Barriers, drivers and prospects of the adoption of artificial intelligence property valuation methods in practice

Abidoye, Rotimi Boluwatife; Ma, Junge; Lee, Chyi Lin

doi:10.1080/14445921.2021.2001724

Cited by 7 publications

(3 citation statements)

References 61 publications

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“…The use of AVMs for property valuations has increased (Grover [32]; Wilkinson et al [6]) because it is more efficient and superior to traditional valuation methods in terms of accuracy (Abidoye et al [33]; Chaphalkar and Sandbhor [34]; Kok et al [35]). Furthermore, some property service providers in Australia, like CoreLogic and PropTrack, provide property valuation in real time using AVMs (CoreLogic [36]; PropTrack [37]).…”

Section: Property Technologymentioning

confidence: 99%

The Future Property Workforce: Challenges and Opportunities for Property Professionals in the Changing Landscape

Lee,

Yam,

Susilawati

et al. 2024

Buildings

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The rapid advancement of technology has revolutionised how we live and work, posing challenges and opportunities for various professions, including the property and construction workforce. The COVID-19 pandemic has further accelerated the pace of change. Therefore, in this study, we examined the future property workforce and the required skills for Property Industry 4.0 by conducting semi-structured interviews with property leaders. The findings suggest that digitisation and automation are reshaping the property workforce, including those working in development and construction, necessitating efforts to bridge the gap between graduates’ technology proficiency and practical application. Moreover, calls for proactive regulation of artificial intelligence (AI) use in the property sector highlight the need for regulator and professional body involvement. This study also shows the challenges and opportunities for property professionals with an increased focus on environmental, social, and governance (ESG) matters and the challenges of balancing global expansion with local adaptability due to globalisation. Furthermore, this work highlights a concerning decline in communication skills among graduates, which is partly attributed to the pandemic. Collaborative efforts between universities and industry are essential to cultivate these vital skills among future property professionals. The implications of this study are also discussed.

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Section: Property Technologymentioning

confidence: 99%

The Future Property Workforce: Challenges and Opportunities for Property Professionals in the Changing Landscape

Lee,

Yam,

Susilawati

et al. 2024

Buildings

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“…Property technologies such as "Blockchain", "Property Passports", or "Automated Valuation Models" in recent years have made valuation much faster and cheaper. Abidoye et al (2022) investigate the barriers, drivers, and prospects of adopting artificial intelligence valuation methods in practice. Whether home buyers and sellers anchor to the traditional appraisal values or to market transaction information is the fundamental research question of this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Homebuyer Purchase Decisions: Are They Anchoring to Appraisal Values or Market Prices?

Cheung

Yiu

Guan

2022

JRFM

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Price discovery is an important research topic in real estate due to the heterogeneous nature of housing attributes and relatively thin trading activities compared to other assets. In Commonwealth countries, including New Zealand, governments usually conduct periodic appraisals for the purpose of collecting rates and levies. Such official appraisal values of properties, also known as capital values (CVs), are considered a price anchor for market participants in their negotiation processes. Real estate agents often use these appraisal values to advertise their listings and negotiate transaction prices. In this study, we aim to make an initial attempt to study the influence of CV on market prices using Granger causality tests and a hedonic pricing model. To test the lead-lag relationships, three million housing transactions from 1990 to 2020 in New Zealand are used to construct the capital values (CVs) and transacted prices (TPs) indices in both primary and secondary housing markets. The Granger causality test suggests that the indices of TPs and CVs have a bi-directional lead-lag relationship in the secondary housing market, whereas the relationship does not follow in the primary market where the information on CVs is unavailable. The results imply the existence of a CV anchoring effect. Such anchoring effects are also contingent on the timeliness of price anchors, which is consistent with the availability heuristic from behavioural economics.

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“…Bogin and Shui (2020) argued that rural properties are estimated above contract prices and suggested alternative valuation techniques to reduce this upward bias in rural areas. Abidoye et al (2021) provided prospects for the adoption of artificial intelligence in property valuation, and several studies have attempted to enhance the accuracy of property valuation using advanced models such as neural networks (Peterson and Flanagan, 2009;Morano et al, 2015;Sing et al, 2021). Most recently, Lee (2021a, b) predicted land prices in Seoul, South Korea by combining supervised and unsupervised learning models.…”

Section: Property Valuationmentioning

confidence: 99%

Designing an optimal neural network architecture: an application to property valuation

Lee

2022

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PurposeThe success of a neural network depends on, among others, an architecture that is appropriate for the task at hand. This study attempts to identify an optimal architecture of a neural network in the context of property valuation, and aims to test the ability of connecting related neural networks to reduce the property valuation error.Design/methodology/approachThis study explores efficient network architectures to estimate land and house prices in Seoul, South Korea. The input is structured data, and the embedding technique is used to process high-cardinality categorical variables.FindingsThe shared architecture of a network for simultaneous estimation of both land and houses was revealed to be the best performing network. Through weight sharing between relevant layers in networks, the root-mean-square error (RMSE) for land price estimation was reduced significantly, from 0.55–0.68 using the baseline architecture, to 0.44–0.47 using the shared architecture.Originality/valueThe study results are expected to encourage active investigation of efficient architectures by using domain knowledge, and to promote interest in using structured data, which is still the dominant type in most industries.

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Barriers, drivers and prospects of the adoption of artificial intelligence property valuation methods in practice

Cited by 7 publications

References 61 publications

The Future Property Workforce: Challenges and Opportunities for Property Professionals in the Changing Landscape

The Future Property Workforce: Challenges and Opportunities for Property Professionals in the Changing Landscape

Homebuyer Purchase Decisions: Are They Anchoring to Appraisal Values or Market Prices?

Designing an optimal neural network architecture: an application to property valuation

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