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.
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.
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.
Unemployment South Carolina- April 2010
The Post and Courier missed the main point in the April unemployment numbers. The story here is the labor force, or in this case the decrease in labor force (moving in the wrong direction). Mary Graham from the Charleston Metro Chamber got it right by thinking about this from a seasonal perspective. The surprise is that there should have been an up tick, but instead the labor force dropped a whopping 18K from April 2009, a bad year in itself. In addition to these data, employment actually DROPPED 17K from 2009 (BLS). There is nothing in the data to be pleased with.
I would encourage the Post to speak with experts on these data or become more educated before writing an article. These are important data that need to be reported accurately since they affect so many.
The pdf link tells the real story of unemployment for the state of SC. Note the disturbing drop in labor force and the deep employment recession. (pdf)
Economic-Ecologic Commodity Flow Impact
Detailed Example of Economic-Ecologic Commodity Flows
The ‘TEAM’ model has done the heavy lifting calculating coefficients for a ecologic commodity flows. The classification was first created by Johnson and Bennett (1981). They used the ecologic terms commodities for inputs and sinks for discharges or outputs. Miller and Blair (2009)
The ecologic matrix is defined using matrix algebra. In example (econ_ecol) (pdf), matrices A, R and Q are created from the table of commodity flows. Matrices R* and Q* are calculated using the following formula:
R* =R(I – A)ˉ¹ Reflect the amount of ecologic inputs required directly and indirectly to deliver a dollar’s worth of industry output. In the example, 0.455 represents 0.455 units of water required to deliver a dollar’s worth of agricultural products!
Q* =Q(I – A)ˉ¹ Reflect the amount of ecologic output associated directly and indirectly with a dollar of industry output. In the example, 0.358 means that associated with one dollar’s worth of manufacturing goods to final demand is production of 0.358 units of hydrocarbon pollutants!
Industry may shy from these formula. The opportunity for industry however is to review manufacturing processes using these data to uncover opportunities to reduce waste or identify material/process substitutions (cost savings). For those interested in economic-ecologic impact, this opens, for the first time through the TEAM model, the capability for applied analysis in both industry and transportation development projects.
Benefits of Clean Energy
EPA has just released a resource for states and other interested parties for assessing the benefits of clean energy. This document is a absolute necessity for any organization which analyzes energy development, production, transmission and the associated effects these systems have on economies, public health and policy.
Assessing the Multiply Benefits of Clean Energy(pdf)
Accessing Deleted Microsoft Access Files
If you have ever lost a Microsoft Access file, check out a way to get it back from Black-YoYo
Sorting text and numbers within an Microsoft Excel Spreadsheet Cell
I was cleaning some data for a project and ran across the “text/number in the same cell” sort problem. I researched the web and found three links that were very useful for sorting text and numbers when displayed in the same spreadsheet cell. Cleaning and organizing data is a big part of what I do to prepare information for analysis. This process is critical since, accurately loading data into a data base requires consistent data type; otherwise data will default to text (unknown), not useful for making calculations. Cleaning data is a great way to begin immersing one’s self in what one actually has in terms of data. This process takes the information into its most basic form.
I have included a link to a sheetsheet that shows the two different formula with comments and links. I have also used the “iserror” function in one of the formula, just in case you have forgotten that one..hmmm!
Statistics and Helmets: Linking Data to Emotion
Recently I had coffee with my past professor and friend Chris Nachtsheim, (PhD. Operations Research) of the University of Minnesota. Chris and I often discuss how the world looks through the eyes of a statistician. Most events that we see or experience can be described numerically. Knowing this is important, since one can remove a degree of speculation and emotion out of everyday “crazy” events we witness, by locating available statistical data to help interpret what we experience. One data group, particularly useful to me, pertains to helmets use. The statistics on helmet use is significant, thus providing quite a variety of statistical method and data-my real interest.
I often see children (just like me when I was a kid) not using a helmet. In the past, I would jump to the conclusion of an inevitable brain injury for one that does not wear a helmet. The statistics however, do not support that emotional speculation. Having said that, this is one of those areas where the statistics show how a $10 dollar helmet produces significant positive results when an accident does happen. The old saying is “the natural state of any two wheeled vehicle is on it side”! All my helmets unfortunately, have indicated that that in fact is true.
I have included a link here to the Bicycle Helmet Safety Institute (BHSI). These folks have done a great job (on all sides of the issue) of gathering a number of third party statistics that are interesting and informative both from a research and safety perspective.
