The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has already been applied to the real estate market to identify which variables influence dwelling prices, its use for estimating the price of properties is not so frequent. The application of this methodology has become more sophisticated over time, from applying simple methods to using the so-called ensemble methods and, while the estimation capacity has improved, it has only been applied to specific geographical areas. The main contribution of this article lies in developing an application for the entire Spanish market that fully automatically provides the best model for each municipality. Real estate property prices in 433 municipalities are estimated from a sample of 790,631 dwellings, using different ensemble methods based on decision trees such as bagging, boosting, and random forest. The results for estimating the price of dwellings show a good performance of the techniques developed, in terms of the error measures, with the best results being achieved using the techniques of bagging and random forest.

The aim of this article is to propose a new breakdown of the Gini inequality ratio into three components (within-group inequality, between-group inequality, and intensity of transvariation between groups to the total inequality index). The between-group inequality concept computes all the differences in salaries between men and women. The main contribution is to show this relationship when dealing with noninteger frequency data appearing on survey data. This article also proves the relationship between the very intuitive Gini concentration ratio and the very commonly used Gini index in the case of discrete distributions with a finite number of observations. The former refers to the relationship between the cumulative proportion of population and income, while the latter is the absolute mean difference divided by twice the arithmetic mean. This article complements the inequality decomposition literature that has stemmed from Gini’s work. The research is applied to analyze gender income inequality in a Spanish region using Spanish Structure of Earnings Survey data from 2010.

This article assesses the main problems facing the Spanish Tagus River basin, and the management that has caused (or allowed) them. It examines the economic, social, environmental and political-institutional dimensions of Tagus River management in terms of sustainable development. We find that the institutions responsible for designing and implementing water policy in the Tagus basin have been unsuccessful in balancing the three classic dimensions of sustainability, resulting in systematic and recurrent failure to comply with the European Water Framework Directive and the principles of sustainable development.

La desigualdad de género, y en particular la desigualdad salarial de género, ha sido considerada una prioridad política en muchos países durante las últimas décadas, con foco puesto en reducir la llamada brecha salarial, es decir, en buscar una convergencia en media para los salarios de hombres y mujeres. Una aproximación de medias, sin embargo, no implica distribuciones salariales más justas (totales y de género). Utilizando los microdatos de una muestra nacionalmente representativa de la población ocupada de España (N = 216,769), descomponemos la desigualdad salarial total en desigualdad salarial femenina y masculina (intra-grupos) y desigualdad de género (entre-grupos) y exploramos hasta qué punto estos índices dependen de las características laborales previamente identificadas como determinantes de las diferencias promedio entre hombres y mujeres ("brecha salarial de género"). Para este conjunto de datos, los análisis muestran que la desigualdad salarial de género es más alta que la desigualdad salarial total y ambas la desigualdad salarial total y la desigualdad de género aumentan con la edad y el nivel educativo de los trabajadores.