Purpose\ud– Recent research has found significant relationships between internet search volume and real estate markets. This paper aims to examine whether Google search volume data can serve as a leading sentiment indicator and are able to predict turning points in the US housing market. One of the main objectives is to find a model based on internet search interest that generates reliable real-time forecasts.\udDesign/methodology/approach\ud– Starting from seven individual real-estate-related Google search volume indices, a multivariate probit model is derived by following a selection procedure. The best model is then tested for its in- and out-of-sample forecasting ability.\udFindings\ud– The results show that the model predicts the direction of monthly price changes correctly, with over 89 per cent in-sample and just above 88 per cent in one to four-month out-of-sample forecasts. The out-of-sample tests demonstrate that although the Google model is not always accurate in terms of timing, the signals are always correct when it comes to foreseeing an upcoming turning point. Thus, as signals are generated up to six months early, it functions as a satisfactory and timely indicator of future house price changes.\udPractical implications\ud– The results suggest that Google data can serve as an early market indicator and that the application of this data set in binary forecasting models can produce useful predictions of changes in upward and downward movements of US house prices, as measured by the Case–Shiller 20-City House Price Index. This implies that real estate forecasters, economists and policymakers should consider incorporating this free and very current data set into their market forecasts or when performing plausibility checks for future investment decisions.\udOriginality/value\ud– This is the first paper to apply Google search query data as a sentiment indicator in binary forecasting models to predict turning points in the housing market.
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机译：目的\ ud –最近的研究发现，互联网搜索量与房地产市场之间存在着显着的关系。本文旨在研究Google搜索量数据是否可以作为领先的情绪指标，并能够预测美国住房市场的转折点。主要目标之一是找到一个基于互联网搜索兴趣的模型，该模型可以生成可靠的实时预测。\ udDesign /方法/方法\ ud–从七个与房地产相关的独立Google搜索量指数开始，一个多元概率模型通过遵循选择过程得出。然后测试最佳模型的样本内和样本外预测能力。\ udFindings \ ud–结果表明，该模型可以正确预测月度价格变化的方向，样本内超过89％的样本处于正上方一到四个月超出样本的预测为88％。样本外测试表明，尽管Google模型在时序方面并不总是准确的，但在预见即将到来的转折点时，信号始终是正确的。因此，由于信号会在六个月前产生，因此它可以作为令人满意的，及时的未来房屋价格变化的指标。\ ud实践意义\ ud–结果表明Google数据可以用作早期市场指标，并且可以应用通过Case–Shiller 20城市房价指数衡量，二元预测模型中的此数据集可以对美国房价的向上和向下变动产生有用的预测。这意味着房地产预测师，经济学家和政策制定者应考虑将这些免费且最新的数据集纳入他们的市场预测中，或在进行未来投资决策的合理性检查时考虑。\ udOriginality / value \ ud–这是第一篇应用Google搜索的论文查询数据作为二元预测模型中的情绪指标，以预测房地产市场的转折点。
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