Hotel room rates in the top-25 most popular U.S. destinations average $247.08 this March, up from $213.85 in February, according to hotel online room rates derived from real time global multi-provider database of reservations maintained by e−forecasting.com.
The U.S. average online room rate (e−Room Rate ®)¹ – the world’s best predictive analytic for info about now as compared to the past – and hoteliers’ only tool for “predictive benchmarking ®”, ranges among the top-25 destinations from a high of $416 to a low of $149 this March. Based on industry surveys, e−forecasting.com estimates that in 2018 about 75% of all reservations are made online via brand websites and travel agent merchant websites, compared with only one-fourth nine years ago.
On year-over-year basis, the U.S. average online room rate is now down (−2.0%), this March, from a year ago, which is higher than the previous month’s year-over-year growth rate of (−6.4%). This March, the average online room rate in Washington D.C., despite a dip of (−4.5%) from last year, hit $416 a night, making the city the most expensive destination among the top-25 U.S. hotel markets. San Francisco takes the second place now, in March, with an online room rate at $382, after a drop of (−8.7%) from a year ago. In San Jose, the online room rate in March is now growing (+6.5%) from last year to $365 a night, ranking the city in the third place of the most expensive destinations in the United States.
At the bottom of the list, the three least expensive, or most affordable, cities to visit this March are: Las Vegas hitting a monthly online room rate of $162 a night, after a (−3.5%) change from a year ago; St. Louis posts now an online room rate of $152, following a (+9.0%) change from last year; and lastly, the most affordable popular destination in the country is now Minneapolis with an online room rate of $149, after a nil change from a year ago. With a median online room rate of $224 amongst the top-25 most popular U.S. destinations, Dallas is placed to be the country’s average affordable city to visit this March.
HOTEL BIZ ANALYTICS
Moving from data to Hotel Biz Analytics ®, e−forecasting.com’s Smoothed Seasonally Adjusted (SSA)² predictive analytic, which measures the so called (underlying) trend in online room rates, posts $239.74 this March. On a month-over-month basis – the hoteliers’ predictive analytic for tracking what’s now and not what’s the past – “estimates the national trend in online room rate this March to have declined (−0.3%) from the previous month, which is the same percent change as in February”, said Evangelos Simos, editor of predictive analytics databases and professor of economics at the University of New Hampshire.
Combining the demand-driven trend effect with the market-specific periodic effect, e-forecasting.com offers hoteliers unparalleled predictive intelligence to optimize both what’s now and what’s next “predictive benchmarking ®”. “Looking at the top-25 hotel destinations, the month-to-month percent change in the trend of online room rates in March ranges from a high of (+1.6%) in Salt Lake City to a low of (−1.3%) in Seattle. Amongst the top-25 destinations, the trend (SSM) in online monthly room rates is growing in 14 cities; and is falling or staying flat in 11 cities,” Simos added.
“Looking at profitability, hoteliers’ ultimate gauge for decision-making, profits per available room (e−ProfitPAR ®) are down (−4.5%) on a year-over-year basis in March”, said Maria Sogard, CEO at e-forecasting.com. e−ProfitPAR is the result of changes in two predictive analytics: the online revenue per available room, e−RevPAR ®; and e−forecasting.com’s estimate of hotel unit (per room) cost, e−Unit Cost ®, derived from prices of all relative contributing inputs – including wages – which are mined in real time from hundreds of internet databases. “In our previous month’s report, February 2018, year-over-year percent change in e−ProfitPAR posted a reading of (−8.9%). In a longer perspective last year, March 2017, year-over-year percent change in e−ProfitPAR posted a reading of (+0.1%). Using real time e−RevPAR for the top-25 U.S. destinations and market-centric e−Unit Cost index, year-over-year percent change in e−ProfitPAR range from a high of (+24.1%) in Salt Lake City to a low of (−23.7%) in New Orleans in March. Amongst the top-25 destinations, predictive analytics on e−ProfitPAR are up in 13 cities; they are down or are flat in 12 cities.
On tracking monthly the risk of business losses from online reservations, e−forecasting.com uses sophisticated econometric techniques to estimate the probability for room related losses (negative profits), when room revenues are not sufficient to meet room costs, in each of the 25 destinations and the national economy. For U.S. hoteliers, the probability of losses is forecast to have hit 100% this month – March, which is the same as in February. In the top-25 hotel destinations, the risk for hoteliers being in a period of business losses per room – from online reservations – this March, ranges from a high of 100% in Philadelphia to a low of 1% in Salt Lake City. “The predictive analytic for business losses per room is above 50% in 14 cities out of 25 cities; it is 50% or below in 11 cities,” Maria added.
Founded in Durham, NH in 1994, e-forecasting.com is a predictive intelligence consulting firm offering to clients customized solutions for what’s next. For over 20 years, its hotel insights division has focused on hotel predictive analytics and forecasting products for the top destinations around the world to enhance its clients competitive advantage and improve their bottom line.
Notes and Exhibits
¹The e−Room Rates are a snapshot (real time estimate) of the monthly online room rate for the world’s most popular destinations on the basis of millions of daily inquiries for hotel room rates to hundreds of online travel agents, hotel chains and websites of single properties.
²For each market, e-forecasting.com adjusts each e−Room Rate ® to seasonally adjusted and smoothed (SSA) hotel-biz-analytic, also called trend, using the BLS methodology comprised of two statistical approaches for seasonality: the model-based Signal Extraction in ARIMA Time Series (SEATS) or the non-model-based X-12 adjustment; and, the Henderson smoothing technique using the minimum mean square error revision for the end points.
³For each market, e-forecasting.com generates monthly – starting in 1969 – unit cost or cost per room index, a hotel-biz-analytic consisting of three analytics: a raw (actual) cost per unit index as a weighted average of operational costs in “purchasing’ all inputs from wages to utilities, repairs to several types of professional services; and two variants of the cost index: a seasonally adjusted (SA) cost index and a trend cost index which smooths out the seasonally adjusted index (SSA).
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