| Summary: | Real estate is a complex and unpredictable industry because of the many factors that influence it, and conducting a thorough analysis of these factors is challenging. This study explores why house prices have continued to increase over the last 10 years in Taiwan. A clustering analysis based on a double-bottom map particle swarm optimization algorithm was applied to cluster real estate–related data collected from public websites. We report key findings from the clustering results and identify three essential variables that could affect trends in real estate prices: money sup-ply, population, and rent. Mortgages are issued more frequently as additional real estate is created, increasing the money supply. The relationship between real estate and money supply can provide the government with baseline data for managing the real estate market and avoiding unlimited growth. The government can use sociodemographic data to predict population trends to in turn prevent real estate bubbles and maintain a steady economic growth. Renting and using social housing is common among the younger generation in Taiwan. The results of this study could, therefore, assist the government in managing the relationship between the rental and real estate markets. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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