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April 2008 Edition
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| Spotlight: Foreclosures and the Inner City |
The Battle Against the Blight: The Current Mortgage Crisis and its Inner City Implications The subprime mortgage crisis has hit with brutal force. Across the country, homeowners are losing their houses; the finanical markets are at full boil; and no one knows quite what to do. In all the media coverage, however, few have asked the vital question: what's happening in the inner cities? ICIC's Research team, headed by Senior Vice President Teresa Lynch, takes a look at the data. In 2007, 0.37% of the estimated national housing stock became the property of lending institutions.[1] That is, for every 1,000 homes in Table 1. Foreclosure Statistics by Location
Unfortunately, these data paint a troubling picture of the incidence of foreclosures in Even these numbers, however, underestimate the full impact of foreclosures on inner city neighborhoods. A better measure of the extent of the crisis is foreclosures as a percent of owner-occupied properties, a measure that excludes public housing units, which do not have residential mortgages, and multi-family rental properties with five or more units, which will carry commercial rather than residential mortgages. Using this measure, foreclosure rates per unit are two times higher in inner cities than in the rest of central cities and three times higher than elsewhere in the Although the foreclosure problem is fairly widespread across US inner cities--70% have REO rates that are higher than in the rest of their central city--the problem is particularly acute in some areas. Table 2 lists the inner cities with the highest 2007 foreclosure rates. Table 2. Inner Cities with the Highest REO Rates, 2007
Some analyses of the foreclosure crisis identify the expansion of home ownership opportunities for low-income persons as a driver of the current crisis. In this view, increases in home ownership rates among low-income groups is responsible for rising foreclosure rates, a claim that if true, could explain high foreclosure rates in the inner city, where income levels are significantly lower than in the rest of the United States. However, our analysis calls into question the accuracy and completeness of this argument: even after controlling for median income, a owner-occupied housing unit in an inner city zip code was twice as likely to have gone into foreclosure in 2007 as a unit elsewhere in the country. This finding is particularly difficult to explain in light of evidence that housing price increases, which usually correlate negatively with foreclosures, actually grew faster in inner city neighborhoods than other areas in the years leading up to the crisis.
We believe that the root causes of these trends are not the financial behavior of residents but the unique characteristics of the physical environment in inner cities, especially the high density of housing. As was recently pointed out by Federal Reserve Chairman Ben Bernanke, neighborhoods with high concentrations of foreclosures suffer additional fallout in terms of financing options, home sales, prices, and abandoned properties.[3] Where these effects have been quantified, the numbers are sobering. A 2005 study in Price decline along with other products of foreclosures in a neighborhood in one time period will act as causes of additional foreclosures in that neighborhood in the next time period. These feedback effects are likely to be felt most acutely in inner cities, where housing density (housing units/square mile) is 2.5 times higher than in other urban neighborhoods and almost twenty times higher than in the rest of the United States. In 2007, the combination of high foreclosure rates and dense housing stock created foreclosures per square mile that were almost forty times higher in the inner city than in the rest of the country. The distribution of foreclosures within inner cities is also troubling. Within inner cities, the highest incidence of foreclosures is not in higher-income neighborhoods where gentrification lead to price appreciation and speculation, nor in the lowest-income neighborhoods, where residents might struggle most financially. When ranked by median income, neighborhoods in the middle of the range suffered higher average foreclosure rates (0.67% of housing stock) than either the poorest neighborhoods (0.62%) or the highest-income inner city neighborhoods (0.44%). These patterns suggest that those neighborhoods that were improving in terms of livability and stability are at greatest risk of widespread foreclosures and the attendant problems. This raises fears that the current crisis could undermine decades of hard-won gains in inner city neighborhoods across the country. Our data do show that foreclosures are likely to increase in 2008 in the inner cities and across the Data Evaluation and Methodology This appendix discusses the data used for the ICIC foreclosure analysis, as well as test the quality of that data for inferential use. First we will discuss the variables and their origins, then how we have compared this data to specific local foreclosure data, and finally how we have compared it to a national sample. Our analysis utilizes an internally constructed database collected at the zip code level for the largest 100 inner cities, their central cities, and the United States overall. Database variables include foreclosure data (by stage of foreclosure) for 2006 and 2007; housing stock data by zip code for 2001-2007; annual housing price growth by zip code from 1997-2007; employment and establishment data by zip code, inner city, central city, and the U.S. for 1998-2005; demographic data by inner city, central city, and the U.S. for 1990-2005; demographic data by zip code for 2001-2007, and current foreclosure laws by state. Foreclosure data were supplied by RealtyTrac; housing price data by Fannie Mae; demographic data by Geolytics, a private vendor; employment data from ICIC’s SICE (State of the Inner City database); and publicly available information on foreclosure laws by region. With the exception of the foreclosure data, which come from a special