Housing Market vs. United States Economy - Regression Analysis



Abstract

The market value of the houses in any economy is linked with numerous economic variables. Similar is the case with the United States. In order to investigate the interrelationship between the various economic variables such as income, population, location and cost of living and the market values of the homes in the United States, the regression analysis is conducted that shall aid in accomplishing such purpose. The research report is written with an aim to identify, examine and interpret the regression analysis conducted for the aforementioned purpose.

Introduction1

Research Statement2

Research Aims and Objectives2

Research Hypothesis3

Variables Categorization4

Regression Analysis4

Regression Model5

Determining the relationship between population and housing prices6

Determining the relationship between income level and housing prices8

Determining the relationship between cost of living and housing prices10

Overall Model13

Conclusion14

References15

Housing Market vs. United States Economy - Regression Analysis

Introduction

Housing Market is defined as the sales, development or construction of the homes. The U.S. Housing Market is one of the dominant markets in the country. In U.S., National Association of Home Builder (NAHB) looks after the interest vested in the industry. The influence and the relationship with the economy is indeed strong as we can see the example of global recession that deteriorated the financial and economic conditions of the country because of the mortgages i.e. CDS and sub-prime mortgages on the real estate. The market value or price of the house in an economy is interlinked with the several economic indicators and changes in those independent variables influence the price of the house, either positively or negatively. The economic analysis is conducted to gather the possible determinants that can affect the housing prices. As aforementioned, there are numerous economic indicators that can impact the housing prices, few most important are the income levels, total population, location and cost of living. Economic analysis makes us understand about the financial conditions and the real activity in an economy (Schumpeter, 2013). One of the most eminent and useful statistical tool that can help us in determining the relationship between the housing prices and the cost of living, population and the income levels is regression. Regression analysis aids in determining a relationship among variables (Chatterjee & Hadi, 2013). The representative variables used for the analysis are the Gross National Income per capita (purchasing power parity adjusted) for income level; total population in the United States for population, and cost of living is represented through Consumer Price Index (CPI) for the years ranging from 1960 to 2012. The data for the independent variables is accessed or obtained through the Online Worldbank Development Indicators (WDI, n.d.) and the data for the dependent variable is extracted from Standard & Poor Case-Schillers Indices (okfn.org, n.d.).

The research is undertaken to examine the interrelationship between the income levels, population, location and the cost of living in the country and their impact upon the market value of the homes located in the country. The research methodology includes the usage of the widespread and renowned statistical tools that can aid in concluding the research ...