Data that hotels need to predict demand is readily available: PolyU study

The combination of portfolios will allow both parties to reach into different hotel categories and segments

A new study is demonstrating how hotels, even the most resource-strapped of them, can use publicly available data to improve occupancy predictions.

While international hotel chains often have sufficient resources for forecasting demand, smaller and independent hotels can “rarely afford to invest in such resources”, although their need for accurate predictions is just as great.

Brian King and Stephen Pratt from the School of Hotel and Tourism Management (SHTM) at The Hong Kong Polytechnic University, plus a co-author, say the method they used to make demand predictions can be adopted by even the smaller hotels.

With relevant data publicly available, even the smaller actors will be able to make relatively accurate demand predictions

How data from wider economic context of could speak to tourism
Given that tourism is a global industry consuming a diversity of goods and services, the prediction of future trends needs to take account of the wider economic context, the researchers say.

They hence looked at easily accessible online data available from the Organisation for Economic Cooperation and Development (OECD). The OECD, established in 1957, comprises 34 member states and a further 25 non-member states, including China, that participate as committee observers.

The OECD produces various quantitative indicators of specific aspects of the global economy, three of which were used by the researchers. First, the composite leading indicator (CLI) combines various economic variables, such as GDP, that indicate a country’s economic situation and provide “early signals of turning points in economic activity”. The researchers expected that the CLI for tourist origin countries would predict hotel occupancy rates in the destination country.

The business survey index (BSI) collects qualitative information from business executives and managers that is reflective of “confidence within the business community about prevailing economic conditions”. The researchers argued that the BSI reflects the “motives of business travellers and conference delegates”, which affect the volume of business in the accommodation sector.

The consumer confidence index (CCI), in contrast, reflects consumer sentiment based on the economic climate and household finances. The information is collected through a monthly survey of 19 member and non-member countries. The researchers predict that more positive feelings towards the local economy expressed through the CCI would be associated with increased hotel occupancies in the destination.

To test their predictions, the researchers used quarterly data on hotel occupancy rates in Hong Kong from the first quarter of 1972 up to the final quarter of 2010. They initially applied a method of “smoothing” the data to reduce the effects of seasonal fluctuations, so that they could identify the real peaks and troughs that reflected upturns and downturns in demand.

In the next step, they assessed the abilities of the three OECD indicators to predict peaks and troughs in the Hong Kong hotel occupancy data, categorised according to the Hong Kong Tourism Board’s classification of hotels as “high tariff A, high tariff B and medium tariff hotels”.

They showed that the three OECD indices are leading indicators of hotel occupancy rates. Then, they determined the correlations between each OECD indicator and the peaks and troughs in demand for each hotel type, finding that the CCI is the best predictor of overall Hong Kong hotel occupancy rates. However, the CLI provides better predictions for tariff B hotels.

Furthermore, the researchers note that other sources are available, such as the World Tourism Barometer which is produced by the UNWTO and outputs from the Australian government’s Tourism Forecasting Reference Panel.

There is, they explain, “growing interest at both national and international levels in improving the accuracy of predictions through multiple inputs”. The greater availability of such data, and the use of relevant methods to exploit them, means that policymakers and hoteliers will be better equipped to predict future demand.

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