Analysis of Residential Satisfaction Changes by the Land Bank Program Using Text Mining

Abstract

Many American manufacturing cities have experienced depopulation and economic downturns over the past five decades, and various revitalization strategies have been suggested to overcome the decline issue—ranging from redevelopment to smart decline. However, while most land bank-related studies have focused on socioeconomic dynamics (income levels, unemployment rate, etc.) through the program, there is a lack of direct research on residential satisfaction changes. Additionally, surveys were frequently used in previous studies to evaluate residential satisfaction; however, this method has disadvantages, including constraints on time and cost, and the inability to take into account external factors that may affect residential satisfaction. Furthermore, most studies on urban decline have focused primarily on declining factors, and there have been few investigations into how cities change as urban regeneration strategies advance. Therefore, the primary purpose of this study is to identify the influence of the land bank program on residential satisfaction by using Twitter data. Approximately 300,000 Twitter posts containing location information generated within the city of Detroit were collected to determine the degree of sensitivity to each tweet and categorized into positive and negative emotions to determine the relationship between residential satisfaction and the land bank program. As a result, the increase in homeownership, built year, house value, and the number of land banking sold properties were found to have a negative effect on neighborhood satisfaction in Detroit. Although the research results indicated that while the land bank program did not significantly improve residential satisfaction in Detroit, it has made a partial contribution to improving living standards. These findings emphasize the importance of enhancing residential satisfaction and suggest the need for policy change. In response to the problem of urban contraction, it seems that indiscriminately distributing houses is not the only solution to prevent urban shrinkage. Furthermore, this study shows meaningful results on text mining and provides the possibility of developing research using social network services.