Summer 2021 Research Assistant with Scott Nelson

This internship is part of the University’s Jeff Metcalf Internship Program. Please review the Metcalf Interns’ Responsibility Notice to learn more about program requirements for Metcalf interns.

Please make sure that if selected for an interview, you communicate to your prospective host organization/employer where you will be physically located during the internship as your location may affect your (or your host organization/employer’s) ability to pursue this opportunity. 

If you are an international student, please make sure to visit the OIA website to familiarize yourself with your work authorization eligibility and requirements as soon as possible. If you’d like to make an appointment with your international adviser, please visit this page.

Scott Nelson, Booth School of Business

Scott.Nelson@chicagobooth.edu 

There has been rapid growth in firms’ use of novel information to screen potential customers: FinTech startups, for example, now use data beyond consumer credit reports to screen borrower risk. However this information boom has been slow to come to the business of tenant screening in rental housing markets. We are partnering with a Chicago-area startup with expertise in low-income tenant screening to develop data repositories and risk models that hold promise to grow profitability for landlords, enhance housing access for low-income renters, and support academic research.

With regard to academic research, this project will use detailed data shared by landlords operating in the low-income housing market to study the screening decisions they make in determining which tenants to rent to. We will use the findings to study how different regulatory policies might affect such screening decisions and consequently the access that low-income renters have to different housing options.

A summer RA would maintain the database of tenant and landlord information, run preliminary statistical analysis, and work closely with the research team to develop equilibrium models of this market.

Experience in R, Stata, or Python is required, with proficiency preferred