Hotel room rates in the top-25 most popular U.S. destinations average $294.53 this June, up from $274.76 in May, 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 $501 to a low of $109 this June. 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 up (+12.4%), this June, from a year ago, which is better than the previous month's year-over-year growth rate of (+4.1%). This June, the average online room rate in Boston, after rising (+28.3%) from last year, hit $501 a night, making the city the most expensive destination among the top-25 U.S. hotel markets. New York takes the second place now, in June, with an online room rate at $499, after an increase of (+17.2%) from a year ago. In San Jose, the online room rate in June is now growing (+25.1%) from last year to $489 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 June are: Miami hitting a monthly online room rate of $150 a night, after a (+2.5%) change from a year ago; Las Vegas posts now an online room rate of $118, following a (−17.8%) change from last year; and lastly, the most affordable popular destination in the country is now Phoenix with an online room rate of $109, after a nil change from a year ago. “With a median online room rate of $256 amongst the top-25 most popular U.S. destinations, Minneapolis is placed to be the country's average affordable city to visit this June,” said James Webber, economic analyst for hotel databases at e-forecasting.com.
Moving from data to Hotel Biz Analytics ®, e−forecasting.com's Smoothed Seasonally Adjusted (SSA)² predictive analytic, which measures the so-called trend in online room rates, posts $254.01 this June. On a month-over-month basis - the hoteliers' predictive analytic for tracking what's now and not what's history - "shows the trend in the national online room rate this June to have increased (+1.0%) from the previous month, which is the same percent change as in May", said Evangelos Simos, editor of predictive analytics databases and professor 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 June ranges from a high of (+6.3%) in Chicago to a low of (−3.5%) in St. Louis. Amongst the top-25 destinations, the trend in online monthly room rates is growing in 18 cities; and is falling or staying flat in 7 cities," Simos 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.
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.
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