Neural networks support vector machine for mass appraisal of properties

Yacim, Joseph Awoamim; Boshoff, Douw Gert Brand

doi:10.1108/pm-09-2019-0053

Cited by 10 publications

(6 citation statements)

References 86 publications

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“…This is consistent with the findings of previous studies in which there has been an increased use of AVMs for property valuations (Grover [32]; Wilkinson et al [6]). Research has also explored the use of hybrid AVM systems to enhance accuracy in mass valuation (Yacim and Boshoff [30]). Despite variations amongst participants in terms of expertise, all participants perceive changing technology as a key to future change in the property industry.…”

Section: Evolving Technology Drives the Future Of The Property Industrymentioning

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: Evolving Technology Drives the Future Of The Property Industrymentioning

confidence: 99%

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

Lee,

Yam,

Susilawati

et al. 2024

Buildings

View full text Add to dashboard Cite

show abstract

“…The primary challenge associated with the utilization of ANNs is the impact that differences in the architecture of the ANN model can have on the results (Renigier-Biłozor et al, 2022). ANNs operate as a "black box" system (Yacim & Boshoff, 2020), meaning that they do not provide a clear explanation of how the output values were generated. This lack of transparency makes ANNs challenging to use in certain applications, such as property taxation and expropriation, where the valuations may be questioned and need to be defended in a court of law.…”

Section: The Artificial Neural Network (Ann)mentioning

confidence: 99%

Identifying the Current Status of Real Estate Appraisal Methods

Numan,

Yusoff

2024

Real Estate Management and Valuation

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Real estate appraisal, also known as property valuation, plays a crucial role in numerous economic activities and financial decisions, such as taxation assessment, bank lending, and insurance, among others. However, the current methods used in real estate appraisal face several challenges related to fundamental aspects such as accuracy, interpretation, data availability, and evaluation metrics. Therefore, the purpose of this research is to identify the current status of real estate appraisal methods, highlighting challenges and providing guidance for scholars to undertake further research in addressing them. The methodology retrieves the most recent papers published in the Scopus database over the past five years, covering the period from 2019 to the end of 2023, with an emphasis on empirical studies. These retrieved papers serve as references to capture the current status of real estate appraisal methods. The research findings confirm a clear trend towards increased utilization of artificial intelligence techniques, especially machine learning, but with unfinished work regarding related challenges. Artificial intelligence techniques enhance the accuracy of real estate appraisal, paving the way for improved decision support systems in business, financial, and economic sectors.

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“…Hence, some researchers reported that the performance of ANN is better than other methods (Abidoye et al, 2019;Mccluskey et al, 2013;Mimis et al, 2013). While, in comparing hybrid techniques to other models, it is affirmed that hybrid systems are better (Guan et al, 2008;Liu et al, 2006;Yacim & Boshoff, 2020). selected.…”

Section: Related Researchesmentioning

confidence: 99%

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

Yakub

Ali

Achu

et al. 2021

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A relatively high level of precision is required in real estate valuation for investment purposes. Such estimates of value which is carried out by real estate professionals are relied upon by the end-users of such financial information having paid a certain fee for consultation hence leaving little room for errors. However, valuation reports are often criticised for their inability to be replicated by two or more valuers. Hence, stirring to a keen interest within the academic cycle leading to the need for exploring avenues to improve the price prediction ability of the professional valuer. This study, therefore, focuses on overcoming these challenges by introducing an integrated approach that combines ANFIS with ANN termed ANFIS-AN, thereby having a reiteration in terms of ANN application to fortify price predictability. Using 255 property data alongside 12 variables, the ANFIS-AN model was adopted and its outcome was compared with that of ANN. Finally, the results were subjected to 3 different error testing models using the same training and learning benchmarks. The proposed model’s RMSE gave 1.413169, while that of ANN showed 9.942206. Similarly, using MAPE, ANN recorded 0.256438 while ANFIS-AN had 0.208324. Hence, ANFIS-AN’s performance is laudable, thus a better tool over ANN.

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Neural networks support vector machine for mass appraisal of properties

Cited by 10 publications

References 86 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

Identifying the Current Status of Real Estate Appraisal Methods

An Integrated Approach Based on Artificial Intelligence Using Anfis and Ann for Multiple Criteria Real Estate Price Prediction

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