Summary Of The Foreclosure Crisis

In the late 2000s, the United States housing crisis was one of the most extreme residential catastrophes of the century. The disaster pushed millions of families into foreclosing their homes and many more into severe financial distress. The journal article utilized for this assignment uses data on every foreclosure event between 2005 and 2009 to determine foreclosure rates in neighborhoods for nearly all ethnic groups. Racial segregation within residential areas has been a defining characteristic for cities in the United States. It has played a large role in driving racial/ethnic inequality. The theory in this article seeks to explain why neighborhoods became extremely racially/ethnically segregated after the housing disaster in the late

sets the stage for the housing crisis. Mostly in the form of banking deregulation that heightened the uses of high-risk lending of money. The causal direction of this issue has a multitude of parts that lead up to it (multiple causal directions). The largest causal direction is the banking deregulation and the larger use of high-risk lending than before the housing crisis. There were efforts put in place to stop predatory lenders, however, each state varies dramatically in the terms of how aggressive their laws are in protecting consumers from predatory lenders. This is the temporal priority that made the lead-up to the housing crisis occur at an even quicker pace than it was

The analysis is restricted to the dynamics of the primary concepts within metropolitan areas, using 2010 Office of Management and Budget definitions for Core-Based statistical areas. The foreclosure data that was used came from Realty Trac, which included complete records on almost every foreclosure as well as pre-foreclosure within the U.S. from 2005 to 2012. Importantly, for the purposes of this article, the data included the physical addresses of all properties in the foreclosure process and the timing of fillings, which allowed the researchers to calculate the foreclosure rates at almost any point in time for specific geographic areas. Using the data, a panel file composed of unique foreclosure events that tracks individual properties through the foreclosure process: from the lis pendens, to bank repossession. To do this, an algorithm was used based on multiple fields. The fields included addresses, tax parcel numbers and transaction IDs, and judicial case IDs that help to identify the unique properties and impute any information that is not