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Index Methodology

Comprehensive documentation of how we calculate Regulatory Velocity Index (RVI) and Regulatory Friction Index (RFI) scores for cities.

Regulatory Velocity Index (RVI)

Measuring regulatory process efficiency

The Regulatory Velocity Index quantifies how efficiently a city's government processes regulatory matters. A higher RVI indicates a faster, more predictable regulatory environment that is generally more favorable for business operations.

Data Source

RVI is calculated from Legistar municipal records, which track the lifecycle of legislative matters including ordinances, permits, contracts, and land use decisions. We analyze matters from introduction through final action.

Scoring Components

RVI is composed of four weighted components:

Time to Approval
35% weight

Based on median days from matter introduction to final passage. Lower median days result in higher scores. Uses percentile ranking across all scored cities.

Procedural Complexity
25% weight

Based on median number of procedural steps (history records) per matter. Fewer steps indicate streamlined processes and result in higher scores.

Approval Rate
20% weight

Ratio of successful matters (approved, passed, adopted) to failed matters (denied, rejected). Excludes inconclusive outcomes (withdrawn, tabled).

Predictability
20% weight

Based on Interquartile Range (IQR) relative to median. Lower variance in approval timelines indicates more predictable processes.

Matter Categories

Matters are classified into five business-relevant categories:

Land Use & Zoning

Zoning changes, variances, subdivisions

Development & Construction

Capital improvements, building projects

Contracts & Agreements

Service contracts, leases, procurement

Ordinances & Policy

Municipal code changes, regulations

Permits & Licenses

Operating permits, business licenses

Confidence Levels

Each RVI score includes a confidence level based on data quality:

HIGH
100+ matters/category

Robust data coverage

MEDIUM
30–99 matters/category

Adequate for scoring

LOW
Below minimum thresholds

Interpret with caution

Data Quality Filters

Filters applied
  • Exclude procedural items
    Call to order, agenda approval, and similar non-substantive items.
  • Exclude extreme outliers
    Matters taking 5+ years are excluded.
  • Minimum data thresholds
    Cities need at least 500 total matters and 100 with lifecycle data.

Regulatory Friction Index (RFI)

Measuring regulatory discourse quality

The Regulatory Friction Index measures the quality of regulatory discourse by analyzing meeting transcripts. A higher RFI indicates smoother, less contentious discussions that suggest a more collaborative regulatory climate.

Data Source

RFI is calculated from AI-analyzed meeting transcripts. We use Claude (Anthropic's AI) to analyze transcript segments and identify friction indicators such as deferrals, opposition, and contentious debates.

Scoring Components

RFI is composed of four equally-weighted components (25% each):

Discussion Intensity
25% weight

Average segment length compared to baseline. Longer discussions often indicate complexity or contention. Shorter, efficient discussions score higher.

Contention Rate
25% weight

Percentage of discussions rated as contentious (controversy score 3+ out of 5). Lower contention rates indicate smoother proceedings.

Deferral Rate
25% weight

Percentage of agenda items deferred, tabled, or continued. Higher deferral rates suggest difficulty reaching decisions.

Opposition Frequency
25% weight

Frequency of expressed opposition, dissent, or "no" votes per discussion. Lower opposition indicates more consensus-driven processes.


Technical Details

Update frequency and data processing

Update Schedule

  • • Both indices calculated weekly (Sundays)
  • • RVI runs at 4:00 AM UTC
  • • RFI runs at 5:00 AM UTC
  • • Historical trends preserved for comparison

National Percentile

  • • Cities ranked against all scored cities
  • • 90th percentile = better than 90% of cities
  • • Percentiles update with each calculation

Important Disclaimers

  • • These indices are intended for informational purposes and should not be the sole factor in business decisions.
  • • Score comparisons are most meaningful between cities with similar population sizes and governance structures.
  • • Data availability varies by city; some jurisdictions may have limited historical records.
  • • AI analysis may occasionally misclassify discussion topics or sentiment.