LienIQ

LienIQ decentralizes residential non-performing second lien analytics-based investing.

In this late-cycle credit environment, several factors suggest off-the-run, short-duration, high-yielding assets may be the sweet spot in the credit investment space as these types of assets may outperform through a much overdue downturn. A firm with the right expertise in real estate, credit modeling, and application development can position itself to capture this opportunity. These factors include, but are not limited to: (1) Low rates and flat yield curves, which indicate that the market may not be compensating investors for moving out the maturity spectrum. (2) A re-levered consumer base that may not be able to meet their financial obligations and may therefore change spending behavior. (3) Punitive regulations instituted after the last financial crisis potentially making originators, mainly banks, less apt to hold distressed assets on balance sheet. (4) A reluctance of traditional managers to engage in smaller, fragmented asset markets that may be costly to enter. LienIQ is that firm. We are made up of fixed income and technology professionals that are committed to capturing unique investment opportunities in the consumer credit space. By marrying cutting-edge, machine-learning based, financial modeling and traditional real estate capital structure analysis with the distributed, bottom-up power of blockchain technology, we can engage the broader investment community and democratize the rewards from this endeavor. Our first order of business is purchasing and working out Non-Performing Loans (NPL’s) in the second lien mortgage space. NPL’s present a unique opportunity because they are rising in volume, can be sourced effectively through our network, and traditional funds tend to avoid them because are smaller in size and carry headline risk. Furthermore, they are buttressed by home prices, which may continue to rise through the next downturn. By researching the market, the team discovered that a rigorous, model-driven approach was lacking and that a scale-able second lien portfolio pricing engine may provide smaller participants a marked advantage. In response to this insight, the team created the first version of a small-scale, proprietary lien scoring system to evaluate claims. Our vision is to institute a best-in-class investment process for the consumer credit space through the next downturn so we may generate dividends for our token-holders. With improvements of our quanta-mental approach, LienIQ will solidify this competitive advantage. We already built and used the first version of this model to evaluate ~$5MM in second lien portfolios and purchase several individual liens. In addition, we developed similar models for other consumer credit assets, like unsecured term loans used for pricing and underwriting purposes. By enhancing the model suite with more relevant and timely historical performance data, additional human capital, and an expanded sourcing network, we may be able to not only project future cash-flows, but also the borrower order of payments across many financial obligations. We then may be able to intelligently select, purchase, and workout those portfolios to monetize this informational advantage and outperform other market participants.