Professor Paul Palley's
          Economics Seminars

One index used to track home prices is the S&P/Case-Shiller Home Price Indices (http://www2.standardandpoors.com/portal/site/sp/en/us/page.topic/indices_csmahp/0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0.html). The website describes the S&P/Case-Shiller Home Price Indices as measuring “the residential housing market, tracking changes in the value of the residential real estate market in 20 metropolitan regions across the United States.” These indices use the repeat sales pricing technique to measure housing markets. The data is collected using single-family home re-sales. This means that its observations are comprised of re-sold sale prices in order to establish sale pairs. There are 20 regional indices and two composite indices that serve as aggregate price indices for the regions. Also published is the S&P/Case-Shiller U.S. National Home Price Index. This is a broader composite index of single-family home price indices for the nine U.S. Census divisions. This index is calculated on a quarterly basis.

There are two weighted composite indices of home prices. The first is S&P/Case-Shiller Composite 10 Home Price Index which reflects home prices in Boston, Chicago, Denver, Las Vegas, Los Angeles, Miami, New York (commuter index), San Diego, San Francisco and Washington DC. The second is the S&P/Case-Shiller Composite 20 Home Price Index in which the following additional cities are included; Phoenix, Tampa, Atlanta, Detroit, Minneapolis, Charlotte, Cleveland, Portland, Dallas, and Seattle.

Another resource for a home price index is the Office of Federal Housing Enterprise Oversight (http://www.ofheo.gov/). The House Price Index (HPI) is described as, “a broad measure of the movement of single-family house prices.  The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties.” A key point here is that the data used to compile and compute the index is based on observing, “repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.” The sample that is used is very broad providing more complete data than other housing price indices. On the other hand, because it refers to the transactions that it does, it will not fully capture transactions financed by alternative mortgages including subprime mortgages.

The graphs below were produced using the S&P/Case-Shiller Home Price Indices for Phoenix and the Composite 20 Home Price Index. The first graph illustrates a comparison between the S&P/Case-Shiller Home Price Indices for Phoenix and the long-term trend developed using data for the period of 1989 to 2003 (the housing bubble arguably started in 2004). This graph demonstrates the excessive nature of the bubble during the 2004 to 2006 time period and the subsequent adjustment that has been occurring since then. As of June 2008 home prices were 11.7% above trend versus 43.9% as of April 2006. Considering what occurred during the months of July and August 2008, prices are at this time probably 6% to 8% above trend. The second graph demonstrates a comparison between trends for home prices in Phoenix versus the national composite. Historically home prices as measured by this index in Phoenix had been below the Composite 20 Home Price Index, but this was reversed during 2005 because of the housing market bubble, and now the index for Phoenix is once again below the Composite 20 Home Price Index.

These more recent trends are positive for the local economy in terms of the fact that it accelerates the rate at which the local economy will begin its recovery. Given current conditions, and realizing the fact that markets tend to over shoot on the way up and do the same thing on the way down, it is clear that home prices are nearing but not at the bottom. Furthermore, the steps taken by the United States Government and the Federal Reserve to stabilize the financial markets, while they are controversial, will help the financial markets and as a result the housing market. I have indicated that it is Fiscal Year 2010-11 in which we should expect to see the housing market and local economy start its recovery. This date is influenced in part by the large number of homes on the market, and homes that will in all likelihood hit the market at any sign of recovery. However, we must watch the financial markets for stability, the credit markets for more active financial intermediation, and the expectations that home buyers have related to prices. Once home buyers believe a bottom for home prices has been reached then we can expect more sales activity. Once the credit markets unfreeze then we should expect to see loans more easily being made again. Therefore it is possible, but not probable, that the recovery of the housing market and economy in Phoenix will start to be evident in 2009-10.

The recent history of the economy of the United States can be interpreted as one that is increasingly reliant on short-term speculative bubbles. The data indicates that over the last twenty-five years (1983 to 2007) the economy, as measured by real GDP, grew at an average annual rate of 3.2%. This starting point was selected in order to correspond to the first year of economic growth following the recessions of 1980-1982. Economic growth during the 1980s averaged just less 3.1%, averaged 3.1% during the 1990s, and 2.5% during the period of 2000-2007. Given the poor economy we are experiencing today, economic growth during the period of 2000-2009 should only average approximately 2.2% to 2.8%. The trend appears to be downward. Taking a closer look at the data indicates an increasingly greater reliance on brief periods of bubbles for economic growth, and that these periods are having a smaller impact on the economy. It is like the United States is trying to grow on a weaker platform.

Economic growth during the period of 1992 to 1997 averaged 3.5% versus 4.1% during the period of the stock market bubble of 1998-2000. On the other hand, during the period of 2002-2003 the economy grew at an average rate of 2.1% versus the 3.1% rate seen during the housing market bubble of 2004-2006. More would normally be expected after an economy bounces back from a recession (we experienced a recession in 2001). This is a lot of data to consider, but we see a trend of weaker growth and this raises a question; is there historical evidence for an approach that can be used to address this? The answer is yes; government policies combined with business investment can positively and significantly impact the economy.

We can look at decisions made by policy makers during and after the Great Depression, and in the period after World War II for examples of how an economic transformation triggers greater prosperity and global competitiveness. After 1932, the government implemented a series of reforms, new regulations, and spending and investment programs that coupled with World War II lifted the country out of the Great Depression. These investments, especially infrastructure investments and regulatory reforms, and an increase in manufacturing capacity, established a platform for stronger economic growth after World War II. Additional investments in infrastructure (the national highway program being an example) after World War II and other investments especially by businesses now feeling freer to engage in the United States and global economy triggered strong economic growth through the 1950s and 1960s. In many ways much of the United States still operates on the infrastructure and regulations put in place during the years described in this paragraph, and this is an aging platform to build on.

