Predicting price of Taiwan real estates by neural networks and support vector regression

Authors:
Hong-Yu Lin;Kuentai Chen
Affiliations:
Department of Business and Management, Mingchi University of Technology, New Taipei, Taiwan;Department of Industrial Engineering and Management, Mingchi University of Technology, New Taipei, Taiwan
Venue:
Proceedings of the 15th WSEAS international conference on Systems
Year:
2011

Citing 7
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
The nature of statistical learning theory

The nature of statistical learning theory
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems

Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Fuzzy and Neural Approaches in Engineering

Fuzzy and Neural Approaches in Engineering
Support Vector Machine for Regression and Applications to Financial Forecasting

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Combinatorial time series forecasting based on clustering algorithms and neural networks

Neural Computing and Applications
Forecasting stock market movement direction with support vector machine

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The main purpose of this study is to predict the real estate price in Taiwan efficiently. Neural networks and Support Vector Regression are applied and compared. Variables are first selected from previous research and than chose 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.