In this paper, the heterogeneity of the Paris apartment market is addressed. For this purpose, quantile regression is applied – with market segmentation based on price deciles – and the hedonic price of housing attributes is computed for various price segments of the market. The approach is applied to a major data set managed by the Paris region notary office (Chambre des Notaires d’Île de France), which consists of approximately 156,000 transactions over the 2000–2006 period. Although spatial econometric methods could not be applied owing to the unavailability of geocodes, spatial dependence effects are shown to be adequately accounted for through an array of 80 location dummy variables. The findings suggest that the relative hedonic prices of several housing attributes differ significantly among deciles. In particular, the elasticity coefficient of the apartment size variable, which is 1.09 for the cheapest units, is down to 1.03 for the most expensive ones. The unit floor level, the number of indoor parking slots, as well as several neighbourhood attributes and location dummies all exhibit substantial implicit price fluctuations among deciles. Finally, the lower the apartment price, the higher the potential for price appreciation over time. While enhancing our understanding of the complex market dynamics that underlie residential choices in a major metropolis such as Paris, this research provides empirical evidence that the QR approach adequately captures heterogeneity among house price ranges, which simultaneously applies to housing stock, historical construct and social fabric.