Summary of Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action, by Tasfia Mashiat et al.
Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action
by Tasfia Mashiat, Alex DiChristofano, Patrick J. Fowler, Sanmay Das
First submitted to arxiv on: 27 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effectiveness of eviction prediction methods in informing targeted outreach efforts to prevent housing instability. The authors use a novel dataset that matches property, eviction, and owner information to predict eviction risk scores. They then utilize these risk scores to design targeted outreach policies and demonstrate their usefulness in reaching more eviction-prone properties with the same resources. The study highlights the importance of neighborhood and ownership features in both risk prediction and targeted outreach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computer programs to help social workers find homes that might be at risk of being evicted. The goal is to give these workers the right information so they can help these families before it’s too late. The researchers created a new way to use data about properties, evictions, and who owns them to predict which homes are most likely to be evicted. They then used this information to design a plan for social workers to reach these homes more efficiently. The study shows that this approach can help social workers do their job better. |
