Sorry we're closed : what closes malls and community centers in the United States? an analysis and predictive modeling of distressed centers

Abstract

The retail industry has transformed markedly in the last decade driven by the confluence of technology, evolving consumer behavior, and innovation. As the sector continues to evolve, retail real estate is coming under significant pressure to keep up with the pace of change. Some centers are poised to quickly adapt and come out stronger, others are left behind and going dark. This paper is particularly interested in examining the geography of distressed retail centers, specifically malls and community centers, understanding what factors lead to their closure, and coming up with a predictive model to measure the properties' probability of defaulting. We analyze the geography of approximately 4,900 malls and community centers across the United States at two intervals, 2010 and 2020. First, we isolate the centers that have died within the last decade to identify what distinguishes between a dead and survived center. Second, for each dead center we estimate the distance of its competitors to assess impact of spatial competition. Third, we use a linear regression to identify determinants that influence the death of a center. Fourth, we run a profit model on the center's survived-dead response based on each variable. We conclude by developing a predictive model to assess which centers are at greatest risk of underperforming. Research shows that a property's net rentable area has an outsized impact on the probability of defaulting, with opposite effects for malls and community centers. As malls grow larger, they are less likely to become distressed -- whereas the growth of community centers leads to a higher probability of failure. The center's proximity to competition, and the amount of available space both increase the impact on the likelihood of going under. The opportunity to renovate, on the other hand, mitigates the impact..