research-article

Using machine learning to help vulnerable tenants in New York city

Authors:

Rebecca Johnson,

Bridgit Donnelly,

Rayid GhaniAuthors Info & Claims

COMPASS '19: Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies

Pages 248 - 258

https://doi.org/10.1145/3314344.3332484

Published: 03 July 2019 Publication History

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.

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Chor KLuo ZRodolfa KGhani R(2025)Exploring Machine Learning to Support Decision-Making for Placement Stabilization and Preservation in Child WelfareJournal of Child and Family Studies10.1007/s10826-024-02993-x34:1(282-297)Online publication date: 7-Jan-2025
https://doi.org/10.1007/s10826-024-02993-x
Hasan Chy MNana Buadi O(2024)Role of Machine Learning in Policy Making and EvaluationInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24OCT687(456-463)Online publication date: 19-Oct-2024
https://doi.org/10.38124/ijisrt/IJISRT24OCT687
Mashiat TDiChristofano AFowler PDas S(2024)Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform ActionProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658978(1383-1394)Online publication date: 3-Jun-2024
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Index Terms

Using machine learning to help vulnerable tenants in New York city
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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cover image ACM Conferences

COMPASS '19: Proceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies

July 2019

290 pages

ISBN:9781450367141

DOI:10.1145/3314344

Program Chairs:
Jay Chen
NYU-AD
,
Jennifer Mankoff
University of Washington
,
Carla Gomes
Cornell

Copyright © 2019 ACM.

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Published: 03 July 2019

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COMPASS '19: ACM SIGCAS Conference on Computing and Sustainable Societies

July 3 - 5, 2019

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Cited By

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https://doi.org/10.1007/s10826-024-02993-x
Hasan Chy MNana Buadi O(2024)Role of Machine Learning in Policy Making and EvaluationInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT24OCT687(456-463)Online publication date: 19-Oct-2024
https://doi.org/10.38124/ijisrt/IJISRT24OCT687
Mashiat TDiChristofano AFowler PDas S(2024)Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform ActionProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658978(1383-1394)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658978
O’Connor RBolton MSaeri AChan TPearson R(2024)Artificial intelligence and complex sustainability policy problems: translating promise into practicePolicy Design and Practice10.1080/25741292.2024.23488347:3(308-323)Online publication date: 7-May-2024
https://doi.org/10.1080/25741292.2024.2348834
Barrera Ferro DBrailsford SChapman A(2024)Improving fairness in machine learning-enabled affirmative actions: a case study in outreach activities in healthcareJournal of the Operational Research Society10.1080/01605682.2024.235436476:2(363-374)Online publication date: 24-May-2024
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Lomotey RKumi SHilton MOrji RDeters R(2023)Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their TweetsIEEE Access10.1109/ACCESS.2023.326705011(37570-37601)Online publication date: 2023
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Amarasinghe KRodolfa KLamba HGhani R(2023)Explainable machine learning for public policy: Use cases, gaps, and research directionsData & Policy10.1017/dap.2023.25Online publication date: 20-Feb-2023
https://doi.org/10.1017/dap.2023.2
Tabar MJung WYadav AWilson Chavez OFlores ALee DAl Hasan MXiong L(2022)WARNER: Weakly-Supervised Neural Network to Identify Eviction Filing Hotspots in the Absence of Court RecordsProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557128(3514-3523)Online publication date: 17-Oct-2022
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Chor KLuo ZDworsky ARaman RCourtney MEpstein R(2022)Development and validation of a predictive risk model for runaway among youth in child welfareChildren and Youth Services Review10.1016/j.childyouth.2022.106689143(106689)Online publication date: Dec-2022
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Green B(2022)Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic FairnessPhilosophy & Technology10.1007/s13347-022-00584-635:4Online publication date: 8-Oct-2022
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