Mass valuations

Abstract

Purpose – The purpose of this paper is to review the issues involved in the implementation of mass valuation systems and the conditions needed for doing so. Design/methodology/approach – The method makes use of case studies of and fieldwork in countries that have either recently introduced mass valuations, brought about major changes in their systems or have been working towards introducing mass valuations. Findings – Mass valuation depends upon a degree of development and transparency in property markets and an institutional structure capable of collecting and maintaining up-to-date price data and attributes of properties. Countries introducing mass valuation may need to undertake work on improving the institutional basis for this as a pre-condition for successful implementation of mass valuation. Practical implications – Although much of the literature is concerned with how to improve the statistical modelling of market prices, there are significant issues concerned with the type and quality of the data used in mass valuation models and the requirements for successful use of mass valuations. Originality/value – Much of the literature on mass valuation takes the form of the development of statistical models of value. There has been much less attention given to the issues involved in the implementation of mass valuation.

Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network

“…However, the credibility of the models could be improved by the use of more robust and quality data (Grover, 2016). When this is in place, as obtainable in most developed nations (Hofmann, 2003), accurate property valuation estimates could be achieved.…”

“…This will in turn reduce the high property valuation inaccuracy prevalent in such emerging markets. Subsequently, AI property modeling techniques could be introduced in the property valuation practice of emerging property markets as obtainable in some developed property markets (Mora-Esperanza, 2004;Grover, 2016).…”

“…Most of the Nigerian property valuers are not aware of and do not use the ANN valuation technique in practice, but mostly adopt traditional methods of valuation (Abidoye and Chan, 2016b). Whereas, if the pre-conditions for property value modeling (robust and quality databank, appropriate training of valuers and transparent property market, amongst others) (Grover, 2016) are in place, property valuation inaccuracy could be reduced to a barest minimum in the property valuation domain. The data set used for this study was collected from property firms operating in the study area, and hence the use of a small sample size.…”

“…This findings of Worzala et al (1995) corroborates those of Lenk et al (1997), McGreal et al (1998 and McCluskey et al (2013), that reported that the ANN technique is not actually superior to the HPM approach. The differences in the findings of the ANN property valuation studies could be attributed to the quality of the data available for use in each respective property market (Lenk et al, 1997), because this is an important requirement for developing robust property valuation models (Grover, 2016).…”

Meeting Changing Industry Expectations from Australian Property Valuation Graduates

“…For instance, digital technologies and artificial intelligence (AI) pose significant threats to the existing valuation practice. Many of the steps in the valuation process -data collection, data analysis and data formatting are performed by computerised models and the valuation profession is moving towards automated processes (Grover 2016). In Australia, this is evidenced by companies such as CoreLogic, APM Price Finder and Monitor, all offering computer generated valuation reports, which have been developed to allow for rapid, cost-effective outputs.…”