Tag Archives: machine learning

Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

In recent years big financial institutions are interested in creating and maintaining property  valuation models. The main objective is to use reliable historical data in order to be able to forecast the price of a new property in a comprehensive manner and provide some indication for the uncertainty around this forecast. In this paper we develop an automatic valuation model (AVM) for property valuation using a large database of historical prices from Greece.

The Greek property market is an inefficient, nonhomogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem.

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An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Rainfall is a crucial phenomenon within a climate system, whose chaotic nature has a direct influence on water resource planning, agriculture and biological systems. Within finance, the level of rainfall over a period of time is vital for estimating the value of a financial security. In this study we evaluate seven machine learning methods for rainfall prediction in the context of weather derivatives.
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