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Abstract

Both, the housing bubble and financial crisis, are prime examples of complex events. Complex in the sense that there were several interconnected and interdependent root causes. This paper presents an agent-based model (ABM) to model the housing market from 1986 to 2017. We provide a unique approach to simulating the financial market along with analyzing the phenomenon of emergence resulting from the interactions among consumers, banks and the Federal Reserve. This paper specifically focuses on the emergence of “underwater mortgages” and the macroeconomics of the housing market. The market value of a property is heavily influence by the value of a neighboring property; therefore, individuals are able to gauge the probable value of a property that has not been developed yet. The blend of available financial products to consumers (i.e., ARM versus Fixed-Rate) certainly influences demand within the housing market given that ARM products are more affordable than fixed-rate products. Policymakers and financial institutions should work together to develop programs, which monitor the supply of these historically easy to access financial products and prevent the risk of underwater mortgages and crashes.

Keywords

Agent-based simulation Housing market Systemic risk Complexity and emergence Financial crisis 

References

  1. Abdollahian, M., et al.: Human development dynamics: an agent based simulation of macro social systems and individual heterogeneous evolutionary games. Complex Adapt. Syst. Model. 1(1), 18 (2013a)CrossRefGoogle Scholar
  2. Abdollahian, M., et al.: Techno-social energy infrastructure siting: sustainable energy modeling programming (SEMPro). J. Artif. Soc. Soc. Simul. 16(3), 6 (2013b)Google Scholar
  3. Archer, W.A., Smith, B.C.: Residential mortgage default: the roles of house price volatility, euphoria and the borrower’s put option. Working paper, March 2010, No. 10-02 - Federal Reserve Bank of RichmondGoogle Scholar
  4. Aschcraft, A.B., Schuerman, T.: Understanding the securitization of subprime mortgage credit. Federal Reserve Bank of New York staff reports, no. 318. March 2008Google Scholar
  5. Axtell, R., Epstein, J.: Agent based modeling: understanding our creations. Bull. Santa Fe Institute 9(4), 28–32 (1994)Google Scholar
  6. Bord, V.M., Santos, J.A.C.: The rise of the originate-to-distribute model and the role of banks in financial intermediation. Econ. Policy Rev. 18(2), 21–34 (2012)Google Scholar
  7. Collins, A.J., Seiler, M.J., Gangel, M., Croll, M.: Applying Latin hypercube sampling to agent-based models: understanding foreclosure contagion effects. Int. J. Hous. Markets Anal. 6(4), 422–437 (2013). ISSN: 1753-8270Google Scholar
  8. Dosi, G., et al.: Fiscal and monetary policies in complex evolving economies. J. Econ. Dyn. Control 52, 166–189 (2015)MathSciNetCrossRefGoogle Scholar
  9. Duca, J.V., Muellbauer, J., Murphy, A.: House prices and credit constraints: making sense of the US experience. Econ. J. 121(552), 533–351 (2011)Google Scholar
  10. Gangel, M., Seiler, M.J., Collins, A.: Exploring the foreclosure contagion effect using agent-based modeling. J. Real Estate Finance Econ. 46(2), 339–354 (2013a)CrossRefGoogle Scholar
  11. Gangel, M., Seiler, M.J., Collins, A.J.: Latin hypercube sampling and the identification of the foreclosure contagion threshold. J Behav. Fin. 14(2), 149–159 (2013b). ISSN: 1542-7560Google Scholar
  12. Geanakoplos, J., et al.: Getting at systemic risk via an agent- based model of the housing market. Am. Econ. Rev. 1023, 53–58 (2012)CrossRefGoogle Scholar
  13. Gilbert, N., Hawksworth, J.C., Swinney, P.A.: An agent-based model of the English housing market. In: AAAI Spring Symposium: Technosocial Predictive Analytics (2009)Google Scholar
  14. Goldstein, J.: Rethinking housing with agent-based models: models of the housing bubble and crash in the Washington DC area 1997–2009. Dissertation, George Mason University (2017)Google Scholar
  15. Gorton, G.B.: The panic of 2007. Working paper, National Bureau of Economic Research, September 2008Google Scholar
  16. Haldane, A.G.: The dappled world. Speech (2016)Google Scholar
  17. Khan, F., Yang, Z.: Simulation of financial systemic risk and contagion in the U.S. housing market. In: Cassenti, D., (ed.) Advances in Human Factors in Simulation and Modeling (AHFE 2018). Advances in Intelligent Systems and Computing, vol. 780. Springer, Cham (2019)Google Scholar
  18. Khandani, A.E., Lo, A.W., Merton, R.C.: Systemic risk and the refinancing ratchet effect. Working paper, National Bureau of Economic Research, September 2009Google Scholar
  19. Ling, D., Archer, W.: Real Estate Principles: A Value Approach. McGraw-Hill Irwin, Boston (2009)Google Scholar
  20. McMahon, M., Berea, A., Osman, H.: An agent based model of the housing market. Housing. Market Rev. 8, 3 (2009)Google Scholar
  21. Mian, A., Sufi, A.: The consequences of mortgage credit expansion: evidence from the U.S. mortgage default crisis. Quart. J. Econ. 124(4): 1449–1496 (2009).  https://doi.org/10.1162/qjec.2009.124.4.1449CrossRefGoogle Scholar
  22. Vernon-Bido, D., Collins, A.J., Sokolowski, J.A., Seiler, M.J.: Using real property layouts to study the foreclosure contagion effect in real estate with agent-based modeling and simulation. J. Hous. Res. 26(2): 137–155 (2017). ISSN: 1052-7001Google Scholar
  23. Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1(2), 143–186 (1971)CrossRefGoogle Scholar
  24. Seiler, M., Collins, A.J., Fefferman, N.H.: Strategic mortgage default in the context of a social network: an epidemiological approach. J. Real Estate Res. 35(4), 445–475 (2013). ISSN: 0896-5803Google Scholar
  25. Ghoulmie, F., Cont, R., Nadal, J.-P: Heterogeneity and feedback in an agent-based market model. J. Phys. condens. matter 17(14), S1259 (2005)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of International StudiesClaremont Graduate UniversityClaremontUSA

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