Home is where the ad is: online interest proxies housing demand

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...

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Main Authors: Pangallo, M, Loberto, M
Format: Journal article
Published: EDP Sciences 2018
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author Pangallo, M
Loberto, M
author_facet Pangallo, M
Loberto, M
author_sort Pangallo, M
collection OXFORD
description 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.
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spelling oxford-uuid:c89d9ab2-3184-4bde-8a3b-4056d2d320d42022-03-27T06:53:32ZHome is where the ad is: online interest proxies housing demandJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:c89d9ab2-3184-4bde-8a3b-4056d2d320d4Symplectic Elements at OxfordEDP Sciences2018Pangallo, MLoberto, MOnline 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.
spellingShingle Pangallo, M
Loberto, M
Home is where the ad is: online interest proxies housing demand
title Home is where the ad is: online interest proxies housing demand
title_full Home is where the ad is: online interest proxies housing demand
title_fullStr Home is where the ad is: online interest proxies housing demand
title_full_unstemmed Home is where the ad is: online interest proxies housing demand
title_short Home is where the ad is: online interest proxies housing demand
title_sort home is where the ad is online interest proxies housing demand
work_keys_str_mv AT pangallom homeiswheretheadisonlineinterestproxieshousingdemand
AT lobertom homeiswheretheadisonlineinterestproxieshousingdemand