The role of information externalities and scale economies in home mortgage lending decisions

Blackburn, McKinley L.; Vermilyea, Todd A.

doi:10.1016/j.jue.2006.05.005

Cited by 18 publications

(31 citation statements)

References 10 publications

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“…Consistent with the Blackburn and Vermilyea () study that used data almost 15 years prior to ours, our data show little tendency by banks to dominate lending activity within geographic areas. While the HMDA‐based overall volume of home purchase loans (number of house sales in the context of our study) per census tract ranges, on average, from 251 to 560 loans across samples, the bank‐specific average number of applications per census tract is less than six.…”

Section: Methodssupporting

confidence: 92%

“…However, our results suggest that the lack of previous house sales in a neighborhood is an economically and statistically important impediment to obtaining a low mortgage rate for some applicants. These findings are similar to those obtained by Blackburn and Vermilyea () with respect to the underwriting decision.…”

Section: Pricingsupporting

confidence: 91%

“…Our findings of generally weaker information effects than those in Blackburn and Vermilyea () are consistent with the softer underwriting standards during the first half of this decade and the consequent diminishing importance of accurate appraisals. Our findings of stronger information externalities in the regressions for refinance lending are consistent with our prediction that appraisal accuracy may matter more for refinancing, where the property sale price is not available.…”

Section: Underwritingsupporting

confidence: 82%

“…Following Blackburn and Vermilyea (), we assume that the effect becomes negligible if its magnitude falls below 0.2%. This threshold is attained at 2 sales for Bank 1 and at 5 sales for Bank 2.…”

mentioning

confidence: 99%

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Information Externalities, Neighborhood Characteristics and Home Mortgage Pricing and Underwriting

Voicu

Paley

Lopez³

et al. 2015

Real Estate Economics

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Theories of rational redlining suggest thinness in housing markets should lead to greater uncertainty in house price appraisals, increasing mortgage denial rates or pricing. Empirical tests found support for this theory in mortgage underwriting using 1990s data. Using 2006 data and bank-specific regression models, we revisit this topic in light of two developments leading to the recent mortgage bubble: the widespread securitization that allowed banks to shift loan risk to investors and the advent of risk-based pricing. Consistent with expectations, we find that information externalities have become economically very small and have shifted from underwriting to pricing decisions.Theories of rational redlining suggest that the number of recent house sales in a given neighborhood represents an "information externality," with the implication that a low volume of sales should lead to greater uncertainty about appraised house values, making the mortgage loan less attractive to lenders and consequently more likely to be denied or charged a higher price. Previous empirical tests of this theory are limited solely to the implications with respect to the underwriting (accept/reject) decision. Additionally, those tests are based on data that preceded the recent mortgage bubble. The period of the mortgage bubble, however, offers a unique time frame for studying information externalities in lending. First, easy securitization enabled banks to sell many of the loans they underwrote, making them unaccountable for the riskiness of their loans. 1 We therefore test whether the effect of information externalities on denial decisions decreased during the mortgage bubble. Second, in the early 2000s, lenders extended mortgage underwriting to applicant segments previously deemed too risky, with pricing being the mechanism by which investors were compensated for the increased risk. We therefore test whether the effect of information externalities has shifted from underwriting to pricing, this being the first study of the pricing implications of the information externality theory.We further contribute to the existing literature by testing whether information effects play a lesser role in home purchase lending than in refinancing. Underlying this hypothesis is the fact that appraisal accuracy may be less important in home purchase lending, where the property sale price is known.Testing the information externality theory has important public policy implications. First, if information externality theory is true, government intervention is justified even in the absence of "traditional" discriminatory redlining. Second, it has fair lending implications: if low sale volume areas tend to have higher concentrations of minorities, omission of information variables may lead to the misidentification of lending patterns resulting from economically rational decisions by lenders as the presence of irrational redlining.The empirical analysis in this article uses extremely rich data and employs wellspecified, bank-specific regression models that explain ...

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Section: Methodssupporting

confidence: 92%

Section: Pricingsupporting

confidence: 91%

Section: Underwritingsupporting

confidence: 82%

mentioning

confidence: 99%

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Information Externalities, Neighborhood Characteristics and Home Mortgage Pricing and Underwriting

Voicu

Paley

Lopez³

et al. 2015

Real Estate Economics

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“…Empirical works combine HMDA data with census data (Silverman, 2005;E. Blank et al, 2005;Blackburn & Vermilyea, 2007;Ding et al, 2008;Ezeala-Harrison et al, 2008;Wyly et al, 2008;Rugh & Massey, 2010;Squires et al, 2009;Vicki et al, 2009;G. A. Dymski et al, 2011) to test for such a form of indirect discrimination.…”

Section: Credit Marketsmentioning

confidence: 99%

Discrimination Data Analysis: A Multi-disciplinary Bibliography

Romei

Ruggieri

2013

Studies in Applied Philosophy, Epistemology and Rational Ethics

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Discrimination data analysis has been investigated for the last fifty years in a large body of social, legal, and economic studies. Recently, discrimination discovery and prevention has become a blooming research topic in the knowledge discovery community. This chapter provides a multi-disciplinary annotated bibliography of the literature on discrimination data analysis, with the intended objective to provide a common basis to researchers from a multi-disciplinary perspective.We cover legal, sociological, economic and computer science references

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Appraisal Accuracy and Automated Valuation Models in Rural Areas

Bogin

Shui

2019

J Real Estate Finan Econ

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Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage's loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home's sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

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The role of information externalities and scale economies in home mortgage lending decisions

Cited by 18 publications

References 10 publications

Information Externalities, Neighborhood Characteristics and Home Mortgage Pricing and Underwriting

Information Externalities, Neighborhood Characteristics and Home Mortgage Pricing and Underwriting

Discrimination Data Analysis: A Multi-disciplinary Bibliography

Appraisal Accuracy and Automated Valuation Models in Rural Areas

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