The economic and social importance of the real estate market corresponds to overall economic development, but the housing sector can also be the cause of vulnerability and crisis. Therefore, the problem of estimating the value of real estate is always current and complex due to the influence of a large number of variables (macroeconomic, construction, etc.). Contrary to conventional real estate estimation methods, new approaches have been developed to evaluate prices in real estate markets. In addition to the classic time series method in the last decades, models of artificial neural networks that provide more objective and accurate estimates have been developed. This research provides a prognostic model of real estate market prices for Santiago of Chile based on a hybrid model with ARIMA and autoregressive artificial neural networks. For an accurate and fast estimation of house prices with about 87% reliability, it is possible to use that hybrid model. We consider the obtained level of reliability as very high, it is about modeling the sociotechnical system. More accurate pricing and reliable information could be obtained if a larger set of input parameters would be included. It shows that neural networks can model nonlinear behavior of input variables and generalize real estate prices data for random inputs in the network training range. The model shows a satisfactory degree of forecasted precision.