Home prices are determined in part by income. Assuming the demand for housing is a normal good, an increase in income shifts the housing demand curve to the right. The equilibrium price of housing increases, ceteris paribus. The purpose of this section of the paper is to pick an appropriate measure of income and compare it with the housing prices discussed in section 1.
There are several different measures of income reported by statistical agencies. For example, there is per capita personal income, per capita disposable income, money income, household income, family income, and a few more. Your first task should be to identify the different income measures available and pick the best one. If we want to evaluate the effect of income on median home prices what is the best measure of income? I'm not going to advise you as to which one may be best. This effort merits a one paragraph discussion on why the income measure you selected is preferred to the other measures. My recommendation - look closely at the definitions of household income and family income.
Once you have picked a measure of income the next task is to compare median (or average) incomes with housing prices. In part one you should have presented the U.S. median housing price as a time series graph. That means you should want to compare the housing price time series with a median annual U.S. income time series that covers the same years.
Second, you should have included in part 1 a table comparing some regional median housing prices. You should also get regional median annual income measures that cover the same year and regions. For example, if you compared 2003 housing prices in Washington, DC with New York City, you should get 2003 median incomes for those two regions.
Now, what is the best way of comparing housing prices and income? You could simply plot the two time series on the same graph or have a column with housing prices next to a column with incomes in the same table. But that is not very revealing. Graphing or comparing annual percent changes is a bit better but it remains difficult for the reader to assess cumulative changes. In other words, we might easily identify single years in which housing prices rose much faster or much slower than income but it is difficult to see the cumulative effect over several years.
I would recommend creating an index. Divide the median home price by the median annual income. When you start looking to buy a house you might find that many real estate agents assess your ability to afford a house using a rule-of thumb based on the home price as a multiple of your income. For example, with current low interest rates you might be told that you should limit yourself to houses that are no more than 3 times you annual salary.
For the time series create a graph that has only the home price/income index. Do not include the median housing price and median annual income in the graph. I would not even bother with a separate graph of the median housing price and median annual income series. This would be an overload of information that is not particularly useful.
For the cross-section table you should have four columns: (1) The name of the city/region, (2) the median home price, (3) the median annual income, and (4) a home price/income index. The fact that a house in one region costs 50 percent more that one in another loses its wow factor if we recognize that income in the first is also 50 percent greater than income in the second.
Income statistics are available at:
|Bureau of Economic Analysis||http://www.bea.gov/bea/dn/nipaweb/SelectTable.asp?Selected=N|
|Bureau of Labor Statistics||http://www.bls.gov/|
Note that I'm giving you a little less information about what data series your should get and where it is located. So be aware it will take a more effort this time to get the numbers you will be happy with. Getting the numbers is the key to this exercise. If you have problems I recommend consulting with the reference librarian at the GMU Library. Looking at the printed copy of the Census Bureau's annual Statistical Abstract of the United States may also help.