Implicit Prices in Urban Real Estate Valuation

Abstract

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

Estimation of Residential Property Market Price: Comparison of Artificial Neural Networks and Hedonic Pricing Model

“…Based on the results, it can be stated that pricing models based on ANNs achieved a better predictive ability than the pricing model based on the regression method. Better prediction accuracy of ANNs compared to regression pricing models was reported by Moreno-Izquierdo et al (2018), Tabales et al (2013), Kutasi and Badics (2016), and Abidoye and Chan (2018) [7,[14][15][16]. These results are also supported by the literature review by Valier (2020), which examined research papers analyzing the accuracy of automated valuation models [32].…”

“…He reported a slightly higher accuracy for the GWR model compared to ANN. Tabales et al (2013), Kutasi and Badics (2016), and Abidoye and Chan (2018) conducted a comparison of the accuracy of real estate property appraisal using ANN and multiple regression analysis (MRA) representing hedonic pricing model [14][15][16]. Reported results show ANN's ability to achieve higher accuracy in price estimation.…”

Estimación de los costes de construcción de viviendas rústicas mediante Redes Neuronales Artificiales

“…La implementación de la red se ha llevado a cabo mediante el software informático MATLAB (30). Todos los datos se han normalizado entre 0 y 1 para homogeneizarlos y evitar grandes dispersiones (31). El entrenamiento se ha configurado con 300 iteraciones.…”

Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina

“…ANNs have gained recognition as a cost-effective and robust system capable of handling model misspecification and measurement errors in explanatory variables (Peterson and Flanagan, 2009). In the early stages of investigation, a number of studies compared ANNs to hedonic regression models, with some reporting superior predictive accuracy for ANNs (Do and Grudnitski, 1992;Tay and Ho, 1992;Pearson et al, 1995;Evans et al, 1992;Collins and Evans, 1994;Liu et al, 2011;N uñez Tabales et al, 2013;Ecer, 2014;Aminuddin and Maimun, 2020;Valier, 2020). Nonetheless, other scholars such as Worzala et al (1995), McGreal et al (1998 have expressed reservations about the reliability and comprehensibility of ANNs.…”