A Hybrid Model Using Autoregressive Neural Networks to Assessment the Real Estate Market Values in Santiago of Chile

The economic and social importance of the real estate market corresponds to overall economic development, but the housing sector can also be the cause of vulnerability and crisis. Therefore, the problem of estimating the value of real estate is always current and complex due to the influence of a large number of variables (macroeconomic, construction, etc.). Contrary to conventional real estate estimation methods, new approaches have been developed to evaluate prices in real estate markets. In addition to the classic time series method in the last decades, models of artificial neural networks that provide more objective and accurate estimates have been developed. This research provides a prognostic model of real estate market prices for Santiago of Chile based on a hybrid model with ARIMA and autoregressive artificial neural networks. For an accurate and fast estimation of house prices with about 87% reliability, it is possible to use that hybrid model. We consider the obtained level of reliability as very high, it is about modeling the sociotechnical system. More accurate pricing and reliable information could be obtained if a larger set of input parameters would be included. It shows that neural networks can model nonlinear behavior of input variables and generalize real estate prices data for random inputs in the network training range. The model shows a satisfactory degree of forecasted precision.

Silvia Salinas-Ayaviri and Marcelo Villena
Tuesday, September 25, 2018 - 18:00 to 18:15


The official Hotel of the Conference is
Makedonia Palace.

Conference Organiser: NBEvents

The official travel agency of the Conference is: Air Maritime

Photo of Thessaloniki seafront courtesy of Juli Bellou
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