STR: Preliminary May data for Sydney hotels
 
STR: Preliminary May data for Sydney hotels
14 JUNE 2018 7:51 AM

Hotels in Sydney reported occupancy rose 2.3% to 84.3% in May, according to preliminary STR data. ADR increased 7.7% to 214.48 Australian dollars ($162) and RevPAR rose 10.1% to AU$180.85 ($136.60).

LONDON—STR’s preliminary May 2018 data for hotels in Sydney, Australia, indicates strong demand and significant performance levels.

Based on daily data from May, Sydney reported the following in year-over-year comparisons:

• Supply: +5.6%
• Demand: +3.9%
• Occupancy: -1.7% to 83.0%
• Average daily rate (ADR): +0.6% to AUD215.64
• Revenue per available room (RevPAR): -1.0% to AUD178.95

The absolute occupancy and RevPAR levels would be the second-highest for any May on record in the market. The absolute ADR value would be the highest for any May in STR’s Sydney database.

STR analysts note that performance was helped by a combination of events, such as Mercedes-Benz Fashion Week Australia (13-18 May) and Vivid Sydney (25 May-16 June). The opening week of Vivid Sydney 2018 coincided with occupancy growth of 4.6% and an ADR increase of 5.4% when compared with the same Vivid Sydney 2017 dates.

STR will release full May results later this month. The May edition of STR’s Market Forecast is available now for Sydney and a host of other markets across the globe.

Media Contacts:

Alex Anstett
Marketing & Communications
aanstett@str.com
+44 (0)207 922 1979

Naureen Ahmed
Director of Marketing, Research & Analysis
media@str.com
+44 (0)207 922 1965

The above is a news release written by a third party. While HNN’s editorial mission is to produce unique content, it occasionally publishes timely, newsworthy news releases to complement in-house reporting efforts. All news releases are clearly marked as such. For questions and clarification, please contact Editor-in-Chief Stephanie Ricca at sricca@hotelnewsnow.com.

Correction: An updated version of this press release was provided by STR on 22 June to address several incorrect data points.

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