Foreclosure Echo: How Abandoned Foreclosures are Re-Entering the Market Through Debt Buyers

It is common knowledge that mortgage defaults increased steadily from 2006 through 2011. In some situations, lenders moved swiftly after default to foreclose the property; but for other homeowners the foreclosure process began and then stalled or was completely abandoned by the lender. The result of these abandoned foreclosures has been devastating to cities and consumers throughout the country. This article explores what is happening to homeowners caught up in the strange world of bank walkaways as the economy is beginning to improve. This second wave of collection activity, an echo of the original foreclosure crisis, could easily throw thousands of consumers back into financial hardship just as the economic recovery begins. Part I of this article explores the evidence of foreclosures started and then stalled or abandoned and their impact on consumers and communities. In Part II the real zombie title is introduced through evidence gathered in foreclosures in Indiana. This new form of zombie loan is a mortgage loan that has been foreclosed, but is suddenly and inexplicably un-foreclosed. The effect of zombie loans on homeowner, judicial system and communities is also explored. Finally, Part III discusses the increased presence of debt buyers in both the buying of loans and the collection of deficiency judgment in relation to the overall concern currently being voiced regarding the debt buying industry. The clever ways banks are managing their foreclosure inventory make clear that the effects of zombie loans must be mitigated in order to avoid a second economic downturn, the foreclosure echo

The Future of Foreclosure Law in the Wake of the Great Housing Crisis of 2007-2014

As 2014 came to an end so, perhaps, did the worst foreclosure crisis in U.S. history. On January 15, 2015, RealityTrac, one of the nation’s leading reporters of housing data, declared the foreclosure crisis had ended. Whether or not their declaration proves true, the aftermath of the crisis will be felt for years to come. During the crisis it is estimated more than five million families lost their homes to foreclosure. Federal, state and local responses to the crisis changed laws and perceptions regarding foreclosure. Despite these changes, we end the crisis much the way we began -- with a nationwide foreclosure system mistrusted and disliked by lenders and consumers alike. This paper examines the responses to the crisis in an effort to determine what worked, what did not, and where foreclosure law should go from here. In the end, it is clear that we need a more uniform system, but one that also prioritizes homeownership, or at least home occupancy

Foreclosure Echo: How Abandoned Foreclosures are Re-Entering the Market Through Debt Buyers

It is common knowledge that mortgage defaults increased steadily from 2006 through 2011. In some situations, lenders moved swiftly after default to foreclose the property; but for other homeowners the foreclosure process began and then stalled or was completely abandoned by the lender. The result of these abandoned foreclosures has been devastating to cities and consumers throughout the country. This article explores what is happening to homeowners caught up in the strange world of bank walkaways as the economy is beginning to improve. This second wave of collection activity, an echo of the original foreclosure crisis, could easily throw thousands of consumers back into financial hardship just as the economic recovery begins. Part I of this article explores the evidence of foreclosures started and then stalled or abandoned and their impact on consumers and communities. In Part II the real zombie title is introduced through evidence gathered in foreclosures in Indiana. This new form of zombie loan is a mortgage loan that has been foreclosed, but is suddenly and inexplicably un-foreclosed. The effect of zombie loans on homeowner, judicial system and communities is also explored. Finally, Part III discusses the increased presence of debt buyers in both the buying of loans and the collection of deficiency judgment in relation to the overall concern currently being voiced regarding the debt buying industry. The clever ways banks are managing their foreclosure inventory make clear that the effects of zombie loans must be mitigated in order to avoid a second economic downturn, the foreclosure echo

A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

Fairness and Privacy in Federated Learning and Their Implications in Healthcare

Currently, many contexts exist where distributed learning is difficult or otherwise constrained by security and communication limitations. One common domain where this is a consideration is in Healthcare where data is often governed by data-use-ordinances like HIPAA. On the other hand, larger sample sizes and shared data models are necessary to allow models to better generalize on account of the potential for more variability and balancing underrepresented classes. Federated learning is a type of distributed learning model that allows data to be trained in a decentralized manner. This, in turn, addresses data security, privacy, and vulnerability considerations as data itself is not shared across a given learning network nodes. Three main challenges to federated learning include node data is not independent and identically distributed (iid), clients requiring high levels of communication overhead between peers, and there is the heterogeneity of different clients within a network with respect to dataset bias and size. As the field has grown, the notion of fairness in federated learning has also been introduced through novel implementations. Fairness approaches differ from the standard form of federated learning and also have distinct challenges and considerations for the healthcare domain. This paper endeavors to outline the typical lifecycle of fair federated learning in research as well as provide an updated taxonomy to account for the current state of fairness in implementations. Lastly, this paper provides added insight into the implications and challenges of implementing and supporting fairness in federated learning in the healthcare domain

Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series Models

Interpretable machine learning plays a key role in healthcare because it is challenging in understanding feature importance in deep learning model predictions. We propose a novel framework that uses deep learning to study feature sensitivity for model predictions. This work combines sensitivity analysis with heterogeneous time-series deep learning model prediction, which corresponds to the interpretations of spatio-temporal features. We forecast county-level COVID-19 infection using the Temporal Fusion Transformer. We then use the sensitivity analysis extending Morris Method to see how sensitive the outputs are with respect to perturbation to our static and dynamic input features. The significance of the work is grounded in a real-world COVID-19 infection prediction with highly non-stationary, finely granular, and heterogeneous data. 1) Our model can capture the detailed daily changes of temporal and spatial model behaviors and achieves high prediction performance compared to a PyTorch baseline. 2) By analyzing the Morris sensitivity indices and attention patterns, we decipher the meaning of feature importance with observational population and dynamic model changes. 3) We have collected 2.5 years of socioeconomic and health features over 3142 US counties, such as observed cases and deaths, and a number of static (age distribution, health disparity, and industry) and dynamic features (vaccination, disease spread, transmissible cases, and social distancing). Using the proposed framework, we conduct extensive experiments and show our model can learn complex interactions and perform predictions for daily infection at the county level. Being able to model the disease infection with a hybrid prediction and description accuracy measurement with Morris index at the county level is a central idea that sheds light on individual feature interpretation via sensitivity analysis

World of Viruses: the Frozen Horror

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