Conference « Alternative technics in housing and construction during the Covid crisis »

A conference was organized by the Alter Property Data Network on November 21st, 2022, hosted by ReFinE/Institut Louis Bachelier. This network gathers economists, data scientists and practitioners from all over the world (from the IMF, World Bank, OECD, European Commission, BIS, central banks, universities…) who share information, data, programs and papers on alternative techniques (web-scraping, mobile data, satellite data, machine learning, text mining…) related to housing and construction in general.

The organizers were, in alphabetical order:

  • Alexandre Banquet, OECD
  • Kevin Beaubrun-Diant, ReFinE, Institut Louis Bachelier and Paris-Dauphine University
  • Daniela Behr, International Finance Corporation (IFC), World Bank Group
  • Nina Biljanovska, IMF
  • Jean-Charles Bricongne, Banque de France
  • Boris Cournède, OECD
  • Ludovic Gauvin, Yanport
  • Deniz Igan, BIS
  • Basile Pfeiffer, Ministère de la transition écologique
  • Pierre-Alain Pionnier, OECD
  • Volker Ziemann, OECD


The theme of this conference was:  To what extent do alternative techniques (web-scraping, satellite data…) help to better understand what happened during the Covid crisis in housing and construction?


After a transversal introductory statement by Yunhui Zhao (IMF), this conference gave insights mainly from two different perspectives, first showing how alternative tools such as web-scraping or satellite data can give additional information on real estate during the Covid-19 and showing the latest developments on methodological tools on housing and construction.


The first part included:


The second part on methodological tools included the following presentations:

  • Alexandre Banquet (OECD): presentation of advances in satellite data for the monitoring of built areas: Dynamic World and new version of GHSL
  • Nina Biljanovska (IMF), Chenxu Fu (IMF) & Deniz Igan (BIS), Housing Affordability: A New Panel Dataset
  • Emmanuel Flachaire, Gilles Hacheme, Sullivan Hué and Sébastien Laurent (AMSE): GAM(L)A: An econometric models for interpretable Machine Learning, used to forecast housing prices in Boston (USA);  
  • Pierre-Alain Pionnier (OECD), jointly with Johannes Schuffels: Estimating regional house price levels; Methodology and results of a pilot project with Spain,  


The replay of this event is available here.