High Rises and Housing Stress: A Spatial Big Data Analysis of Rental Housing Financialization

Abstract

Problem, research strategy, and findings: The financialization of housing is a rapidly growing concern for planning researchers and policymakers, but the opacity of property ownership in most cities has hampered efforts to rigorously measure the phenomenon. Here we introduce a new approach based on big data methods. By combining web scraping of property assessment, business registry, and rental advertisement data, we reliably identified the networks of property ownership lurking behind anonymous numbered companies and established the extent of financialized rental housing ownership. We demonstrate the effectiveness of this approach with a quantitative case study of the financialization of rental housing in Montreal (Canada). Using spatial regression and clustering analyses, we found that there are two distinct types of financialized rental housing ownership in Montreal: one characterized by precarious and student tenants and another characterized by affluent tenants. In general, high proportions of financialized ownership are associated with higher levels of housing stress and dense housing typologies. Takeaway for practice: By demonstrating meaningful differences in housing market outcomes across financialization status—which has not usually been readily accessible to either renters or planners—our findings show the importance of rental market information asymmetry. Planners should treat landlord data as one component of the information necessary to properly regulate a rental housing market. Municipalities should make property ownership information publicly accessible to facilitate public scrutiny of residential land use and more effective protection of tenant rights.