House Price Prediction: Hedonic Price Model vs. Artificial Neural Network
Visit Limsombunchai, Christopher Gan and Minsoo Lee
DOI : 10.3844/ajassp.2004.193.201
American Journal of Applied Sciences
Volume 1, Issue 3
The objective of this study is to empirically compare the predictive power of the hedonic model with an artificial neural network model for house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.
© 2004 Visit Limsombunchai, Christopher Gan and Minsoo Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.