Using LightBox True Owner™

Using the Data

• Identify Actionable Property Contacts

  • Use the ASSESSMENT_LID to retrieve specific True Owner records.

  • Access direct emails and phone numbers for individual owners, corporate officers, or key business representatives.

  • Confidence scores help prioritize the most reliable records.

• Expand Ownership Research

  • Utilize PERSON_ID to find additional properties linked to the same individual.

  • Discover entire portfolios owned or managed by a contact.

• Enhance Due Diligence & Outreach

  • Filter contacts by corporate roles (CEO, CFO, Manager etc.) to target decision-makers.

  • Validate last-seen contacts timestamps to ensure outreach accuracy.

  • Access business phone/email details when individual contacts are not available.

Linking True Owner Data to Other Datasets

Beyond direct property contact identification, True Owner data provides a rich foundation for advanced analytics, AI-driven insights, and cross-dataset linking. By integrating this dataset with other data sources and leveraging machine learning and other technologies, businesses can uncover hidden relationships, predict market opportunities, and enhance decision-making.

By connecting True Owner with additional datasets, organizations can:

• Uncover Hidden Ownership Patterns

  • Link PERSON_ID across property tax records, corporate registrations, and leasing databases to map entire portfolios owned by individuals or businesses.

  • Identify related entities (e.g., subsidiaries, investment groups) by analyzing shared contacts across multiple properties.

  • Detect shell companies and indirect ownership structures often used in real estate transactions.

• Enrich Market & Investment Insights

  • Integrate with lending & financial data to assess borrower risk and loan exposure based on the true breadth of a person’s or company’s holdings.

  • Cross-reference with tenant and lease datasets to evaluate portfolio occupancy risks and opportunities.

• Improve Contact Intelligence & Sales Targeting

  • Link True Owner contacts with business registries, LinkedIn, or CRM databases to build a more complete profile of decision-makers.

  • Apply NLP to email domains and phone numbers to categorize contacts by industry, business size, and role relevance.

  • Use transactional history to identify owners more likely to sell, refinance, or lease properties.

Advanced Analytics Opportunities: Predicting Relationships & Market Trends

True Owner is more than just a contact dataset—it is a powerful foundation for uncovering ownership relationships, optimizing outreach, and enhancing decision-making. By integrating with external data sources and leveraging AI/ML-driven analytics, real estate professionals, investors, and financial institutions can:

• Map hidden ownership structures with greater accuracy.

• Predict market trends and identify high-value opportunities.

• Enhance lead targeting by identifying key decision-makers faster.

• Automate due diligence with AI-powered risk assessments.

• Predictive Analytics & Lead Scoring for CRE Deals

  • Apply ML-based contact prioritization based on activity signals, confidence scores, and past engagement history.

  • Forecast property turnover likelihood by analyzing ownership patterns, economic trends, and market movements.

  • Identify high-value acquisition or leasing targets by spotting distressed or underperforming assets linked to the same owner network.

• Ownership Discovery & Portfolio Analysis

  • Use generative AI to generate intelligent ownership reports based on linked datasets, summarizing potential business relationships and property holdings.

  • Automate due diligence reports by integrating property records, liens, and financial data to assess ownership risk.

  • Apply clustering algorithms to segment high-growth investors, active sellers, and passive holders based on historical behavior.

• Machine Learning for Entity Resolution & Relationship Mapping

  • Train ML models to group multiple properties under the same true owner, even when ownership is fragmented across trusts, LLCs, or affiliated corporations.

  • Use graph analytics to visualize networks of related businesses, property holdings, and financial interests.

  • Automate anomaly detection to flag potential ownership obfuscation (e.g., circular transactions, frequent entity name changes).