Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Concepts, Principles, and Definitions Read chapter Read first chapter

2014 | OriginalPaper | Chapter

5. Using Near Repeat Analysis for Investigating Mortgage Fraud and Predatory Lending

Authors : Ronald E. Wilson, Ann D. Fulmer

Published in: Forensic GIS

Publisher: Springer Netherlands

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Spatial Technology Applications
next chapter State Registration of Sex Offenders: Public Notification, Web Mapping, and Spatial Issues
Footnotes
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
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
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%20​Ann%20​Fulmer%20​Supplemental%20​Written%20​Testimony.​pdf (accessed August 17, 2012).
 
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
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
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
Tax officials in Boulder County, Colorado, reported tax assessment inflation up to six miles away from the affected neighborhoods.
 
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
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
Parcel data were provided by the Geography Department at the University of North Carolina, Charlotte.
 
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
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
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
A standard deviation distance is also calculated in CrimeStat 3.3 to report the variation of the minimum distances.
 
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
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
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
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
The observed and expected counts are reported in another table produced by the NRC, which is entitled “Verbose.”
 
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
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
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
Other studies have shown that lower-income populations took advantage of the opportunity to purchase a house in their neighborhood.
 
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.
 
Literature
Apgar WC, Calder A (2005) The dual mortgage market: the persistence of discrimination in mortgage lending. In: de Souza Briggs X (ed) The geography of opportunity: race and housing choice in metropolitan America. Brooking Institution Press, Washington, DC, pp 101–126
Arnio AN, Baumer EP (2012) Demography, foreclosure, and crime: assessing spatial heterogeneity in contemporary models of neighborhood crime rates. Demogr Res 26(18):449–488CrossRef
Arnio AN, Baumer EP, Wolff KT (2012) The contemporary foreclosure crisis and U.S. crime rates. Soc Sci Res 41(6):1598–1614CrossRef
Baumer EP, Wolff KT, Arnio AN (2012) A multicity neighborhood analysis of foreclosure and crime. Soc Sci Q 93(3):577–601CrossRef
Baxter V, Lauria M (2000) Residential mortgage foreclosure and neighborhood change. Hous Policy Debate 11(3):675–699CrossRef
Bowers KJ, Johnson SD (2004) Who commits near repeats? A test of the boost explanation. West Criminol Rev 5(3):12–14
Calem PS, Hershaff JE, Wachter SW (2004) Neighborhood patterns of subprime lending: evidence from disparate cities. Hous Policy Debate 15(3):603–622CrossRef
Campbell J, Giglio S, Pathak P (2009) Forced sales and house prices. Working paper. National Bureau of Economic Research, 14866
Carswell AT (2009) Effects of mortgage fraud on property tax assessments. J Prop Tax Assess Adm 6(2):5–17
Chambers M, Garriga C, Schlagenhauf DE (2007) Accounting for changes in homeownership rate. Working paper 21. Federal Reserve Bank of St. Louis
Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44:588–608CrossRef
Collins CM, Harvey KD, Fulmer A, Nigro PJ (2009) Mortgage Fraud’s impact on housing markets: an Atlanta case study in neighborhood collateral damage. White paper submitted to a meeting to Mortgage Fraud, Foreclosures and Neighborhood Decline. National Institute of Justice
Crossney KB (2010) Is predatory mortgage lending activity spatially clustered? Prof Geogr 62(2):153–170CrossRef
Crump J, Newman K, Belsky ES, Ashton P, Kaplan DH, Hammel DJ, Wyly E (2008) Cities destroyed (again) for cash: forum on the U.S. foreclosure crisis. Urban Geogr 29(8):745–784CrossRef
Doms M, Motika M (2006) The rise in homeownership. Federal Reserve Bank, San Francisco Economic Letter, 3
Ellen IG, Lens MC, O’Regan K (2012) Memphis murder mystery revisited: do housing voucher households cause crime? Hous Pol Debate 22(4):551–572CrossRef
Fisher LM, Lambie-Hanson L, Willen PS (2013) The role of proximity in foreclosure externalities: evidence from condominiums. Public Policy Discussion Papers, No. 13–2. Federal Reserve Bank of Boston
Fulmer A (2009) Burning down the house: mortgage fraud and the destruction of residential neighborhoods. White paper submitted for a meeting on Mortgage Fraud, Foreclosures and Neighborhood Decline. U.S. Department of Justice, National Institute of Justice, Washington, DC
Galster G (1982) The social neighborhood: an unspecified factor in homeowner maintenance. Urban Aff Q 15(2):235–254CrossRef
Gibbons S, Machin S (2008) Valuing school quality, better transport, and lower crime: evidence from house prices. Oxford Rev Econ Policy 24(1):99–119CrossRef
Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, Stanford
Grubesic T, Mack E (2008) Spatial and temporal interaction of crime. J Quant Criminol 24:285–306CrossRef
Harding JP, Rosenblatt E, Yao VW (2009) The contagion effect of foreclosed properties. J Urban Econ 66(3):164–178CrossRef
Immergluck D, Smith G (2005) Measuring the effect of subprime lending on neighborhood foreclosures: evidence from Chicago. Urban Aff Rev 40(3):362–389CrossRef
