{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/126966"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/126966","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"FinTech mortgage lenders solving or exploiting a friction? evidence on risk layering and prepayment risk of conforming loans","abstract":"Fintech mortgage lenders have become an increasingly important source of mortgage credit in the US. Using loan-level data on mortgages sold to Fannie Mae and Freddie Mac (GSEs), I find that compared to traditional lenders, Fintech lenders are more likely to address credit demand from low credit score borrowers. However, they may be able to exploit two frictions in the GSEs' pricing and securitization setup. First, Fintech loans tend to have more risk layers conditional on paying the same guarantee fee, which are charged 15 basis points less of interest rate but translate to 0.5% higher delinquency rate ex-post. Second, Fintech loans get prepaid more often (11%). They get cross-subsidies in the to-be-announced mortgage-backed-securities market since these loans are pooled together with low prepayment risk loans in the same contract.","abstract_html":"Fintech mortgage lenders have become an increasingly important source of mortgage credit in the US. Using loan-level data on mortgages sold to Fannie Mae and Freddie Mac (GSEs), I find that compared to traditional lenders, Fintech lenders are more likely to address credit demand from low credit score borrowers. However, they may be able to exploit two frictions in the GSEs' pricing and securitization setup. First, Fintech loans tend to have more risk layers conditional on paying the same guarantee fee, which are charged 15 basis points less of interest rate but translate to 0.5% higher delinquency rate ex-post. Second, Fintech loans get prepaid more often (11%). They get cross-subsidies in the to-be-announced mortgage-backed-securities market since these loans are pooled together with low prepayment risk loans in the same contract.","abstract_has_math":false,"creators":["Wang, Yupeng(Scientist in business management)Massachusetts Institute of Technology."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Antoinette Schoar."],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020","date_published":"2020","updated_at":"2026-07-22T22:20:59Z","subjects":["Sloan School of Management."],"languages":["eng"],"rights":["MIT theses may be protected by copyright. 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Using loan-level data on mortgages sold to Fannie Mae and Freddie Mac (GSEs), I find that compared to traditional lenders, Fintech lenders are more likely to address credit demand from low credit score borrowers. However, they may be able to exploit two frictions in the GSEs' pricing and securitization setup. First, Fintech loans tend to have more risk layers conditional on paying the same guarantee fee, which are charged 15 basis points less of interest rate but translate to 0.5% higher delinquency rate ex-post. Second, Fintech loans get prepaid more often (11%). They get cross-subsidies in the to-be-announced mortgage-backed-securities market since these loans are pooled together with low prepayment risk loans in the same contract."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M. in Management Research"]},{"key":"dc:title","label":"Title","values":["FinTech mortgage lenders solving or exploiting a friction? evidence on risk layering and prepayment risk of conforming loans"]}]}],"canonical_facts":{"dc:contributor.advisor":["Antoinette Schoar."],"dc:contributor.department":["Sloan School of Management","Sloan"],"dc:contributor.other":["Sloan School of Management."],"dc:creator":["Wang, Yupeng(Scientist in business management)Massachusetts Institute of Technology."],"dc:date.accessioned":["2020-09-03T16:45:46Z"],"dc:date.available":["2020-09-03T16:45:46Z"],"dc:date.issued":["2020"],"dc:description":["Thesis: S.M. in Management Research, Massachusetts Institute of Technology, Sloan School of Management, May, 2020","Cataloged from the official PDF of thesis.","Includes bibliographical references (pages 55-56)."],"dc:description.abstract":["Fintech mortgage lenders have become an increasingly important source of mortgage credit in the US. Using loan-level data on mortgages sold to Fannie Mae and Freddie Mac (GSEs), I find that compared to traditional lenders, Fintech lenders are more likely to address credit demand from low credit score borrowers. However, they may be able to exploit two frictions in the GSEs' pricing and securitization setup. First, Fintech loans tend to have more risk layers conditional on paying the same guarantee fee, which are charged 15 basis points less of interest rate but translate to 0.5% higher delinquency rate ex-post. Second, Fintech loans get prepaid more often (11%). They get cross-subsidies in the to-be-announced mortgage-backed-securities market since these loans are pooled together with low prepayment risk loans in the same contract."],"dc:description.degree":["S.M. in Management Research"],"dc:identifier.uri":["https://hdl.handle.net/1721.1/126966"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Sloan School of Management."],"dc:title":["FinTech mortgage lenders solving or exploiting a friction? evidence on risk layering and prepayment risk of conforming loans"],"dc:type":["Thesis"],"thesis:degree_name":["Master"]},"updated_at":"2026-07-22T22:20:59Z"}