Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public
  Records to Inform Action
Tasfia Mashiat Alex DiChristofano Patrick J. Fowler Sanmay Das
There has been considerable recent interest in scoring properties on the
basis of eviction risk. The success of methods for eviction prediction is
typically evaluated using different measures of predictive accuracy. However,
the underlying goal of such prediction is to direct appropriate assistance to
households that may be at greater risk so they remain stably housed. Thus, we
must ask the question of how useful such predictions are in targeting outreach
efforts - informing action. In this paper, we investigate this question using a
novel dataset that matches information on properties, evictions, and owners. We
perform an eviction prediction task to produce risk scores and then use these
risk scores to plan targeted outreach policies. We show that the risk scores
are, in fact, useful, enabling a theoretical team of caseworkers to reach more
eviction-prone properties in the same amount of time, compared to outreach
policies that are either neighborhood-based or focus on buildings with a recent
history of evictions. We also discuss the importance of neighborhood and
ownership features in both risk prediction and targeted outreach.