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.
Unemployment and Productivity
Productivity Enhances Job Growth
The Post and Courier recently published an article on unemployment and productivity. The article suggests that productivity is a cause of unemployment. In fact, it is the other way around! Being more productive (efficient), means gaining market share, which creates an opportunity for more employment. The alternative, low productivity and inflated cost structure, decreases employment.
Demand: The Lost link
The link between productivity and employment rests with demand. Decline in demand shrinks employment. The best example is the housing market. A productive builder may be able to capture a greater market share, but that share is in a declining market. The overall effect is that his employees work longer hours with the same technology (they do more with less). But the industry sheds workers due to limited demand, not because of increased productivity. Wages during this time stay flat or decline due to thin margins and little growth potential.
Show Me The Data
Two data tools that assist us in this discussion are Current Employment Statistics (CES) and Labor Productivity databases from the Bureau of Labor Statistics (BLS). CES tracks hours worked, while the labor productivity database tracks hours as well as output. A quick glance at these data indicate that – sure enough – hours have increased. Output per person is up while earnings have declined and employment has shrunk. The astute observer will, however, note that hours have been dropping since 2007 and likely rebounded recently only in response to the need to rebuild critical inventory levels. I would expect hours to continue their slide in the short term.
Easy to Cut, Hard to Invent
Looking forward, cutting cost is easy: Don’t sign the check, reduce the crew, punish vendors for late shipments – the list is endless. But what is needed now in part, is new products that create demand. In the long run, this is the only way employment will increase.
All of which means businesses have plenty of hard work cut out for them.
South Carolina Lazy? I don’t think so!
Lazy: When Noise Interferes with the Signal
Recently the Post and Courier ran an article highlighting a Business Week analysis that said South Carolina was the eighth laziest state in the union! Typically, subjective words used to describe data pop a red flag that warns me of impending data misuse doom.
The Data Set
The American Time Use Survey (ATUS), measures the time people spend doing various activities such as work, childcare, housework, watching television, volunteering and socializing. Hence this is an activity survey, not a lazy survey. The data are collected by the Census Bureau and sponsored by the Bureau of Labor Statistics (BLS). I ran a query to understand the nature of the survey, data availability and error rates. I called in the big guns from Global Pragmatica LLC to assist in converting the data from a ASCIDAT file to my JMP statistical software package format. These folks are experts in scripting and were a huge help. Thank you!
These data are collected regionally but analyzed nationally. There is about a 90-percent chance, or level of confidence, that an estimate based on a sample will differ by no more than 1.6 standard errors from the “true” population value because of sampling error. No estimates are made for state level data, and one University of Minnesota analyst stated she was not aware of state level error estimates.
It is inappropriate to analyze these data at the state level without calculating the error inherent in the data. If you did that, the analysis would be interesting but useless when comparing one state to another. Why?
Sports Activity Variable Analysis
For a test sample, I choose state level geography,with sports as a variable activity. This category captures the respondent’s participation in sports, exercise and recreational activities. To extract the data from the system, I used a tool created by the University of Minnesota called the American Time Use Survey -X. The data needs to be processed by a statistical package, in this case my JMP program. An analysis of people participating in sports activities indicates that South Carolina would rank 22nd out of 50 states in terms of average minutes spent participating in sports in a 24 hour period – not bad. However, upon further inspection of South Carolina’s 2009 detailed weighted data, the state could rank anywhere from 12th to 23rd,based on national error rates! (PDF) Unfortunately, since these are state data, the results are meaningless. That’s because the sample is simply too small, which is one of many buried statistical problems. This 2009 sample included a total of 200 people, where 166 recorded zero sports activity minutes. (PDF) In fact, the median is zero, which is another red flag for this data set. A review of other states’ data revealed the same issue. This is a fascinating national data set. But unfortunately, analysis of non-national geographies yields unreliable results.
