ANOVA Hypothesis Test to Determine the Price of Houses in Suburbs and the City

ANOVA Hypothesis Test to Determine the Price of Houses in Suburbs and the City

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Living near a major city can be a positive aspect of being a homeowner or someone who uses real estate as an investment. Increasing population contributes to land and space diminishing, resulting in high demand for what is available. Industry and markets are in the city, attracting buyers who want to have the convenience of living near commercial properties. The difference in the pay scale between jobs in the city and jobs in the suburbs could contribute to the home prices being less expensive in the suburbs. Many people do not want to live in the middle of the hustle and bustle of the city, causing an increasing number of communities to be built further away from the city.
Team C will use an ANOVA hypothesis test to determine whether the price of homes become more expensive towards the center of the city, or less expensive as home buyers look outside the city center. Is the price discrepancy of a home reflected solely on the distance from the city or do other factors (such as buyers, the economy, amenities) contribute to the price of a home? An ANOVA test allows researchers to compare more than two means simultaneously, and trace sources of variation to potential explanatory factors (Doane & Seward. p 439). The team will be reviewing the information presented in the data set and with supporting research will be able to determine if price discrepancies exist.

Importance of Research
Nothing affects a home value as much as economy. This particular research problem is highly important for individuals looking to purchase a home to be aware of the price discrepancies. There are two categories of people whom it would be crucial to have a constant update on home prices. Buyers and sellers hold an accountable interest in the real ...


... middle of paper ...


...are right-tailed tests) are:
F3,7 = 4.35 for factor A
F4,7 = 4.12 for factor B
We will reject the null hypothesis (no factor effect) if the F test statistic exceeds the critical value.

Step III – Perform the Calculations / Make the Decision
Since FA = 2.27 (rows) does not exceed F3.7 = 4.35, we see that factor A (square footage) has a significant effect on home pricing. The p-value for square footage is very small (p = .131868). Similarly, FB = .092 does not exceed F4,7= 4.12, so we see that factor B (distance) also has a significant effect on home pricing being more expensive toward the center of the city. In short, we conclude that we will reject the null hypothesis and:
• Pricing discrepancies is significantly affected by square footage (p = 0.131868).
• Pricing discrepancies is significantly affected by distance from city center (p = 0.484129).

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