Total international visitor arrivals to the Greater Bay Area (GBA) are forecasted to recover to the pre-pandemic level, reaching close to 130 million by the end of 2024, and the total number of inbound visitor arrivals to Hong Kong will recover to the 2018 level of 65 million by 2025. These were some of the findings released by the SHTM at the launch of a new forecasting platform on 25 January 2024 as part of "The Development of an Automated
and Self-Adaptive Tourism Demand Forecasting Platform for the Greater Bay Area" (GBA-TDFP) project.
Led by Professor Haiyan Song, Principal Investigator and SHTM Associate Dean, Chair Professor and Mr and Mrs Chan Chak Fu Professor in International Tourism, the project adopts an interdisciplinary approach, integrating well-established theories in economics, tourism management and computer science to develop the GBA-TDFP. Its key functions include big data visualisation, market
sensitivity analysis, short-, medium- and long-term forecasting, sentiment analysis and interactive scenario forecasting, and it will serve as a valuable tool for industry professionals, policy makers and academics, enabling them to adapt and generate forecasts of visitor arrivals to the GBA under different economic scenarios.
Tourism is considered a key industry in the GBA, which has the potential to become a world-leading destination by 2035. Although the economy has largely recovered
from the shocks caused by the travel restrictions and public health measures taken during the COVID-19 pandemic, there are still challenges to overcome, including labour shortages, supply constraints, changing economic conditions and shifts in consumer behaviour. According to Professor Song, all of the indicators point to the fact that to sustain ongoing recovery, accurate forecasts of tourism demand recovery are crucial, as they enable policy makers and practitioners to develop sustainable
tourism strategies that foster long-term economic growth in the region.
To facilitate accurate forecasting, the project has collected macroeconomic data such as the GDPs, CPIs and exchange rates of the GBA cities and their key source markets from statistical departments and international organisations such as the International Monetary Fund. For short-term tourism demand forecasting, the project has leveraged big data collected from popular online and social media platforms such as
Google, Ctrip and Baidu.
The project's analysis reveals the following insights.
- The short-term forecasting results indicate that there will be a significant rebound in inbound visitor arrivals to Hong Kong and Macau by the end of 2024. Mainland Chinese cities within the GBA are also projected to experience a substantial recovery in domestic visitor arrivals and visitor flows within the GBA by the end of 2024. These projections suggest that the volume of visitors will reach
levels comparable to those observed before the COVID-19 pandemic.
- The 5-year-ahead long-term tourism demand forecasting results indicate that the total number of domestic and inbound visitor arrivals to the GBA, as well as visitor flows within the GBA, will return to pre-pandemic levels by the end of 2024. By 2027, the GBA as a whole is expected to witness over 335 million domestic visitor arrivals and 195 million inbound visitor arrivals. Visitor flows within the GBA are projected
to reach 200 million.
- In the GBA, visitor reviews are overwhelmingly positive for all destinations. These encouraging reviews not only validate the predictions for a robust recovery of the tourism industry in all GBA destinations but also signify the immense potential for further growth in the GBA tourism market. At the same time, the moderate and negative reviews point out critical areas in which the destinations could further improve, such as service quality and border
control.
- Across all destinations, the monthly average satisfaction levels of visitors, as reflected by sentiment scores extracted from their reviews, are consistently positive. However, there are fluctuations in daily satisfaction levels, indicating that experiences may vary from day to day. There are also noticeable discrepancies in satisfaction levels across different tourism activities.
For policy makers and industry leaders, the GBA-TDFP serves to simplify the process of
analysing "what-if" scenarios in tourism demand. Users can input hypothetical values for the determinant variables (such as GDP and price levels) into their Web browsers, which are then incorporated into the estimated econometric models to generate scenario forecasts. This functionality is particularly valuable for policy evaluation and decision-making purposes.
With advances in technology, destinations and visitors are increasingly dependent on information and communications
technologies. By integrating cloud computing, big data and artificial intelligence techniques with advanced forecasting methods, the GBA-TDFP offers innovative insights and valuable guidance for both industry professionals and academics, effectively transforming vast amounts of data into actionable information and enabling stakeholders to make informed decisions and maximise the value derived from them.
Professor Kaye Chon, SHTM Dean, Chair Professor and Walter and Wendy Kwok Family
Foundation Professor in International Hospitality Management remarked that this is another contribution that the School has made to the tourism industry. The SHTM is committed to applying the results of cutting-edge research to business practices to address the global challenges that the tourism industry faces.
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