Abstract
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.
Highlights
Online activity makes it possible to quantify aspects of human behavior that were not previously measurable at a comparable scale
We first establish that online interest is a good proxy of actual demand, and on a more technical level, we combine econometric and machine learning ideas to investigate the causal link from prices to demand
4 Evidence that online interest proxies demand Is online interest a good proxy for actual demand? we provide evidence that supports this hypothesis, showing that online interest has the same effect of demand on time on market, liquidity and prices
Summary
Online activity makes it possible to quantify aspects of human behavior that were not previously measurable at a comparable scale. If the dwelling is the same and only the price of the corresponding duplicate ad is different—for example, because the agency posted a new ad with a different price—the elasticity can be estimated consistently from pairs of ads.
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