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Published in: The Journal of Real Estate Finance and Economics 1/2013

01-07-2013

Testing for Fraud in the Residential Mortgage Market: How Much Did Early-Payment-Defaults Overpay for Housing?

Author: Paul E. Carrillo

Published in: The Journal of Real Estate Finance and Economics | Issue 1/2013

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Abstract

Current explanations for the high rate of default and foreclosure in the U.S. emphasize house price fluctuations and lax lending criteria. Another explanation for default and foreclosure, which has generally been neglected in the academic literature but not by the FBI, is fraud. One impediment to identifying and measuring fraud is the lack of statistical tests capable of detecting it. This paper proposes a simple method to detect transactions where fraud may have occurred. The models proposed here are important for at least three reasons. First they can document the role of fraud in the mortgage foreclosure crisis. Second, they can serve as part of a forensic effort designed to detect and deter mortgage fraud. Third, they demonstrate that mortgage fraud distorts house price indexes because it artificially elevates house prices during the period of fraud followed by a subsequent collapse due to the foreclosure sales. Accordingly, fraud can give the false impression that foreclosure lowers area house prices when it actually artificially inflates them. This suggests an alternative interpretation for the recent empirical literature on externalities from foreclosure.

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Appendix
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Footnotes
1
See, for example, Gerardi et al. (2007), Danis and Pennington-Cross (2008), Glaeser et al. (2008), Haughwout et al. (2008), Mayer et al. (2009), Demyanyk and O. van Hemert (2009), Gerardi et al. (2008).
 
2
This is one example of many schemes used to perpetrate mortgage fraud. An excellent overview of mortgage fraud schemes is found in reports prepared by the Federal Financial Institutions Examination Council (FFIEC), the Financial Crimes Enforcement Network (FinCEN) (see, for example, FFIEC 2009; FinCEN 2006; FinCEN 2008), the FBI (http://​www.​fbi.​gov/​publications/​fraud/​mortgage_​fraud06.​htm) and the Mortgage Bankers Association (mortgage fraud case reports also known as MARI’s reports http://​www.​marisolutions.​com/​resources-news/​reports.​asp).
 
3
SARs are reports regarding suspicious or potentially suspicious activity (known or suspected violations of law observed by financial institutions subject to the regulations of the Bank Secrecy Act), filed with the Financial Crimes Enforcement Network (an agency of the U.S. Department of the Treasury).
 
4
For instance, Elul (2009) reports that by 2006 the fraction of 2/28 subprime ARMs loans that defaulted within the first 3 months following origination reached 9%.
 
5
We are aware of only one study that discusses mortgage fraud (Shroder 2008).
 
6
There are many commercial products (generally, fraud detection software) available to lenders, investors and regulators to detect potential fraudulent activity in the mortgage market. This software generally validates data from loan applications and third-party sources, confirms a property’s value, and assesses it risk potential, providing an important decision-support tool for the mortgage industry. According to FFIEC (2009), the use of fraud detection software is one of the “best practices” lenders can do to mitigate mortgage fraud schemes. To the best of our knowledge, the methods employed by these products are proprietary and have not been publicly disclosed.
 
7
We use the term “mortgage fraud” to refer to “mortgage fraud-for-profit,” hereafter. We do not discuss the implications of “mortgage fraud-for-housing.”
 
8
Related literature is discussed in the second section of the paper.
 
9
The name of the county will remain anonymous.
 
10
FFIEC (2009), Fannie Mae (2009) provide a long list of “red flags,” indicators that call for further scrutiny of loan files and/or loan applications. Red flags include large differences between the appraisal value and sale prices of comparable units, quick (early) defaults, sudden defaults, high debt-to-income ratio, high loan-to-value ratio, among many others. Insights from these recommendations are taken when searching for circumstantial evidence of mortgage fraud.
 
11
EPDs are generally defined in our paper as loans that turn into a default within 1 year of the transaction. Some EPDs, however, default faster than others. Thus, one can compare the price premium paid by EPDs that default very fast (say after 2 months) with the premium paid by those EPDs that default not as fast (say after 11 months).
 
12
Actors of real estate transactions include loan originators, appraisals and real estate agents, for example. Data constraints force us to focus on the latter group.
 
13
In a recent paper, Foote et al. (2008) find that negative equity is a necessary but not a sufficient condition for delinquency (foreclosure). Besides negative equity, borrowers should face cash-flow constraints that make monthly mortgage payments unaffordable.
 
14
For simplicity and tractability, we also ignore the buyer’s option to refinance. Notice that, given the focus on EPD, refinancing is irrelevant.
 
15
For the EPD period, the assumption of no amortization is approximately correct even for a self-amortizing loan.
 
16
Search models have been widely used in the literature to analyze the housing market. Applications include Yinger (1981), Yavas (1992), Horowitz (1992), Haurin (1998), Arnold (1999), Albrecht et al. (2007) and Carrillo (forthcoming), among many others.
 
17
Glower et al. (1998) show that differences in seller’s motivation to trade influence their reservation values and, ultimately, transaction prices. Albrecht et al. (2007) assume that home buyers’ and sellers’ motivation to trade is heterogeneous (some are “relaxed” while others are “desperate” ); this assumption is crucial to generate price dispersion in equilibrium.
 
