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Using Near Repeat Analysis for Investigating Mortgage Fraud and Predatory Lending

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Forensic GIS

Part of the book series: Geotechnologies and the Environment ((GEOTECH,volume 11))

Abstract

Mortgage fraud and predatory lending are pervasive white-collar crimes that are difficult to investigate . As such, fraud investigators must use analytical techniques that can identify loans with the greatest potential to show systematic irregularities in the lending process. Foreclosures from the 2000 decade housing crisis are widely known to have occurred because of large-scale fraud, to which many were clustered . Despite displaying geographic patterns, an approach that has rarely been considered for fraud investigations from foreclosures is to use spatial analysis to identify geographic patterns of these crimes. We demonstrate in this chapter a spatial analysis method that can be applied more widely to help fraud investigators identify loans for scrutiny that show geographically systematic patterns of foreclosure.

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Notes

  1. 1.

    Mortgage fraud involves deceptive practices that use intentional misstatements, misrepresentations, or outright omissions in the process of making a loan. Following the FBI’s definition of activities, mortgage fraud is comprised of the following: (1) encouraging a borrow to lie about identity, (2) equity theft , (3) inflating borrower income, (4) falsifying appraisals, and (5) encouraging borrows to lie about income. These activities are generally classified as either fraud for profit or fraud for housing and target anyone who has the ability to qualify for a loan.

  2. 2.

    Predatory lending involves illegal practices that apply exorbitant fees, rates, and unnecessary terms to loans to strip housing wealth from the borrower. Predatory practices are not defined in federal law and states differ in their definitions, but Freddie Mac identifies the following as general practices: (1) charging interest rates and/or fees that far exceed reasonable lender compensation, (2) repeatedly refinancing (flipping) at a lower interest rate after originally lending at a high rate to strip the borrower’s equity in order to pay new points and fees, (3) failing to report borrower credit information to limit borrowers to obtain the lowest interest rate available, (4) steering to higher-cost mortgages when they are eligible for lower-cost financing, and (5) requiring credit insurance products to be financed upfront.

  3. 3.

    In testimony to the Financial Crisis Inquiry Commission, Ann Fulmer of Interthinx Corp. stated that about 38 percent of foreclosures were the results of mortgage fraud , September 21, 2010, http://fcic-static.law.stanford.edu/cdn_media/fcic-docs/2010-09-21%20Ann%20Fulmer%20Supplemental%20Written%20Testimony.pdf (accessed August 17, 2012).

  4. 4.

    Statement of Assistant Director Chris Swecker before the House Financial Services Subcommittee on Housing and Community Opportunity, October 7, 2004. http://www.fbi.gov/congress/congress04/swecker100704.htm (accessed March 29, 2009)

  5. 5.

    Mortgage-backed securities allowed banks to reduce the risk of any one loan, even a group of loans, from defaulting because they bundle all these loans together as a single product. As a bundle, the impact of any loan going into default is minimized for investors because the loss is minimal due to the other loans being stable.

  6. 6.

    The General Theory of Crime allows the application of Routine Activities Theory – typically applied to street crime – to white-collar crime due to attitude and behavioral characteristics underpinning the motivations for the perpetration of mortgage-related crimes.

  7. 7.

    Tax officials in Boulder County, Colorado, reported tax assessment inflation up to six miles away from the affected neighborhoods.

  8. 8.

    This phrase was borrowed from John Hoerr’s 1988 book And the Wolf Finally Came: The Decline and Fall of the American Steel Industry that described the collapse of the steel industry and the devastating geographic impact on the Pittsburgh metropolitan region and connected geographies. This phase is aptly appropriate here because of the geographic magnitude of the fallout from one industry.

  9. 9.

    Property types were condominium, manufactured, and single- and multifamily dwellings of all types. Condominiums and multifamily dwellings will appear in the same results cell in the results because they are at the same location, i.e., they are vertically integrated in this analysis. Single-family and manufactured homes will be spatially dispersed and appear in the distance interval cells of the results.

  10. 10.

    Parcel data were provided by the Geography Department at the University of North Carolina, Charlotte.

  11. 11.

    An analysis of block group data shows a wedge radiating from center city Charlotte to the south in which there is no concentrated disadvantage and many of the block groups show the top 40 % of median incomes in the county.

  12. 12.

