For mass appraisal in real estate, the hedonic pricing method (HPM) tends to be most commonly used by academic researchers, while the comparable sales approach (CSA) is mostly preferred by professionals. This paper shows how CSA is a constrained version of a spatial autoregressive model, which can be implemented by simple matrix calculations. The CSA takes into account information on individual characteristics identifying similar complex goods, spatial proximity reflecting similar spatial amenities and temporal constraints by only selecting past sales. Using US transaction data from Lucas County, Ohio, we compare CSA to a-spatial HPM results and conduct an out-of-sample exercise to gauge the prediction performance of the two approaches. The findings suggest that CSA is a very useful tool for mass appraisal, especially when the number of independent variables available is limited.