In this paper we explore spatial effects in a hedonic price function framework for a large sample of apartments in Moscow. We find strong evidence of both spatial lag and spatial autocorrelation. Our results are robust across both the spatial model specifications and the choice of the spatial weight matrices. The fact that the quality attributes’ shadow prices we estimate are not much different from the OLS (ML) estimates suggests that spatial effects are orthogonal to the quality characteristics. One interesting finding is that an increase in the kitchen area contributes much more significantly to the apartment’s price compared a marginal increase in the living area, which is reflecting the traditional role kitchen has been playing in the Russian households as a dining and communication area. House type, time needed to walk to the nearest subway station and subway time to the city center are other important apartment attributes. Methodologically, we believe our study is demonstrating the need to develop spatial econometric techniques for application in the environment where both types of spatial effects are simultaneously present.