Academic journal article Cityscape

Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons

Academic journal article Cityscape

Geographic Patterns of Serious Mortgage Delinquency: Cross-MSA Comparisons

Article excerpt

Abstract

This article examines the distribution of impaired mortgages across neighborhoods, defined at the ZIP Code level, in 91 metropolitan areas as of the fourth quarter of 2008, well into the recent U.S. mortgage crisis. We catalogue serious mortgage delinquency patterns by metropolitan area based on features of the geographic distribution, including measures of dispersion across neighborhoods and of spatial autocorrelation. The findings are potentially informative for assessing local and neighborhood consequences of the mortgage crisis and for selecting and implementing strategies to ameliorate the effects of foreclosure.

(ProQuest: ... denotes formulae omitted.)

The views expressed in this arüde are those ojthe authors and do not necessarily reflect those ojthe Federal Deposit Insurance Corporation, the Federal Housing Finance Agency, the Federal Reserve Board, or Freddie Mac.

Introduction

The tremendous volume of mortgage delinquencies and foreclosures since 2007 is an ongoing national crisis, but fashioning an appropriate policy or private-sector response requires assessing the local manifestations of the crisis. That the appropriate response depends on the neighborhood distribution of seriously delinquent mortgages in a metropolitan area - the extent to which such mortgages are concentrated in high-foreclosure neighborhoods and whether the latter are sparse or numerous, and are clustered together, dispersed, or isolated - has become increasingly clear.

For example, Goldstein (2010) introduced a data-based tool labeled "Market Value Analysis" that can be used to target public-sector and nonprofit neighborhood stabilization funds.1 The author emphasized that "targeting places where the problem is manageable and the surrounding markets have strength is critical to success" (Goldstein, 2010: 73). An illustrative application to the city of Philadelphia identified neighborhoods where vacancy and foreclosure were geographically confined so that interventions are likely to succeed.

This article surveys and classifies the variety of spatial patterns of serious delinquency observed across U.S. metropolitan areas. The article's primary objectives are to highlight important differences in the spatial distribution of mortgage delinquency across metropolitan areas and to promote discussion of what public- and private-sector strategies are most suitable in each context. In particular, our typology may facilitate information sharing among cities with similar circumstances.

Secondarily, the article examines some housing market and economic conditions associated with the different spatial patterns. Although overall delinquency rates are highest in cities with large house price declines or high unemployment rates, this examination highlights how most other cities have high- delinquency pockets, mostly because of subprime lending concentrations.

Specifically, this article examines the mortgage delinquency distribution across neighborhoods, defined at the ZIP Code level, within U.S. metropolitan statistical areas (MSAs) as of the fourth quarter of 2008, well into the mortgage crisis. The results classify metropolitan areas into six groups:

1 . Low-to-moderate mean and high spatial autocorrelation: a modest number of high- or moderately high-delinquency neighborhoods that are clustered together.

2. High mean and standard deviation: wide variation across neighborhoods, with most delinquencies occurring in distressed neighborhoods.

3. High positive skewness: mostly multiple high- delinquency neighborhoods, some with extremely high delinquency rates.

4. Low-to-moderate mean, high positive skewness, and steep gradient around the peak delinquency neighborhood: a modest number of neighborhoods distinguished by high delinquency rates, including at least one spatial outlier.

5. Steep gradient around the peak delinquency neighborhood, indicating at least one spatial outlier: in general, isolated problem neighborhoods. …

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