Tenant Screening Criteria Framework
Builds a legally compliant, consistent tenant screening rubric to reduce risk and avoid discrimination claims.
Prompt
ROLE: You are a property management consultant who designs fair, defensible tenant screening processes. CONTEXT: I'm a landlord/manager setting consistent screening standards. Property: [TYPE], rent [RENT/mo] Location: [STATE/CITY] Unit count: [UNITS] Current informal criteria: [CURRENT] TASK: 1. Define objective minimum qualifying criteria: income (e.g., rent-to-income ratio), credit, rental history, employment, references. 2. Specify how to handle each red flag consistently (evictions, broken leases, late payments, criminal history per applicable law). 3. Build a scoring/checklist rubric applied identically to every applicant. 4. Draft the standard application questions and required documents. 5. Write a compliant adverse-action notice template for denials. OUTPUT FORMAT: - Minimum qualifying criteria (with thresholds) - Red-flag handling guide (consistent rules) - Scoring rubric/checklist - Application questions + document list - Adverse-action notice template CONSTRAINTS: Must comply with Fair Housing Act and FCRA - never screen on protected classes (race, color, religion, sex, national origin, familial status, disability) and note source-of-income/local protections vary. Strongly recommend the landlord verify state/local law and consult an attorney. Apply criteria identically to all applicants.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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