Who Owns America? A Methodology for Identifying Landlords’ Ownership Scale and the Implications for Targeted Code Enforcement

Abstract

Problem, research strategy, and findings Scholars and practitioners are increasingly interested in understanding who owns real estate in communities and resultant implications for targeted planning approaches. Yet, practitioners lack an efficient and comprehensive methodology to assess landlords’ ownership scale, namely, how many properties they own in each geographic area. The existence of variegated ownership, multiple legal entities, siloed databases within government bureaucracies, and inconsistencies in spelling and documentation across data entries make it time-consuming and costly to determine the extent of real estate ownership by the same landlords. To address these challenges, we have created a data-driven natural language processing solution. Using OpenRefine, an open-source software, we present a step-by-step, practice-oriented methodology for amassing data, cleaning textual inconsistencies, and clustering properties to uncover the truer ownership scale in local housing markets. Applied to a large U.S. urban county—Fulton, home to Atlanta (GA)—our proposed methodology demonstrated its superior efficiency, comprehensiveness, and accuracy compared with traditional approaches. Using code enforcement as a study frame, we then empirically examined a linkage between landlords’ ownership scale and their code violation patterns. With the proposed methodology in place, the analysis consistently showed that the ownership scale was related to both the likelihood and number of code violations. In contrast, the analysis missed this critical linkage without applying the methodology. Our methodology can yield practical implications regarding targeted code enforcement. Takeaway for practice Our methodology can serve as a useful toolbox for both practitioners and fellow researchers to unravel real estate ownership and its concentrations in housing markets. Using the methodology presented here, they can uncover all types and scales of landlords and monitor their code violation frequencies for targeted outreach and resource allocation in enforcement