<p>The selling process of a house often involves generating prospective buyer interest and driving search traffic to the property. While it is typically expected that additional traffic received by a property will result in better outcomes for the seller – such as a higher sale price – this is challenging to empirically evaluate as data on search traffic is not readily available. This paper utilizes a novel dataset that contains information on the search traffic to properties, along with MLS listing information, to examine and estimate the impact of search traffic on the key outcomes of sales prices and time on market. To deal with problems of endogeneity, we employ an instrumental variables approach where we use exogenous variation in house search traffic generated randomly by variability in local weather, to estimate the causal effect of search traffic on outcomes. We find that greater search traffic leads to increases in sales prices, and shorter times on market. We further estimate the magnitude and heterogeneity of these effects.</p>