This research used several imputation methodologies to predict the race/ethnicity and gender of individuals who faced an eviction filing in the Cleveland Municipal Court - Housing Division (Cleveland Housing Court) between January 2016 and June 2023. The analysis of the imputed data assesses racial/ethnic and gender disparities in eviction filings and case outcomes, the geographic distribution of eviction cases and outcomes, and prolific evictors and the composition of tenants receiving filings from these landlords.
Why This Matters
Each year, millions of people engage with the civil legal system for a range of matters, including evictions, traffic violations, small claims, probate, debt collection, divorce, and child custody and support. Civil court case outcomes can have significant impacts on the lives of these individuals, their families, and their communities. Understanding racial/ethnic and gender disparities in the civil justice system will enable advocates to build an evidence base to support reforms that address disparities.
What We Found
- Black people and women disproportionately face eviction: 64.4 percent of eviction filings are against Black people while 51.9 percent of all tenants in the Cleveland Housing Court’s jurisdiction are Black; 59.7 percent of eviction filings are against women while 51.4 percent of all tenants in the court’s jurisdiction are women.
- Defendants without legal representation were far more likely to have a financial judgment rendered against them, with 16.6 percent of unrepresented defendants receiving financial judgments compared to only 2.26 percent of represented defendants.
- We found widespread disproportionate eviction filings against Black renters. In 90 percent of the City of Cleveland’s block groups with five or more eviction filings in our data, the share of tenants facing eviction who were Black was greater than the share of renter households that were Black.
- Although our findings indicate clear racial/ethnic and gender disparities regarding who enters the eviction court system, our analysis of eviction case outcomes did not reveal clear racial/ethnic or gender disparities after an individual entered the system. However, data limitations prompted us to analyze a subset of outcomes, including whether defendants (tenants) or plaintiffs (landlords) had legal representation; whether a writ of eviction was filed or issued, a move was scheduled, or a second cause hearing was held; whether a financial judgment occurred and the amount; and whether wage garnishment occurred. Future analyses should explore additional outcomes of interest.
How We Did It
This research tests multiple methods to impute race/ethnicity on civil court data from the Legal Services Corporation: Bayesian Improved Surname Geocoding (BISG), a BISG extension to address measurement error in census datasets used for imputation, and the Bayesian Instrumental Regression for Disparity Estimation (BIRDiE) method to improve estimates of racial/ethnic disparities using imputed data. We also test multiple gender imputation approaches using data from social media profiles and the Social Security Administration. We had the rare opportunity to validate our imputation methodology using eviction records linked to the Case Western Reserve University Center on Poverty and Community Development’s Child and Household Integrated Longitudinal Data (CHILD) system. The validation found that our methodology correctly imputed gender for 97.1 percent, and race/ethnicity for 83.8 percent of the linked observations.