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The purpose of this paper is to examine the real estate journal ranking by comparing journal performance statistics to researcher preferences.

The study is based on a survey of members of the International Real Estate Society and sister societies with comparison to impact statistics for real estate journals which are analysed using data from Google Scholar.

The findings show a high correlation between researcher preferences and the empirical results, supporting the findings of previous research in this area. However, while most previous studies were conducted on high-impact US journals only, these are still found to be amongst the highest ranked overall even with inclusion of other international journals. There are, however, some differences found, such as the perception of researchers on electronic vs hard copy journals, which were found to be moving more towards the former.

The results provide a ranking of various real estate journals, especially with regard to other international journals not included in the previous studies that are dominated by high-impact US journals, providing a guideline on where to publish. The paper also shows the methodology used in order to determine how the journals are ranked, which could be applied to other journals not included in this study in order for researchers to make informed decisions concerning publication choices.

This paper extends the research on this topic by analysing the preferences and statistics of a broader international sample of journals and compares researcher preferences to empirical analysis.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 paper introduced the use of a hybrid system of neural networks support vector machines (NNSVMs) consisting of artificial neural networks (ANNs) and support vector machines (SVMs) to price single-family properties.

The mechanism of the hybrid system is such that its output is given by the SVMs which utilise the results of the ANNs as their input. The results are compared to other property pricing modelling techniques including the standalone ANNs, SVMs, geographically weighted regression (GWR), spatial error model (SEM), spatial lag model (SLM) and the ordinary least squares (OLS). The techniques were applied to a dataset of 3,225 properties sold during the period, January 2012 to May 2014 in Cape Town, South Africa.

The results demonstrate that the hybrid system performed better than ANNs, SVMs and the OLS. However, in comparison to the spatial models (GWR, SEM and SLM) the hybrid system performed abysmally under with SEM favoured as the best pricing technique.

The findings extend the debate in the body of knowledge that the results of the OLS can significantly be improved through the use of spatial models that correct bias estimates and vary prices across the different property locations. Additionally, utilising the result of the hybrid system is thus affected by the black-box nature of the ANNs and SVMs limiting its use to purposes of checks on estimates predicted by the regression-based models.