The Challenge: Reactive Collections in Affordable Housing

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How ExactEstate Is Building Machine Learning to Help Property Managers Stay Ahead of Collections

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For affordable housing operators, delinquency management has traditionally been reactive. Staff review aging reports, manually identify residents who have fallen behind, and prioritize collections based on gut instinct or simple dollar thresholds. This approach has real costs: delayed interventions mean larger balances, strained resident relationships, and compliance risks when delinquencies affect HAP payments or regulatory standing.

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What if you could see which residents are likely to become delinquent before they miss a payment?  What if you knew which delinquent residents are most likely to cure on their own versus those who need immediate outreach? 

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That's exactly what we're building into ExactEstate.

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What We're Building: Three Predictive Models

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ExactEstate is developing a suite of machine learning models designed specifically for affordable housing delinquency management. Each model answers a different operational question:

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Risk of Becoming Delinquent

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This classification model identifies residents currently in good standing who are at risk of falling behind. Early identification enables proactive outreach—a check-in call, a payment plan offer, or a resource referral—before a missed payment becomes a pattern.

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Current Performance:

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• ROC AUC: 0.944- The rate at which the current model distinguishes between residents who will and won't become delinquent
• Accuracy: 97%- Overall prediction accuracy
• Precision: 80.6%- When the model flags a resident as at-risk, it's correct 81% of the time
• Recall: 79%-The model catches 79% of residents who actually become delinquent

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Delinquency Severity

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Not all delinquencies are equal. This regression model predicts the dollar amount at risk, helping staff prioritize by financial exposure rather than just days past due.

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Current Performance:

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• R² Score: 0.998-The model explains 99.8% of variance in debt amounts
• Mean Absolute Error: $66.73- On average, predictions are within $67 of actual amounts
• Median Absolute Error: $29.04- Half of all predictions are within $29

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Cure Probability

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For residents who are already delinquent, this model predicts the likelihood they'll cure on their own. High-cure-probability residents may just need a reminder, while low-probability residents need more intensive intervention.

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Current Performance:

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• ROC AUC: 0.968- Excellent discrimination between who will and won't cure
• Accuracy: 88%- Strong overall accuracy
• Recall: 84.7%- Catches 85% of residents who will actually cure

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How It Works: Automated and Privacy-First

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The system is designed to run automatically without manual intervention. Every two hours, ExactEstate refreshes a comprehensive feature set for each resident—payment history, balance trends, lease tenure, and dozens of other factors—and generates fresh predictions.

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Importantly, the models train on historical outcomes (what actually happened to residents over 90-day windows), not on personally identifiable information. The system learns patterns from aggregated behavior, not individual circumstances. No PII is used in model training or prediction.

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As more outcome data accumulates over time, we can retrain the models to improve accuracy—ensuring predictions stay relevant as market conditions and resident populations evolve.

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The ROI: Time Savings and Better Outcomes

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The value proposition is straightforward:

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• Replace manual review with automated risk scoring. Instead of staff spending hours each week pulling reports and manually categorizing residents, the system automatically surfaces prioritized lists.
• Intervene earlier. Proactive outreach to at-risk residents prevents small issues from becoming large balances.
• Prioritize by impact. Focus staff time on residents with high severity predictions and low cure probability—the cases that actually need attention.
• Improve resident relationships. Early, supportive outreach is better for residents than late-stage collections pressure.

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What's Next

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We're actively developing this capability and will be rolling it out to ExactEstate clients in the coming months. The models are trained, the automation is built, and we're now focused on the user experience—making predictions actionable through dashboards, alerts, and workflow integrations.

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If you're interested in learning more about how predictive analytics can transform your delinquency management, reach out to our team. We'd love to show you what's possible.

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ExactEstate: Property Management Built for Affordable Housing

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