{"type":"ScholarlyArticle","title":"Forecasting the Number of Tenants At-Risk of Formal Eviction: A Machine Learning Approach to Inform Public Policy","authors":[{"name":"Maryam Tabar","slug":"maryam-tabar","given":"Maryam","family":"Tabar","affiliation":"The Pennsylvania State University , University Park , PA , USA; {mfg5544 ,"},{"name":"Wooyong Jung","slug":"wooyong-jung","given":"Wooyong","family":"Jung","affiliation":"The Pennsylvania State University , University Park , PA , USA; {mfg5544 ,"},{"name":"Amulya Yadav","slug":"amulya-yadav","given":"Amulya","family":"Yadav","affiliation":"The Pennsylvania State University , University Park , PA , USA; {mfg5544 ,"},{"name":"Owen Wilson Chavez","slug":"owen-wilson-chavez","given":"Owen","family":"Wilson Chavez","affiliation":"Child Poverty Action Lab , Dallas , TX , USA"},{"name":"Ashley Flores","slug":"ashley-flores","given":"Ashley","family":"Flores","affiliation":"Child Poverty Action Lab , Dallas , TX , USA"},{"name":"Dongwon Lee","slug":"dongwon-lee","given":"Dongwon","family":"Lee","affiliation":"The Pennsylvania State University , University Park , PA , USA; {mfg5544 ,"}],"abstract":"Eviction of tenants has reached a crisis level in the U.S. and its consequences pose significant challenges to society. To tackle this eviction crisis, policymakers have been allocating financial resources but a more efficient resource allocation would need an accurate forecast of the number of tenants at-risk of evictions ahead of time. To help enhance the existing eviction prevention/diversion programs, in this work, we propose a multi-view deep neural network model, named as MARTIAN, that forecasts the number of tenants at-risk of getting formally evicted (at the census tract level) n months into the future. Then, we evaluate MARTIAN’s predictive performance under various conditions using real-world eviction cases filed across Dallas County, TX. The results of empirical evaluation show that MARTIAN outperforms an extensive set of baseline models in terms of predictive performance. Additionally, MARTIAN’s superior predictive performance is generalizable to unseen census tracts, for which no labeled data is available in the training set. This research has been done in collaboration with Child Poverty Action Lab (CPAL), which is a pioneering non-governmental organization (NGO) working for tackling poverty-related issues across Dallas County, TX. The usability of MARTIAN is under review by subject matter experts. We release our codebase at https://github.com/maryam-tabar/MARTIAN.","doi":"10.24963/ijcai.2022/719","url":"https://ijcai.org/proceedings/2022/719","pdf":"https://ijcai.org/proceedings/2022/0719.pdf","markdown":"https://ijcai.org/proceedings/2022/719.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/719","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Research Papers","track":"AI for Good","number":719,"pages":{"first":5178,"last":5184},"keywords":["Multidisciplinary Topics and Applications: Computational Sustainability","Multidisciplinary Topics and Applications: Sustainable Development Goals"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987145","length_sec":88,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985288","length_sec":580,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Arik and Pfister, 2021] Sercan Ö Arik and Tomas Pfister. 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