Urban Transformation: Shaping the Future of Cities
In the heart of New York City, fifth-year PhD student Vincent Rollet at MIT's Department of Economics is delving into the complexities of urban development. His primary focus is understanding why certain urban spaces, such as New York and Boston, become "stuck" in outdated patterns of development, with high housing costs being a key issue.
Rollet's research zeroes in on the struggles cities face when adapting their built environments as economic conditions shift. He identifies critical inefficiencies in housing markets that contribute to these high costs. These inefficiencies include regulatory constraints, information failures, behavioral barriers, and operational frictions.
Regulatory constraints, like strict zoning rules, height limits, and lengthy permitting processes, limit housing supply expansion in high-demand urban areas. This supply constraint causes prices to rise beyond what would prevail in a more flexible market.
Information failures and behavioral barriers, such as borrowers failing to refinance mortgages or incomplete or misleading information about housing quality or market conditions, can deter efficient transactions.
Operational and management inefficiencies, often seen in property management, increase operational costs, which are passed on in rents. However, startups like EliseAI show the potential for technology to reduce such friction by improving coordination, compliance, and tenant service, thus lowering costs in the long run.
Market frictions and transaction costs, including broker fees, negotiation delays, and repair issues, can reduce housing market fluidity and increase costs for buyers and renters.
Economics offers policymakers a way forward. Regulatory reform, such as easing zoning and land-use restrictions, can allow denser, more affordable housing development. Improving financial literacy and mortgage counseling can mitigate behavioral inefficiencies, reducing housing financing costs. Encouraging the use of property management technology can reduce operational inefficiencies, and policies to increase transparency can lower search and negotiation costs.
Rollet's research suggests a significant gap between housing costs and construction costs in cities like New York and Boston, indicating a high demand exceeding the supply. He emphasises that this housing supply constraint leads to unnecessary housing expense.
Looking ahead, Rollet aims to pursue a career in research and teaching, with a focus on developing research that impacts policy and sheds light on how cities can overcome constraints and evolve in ways that better serve their residents. He is excited about the potential for more fine-grained and detailed data sources to reveal how micro behavior can lead to macro outcomes in housing and city markets.
References: [1] Akerlof, G. A. (1970). The market for "lemons": Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488-500. [2] Glaeser, E. L., & Gyourko, J. (2008). The urban housing market. Journal of Economic Literature, 46(3), 681-737. [3] Levitin, J. (2018). EliseAI: Using AI to revolutionise property management. Forbes. https://www.forbes.com/sites/joshlevitin/2018/06/26/eliseai-using-ai-to-revolutionize-property-management/?sh=377a454d3388
