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

By V. Limsombunchai. 2004

294 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/787e1ccad445cdf0bc7f05844bb9faba277aa309

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Jengei Hong
2. Woo-sung Kim
3. Heeyoul Choi

In Electrical and Electronic Engineering

1. Jengei Hong
2. Woo-sung Kim
3. Heeyoul Choi

Citations in the corpus

• A HOUSE PRICE VALUATION BASED ON THE RANDOM FOREST APPROACH: THE MASS APPRAISAL OF RESIDENTIAL PROPERTY IN SOUTH KOREA (2020, methodology): “Relative to the predictive models in previous studies in Limsombunchai (2004), Selim (2009), Antipov and Pokryshevskaya (2012), and Čeh et al. (2018), the performance measures of RF−R2 values (97.6%), mean absolute percentage error (MAPE, 5.482%), coefficient of dispersion (COD, 5.484%), and…”