# Who Pays the RENT? Implications of Spatial Inequality for Prediction-Based Allocation Policies

arXiv: https://arxiv.org/abs/2508.08573 · HTML: https://mlsanity.com/paper/2508.08573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization](https://mlsanity.com/paper/1809.05504/) (2018)
- [Decision Trees for Decision-Making under the Predict-then-Optimize Framework](https://mlsanity.com/paper/2003.00360/) (2020)
- [Difficult Lessons on Social Prediction from Wisconsin Public Schools](https://mlsanity.com/paper/2304.06205/) (2023)
- [Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Making](https://mlsanity.com/paper/2308.05224/) (2023)
- [Concentric mixtures of Mallows models for top-k rankings: sampling and identifiability](https://mlsanity.com/paper/2010.14260/) (2020)
- [Learning Optimal and Fair Policies for Online Allocation of Scarce Societal Resources from Data Collected in Deployment](https://mlsanity.com/paper/2311.13765/) (2023)
- [A Human-Centered Review of Algorithms in Homelessness Research](https://mlsanity.com/paper/2401.13247/) (2024)
- [Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning](https://mlsanity.com/paper/2312.10694/) (2023)
- [Decision-Focused Evaluation of Worst-Case Distribution Shift](https://mlsanity.com/paper/2407.03557/) (2024)
- [Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action](https://mlsanity.com/paper/2401.16440/) (2024)

## Built on by (most cited first, 2 of 2)

- [Good Allocations from Bad Estimates](https://mlsanity.com/paper/2601.05597/) (2026)
- [On the Meta-Design of Allocation Problems](https://mlsanity.com/paper/2602.08786/) (2026)

## Abstract (arXiv)

AI-powered scarce resource allocation policies rely on predictions to target either specific individuals (e.g., high-risk) or settings (e.g., neighborhoods). Recent research on individual-level targeting demonstrates conflicting results; some models show that targeting is not useful when inequality is high, while other work demonstrates potential benefits. To study and reconcile this apparent discrepancy, we develop a stylized framework based on the Mallows model to understand how the spatial distribution of inequality affects the effectiveness of door-to-door outreach policies. We introduce the RENT (Relative Efficiency of Non-Targeting) metric, which we use to assess the effectiveness of targeting approaches compared with neighborhood-based approaches in preventing tenant eviction when high-risk households are more versus less spatially concentrated. We then calibrate the model parameters to eviction court records collected in a medium-sized city in the USA. Results demonstrate considerable gains in the number of high-risk households canvassed through individually targeted policies, even in a highly segregated metro area with concentrated risks of eviction. We conclude that apparent discrepancies in the prior literature can be reconciled by considering 1) the source of deployment costs and 2) the observed versus modeled concentrations of risk. Our results inform the deployment of AI-based solutions in social service provision that account for particular applications and geographies.