Valuers’ receptiveness to the application of artificial intelligence in property valuation

Abidoye, Rotimi Boluwatife; Chan, Albert P.C.

doi:10.1080/14445921.2017.1299453

Cited by 18 publications

(13 citation statements)

References 58 publications

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“…This conforms to the findings of Abidoye and Chan (2017b), who found that the professional bodies and educational institutions are partly responsible for the lack of awareness and adoption of the advanced valuation methods by valuers in Nigeria.…”

Section: Resultssupporting

confidence: 91%

Property valuation methods in practice: evidence from Australia

Abidoye

Lam

et al. 2019

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Purpose Improving valuation accuracy, especially for sale and acquisition purposes, remains one of the key targets of the global real estate research agenda. Among other recommendations, it has been argued that the use of technology-based advanced valuation methods can help to narrow the gap between asset valuations and actual sale prices. The purpose of this paper is to investigate the property valuation methods being adopted by Australian valuers and the factors influencing their level of awareness and adoption of the methods. Design/methodology/approach An online questionnaire survey was conducted to elicit information from valuers practising in Australia. They were asked to indicate their level of awareness and adoption of the different property valuation methods. Their response was analysed using frequency distribution, χ2 test and mean score ranking. Findings The results show that the traditional methods of valuation, namely, comparative, investment and residual, are the most adopted methods by the Australian valuers, while advanced valuation methods are seldom applied in practice. The results confirm that professional bodies, sector of practice and educational institutions are the three most important drivers of awareness and adoption of the advanced valuation methods. Practical implications There is a need for all the property valuation stakeholders to synergise and transform the property valuation practice in a bid to promote the awareness and adoption of advanced valuation methods, (e.g. hedonic pricing model, artificial neural network, expert system, fuzzy logic system, etc.) among valuers. These are all technology-based methods to improve the efficiency in the prediction process, and the valuer still needs to input reliable transaction data into the systems. Originality/value This study provides a fresh and most recent insight into the current property valuation methods adopted in practice by valuers practising in Australia. It identifies that the advanced valuation methods could supplement the traditional valuation methods to achieve good practice standard for improving the professional valuation practice in Australia so that the valuation profession can meet the industry’s expectations.

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Section: Resultssupporting

confidence: 91%

Property valuation methods in practice: evidence from Australia

Abidoye

Lam

et al. 2019

Self Cite

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“…This suggests that most of the valuers practising in the Australian property market are male. This is the similar gender mix reported about real estate professionals in previous studies by Mooya (2015) (South Africa), Wilkinson et al (2018) (Australia), and Abidoye and Chan (2017) (Nigeria), among others. This suggests that the real estate profession is dominated by men.…”

Section: Resultssupporting

confidence: 88%

“…Also, the Chi-square ( χ 2 ) test was conducted to examine the statistical relationship that exists between valuers' profile and factors that affect property valuation inaccuracy in Australia. A similar approach was adopted in previous studies (see, Abidoye and Chan, 2017 among others). In order to ensure the reliability, validity and consistency of the collected data, the Cronbach's alpha test was performed.…”

Section: Data and Research Methodologymentioning

confidence: 99%

An updated survey of factors influencing property valuation accuracy in Australia

Abidoye

Huang

Amidu

et al. 2021

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PurposeThis study updates and extends the current work on the issue of accuracy of property valuation. The paper investigates the factors that contribute to property valuation inaccuracy and examines different strategies to achieve greater accuracy in practice.Design/methodology/approachAn online questionnaire was designed and administered on the Australian Property Institute (API) registered valuers, attempting to examine their perceptions on the current state of valuation accuracy in Australia. The variables/statements from responses are ranked overall and compared for differences by the characteristics of respondents.FindingsUsing mean rating point, the survey ranked three factors; inexperience valuers, the selection, interpretation and use of comparable evidence in property valuation exercise and the complexity of the subject property in terms of design, age, material specification and state of repairs as the most significant factors currently affecting valuation inaccuracy. The results of a Chi-square test did not, however, show a significant statistical relationship between respondents' profile and the perception on the comparative importance of the factors identified. Except for valuers' age and inexperience valuers and valuers' educational qualification and inexperience valuers and the selection, interpretation and use of comparable evidence in property valuation exercise. Also, the three highly ranked strategies for reducing the level of inaccuracy are: developing a global mindset, use of advanced methodology and training valuers on market forecasting skills.Practical implicationsIn order for valuers to provide state-of-the-art service to the public and to remain relevant, there is a need to accurately and reliably estimate valuation figures. Hence, the strategies highlighted in this study could be considered in a bid to reduce property valuation inaccuracy in practice.Originality/valueThis study provides an updated overview of the issue of property valuation inaccuracy in the Australia valuation practice and examines the strategies to reduce it.

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“…They will be able to take advantage of the services sold to them by the largest players in the industry. Technological innovation will therefore bring radical changes to the current structure of the professional sector of evaluators (Abidoye and Chan, 2017).…”

Section: Avms Vs Hedonic Modelsmentioning

confidence: 99%

Who performs better? AVMs vs hedonic models

Valier

2020

JPIF

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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

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Valuers’ receptiveness to the application of artificial intelligence in property valuation

Cited by 18 publications

References 58 publications

Property valuation methods in practice: evidence from Australia

Property valuation methods in practice: evidence from Australia

An updated survey of factors influencing property valuation accuracy in Australia

Who performs better? AVMs vs hedonic models

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