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Information verification is an important factor in commercial valuation practice. Valuers use their professional autonomy to decide on the level of verification required, thereby creating an opportunity for client-related judgement bias in valuation. The purpose of this paper is to assess the manifestation of client attachment risks in information verification.

A case-based questionnaire was used to retrieve data from 290 commercial valuation professionals in the Netherlands, providing a 15 per cent response rate of the Dutch commercial valuation population. Descriptive and inferential statistics have been used to test research hypotheses involving relations between information verification and professional features that may indicate client attachment such as an executive job level and brokerage experience.

The results reveal that valuers acting at partner level within their organisation obtain lower scores on information verification compared to lower-ranked valuers. Also, brokerage experience correlates negatively to information verification of valuation professionals. Both findings have statistical significance.

The results reflect valuers’ reasoning behaviour rather than actual behaviour. Replication of findings through experimental design will contribute to research validity.

Maintaining close client contact in a competitive environment is important for business continuity yet may foster client attachment. The associated downside risks in valuation practice call for higher awareness of (subconscious) client influence and the development of attitudinal scepticism in valuer training programmes.

This paper is one of the few that explore possible sources of valuer judgement bias by relating client-friendly valuer features to a key area of valuation i.e. information verification.

The monetary valuation of cultural heritage of a selection of 16 major public, not-for-profit Australian cultural institutions is examined over a period of almost three decades (1992–2019) to understand how they have responded to the paradoxical tensions of heritage valuation for financial reporting purposes.

Accounting for cultural heritage is an intrinsically paradoxical practice; it involves a conflict of two opposite ways of attributing value: the traditional accounting and the heritage professionals (or curatorial) approaches. In analysing the annual reports and other documentary sources through qualitative content analysis, the study explores how different actors responded to the conceptual and technical contradictions posed by the monetary valuation of “heritage assets”, the accounting phraseology of accounting standards.

Four phases emerge from the analysis undertaken of the empirical material, each characterised by a distinctive nature of the paradox, the institutional responses discerned and the outcomes. Although a persisting heterogeneity in the practice of accounting for cultural heritage is evident, responses by cultural institutions are shown to have minimised, so far, the negative impacts of monetary valuation in terms of commercialisation of deaccessioning decisions and distorted accountability.

In applying the theoretical lens of paradox theory in the context of the financial reporting of heritage, as assets, the study enhances an understanding of the challenges and responses by major public cultural institutions in a country that has led this development globally, providing insights to accounting standard setters arising from the accounting practices observed.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.