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Credit Scores (credit + score)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Selected Abstracts

Credit scores, race, and residential sorting

JOURNAL OF POLICY ANALYSIS AND MANAGEMENT, Issue 1 2010
Ashlyn Aiko Nelson
Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households' residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data. I employ the data set to estimate a discrete-choice residential sorting model. I find that credit scores significantly predict residential sorting behavior and models that do not account for credit score provide biased estimates of housing utilities for black households in particular. Simulation results show that increases in credit score are associated with increases in the consumption of higher-priced homes in more expensive school districts, higher-quality public schools, and proximity to urban/metropolitan areas. © 2010 by the Association for Public Policy Analysis and Management. [source]

Unobserved Heterogeneity in Models of Competing Mortgage Termination Risks

REAL ESTATE ECONOMICS, Issue 2 2006
John M. Clapp
This article extends unobserved heterogeneity to the multinomial logit (MNL) model framework in the context of mortgages terminated by refinance, move or default. It tests for the importance of unobserved heterogeneity when borrower characteristics such as income, age and credit score are included to capture lender-observed heterogeneity. It does this by comparing the proportional hazard model to MNL with and without mass-point estimates of unobserved heterogeneous groups of borrowers. The mass-point mixed hazard (MMH) model yields larger and more significant coefficients for several important variables in the move model, whereas the MNL model without unobserved heterogeneity performs well with the refinance estimates. The MMH clearly dominates the alternative models in sample and out of sample. However, it is sometimes difficult to obtain convergence for the models estimated jointly with mass points. [source]

Credit scores, race, and residential sorting

JOURNAL OF POLICY ANALYSIS AND MANAGEMENT, Issue 1 2010
Ashlyn Aiko Nelson
Credit scores have a profound impact on home purchasing power and mortgage pricing, yet little is known about how credit scores influence households' residential location decisions. This study estimates the effects of credit scores on residential sorting behavior using a novel mortgage industry data set combining household demographic, credit, and financial data with property location information and detailed community attribute data. I employ the data set to estimate a discrete-choice residential sorting model. I find that credit scores significantly predict residential sorting behavior and models that do not account for credit score provide biased estimates of housing utilities for black households in particular. Simulation results show that increases in credit score are associated with increases in the consumption of higher-priced homes in more expensive school districts, higher-quality public schools, and proximity to urban/metropolitan areas. © 2010 by the Association for Public Policy Analysis and Management. [source]