University of Pittsburgh's Quantitative Economics Students Explore the Demographics of Evictions
In a blog post on May 5, we released the results of the work conducted by three outstanding students from the University of Pittsburgh’s new Master of Science in Quantitative Economics (MQE) program:
We asked Gayatri, Mason, and Natalee to utilize publicly available data from the US Census Bureau, Department of Housing and Urban Development (HUD), and the Eviction Lab at Princeton University to help us understand how underlying demographic and economic conditions might predict future changes in evictions for renters. This is an especially timely question given the economic impacts of Covid-19 — especially considering the pronounced spikes in housing insecurity that continue to affect households around the country. Now that the students have had some time to recover from their semester, we asked them to reflect on their experience. Their answers are below:
You all sought out this topic. Why did it interest you? Why do you think this is an important area for economic analysis?
Studying evictions and housing affordability is interesting because it will always be a relevant and ongoing topic. With respect to the pandemic, it is especially important to be able to identify factors that might contribute to eviction rates and identify how housing shortages impact different income groups.
Even though problems with evictions have been around for a while, studying evictions on a national scale is still in its infancy. This project presented a chance to gain a better understanding of the housing affordability problem.
What were the most exciting parts of the project?
One of the most exciting parts of this project was developing interactive visualizations. It was a great opportunity to learn more about data visualization through Python and Google Data Studio. Creating these visualizations allowed for a clear picture of our analysis and trends.
Overall, it was exciting to pull together a number of tools including Jupyter Notebook and a variety of R packages to tell a story about housing affordability in the United States.
What were the most interesting or unexpected things you learned?
It was surprising to learn that there are many places in the US that either do not report on evictions or do not make these records accessible for research. There is little existing literature on eviction forecasting; in addition, many evictions go unreported, making this topic especially difficult to study.
Strong geographic trends in housing affordability were also unexpected, with many coastal states having incredibly high-cost burden rates.
How would you like to see work like this used by others? What value could it bring to dialogue around housing policy?
We would like to see our work used as a tool to enable holistic affordab