Data Collection Paper
Research and Evaluation I
April 19, 2010
Data Collection Paper
Team B is conducting research to examine variables that affect the price of homes. The problem statement used in our research is: what effect, if any, does proximity to the city have on the value of a home? To better answer this question, Team B will review literature related to the topic in an effort to provide context to the situation, and assess the sampling used in the research to determine if there is potential for bias based on the population and the size. Data collection methods are considered to determine the most appropriate approach and data is displayed in various formats to provide a visual review as well as an in-depth analysis. Additionally, Team B must address ethical considerations related to this research to ensure that any conclusions drawn do not compromise the validity of the conclusions that are drawn.
During last week’s research, three testable outcomes were identified: the value will increase if the home is closer to the city, the value will decrease if the home is closer to the city, or there is no direct correlation. Team B hypothesizes that if a property is closer to the city, the value will be higher. It has been determined that the median, mean, minimum, and maximum values should be assessed and examined to determine if this hypothesis is accurate. Team B will be using the mean home prices in group one and two to determine if there is a significant difference in home prices for homes less than 15 miles from the city compared to those equal to or greater than 15 miles from the center of the city. Based on the possible testable outcomes, Team B will use the Null Hypothesis and Alternate hypothesis with the mean prices in group one denoted as µH1 and mean home prices in group two denoted as µH2. 1. Null Hypothesis: There is no significant difference in the mean Home Prices in group one and the mean of Home Prices in group two. Ho: μH1 = μH2
2. Alternate Hypothesis: There is a significant difference in the mean of Home Prices in group one and the mean of Home Prices in group two. H1: μH1 ≠ μH2
Review of Literature
In the article The Dynamics of Location in Home Price, the Journal of Real Estate Finance and Economics discussed the complexity of determining if location can determine the value of a home. According to the authors, Gelfand, Ecker, Knight, and Sirmans (2004), “external variables such as changes in market, fluctuation, time and characteristics of the home provide endless changes in parameter.” Spatial variation, in this case distance, proved to be a very important factor leading to the rise or decrease in home values. Samples have possibility for bias when only involving one type of house or a type of sale such as first-time or resale (Gelfand et al., 2004, p. 164). This article can be used with Team B’s research as it sheds light on factors addressed, such as external forces, influences, and possible areas of bias. In a study done in 2007 titled Determinants of House Prices: A Quantile Regression Approach, The Journal of Real Estate Finance and Economics published a quantile regression analysis of home prices values. Using quantile regression analysis provided reasons behind outliers by considering variables such as the type of consumer (tax brackets) and by categorizing many of the characteristics of the home such as square footage, decks, and pool availability (Zietz, Zietz, Sirmans, 2007, p. 319). By reviewing individual characteristics, a more robust analysis provided light on how these variables, in addition to location, affect the value of a home. Team B will be analyzing the effects of these additional variables on home prices. The results of this analysis and the review of the following pieces of literature will help spearhead the development of Team B’s statistical review. The article by The Journal of...
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