Soft computing algorithms in price of Taiwan real estates

The prediction of real estate price is important because it concerns both individuals and government. Other than traditional statistic methods, Neural networks and Support Vector Regression have demonstrated their advantages in previous research, and thus are applied and compared in this study. Variables are first summarized and concluded from earlier research and than selected by stepwise procedure and trial-and-error methods. It is found that SVR with trial-and-error method performed the best with MAPE=4.466% and R2=0.8540. In addition, Rediscount rate, Money supply, and Price of last month are the three common variables for both BPNN and SVR. The economic explanation and relations to the housing price for selected variables are also provided.

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