Academic journal article Journal of Housing Research

The Effect of Neighborhood Density and GIS Layout on the Foreclosure Contagion Effect

Academic journal article Journal of Housing Research

The Effect of Neighborhood Density and GIS Layout on the Foreclosure Contagion Effect

Article excerpt

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Defaults are the result of property owners being either unwilling or unable to meet their mortgage obligations. Studies have shown that the resulting foreclosures cause a burden to more than the defaulter and creditor; the community bears the cost through an overall decrease in nearby property values and municipal expenses (Frame, 2010). From 2000 to 2007, Federal Reserve statistics show that Americans assumed greater mortgage debt than is historically or financially sustainable (Federal Reserve Bank of San Francisco, 2009). Housing prices that reached an all-time high in 2006 began to tumble. According to the Standard and Poor's (S&P)/Case-Shiller Home Price index, home prices by the end of 2008 fell 21% from their peak value. High-risk mortgage securities failed and a wave of foreclosures followed these defaults. Census bureau data determined that the foreclosure rate in 2005 was 1.0%; by 2009 it had risen to 4.3%. In 2008, there were more than 3.1 million foreclosure filings; one out of every 54 U.S. households received a foreclosure notice (Census Bureau Data, 2012).

We use an agent-based simulation (ABS) to investigate the impact of housing neighborhood layout on the foreclosure contagion effect. The topologies of five neighborhoods in Virginia Beach, Virginia were used in this investigation, and the simulation was implemented using the Repast Simphony Agent-based simulation software suite (North et al., 2013). In an earlier study, Gangel, Seiler, and Collins (2013) focused on a homogenous torus grid layout of properties when investigating the foreclosure contagion effect. We introduce real-world neighborhood layouts to the ABS that are heterogeneous, non-linear, and difficult to express in a quantitative way. The results demonstrate that the density of the neighborhood has a significant impact on the rate of foreclosures.

Foreclosed properties often suffer from neglect and poor maintenance (Immergluck and Smith, 2006; Harding, Rosenblatt, and Yao, 2009; Leonard and Murdoch, 2009; Frame, 2010; Daneshvary and Clauretie, 2012). Neighborhoods decline as property owners who cannot or choose not to make mortgage payments frequently discontinue internal and external maintenance of the property. Nearby properties not previously in distress lose additional value from the neighborhood decline and occasionally become worth less than the mortgage obligation (a condition known as being underwater, or more formally, as having negative equity). Endogeneity concerns make it difficult to determine whether the foreclosures caused the decline or the decline caused the foreclosures. However, foreclosures appear to be infectious, which has been labeled the foreclosure contagion effect (Immergluck & Smith, 2006). The foreclosure contagion effect is formally defined as the reduction in property values due to neighboring foreclosures leading to an increased probability of foreclosure of the subject property (Gangel, Seiler, and Collins, 2013). Researchers have defined the decrease in nearby property values as the foreclosure discount (Immergluck and Smith, 2006; Harding, Rosenblatt, and Yao, 2009; Lin, Rosenblatt, and Yao, 2009). They have determined that the magnitude of the foreclosure contagion effect is a function of the distance between the foreclosed property and the subject property, as well as the length of time since the foreclosure occurred.

The goal of this study is to understand the potential differences spatial layouts and density have on property values with respect to the foreclosure contagion effect. We use geographic information system (GIS)-based property layouts in the Virginia Beach, Virginia area instead of the traditional x-y uniformly distributed property grid format to explore whether the effect of foreclosure contagion in a neighborhood is dependent on its layout and density. The paper is organized in the following manner. We provide a brief review of related studies followed by a description of our simulation and a discussion of the results. …

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