AI-Assisted Decision Support in Housing Policy Implementation: Distinguishing Deterministic Rules, Manual Review, and Heuristic Suggestion

Jingyuan Huang

doi:10.71222/e9ph5b75

Authors

Jingyuan Huang Independent Researcher, New York, United States Author

DOI:

https://doi.org/10.71222/e9ph5b75

Keywords:

Housing policy implementation, Accessory dwelling unit, Parcel-level feasibility, Decision support framework, Deterministic rules, Heuristic suggestion, Built environment, Digital governance

Abstract

U.S. housing policy directly shapes the housing options available to homeowners and families, but easing zoning rules and broadening legal permission do not, on their own, translate into completed homes. Implementation difficulties surface at two distinct stages: many eligible homeowners never formally apply once they encounter the cost, complexity, and uncertainty of pre-application analysis, and even among those who do apply, a substantial share of permitted projects never reach completion. The principal site of friction is the implementation phase, not the legalization phase—and addressing it requires mechanisms that translate policy rules into parcel-level feasibility judgments that property owners, designers, and local governments can rely on. This article examines how artificial intelligence and related computational methods should be applied to residential buildability assessment. The central question is where AI should sit—and where it should not—within decisions that involve regulatory compliance. The article proposes a three-layer architecture distinguishing rule sets, deterministic calculation, and heuristic suggestion. Matters involving regulatory boundaries—setbacks, lot coverage, building separation—should be handled through deterministic rule sets and calculation; AI may legitimately assist in building and maintaining such systems, but should not perform runtime compliance adjudication. Site conditions that cannot be reliably confirmed from public data should enter manual review. Heuristic methods are appropriate for ranking, placement suggestion, and scenario comparison within the feasible space defined by deterministic rules. Accessory dwelling units (ADUs) serve as the principal scenario for discussion and validation, but the architecture is not specific to ADUs—it applies more broadly to small-scale residential development, adaptive reuse, accessory-structure approval, and land-use change review. The article is presented as a normative contribution to the literatures on AI in public-rule systems, housing policy implementation, and digital governance of the built environment.

References

1. K. Chapple, A. Lieberworth, D. Ganetsos, E. Valchuis, A. Kwang, and R. Schten, ADUs in California: A Revolution in Progress. Berkeley, CA: Terner Center for Housing Innovation and Center for Community Innovation, University of California, Berkeley, 2020.

2. J. Wegmann and K. Chapple, "Hidden density in single-family neighborhoods: Backyard cottages as an equitable smart growth strategy," Journal of Urbanism: International Research on Placemaking and Urban Sustainability, vol. 7, no. 3, pp. 307–329, 2014.

3. M. Veale and I. Brass, "Administration by algorithm? Public management meets public sector machine learning," in Algorithmic Regulation, K. Yeung and M. Lodge, Eds. Oxford, UK: Oxford University Press, 2019, pp. 121–149.

4. J. Huang, "Accessory dwelling units as a policy execution challenge: Feasibility, regulatory risk, and early-stage decision modeling," Global Journal of Science & Innovation, Vol. 3, No. 1, pp. 1–8, 2026.

5. J. Huang, "From policy authorization to practical execution: A decision-support framework for implementing housing supply strategies in the United States," Strategic Management Insights, vol. 3, no. 1, pp. 24–31, 2026.

6. A. V. Moudon and M. Hubner, Eds., Monitoring Land Supply with Geographic Information Systems: Theory, Practice, and Parcel-Based Approaches. New York, NY: John Wiley & Sons, 2000.

7. M. A. Lawrimore, G. M. Sanchez, C. Cothron, M. G. Tulbure, T. K. BenDor, and R. K. Meentemeyer, "Creating spatially complete zoning maps using machine learning," Computers, Environment and Urban Systems, vol. 112, art. no. 102157, 2024.

8. Y. Yao, Y. Jiang, Z. Sun, et al., "Applicability and sensitivity analysis of vector cellular automata model for land cover change," Computers, Environment and Urban Systems, vol. 109, art. no. 102090, 2024.

9. C.-L. Hwang and K. Yoon, Multiple Attribute Decision Making: Methods and Applications—A State-of-the-Art Survey, Lecture Notes in Economics and Mathematical Systems, vol. 186. Berlin, Germany: Springer-Verlag, 1981.

10. J. Malczewski, "GIS-based multicriteria decision analysis: A survey of the literature," International Journal of Geographical Information Science, vol. 20, no. 7, pp. 703–726, 2006.

11. J. Greenberg, H. Phalen, K. Chapple, D. Garcia, and M. Alameldin, ADUs for All: Breaking Down Barriers to Racial and Economic Equity in Accessory Dwelling Unit Construction. Berkeley, CA: Terner Center for Housing Innovation and Center for Community Innovation, University of California, Berkeley, 2022.

12. K. Chapple, D. Ganetsos, and E. Lopez, Implementing the Backyard Revolution: Perspectives of California's ADU Owners. Berkeley, CA: Center for Community Innovation, University of California, Berkeley, 2021.