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.

基于驱逐风险的评分属性近年来引起了广泛关注。本文利用一个新的数据集，在驱逐、财产和业主信息之间进行匹配，对驱逐进行预测并制定有针对性的外展政策，证明了风险评分的实用性，使理论团队的工作人员在相同时间内能够接触到更多面临驱逐风险的财产。与基于社区或最近有驱逐记录的建筑物为重点的外展政策相比，我们还讨论了社区和所有权特征在风险预测和有针对性外展中的重要性。

超越驱逐预测：利用本地时空公共记录支持行动