The 1980s was a period of substantial growth. It is logical that a large and dynamic economy such as the economy of the United States would bounce back substantially after the years of uncertainty and periodic recessions experienced during the 1970s and early 1980s, but other factors were also at work. The growth of the 1980s was also fueled by substantial increases in government spending and tax cuts resulting in significant amounts of new public debt. A more fundamentally positive influence on the 1980s included declining rates of inflation that resulted from tight monetary policies and declining oil prices that led to lower interest rates. This period then led to the 1990s and 2000s. Economic growth during the 1990s was greater than the 1980s, but the non-bubble years of 1992 to 1997 were weaker than the period of 1983 to 1989. There were multiple causes for these differences and many would argue if not for the bounce after the 1970s and early 1980s economic growth during these periods would be about equal. However, as noted above, the period of 2000 to 2007 characterized relatively low levels of economic growth and this coupled with the housing bust has led to the weak economy that emerged in 2007.

As described above, in many respects our economy has operated on a bubble economy model that has stood on an aging infrastructure and regulatory platform. The model ahead from here should not be the one that has been in place since the 1980s. This model is to continue to work with this aging platform, tax cuts and increases in government spending on non-economically productive initiatives, and growing levels of private and public debt. Short-term bursts of growth result from these policies, but this is not a basis for increases in long-term sustainable growth. Instead the direction should come from the earlier models used from the 1930s through the 1950s that are modernized to reflect the needs of the global economy we are competing in today and new technology demands. This means new and substantial investments in infrastructure, research and development, education, business investment, and modernized regulations. Furthermore, to relieve businesses and households of some uncertainty, a comprehensive health care policy and resolution of the short-fall in Social security is necessary.

Descriptive statistics are used to help us understand and interpret the data that we collect, and as a result facilitate efforts to analyze and interpret sample data and statistical inference. The point of sample data is to better understand the population of interest. We use the mean, median, and mode to better understand the middle or average of a series of data. We use the range, variance, and standard deviation as measures of dispersion or to better understand how spread out data is, or in other words to get a sense of how well the middle describes the data. What follows is a brief description of how these measures of central tendency or descriptive statistics are used to better understand the population of interest.

The mean, median, and mode are used to describe the middle or average. Each measure indicates different things, and as a result is used for different purposes. The sample mean is the average, and as long as the data used to calculate it comes from a random sample it is used as the best unbiased estimate of the population mean. It works well as long as the data it represents is not skewed either by an exceptionally large or small observation. The reason for this is that an observation that is much lower than what is typical of the majority of the data will make the mean smaller than what is represented by the majority of the data. On the other hand, an observation that is much higher than what is typical of the majority of the data will make the mean larger than what is represented by the majority of the data. We will use the median if this is the case.

The median is the 50^th percentile. In other words, fifty percent of the observations will be greater than the median and fifty percent of the observations will be smaller than the median. It is the middle observation, and is not skewed by outliers in the data like the mean is. We see this used to describe demographic data like the median household income or the median home price because these measures can be skewed by exceptional big or small observations. An example where this is important would be that of a business person looking to locate a store. The business person would be interested in household incomes and home prices in zip codes because these measures are indicators of buying power. Looking at the mean as a measure of central tendency can be a mistake because it can misrepresent the characteristics of potential buyers. Imagine the owner of antique shops who sells expensive products. Suppose this business person used the mean or average household income and home price as an indicator to select the zip code to locate a store, and a zip code included a handful of very wealthy homeowners. This could misleadingly lead to the assumption that that zip code should be where the store should be, and this could be a disastrous decision because their only potential customers would be the few wealthy homeowners.

The mode or modal observation is the number that occurs most often. It is not used as often as the mean or median because it is not useful for statistical inference or hypothesis testing, and is not as robust an indicator of the middle as the median. One place where it is used is in retail as businesses want to identify the items that sell the most. Knowing this allows businesses to strategically locate items in the store in places that facilitate the sales of other items. For example, the grocery store places staple items in the back of the store and separates them from one end of the store to another, forcing shoppers to walk through the store and increasing the probability that other items will be purchased.

The range, variance, and standard deviation are measures of dispersion. The range is rarely used because it is based on only two observations, the biggest and the smallest. It is also can be affected by exceptionally large or small observations. Where we do see this used is in statistical quality control. Firms use performance measures to track productivity, production targets, and whether or not the products that are produced meet design guidelines. The range can be used as a measure that indicates whether or not targets are being met, and the firm will want to reduce the range indicating increasing consistency. For example, the call center will want to reduce the range of hold times in order to ensure consistent customer service.

The variance and standard deviation are other measures of dispersion or how spread out data is that are used far more often. The variance is the average squared difference between each observation and the mean. The standard deviation is the square root of the variance. We use the variance and standard deviation as measures of consistency, reliability, and risk. An example would be evaluating the productivity of two groups of employees each performing the same task. If the average or mean rate of production of the two groups are about equal then one may conclude the groups are equally productive. However, if the standard deviation of the productivity of one group is significantly greater than the second group, then its productivity measures would be far more disperse. This indicates greater inconsistency or unreliability in their productivity. You also see variance and standard deviation as a measure of risk in finance as it is used to evaluate the variability of the returns generated by an investment portfolio.

These examples were meant to illustrate key points, and reinforce concepts related to statistical measures we see used everyday. Statistics is a powerful tool used to facilitate decision-making, but its use is most profitable if the measures that are used are well understood and are properly targeted. Measures of central tendency like the mean, median and mode are each interpreted differently and have their own strengths and weakness. Similarly, the measures of dispersion we use each have their own interpretations and applications.