Structyx transforms property, loan, and market data into forward-looking credit outcomes and investment signals.
Designed to integrate with existing infrastructure including Intex, Trepp, and Bloomberg.
Get in TouchCapabilities
01
Enhances property-level data with alternative signals, including continuously updated consumer activity, tenant performance, and location-based dynamics, transforming unstructured inputs into structured indicators using AI-enabled methods.
02
Models loan-level outcomes including default probability, timing, and loss severity using a combination of structural logic, data-driven approaches, and market-tested methodologies refined over more than a decade.
03
Connects asset and credit outcomes to bond-level valuation, enabling identification of relative value, systematic investment opportunities, and portfolio-level insights.
Methodology
Step 01
Property & Transaction Data
Enhanced with alternative signals
Step 02
Loan & Structure
Intex / Trepp / Bloomberg
Step 03 · Core Engine
Structyx Modeling Engine
Default, timing, and loss modeling using structural and data-driven methodologies
Step 04
Cash Flow & Scenario Outputs
Stress testing and simulation
Step 05
Investment Signals
Decision-relevant outputs
Existing platforms provide data and infrastructure. Structyx drives forward-looking default, loss, and timing outcomes that power investment decisions.
Value Proposition
Most existing systems stop at data, analytics, or scenario modeling. Structyx focuses on generating forward-looking credit outcomes and translating them into investment decisions.
Leadership
Structyx was founded by a veteran CMBS portfolio manager with over 17 years of experience at a leading global multi-strategy hedge fund.
Throughout his career, he has bridged the gap between investment logic and technical execution, personally architecting analytical systems comprising thousands of lines of code. This "PM-as-Developer" approach supported investment decisions that generated top-tier returns across multiple market cycles, including the Global Financial Crisis and the COVID-19 dislocation.
A pioneer in the integration of alternative data within CMBS, he was among the first to incorporate credit card transactions, geolocation data, and property-level datasets into a systematic investment framework — well before such approaches became industry standard. This work also incorporated early forms of data-driven modeling and pattern recognition applied to large-scale alternative datasets, reflecting early applications of data-driven approaches that have since become more prevalent in modern AI.
His work has consistently influenced broader industry practices through deep collaboration with data providers, servicers, and market participants, improving transparency and modeling frameworks.
Structyx is the culmination of this perspective — an intelligence layer built at the intersection of portfolio management expertise, alternative data, and quantitative modeling.
Currently focused on commercial real estate and securitized credit
CMBS · CRE · Structured Credit