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Eviction is a central mechanism driving housing instability in the United States, with significant implications for health, economic mobility, and social inequality. Despite a growing body of research, most empirical studies rely on rate-based measures of eviction, which may obscure the frequency and cumulative burden of eviction related interactions. Emerging evidence suggests that eviction filings often function as routine rent collection practices, exposing households to repeated system contact without formal displacement. Drawing on institutional churning and administrative burden frameworks, this study examines whether conventional rate-based indicators adequately capture housing instability or whether count-based measures provide a more accurate and policy relevant understanding. The study aims to compare long term trends in eviction activity in Maryland and assess how measurement choices shape interpretations of housing instability.
Methods:
This study uses longitudinal data from the Eviction Lab National Database for the state of Maryland from 2000 to 2018. Annual observations include total eviction filings, eviction filing rates, households threatened with eviction, and corresponding household rates. A standardized measure of eviction filings per day was constructed to enhance interpretability of volume. The analytic strategy included descriptive statistics, Pearson correlation analyses, and exploratory linear trend models to assess temporal changes across indicators. Variability was evaluated using standard deviation and coefficients of variation. All analyses were descriptive and exploratory in nature, with no causal inference intended.
Results:
Findings demonstrate a clear divergence between rate based and count based indicators. Rate based measures of eviction filings and households at risk remained relatively stable and showed no statistically significant trends over time. In contrast, count based indicators revealed significant increases in total eviction filings (b = 7,641.55, p < .001), filings per day (b = 20.94, p < .001), and households threatened with eviction (b = 2,652.20, p < .001). Correlational analyses indicated strong positive relationships among count-based measures, while rate-based measures showed weaker associations, suggesting reduced sensitivity to changes in housing instability. These results indicate that housing instability is increasing in scale and frequency, even when normalized rates suggest stability.
Conclusions and Implications:
This study advances measurement approaches in housing research by demonstrating that rate-based indicators alone may underestimate the scope and dynamics of housing instability. Consistent with an institutional churning perspective, findings suggest that repeated exposure to eviction processes rather than singular displacement events plays a central role in producing instability. Incorporating count-based indicators into research and policy monitoring systems can improve detection of emerging risks and support the development of early warning systems. For social work research and practice, these findings underscore the need to shift from outcome focused definitions of eviction toward process-oriented frameworks that account for cumulative administrative burden. Policy interventions should address not only displacement prevention but also the structural conditions that normalize repeated eviction filings.