Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914658613460992 |
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| author | Mashiat, Tasfia DiChristofano, Alex Fowler, Patrick J. Das, Sanmay |
| author_facet | Mashiat, Tasfia DiChristofano, Alex Fowler, Patrick J. Das, Sanmay |
| contents | There has been considerable recent interest in scoring properties on the basis of eviction risk. The success of methods for eviction prediction is typically evaluated using different measures of predictive accuracy. However, the underlying goal of such prediction is to direct appropriate assistance to households that may be at greater risk so they remain stably housed. Thus, we must ask the question of how useful such predictions are in targeting outreach efforts - informing action. In this paper, we investigate this question using a novel dataset that matches information on properties, evictions, and owners. We perform an eviction prediction task to produce risk scores and then use these risk scores to plan targeted outreach policies. We show that the risk scores are, in fact, useful, enabling a theoretical team of caseworkers to reach more eviction-prone properties in the same amount of time, compared to outreach policies that are either neighborhood-based or focus on buildings with a recent history of evictions. We also discuss the importance of neighborhood and ownership features in both risk prediction and targeted outreach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16440 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action Mashiat, Tasfia DiChristofano, Alex Fowler, Patrick J. Das, Sanmay Machine Learning Artificial Intelligence There has been considerable recent interest in scoring properties on the basis of eviction risk. The success of methods for eviction prediction is typically evaluated using different measures of predictive accuracy. However, the underlying goal of such prediction is to direct appropriate assistance to households that may be at greater risk so they remain stably housed. Thus, we must ask the question of how useful such predictions are in targeting outreach efforts - informing action. In this paper, we investigate this question using a novel dataset that matches information on properties, evictions, and owners. We perform an eviction prediction task to produce risk scores and then use these risk scores to plan targeted outreach policies. We show that the risk scores are, in fact, useful, enabling a theoretical team of caseworkers to reach more eviction-prone properties in the same amount of time, compared to outreach policies that are either neighborhood-based or focus on buildings with a recent history of evictions. We also discuss the importance of neighborhood and ownership features in both risk prediction and targeted outreach. |
| title | Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16440 |