Bare ownership evaluation. Hedonic price model vs. artificial neural network

Authors:
Pierluigi Morano;Francesco Tajani
Affiliations:
Department of Science of Civil Engineering and Architecture, Polytechnic of Bari, Via Orabona 4, 70125 Bari, Italy;Department of Civil Engineering, University of Studies of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano SA, Italy
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2013

Citing 7
Cited 0

Multilayer feedforward networks are universal approximators

Neural Networks
Neural networks and non-linear statistical methods: an application to the modelling of price-quality relationships

Computers and Operations Research
Real Estate Value at Porto Alegre City Using Artificial Neural Networks

SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
LIA: An Intelligent Advisor for e-Learning

WSKS '08 Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society
Determinants of house prices in Turkey: Hedonic regression versus artificial neural network

Expert Systems with Applications: An International Journal
Application of fuzzy neural network for real estate prediction

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process

MICAI '11 Proceedings of the 2011 10th Mexican International Conference on Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the current downturn of European real estate market, the sale of the bare ownership is creating a growing interest of market operators. The attention to this formula is not only local, but also comes from foreign investors, attracted by the substantial saving on the purchase price, the revaluation of the property over time, as well as the breakdown of the tax burden and the costs of management and maintenance. The aim of this research is, first of all, to deepen the knowledge of the bare ownership market of residential properties. Secondly, it is to develop an effective tool for estimating the bare ownership. Therefore, on the same database and with reference to the same explanatory variables of the price of bare ownership, two estimation models are implemented, one based on hedonic prices theory and another using artificial neural networks ANN, in order to compare the respective performance.