Running Head: A REAL ESTATE ANALYTICAL PERSPECTIVE
A Real Estate Analytical Perspective
Ceci Leon, Chris Petz, and Lori Williams
University of Phoenix
RES342: Research and Evaluation II
October 6, 2008
A Real Estate Analytical Perspective
The economy of the United States is said to have had an influence on real estate values in recent years. In an article titled “Keeping That Hard Hat Handy,” the financial struggles of homebuilders such as D.R. Horton are analyzed. According to the article, D.R. Horton “ended its fiscal year on Sept. 30, 2007, with a net loss of $712.5 million” (Cocheo, 2008). As a result, the largest U.S. homebuilder began to significantly slash home prices in an attempt to save face and gain some positive cash flow. D.R. Horton is not the only home builder faced with significant losses, and furthermore, not the only firm drastically reducing their prices. The effects of this action have rippled through the real estate market with shocking results.
In an attempt to measure this trend in real estate, an experiment was conducted in an effort to analyze this trend. Samples were taken involving numerous homes with varying features. A null and alternate hypothesis follows that illustrates the trends in real estate prices for the sample.
Numerically, the hypothesis posed is the following:
H0: m = 190,000
H1: m > 190,000
This null and alternate hypothesis is based on preliminary research done in the field of real estate for average home costs. If the alternate hypothesis is accepted and the z value is proven to be less than -1.96, then we reject the null hypothesis and there will be a significant impact will be seen in terms of the influence of the US economy. If the null hypothesis is accepted and the alternate hypothesis is rejected, then the test was not statistically significant and therefore, there is no impact because the data was not conclusive.
Hypothesis test of one population mean
There are five steps to derive a hypothesis test of one population mean of which directly corresponds to the observation of the data.
1. The null hypothesis needs to be stated in mathematical or statistical terms. The null hypothesis is done in order to make it possible to calculate the probability of possible samples assuming the hypothesis is correct.
2. Test statistic must be summarized as well as the information in the sample that is relevant to the hypothesis.
3. There needs to be the distribution of the test statistics used to calculate the probability sets of possible values (usually an interval or union of intervals).
4. Sets of possible values that represents the most extreme evidence against the hypothesis needs to be chosen.
5. Probability that a sample falls in the critical region when the parameter is θ. This is the area where θ is for the alternative hypothesis which is called the power of the test at θ. The power function of a critical region is maps θ to the power of θ.
Suppose in a collection of data on real estate and the impact the housing market played on the U.S. economy, there is a higher level of foreclosures. Here the mean of real estate and the impact the housing market played on the U.S. economy. The relevant hypothesis test for real estate will be stated as follows:
H0: m = 190
H1: m > 190
When the test is conducted for 100 homes in the U.S. real estate market, the result is:
x = 198 and s = 15
The question is related to the evidence to suggest that the U.S. housing market crash has a high impact on the economy. The derivation is stated as follows:
The z is called the test statistic.
Since z test result is so high, and the probability that Ho is true is so small the results H0 should be rejected, and H1 should be accepted. Therefore, we can conclude that the U.S. housing market crash had a tremendous impact on our economy.
To clarify further, there are 160 homes in real estates and there is a direct impact the abundance of real estate foreclosures in the housing market on the U.S. economy. To test the hypothesis of the buyers needing to have the minimum cost of a mortgage. The generally estimated average will be 7.7. There is the need to have a solution.
Compute a rejection region for a significance level of .05.
If the sample mean is 7.5 and the standard deviation is .5, the conclusion will be as follows:
First, we need to write the null and alternative hypotheses;
H0: m = 7.7 H1: m < 7.7
This test falls under the criteria of a left tailed test. The z-score corresponds to .05, and the result is -1.96. The critical region of the specified area that lies to the left of -1.96 is represented. If the z-value is less than -1.96 then there is the possibility of rejection of the null hypothesis and acceptance of the alternative hypothesis. However, in the case where the z values is greater than -1.96, then there will be no rejection of the null hypothesis and the test was not be statistically significant.
The test results are the following:
Since -2.83 is situated to the left of -1.96, it falls in the real estate estimation. Thus, there is the rejection of the null hypothesis and accept the alternative hypothesis. Conclusively, the real estate estimation needs to be thoroughly reviewed by the U.S. economists for a balanced situation.
The fact of the matter is, our housing market is definitely on a day-to-day analysis due to the present state we find ourselves in as a nation. The average home in America is now costing thousands more due to higher productivity costs and the inflation on construction materials. With foreclosures on the rise, and the situation our banks now find themselves in, the possibility of owning a new home is for many a distant possibility. Our housing market has proven its instability due to the various factors, but our research proves that the mean average of an American home is now a plausible insight into the future of the economy.
With a recent bailout of Wall Street, there are still many uncertainties that are in dire need of some answers. Can corporate America survive the crunch and eliminate any fears of a depression? Well, with last week’s two hundred point market drop, even the market is susceptible to alternate hypothesis and unstable variables constantly affecting the status of real estate. In a published article titled “Real Estate Home Appreciation” (2008), the figures which null the original hypothesis are as follows:
Further evidence proves that the average household over the course of the past year has fluctuated dramatically. With an average cost of $212,00, the impreciseness of the market is shown to have limited different variables at play—financing, accountability, issues dealing with ownership, and potential losses. The numbers and factors are, thus, the cause for many legislative reforms. The $850 billion dollar bailout of our nation’s top corporate institutions are now bound to a new line of reasoning, one that can flop to either side of the spectrum. With thousands of families losing their homes of the past few years, our market is highly dependent on an issue many of us in this country seek to make a reality—the American dream.
Cocheo, Steve. (2008). “Keeping that hard hat handy.” American Bankers Association.
ABA Banking Journal, 100(1), 1.
Hypothesis Testing For a Population Mean. (2008).
Real Estate and Mortgage Resources. (2008). Real Estate Home Appreciation. Retrieved October 4, 2008