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It is well‐known from the literature that locational externalities influence the price formation of residential property. This effect is usually studied empirically with the hedonic price models, by including various neighbourhood and proximity variables in the model. These regression based techniques have, however, been criticised for a number of reasons. The arguments pertain partly to technical issues such as model flexibility, functional discontinuity and nonlinearity, and data quality, and partly to more fundamental problems regarding the nature of the value formation process. The criticism has attracted experiments with new modelling approaches, each of which adds something substantial to the hedonic approach. The study comprises two parts: it first highlights the rationale behind each broad approach composed of specific modelling techniques currently available, and then demonstrates an improvement of the demand side analysis by applying the analytic hierarchy process. This method enables quantification of qualitative expert judgements, and may lead to conclusions that go beyond the purely economic value framework.

Property valuers in England and Wales face a difficult task; they must collect and assimilate data from a variety of sources which differ widely with regard to quality, currency and coverage. This is because of legislative restrictions on access to public sector data sources and organizational constraints on access to property data within the private sector, such as confidentiality and commercial secrecy. Proposes a system for the dissemination of property data for valuation and other property‐related procedures, namely, a National Land Information Service (NLIS). Suggests that a NLIS should be developed using geographical information system technology. Describes a methodology which shows how a spatial analysis technique available on a GIS was used to examine the influence of accessibility on property value.

Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.

The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.

This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.

This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

In the context of a corporate case study, this paper aims to propose and test a talent analytics model that enables organisations to optimise their employer value propositions and branding strategies in a comparative manner.

After the conceptual framework was translated into a quantitative model, data were collected via online self-completed questionnaires and analysed with statistical techniques.

Revealing misalignments with the employment preferences of Gen Z, the model’s capability to optimise employer value propositions and branding strategies is effectively demonstrated.

Providing an actionable framework for corporate and academic readers, this paper has scope to serve as a guide for scholarly or practitioner talent analytics projects focused on attraction.

Focused exclusively on attraction, to the best of the author’s knowledge, this is the first paper to apply an in-depth talent analytics model which prescribes employer branding as a mediator variable.

This paper proposes locational ambidexterity as a location-specific factor based on an operation flexibility perspective, and explores why and how multinational corporations (MNCs) proactively deal with uncertainty by valuing locational ambidexterity in making location decisions.

Location choice data for foreign direct investment (FDI) at a sub-national level in China is used to test the role of locational ambidexterity.

We find that FDI generally prefers locations with high ambidexterity. Moreover, investments from a heterogeneous country context are more sensitive to locational ambidexterity than those from a similar country context. However, there is no significant evidence that wholly owned investments favor locational ambidexterity more than do international joint ventures.

An alternative operationalization of locational ambidexterity may be needed. Future research could explore the sources of locational ambidexterity, identify other firm- and industry-level factors that could alter the value of ambidexterity, investigate how MNCs integrate locational ambidexterity into organization-specific option creation strategies and test the ambidexterity perspective with micro-level location choice data.

Locational ambidexterity may reduce the overall risk and adjustment cost of future changes. FDI may choose a location with high ambidexterity, i.e. a balanced portfolio of location-specific determinants, under uncertainty about the future.

Drawing on the notion of location flexibility from Buckley and Casson (1998), this study identifies a new location character, locational ambidexterity, and proposes that MNCs address uncertainty by choosing ambidextrous locations that offer more flexibility for MNCs to change or respond to potential volatility. Selecting locations with high ambidexterity is thus an alternative and complement to the organization-specific flexibility creation strategies suggested by the literature on real option and flexibility.

Investigates and evaluates land use data and valuation in Sydney′s central area. Proposes that, in order to make informed investment decisions, there should be an understanding of the nature and functioning of the physical environment in which funds are to be committed. Investigates what things belong in an urban area. Concludes with data related to how land is used. States that lowest land value and floor level classes tend to be allocated to goods handling and destinations for mass gatherings of people; highest land value and floor‐level are allocated to business categories.

In a largely exploratory study, the authors investigate: how do managers’ experiences with exogenous hazards (e.g., natural disaster risk) affect their identification of those hazards as salient to the firm? This analysis is based on an international survey of 575 managers across 18 disaster-prone countries. The authors examine whether and how locational hazard risk and managerial experience influence the identification of natural disaster risk as an important firm issue. The authors find that locational natural hazard risk, and direct and indirect experience with natural disasters, increases the likelihood that managers’ will identify firm-specific natural disaster risk as an important firm issue. In addition, the authors also find that managers are likely to identify natural hazards as a threat when natural hazard risk is high and when managers have experience in natural disasters that directly affected their businesses.

As regards the assessment of the market values of properties that compose real estate portfolios, the purpose of this paper is to propose and test an automated valuation model. In particular, the method defined allows for providing for objective, reliable and “quick” valuations of the assets in the phases of periodic reviews of the property values.

Aiming at both predictive and interpretative purposes, the method, based on multi-objective genetic algorithms to search those model expressions that simultaneously maximize the accuracy of the data and the parsimony of the mathematical functions, is applied to a sample data of office properties characterized by medium and large size, located in the city of Milan (Italy) and sold in the period between 2004 and 2015.

The model obtained could be an integration of the canonical methodologies (market approach, income approach, cost approach) implemented in the assessment of the market values of properties, so as to provide an additional tool to verify the results. In particular, the inclusion of economic variables in the model is consistent with the need to reiterate the valuations, contextualizing them to the locational characteristics and to the current property cycle phase in the specific area.

The model can be applied by all the operators involved in the periodic reviews of the values of property portfolios: from real estate funds’ insiders, in order to monitor the values obtained through the canonical approaches, to the public institutions, such as the revenue agencies, in order to ensure the fair payment of the taxes through the updating values of the properties according to the actual and current market trends.

The method proposed can be a valid support for all public and private entities that hold significant property assets and that, for various reasons (periodic reviews of the balance sheets, sales, enhancement, investment, etc.), require cyclical updated values of the properties. The automated valuation model developed can be used for the assessment of “comparison” values with the estimates values obtained by other assessment techniques, in order to ensure a further monitoring tool of the results from the subjects involved.

Drawing on social learning theory (SLT), this study aims to investigate how previous cross-cultural work experience influences individual adjustment in a foreign environment over time. For this purpose, the authors study foreign service employees who are characterized by permanent high mobility and frequent rotations.

Two cross-sectional surveys conducted in 2011 (analytical sample N = 1,097) and 2019 (analytical sample N = 1,431) amongst German Foreign Service (GFS) employees are used to analyse employees' adjustment, measured by self-perceived quality of life (QOL) and its development over time based on four time points. Locational adjustment trajectories serve as robustness checks.

Younger and therefore less experienced employees fit J-shaped patterns of adjustment, while more experienced employees show rather flat adjustment curves. Accordingly, work experience matters and “one curve fits all” approaches do not suffice to explain adjustment over time. Moreover, neither more nor less experienced employees experienced U-trajectories as proposed by previous literature on business expatriates.

The study findings are based on cross-sectional surveys, but longitudinal designs should be preferred in future research.

Sending institutions may develop special support systems for inexperienced expatriates prior to departure to weaken the negative impacts of culture shock.

Existing literature only sparsely analysed adjustment and QOL for foreign service employees/diplomats so far. To the authors’ knowledge, no study analysed trajectories of adjustment over time for this population. This study profits from the analysis across two surveys. Both samples benefit from a high diversity, among others, regarding gender, age, education and host countries.