There is now a prior focus on transaction prices in purchasing civil airplanes by airlines and airplane financial leasing companies. Few investigations have been conducted in order to develop prediction models of civil airplane transaction price, while most of those proposed models are based on a variety of regression analysis methods which are usually limited to the estimation of prices of brand new airplanes. In the current study, a prediction model of civil airplanes transaction prices based on BP ANN （artificial neural network）is established, selecting length,wingspan, height, maximum take-off weight, maximum payload, range, cruising speed, seat number, capacity of cargo, thrust of engine and age as the inputs of the forecasting model. The BP ANN model is trained and calibrated using selected transaction sample data, and is then validated. Results show that it has the ability to generalize regulations of data and precise prediction. It is thus feasible to be used for predicting airplanes transaction prices.
