The Research On Mass Appraisal Model Of Forest Resources Assets

After the reform of forest ownership, the assets evaluation of forest resources has more batches and its scale is smaller with assigning mountains to families. The assets evaluation of forest resources will consume large amount of personnel resources and its efficiency is low. While mass appraisal, a method this paper introduces, can efficiently to complete the evaluation task and save cost. It gives a new idea to assets evaluation of forest resources.This study is based on the features of the assets evaluation of forest resources, which are brought by the reform of forest ownership. For the first time to introduce mass appraisal principle and method to the assets evaluation of forest resources. We analyze the main factors of assets evaluation of forest resources. In accordance with different trees and age groups we use the conventional method to evaluate the assets. And then in view of age groups, the assets evaluation of juvenile forest, semi-mature forest, mature forest is carried out by setting up multiple regression analysis model, bayesian-regularization BP model, finally we use sample to text the model accuracy.Our results demonstrate that by using multiple regression analysis model to predict can get high accuracy, which can meet the requirements of forest resources assets evaluation. But it is based on specific theoretical model, it has some limitations. While Bayesian regularization BP neural network has no demands and can avoid effectively over-fitting in neural network training, and it has excellent generalization capabilities. The Bayesian regularization BP neural network model can improve the accuracy of the evaluation results. And so it gives new ideas for the application of mass appraisal in the assets evaluation of forest resources. It promotes the forest resources assets appraisal system to be more perfect, which can provide better service to "Agriculture, Ruralareas and Peasantry".