CoreLogic (Cotality) for Property Data, Replacement Cost, and Hazard Risk

In-Depth Review

CoreLogic, in the process of rebranding to Cotality, operates the largest property data platform in insurance. With COPE data on 150M+ US properties, AI-powered replacement cost estimation, natural hazard risk models, and claims analytics, it is the data backbone that most P&C carriers already rely on. The question for most carriers is not whether to use CoreLogic, but which of its AI-powered modules justify activation beyond current licensing.

What CoreLogic Does for Insurance Professionals

CoreLogic’s position in insurance is foundational. It provides the property characteristics data (construction type, square footage, year built, roof type, protection class) that feed underwriting decisions, pricing models, and catastrophe exposure analysis. The Araya AI platform, launched more recently, applies machine learning to this data to generate automated replacement cost estimates, property intelligence scores, and risk classifications designed to be consumed at the point of quote.

The company also maintains natural hazard risk models covering earthquake, flood, wildfire, and wind/hail, plus a weather verification service that confirms whether reported weather conditions actually occurred at a specific loss location and date. This breadth means a carrier can theoretically source property data, replacement cost, hazard risk, and claims support from a single vendor.

Key Capabilities

COPE data is CoreLogic’s foundation. Construction, Occupancy, Protection, and Exposure data on 150M+ properties includes building age, construction type, square footage, stories, roof material, foundation type, fire protection class, distance to nearest fire station, and occupancy classification. This is the raw material that feeds underwriting eligibility rules, rating algorithms, and CAT model inputs. No competitor matches this coverage at scale.

Replacement cost estimation uses property characteristics, local construction cost indices, and historical data to calculate what it would cost to rebuild a property. AI-powered models (via Araya) improve on traditional replacement cost calculators by incorporating more data points and detecting anomalies. Under-insurance is a persistent problem in homeowners; CoreLogic’s RCE data helps carriers identify policies where the dwelling limit is materially below actual replacement cost.

Natural hazard models cover earthquake, flood (including inland flood beyond FEMA zones), wildfire, and wind/hail. Each model scores risk at the property level using terrain, soil composition, vegetation, proximity to hazard sources, and building characteristics. These are not the same as CAT models used for portfolio loss estimation, but rather property-level risk scores used for underwriting and pricing.

Weather verification is a differentiated capability. When a claim reports hail damage on a specific date, CoreLogic can confirm whether hail of the reported size actually occurred at that address. This supports claims accuracy, identifies date-of-loss discrepancies, and provides evidence for fraud investigations.

Pricing

Enterprise contracts. No public pricing. CoreLogic’s product portfolio is broad, and licensing structures can be complex. Carriers often discover they are paying for overlapping data products across different departments (underwriting, claims, actuarial) without a consolidated view of total spend. Before adding new modules, audit your existing CoreLogic licensing to understand what you already have access to.

Honest Assessment

CoreLogic’s scale advantage is real: 150M+ properties, decades of data history, and integrations with every major policy admin platform. The challenge is that scale also means complexity. The product portfolio is large, the Cotality rebrand adds transition uncertainty, and the Araya AI platform is still establishing its track record alongside the company’s traditional data products.

For most carriers, the practical decision is incremental: you already use CoreLogic data somewhere in your operations. The question is whether activating Araya, adding hazard models, or licensing weather verification generates enough underwriting or claims ROI to justify the additional spend. That calculation requires mapping the specific module against a specific workflow improvement with measurable outcomes.

One Thing to Test Before Committing

Audit your existing CoreLogic licensing across all departments: underwriting, claims, actuarial, and any third-party vendors who may be passing through CoreLogic data. Map what you already have access to, what you are paying for but not using, and where the data gaps are. Then evaluate new modules (Araya, hazard models, weather verification) against those specific gaps rather than the full product portfolio. The ROI on incremental modules is clearer when you know your baseline.