Consumer Expertise or Credit Risk? An empirical analysis of mortgage pricing

Loan mortgage interest rates are usually the result of a bank-customer negotiation process. Credit risk, consumer cross-buying potential, bundling, financial market competition and other features affecting the bargaining power of the parties could affect price. We argue that, since mortgage loan is a complex product, consumer expertise could be a relevant factor for mortgage pricing. Using data on mortgage loan prices for a sample of 1055 households for the year 2005 (Bank of Spain Survey of Household Finances, EFF-2005), and including credit risk, costs, potential capacity of the consumer to generate future business and bank competition variables, the regression results indicate that consumer expertise-related metrics are highly significant as predictors of mortgage loan prices. Other factors such as credit risk and consumer cross-buying potential do not have such a significant impact on mortgage prices. Our empirical results are affected by the credit conditions prior to the financial crisis and could shed some light on this issue.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.