Mortgage fraud: A risk factor analysis of affected communities

Abstract

Mortgage fraud is a fast-growing form of white-collar crime that has received much press coverage in the United States of America. Mortgage fraud has an adverse effect on individual homeowners, communities, and many indirect victims of the crime. While past research has focused on the personal motivating factors behind the commission of white-collar crime, this particular article reviews several facets of the crime itself and explores the potential neighbourhood risk factors that help attract the crime. From a national perspective, mortgage fraud seems to occur more frequently in neighbourhoods that have low socioeconomic indicators. These associations become even more pronounced when the degree of fraud occurrences within the community is factored in as a variable. Upon disaggregating the data according to region, the fraud indicator variables also display differing trend levels, perhaps indicating that as mortgage fraud practices begin to mature within an area, its community dynamics tend to change as well. The article concludes with recommendations for policymakers, community organizations, and law enforcement officials as to how to address mortgage fraud once it appears within a community, and also addresses future avenues of research for what is largely an untapped area of financial crime research.

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Notes

  1. 1.

    In 2000, the number of potential mortgage fraud cases only accounted for 3,515 total cases (as measured by the housing industry’s count of Suspicious Activity Reports (SARs), but that figure has increased considerably to an estimated 28,000 potential cases by 2006.

  2. 2.

    Suspicious Activity Reports (SARs) are generated by banks and other money service businesses when the financial institution suspects that funds obtained from a client either: a) come from illegal activity, b) serves no known business or apparent lawful purpose, and/or c) is being used to facilitate criminal activity. Such reports are submitted to the Financial Crimes Enforcement Network, a subdivision of the U.S. Treasury.

  3. 3.

    In their analysis of savings and loan fraud, Spahr and Alison [73] also found that when accomplices were involved in that crime, they were usually outside the company.

  4. 4.

    While Ohio and Missouri data were largely inclusive of the entire state, Georgia’s data were limited to the metro-Atlanta area.

  5. 5.

    Census tracts tend to be homogeneous geographic areas, in which the residents share many of the same demographic and economic characteristics. Unlike U.S. municipalities, counties, and states, Census tracts are not based upon political boundaries but were designated because of their similarities and applicability toward the concept of community.

  6. 6.

    The U.S. Census Bureau officially classifies a property as vacant if no one is living in it at the time of the interview, unless its occupants are only temporarily absent. In addition, a vacant unit may be one which is entirely occupied by persons who have a usual residence elsewhere. New units not yet occupied are classified as vacant housing units if construction has reached a point where all exterior windows and doors are installed and final usable floors are in place.

  7. 7.

    Median house price value was not used in broad-based geographic analysis, due to the vast difference in local area house prices across regions of the country. This variable was included when analysis was confined to localized areas.

  8. 8.

    Immergluck [41] is one of several researchers who have made this connection.

  9. 9.

    HUD’s definition of “underserved” areas applies to Fannie Mae’s and Freddie Mac’s affordable housing goals, which were set by the U.S. Congress. Specifically, the U.S. Code of Federal Regulations 24, Section 81.2, states that an underserved geographical area consists of either “...[a] Census tract with median income at or below 120 percent of the median income of the metropolitan statistical area(MSA) and a minority population of 30 percent or greater; or a Census tract with median income at or below 90 percent of median income in the MSA”.

  10. 10.

    The research on underserved variables could not be performed for Ohio due to difficulties in matching up information on these areas with the proper Census tract variables.

  11. 11.

    Some of the variables from the earlier analysis, such as poverty rate and unemployment, were dropped from the model due to multicollinearity concerns.

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Acknowledgements

The authors would like to thank two anonymous referees for their part in improving the quality of this manuscript. In addition, we are grateful to Ann Fulmer and her colleagues at Interthinx for providing data used in this project. Thanks also to Nikki Williams for providing excellent editorial assistance. Without her expert guidance, we could not have completed this task.

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Correspondence to Andrew T. Carswell.

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Carswell, A.T., Bachtel, D.C. Mortgage fraud: A risk factor analysis of affected communities. Crime Law Soc Change 52, 347–364 (2009). https://doi.org/10.1007/s10611-008-9186-5

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Keywords

  • House Price
  • Census Tract
  • Housing Market
  • Vacancy Rate
  • Underserved Area