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A Neurological Explanation of Strategic Mortgage Default

  • Michael J. Seiler
  • Eric Walden
Article

Abstract

This study examines strategic mortgage default on a neurological level. Specifically, we test two mainstream behavioral finance/economic theories: sunk cost fallacy and cognitive dissonance. Using fMRI technology, we identify a number of substrates within the brain that provide a neurobiological explanation for why some homeowners exercise their mortgage put option while others do not. We find that borrowers rationally do not suffer from the sunk cost fallacy as it relates to strategic default in that they significantly prioritize their negative equity position over the amount of their initial down payment. We do, however, find neurological support that cognitive dissonance is relevant in homeowners’ thought processes as they toil with the hesitancy brought on by the believe that strategic default is immoral against the strong financial incentive to walk away from a substantially underwater mortgage.

Keywords

Neurological real estate Forensic real estate fMRI Strategic mortgage default 

JEL Classification

C91 D81 G02 R39 

References

  1. Andersson, J.L., Jenkinson, M., & Smith, S.M. (2007). Non-linear Registration, aka Spatial Normalisation. FMRIB technical report TR07JA2.Google Scholar
  2. Baker, H. K., & Chinloy, P. (2012). Real Estate: Markets and Investment Opportunities. New York: Oxford University Press.Google Scholar
  3. Baliga, S., & Ely, J. (2011). Mnemonomics: the sunk cost fallacy as a memory kludge. American Economic Journal: Microeconomics, 3(4), 35–67.Google Scholar
  4. Barber, B. M., & Odean, T. (2000). Trading is hazardous to your wealth: the common stock investment performance of individual investors. Journal of Finance, 55(2), 773–806.CrossRefGoogle Scholar
  5. Barberis, N., & Xiong, W. (2012). Realization utility. Journal of Financial Economics, 104(2), 251–271.CrossRefGoogle Scholar
  6. Belliveau, J., Kennedy, D., McKinstry, R., Buchbinder, B., Weisskoff, R., Cohen, M., Vevea, J., Brady, T., & Rosen, B. (1991). Functional mapping of the human visual cortex by magnetic resonance imaging. Science, 254(17), 716–719.CrossRefGoogle Scholar
  7. Bhutta, N., Dokko, J., & Shan, H. (2010). The Depth of Negative Equity and Mortgage Default Decisions. Federal Reserve Board of Governors Finance and Economics Discussion Series, Working Paper 2010–35.Google Scholar
  8. Brennan, T. J., & Lo, A. W. (2011). The origin of behavior. Quarterly Journal of Finance, 1(1), 55–108.CrossRefGoogle Scholar
  9. Bruguier, A. J., Quartz, S. R., & Bossaerts, P. (2010). Exploring the nature of trader intuition. Journal of Finance, 65(5), 1703–1723.CrossRefGoogle Scholar
  10. Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2009). Reinforcement learning and savings behavior. Journal of Finance, 64(6), 2515–2534.CrossRefGoogle Scholar
  11. FICO. (2011). Predicting Strategic Default. April, white paper.Google Scholar
  12. Fryman, C., Barberis, N., Camerer, C., Bossaerts, P., & Rangel, A. (2014). Using neural data to test a theory of investor behavior: an application of realization utility. Journal of Finance, 69(2), 907–946.CrossRefGoogle Scholar
  13. Grinblatt, M., & Keloharju, M. (2009). Sensation seeking, overconfidence, and trading activity. Journal of Finance, 64(2), 549–578.CrossRefGoogle Scholar
  14. Guiso, L., Sapienza, P., & Zingales, L. (2013). The determinants of attitudes towards strategic default on mortgages. Journal of Finance, 68(4), 1473–1515.CrossRefGoogle Scholar
  15. Huesing, B., Jancke, L., & Tag, B. (2006). Impact Assessment of Neuroimaging. Zurich: Hochschulverlag.Google Scholar
  16. James, R. N., & O’Boyle, M. W. (2014). Charitable estate planning as visualized autobiography: an fMRI study of its neural correlates. Nonprofit and Voluntary Sector Quarterly, 43(2), 355–373.CrossRefGoogle Scholar
  17. Jenkinson, M., Bannister, P. R., Brady, J. M., & Smith, S. M. (2002). Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.CrossRefGoogle Scholar
  18. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decisions under risk. Econometrica, 47(2), 313–327.CrossRefGoogle Scholar
  19. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150–157.CrossRefGoogle Scholar
  20. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.Google Scholar
  21. Ogawa, S., Lee, T. M., Nayak, A. S., & Glynn, P. (1990). Oxygenation-sensitive contrast in magnetic resonance image of rodent brain at high magnetic fields. Magnetic Resonance Medicine, 14(1), 68–78.CrossRefGoogle Scholar
  22. Ogawa, S., Tank, D. W., Menon, R., Ellermann, J. M., Kim, S. G., Merkle, H., & Ugurbil, K. (1992). Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proceedings of the National Academy of Sciences, 89, 5951–5955.CrossRefGoogle Scholar
  23. Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008). Human insula activation reflects risk prediction errors as well as risk. Journal of Neuroscience, 28(1), 2745–2752.CrossRefGoogle Scholar
  24. Seiler, M. J., Seiler, V. L., Lane, M. A., & Harrison, D. M. (2012). Fear, shame, and guilt: economic and behavioral motivations for strategic default. Real Estate Economics, 40(S1), 199–233.CrossRefGoogle Scholar
  25. Staw, B. M. (1976). Knee-deep in the Big muddy: a study of escalating commitment to a chosen course of action. Organizational Behavior and Human Performance, 16(1), 27–44.CrossRefGoogle Scholar
  26. Veen, V. V., Krug, M. K., Schooler, J. W., & Carter, C. S. (2009). Neural activity predicts attitude change in cognitive dissonance. Nature Neuroscience, 12(11), 1469–1475.CrossRefGoogle Scholar
  27. White, B. (2010). Underwater and Not walking away: shame, fear, and the social management of the housing crisis. Wake Forest Law Review, 45, 971–1023.Google Scholar
  28. Wilkinson-Ryan, T., & Hoffman, D. A. (2010). Breech is for suckers. Vanderbilt Law Review, 63(4), 1003–1045.Google Scholar
  29. Woolrich, M. W., Ripley, B. D., Brady, J. M., & Smith, S. M. (2001). Temporal autocorrelation in univariate linear modelling of FMRI data. NeuroImage, 14(6), 1370–1386.CrossRefGoogle Scholar
  30. Woolrich, M. W., Behrens, T. E., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2004). Multi-level linear modelling for FMRI group analysis using Bayesian inference. NeuroImage, 21(4), 1732–1747.CrossRefGoogle Scholar
  31. Worsley, K.J. (2001). Statistical Analysis of Activation Images. Ch 14, in Functional MRI: An Introduction to Methods, In P. Jezzard, P.M. Matthews and S.M. Smith (eds) OUPGoogle Scholar
  32. Wu, C. C., Bossaerts, P., & Knutson, B. (2011). The affective impact of financial skewness on neural activity and choice. Open Access, 6(2), 1–7.Google Scholar
  33. Zeng, J., Zhang, Q., Chen, C., Yu, R., & Gong, Q. (2013). An fMRI study on sunk cost effect. Brain Research, 1519(26), 63–70.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.K. Dane Brooksher Endowed Chair Professor of Real Estate and FinanceThe College of William & Mary Mason School of Business Department of FinanceWilliamsburgUSA
  2. 2.James C. Wetherbe Professor of Information Systems and Quantitative SciencesTexas Tech UniversityLubbockUSA

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