Community Study Shelter

What are the variables to predict evictions?

9bCorp

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Details
Research Type Community Study
Researcher 9bCorp
Categories
Last Updated November 14, 2024

Source: We All Count

Project Restrictions

Data analysis: some census tracts without evictions listed (those were removed).

Project Rewards

Who would benefit: Tulsa community, renters, policymakers, data working group, data analysis company, nonprofit managers/directors.

Definitions Of Success

Nonprofits: measures they can use/action items to remediate the pain caused by eviction, to reduce the number of evictions. Policymakers: understanding the root cause of evictions in Tulsa county and getting actionable items that they can start acting on it and developing policy to reduce eviction cases and serial evictions. Population: being evicted less. Data analysis company: increasing reputation in analyzing complex social issues.

Project Goals

Discover variables that can be used to predict eviction/risks of eviction. Out of the variables used for prediction, see the ones that will provide more positive impact to the community. Determine the root causes for evictions in Tulsa county.

Implicit Bias

Discussed: preconceptions of what caused evictions, having institutional knowledge of some of the variables (cognitive bias), justice-oriented project

Centered Perspectives

People at risk of being evicted

Data Sources

Census (5 year estimates 2020), Tulsa county assessor data (obtained May 2022), eviction data provided by Oklahoma policy institute (mid-June)

Research Questions

What tools can be used to predict eviction rates? Is there a way to predict eviction rates in Tulsa County? What variables will yield highest impact to move Census Tracts from a high or extreme risk towards eviction to a moderate or low risk? [what variables have the biggest impact on reducing eviction rates?] what are the main causes of evictions in tulsa county?

Onus To Change

Policymakers, landlords

Descriptive
Predictive
Causal

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