Who performs better? AVMs vs hedonic models

Valier, Agostino

doi:10.1108/jpif-12-2019-0157

Cited by 43 publications

(34 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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]. ANNs were indicated as more effective and reliable for mass evaluation of residential properties compared to regression models in 29 research papers.…”

Section: -Results and Discussionsupporting

confidence: 57%

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

et al. 2020

View full text Add to dashboard Cite

The correct real estate property price estimation is significant not only in the real estate market but also in the banking sector for collateral loans and the insurance sector for property insurance. The paper focuses on both traditional and advanced methods for real estate property valuation. Attention is paid to the analysis of the accuracy of valuation models. From traditional methods, a regression model is used for residential property price estimation, which represents the hedonic approach. Modern advanced valuation methods are represented by the artificial neural network, which is one of the soft computing techniques. The results of both methods in residential property market price estimation are compared. The analysis is performed using data on residential properties sold on the real estate market in the city of Nitra in the Slovak Republic. To estimate the residential property prices, artificial neural networks trained with the Levenberg-Marquart learning algorithm, the Bayesian Regularization learning algorithm, and the Scaled Conjugate Gradient learning algorithm, and the regression pricing model are used. Among the constructed neural networks, the best results are achieved with networks trained with the Regularization learning algorithm with two hidden layers. Its performance is compared with the performance of the regression pricing model, and it can state that artificial neural networks can considerably improve prediction accuracy in the estimation of residential property market price. Doi: 10.28991/esj-2020-01250 Full Text: PDF

show abstract

Section: -Results and Discussionsupporting

confidence: 57%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Future Price [60] Used Jointly SVR LASSO Transport Future Demand [26] Used Jointly RF FE-R Trade Future Performance [46] Used Jointly DL Regression Tourism Future Performance [53] ML outperforms SVR/KNN PCR Tourism Future Price [72] ML outperfoms AVM ML Regression investment Theorical [22] ML outperfoms ANN Regression Tourism Future Demand [2] ML outperfoms DNN ARIMA Investment Future Price [27] Used Jointly SVR LASSO Transport Future price [47] ML outperforms NN ARIMA Investment Future Price [69] Used Jointly D-ML Regression Theorical Theorical Method [38], [62] [15], [31], [29], [14], [56] [17]…”

Section: Ga Hodrick-prescott Stock Marketmentioning

confidence: 99%

Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

Pérez-Pons

Parra-Domínguez

Omatu

et al. 2021

Journal of Artificial Intelligence and Soft Computing Research

View full text Add to dashboard Cite

Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.

show abstract

“…Given the rise of machine learning and applications to automated valuation models in real estate, the future of valuation is likely to be automated. However, the success of these models will rely heavily on making sure the users understand how the new technologies work so they can comprehend their inherent strengths and possible weaknesses (Valier, 2020).…”

Section: Impact Of Industry 40mentioning

confidence: 99%

The rise of PropTech: emerging industrial technologies and their impact on real estate

Starr

Saginor

Worzala

2020

JPIF

View full text Add to dashboard Cite

PurposeIndustry 4.0 recognizes a broad set of technologies that rapidly redefine industry, including real estate. These broad technologies include the Internet of things (IoT), cloud computing, decision automation, machine learning and artificial intelligence. This paper explores applies Industry 4.0 to commercial real estate, resulting in a framework defined here as Real Estate 4.0, a concept that encompasses fintech and proptech.Design/methodology/approachThis research paper examines Industry 4.0 technology to construct a framework for Real Estate 4.0. We also focus on how the COVID-19 pandemic is accelerating proptech, particularly as it relates to getting employees back into their traditional work environments.FindingsAs a research paper, this is not a traditional research project with empirical findings. It is a primer on how the rapidly changing technologies of Industry 4.0 are now disrupting and transforming real estate today into what we are calling Real Estate 4.0.Practical implicationsPractitioner insight and future research are informed by a framework for Real Estate 4.0 drawn from the technologies of Industry 4.0. Additional implications are outlined for practical, systemic change as a result of the COVID-19 pandemic within the scope of Real Estate 4.0 technology.Originality/valueThis is a combined effort by experts in three contributing disciplines: systems science, planning and real estate. Our intent is to provide a primer for those of us in the latter two fields so that we can embrace the rapidly changing built environment landscape as it adjusts and adapts to a post COVID-19 environment that will be critical to maintain real estate investment values and enhance the real estate user's experience.

show abstract

Who performs better? AVMs vs hedonic models

Cited by 43 publications

References 40 publications

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

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

Machine Learning and Traditional Econometric Models: A Systematic Mapping Study

The rise of PropTech: emerging industrial technologies and their impact on real estate

Contact Info

Product

Resources

About