House Price Prediction: Hedonic Price Model vs. Artificial Neural Network

The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on 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.


Issue Date:
2004-06
Publication Type:
Conference Paper/ Presentation
PURL Identifier:
http://purl.umn.edu/97781
Total Pages:
15
JEL Codes:
C53; L74




 Record created 2017-04-01, last modified 2017-08-25

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