Impact of mortgage soft information in loan pricing on default prediction using machine learning

Luong, TM; Scheule, H; Wanzare, N

Impact of mortgage soft information in loan pricing on default prediction using machine learning

Luong, TM Scheule, H

Wanzare, N

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: International Review of Finance, 2023, 23, (1), pp. 158-186
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Luong, TM
dc.contributor.author	Scheule, H https://orcid.org/0000-0001-7283-5654
dc.contributor.author	Wanzare, N
dc.date.accessioned	2023-03-03T00:11:45Z
dc.date.available	2023-03-03T00:11:45Z
dc.date.issued	2023
dc.identifier.citation	International Review of Finance, 2023, 23, (1), pp. 158-186
dc.identifier.issn	1369-412X
dc.identifier.issn	1468-2443
dc.identifier.uri	http://hdl.handle.net/10453/166641
dc.description.abstract	We analyze the impact of soft information on US mortgages for default prediction and provide a new measure for lender soft information that is based on the interest rates offered to borrowers and incremental to public hard information. Hard and soft information provide for a variation in annual default probabilities of approximately 3%. Soft information has a lesser impact over time and time since origination. Lenders rely more on soft information for high-risk borrowers. Our study evidences the importance of soft information collected at loan origination.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	International Review of Finance
dc.relation.isbasedon	10.1111/irfi.12392
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	1501 Accounting, Auditing and Accountability, 1502 Banking, Finance and Investment
dc.title	Impact of mortgage soft information in loan pricing on default prediction using machine learning
dc.type	Journal Article
utslib.citation.volume	23
utslib.for	1501 Accounting, Auditing and Accountability
utslib.for	1502 Banking, Finance and Investment
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Finance Discipline
pubs.organisational-group	/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2023-03-03T00:11:43Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	23
utslib.citation.issue	1

Abstract:

We analyze the impact of soft information on US mortgages for default prediction and provide a new measure for lender soft information that is based on the interest rates offered to borrowers and incremental to public hard information. Hard and soft information provide for a variation in annual default probabilities of approximately 3%. Soft information has a lesser impact over time and time since origination. Lenders rely more on soft information for high-risk borrowers. Our study evidences the importance of soft information collected at loan origination.

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http://hdl.handle.net/10453/166641