Algorithms for All: Can AI in the Mortgage Market Expand Access to Homeownership?

• Published in: AI (Basel) Vol. 4; no. 4; pp. 888 - 903
• Main Authors: Perry, Vanessa G., Martin, Kirsten, Schnare, Ann
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Accuracy; algorithmic bias; Algorithms; Artificial intelligence; Bias; BIPOC homeownership; Credit scoring; Customer relationship management; Digital marketing; Digital technology; ethics of AI; Ethnicity; Fraud prevention; Hispanic Americans; Home ownership; Households; Low income groups; Machine learning; Minority & ethnic groups; mortgage market; Property values; Race; Underwriting; Valuation; Variables
• ISSN: 2673-2688; 2673-2688
• DOI: 10.3390/ai4040045

Artificial intelligence (AI) is transforming the mortgage market at every stage of the value chain. In this paper, we examine the potential for the mortgage industry to leverage AI to overcome the historical and systemic barriers to homeownership for members of Black, Brown, and lower-income communities. We begin by proposing societal, ethical, legal, and practical criteria that should be considered in the development and implementation of AI models. Based on this framework, we discuss the applications of AI that are transforming the mortgage market, including digital marketing, the inclusion of non-traditional “big data” in credit scoring algorithms, AI property valuation, and loan underwriting models. We conclude that although the current AI models may reflect the same biases that have existed historically in the mortgage market, opportunities exist for proactive, responsible AI model development designed to remove the systemic barriers to mortgage credit access.