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The aim of this paper is to attempt to measure the effect of location on residential house prices and to endeavour to integrate spatial and aspatial data in terms of developing a hybrid predictive model. The research methodology investigates the traditional hedonic approach to modelling location using multiple regression techniques. Alternative approaches are considered which specifically model the spatial distribution of house prices with the objective of developing location adjustment factors. These approaches are based on the development of surface response techniques such as inverse distance weighting and universal kriging. The results generated from the surfaces created are then calibrated within MRA.

The purpose of this research is to explore from a mass appraisal perspective how the effects of location are reflected within valuation models. The paper sets out to detail the various techniques and the efficacy of their application.

The approach adopted is analytical and based upon the development of locational attributes. An extensive literature base is synthesized with methods being evaluated in their application to mass appraisal.

This research has identified that the three main groups interested in residential property valuation, namely, academia, industry and commerce have to a certain extent been unfamiliar with the research developments occurring in the other groups. The impact of this is important, given the need for integration and collaboration in terms of future model development.

The research underpinning this paper will provide a solid basis for further research into this area. The importance of measuring the effect that location has on value is of major significance in the determination of objective estimates of property value.

Those within the assessment community could be described as pragmatists working in a situation that requires feasible and suitable solutions to the problem of measuring location value. It is our contention that the third generation techniques of spatially varying parameter models and spatial autocorrelation models will require greater industry verification before their use becomes more widely accepted.

This paper provides a detailed analysis of methodologies used to reflect the value of location over the last 50 years. The debate is taken forward by describing what will be the contribution to the development of the next generation of location‐specific modeling techniques.

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify submarkets which could be applied within the mass appraisal environment.

Given the spatial dimension within which neighbourhoods/submarkets exist, this paper has sought to utilize the geostatistical technique of GWR to identify them.

The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas. Optimal number of segments is obtained by measures of predictive accuracy, spatial autocorrelation in the residual errors and the Akaike information criterion.

The three datasets used allowed for an evaluation of the robustness of the method. Nonetheless it would be beneficial to test it on other datasets, particularly from different regions of the world.

Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the effects of location within mass valuation models. This approach develops the existing knowledge.

The research ultimately is developing more accurate valuation models upon which the property tax is based. This should create an environment of fair and acceptable assessed values by the tax paying community.

The contribution of this work lies in the methodological approach adopted which incorporates a market basket approach developed through a process of GWR. The importance of the research findings illustrate that submarket segmentation need no longer be an arbitrary process.

Provides an outline of research seeking to apply to computer assisted mass appraisal (CAMA) model capable of use within a geographic information system (GIS). The end product will be a working GIS/valuation integrated model. The model, in an operational context, can be utilized for property taxation purposes, to facilitate the rating and revaluation of residential properties in Northern Ireland. As the value of land and property is a function of economic, legal, physical and locational factors, consequently access to comprehensive, reliable and up‐to‐date transaction evidence is a prerequisite to property valuation. Valuation techniques depend on the collection and analysis of relevant data. Historically, the application of these techniques took place within a non‐spatial environment. Ultimately, market data support any estimate of value. Data searches and collection can prove both time consuming and expensive in relation to the fee earning potential of a valuation report. GIS can facilitate, in a spatial and aspatial context, the storage, manipulation and analysis of data, in a fraction of the time previously required. Current techniques for the mass appraisal of property, and for the prediction of residential property values, can be enhanced by utilizing the data handling capacity of GIS. Integration of a mass appraisal model within a GIS will add value to the valuation process.

Despite the importance of cognitive monitoring, limited studies attempted to continuously monitor cognitive status of workers regarding mental fatigue effects on fall hazard. Thus, the objective of this study is to investigate and understand the effects of working at height on mental fatigue development for fall hazard prevention.

A quantitative framework using two well-known methods, i.e. Wavelet Packet Decomposition and Sample entropy, is developed to analyze the captured brain signals from Electroencephalography (EEG) to quantitatively assess mental fatigue levels, and seven mental fatigue indices were obtained. Between-subjects lab experiment was designed and conducted to assess mental fatigue in Virtual Reality (VR) environment.

Both of the quantitative methods confirmed that height exposure can adversely affect subjects' vigilance levels and indicated higher levels of mental fatigue. Significant differences were found between the two tested groups (i.e. working at height or on the ground) for six out of seven indices. The results suggested that working-at-height group had higher mental fatigue levels.

One limitation of this study is the limited number of subjects recruited for the experiment. Overall, this study is a preliminary and exploratory work towards mental fatigue monitoring and assessment in subjects exposed to fall risk.

This is the first study to explore and focus on mental fatigue assessment, particularly for construction falling-from-height hazard prevention by continuously monitoring mental fatigue levels of workers. The research provides insight into construction safety enhancement using smart technologies.

Why do K-12 schools not perform better in educating English Language Learners (ELLs)? Part of the problem lies with higher education: We continue to produce pre-service teachers who are not prepared for today’s multilingual student population and, more importantly, most currently practicing teachers lack any such preparation.

The purpose of this paper is to investigate how the degree of investment and involvement attributed to specific product categories, affect content marketing plans and practices on the Web and social media.

This is a conceptual paper based on the classification proposed by Morton and Devine (1985) on the axes of investment and involvement. The author uses secondary research evidence from both academic and industry sources to document content marketing trends in the US and the EU markets and allocates such trends using the semiotic square.

The findings indicate that products in each quadrant follow similar practices regarding content publishing, campaign planning and community management.

Further research may test this model empirically and assess its merits in different markets.

Managers can use this model for content planning, considering category-related opportunities and limitations. The model may also serve as a teaching tool to familiarize students with older research and its potential contribution in current settings.

By applying an old model in the current US/EU context, this paper helps document and understand content marketing practices, paving the way toward their optimization.

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.

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.

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.

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.

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.