Short-Term Rental Regulation Change Intelligence

Turn municipal short-term rental rules into jurisdiction-level licensing, night-cap, restriction and effective-date change intelligence.

Why use this Actor

Hosts, operators and property teams need to know when local short-term rental rules materially change, not merely collect municipal pages. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Jurisdiction-level rule change intelligence instead of raw municipal text.

• Snapshot comparison for licensing, night caps and restriction changes.

• Effective-date and rule-breadth signals for operational review.

• Decision-ready actions for recurring compliance monitoring.

• Low-compute dataset-first Gen2 architecture.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Short-term rental operators.
• Property managers.
• Real-estate investors.
• Compliance teams.
• AI agents.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems as JSON records or point currentDatasetId to an Apify Dataset. For recurring comparisons, provide previousItems or previousDatasetId. Keep stable entity identifiers across snapshots whenever possible. maxItems limits Dataset reads. Optional Gen2 fields can compare the current decision metric with a prior analysis and estimate economic impact only when the user explicitly supplies an impact value.

Output

The default Dataset receives one structured intelligence report with record counts, new and removed records, ranked entity signals, decision confidence, regression status, executive decision, recommended action, and transparent economic-impact fields when enabled. The same report is stored in INTELLIGENCE_REPORT for downstream automations and agent workflows.

Compatible upstream datasets

This Actor analyzes normalized records rather than scraping restricted sources itself. Useful upstream families include:

• municipal ordinance and licensing datasets.
• normalized short-term-rental regulation feeds.
• compliance datasets used by Airbnb/Vrbo operators.

The input aliases were expanded for these workflows. Verify field semantics when an upstream dataset uses source-specific names; snapshot identifiers should remain stable between periods.