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Book
Data science : fondamentaux et études de cas : machine learning avec Python et R
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ISBN: 9782212142433 2212142439 Year: 2016 Publisher: Paris: Eyrolles,

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CVPRW '06 : 2006 Conference on Computer Vision and Pattern Recognition Workshop : 17-22 June 2006.
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Year: 2006 Publisher: New York : IEEE,

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Book
CVPRW '06 : 2006 Conference on Computer Vision and Pattern Recognition Workshop : 17-22 June 2006.
Authors: --- ---
Year: 2006 Publisher: New York : IEEE,

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Digital
Reassessing FHA Risk
Authors: --- --- --- --- --- et al.
Year: 2010 Publisher: Cambridge, Mass National Bureau of Economic Research

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Federal Housing Administration (FHA) insurance has doubled over the past two years and is projected to redouble to $1.5 trillion over the next five. Despite clear signs of strain in the FHA's Mutual Mortgage Insurance Fund, a recent actuarial review indicates that the FHA will not need any form of government support. We identify four risk factors that make such a funding request more likely; the review underestimates how many FHA borrowers are underwater and in economic distress; it uses measures of house values that lower loss estimates; it does not incorporate early-warning signals of future losses that are available from mortgage delinquency; and it ignores potential risks associated with recent down-payment assistant programs despite higher losses on previous programs of this type. We propose measures that could be taken to improve the predictive accuracy of FHA risk assessment.


Book
Reassessing FHA Risk
Authors: --- --- --- --- --- et al.
Year: 2010 Publisher: Cambridge, Mass. National Bureau of Economic Research

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Abstract

Federal Housing Administration (FHA) insurance has doubled over the past two years and is projected to redouble to $1.5 trillion over the next five. Despite clear signs of strain in the FHA's Mutual Mortgage Insurance Fund, a recent actuarial review indicates that the FHA will not need any form of government support. We identify four risk factors that make such a funding request more likely; the review underestimates how many FHA borrowers are underwater and in economic distress; it uses measures of house values that lower loss estimates; it does not incorporate early-warning signals of future losses that are available from mortgage delinquency; and it ignores potential risks associated with recent down-payment assistant programs despite higher losses on previous programs of this type. We propose measures that could be taken to improve the predictive accuracy of FHA risk assessment.

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