Consumer Price Index (CPI)
Issue
The Consumer Price Index (CPI) is one measure of consumer prices. The Bureau of Labor Statistics (BLS) CPI program produces monthly data on changes in the prices paid by urban consumers for a representative basket of goods and services. The BLS data sets allows us to review price increases (or declines) on more than 200 categorical items. (Link)
While my home shopping econometric expert and I were comparing prices from a recent trip to the grocery store, we realized there had been a significant price increase in our Cream of Wheat®. In fact, the cereal had increased by more than 6 percent. However, the CPI November report from the BLS stated:
The Consumer Price Index for All Urban Consumers (CPI-U) was unchanged in November (2011) on a seasonally adjusted basis, the U.S. Bureau of Labor Statistics reported today. Over the last 12 months,the all items index increased 3.4 percent before seasonal adjustment.
So how do the items we purchase prices relate to the CPI?
CPI: Index Weighting
It turns out there is a relationship between our breakfast cereal and the CPI. Although our individual cereal price is in the “basket of goods”, it is a small contributor to the overall index. The index is heavily weighted toward food, but even more so for housing and transportation. 
This is one criticism of the index – that it does not clearly reflect the items that we purchase every day. Who of us purchases a home once a week?! In other words, our cereal is mixed in with a variety of goods and services, some of which we seldom use.
CPI: Applied
The CPI however, is actually a pretty handy consumer tool, especially if one is going to make a major purchase beyond daily consumables. As an example, in 2006 a price drop in televisions at Best Buy® caught my eye. I decided to save the January/February sale catalog and watch prices over the years. After our recent morning cereal discussion, I recovered the January 2006 catalog and starting comparing prices. What I found is that a 32-inch television in the 2006 catalog had decreased in price by 86.3 percent in 2012. When I looked at the “basket” of television items in the CPI, the index noted a 61.5 percent decrease from 2006 to 2010, and 82.7 percent decrease from 2001 to 2010. (PDF) Another way to look at this is that your 32-inch television, if you purchased one in 2006, is worth 61.5 percent less, not including depreciation!
Conclusion
The CPI is a good tool for not only reviewing price trends, but forecasting what we might expect in price increases or decreases from a large assortment of items we purchase. This broad base of items range from daily purchases on consumables to housing purchases, which may only happen once or twice in a lifetime.
Maybe I will wait another year before I get that 52 inch big screen.
For a monthly analysis of these data see the Center for Economic and Policy Research (cepr).
Hotel Occupancy and Revenue Per Room
The Issue
The Post and Courier recently published and article on the regional hotel occupancy forecast for 2011. We are not exactly sure how they derived these numbers, but they are important enough in this community that we need to shed some light on the impact of these data.
I am not convinced there is going to be a rebound in occupancy levels with national growth recently being reported to be under 2 percent. Having said that, those results are probably statistically insignificant as a result of noise in the data. In addition, we always need to include this caveat in South Carolina hotel research: With any inkling of a hurricane, all bets are off. This is one reason that August, September and October tend to be very volatile months, with occupancy slipping from highs achieved in the first six months of the year.
Demand, Occupancy and Money: The Trifecta of the Hotel Business
To make forecasts in the hotel industry, a researcher needs historical occupancy data. My source is the venerable STR Global. There definitely is seasonality to these data, so I stay with the annual data which are adjusted slightly by STR at the end of each year.
I reviewed two data sets: occupancy and revenue per available room (RevPAR). I chose to compare three geographies; Charleston/West Ashley, the state of South Carolina and the entire nation (PDF). It appears that Charleston has done a better job of managing occupancy than the state as a whole, but has paid the price in a lower RevPAR. Instead, it opted for market share. In a market with declining demand, hotels need to create and execute a strategy to grab market share. However, if hoteliers believe there is going to be a measurable increase in demand, keeping prices high likely would be the winning strategy. As capacities creep to more than 80 percent, they can demand higher room rates. Unfortunately, this is not likely the baseline scenario going into 2011.
Community Economic Impact
A back of the envelope calculation nets the following impacts: On average there about 6,ooo hotel rooms in STR’s study area. A calculation using annual RevPAR, occupancy and days returns a gross annual income of $147 million. A small adjustment of just 1 percent in occupancy increases this number by $2.1 million. In 2005 the average daily rate, sightly different than RevPAR, was $128.61 with occupancies at 70 percent. Those numbers result in $51.5 million positive difference in revenue.
Therefore, from a community standpoint, it is important to consider the effect policy has on these numbers. As we can see, small changes in the customers perception of Charleston and/or the state of the economy can and do have significant impact occupancy and room rates, which ultimately affect the community as a whole.
Economic Impact – Forecasting Definition
As a result of the BP Gulf Oil spill, persons are using the term “Economic Impact” or “Impact Analysis” more frequently. The definition of impact analysis is: “When the exogenous changes occur because of the actions of only one “impacting agent” (or a small number of such agents) and when the changes are expected to occur in the short run (e.g., next year), this is usually called impact analysis”. (Miller and Blair, 2009)
The definition for forecasting is: “If we project the levels of final demand for outputs of all sectors in an economy fives years hence, and estimate, using the Leontief inverse, the outputs from all sectors that will be needed to satisfy this demand, this is an exercise in forecasting”. (Miller and Blair, 2009)
The issues in the Gulf are going rely on a combination of these to analysis techniques.
