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Tenant screening is one of the most repetitive and high-stakes tasks in property management. Each application requires pulling credit reports, verifying income, checking eviction and criminal records, and comparing results against your screening criteria. When this is done manually, turnaround times stretch to days, criteria are applied inconsistently across properties, and good applicants lose patience and sign elsewhere. Wayak automates the screening workflow. A playbook triggers on each new application, pulls data from your credit and background check APIs, scores the applicant against your configurable criteria, and sends notifications to both the leasing team and the applicant. An agent gives leasing managers instant access to application details, scoring breakdowns, and historical approval patterns.

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What you need

Data sources

  • Property management system — tenant applications, unit availability, lease terms, and applicant contact information
  • Credit/background check API — credit scores, payment history, eviction records, and criminal background results
  • Accounting database — current tenant payment history and collections data for reference comparisons

Knowledge spaces

  • Tenant screening criteria — upload your screening standards by property and unit type, including minimum credit score, income-to-rent ratio, eviction lookback period, and disqualifying conditions
  • Fair housing guidelines — upload federal and local fair housing requirements to ensure screening criteria comply with anti-discrimination regulations
Semantic layer: Define these in your ontology before setting up the agent.
ComponentNameDefinition
ObjectApplicationMaps to the applications table in the property management system. Represents a single tenant application with applicant details and status
ObjectTenantMaps to tenants in the property management system. Represents a current or former tenant with lease and payment history
MetricApproval RatePercentage of applications approved over a rolling 30-day period, segmented by property and unit type
MetricAverage Screening TimeMean elapsed hours from application submission to decision notification
DimensionPropertyGroups applications by property name and location
DimensionDecision OutcomeClassifies applications as approved, conditionally approved, denied, or withdrawn
See building a semantic layer for a step-by-step guide.

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Agent setup

1

Create the agent

Go to Agent Space → New agent.
FieldValue
NameTenant Screening Analyst
RoleLeasing Application Reviewer
GoalEvaluate tenant applications against screening criteria, explain scoring decisions, and provide leasing teams with actionable application summaries
2

Set the description

You are a tenant screening analyst who evaluates rental applications against defined criteria. You pull credit, background, and income data and score each application consistently using the property’s screening standards. You explain every scoring decision by citing the specific criteria that the applicant met or failed. You flag any fair housing considerations and never make final approval decisions — you present a scored recommendation for the leasing manager to act on.
3

Scope data access

Grant access to:
  • Property management system (applications, units, lease terms)
  • Credit/background check API (credit scores, eviction records, criminal checks)
  • Accounting database (tenant payment history)
  • Tenant screening criteria knowledge space
  • Fair housing guidelines knowledge space
  • Application and Tenant objects, Approval Rate and Average Screening Time metrics
4

Add skills

Trigger: User asks the agent to score a specific application.
  1. Retrieve the application record from the property management system, including the requested unit and lease terms.
  2. Pull the applicant’s credit score, payment history, and delinquency records from the credit/background check API.
  3. Check eviction history and criminal background results from the same API.
  4. Load the screening criteria for the specific property and unit type from the tenant screening criteria knowledge space.
  5. Score the application against each criterion (credit score, income-to-rent ratio, eviction history, criminal background).
  6. Assign an overall recommendation (approve, conditionally approve, or deny) based on the combined results.
  7. Return a scoring summary with each criterion’s result, the overall recommendation, and any conditions or flags.
Trigger: User asks the agent to compare multiple applicants for the same unit.
  1. Retrieve all active applications for the specified unit.
  2. Score each application using the scoring skill.
  3. Rank applicants by overall score, highlighting the strongest and weakest criteria for each.
  4. Flag any applicants with equivalent scores for manual tiebreaking.
  5. Return a side-by-side comparison table with scores, key differentiators, and the recommended top applicant.
Trigger: User asks about application trends, approval rates, or screening performance.
  1. Query application data for the requested time period and property from the property management system.
  2. Calculate approval rate, denial rate, and average screening time.
  3. Identify the most common denial reasons.
  4. Compare current metrics against the prior period.
  5. Return a trend summary with rates, top denial reasons, and screening time performance.

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Automation

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Playbook: Application intake and scoring

1

Set the trigger

Set the trigger to Event — New record on the applications table in the property management system. The playbook fires each time a prospective tenant submits an application.
2

Build the workflow

The workflow scores each application and notifies all parties:
  1. Query the application record including applicant demographics, income documentation, and the requested unit.
  2. Action — call the credit/background check API with the applicant’s identifying information to pull credit score, eviction history, and criminal background.
  3. Condition — check whether the credit and background data returned successfully. If the API returns an error or incomplete data, route to a manual review queue.
  4. Action — score the application against the property’s screening criteria (credit score threshold, income-to-rent ratio, eviction lookback, criminal disqualifiers).
  5. Condition — if the application meets all criteria, set the status to “approved.” If it fails any criterion, set the status to “denied” and record the denial reason. If it falls within conditional ranges (e.g., credit score within 20 points of threshold), set to “conditionally approved” pending additional deposit or guarantor.
  6. Action — update the application status in the property management system.
  7. Delivery — notify the leasing team and the applicant.
3

Configure delivery

  • Email to applicant — send the application decision with next steps (lease signing instructions for approvals, denial reason for denials, conditional requirements for conditional approvals)
  • Slack — post the scoring result to #leasing with the applicant name, property, unit, score, and decision
  • Email — notify the property manager of conditional approvals that need review
4

Test and activate

Click Run now to test with a recent application, then toggle to Active.

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What’s next

Lease management

Track lease expirations, automate renewal workflows, and manage document processes across your portfolio.

All Real Estate use cases

See the full list.