Market Heterogeneity and Investment Risk – Applying Quantile Regression to the Paris Apartment Market, 1990-2006

Since the second quarter of 1998, and with the exception of the 10% downturn experienced in the first half of 2009, apartment prices in Paris have been rising steadily. Their pace of growth even accelerated from early 2010 onward and, unless interest rates are raised to their historical trend levels – which remains highly improbable, most “arrondissements” are still expected to experience value rises over the years to come due to both the relative scarcity of apartment supply and the profile of buyers that are little affected by the current European economic crisis. In the presence of market heterogeneity, the ability of traditional appraisal methods to capture the true property market value may be questioned and emerges as a major issue for local authorities that collect property tax as well as for mortgage lenders confined to tight lending provisions in a crisis context. Assessing such differences in a reliable way is a step forward towards improving mortgage lending risk management. In this paper, the heterogeneity of the Paris apartment market is addressed through assessing the differences in both the hedonic price of housing attributes and price appreciation over time for various price, hence income, segments of the housing market. For that purpose, quantile regression is applied to the 20 “arrondissements” as well as to the 80 neighborhoods, or “quartiers” (each “arrondissement” is composed of four “quartiers”) and segmented constant-quality house price indices are computed and compared with an overall index. The database, which is provided by the Chambre des Notaires de France, includes cases spread over a 17 year period, that is, from 1990 to 2006. Housing descriptors include, among other things, the building age, the apartment size and the number of rooms, the floor level, the presence of a garage, the type of street and access to building (boulevard, square, alley, etc.) as well as a series of location dummy variables standing for the “arrondissements” and “quartiers”. Preliminary findings suggest that, during the slump, extreme prices by and large define the efficient frontier, with higher returns being reported for lower-price, central apartments as opposed to a lower volatility for upper-price, outlying ones. During the recovery and boom stages of the cycle, the optimal portfolio is obtained through a blend of middle-price apartments, with a lower volatility and higher returns for central and outlying “arrondissements”, respectively.