Pricing Comparables

Pricing Comparables allow users to evaluate insurance pricing by comparing assets and policies with similar characteristics.

Users begin by selecting the attributes that matter most for the analysis—such as asset type, coverage structure, geography, and property characteristics. These selections define a comparison set of relevant policies drawn from Advocate’s validated insurance data.

As filters are adjusted, users can explore different comparison perspectives, evaluate alternative peer group definitions, and view results side by side to understand how pricing shifts across comparable risks.

Pricing Comparable tables present multiple statistical views of pricing, enabling users to assess the full range of observed market behavior rather than relying on a single point estimate.

Available Filters

• Asset type (e.g., Multifamily, Assisted Living)
• Coverage type (Property, Liability, Package)
• Geography, including state and city
• Property characteristics, including number of units, year built, Replacement Cost Value (RCV), and Effective Gross Income (EGI)
• Policy effective date

Table Configuration

Tables can be configured to surface the specific data points required for the task at hand, including:

• Group size and dimensional breakdowns
• Rate on Line (RoL) metrics such as averages, medians, and percentile ranges
• Premium normalization metrics, including premium per unit and per square foot
• Replacement cost metrics, including total RCV and RCV normalized by unit or square footage
• Coverage participation indicators across major perils and policy components

Building an Analysis

The My Comps section allows users to save and compare multiple peer groups side by side. To build an analysis:

1. Search for comparable groups using the Filters panel to define peer group criteria
2. Browse Available Comp Groups in the results table that appears based on selected filters
3. Add to the Analysis by clicking the Add button on any comp group row
4. Name saved groups to keep track of different comparison perspectives (e.g., “TX Package 50+ Units”, “FL Coastal Properties”)
5. Compare across groups by viewing all saved peer groups in a single table with consistent metrics
6. Create an artifact to export the analysis for use in reports

This workflow enables users to build a comprehensive view of how pricing varies across different market segments, property types, or geographic regions.

Exploring the Comp Worksheet

Click on any comp group row to open the Comp Worksheet, which provides deep insights into that specific peer group:

Overview

High-level counts of policies, carriers, brokers, and brokerages in the peer group.

Property Valuation

Detailed metrics on replacement cost and income characteristics:

• Average and median RCV
• RCV per door and per square foot
• Average EGI (Effective Gross Income)

Pricing & ROL

Interactive distribution charts showing the full range of pricing outcomes:

• ROL Distribution — Visualize min, Q1, median, mean, Q3, and max Rate on Line values
• Premium/Door Distribution — See how premium per unit varies across the peer group
• Hover over charts to see detailed percentile breakdowns

Rate on Line Price Factors

For Multi Family properties, the worksheet displays key factors influencing pricing in the segment:

• See which property characteristics (units, year built, RCV, EGI) have the strongest impact on price
• Understand whether each factor increases or decreases expected ROL
• Click on any factor to see a detailed breakdown by value range

Coverage Breakdown

Visual representation of coverage participation rates:

• Percentage of policies including flood, wind/hail, earthquake, and other coverages
• Helps identify common coverage structures in the market segment

Market Players

Paginated tables showing carriers, brokers, and brokerages active in this segment:

• Carriers — With average ROL, total premium, and policy counts
• Brokers — Individual brokers with their brokerage affiliations
• Brokerages — Aggregated view with broker counts

Expanding Coverage

As data coverage expands, additional filters and analytical dimensions may become available. Where supported by the underlying data and aggregation standards, users may also request custom groupings.