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This paper addresses the issue of data quality in the real estate market. In many countries, the returns indices for direct markets are provided by several sources differing in terms of the methodology adopted and index weights. These differences produce a lack of informative standardization, which could negatively affect the ability of market participants to make predictions. Focusing on the Italian real estate market, the aim of this paper is therefore twofold: to investigate the reliability of property data sources and to assess the impact for financial intermediaries involved in real estate investing. Our results show a significant level of divergence between the data,and considerable implications for those financial institutions dealing with them. These findings conflict with the requirements of an efficient (or at least sub-efficient) market. [ABSTRACT FROM AUTHOR]

The deregulation of the electricity industry has caused for the generation company (Genco) the need of tools for measuring and managing the risk, beyond the classical problem of generating unit scheduling. In this paper, a probabilistic framework for the problem of managing risk faced by Gencos trading in day-ahead energy market is proposed. In particular, a stochastic forecast of electricity price and the technical features of hydrothermal units are considered. The approach is based on an optimization procedure for maximizing expected profits in the presence of risk constraints. Conditional value at risk for the distribution of daily profit is used as risk measure. The effectiveness of the proposed model is tested for the case of one producer of the Italian electricity system with a fleet of hydrothermal generating units. [ABSTRACT FROM AUTHOR]