The Role of Technology in Mortgage Lending

Fuster, Andreas; Plosser, Matthew; Schnabl, Philipp; Vickery, James

doi:10.1093/rfs/hhz018

Cited by 683 publications

(341 citation statements)

References 49 publications

(60 reference statements)

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“…The authors perform robustness tests to address this alternative explanation, but it is difficult to completely rule out. Fuster et al (2018b) also find that among FHA loans, the default rate for fintech loans is lower after controlling for observable borrower risk factors, suggesting that the applicants, or at least the originated loans, differ in some unobservable way between lender types. Buchak et al (2018) find that among conventional mortgages (rather than FHA), the default rates are not significantly different across lender types, conditional on controlling for observable borrower and loan characteristics.…”

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 82%

“…First, fintech mortgage loans are processed more quickly. Fuster et al (2018b) find that the processing duration from application to origination is one week shorter for fintech loans compared with nonfintech loans. Relatedly, fintech firms appear to sell the mortgage faster on the secondary market after origination.…”

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 83%

“…Second, fintech lenders charge higher interest rates on conventional loans and lower interest rates on FHA loans (Buchak et al, 2018;Fuster et al, 2018b). 3 Taken together, these results are consistent with fintech firms charging consumers of conventional loans a premium for convenience while pricing lower for potentially more price-sensitive FHA borrowers.…”

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 99%

“…Last, Buchak et al (2018) and Fuster et al (2018b) provide evidence on how local lending conditions and credit accessibility impact fintech market share. More concentration in the local lending market, measured either by the Herfindahl Hirschman Index (HHI) of mortgage lending in a county or the number of lenders, is correlated with greater fintech market share (Buchak et al, 2018).…”

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 99%

“…1 One growing segment of nonbank mortgage originations is financial technology (fintech) lending, in which nonbank lenders use online platforms and advanced technology to process mortgage applications more quickly than traditional lenders. Following previous literature, we define fintech lenders as those that allow the borrower to apply for a mortgage online, such that the lender does a credit pull without the borrower needing to speak to anyone (Buchak, Matvos, Piskorski, and Seru, 2018;Fuster, Plosser, Schnabl, and Vickery, 2018b).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fintech Lending and Mortgage Credit Access

Jagtiani¹,

Lambie‐Hanson²,

Lambie-Hanson³

2019

Working Paper (Federal Reserve Bank of Philadelphia)

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Following the 2008 financial crisis, mortgage credit tightened and banks lost significant mortgage market share to nonbank lenders, including to fintech firms recently. Have fintech firms expanded credit access, or are their customers similar to those of traditional lenders? Unlike in small business and unsecured consumers lending, fintech mortgage lenders do not have the same incentives or flexibility to use alternative data for credit decisions because of stringent mortgage origination requirements. Fintech loans are broadly similar to those made by traditional lenders, despite innovations in the marketing and the application process. However, nonbanks market to consumers with weaker credit scores than do banks, and fintech lenders have greater market shares in areas with lower credit scores and higher mortgage denial rates.

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Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 82%

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 83%

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 99%

Section: Ii1 Fintech In the Us Mortgage Marketmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fintech Lending and Mortgage Credit Access

Jagtiani¹,

Lambie‐Hanson²,

Lambie-Hanson³

2019

Working Paper (Federal Reserve Bank of Philadelphia)

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Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads

Goldberg

Zaman

Brahma

et al. 2021

Asso for Info Science & Tech

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Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk. Substantial work experience is required to predict delays, and we find that even highly trained employees have difficulty predicting delays by reviewing discussion threads. We develop an array of methods to predict loan delays. We apply four modern out-of-the-box sentiment analysis techniques, two dictionary-based and two rule-based, to predict delays. We contrast these approaches with domain-specific approaches, including firm-provided keyword searches and "smoke terms" derived using machine learning. Performance varies widely across sentiment approaches; while some sentiment approaches prioritize the top-ranking records well, performance quickly declines thereafter. The firm-provided keyword searches perform at the rate of random chance. We observe that the domain-specific smoke term approaches consistently outperform other approaches and offer better prediction than loan and borrower characteristics. We conclude that text mining solutions would greatly assist mortgage firms in delay prevention.

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The impact of Fintech on corporate carbon emissions: Towards green and sustainable development

Wang,

Zhao

et al. 2024

Bus Strat Env

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Fintech, as the fusion of finance and technology, has not only transformed the traditional financial industry and contributed to reshaping the real economy. But also, it holds the potential to offer a feasible solution for achieving green and sustainable development. This paper investigates the impact of Fintech on corporate carbon emissions (CCEs) by using data from the National Tax Survey Database (NTSD). The results suggest that Fintech development leads to a reduction in CCEs. Our findings remain robust even after using the instrumental variable approach to alleviate endogeneity problems. The mechanism analysis reveals that Fintech reduces CCEs via alleviating financing constraints, improving energy efficiency, and promoting green innovation. Heterogeneity analysis demonstrates that Fintech dramatically decreases CCEs from coal energy consumption, while increasing CCEs from consuming power and gas energy. Additionally, carbon emissions from state‐owned and foreign companies experience a more pronounced reduction through Fintech compared to those from private firms. Furthermore, firms in eastern and middle regions are more vulnerable to Fintech development. Moreover, enterprises in non‐high‐tech industries and high‐polluting industries exhibit noteworthy performance in reducing carbon emissions through Fintech adoption. This research offers policymakers a path to effectively govern CCEs and achieve their carbon reduction targets.

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The Role of Technology in Mortgage Lending

Cited by 683 publications

References 49 publications

Fintech Lending and Mortgage Credit Access

Fintech Lending and Mortgage Credit Access

Are mortgage loan closing delay risks predictable? A predictive analysis using text mining on discussion threads

The impact of Fintech on corporate carbon emissions: Towards green and sustainable development

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