Real estate accounts for 61% of France's national net wealth. Housing is the largest item of  expenditure of French households. Indices that track real estate prices evolution are thus crucial  instruments for decision makers of all kinds: households, investors, the scientific community,  local governments, etc. Yet, the available public statistics fail to cope with the heterogeneity of  the housing prices dynamics across the country. 

In France, Notaire-Insee indices are considered as the reference, especially because their  methodology and indices are open source. Quarterly, the institute produces indices for  apartments and houses in big agglomerates. With 9 indices for house prices in France, the  division proposed by this methodology hides a lot of disparities. For instance, the “Province”  house index includes more than 25000 cities as diverse as Toulouse (450k inhabitants) and  Malroy (350 inhabitants), which represents 36% of the French housing stock. This indicator  does not make it possible to highlight the differences in dynamics between cities geographically  distinct and drived by different fundamentals due to different economic conditions. 

This work aims at producing a library of open data real estate price indices that track price  evolution at fine geographical scale. To do so we develop a methodology for real estate price  index computation, and then apply it on geographical clusters close to local markets. We want to be part of an open-source approach. Indeed, the methodology will be published, and  all the indices will be made available for free to all. 

The proposed method is applied on the fiscal database of real estate transactions DV3F,  containing all the transactions in France (except Alsace, Moselle and Mayotte) between 2010  and 2020. 

Our approach is based on classic hedonic price index methods. Each aspect and hypothesis of  the hedonic method have been justified to produce precise indices. 

Producing indices close to local markets requires working in a low data environment, and  increases the probability of encountering outliers. Hedonic methods being very sensitive to  outliers, we tackle this issue by testing the impact of different dynamics filters methods. To reduce the heteroscedasticity and improve the precision of the model, different forms and  combinations of the regression have been tested. 

This method is applied to 2 divisions of France: one for apartments, another for houses. In order  to produce indices close to local markets, a clusterization of cities of France is computed as  finely as possible and based on socio economic and local housing stock criteria. To preserve the  quality of indexes, all clusters respect constraints of minimum transaction volumes. This  division is based on a clusterization of urban areas thanks to Ascending Hierarchical  Classification and Kohonen algorithms. 

This clusterization resulted in the computation of 350 apartments and 400 houses indices. The  application of our approach on these geographical clusters reveals a great diversity of house  price dynamics. For instance, the “Province” index produced by Notaire-Insee is divided into  220 clusters, with variations between 2015 and 2020 of 2% and 29% respectively for the first  and ninth decile of these indices. 

By highlighting the plurality of real estate price dynamics in France and urban centers, our  approach emphasizes the need for indices to be computed on a local scale to be useful.