The Effects Geographic Location and Age Have on a Credit Score

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Term Project

The Effects Geographic Location and Age
Have on
A Credit Score

Nicholas Rogers
EC315
10 March 2013

TABLE OF CONTENTS

PURPOSE STATEMENT AND MODEL………………………………………………………..3

DEFINITION OF VARIABLES…………………………………………………………………3

DATA DESCRIPTION…………………………………………………………………………..5

PRESENTATION AND INTERPRETATION OF RESULTS………………………………….7

WORKS CITED…………………………………………………………………………………10 DATA SOURCES……………………………………………………………………………….10 REGRESSION ANALYSIS AND CORRELATION MATRIX……………………attachment 1

PURPOSE STATEMENT AND MODEL

With the current state of the economy and job market in the United States, many individuals are shocked to hear that credit scores are becoming even more important now than before not only when it comes to making purchases or financial decisions, but also in other aspects of their everyday life. According to Forbes.com staffer, Heather Struck, in A Bad Credit Score Affects A Lot More Than Credit, ones FICO score can affect their ability to get a job, their insurance rates, and even how their personal relationships work out (2011). Some people have differing opinions on what can hurt or help a credit score. To keep from going into each of those possible outcomes, I am just going to pay attention to geographic location and age of the person seeking credit. The purpose of this project is to see what, if any, effect where a person lives and his age will have on his credit score. The dependent variable, CREDIT SCORE, is determined by the independent variables of geographic location (geo_loc) and age. The primary independent variable in this relationship is age or length of credit history because it has the most weight in the calculation between the two variables. According to a graph from MyFico.com, this is responsible for 15% of how a FICO score is obtained (MyFico.com, 2013). A person’s age is important to factor into a credit score. In general, the older a person is the higher credit score he will have. This study will show what relationship, if any, where a person lives and how old he is has on his credit score. The model is: Credit_score = consumer_age + geo_loc

DEFINITION OF VARIABLES
The dependent variable Credit_score is the purchasing power and the risk to creditors a consumer possesses. An individual’s credit score can be determined by the use of several factors. Whether the person meets all the criteria or not, he will still have a credit score. A lower score will result in not paying attention or simply not applying for credit. On the opposite end, if attention is paid to all the important factors and actions are swift, a higher score will be the result.

The primary independent variable in this relationship is age or length of credit history because it has the most weight in the calculation between the two variables. Longer credit age and payment history will allow for increased credit scores. “Older borrowers have an advantage in maintaining a higher credit score not only because of the ability to have a more extensive payment history and older credit age, but also because they have had more time to clear negative debt or marks from their credit report if they had a less than spotless payment history” (freescore, 2012). This is not to say that younger people cannot have a high credit score, rather it is not necessarily that common. For this study, the median age of members of each state will be used to see how much age plays a part in one’s credit score. I expect to see that this independent variable will have a positive coefficient. The relationship between age and the independent variable is direct. All other things equal and if the individual takes care of what he is supposed to, the older he gets the higher his credit score will become until it reaches the highest echelon.

The next independent variable is geographical location or a person’s region of residence. Just a bit of...
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