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.
Port Index: A Skewed East-West Ranking
Issue
Jones Lang LaSalle (JLL) recently released a summer 2010 port analysis (PDF). This is actually a solid overview of primary port data, including port investment and TEU’s (twenty-foot equivalent units). The troubles at the South Carolina Ports Authority are clear. The data indicate SC ports lost nearly 50 percent of their TEU volume from their peak of almost 2 million TEUs. That is a huge number, a statistic that can only be explained by mismanagement, based on other port TEU volume declines. Included with these data, JLL summarizes key findings from a cursory review of the data, and add what they label an industry index. It is this industry index that caught my attention.
Indices
A number is expressed as a proportion of a base value – usually converted into a percentage. For example, 20 and 30 with the first as the base value are 100 (= 20/20 *100) and 150 (30/20*100) in index form. Where raw data are irrelevant, misleading or distracting, numbers are often converted into indices. When several factors are combined, the components form a weighted average. Such an index may be base-weighted (Laspeyres) or current-weighted (Paasche) index. (Source: The Economist)
Indices contain a set of variables that allow the analyst to forecast over a time horizon for a specific group of variables. In this case, it’s port transportation industry performance. Forecasting methods usually include statistical regression analysis. I believe what JLL has is actually interval data, which is data used to rank, based on magnitude. Ranking is simpler: a total of the sum of ranked characteristics of one entity compared with others. In this case, it’s where one port falls in relation to the others. There is nothing wrong with this, it just isn’t an index.
Port Index Goal
To this analyst, the primary reason for an index is to forecast. We can see a data problem immediately with what JLL has assembled. In particular, we note the vast difference in TEU volumes. The LA-Long Beach port is almost 14 times the size of the Charleston port, so it’s an apples and oranges comparison. At the same time, Charleston’s port is investing 67 percent more per TEU than LA. To complicate matters, Virginia ports are actually investing a little less than 14 times the LA investment per TEU, while only having one-seventh LA’s volume. So these numbers when combined do not make any sense and are confusing when we try to determine both current and future performance.
Conclusion
To evaluate port performance, start with an article such as this one from Kek Choo Chung (PDF). His research lists port performance variables. We could add more to this list, such as share of potential market, population served or any number of measures outside the port’s control that would affect performance. These are gathered from historical data, weighted, then used to produce a forecast index of performance going forward. What we should end up with is a index trend line showing the port’s past performance with a prediction of future performance that is relevant to other ports in the study.
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.
Real Estate and In-Migration
The Post and Courier covered a local real estate economist’s presentation on the Real Estate Recovery. Core to any real estate recovery is, of course, employment and wage growth. However, a key statistic overlooked in this presentation was migration patterns. I had mentioned in my June Unemployment post that areas such as Detroit were having problems as a result of a declining labor force. This map from Forbes graphically displays the migration problems Detroit is having.
But when you click on Berkley, Charleston, or Dorchester counties, a picture of in-migration emerges. This is an important indicator of growth potential because people have jobs when they move here, have decided to collect transfer payments (retirement) in this region or believe there is potential for work in the area.
Another important statistic this map displays is how our rural population is moving to metro areas (short black lines). This is important for two reasons: 1) unemployed people may have the opportunity to find work and 2) if they find work, the state increases its tax base while decreasing social services.
Unlike the economist quoted in the article, I predict our real estate growth will be better than the median national real estate growth, primarily because of in-migration. This is not to say it will be even close to the bubble years (when we had an unrealistic and unsustainable market), but we should see steady improvement as a result of our region’s possibilities.
I am bullish, for a change. I do believe we have significant control over our own growth since the most important contributors to growth and sustainability include education, health care, public safety, urban planning, convenience and infrastructure (including biking and walking trails), which all are within our control.
Thank you to Keihly Moore for her assistance with this article.
Discouraged Workers
A recession can really change people’s attitudes about looking for work, and many become discouraged (PDF). Unemployment rates in South Carolina are hovering between 10 and 12 percent. But another way to look at that is 9 out of 10 people are actually working! In addition, even with high unemployment, there is still considerable churn in the market place, meaning that jobs are being filled and vacated every day.
So what is the problem? One issue is that the right work is more difficult to find, and takes more time to find, which is discouraging. Matching one’s skills to a job is challenging especially when jobs skills for work are in a constant state of flux. Unlike some employed workers, who may receive retraining assistance from their employer, the unemployed need to finance their own retraining with no guarantee of a job at the end of the training.
