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Religion and Mortgage Misrepresentation

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Abstract

We investigate whether religion acts as a deterrent to the types of mortgage misrepresentation that played a significant role in the recent housing boom and bust. Using a large sample of mortgages originated from 2000 to 2007, we provide evidence that local religious adherence (religiosity) is associated with a lower likelihood of home appraisal overstatement and owner occupancy misreporting. The evidence on borrower income misrepresentation is mixed. Religiosity does not appear to reduce the incidence of income misrepresentation; however, it seems to restrain the degree to which income is misrepresented. Our results are generally consistent with the hypothesis that religion, as a set of social norms, fosters ethical behavior, and possibly risk aversion, in the mortgage market.

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Notes

  1. We will use the terms misrepresentation and fraud interchangeably throughout the paper. Calem et al. (2015), Cho and Megbolugbe (1996), Ding and Nakamura (2016), Kruger and Maturana (2020), Shi and Zhang (2015), and Eriksen et al. (2016), among others, show the prevalence of appraisal inflation. For evidence on occupancy misrepresentation, see Piskorski et al. (2015) and Griffin and Maturana (2016b). See Jiang et al. (2014), Blackburn and Vermilyea (2012), Ambrose et al. (2016), Mian and Sufi (2017), Mian and Sufi (2016), and Elul et al. (2010) for evidence on the incidence and economic consequences of income misrepresentation. A simultaneously-closed second lien mortgage is a junior (second) mortgage that closes concurrently with a more senior (first) mortgage. Although the second lien affects the default risk of the first mortgage, its existence was often not disclosed to first-mortgage investors. Refer to Piskorski et al. (2015) for evidence of undisclosed simultaneously closed second liens.

  2. Some religious groups may approach risk taking differently. For example, Catholics have been found to exhibit higher risk-taking behavior than Protestants (Shu et al. 2012). However, Noussair et al. (2013) finds religious adherence to be positively related to risk aversion in both groups with Protestants being more risk averse. As discussed later, we are not able to separately test for unethical behavior in mortgage fraud and risk aversion. However, we expect the ethics channel to be dominant in mortgage fraud.

  3. Religion and religiosity have the same meaning in this paper and are used interchangeably to refer to the degree of local participation in religious organizations. Specifically, following previous research (e.g., Adhikari & Agrawal, 2016; Hilary & Hui, 2009; and Jiang et al., 2018, among others), we use the total rate of religious adherence across all denominations within a county as our measure of religiosity. Mortgage-backed securities are investment products issued by Wall Street firms that allocate the cash flows from the underlying mortgages to the security investors.

  4. The Federal Bureau of Investigation (FBI) classifies mortgage fraud as a subcategory of Financial Institution Fraud (FIF). According to the FBI, mortgage fraud “is crime characterized by some type of material misstatement, misrepresentation, or omission in relation to a mortgage loan which is then relied upon by a lender. A lie that influences a bank’s decision—about whether, for example, to approve a loan, accept a reduced payoff amount, or agree to certain repayment terms—is mortgage fraud” (https://www.fbi.gov/investigate/white-collar-crime/mortgage-fraud).

  5. As a federal crime, mortgage fraud may entail significant risk and potentially serious punishment when committed at a large scale. For example, in 2018 the U.S. Department of Justice ordered Wells Fargo to pay a $ 2.09 billion penalty for allegedly misrepresenting loan quality to investors in pre-crisis era mortgages.

  6. We also refer to appraisal fraud as appraisal inflation and appraisal overstatement. These terms have the same meaning in this paper and are used interchangeably.

  7. We use investment property to refer to any property that is not actually owner-occupied.

  8. We believe this is a reasonable assumption since the Equal Credit Opportunity Act (ECOA) and Fair Housing Act (FHAct) prohibit lenders from discriminating in credit decisions on the basis of protected classes (e.g., race, religion, national origin, marital status, age).

  9. Interestingly, Bai et al. (2020) find that the effect of religiosity on financial adviser misconduct loses statistical significance in competition with social capital, a measure capturing various aspects of the society including, but not limited to, religion and political environment.

  10. The Home Valuation Code of Conduct (HVCC) and the Dodd–Frank Act both include provisions that address appraiser independence. For a detailed discussion, refer to Tzioumis (2017).

  11. Unfortunately, we cannot identify individual appraisers in our data, so we are unable to definitively determine whether the appraiser is located (or primarily works) within the county of the appraised property. But, analysis on a separate dataset (not directly available to the authors of this study) helps shed light on this issue. The dataset includes a large sample of mortgages originated between 2003 and 2007 where the individual appraiser can be identified. Of the 38,289 appraisers in that data, 89% perform the majority of their business in a single county. We thank Brent Ambrose and the Penn State Institute for Real Estate Studies for performing this analysis. Since appraisers tend to concentrate their business in a single county, it seems reasonable that they could be influenced by the religious and social norms of that county, even if they do not reside there.

  12. It is important to note that some of the parties to the transaction may not be located within the same county as the subject property, which introduces noise into our empirical test. For example, the loan officer may work at a regional or national office. However, it is likely that at least one of the parties to the transaction is located in the same county as the subject property, particularly the real estate agent or the borrower. Moreover, this noise should bias us away from finding significant results.

  13. A potential concern is that investors may not live in close proximity to their investment properties. If this is the case, then religiosity in the area where the subject property is located may not impact investor behavior. However, there is empirical evidence that the majority of investment properties are located in close proximity to the owners’ primary residence. For example, Tables 2 and 3 of Chinco and Mayer (2015) show that the majority (75%) of investment properties are purchased by “local investors”—those living in close proximity to the investment property. Thus, this should not be a major concern for our study. Furthermore, similar arguments in footnote 12 also apply here. For example,“out-of-town” investors introduce noise into the empirical tests, hence biasing us away from finding a significant relationship between local religiosity and occupancy fraud.