    The random distance is the expected nearest neighbor distance that represents the distribution of observations if their patterns are completely spatially random. The random distance is calculated with an approximate value, which is 0.5 × sqrt(A/N) where A is the total area being analyzed and N is the total number of observations in the area.

  13. 13.

    The value 2.149 is the empirical ceiling of the nearest neighbor index. Theoretically a value could be higher, but none have been observed in previous research.

  14. 14.

    A standard deviation distance is also calculated in CrimeStat 3.3 to report the variation of the minimum distances.

  15. 15.

    A contingency table in this context is matrix of two or more categories that depict the relationship between each factor across all rows and columns.

  16. 16.

    The number of randomization trials is based on a selected level of significance to be achieved; p ≤ 0.5: 19 randomizations; p ≤ 0.01: 99 randomizations; p ≤ 0.001: 999 randomizations.

  17. 17.

    The distances are only measured the first time and stored in a matrix. Because the locations remain fixed, there is no need to remeasure them for each permutation run.

  18. 18.

    The likelihood table can present which ratios are significant alone. However, we present both tables so that users understand the output of the Near Repeat Calculator.

  19. 19.

    The observed and expected counts are reported in another table produced by the NRC, which is entitled “Verbose.”

  20. 20.

    If further spatial analysis is to be conducted, plotting the values in a graph will show a distance decay curve that can be be used to guide the selection of a mathematical function to produce a similar distance decay curve for weighting distance.

  21. 21.

    Concentrated disadvantage is comprised of five census variables, which are the number of (1) families below poverty, (2) families receiving public assistance, (3) unemployed individuals in the civilian labor force , (4) families with children that are female headed, (5) residents who are black, and (6) the median family income in 1999 in a census tract. Criminological studies examining neighborhood effects have noted these factors being highly correlated (Morenoff et al. 2001) but represent a conceptual idea that represents more than just being economically impoverished. To ensure that any analysis did not suffer from correlation problems, a factor analysis was used with varimax rotation using maximum likelihood estimation to calculate factor loadings.

  22. 22.

    We took the log of the ratio for displaying the relationship order to remove the distribution asymmetry that ratios can create. In many instances ratio values above 1 stretch to the maximum of their range, whereas values below 1 are confined to a range of 0 and 1. This creates a long tail in the distribution of 1 that can skew distribution and affect thematic mapping for comparing the distribution around the center point of 1. Taking the log of the ration alters the values by pulling in the larger values in the tail creating a range of values above 1 that is similar to the range of values below 1. This makes the values on either side of 1 comparable with each other in thematic mapping .

  23. 23.

    Other studies have shown that lower-income populations took advantage of the opportunity to purchase a house in their neighborhood.

  24. 24.

    Events located outside the specified distance are excluded, which is the case for all the mathematical functions in CrimeStat 3.3 except for the normal function. The normal function includes all observations across the geography.

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Acknowledgments

The authors would like to thank Tony Grubesic of Oregon State University, Peter Frandsen of the US Department of Justice, Joe Stachyra of the US Geological Survey, and our blind reviewers for providing comments to refine and improve this chapter. The authors would also like the thank Michael Bess from the Charlotte-Mecklenburg Police Department for providing insight into trends and findings that led to a better understanding of the geography of the city and county.

The views expressed in this chapter are those of the authors and do not represent the official positions or policies of the Office of Policy Development and Research or the US Department of Housing and Urban Development; the University of Maryland, Baltimore County; or Interthinx Corporation.

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Authors and Affiliations

  1. U.S. Department of Housing and Urban Development, University of Maryland, Baltimore County, Washington, DC, USA

    Ronald E. Wilson

  2. Interthinx Corporation, 30005 Ladyface Court, Agoura Hills, CA, 91301, USA

    Ann D. Fulmer

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  1. Ronald E. Wilson
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Correspondence to Ronald E. Wilson .

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Editors and Affiliations

  1. Department of Geology and Geography, West Virginia University, Morgantown, West Virginia, USA

    Gregory A. Elmes

  2. Department of Geology and Geography, West Virginia University, Morgantown, West Virginia, USA

    George Roedl

  3. Department of Geology and Geography, West Virginia University, Morgantown, West Virginia, USA

    Jamison Conley

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Wilson, R.E., Fulmer, A.D. (2014). Using Near Repeat Analysis for Investigating Mortgage Fraud and Predatory Lending. In: Elmes, G., Roedl, G., Conley, J. (eds) Forensic GIS. Geotechnologies and the Environment, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8757-4_5

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