Anova Hypothesis Test

ANOVA Hypothesis Test

ANOVA Hypothesis Test

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, ch. 11, 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 estate field whether the home is being sold through a local real estate agent, or using corporate connections. Individuals who are looking into purchasing a house must understand the variables that affect pricing such as; school for children, crime rate, size of lot, size of home and home location.

Sellers who need to put their property on the market usually desire that the sell comes in a speedy form. It takes time to sell a house and sellers must make sure that their home value is not under-priced or over-priced for the area. Statistics are imperative in the real estate business, because it is a competitive world that shifts up and down the scale of costs and thrives off the economy. Informing the public by researching real estate industry questions provides individuals with wisdom in price comparison to others. Team C has chosen to look at the Real Estate Data Set with a focus on homes that are 30 miles or less away from the center of the city.

Possible Reasons for Pricing Difference

Home prices are more expensive as you get closer to the center of a city as demand remains high for these units. Several reasons can explain this phenomenon. First, there tends to be a high population density towards the centers of cities and therefore more demand for housing. Second in the recent past, skyrocketing gas prices have made suburban living less appealing. The cost to commute to and from work can play a major distraction in people trying to maintain a budget. The result being that those people given a choice would prefer to be located closer to work or metropolitan advantages.

According to the Wall Street Journal in March of 2008, “Higher gas prices negatively impact housing prices by sapping home buyers’ budgets and leaving less to spend on housing, and by making consumers less apt to bid more for homes in less centrally located suburbs” (King, 2008). Furthermore, the article states, “that in metropolitan areas like Chicago, Los Angeles, Pittsburgh, Portland and Tampa, home prices have fallen more in farther-flung ZIP codes than in close-in neighborhoods. For instance, in Chicago, while housing prices have remained stable in close-in neighborhoods within three miles of the city’s central business district over the past 12 months, home prices have fallen 4% in “distant” neighborhoods 13 miles from the central business district. And in Los Angeles, while home prices have dropped 6% in close-in neighborhoods, they have decreased 10% in distant neighborhoods, according to the report” (WSJ, 2008).

In the past 18-24 months, the overall trend for house pricing has been dramatically lower. Many urban areas are facing their worst economic downturn in 30 plus years. The clear delineation between pricing trends is the relative location of housing to centers of cities. Cutting time and cost from commuting as well as providing stability in pricing makes these homes much more attractive and valuable to owners and buyers.

Numerical Hypothesis

Using a population of randomly selected 105 homes, we will be taking samples of the prices of homes in four different distance ranges. With a level of significance of 0.05, this will help determine if