Regression and the Value of Homes
S Jennings
MBA 502: Applied Quantitative Methods for Business
23 September 2013

Regression and the Values of Houses
The values of homes can inflate due to the market at any time. Recently, the housing market has been very kind to the buyer with reasonable interest rates and various programs to help receive a mortgage loan. In order to know what type of mortgage loan to look for a consumer must know the value of the home. What drives the value of a home? Is it the neighborhood, amenities, structure, or the year it was built? Actually it is a combination of all of these things plus more that make up the value of a house. Regression is often used to determine the relationship between multiple characteristics and the value of a house. For this study a regression analysis was performed to show the variables that can predict the value of a home. The results show that a new kitchen, bathrooms, and rooms can really drive the price of a house along with location. It is known that certain characteristics of a house can increase or decrease its value. As a consumer it is important to come up with a reasonable asking price when inquiring a home, but how does one do that? Some people would hire an experienced real estate agent that can value the home on different characteristics, like location, size, and amenities. Whereas other consumers would like to do their own research and come up with a figure. One way that a consumer can come to a reasonable asking price is by using regression analysis. Regression analysis is used to determine the relationship between various characteristics and the value of a home. In the regression analysis any given characteristic would be known as a variable. It is up to the individual to determine which variables are important enough to include in the analysis. Each variable will yield a different estimated cost, also variables may not be valued the same through a distribution of housing prices. It is safe to say that consumers will value housing characteristics differently from one another. For example, Consumer A may value a new kitchen and full baths while Consumer B value a garage and partial baths. With this study, certain characteristics were used based off of what a group of individuals believed to be substantial to the value of a home. With regression analysis, adjustments can be made with variables. A consumer could add more variables or take out variables to see the differences between the values. Our group discussed and came up with a null and alternate hypothesis. Our hypotheses stand as: Null hypothesis: Location is the most important factor when determining the value of a home; Alternate hypothesis: Location is not the most important when determining the value of a home. Location is deemed to be important when valuing a home; however the amenities of the home may or may not have a larger impact.

DATA DESCRIPTION OF CHARACTERSITICS Housing characteristics can be far and wide when the determining the value of a home depending on the buyer, relator or appraiser. In valuing a home geographic characteristics along with house characteristics can play major roles. It is important to understand the variables that go into these different characteristics to make an informed decision on the regression model.

Geographic Characteristics Where a home is located is one of the important factors for a consumer in the market for buying a home. Is the home located in a crime infested area? Metropolitan? Rural? Subdivision? Depending on the consumer he/she can determine if they would rather live in area that is close to shopping areas and attractions. Homes that are typically in the metropolitan area are usually priced higher than a home in a rural area. Why? Because it is a convenience for the consumer, he/she is closer to the markets, shopping malls, restaurants, attractions, etc. The drawback of being in a metro area is