18
Notice that it is assumed that all the search process is fully completed during the initial period.
 
19
Due to language barriers and lower education levels, certain demographic groups may be less aware of the distribution of home prices and, in general, of the risks and benefits of homeownership. For instance, using housing sales data from two census tracts from the Washington D.C. region, Shroder (2008) finds that Hispanics paid a 5% premium for their homes. Since subprime origination was higher among this demographic group and default rates of subprime mortgages are higher than primes, a positive correlation between home prices and loan defaults could be expected.
 
20
This two-period stylized model suggests that there is a positive correlation between home price and loan default but without extending the time horizon of borrowers, it cannot explain the correlation between prices and time-to-default. It is clear from Eq. 1, however, that default is more likely to occur in any period if prices (P0) are higher; thus, the multi-period model can explain a positive correlation between prices and time-to-default.
 
21
Because lagged prices are endogenous, Eq. 5b cannot be estimated using OLS. For estimation, we use the generalized spatial two-stage least squares (GS2SLS) suggested by Kalejian and Prucha (1998, 1999).
 
22
In the empirical section, we also introduce spatial dependence in Eq. 5c.
 
23
The methods we propose here are similar in spirit to other algorithms used in the literature to detect corruption and fraud (Duggan and Levitt 2002; Jacob and Levitt 2003).
 
24
In the empirical section we also estimate the relationship between price premiums and time-to-default and allow for spatial dependence among housing transactions (using generalizations of Eqs. 5b and 5c). Specific details about these additional models are described in the results section.
 
25
Similarly, in the results section we estimate additional models that describe the relationship between price premiums and LTV ratios and allow for spatial dependence among housing transactions (using generalizations of Eqs. 5b and 5c).
 
26
The author thanks the local association of real estate agents for sharing these data with him.
 
27
A comprehensive review of this process is found in Pennington-Cross (2006).
 
28
Once a mortgage has become delinquent, the foreclosure process begins when lenders file a public default notice. This process ends when a) the borrower repays the default amount during a pre-foreclosure grace period; b) the owner sells the property to a third party during the pre-foreclosure period which allows the borrower to pay off the loan; c) the property is sold to a third party during the public auction after the pre-foreclosure grace period; or d) the lender takes ownership of the property.
 
29
This includes all listings regardless if they were ultimately sold or withdrawn from the market.
 
30
Careful data mining was needed to identify cases where the remarks indicated that the listing was not a foreclosure or not a short-sale.
 
31
Similarly, data cleaning was needed to identify the time when listings appeared in 2007 and 2008 for the first time. In particular, a few properties that were withdrawn and later re-entered the market appear in more than one listing during 2007 and 2008.
 
32
Excluding missing observations and a few cases with implausible high LTVs, the number of observations with valid LTV records decreases to 11,411.
 
33
To maintain the anonymity of the county, the number of census block groups is not disclosed. We report, however, that this number is above 500.
 
34
We exclude from the sample the upper 0.5 percentile of home sales (109 observations).
 
35
Again, to keep the anonymity of the county, we do not report descriptive statistics of the census block groups.
 
36
Since home sellers are not required to disclose on the MLS listing if they have defaulted on their loans, our estimates of EPDs are likely underestimated.
 
37
Estimation follows the spatial generalized two-stage least squares (SG2SLS) three-step procedure described in Kalejian and Prucha (1998). To compute the spatial weights, wij, we first calculate the linear distance between each pair of properties in our sample. If the distance between two properties is greater than 1 km, the weight is assumed to be zero. Otherwise, the weights are computed using a normal kernel with a bandwidth of 0.1 km. For each property, weights are normalized so that they add up to one (row-standardization). Other studies that have used a similar approach to compute spatial weights include Pace and Barry (1997), Pace and Guilley (1997). Alternatively, other studies (Pace et al. 1998; Clauretie and Daneshvary 2009, for example) compute spatial weights using a fixed number of nearest neighbors. We have estimated the SARAR models using this alternative definition for the spatial weights and found that it has little effect on our coefficient of interest (α).
 
38
Because the hedonic model used to predict prices include location fixed effects, it is not surprising that the coefficient of the spatial lagged premium is less precise.
 
39
Notice that the coefficient on LTV is negative and statistically significant suggesting that buyers with high LTV pay on average lower prices (0.7% percent less) for their homes. Genesove and Mayer (1997), on the other hand, find that sellers with high LTVs stay longer on the market and obtain higher prices.
 
40
Selected broker’s offices had the same number of EPDs.
 
41
EPDs occurred in 132 broker offices. We focus on offices with at least 3 transactions. The maximum number of EPDs in a broker office is 10.
 
42
According to the indictment, the scheme involved real estate agents, loan officers, and individuals recruited to serve as buyers just as hypothesized in this paper.
 
43
Some properties named were traded in other periods (either before or after 2006) and would not have been part of the sample.
 
44
To conduct the tests we estimate the distribution of several statistics under the null hypothesis that the 42 sales identified by our method are a random draw from the sample. The statistics include a) the average minimum distance between the indicted units and the sample of 42 properties, and b) the share of the indicted residences that are located within 0.1, 0.2 and 0.5 miles from one of the 42 selected addresses.
 
45
Earlier studies that include a binary variable for foreclosure status in a hedonic model include Shilling et al. (1990), Forgey et al. (1994), Springer (1996), Carroll et al. (1997).
 
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Metadata
Title
Testing for Fraud in the Residential Mortgage Market: How Much Did Early-Payment-Defaults Overpay for Housing?
Author
Paul E. Carrillo
Publication date
01-07-2013
Publisher
Springer US
Published in
The Journal of Real Estate Finance and Economics / Issue 1/2013
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI
https://doi.org/10.1007/s11146-011-9343-y

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