  14. Holding all else equal, the individual stating the higher level of income on the low-doc loan would be able to obtain a larger loan amount. In addition to inflating income on the application, the borrowers are not truthfully reporting all income to the tax authority.

  15. Data link: http://www.thearda.com/Archive/ChCounty.asp.

  16. We use the “totrt” variable in the dataset, which is the number of adherents per 1000 in population as of 2000. We divide this number by 1000 to convert it into percentage terms. Note that this rate can be greater than one if adherents come from outside the county. The religiosity data is only available on a decennial basis. We use the 2000 data since the study covers 2000 to 2007.

  17. ABSNet data is commonly used to examine issues related to U.S. mortgage markets during our sample period (Conklin et al., 2019, 2020; Demiroglu & James, 2012, 2018; Demiroglu et al., 2014; Di Maggio et al., 2019; Griffin & Maturana, 2016a, b; Griffin et al., 2020; Korgaonkar, 2018).

  18. Our full sample includes 1,257,039 loans where 7% appear to have inflated appraisal. When we focus on refinance loans, the sample size drops to 709,268 loans where 9% appear to have inflated appraisal. In untabulated analysis using both refinance and purchase loans, our main results continue to hold.

  19. There is one exception to this: ARMs are more common in the full sample than they are in the owner occupancy subsample (i.e. where the owner occupancy indicator is available). Specifically, full sample 66% versus subsample 56%. Besides the loan characteristics at origination, we also use additional variables observed after origination in our regression analysis. Hence, we also check the summary statistics for these variables across the two samples. It is worth noting that the two samples resemble each other except for the average of change in the unemployment rate and CLTV. Specifically, for change in the unemployment rate, full sample 33% versus subsample 66%; for CLTV, full sample 83% versus subsample 93%. Tabulated descriptive statistics for the owner occupancy fraud subsample are available upon request.

  20. The reason for the reduction in sample size for appraisal and occupancy fraud, and its potential implications for our analysis, are discussed in detail in “Fraud Measures” section.

  21. Our main results are similar when we exclude loans originated in California.

  22. The sample size is slightly smaller for some of the variables used in the loan performance analysis (default, change in unemployment rate, and current CLTV). Mortgage performance (default) is not observed for every loan in the origination database. Also, the FHFA Zip code level house price indices, which are used to calculate current CLTV after origination, are not available for all Zip codes in our data. Similarly, there are some counties in our data that do not have unemployment data in every period. Thus, the change in the unemployment rate is missing for some of the observations in our data.

  23. A correlation matrix for the owner occupancy fraud subsample is provided in appendix Table 11. The signs, significance, and magnitudes of the correlations are largely unchanged in this subsample.

  24. Appendix Tables 10 and 11 show that religiosity is unconditionally correlated with many of the controls in our study. To investigate the conditional correlations between our control variables and religiosity, we estimate linear regression models where religiosity is the dependent variable and report the results in Appendix Table 12. Column (1) is a county level regression. Controls variables that are not measured at the county level (e.g., loan characteristics) are averaged at the county level. Column (2) is a loan level regression. Two thirds of the partial correlations in the table are not statistically different from zero, suggesting that high levels of multicollinearity between religiosity and our other control variables are not present. Even if high levels of multicollinearity exist, this is not a major concern in our multiple regression models below as it would tend to bias us away from from finding significant results.

  25. Our main results are not materially affected when we estimate marginal effects using probit regression models. The probit regression results are discussed in “Robustness Checks” section.

  26. To account for the possibility of time-varying differences in fraud across lenders, we also estimated models with lender × year fixed effects and the results were not materially affected.

  27. In a contemporaneous paper, Bai et al. (2020) show that once they control for an area’s level of social capital, religiosity is not related to financial adviser misconduct. As a robustness check, we included the social capital measure used in Bai et al. (2020) and our religiosity results are materially unaffected. In other words, religiosity has an effect on mortgage fraud independent of social capital.

  28. These are calculated as \(\frac{(0.56-0.37) \times -0.180}{0.38} = -9\%\) and \(\frac{(0.56-0.37) \times -0.101}{ {0.06}} = -32\%\), respectively. These effects relative to the mean are statistically different from one another.

  29. Our approach differs slightly from Hilary and Hui (2009) and Jiang et al. (2018). They use religiosity lagged by three years as an instrument for current religiosity. But in those papers, both current and lagged religiosity are on the basis of the same linear extrapolation, which essentially forces a strong correlation between current and past religiosity. In contrast, our approach does not force correlation between past and current religiosity. Even though we are following the literature, we recognize the limitations of using past religiosity as an IV.

  30. The F-statistic (p value = 0.0000) from the first-stage regression suggests that our instrument meets the relevance condition. There is no way to test whether the instrument meets the exclusion restriction.

  31. Additionally, religiosity has been linked to risk aversion.

  32. As discussed previously, other individuals may encourage or be complicit in the fraud as well.

  33. Borrowers select into low-doc loans and thus are responsible, at least in part, for income misrepresentation. Lenders are of course aware of the propensity for income inflation by borrowers on low-doc loans, so they charge higher rates on these loans.

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Correspondence to Mingming Qiu.

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Appendix

Appendix

Table 9 Descriptive statistics
Table 10 Correlation matrix (full sample)
Table 11 Correlation matrix owner occupancy fraud subsample
Table 12 Relationship between religiosity and other controls

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Conklin, J., Diop, M. & Qiu, M. Religion and Mortgage Misrepresentation. J Bus Ethics 179, 273–295 (2022). https://doi.org/10.1007/s10551-021-04831-2

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