How demographics contribute to mortgage default: using binary logistic regression to draw inferences

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dc.contributor.author Rathnasingha, D. L. P. M.
dc.contributor.author Khatibi, A.
dc.date.accessioned 2021-09-14T06:04:40Z
dc.date.available 2021-09-14T06:04:40Z
dc.date.issued 2012
dc.identifier.citation Rathnasingha, D., & Khatibi, A. (2012). How demographics contribute to mortgage default: using binary logistic regression to draw inferences . HOUSING FINANCE INTERNATIONAL , 27-32. en_US
dc.identifier.uri http://archive.cmb.ac.lk:8080/xmlui/handle/70130/5941
dc.description.abstract . Introduction Housing is an indispensable human need. It is predictable as a basic need for shelter. It is apparent that habitable housing contributes to the health, efficiency, positive social behavior and general welfare of the populace, which uplift the living conditions of the community. In present society, housing has a value beyond the essential need for human shelter. As an upshot many agencies have moved to work in this field. This has made for a rapid transformation in the household sector in Sri Lanka. Recent studies expressed concern that substantial numbers of bank customers, who enter into complicated financial contracts such as mortgages, are financially illiterate and fail to understand crucial factors in decision-making, Engin (2011). Further, for most governments the availability of sufficient but basic housing for all is often stated as a priority for enhancing the social needs of the society. For a typical house-owner, the house is a major asset in his portfolio and for many households the purchase of a house represents the largest (and often only) lifelong investment and a store of wealth, Goodman (1989). Housing represents a large proportion of a household’s expenditure and takes up a substantial proportion of lifetime incomes. It has been noted by Rose (2011) that for most Americans, the purchase of a home is the greatest and most important financial decision made during their life time. A central purpose of financial markets is to redistribute risk in an optimal manner. However, external influences such as local currency depreciation, oil and gold price increases plus some internal factors, including higher levels of interest rates, have made the financing of housing a major challenge for the country. It needs attention at a policy level. The provision of housing services depends mostly upon a well-functioning and affordable housing finance system. The housing finance process is very complicated and the beneficiary has to meet a set of difficult criteria which make it harder to meet the requirements for a housing loan. Further, the interest rates on loans are unaffordable for the general working class of people and they went up to nearly 20% per annum in the recent past. This rate has shown vast fluctuations from 20% to 12% in the period 2009-2012 indicating the instability of the market. This rate fluctuation creates a number of issues for lenders and for borrowers in relation to their financial management. It has been mentioned by Vandell et al. (1985) that understanding the forces that drive mortgage default is a necessary prerequisite to appropriate pricing of default in the mortgage market. This study is focused on the demography of defaulting borrowers and provides valuable insight into the demography of mortgage borrowers.
dc.language.iso en en_US
dc.subject Mortgage Default en_US
dc.subject Binary Logistic en_US
dc.subject Inferences en_US
dc.title How demographics contribute to mortgage default: using binary logistic regression to draw inferences en_US
dc.type Article en_US


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