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The Role of Soft Information in a Dynamic Contract Setting: Evidence from the Home Equity Credit Market

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  • SUMIT AGARWAL
  • BRENT W. AMBROSE
  • SOUPHALA CHOMSISENGPHET
  • CHUNLIN LIU

Abstract

Credit underwriting is a dynamic process involving multiple interactions between borrower and lender. During this process, lenders have the opportunity to obtain hard and soft information from the borrower. We analyze more than 108,000 home equity loans and lines‐of‐credit applications to study the role of soft and hard information during underwriting. Our data set allows us to distinguish lender actions that are based strictly on hard information from decisions that involve the collection of soft information. Our analysis confirms the importance of soft information and suggests that its use can be effective in reducing overall portfolio credit losses ex post.

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  • Sumit Agarwal & Brent W. Ambrose & Souphala Chomsisengphet & Chunlin Liu, 2011. "The Role of Soft Information in a Dynamic Contract Setting: Evidence from the Home Equity Credit Market," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(4), pages 633-655, June.
    • Handle: RePEc:wly:jmoncb:v:43:y:2011:i:4:p:633-655
    DOI: 10.1111/j.1538-4616.2011.00390.x
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    1. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F. P. Luttmer & Kelly Shue, 2016. "Screening Peers Softly: Inferring the Quality of Small Borrowers," Management Science, INFORMS, vol. 62(6), pages 1554-1577, June.
    2. Anthony Yezer & Pingkang Yu, 2016. "Costly Screening, Self-Selection, Fraud, and the Organization of Credit Markets," Working Papers 2016-4, The George Washington University, Institute for International Economic Policy.
    3. Min Qi & Harald Scheule & Yan Zhang, 2021. "Positive Payment Shocks, Liquidity and Refinance Constraints and Default Risk of Home Equity Lines of Credit at End of Draw," The Journal of Real Estate Finance and Economics, Springer, vol. 62(3), pages 423-454, April.
    4. Arpit Gupta & Christopher Hansman, 2022. "Selection, Leverage, and Default in the Mortgage Market," The Review of Financial Studies, Society for Financial Studies, vol. 35(2), pages 720-770.
    5. Dean Karlan & Melanie Morten & Jonathan Zinman, 2012. "A Personal Touch: Text Messaging for Loan Repayment," NBER Working Papers 17952, National Bureau of Economic Research, Inc.
    6. Luong, Thi Mai & Scheule, Harald, 2022. "Benchmarking forecast approaches for mortgage credit risk for forward periods," European Journal of Operational Research, Elsevier, vol. 299(2), pages 750-767.
    7. Suzuki, Masatomo & Kawai, Kohei & Shimizu, Chihiro, 2022. "Discrimination against the atypical type of tenants in the Tokyo private rental housing market: Evidence from moving-in inspection and rent arrear records," Journal of Housing Economics, Elsevier, vol. 58(PB).
    8. Jason Allen & H. Evren Damar & David Martinez-Miera, 2016. "Consumer Bankruptcy, Bank Mergers, and Information," Review of Finance, European Finance Association, vol. 20(4), pages 1289-1320.
    9. Milonas, Kristoffer, 2017. "The effect of foreclosure laws on securitization: Evidence from U.S. states," Journal of Financial Stability, Elsevier, vol. 33(C), pages 1-22.
    10. Eriksen, Michael D. & Fout, Hamilton B. & Palim, Mark & Rosenblatt, Eric, 2019. "The influence of contract prices and relationships on appraisal bias," Journal of Urban Economics, Elsevier, vol. 111(C), pages 132-143.
    11. Agarwal, Sumit & Ambrose, Brent W. & Chomsisengphet, Souphala & Liu, Chunlin, 2016. "Joint liability lending and credit risk: Evidence from the home equity market," Journal of Housing Economics, Elsevier, vol. 32(C), pages 47-66.
    12. Juha-Pekka Niinimäki, 2015. "Asymmetric Information, Bank Lending and Implicit Contracts: Differences between Banks," Czech Economic Review, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, vol. 9(2), pages 074-090, December.
    13. Simon Firestone, 2014. "Race, Ethnicity, and Credit Card Marketing," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(6), pages 1205-1224, September.
    14. Sumit Agarwal & Brent W. Ambrose, 2008. "Does it pay to read your junk mail? evidence of the effect of advertising on home equity credit choices," Working Paper Series WP-08-09, Federal Reserve Bank of Chicago.
    15. Wang, Yao & Drabek, Zdenek & Wang, Zhengwei, 2022. "The role of social and psychological related soft information in credit analysis: Evidence from a Fintech Company," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    16. Michael LaCour-Little & Wei Yu & Libo Sun, 2014. "The Role of Home Equity Lending in the Recent Mortgage Crisis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(1), pages 153-189, March.
    17. James Conklin & Kristopher Gerardi & Lauren Lambie-Hanson, 2022. "Can Everyone Tap Into the Housing Piggy Bank? Racial Disparities in Access to Home Equity," FRB Atlanta Working Paper 2022-17, Federal Reserve Bank of Atlanta.
    18. Neil Bhutta & Benjamin J. Keys, 2016. "Interest Rates and Equity Extraction during the Housing Boom," American Economic Review, American Economic Association, vol. 106(7), pages 1742-1774, July.
    19. David C. Ling & Andy Naranjo & Benjamin Scheick, 2021. "There is no place like home: Information asymmetries, local asset concentration, and portfolio returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(1), pages 36-74, March.
    20. Mary L. Z. Ma & Victor Song, 2016. "Discretionary Loan Loss Provisions and Systemic Risk in the Banking Industry," Accounting Perspectives, John Wiley & Sons, vol. 15(2), pages 89-130, June.
    21. Chun Chang & Guanmin Liao & Xiaoyun Yu & Zheng Ni, 2014. "Information from Relationship Lending: Evidence from Loan Defaults in China," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(6), pages 1225-1257, September.
    22. Thi Mai Luong & Harald Scheule & Nitya Wanzare, 2023. "Impact of mortgage soft information in loan pricing on default prediction using machine learning," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 158-186, March.

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