Housing Prices and The Gini Index
Format
Oral Presentation
Faculty Mentor Name
William Herrin
Faculty Mentor Department
Economics
Abstract/Artist Statement
This research paper will develop a regression model and analyze the relationship between the dependent variable, housing prices, and the explanatory variables: crime levels, education, income, income inequality, and racial/ethnic population. Data will be collected using metropolitan areas in the United States over a five year span. The dependent variable represents the median house price in a metropolitan area per year and will be measured in thousands of dollars. Crime levels will be measured as two distinct variables: violent crimes and property crimes. Both will be measured in terms of the rate of crime per hundred-thousandth person per year in a given metropolitan area. Education represents the total funding a metropolitan area gets resourced per student per year. Income represents the median household income within a metropolitan area over median household size per year measured in thousands of dollars. Income inequality represents the gap between household income measured as a value between 0 and 1, the gini index, in a metropolitan area per year. Racial/ ethnic makeup is broken down into Hispanic, Asian/ Pacific Islander, African American, Native American, and White or European descent, all of which will be measured as a percentage of population in a metropolitan area per year. It is expected that the estimated coefficient for crime levels, racial/ethnic population, and income inequality will be negative and for education and income, the sign will be positive. The regression analysis will interpret the coefficients and test for the strength of influence using a separate equation; furthermore, important components of the regression will also be explored. The goal of this regression is to understand how the explanatory variables influence the changes that occur in house prices, how they are related to each other, and the how income inequality plays a role.
Location
DeRosa University Center, Room 211
Start Date
30-4-2016 10:00 AM
End Date
30-4-2016 12:00 PM
Housing Prices and The Gini Index
DeRosa University Center, Room 211
This research paper will develop a regression model and analyze the relationship between the dependent variable, housing prices, and the explanatory variables: crime levels, education, income, income inequality, and racial/ethnic population. Data will be collected using metropolitan areas in the United States over a five year span. The dependent variable represents the median house price in a metropolitan area per year and will be measured in thousands of dollars. Crime levels will be measured as two distinct variables: violent crimes and property crimes. Both will be measured in terms of the rate of crime per hundred-thousandth person per year in a given metropolitan area. Education represents the total funding a metropolitan area gets resourced per student per year. Income represents the median household income within a metropolitan area over median household size per year measured in thousands of dollars. Income inequality represents the gap between household income measured as a value between 0 and 1, the gini index, in a metropolitan area per year. Racial/ ethnic makeup is broken down into Hispanic, Asian/ Pacific Islander, African American, Native American, and White or European descent, all of which will be measured as a percentage of population in a metropolitan area per year. It is expected that the estimated coefficient for crime levels, racial/ethnic population, and income inequality will be negative and for education and income, the sign will be positive. The regression analysis will interpret the coefficients and test for the strength of influence using a separate equation; furthermore, important components of the regression will also be explored. The goal of this regression is to understand how the explanatory variables influence the changes that occur in house prices, how they are related to each other, and the how income inequality plays a role.