Using machine learning to help vulnerable tenants in New York City

Teng Ye; Rebecca Johnson; Samantha Fu; Jerica Copeny; Bridgit Donnelly; Alex Freeman; Mirian Lima; Joe Walsh; Rayid Ghani

doi:10.1145/3314344.3332484

Using machine learning to help vulnerable tenants in New York City

Teng Ye, Rebecca Johnson, Samantha Fu, Jerica Copeny, Bridgit Donnelly, Alex Freeman, Mirian Lima, Joe Walsh, Rayid Ghani

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

To keep housing affordable, the City of New York has implemented rent-stabilization policies to restrict the rate at which the rent of certain units can be increased every year. However, some landlords of these rent-stabilized units try to illegally force their tenants out in order to circumvent rent-stabilization laws and greatly increase the rent they can charge. To identify and help tenants who are vulnerable to such landlord harassment, the New York City Public Engagement Unit (NYC PEU) conducts targeted outreach to tenants to inform them of their rights and to assist them with serious housing challenges. In this paper, we¹ collaborated with NYC PEU to develop machine learning models to better prioritize outreach and help to vulnerable tenants. Our best-performing model can potentially help TSU find 59% more buildings where tenants face landlord harassment than the current outreach method using the same resources. The results also highlight the factors that help predict the risk of experiencing tenant harassment, and provide a data-driven and comprehensive approach to improve the city’s policy of proactive outreach to vulnerable tenants.

Original language	English (US)
Title of host publication	COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies
Publisher	Association for Computing Machinery, Inc
Pages	248-258
Number of pages	11
ISBN (Electronic)	9781450367141
DOIs	https://doi.org/10.1145/3314344.3332484
State	Published - Jul 3 2019
Externally published	Yes
Event	2019 ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 2019 - Accra, Ghana Duration: Jul 3 2019 → Jul 5 2019

Publication series

Name	COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies

Conference

Conference	2019 ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 2019
Country/Territory	Ghana
City	Accra
Period	7/3/19 → 7/5/19

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Machine Learning
Public Policy
Resource Allocation
Social Good
Tenant Harassment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Publisher link

10.1145/3314344.3332484

Find It

Find It at U of M Libraries

Cite this

Ye, T., Johnson, R., Fu, S., Copeny, J., Donnelly, B., Freeman, A., Lima, M., Walsh, J., & Ghani, R. (2019). Using machine learning to help vulnerable tenants in New York City. In COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies (pp. 248-258). (COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies). Association for Computing Machinery, Inc. https://doi.org/10.1145/3314344.3332484

Using machine learning to help vulnerable tenants in New York City. / Ye, Teng; Johnson, Rebecca; Fu, Samantha et al.
COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies. Association for Computing Machinery, Inc, 2019. p. 248-258 (COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, T, Johnson, R, Fu, S, Copeny, J, Donnelly, B, Freeman, A, Lima, M, Walsh, J & Ghani, R 2019, Using machine learning to help vulnerable tenants in New York City. in COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies. COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies, Association for Computing Machinery, Inc, pp. 248-258, 2019 ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS 2019, Accra, Ghana, 7/3/19. https://doi.org/10.1145/3314344.3332484

Ye T, Johnson R, Fu S, Copeny J, Donnelly B, Freeman A et al. Using machine learning to help vulnerable tenants in New York City. In COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies. Association for Computing Machinery, Inc. 2019. p. 248-258. (COMPASS 2019 - Proceedings of the 2019 Conference on Computing and Sustainable Societies). doi: 10.1145/3314344.3332484

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AB - To keep housing affordable, the City of New York has implemented rent-stabilization policies to restrict the rate at which the rent of certain units can be increased every year. However, some landlords of these rent-stabilized units try to illegally force their tenants out in order to circumvent rent-stabilization laws and greatly increase the rent they can charge. To identify and help tenants who are vulnerable to such landlord harassment, the New York City Public Engagement Unit (NYC PEU) conducts targeted outreach to tenants to inform them of their rights and to assist them with serious housing challenges. In this paper, we1 collaborated with NYC PEU to develop machine learning models to better prioritize outreach and help to vulnerable tenants. Our best-performing model can potentially help TSU find 59% more buildings where tenants face landlord harassment than the current outreach method using the same resources. The results also highlight the factors that help predict the risk of experiencing tenant harassment, and provide a data-driven and comprehensive approach to improve the city’s policy of proactive outreach to vulnerable tenants.

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Using machine learning to help vulnerable tenants in New York City

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Publisher link

Find It

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Cite this