Abstract
We augment linear pricing models for the housing market commonly used in the literature with Google trends data in order to assess whether or not crowd-sourced search query data can improve the forecasting ability of the models. We estimate both sets of models (excluding and including the search query data) in order to assess statistical fit. We then compare various performance measures of the augmented linear model’s out-of-sample, dynamic forecasts against a baseline version. We find that augmenting the models to take advantage of the availability of Google trend data does not significantly improve the forecasting performance of the models.
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