del Cacho, Carlos (2010): A comparison of data mining methods for mass real estate appraisal.
Download (292kB) | Preview
We compare the performance of both hedonic and non-hedonic pricing models applied to the problem of housing valuation in the city of Madrid. Urban areas pose several challenges in data mining because of the potential presence of different market segments originated from geospatial relations. Among the algorithms presented, ensembles of M5 model trees consistently showed superior correlation rates in out of sample data. Additionally, they improved the mean relative error rate by 23% when compared with the popular method of assessing the average price per square meter in each neighborhood, outperforming commonplace multiple linear regression models and artificial neural networks as well within our dataset, comprised of 25415 residential properties.
|Item Type:||MPRA Paper|
|Original Title:||A comparison of data mining methods for mass real estate appraisal|
|Keywords:||mass appraisal, real estate, data mining|
|Subjects:||L - Industrial Organization > L8 - Industry Studies: Services > L85 - Real Estate Services|
|Depositing User:||Carlos del Cacho|
|Date Deposited:||12. Dec 2010 20:25|
|Last Modified:||13. Feb 2013 16:48|
Acciani, Claudio et al (2008) - Model Tree: An application in real estate appraisal
Bourassa, Steven (2002) - Do Housing Submarkets Really Matter?
Limsombunchai, Visit et al (2004) - House Price Prediction: Hedonic Price Model vs. Artificial Neural Network
McCluskey, William and Anand Sarabjot (1999) - The application of intelligent hybrid techniques for the mass appraisal of residential properties
Parker, David (2006) - Automated Valuation Models: A Practitioner Perspective
Peterson, Steven - Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal
van Wezel ,Michiel et al (2005) - Boosting the Accuracy of Hedonic Pricing Models