{"product_id":"2940148311911","title":"Analysis of FHA Single-Family Default and Loss Rates","description":"Previous studies of mortgage risk in both the conventional and FHA sectors have \u003cbr\u003efocused almost exclusively on default behavior and on the factors that lead to default. \u003cbr\u003eThis is the approach taken in numerous articles in the professional economics and finance \u003cbr\u003eliterature, as well as in nonacademic studies produced by practitioners within the \u003cbr\u003eindustry.  In addition, recent extensions on FHA mortgage scoring have followed the \u003cbr\u003emain lines of previous research in focusing solely on the default probability as a metric \u003cbr\u003efor risk.  In virtually all of this extensive research virtually no attention is given to other \u003cbr\u003edimensions of loss and to the dollar value of losses in particular; thus, little is known \u003cbr\u003eabout dollar loss and its determinants. \u003cbr\u003eThis focus on default in the mortgage scoring context means that observable \u003cbr\u003efactors affecting the likelihood of default assume a primary role. Because minorities tend \u003cbr\u003eto have less attractive distributions of factors leading to default, mortgage scoring \u003cbr\u003esystems tend to give minorities less favorable scores than nonminorities, justifying such \u003cbr\u003epatterns with well-reasoned arguments of business necessity. Some observers, \u003cbr\u003eunderstandably concerned by this racial discrepancy in scoring outcomes, have suggested \u003cbr\u003ethat minorities generate smaller dollar losses on average when they default, and thus a \u003cbr\u003emortgage scoring system relying on dollar losses rather than default alone might improve \u003cbr\u003eminorities’ lot. In addition, a mortgage scoring system that recognizes both the \u003cbr\u003eprobability of default and the dollar losses attendant upon default would provide a more \u003cbr\u003ecomplete, and thus superior, measure of risk that could be used for policy decisions as \u003cbr\u003ewell as for underwriting. \u003cbr\u003eThe purpose of this paper is to use data on FHA-insured loans from 1992, 1994, \u003cbr\u003eand 1996 to examine the factors that influence both default probabilities and dollar loss \u003cbr\u003erates, as well as the avenue by which impacts arise. En route we pay special attention to \u003cbr\u003ethe possibility that minorities would fare better with a scorecard based in part on dollar \u003cbr\u003elosses.  The analysis ranges from simple statistical summaries and descriptive regressions \u003cbr\u003eto more complete and sophisticated statistical analysis.","brand":"ReadCycle","offers":[{"title":"Default Title","offer_id":47079019249904,"sku":"2940148311911","price":2.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/2940148311911_p0.jpg?v=1763707962","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/2940148311911","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}