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Purpose The South African listed property market has changed its legal basis from property loan stock companies and property unit trusts to adopt the more familiar international structure, real estate investment trusts. The main distinction is how shareholding is structured and investment returns are paid out to shareholders, which results in a different tax treatment. It is hoped that this change would attract more foreign investment, but it is questionable if this is sufficient to convince global investors who, amidst a seeming worsening of the stability in the political and economic environment, would probably need more insight into aspects such as investment decision making within these South African organisations. The paper aims to discuss these issues. Design/methodology/approach Using a balanced scorecard (BSC) framework, this study investigates the relevance of investment decision-making frameworks in South Africa. A survey using a sample of institutional investors that are included in the South African Property Market Index was conducted. Findings The study found similarities in decision-making priorities of South African institutional investors to those of previous studies. With the focus on retail property, tenant mix and secondary to that, quality of the centre management team is found to be important for forecasting expected returns in a retail investment decision environment. Diversification strategies were found to have similar results to previous studies, leaning more towards geographic location than economic location. Further, the study suggested the use of a BSC framework, linking the financial information and different financial ratios to nonfinancial aspects that need specific consideration in a retail investment environment. Research limitations/implications Retail property is considered to be of particular concern due to the business enterprise value that could be created if superior management techniques are applied. The investment decision stage concerned with forecasting expected returns relies on financial and quantitative models such as those derived from Modern Portfolio Theory. In a shopping mall environment, however, future performance is driven by nonfinancial factors, for example, tenant mix and superior customer experience. Therefore, forecasting expected returns in a retail environment requires a nuanced approach relative to other commercial property sectors. Originality/value The paper is considered to be original in its analysis of the retail real estate market in South Africa. This offers new insight into retail properties specifically, but also how investors in South Africa react to decision-making practices. This adds value in the internationalisation of the property market and the consistency and transparent practices applied globally.

Purpose The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models. Design/methodology/approach The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics. Findings The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes. Research limitations/implications A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values. Originality/value This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.

The study examines the influence of four spatial weighting functions and bandwidths on the performance of geographically weighted regression (GWR), including fixed Gaussian and bisquare adaptive kernel functions, and adaptive Gaussian and bi-square kernel functions relative to the global hedonic ordinary least squares (OLS) models. A demonstration of the techniques using data on 3.232 house sales in Cape Town suggests that the Gaussian-shaped adaptive kernel bandwidth provides a better fit, spatial patterns and predictive accuracy than the other schemes used in GWR. Thus, we conclude that the Gaussian shape with both fixed and adaptive kernel functions provides a suitable framework for house price valuation in Cape Town.

This paper assesses the different models, in conjunction with the different theories surrounding the distinction and interdependencies between space-and capital markets. First, the theory of space-and capital markets is discussed with reference to two models, the FDW and the REEFM models. The FDW model provides a diagrammatic explanation of the behaviour of the property market, while the REEFM is an econometric model based on statistical principles that are able to forecast property-market behaviour by interpreting specific given variables. The REEFM model as the perceived more sophisticated model, untested in South Africa, was then analysed to test its applicability in the South African context. The findings confirmed the applicability of the model, although one part is not confirmed and is suggested for further research.