The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.
BackgroundAttentional biases, namely difficulties both to disengage attention from negative information and to maintain it on positive information, play an important role in the onset and maintenance of the disorder. Recently, researchers have developed specific attentional bias modification (ABM) techniques aimed to modify these maladaptive attentional patterns. However, the application of current ABM procedures has yielded, so far, scarce results in depression due, in part, to some methodological shortcomings.The aim of our protocol is the application of a new ABM technique, based on eye-tracker technology, designed to objectively train the specific attentional components involved in depression and, eventually, to reduce depressive symptoms.MethodsBased on sample size calculations, 32 dysphoric (BDI ≥13) participants will be allocated to either an active attentional bias training group or a yoked-control group. Attentional training will be individually administered on two sessions in two consecutive days at the lab. In the training task series of pairs of faces (i.e. neutral vs. sad; neutral vs. happy; happy vs. sad) will be displayed. Participants in the training group will be asked to localize as quickly as possible the most positive face of the pair (e.g., the neutral face in neutral vs. sad trials) and maintain their gaze on it for 750 ms or 1500 ms, in two different blocks, to advance to the next trial. Participants’ maintenance of gaze will be measured by an eye-tracking apparatus. Participants in the yoked-control group will be exposed to the same stimuli and the same average amount of time than the experimental participants but without any instruction to maintain their gaze or any feedback on their performance. Pre and post training measures will be obtained to assess cognitive and emotional changes after the training.DiscussionThe findings from this research will provide a proof-of-principle of the efficacy of eye-tracking paradigms to modify attentional biases and, consequently, to improve depressed mood. If the findings are positive, this new training approach may result in the improvement of cognitive bias modification procedures in depression.Trial registrationThis trial was retrospectively registered on July 28, 2016 with the ClinicalTrials.gov NCT02847793 registration number and the title ‘Attentional Bias Modification Through Eye-tracker Methodology (ABMET)’.Electronic supplementary materialThe online version of this article (doi:10.1186/s12888-016-1150-9) contains supplementary material, which is available to authorized users.
Worry and rumination, two cardinal responses to emotional events, are key for maintaining negative emotion and have been implicated in the etiology and maintenance of anxiety and depressive disorders. Though worry and rumination are highly correlated with one another and people who engage in one often engage in both, they may differentially affect emotion. Specifically, previous work suggests that worry helps people avoid (intense) emotion, while rumination provokes it. Examining the ways in which these two forms of repetitive negative thinking (RNT) influence cognitive processing of emotional material may help us better understand the emotional sequelae of worry and rumination. This study examines visual attention to emotional information, since attending to certain types of information opens the door for further processing of it. The current study induced worry and rumination and then used eye tracking to compare how each form of RNT influenced the allocation of attention to emotional scenes. Participants induced to worry, compared with those induced to ruminate, spent less time viewing positive (vs. neutral) scenes and were the only group to preferentially maintain their attention on negative images when they were paired with positive images. These findings suggest that worry, compared with rumination, leads to the relative avoidance of positive information. Implications of these findings for research on mood and anxiety disorders are discussed.
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