The Surging Reverse Mortgage Market

Momentum continues to build around reverse mortgages and related products. Persistent growth in both home prices and the senior population has stoked renewed interest and discussion about the most appropriate uses of accumulated home equity in financial planning strategies. A common and superficial way to think of reverse mortgages is as a “last-resort” means of…

Permissioned Blockchains–A Quest for Consensus

Conspicuously absent from all the chatter around blockchain’s potential place in structured finance has been much discussion around the thorny matter of consensus. Consensus is at the heart of all distributed ledger networks and is what enables them to function without a trusted central authority. Consensus algorithms are designed to prevent fraud and error. With large,…

Private-Label Securities – Technological Solutions to Information Asymmetry and Mistrust

At its heart, the failure of the private-label residential mortgage-backed securities (PLS) market to return to its pre-crisis volume is a failure of trust. Virtually every proposed remedy, in one way or another, seeks to create an environment in which deal participants can gain reasonable assurance that their counterparts are disclosing information that is both…

Harnessing Machine Learning in Finance to Improve Model Results

Models based on Machine Learning are being increasingly adopted by the finance community in general and the mortgage market in particular. The use of modeling and data analytics has been key in the turnaround of this market; however, anyone who has worked with mortgage loan data knows it is notorious for errors and data gaps. Despite industry-wide efforts to incorporate robust quality control programs, challenges with mortgage data persist. Fortunately, combining machine learning in finance with cloud computing shows promise in addressing mortgage data gaps and producing more accurate results than traditional approaches.

Loan Modification Performance: A Multi-Variate Review Approach

As this article goes to print, our nation is nearing the 100-day mark until the presidential election of 2012. While the candidates debate, most polls rate the economy, jobs, and housing as differentiators. For the issue of housing to be included as top of mind speaks volumes to the challenges the housing market has brought to the policy makers on the one hand, and the confidence of the voters on the other. Policy makers want to f ind answers to the next gen-eration of housing finance and have sought to balance the fragility of the housing market with appropriate loan modification programs as an overall economic recovery takes hold. The questions of how to solve our homeown-ership conundrum are generational; and they are due additional quantitative analysis and scrutiny. Many factors affect the success of the various loan modification programs, and in this article, we review the different loan modification efforts that have been observed in the non-agency market and assess the performance of these different modification types. As the campaign rhetoric fades past November, we will continue to analyze the data surrounding the success and failure of the various loan modification programs and the impact these programs are having on our nation’s housing recovery.