Cómo citar este artículo/Citation: Núñez Tabales, J. M., Rey Carmona, F. J., Caridad y Ocerin, J. M.ª (2016). Redes neuronales (RN) aplicadas a la valoración de locales comerciales. Informes de la Construcción, 69(545) RESUMENLa estimación de precios de inmuebles mediante la utilización de métodos objetivos es de interés para compradores, vendedores y para la propia Administración. Existen diferentes metodologías que permiten la determinación del precio de un inmueble, siendo numerosas las aportaciones cuyo propósito es la estimación de precios de inmuebles residenciales. No obstante, el presente trabajo es pionero en la aplicación de técnicas de Inteligencia Artificial (IA) para la determinación de precios de locales comerciales. Se presenta un estudio de esta tipología de inmueble en la ciudad de Córdoba (España). Los resultados evidencian que las Redes Neuronales (RN) constituyen una alternativa atractiva a los tradicionales Modelos Hedónicos (MH), registrando un mejor ajuste a las no linealidades del mercado y resultando con menores errores. Asimismo, se obtienen los precios implícitos correspondientes a los atributos determinantes del precio de un local comercial a partir de la ecuación de la RN diseñada.
In recent years, the number of sharing economy accommodations has grown exponentially due to the Internet and peer-to-peer networks, which has made researchers increasingly interested in analysing this new type of lodging. This study sought to develop models that determine the significant variables for the daily price of staying in holiday rentals based on data extracted from Booking.com and other sources. The hedonic pricing method (HPM) was selected to conduct the research as this methodology has been widely used in real estate valuation and hotel daily rate determination; however, the HPM is still rarely used for holiday rentals. The study focused on the city of Seville, where a notable increase in holiday rentals has been observed in recent years. Variables related to the accommodation typology, including location, size and equipment, as well as seasonality, are the most influential factors in the proposed models. These results are of interest to both owners and users of holiday rentals and can help these individuals to determine if the price of a stay is what would commonly be offered in the market under normal circumstances.
The enormous expansion of the video game sector, driven by the emergence of live video game streaming platforms and the professionalisation of this hobby through e-sports, has spurred interest in research on the relationships with potential adverse effects derived from cumulative use. This study explores the co-occurrence of the consumption and viewing of video games, based on an analysis of the motivations for using these services, the perceived positive uses, and the gamer profile. To that end, a multilayer perceptron artificial neural network is developed and tested on a sample of 970 video game users. The results show that the variables with a significant influence on pathological gaming are the motivation of a sense of belonging to the different platforms, as well as the positive uses relating to making friends and the possibility of making this hobby a profession. Furthermore, the individual effects of each of the variables have been estimated. The results indicate that the social component linked to the positive perception of making new friends and the self-perceived level as a gamer have been identified as possible predictors, when it comes to a clinical assessment of the adverse effects. Conversely, the variables age and following specific streamers are found to play a role in reducing potential negative effects.
Econometric hedonic models encounter s e v e r a l t h e o re t i c a l a n d p r a c t i c a l difficulties when applied to the real estate market, such as downward biases in the estimation of hedonic prices, subjective decisions in the measurement process of categorical attributes, frontier problems related to an imperfect information framework and uniequational specification. Many of these are linked to the parametric approach. A r t i f i c i a l N e u r a l N e t w o r k s ( A N N ) provide an attractive alternative: better dwelling prices estimates, avoidance of bias at different market segments, direct use of categorical data and full use of the information available. The price to be paid is the difficulties in the economic interpretation of network parameters. Nowadays, if the final objective to produce better estimates of the transaction prices, this methodology show lower errors, provided of a broad representative database of sales are recorded. A case study is presented for a medium size city in the South of Spain. Keywords
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.