The city of Good Year is located in the county of Maricopa, Arizona, United Sates of America. The city is in the Phoenix suburb in the Phoenix Metropolitan area. The area has a population of 65275 people according to the census results of 2010. It is among the fastest growing in cities as compared to the other cities and towns in the state of Arizona. Good Year city is the home for the Goodyear Ballpark a part where the Cincinnati Reds and the Cleveland Indians of the MLB do their practice and training during spring. This city is named Good year after the Good year tire and Rubber Company at a period when this company had farmland to grow a lot of cotton of production of the tires in the region (Zillow).
The city was established in the year 1917 after Good year tire and Rubber Company purchased around 1600 acres in order to cultivate its cotton that would be used in the production of the threads for vehicle tires. Good year became recognized as a city in the year 1946 on November 19th.By the time it became a city, Good year had a total of 151 homes with the residents and about 250 apartments. However, through the years 1990 to 2010, a number of new homes and communities for its inhabitants have cropped up and has led to an enormous increase in the population of the people in this area. Good year has continued to increase in the number of communities and the homes. This partly because of it increasing the population, for instance, it is projected that this city will have a population of around 358000 by the year 2035.
With the increasing population in this area, the demand for more and more housing has led to the establishment of the Goodyear, Arizona, Real estates. These apartments are found in a very organized environment and also have a wide range of prices for those who would be interested in purchasing or renting the houses. Consequently, the factors determining the prices and thus profits made from the purchase of these apartments or houses have been an issue. This research seeks to investigate the major factors determining the profits that are accrued from the sale of these apartments and transfer of ownership from one individual to the other.
The research bellow is going to establish the relationship, and the factors determining the profits that are got from the sale of the apartment or houses in Arizona. The factors to be considered in this research include Profits, Age, Location, and Garage. Profits mean the difference between the buying prices and selling price. In this research, the profit will conceptually mean the amount money that an individual gains from the sale of any given apartment in dollars. The profit variable is an interval variable as in contains the numerical figures of the prices of the profits earned from the sale of the houses/apartments. Age will refer to the total number of years that any given house or apartment has been in existence. The variable is an interval variable as in contains numerical figures if the years. The location will be a nominal variable consisting of the four cities for instance; Kane, Olean, Sheffield, and Tionesta. The last variable in this research will be the Garage variable. The garage variable in this context will be a dichotomous as it contains only two dichotomous attributes i.e. yes and No.
Since this research involves a number of variables, the regression tool will be used to study the influence of the three variables of Age, Location, and garage. These three variables will together form the independent variable in this research, and the Profit variable will consist the dependent variable in this research. Since the variables are a mix of interval and nominal variables, a dummy variable regression model will be adapted to this research. The dummy variables will be made basing on the two nominal variables in the model. The variable location will be separated into four dummy variables consisting of the four cities outlined in the model. However, because of the dummy variable trap, one of the cities will be used as the benchmark for this analysis. The variable Kane will be used as the benchmark since the inclusion of this variable would lead to perfect correlation in the model which will lead to erroneous results. Regression will be run, and the interpretation regarding the location will be made depending/making reference to the Kane city attribute.
Similarly, the garage variable will be separated into the two attributes. For instance, the ‘yes’ attribute and the no attribute and the interpretations made regarding this variable will take the attribute ‘No’ as the benchmark to get rid of the dummy variable trap. The dummy variable trap is an instance that occurs when the variable under study especially the independent variables are perfectly correlated with each other. One way to do analysis with inclusion of all the attributes in any given model will involve suppression of the constant term. However, this is not preferred since this term sometimes contains vital information that would be useful in making inference about the model.
Results:
Preliminary analysis using the box plot mainly to test for the normality of the two interval variables of age and profit in the model revealed that the two interval variables in the model were normally distributed with no outliers. Figure (1) and figure (2) bellow give a summary of the results of the normality tests on these variables.
Figure 1:Box plot of profits
Figure 2: Box plot of Age
The pie chart above shows that out of the 105 houses considered in this analysis. 33.24% of the houses/apartments were from Kane,28.41% of the apartments/houses were from Tionesta, 20.69% were from Sheffield and lastly 17.66% of the apartments were from Olean.
Profit=1546.983+4.978Age-122.315Olean+84.027Sheffield+188.639Tionesta-39.649Garage
Regression equation above indicates that there exists no significant relationship between the profits got from the sale of the apartments and the age of the houses. This was implied by the p-value of the age variable that was above the stated level of significance in the model. The regression model was run with a 0.05% level of significance. Similarly, there was no significant relationship between the profits got from the houses that own garages and those that do not own. However, the results showed that the profit got from the sale of the houses that had garages were lower than those got from those that had no garages though this difference was discovered insignificant in this model. Regression results further showed that the profits got from the sale of the houses in Olean was 122.315 dollars less than that got from the sale of the houses in Kane city. The profits got from the sale of houses in Sheffield were 84.027 dollars less than those got from the sale of the houses in Kane. The results were tested to be insignificant in this analysis. The profits got from the sale of the houses in Tionesta were 188.639 higher than those from the sale of the houses in Kane.
The regression diagnostic tests above indicate that the residuals from this regression analysis were normally distributed with less white noise and minimal collinearity. It is shown by the Normal probability plot of the standardized residuals and the other plots above.
Conclusion: Conclude your argument and prescribe the advice you would give based on your results.
The results indicated that there exists a relationship between the profits got from the sale of the houses and the age variable although this was found out to be insignificant. The results further indicated that there exists no significant difference between the profits earned from the sale of the houses in the cities. What this implies is that the prices of the houses in the four cities were relatively similar. For any individual who would like to sell his her house, having a garage has less significance regarding the profits he or she will earn.
Reference
Zillow. Goodyear Real Estate. April 2015. 20 April 2015 <http://www.zillow.com/goodyear-az/>.
