modeling reliably predict future housing needs?

4. What are the implications of the findings for policymakers and urban developers?

1.5 Research Hypotheses

• H₀: There is no significant relationship between population growth, household income, and housing demand in Jos Metropolis.

• H₁: There is a significant relationship between population growth, household income, and housing demand in Jos Metropolis.

1.6 Significance of the Study

This study contributes to both academic literature and practical urban planning by demonstrating how mathematical models can address real-life housing challenges. For policymakers, it provides a data-driven framework to predict housing needs, prioritize infrastructure investments, and allocate resources efficiently. Furthermore, for researchers and students of applied mathematics, the study illustrates the use of regression modeling in socio-economic contexts.

Additionally, urban planners and real estate developers can apply the findings to design better housing strategies that match population growth trends. The results also serve as a reference point for future studies on urban housing and regional development.

1.7 Scope of the Study

The research focuses on Jos Metropolis in Plateau State and examines data covering the period from 2010 to 2025. Variables analyzed include population growth, average household income, building material costs, and rental prices. Both simple and multiple linear regression techniques are employed to evaluate the relationship between these variables and housing demand.

1.8 Definition of Key Terms

• Housing Demand: The total number of housing units that households are willing and able to purchase or rent at various price levels.

• Linear Regression: A statistical approach used to model and analyze the relationship between one dependent variable and one or more independent variables.

• Population Growth: The rate at which the number of individuals in a given area increases over time.

• Income Level: The average amount of money earned by households, influencing their purchasing power and housing preferences.

• Construction Cost: The financial expenditure associated with building, maintaining, and renovating housing structures.

1.9 Organization of the Study

This work is structured into five chapters for clarity and coherence. Chapter One presents the introduction, background, objectives, and hypotheses. Chapter Two reviews relevant literature and theoretical models. Chapter Three discusses the research methodology, data sources, and analytical tools. Chapter Four presents data analysis, model estimation, and interpretation of results. Finally, Chapter Five summarizes key findings, draws conclusions, and provides actionable recommendations for policymakers.