Online activity leaves digital traces of human behavior. In this paper we investigate if online interest can be used as a proxy of housing demand, a key yet so far mostly unobserved feature of housing markets. We analyze data from an Italian website of housing sales advertisements (ads). For each ad, we know the timings at which website users clicked on the ad or used the corresponding contact form. We show that low online interest—a small number of clicks/contacts on the ad relative to other ads in the same neighborhood—predicts longer time on market and higher chance of downward price revisions, and that aggregate online interest is a leading indicator of housing market liquidity and prices. As online interest affects time on market, liquidity and prices in the same way as actual demand, we deduce that it is a good proxy. We then turn to a standard econometric problem: what difference in demand is caused by a difference in price? We use machine learning to identify pairs of duplicate ads, i.e. ads that refer to the same housing unit. Under some caveats, differences in demand between the two ads can only be caused by differences in price. We find that a 1% higher price causes a 0.66% lower number of clicks.

中文翻译：

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家就是广告所在：在线兴趣代表住房需求。

在线活动留下了人类行为的数字痕迹。在本文中，我们研究了在线兴趣是否可以用作住房需求的代理，这是住房市场的一个关键但迄今为止大多未被观察到的特征。我们分析来自意大利房屋销售广告（广告）网站的数据。对于每个广告，我们都知道网站用户点击广告或使用相应联系表格的时间。我们发现，较低的在线兴趣（相对于同一社区的其他广告而言，该广告的点击/接触次数较少）预示着上市时间较长，价格下调的可能性较高，并且总体在线兴趣是住房的领先指标市场流动性和价格。由于在线兴趣与实际需求一样影响市场时间、流动性和价格，因此我们推断它是一个很好的代理。然后我们转向一个标准的计量经济学问题：价格差异导致了何种需求差异？我们使用机器学习来识别成对的重复广告，即涉及同一住房单元的广告。在某些情况下，两个广告之间的需求差异只能由价格差异引起。我们发现，价格上涨 1% 会导致点击次数减少 0.66%。