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Data-Driven Tenant Screening: Uncovering the Real Drivers of Evictions in North Texas

Updated: Jan 2

Happy New Year! From all of us at Darling Property Management, we hope you have safe, happy, and prosperous 2026! I was watching a video about how a leading Property Manager in Ohio, Peter Lohmann, approaches multiple aspects of running his PM company, RL Property Management. The video is long and quite extensive, but he mentioned something I thought was worth diving into deeper - the why behind some of the basic tenant screening criteria property managers use.


Most property management companies and rental property investors simply use a boilerplate criteria for credit score, criminal background, and income but Peter poses the question why those limits were set the way they were.


First, here's the video if you want to watch. It's a great watch if you are a fellow rental property investor to hear his point of view.




The video made me want to look deeper into North Texas' trends and see if the data shows us any of the why behind certain screening criteria. As a property manager specializing in single-family homes in North Texas, we know that effective tenant screening is the cornerstone of minimizing risks like defaults and evictions. But relying on generic rules of thumb—like requiring 3x rent in income or a minimum credit score—might not cut it in today's market. What if those criteria don't actually address the true culprits behind tenant issues? That's why I dove deep into the data to build a more robust, evidence-based approach. Using advanced analysis, I'll break down what really drives evictions and share tailored screening criteria to protect your investments.


Why Go Beyond the Basics?

In North Texas, especially areas like Dallas County, eviction rates can vary wildly by neighborhood. Traditional screening often focuses on income and credit scores, but these are just proxies. To get to the heart of the matter, I researched key predictors of tenant default and eviction. This involved pulling from large-scale studies and even constructing a regression model to quantify relationships. The goal? Help you screen smarter, reducing turnover and legal headaches while attracting reliable renters.


The Science Behind the Screening: Key Insights from the Data

Evictions aren't random—they're often tied to a handful of interconnected factors. Drawing from reliable sources like TransUnion's resident behavior studies and local Dallas reports, here's what the evidence shows:


  • Housing Cost Burden as the Top Culprit: When rent eats up more than 30-33% of a tenant's income, the risk skyrockets. In Dallas, neighborhoods with higher cost burdens (e.g., 32-37% for some groups) see eviction filing rates up to 4x higher than others. This isn't just anecdotal; aggregate data from the Dallas Equity Indicators reveals clear patterns linking overburdened budgets to instability.

  • Prior History Speaks Volumes: Tenants with past evictions or rental collections are far more likely to repeat the pattern. TransUnion's analysis of millions of rental outcomes found that evicted tenants had nearly 3 times more prior evictions (21.7% vs. 5.5%) and twice the rental-related debts compared to stable renters. Their specialized ResidentScore model outperforms standard credit scores by spotting 15% more high-risk cases.

  • Job Instability and Broader Vulnerabilities: Unemployment or frequent job changes amplify risks. A sophisticated neural network model called MARTIAN, designed specifically for forecasting evictions in Dallas County, emphasized historical eviction data as the strongest predictor, followed closely by labor stats like unemployment rates and sector shifts. Removing unemployment trends from the model increased prediction errors by 8%, underscoring its impact. Other factors, like low education levels (as a proxy for economic vulnerability) and poverty indicators, also play roles, especially in diverse North Texas communities.


In short, while credit scores matter, they're not the full story. A data-driven lens reveals that affordability, history, and stability are the real levers to pull.


Your Optimized Screening Criteria for North Texas Rentals

Based on this analysis, here's a refined set of criteria for screening tenants in single-family homes. It's designed to target the proven drivers without overcomplicating your process:

  • Rent-to-Income Ratio: Aim for gross monthly income at least 3x the rent (e.g., $6,000 for a $2,000 property). This keeps housing costs under 33%, directly tackling the leading eviction driver identified in Dallas data. So 3x income as a screening criteria makes sense, but now you know the why and the data behind the decision

  • Rental and Eviction History: Require no evictions or rental collections in the past 7 years. This simple check leverages TransUnion's findings on repeat risks.

  • Credit or Resident-Specific Score: Set a minimum of 650 on a rental-focused score like ResidentScore (or equivalent credit score). It's a solid proxy for payment habits but weighted less than history and income for better accuracy.

  • Employment Verification: Confirm at least 12 months of stable, full-time employment with no recent gaps. This addresses job instability, a key factor in the MARTIAN model.

  • Holistic Add-Ons: Check for no recent felonies (last 5-7 years), positive landlord references, and consider household factors like size or education if they indicate stability—always in compliance with Texas fair housing laws to avoid discrimination.


Wrapping Up: Empower Your Portfolio with Insights

In the competitive North Texas rental market, smart screening isn't about being strict—it's about being strategic. This analysis shows how blending big data, predictive models like MARTIAN, and custom regression can elevate your approach from guesswork to precision. As your property management partner, we're here to implement these criteria seamlessly, helping you build a resilient portfolio. Ready to refine your process? Contact us today!


Darling Property Management

214.471.3741


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