From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth


Hau Chan, Eric Rice, Phebe Vayanos, Milind Tambe, and Matthew Morton. 2018. “From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth .” In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD).


There are nearly 2 million homeless youth in the United States each year. Coordinated entry systems are being used to provide homeless youth with housing assistance across the nation. Despite these efforts, the number of youth still homeless or unstably housed remains very high. Motivated by this fact, we initiate a first study to understand and analyze the current governmental housing systems for homeless youth. In this paper, we aim to provide answers to the following questions: (1) What is the current governmental housing system for assigning homeless youth to different housing assistance? (2) Can we infer the current assignment guidelines of the local housing communities? (3) What is the result and outcome of the current assignment process? (4) Can we predict whether the youth will be homeless after receiving the housing assistance? To answer these questions, we first provide an overview of the current housing systems. Next, we use simple and interpretable machine learning tools to infer the decision rules of the local communities and evaluate the outcomes of such assignment. We then determine whether the vulnerability features/rubrics can be used to predict youth’s homelessness status after receiving housing assistance. Finally, we discuss the policy recommendations from our study for the local co
See also: 2018