Post-disaster relocation may occur in the aftermath of major disasters, either unplanned or through buyout programs. There is an extensive research on recovery of affected population. However, research on the factors which cause the households to decide in favor of relocation or reconstruction is still in its infancy. This paper suggests a framework for classification and prediction of households' recovery decisions based on their attributes. To achieve this objective, data was collected through a telephone survey of residents affected by the 2013 Moore, Oklahoma and Hattiesburg, Mississippi tornados. The analysis was implemented using conditional inference trees and revealed the significance of level of damage, pre- and post-disaster homeownership, and housing type on the households' relocation decisions. The model can help with devising data-driven recovery policies tailored to a community's characteristics.

灾后迁移可能发生在重大灾难之后，无论是计划外还是通过收购计划。关于受影响人口的康复有广泛的研究。但是，关于导致家庭决定搬迁或重建的因素的研究仍处于起步阶段。本文提出了一个基于家庭属性对家庭恢复决策进行分类和预测的框架。为了实现这一目标，通过电话调查收集了受2013年俄克拉荷马州摩尔和密西西比州龙卷风哈蒂斯堡影响的居民的数据。该分析是使用条件推断树进行的，揭示了破坏水平，灾前和灾后房屋所有权以及房屋类型对家庭搬迁决定的重要性。