pull by RealtyTrac, information on our other data sources is publicly available. [9], [10], [11], [12] Foreclosure data are based on the number of properties at each stage of the foreclosure process for the given time period, as provided by RealtyTrac. That is, each piece of property is represented in only one category, and only once in that category. RealtyTrac was able to accommodate this request by filtering their database by address, so that each property would be counted only once. Data are compiled at the zip code level for each stage; notice of default (NOD), lis pendens (LIS), notice of trustee sale (NTS), notice of foreclosure sale (NFS), or real estate owned (REO). We have data for three time periods: 2006 (full-year), January-October 2007, and 2007. We verified our data in two ways. The first was to compare specific regions to publicly reported numbers. Specifically, we compare our data to Clark (NV) and Cuyahoga (OH) counties, two of the hardest hit foreclosure spots in the country. In a November 18th A study conducted by Case Western Reserve University utilized foreclosure information from the Cuyahoga County Recorder’s office from 2000 through August, 2007 to analyze the changing market. What the county actually tracks are sheriff’s deeds, which encompass any house sold in foreclosure proceedings whether back to the lender or to a 3rd party.[15] According to their work, in the first 8 months of 2007 Beyond regional anecdotes, we undertook to systematically test the veracity of our data by comparing them with foreclosure data used by the Federal Reserve Bank, which has been at the forefront of understanding the sub prime market and its foreclosures. The Fed data are based on First American Loan Performance (LP) data on sub-prime and Alt-A loan performance, including foreclosures. Our LP sample data includes loans that entered REO as of September 2007 by Metropolitan Statistical Area (MSA), as well as the percent of loans in each MSA that are sub prime or Alt-A. Although RealtyTrac captures foreclosures on all residential properties, the two can be compared using reasonable assumptions based on knowledge of loan type and relative default rates on subprime/Alt-A versus prime mortgages. The Federal Reserve sample gives us an undisputed picture of the foreclosure rate for subprime and Alt-A loans in September 2007, along with the proportion of local mortgages of each type. In other work, the Federal Reserve has estimated that subprime loans are six times more likely to enter foreclosure than prime loans.[16] From this, we were able to calculate estimates of the foreclosure rates for the full set of mortgages in the given MSAs from these LP data, by assuming that the remaining mortgages would have a foreclosure rate one sixth that of the subprime mortgages. We combined the prime approximation with the sub-prime foreclosures to create foreclosure estimates for the complete set of mortgages. These estimates from the LP data are highly correlated with the RealtyTrac data for the full year (r(82)=.88, p<.001; see chart 1). The residual variation is to be expected, with foreclosure processes varying by region and the crisis hitting different places at different times. For more information, please contact: Teresa M. Lynch Senior Vice President, Director of Research 727 Atlantic Avenue Boston, Massachusetts 02111 617-292-2363
[1] ICIC thanks RealtyTrac, which provided the foreclosure data used in this analysis, and Fannie Mae, which provided housing price data used in this analysis. A description of data sources and methods is outlined in a technical memorandum, “Foreclosures and the Inner City: Data Evaluation and Methodology.” ICIC Research, March, 2008. [2] ICIC defines inner cities as core urban census tracts with 20% or higher poverty rates or that meet two of the following three criteria: poverty rate of 1.5 times or more that of their Metropolitan Statistical Areas; median household income of 1/2 or less that of their Metropolitan Statistical Areas; and unemployment rate of 1.5 or more that of their Metropolitan Statistical Areas. [3] Bernanke, Ben S. (Speaker). (2007). “Subprime Mortgage Lending and Mitigating Foreclosures.” [4] [5] Apgar, William, and Mark Duda. (2005). Collateral Damage: The Municipal Impact of Today’s Mortgage Foreclosure Boom. [6] Immergluck, Dan and Geoff Smith. (2006). The External Costs of Foreclosure: The Impact of Single-Family Mortgage Foreclosures on Property Values Fannie Mae Foundation. [7] The Center for Responsible Lending. (2008). “Subprime Spillover: Foreclosures Cost Neighbors $223 billion; 44.5 Million Homes Lose $5,000 on Average.” [8] Association of Community Organizations for Reform Now (ACORN). 2004. Separate and Unequal: Predatory Lending in [9] The zip code price data utilizes the same mortgage sample and methods as those used by the Office of Federal Housing Enterprise Oversight (OFHEO) to develop housing price indexes, but with a different treatment of refinance loans. For a technical description of the OFHEO housing price index, see Charles A. Calhoun, “OFHEO House Price Indexes: HPI Technical Description,” (March 1996). [10] A description of Geolytics data and methods can be found at www.geolytics.com. [11] An internally kept database which includes proprietary analysis delineating inner city boundaries in the 100 largest central cities in [12] We are grateful to the organizations who have generously granted us access to their data. [13] Leland, John (2007, Nov 18). As owners feel mortgage pain, so do renters. New York Times, p. A1. [14] The RealtyTrac data indicate the number of properties at each stage of foreclosure, not when the default was initiated. Thus, these data will slightly inflate the RealtyTrac estimates relative to those obtained from the county clerk. [15] Coulton, Claudia, Mikelbank, Kristen and Schramm Michael (2007). Foreclosure and beyond: A report on ownership and housing values following sheriff’s sales, [16] Gerardi, Kristopher, Shapiro, Adam Hale & Willen, Paul S.. (2007). Subprime outcomes: risky mortgages, homeownership experiences, and foreclosures. Working Papers 07-15, Federal Reserve Bank of
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