Another reason is that the employment picture is likely worse than reported in the media. For example, if we hold the workforce steady at April 2009 numbers, 2,185,673 persons, (people don’t just disappear) and use that number to calculate unemployment in June 2010, holding employment constant, the unemployment rate jumps to 12.2 percent not the current South Carolina adjusted rate of 10.7 percent.
As employment data is published in the coming months, it is important to keep in mind these variances:
- Where we were and where we are now,
- Whether the gap is shrinking or growing and
- The effect this has on unemployment prospects for both the employed and the unemployed.
By keeping these points in mind, we will have a better feel for the current labor situation going forward.
June 2010 South Carolina Unemployment
The Post and Courier wrote and excellent article on the June employment situation here in SC. By focusing on the labor force, the P and C is highlighting a major issue not only for our state but for the country. From the labor analyst perspective, one of the disturbing trends is the “discouraged worker”. The Bureau of Labor Statisics defines Discouraged workers (Current Population Survey) as:
“Persons not in the labor force who want and are available for a job and who have looked for work sometime in the past 12 months (or since the end of their last job if they held one within the past 12 months), but who are not currently looking because they believe there are no jobs available or there are none for which they would qualify.” The data suggests a worsening situation on the labor front.
Here is a table (PDF) with the BLS discouraged worker annual data. I would like to think these data are about to turn, especially with the number of temporary workers being hired. However, one issue which may prevent that, is the skills gap.
Reverse Pivot Table: Matrix → xyz Format
I like to add a few technical tools now and again. Here is a sweet piece of programming that could save time converting a matrix to a xyz table. The surface plot on my web page can be created by converting matrix data to an xyz format.
Issue
The problem I often run into with excel spreadsheets, is the data is defined in a matrix. Sometimes it is more convenient to reorgainze the data with a pivot table in order to represent the data as xyz coordinates. At first glace it appears this should be an easy task, but with out the right excel module or the full version of sql- forget it.
Solution
A solution to this problem is provided by The Spreadsheet Page, a reverse pivot table. The link does an excellent job of explaining the process. At the bottom a VBA link allows one to copy the code into your excel application. A big thank you to these guys for sharing this- it saved me many hours of work.
South Carolina May 2010 Unemployment Numbers
Problem- Not Really
I had some questions on the May unemployment numbers which did not seem to make sense to me. The article posts the state unemployment rate. Further down in the article however, a key piece of information reveals the rate quoted was an adjusted state rate.
Issue
In this case it makes a big difference, since the state rate is mentioned in conjunction with the regional unemployment rates. Once this point was brought to my attention, I realized the error of my thinking. Local unemployment rates are not adjusted! So when comparing local and state rates one needs to compare the state unadjusted data with the local data. This process yields similar results with both local and state rates moving in the same direction. In the case of the May data, adjusted state data was quoted (correctly) with unadjusted local data. The result is unemployment rates moving in opposite directions (in this case) since in reality we are comparing apples and oranges.
Conclusion
Even though the adjusted state rate is the most commonly reported rate in the media, I tend to use unadjusted just for this reason, I want to compare it to the local rate. Another reason, which is a little old fashioned, is because the state data will be adjusted again at the end of the year, smoothed in this case. Also as I describe in my Unemployment Definition, I am focused on the overall trend and do not get to hung up on an individual data point. Key point is to make sure we do not compare or imply the state adjusted rate is related to the local unemployment rates.
Deep Water Horizon Rig Employment
Off-Shore Moratorium
The economic impact of shutting down a deep water drilling rig is no doubt massively expensive. But employment impact is different. As a benchmark, the employment on the BP rig was 126 persons. It is reported that wages average about 100k per year for those workers. However, I could not find that person anywhere, except in the management ranks according to National OES data! Furthermore, most if not all support workers, according to the BLS, make less than 1/2 that unsubstantiated amount.
Interestingly, as soon as we get into a multiplier discussion, the numbers start off ridiculously high and go up from there. But a multiplier over two is not reasonable or supported in any research and especially not in this case. One primary reason is the service nature of the JOBS we are talking about, NOT the industry multipliers.
Rig Count
Of all the rigs out there, only 4% are off shore! (Baker Hughes) Therefore few if any support persons are going to be affected by a stoppage of drilling; as a result of 94 percent of their support services not being located off-shore. A potential economic impact is close to 400 million a year, which includes a multiplier. More likely however, those wages are moved to another location, or just paid, the result being no impact. The reason is most companies can not afford to idle (loose) those skilled workers.
Simple calculation: (number of rigs, 33 * employment, 126/rig * wages, 100k)
