Cotality

Property intelligence from the former CoreLogic

Cotality, formerly CoreLogic, supplies property data and analytics for real estate, mortgage, insurance, and other property-related businesses. The official rebrand announcement establishes the current identity, and the former CoreLogic website redirects to Cotality. Its AI-related offerings include computer vision for property imagery, predictive models, and data infrastructure for generative AI applications.

These offerings serve different users: an appraisal or insurance team may need structured observations from photos, a portfolio analyst may need valuations and forecasts, and a product engineering team may need property data for an AI assistant. They are parts of a broader product family, so scope a particular solution rather than assuming one account includes every Cotality service.

Visual asset intelligence

The Visual Asset Intelligence overview describes CoreAI computer vision applied to aerial and ground-level property photos, combined with historical property records. Documented tasks include identifying interior and exterior features, assessing roof-related characteristics, extracting construction classifications, and gathering attributes relevant to flood vulnerability.

The page links to products such as Image Analytics, Age of Roof, and Roof Condition Insights. This is relevant to appraisal review and property-condition workflows where photos contain details absent from a spreadsheet. Image-derived characteristics should be checked against the imagery's date, visibility, and the underlying record; a model's observation is not a substitute for every form of physical inspection.

Predictive models and portfolio analysis

Cotality's predictive forecasting material covers automated property valuation, owner propensity, and longer-term climate-risk modeling. Products listed there include Total Home ValueX, Propensity Scores, and Climate Risk Analytics. These support different questions, such as estimating a property's value, identifying likely listing or refinance activity, or examining portfolio exposure under future scenarios.

For example, a portfolio team could select a relevant property dataset, match its asset records, and investigate properties with changed valuation or risk signals. Analysts should keep a model estimate, an observed transaction, and a scenario projection distinct in reports. A forecast does not establish a future sale price or remove uncertainty from a lending or investment decision.

Grounding AI applications in property data

The LLM Enablement offering provides AI-ready datasets with machine-readable context, including companion YAML definitions and persistent property identifiers. Its purpose is to help an application interpret fields and join information about the same property. The enterprise MCP server gives compatible AI tools a route to query Cotality property data.

An engineering team could evaluate a property-research assistant by asking it to retrieve known attributes for a sample asset list, then checking the answers against the source records. Include ambiguous addresses and missing fields in that evaluation. Data grounding can supply evidence to a model, but generated explanations still need checks for unsupported deductions or incorrect record matches.

Access, coverage, and implementation

The documented solution pages direct buyers to an expert rather than publishing a single price for the complete AI product family. Confirm the selected product, geography, historical depth, update cadence, delivery method, and licensing rights with Cotality. Public descriptions of one dataset or model should not be extended to every market or entitlement. Integration effort and source coverage are material parts of the purchase, especially when the resulting information will be used in a regulated business workflow.