Market Heterogeneity and Determinants of Paris Apartment Prices: A Quantile Regression Approach

According to the INSEE database on second-hand apartment sales, overall apartment prices in Paris have been steadily growing from the first quarter of 1998 until the first half of 2009, when they dropped by an overall 7 percent, before resuming their ascent. Between the beginning of 2010 till the end of 2011, Paris apartment prices experienced a stunning growth of roughly 30% and have stabilized since. But some Paris 'arrondissements' have been doing better than others and are still expected to experience value rises in the near future. 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 the hedonic price of housing attributes over the 2000-2006 period for various price, hence income, segments of the housing market. For that purpose, quantile regression is applied to the 20 Paris 'arrondissements' as well as to the 80 neighbourhoods, or 'quartiers' (each 'arrondissement' is composed of 4 'quartiers'), with market segmentation being based on price deciles (deciles 1 to 9). The database includes some 159,000 sales spread over a seven year period (2000 – 2006). Housing descriptors include in particular, a price index, building age, apartment size, number of rooms and bathrooms, unit storey, the presence of a lift and 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'.Findings clearly suggest that hedonic 'relative' prices of most housing attributes significantly differ among deciles, although discrepancies tend to vary greatly in magnitude depending on the attribute. Among other findings, the elasticity coefficient of the size variable, which stands at 1.070 for the first price decile (cheapest units), is down to 1.026 for units belonging to the ninth one (dearest units). The floor where the apartment is located, the number of rooms and the building period are significant attributes as well and their impact differ depending on the unit price.