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The purpose of the paper is to propose two new procedures that deal with overfitting problem using neural techniques for variable selection and business failure prediction. The first procedure, called HVS-AUC, is based simultaneously on (i) the backward search, (ii) the HVS measure (Heuristic for Variable Selection), and (iii) the AUC criterion (Area Under Curve). The second procedure, called forward-AUC, is based on (i) the forward search and (ii) the AUC criterion. Using a sample of bankrupt and non-bankrupt firms in France, the implementation of the procedures using neural networks shows that the profitability, the repayment capacity, the taxation, and the importance of investment have a strong explanatory power in bankruptcy prediction. These procedures also provide more parsimonious and more efficient models compared to Linear Discriminant Analysis. [ABSTRACT FROM AUTHOR]

This paper reviews real estate price estimation in France, a market that has received little attention. We compare seven popular machine learning techniques by proposing a different approach that quantifies the relevance of location features in real estate price estimation with high and fine levels of granularity. We take advantage of a newly available open dataset provided by the French government that contains 5 years of historical data of real estate transactions. At a high level of granularity, we obtain important differences regarding the models' prediction powers between cities with medium and high standards of living (precision differences beyond 70% in some cases). At a low level of granularity, we use geocoding to add precise geographical location features to the machine learning algorithm inputs. We obtain important improvements regarding the models' forecasting powers relative to models trained without these features (improvements beyond 50% for some forecasting error measures). Our results also reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocoding features are considered. For identifying opportunities in the real estate market through real estate price prediction, our results can be of particular interest. They can also serve as a basis for price assessment in revenue management for durable and non-replenishable products such as real estate. [ABSTRACT FROM AUTHOR]