# Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action

arXiv: https://arxiv.org/abs/2401.16440 · HTML: https://mlsanity.com/paper/2401.16440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 5 of 5)

- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning](https://mlsanity.com/paper/2312.10694/) (2023)
- [Ghosting the Machine: Judicial Resistance to a Recidivism Risk Assessment Instrument](https://mlsanity.com/paper/2306.06573/) (2023)

## Built on by (most cited first, 3 of 3)

- [Allocation Requires Prediction Only if Inequality Is Low](https://mlsanity.com/paper/2406.13882/) (2024)
- [Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies](https://mlsanity.com/paper/2508.08573/) (2025)
- [The Hidden Cost of Waiting for Accurate Predictions](https://mlsanity.com/paper/2503.00650/) (2025)

## Abstract (arXiv)

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.