Mainland visitors hold the key to the revival of Hong Kong’s pandemic-hit travel sector as they account for nearly 80% of visitor arrivals to the city. Following the resumption of normal travel between Hong Kong and the Mainland, it is important for the local tourism industry and stakeholders to better gauge the tourism outlook for pre-emptive policy and business planning.
To support forward-looking strategy development and plans by the industry and relevant agencies, our School (Center for Business and Social Analytics) collaborated with Wisers (Wisers AI) to launch the Wisers-HKUST Tourism Index. The city’s first Tourism Index offers forecasts of key tourism metrics based on a predictive model of mainlanders’ intentions in traveling to Hong Kong. The model was constructed using leading-edge AI technologies to analyze over 10 million data points[1] from social media platforms, online travel agencies, and travel forums in Mainland China.
The project provides a series of predictive tourism indices to forecast Hong Kong’s tourism activity levels, overcoming the limitations of traditional surveys’ limited insights and delays in industry statistics, thereby contributing to the tourism industry’s long-term development.
According to the Hong Kong Hotels Association (HKHA) Chairman, Mr. Peter WONG Chak-fung, this initiative underscores HKUST's and Wisers' dedication to innovation and the utilization of big data to generate crucial tourism indicators for economic and tourism recovery. HKHA understands the importance of accurate and insightful tourism indicators that can aid hotel operators in making informed decisions.
More about the Wisers-HKUST Tourism Index
The Index comprises the Composite Index, and its three component indices, namely Visitor Arrivals Index, Hotel Occupancy Index, and Hotel Average Daily Rate Index, which are the weighted average of the Composite Index, with weights of 50%, 25%, and 25% respectively. The Index is available for viewing on the CBSA website at: https://tourismindex.hkust.edu.hk/
[1] A data point refers to a post made on social media, online travel agencies, and travel forums.