In this paper, we evaluate price indices and hedonic price indices for Italian real estate data using multilevel models. The methodology is based on a random coefficient panel data model. We propose a Laspeyres-type price index and hedonic prices indexes based on some characteristics of the sold properties. The multilevel hierarchical analysis has the advantage of allowing the appraisal analysis for groups, and identified in the same sample data the hierarchical structures of market segmentation according to the parameters of the real estate segment. It allows getting a lot of regression functions as the number of groups identified. Obviously, this depends on the sample size and the variability between groups. Specifically, if the data are also grouped by date, the model allows an analysis of the time series which makes possible the calculation of index numbers and the overall monthly index numbers of real estate properties, consistent with collected data