Immergluck D, Smith G (2006) The impact of single-family mortgage foreclosures on neighborhood crime. Hous Stud 21:851–866CrossRef
Immergluck D, Wiles M (1999) Two steps back: the dual mortgage market, predatory lending, and the undoing of community development. Research report. Woodstock Institute, Chicago
Johnson SD, Bowers K, Hirshfield A (1997) New insights into the spatial and temporal distribution of repeat victimization. Br J Criminol 37(2):224–241CrossRef
Johnson SD, Bernasco W, Bowers KJ, Elffers H, Ratcliffe J, Rengert G, Townsley M (2007) Space–time patterns of risk: a cross national assessment of residential burglary victimization. J Quant Criminol 23:201–219CrossRef
Kaplan DH, Sommers GG (2009) An analysis of the relationship between housing foreclosures, lending practices, and neighborhood ecology: evidence from a distressed county. Prof Geogr 61(1):101–120CrossRef
Knox G (1964) Epidemiology of childhood leukemia in Northumberland and Durham. Br J Prev Soc Med 18:17–24
Lee K (2008) Foreclosure’s price-depressing spillover effects on local properties: a literature review. Community affairs discussion paper. Federal Reserve Bank of Boston, Boston
Leonard T, Murdoch J (2009) The neighborhood effects of foreclosure. J Geogr Syst 11:317–322CrossRef
Levine N (2010) CrimeStat: a spatial statistics program for the analysis of crime incident locations (v 3.3). User’s manual. Ned Levine & Associates, Houston; National Institute of Justice, Washington, DC
Lin Z, Rosenblatt E, Yao VW (2009) Spillover effects of foreclosures on neighborhood property values. J Real Estate Finance Econ 38(4):387–407
Mendelsohn R, Olmstead S (2009) The economic valuation of environmental amenities and disamenities: methods and applications. Annu Rev Environ Resour 34:325–347CrossRef
Metzger JR (2000) Planned abandonment: the neighborhood life-cycle theory and national urban policy. Hous Policy Debate 11(1):7–39CrossRef
Miller HJ (2004) Tobler’s first law and spatial analysis. Ann Assoc Am Geogr 94(2):284–289CrossRef
Morenoff JD, Sampson RJ, Raudenbush SW (2001) Neighborhood inequality, collective efficacy, and the spatial dynamics of urban violence. Criminology 39:517–558CrossRef
O’Sullivan O (2003) New foreclosure phenomenon. ABA Bank J 95(11):77–83
Olson MW (2005) Exploring the benefits and challenges of an ownership society. Remarks by Governor Mark W. Olson at the Community Development Policy Summit: Federal Reserve Bank of Cleveland, Cleveland
Pavlov AD, Wachter SM (2009) Subprime lending and real estate prices. Institute for Law & Economic research paper no. 09–36. University of Pennsylvania, Philadelphia
Pennington-Cross A (2004) The value of foreclosed property. J Real Estate Res 28:193–214
Quercia RG, Stegman MA (1992) Residential mortgage default: a review of the literature. J Hous Res 3(2):341–379
Ratcliffe JH, Rengert G (2008) Near-repeat patterns in Philadelphia shootings. Secur J 21:58–76CrossRef
Rohe WM, Van Zandt S, McCarthy G (2000) The social benefits and costs of homeownership. Working paper no. 00–01. Research Institute for Housing America, Washington, DC
Schuetz J, Been V, Ellen IG (2008) Neighborhood effects of concentrated mortgage foreclosures. NYU Law and Economics research paper no. 08–41
Scott FW (2010) Estimating the effect of mortgage foreclosures on nearby property values: a critical review of the literature. Fed Reserv Bank Atlanta Econ Rev 95(3):1–9
Simons RA, Quercia RG, Maric I (1998) The value impact of new residential construction and neighborhood disinvestment on residential sales price. J Real Estate Res 15(1/2):147–161
Smith JC (2010) The structural causes of mortgage fraud. Syracuse Law Rev 60:433–461
Tobler W (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(2):234–240CrossRef
Townsley M, Homel R, Chaseling J (2000) Repeat burglary victimization: spatial and temporal patterns. Aust N Z J Criminol 33:37–63CrossRef
U.S. Department of Housing & Urban Development (2010) Report to congress on the root causes of the foreclosure crisis. U.S. Department of Housing and Urban Development, Washington, DC
Wachter SM (2009) The foreclosures crisis and what is to be done. Testimony to the Joint Economic Committee, Committee on Financial Services, U.S. House of Representatives on Current Trends in Foreclosure and What More Can be done to Prevent Them
Wachter SM, Russo K, Hershaff JE (2006) Subprime lending: neighborhood patterns over time in US cities. Institute for Law & Economic Research paper no. 06–19. University of Pennsylvania, Philadelphia
Wells W, Ling W, Ye X (2011) Patterns of near-repeat gun assaults in Houston. J Res Crime Delinq 49(2):186–212CrossRef
Welsch A (2008) Economic trends, foreclosures and county budgets. Issue brief. National Association of Counties, Washington, DC
Whitaker S, Fitzpatrick IV TJ (2011) The impact of tax-delinquent and foreclosed property on sales prices of neighboring homes. Working paper 11–23. Federal Reserve Bank of Cleveland, Cleveland
Wilson RE (2015) The neighborhood context of foreclosures and crime. Cartogr Geogr Inform Sci 42(1) (Forthcoming)
Wilson RE, Paulsen D (2009) A theoretical underpinning of neighborhood deterioration and the onset of long-term crime problems from foreclosures. White paper submitted for a meeting on Mortgage Fraud, Foreclosures and Neighborhood Decline. U.S. Department of Justice, National Institute of Justice, Washington, DC
Wyly EK, Hammel DJ, Atia M (2004) Capital is the landlord: class monopoly and new geographies of subprime and predatory mortgage lending. Working paper
Metadata
Title
Using Near Repeat Analysis for Investigating Mortgage Fraud and Predatory Lending
Authors
Ronald E. Wilson
Ann D. Fulmer
Copyright Year
2014
Publisher
Springer Netherlands
Book
Forensic GIS

Print ISBN: 978-94-017-8756-7

Electronic ISBN: 978-94-017-8757-4

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-94-017-8757-4_5

Premium Partner

    Image Credits