What are the variables to predict evictions?
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Public
Source: We All Count
Data analysis: some census tracts without evictions listed (those were removed).
Who would benefit: Tulsa community, renters, policymakers, data working group, data analysis company, nonprofit managers/directors.
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.
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.
Discussed: preconceptions of what caused evictions, having institutional knowledge of some of the variables (cognitive bias), justice-oriented project
People at risk of being evicted
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Census (5 year estimates 2020), Tulsa county assessor data (obtained May 2022), eviction data provided by Oklahoma policy institute (mid-June)
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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?
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Policymakers, landlords
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Predicitive
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