文章运用传统的数据包络分析方法（DEA）对上市房地产企业的效率进行评定，引入相应的分类指标对DEA在不变规模收益下求得的效率值进行分类，然后使用概率神经网络（PNN）和传统的多重判别分析方法（MDA）对分类效果进行模式识别，并比较了两种方法的识别精确度，研究发现：（1）PNN的预测精度要优于传统的判别分析方法；（2）通过DEA的求解结果可以得出所有上市房地产企业的标杆企业；（3）当前上市房地产企业的总体效率值偏低，有巨大的提升空间。

This study investigates the efficiency of real estate public company using data envelopment analysis. At the same time, the study puts forward the classifying index to classify the effective values solved by the data envelopment analysis method on the condition that the return to the scale is unchanged. Then the study uses the PNN method and MDA method to verify the effectiveness of classification and compares the classification accuracy of the probabilistic neural network (PNN) and the traditional multiple discriminant analysis(MDA) and the result shows that (1)the predicting accuracy of the PNN is better than the traditional MDA; (2)The benchmark enterprise can be concluded through the DEA method; (3) The current listed real estate enterprises overall efficiency value are low and have huge promotion space.

国家自然科学基金项目“大股东控制下的中国上市公司资本配置行为研究”(70772100);重庆市教委科技项目(KJ100609)