What do online listings tell us about the housing market?

TL;DR

This paper analyzes online housing ads in Italy to assess their usefulness for market insights, addressing duplicate issues with machine learning and highlighting their potential for real-time demand and supply monitoring.

Contribution

It provides the first comprehensive analysis of online housing ads, identifying duplicate challenges and demonstrating their value for timely market analysis.

Findings

Duplicates are mainly caused by sellers trying to increase visibility.

Machine learning can effectively identify duplicate ads.

Online data can monitor housing demand, supply, and prices more promptly.

Abstract

Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads ("duplicates") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.