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Community Study Shelter

What are the variables to predict evictions?

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Neighborhood Explorer

Details

Research Type
Community Study
Researcher
Neighborhood Explorer
Categories
Shelter
Last Updated
November 14, 2024

More Details

Motivation

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

Dates
Datasources
Data Sources

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

Project Design
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

Answer Type
Descriptive
Predictive
Causal
Distribution
Content
Access
Medium
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