Owning a home is one of the most important ways of building wealth in America. As rises in home prices outpace rises in income, fewer and fewer Americans can afford to buy a home, causing the generational wealth gap to widen. Housing assistance programs are imperative to expand access to homeownership – particularly for Black Americans, who have historically been excluded from stable homeowning through policies and practices including redlining and exclusionary zoning. Designing effective housing assistance requires understanding where gaps in affordability currently exist for potential homeowners. Read more.
The Housing Vertical of the Initiative for Combating Systemic Racism (ICSR) is led by Peko Hosoi (IDSS, MechE) and Catherine D’Ignazio (DUSP).
Since 2021, ten students from IDSS, DUSP, Architecture, and the MSRP program have contributed to our projects, and we have partnered with several visiting MLK scholars including Daniel Auguste (Florida Atlantic University) and Craig Watkins (UT Austin).
The Housing Vertical researches the uneven impacts of data, AI, and algorithmic systems on housing in the US as well as ways that these same tools may be used to address racial disparities. Housing policies enacted today are not applied in a vacuum. They are implemented in an environment that has evolved through a long, complex history which includes discriminatory policies and practices put into effect by various actors, ranging from the government to banks to private citizens. These racially inequitable policies include racial covenants, redlining, predatory lending and more. Emerging research shows that AI and algorithmic systems (including generative AI systems, as we will demonstrate in this paper) are exacerbating rather than ameliorating these existing inequities. In our work, we take a data-driven approach to think beyond algorithmic fairness and account for historical aspects in evaluating housing equity. Our primary objective is to reveal mechanisms that lead to racially disparate outcomes in housing and to identify the most impactful intervention points to disrupt these mechanisms.
The Housing vertical research team, in collaboration with MIT UROPs and MRSP students, is currently working on the following projects:
iBuyers use automated valuation algorithms and streamlined home-buying processes, including exemption of repairs before selling and cash offers, to purchase homes. Previous literature has examined the roles and limitations of iBuyers in the housing market, but there is a lack of empirical research on the racial implications of these algorithmic home-buying processes. Using spatial lag model, this study identifies spatial clustering of iBuyer profit margins and a statistically significant and positive correlation between profit margins and the rise in the proportion of Black or Latinx residents within a given neighborhood tract. This work will be submitted to Journal of Urban Affairs. Read more.
Race-Neutral vs Race-Conscious: Using Algorithmic Methods to Evaluate the Reparative Potential of Housing Programs
The racial wealth gap in the US remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors, and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. Read more.
OpenTSS aims to develop a crowdsourcing tool and/or campaign to audit tenant screening services and reveal the patterns of their inner algorithms, data structures, and representations, by collecting tenant screening reports, as well as denied renters’ experiences. Read more.
What are mechanisms that contribute to racial disparities in evictions? How can we use causal inference methods to evaluate policies that are aimed at reducing evictions? In this poster presentation, authors Aurora Zhang and Anette "Peko" Hosoi find that even after accounting for economic characteristics such as poverty rate, median income, median rent, that there is a significant relationship between neighborhood racial composition and eviction rate. Read more.
Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US
Fairness in Machine Learning (ML) has mostly focused on interrogating the fairness of a particular decision point with assumptions made that the people represented in the data have been fairly treated throughout history. However, fairness cannot be ultimately achieved if such assumptions are not valid. This is the case for mortgage lending discrimination in the US, which should be critically understood as the result of historically accumulated injustices that were enacted through public policies and private practices including redlining, racial covenants, exclusionary zoning, and predatory inclusion, among others. To emphasize such issues, we introduce case studies using contemporary mortgage lending data as well as historical census data in the US. Read More.
Characterization of Affordable Housing Properties and Policy by Reported Concerns: Boston’s RentSmart Dataset
Because of the Housing and Urban Renewal Act of 1983, contracts to receive rental subsidies are now renewable but cannot be awarded to new companies. The study hypothesizes that the experiences of residents with property managers are a function of the amount/lack of competition for contracts. Read more.
- Wonyoung So, Pranay Lohia, Rakesh Pimplikar, A.E. Hosoi, and Catherine D’Ignazio. 2022. Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 988-1004. 2022. https://doi.org/10.1145/3531146.3533160
In the Data Nation podcast episode “Credit Scores, No Friend of Ours,” Dahleh discussed how antagonistic credit scores are to people from underserved communities seeking quality housing. He shares alternatives that can work for everyone – lenders, landlords, and borrowers included.
Savannah Gregory joined the Housing team as an intern through the MIT Summer Research Program. The program seeks to prepare underrepresented students (minorities, women in STEM, or students with low socioeconomic status) for doctoral education.
So presented case studies that emphasize historically accumulated injustices that were enacted through public policies and private practices including redlining and racial covenants.
The Housing vertical team discusses how AI could guide reparations programmes created to redress decades of US housing discrimination against Black homebuyers.
The Housing vertical team consists of vertical co-leads Peko Hosoi (IDSS, MechE) and Catherine D’Ignazio (DUSP), Bhavani Ananthabhotla (TPP), Talla Babou (MIT – IDSS SES), Jola Idowu (MIT – DUSP), Nolen Scruggs (MIT – DUSP), Wonyoung So (MIT – DUSP), and Aurora Zhang (MIT – IDSS SES).
At the Beyond Fairness symposium, we learned the value of “unusual” connections – developing interdisciplinary and cross-domain connections between data scientists, urban planners, activists, practitioners from both academia and industry. These connections will help build a community that can tackle issues of housing justice from many different points of view.
We are very interested in connecting with (local) stakeholders working on projects related to these issues. Please email a description of your project to email@example.com. If you would like to be a sponsor and support our work, please reach out to firstname.lastname@example.org.