Michele Loberto, Andrea Luciani, Marco Pangallo
arXiv 6 Apr 2020 · Econometrics · 74 citations (OpenAlex)
arXiv:2004.02706 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Elliot Anenberg \ Steven Laufer (2017) A More Timely House Price Index | 1.000 | 5 | 3 | 100% |
| 2 | Monika Piazzesi, Martin Schneider \ Johannes Stroebel (2020) Segmented Housing Search | 0.811 | 4 | 2 | 100% |
| 3 | Dorinth W. van Dijk \ Marc K. Francke (2017) Internet Search Behavior, Liquidity and Prices in the Housing Market | 0.811 | 4 | 2 | 100% |
| 4 | Marco Pangallo \ Michele Loberto (2018) Home is where the ad is: online interest proxies housing demand self | 0.737 | 3 | 3 | 67% |
| 5 | Antonio Merlo \ Francois Ortalo-Magne (2004) Bargaining over residential real estate: evidence from England | 0.737 | 3 | 2 | 100% |
| 6 | L. Rachel Ngai \ Kevin D. Sheedy (2020) The Decision to Move House and Aggregate Housing-Market Dynamics | 0.737 | 3 | 2 | 100% |
| 7 | Paul E. Carrillo, Eric R. de Wit \ William Larson (2015) Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Net… | 0.644 | 2 | 2 | 100% |
| 8 | Edward Glaeser \ Joseph Gyourko (2018) The Economic Implications of Housing Supply | 0.644 | 2 | 2 | 100% |
| 9 | Lynn Wu \ Erik Brynjolfsson (2015) The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales | 0.644 | 2 | 2 | 100% |
| 10 | Michele Loberto, Andrea Luciani \ Marco Pangallo (2018) The potential of big housing data: an application to the Italian real-estate market self | 0.585 | 3 | 3 | 33% |
Showing the top 10 of 23 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | On learning agent-based models from data | 0.644 | 2 | 2 |