Not (Officially) in My Backyard

Characterizing Informal Accessory Dwelling Units and Informing Housing Policy With Remote Sensing

Datos Bibliográficos

ID7161985
AutoresNathanael Jo (0000-0003-2295-9952, Twitter (United States), autor de correspondencia), Andrea Vallebueno (0000-0002-3146-8111, Twitter (United States)), Derek Ouyang (0000-0001-7823-0752, Twitter (United States)), Daniel E Ho (0000-0002-2195-5469, Twitter (United States))
Año2025
Volumen91
Número1
Páginas30-45
Fecha de publicación2025-01-02
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of the American Planning Association (JOURNAL)
Identificadores de la revistaISSN: 0194-4363 • E-ISSN: 1939-0130
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/01944363.2024.2345730
OpenAlexW4399281556
IdiomaEN
Citas recibidas2
Referencias citadas13

One promising policy approach to addressing housing needs is liberalizing accessory dwelling unit (ADU) development. Yet, understanding the impact of such policy efforts is fundamentally constrained by the inability to quantify and characterize unpermitted ADUs, which may expose homeowners and tenants to legal, financial, and safety risks and confound policy evaluations. We addressed this gap by leveraging computer vision and human annotations to estimate the population of detached ADU constructions in San José (CA). Our contributions are threefold: 1) We estimated the proportion of unpermitted ADU constructions from 2016 to 2020; 2) we describe the demographic, housing market, and parcel characteristics associated with these informal ADUs; and 3) we provide a data set of labeled small buildings, excluding unpermitted detections, for further research. We found that informal ADU construction was substantial— approximately three to four informal units for every formal unit —and more likely in more diverse, dense, and overcrowded neighborhoods. Though our study was limited to analyzing detached ADUs during one time period, we set the stage for further investigations of informal housing across different typologies and over time. Our approach demonstrates the promise of computer vision and human annotations to enable more robust, comprehensive, and reliable understanding of actual—not just permitted—housing units. We urge planners and other policymakers to consider the growth patterns of unpermitted ADUs to more optimally and equitably address housing needs

Political science · Housing, Finance, and Neoliberalism