7. New Policy Approaches to Discrimination and Subprime Lending

2019 ◽  
pp. 191-206
Keyword(s):  
Author(s):  
Susan M. Wachter ◽  
Karl Russo ◽  
Jonathan E. Hershaff
Keyword(s):  

Author(s):  
Igor Makarov ◽  
Guillaume Plantin
Keyword(s):  

2016 ◽  
Vol 19 (2) ◽  
pp. 171-196
Author(s):  
Tyler Yang ◽  
◽  
Jessie Y. Zhang: ◽  

The recent U.S. financial crisis has been found to be unique compared with previous crises: it started when problems first appeared in the housing market and subprime lending, and then spread to the whole financial system and national economy. Through the securitization of structured private label mortgage products, its impact even reached the international capital markets. To explore the cause of the long and far-reaching effect of the current subprime-induced crisis, we review a series of events and government policies prior, during, and after the subprime and housing crisis. Using qualitative and quantitative models, we show that the low interest rate and passive market supervisory policies made by the U.S. government are among the main drivers of the housing boom. During the housing bust, despite a more aggressive regulatory environment, several conflicting policies that were implemented may have prolonged and deepened the recession. Based on these hypotheses, we argue that contagious real estate cycles can be prevented and/or controlled by more proactive counter-cyclical government intervention.


Author(s):  
Alan N. Rechtschaffen

This chapter discusses the origins of the 2007 financial crisis, subprime lending, and government-sponsored entities. It argues that the events driving financial markets to the precipice of collapse during the global financial meltdown gave rise to a regulatory framework that may have been a rational response to a market in free fall, but need to be reassessed in an era of recovery. In 2018, the U.S. economy may be, by many measures, viewed as wholly recovered from the economic impact of the crisis. The stock market is trading at record highs, having erased all the losses of the crisis period and then some. With this recovery, the Trump administration seeks to restrain the regulatory burden imposed during the crisis.


2016 ◽  
Vol 13 (2) ◽  
pp. 365-377 ◽  
Author(s):  
LeConté J. Dill ◽  
Orrianne Morrison ◽  
Mercedez Dunn

AbstractWaves of migration to and flight from Atlanta by both White and Black residents and businesses have constantly imagined and re-imagined the city as both politically regressive and racially progressive, and from an environmental health perspective, as both a riskscape and a safe haven. We argue that the persistent racial, social, environmental, and health inequities in Atlanta have been fostered and exacerbated by the exponential growth of the city and the persistent rhetoric of it being “the city too busy to hate.” This paper is informed by extant research on housing and transportation policies and processes at work in Atlanta since the end of the Civil War, and in particular, the predatory and subprime lending practices during the past thirty years. This paper examines how young people, living in a neighborhood where over 50% of the houses are currently vacant and contending with threats of school closures, experience the contemporary foreclosure crisis. Using qualitative data from focus groups with middle school youth, this paper offers youth-informed perspectives and local knowledge by offering responses of marginalized populations in Atlanta who inhabit, rather than flee, their built and social environments.


2002 ◽  
pp. 154-166 ◽  
Author(s):  
David West ◽  
Cornelius Muchineuta

Some of the concerns that plague developers of neural network decision support systems include: (a) How do I understand the underlying structure of the problem domain; (b) How can I discover unknown imperfections in the data which might detract from the generalization accuracy of the neural network model; and (c) What variables should I include to obtain the best generalization properties in the neural network model? In this paper we explore the combined use of unsupervised and supervised neural networks to address these concerns. We develop and test a credit-scoring application using a self-organizing map and a multilayered feedforward neural network. The final product is a neural network decision support system that facilitates subprime lending and is flexible and adaptive to the needs of e-commerce applications.


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