| dc.description.abstract | Beginning in 2019, the COVID-19 pandemic caused a global halt to travel activities due to countries began restricting entry and exit. Even some regions saw a decline in COVID cases, travel bubbles were established as a way to facilitate communication, though, only certain individuals were permitted to enter and exit.
As Taiwan is an island nation, the tourism industry has a significant impact of the country′s income. Despite the restrictions on the tourism industry, the real estate industry has been thriving in recent years, with soaring property prices. The government has even had to introduce anti-housing speculation policies, and some have suggested that people are buying homes because they have nowhere else to put their money.
Taiwan is a place where people often invest in real estate, and it is worth considering whether tourism-related factors can truly affect property prices and be capitalized on in the real estate market. With the recent pandemic′s impact, this topic is more pertinent than ever. Therefore, this study focuses on domestic changes, and examines the factors of the number of visitors to various tourist attractions, the number of individuals infected with the virus, and housing prices in different regions of Taiwan in recent years.
＂Food, clothing, housing, transportation, education, and entertainment＂ are all the foundations of people′s livelihood and arranging travel has become one of the little things that Taiwanese people enjoy. Therefore, this study focuses on one of the six major livelihood issues: entertainment. Taking the changes in the prosperity of the tourism industry that are closer to people′s lives as the variables for predicting changes in house prices. Observing and discussing the accuracy of predicting housing prices from the prosperity changes of the Tourism Industry. When people encounter changes in the number of visitors to various tourist attractions, they can incorporate the changes into one of their references for investing in the real estate market.
This study uses the construction factors of the real estate itself and the changes in the number of tourists and epidemics to compare and predict the accurate results of different algorithms for predicting housing prices. Using various regression algorithms, including decision trees, support vector machines, random forests, and artificial neural networks. It is hoped that the prediction results of this study can be used as a reference for inexperienced house buyers to refer to the changes in their lives when buying a property. | en_US |