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The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically weighted regression (GWR) and the generalized additive model (GAM).

The authors assess the asymptotic properties of linear, spatial and non-linear hedonic models based on a very large data set in Germany. The employed functional form is based on the OLS, GWR and the GAM, while the estimation methodology was chosen to be iterative in forecasting, the fitted rents for each quarter based on their 1-quarter-prior functional form. The performance accuracy is measured by traditional indicators such as the error variance and the mean squared (percentage) error.

The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. There exists a strong out-of-sample discrepancy between the GWR and the GAM models, whereas the simplicity of the OLS approach is not substantially outperformed by the GAM approach.

For policymakers, a more accurate knowledge on market dynamics via hedonic models leads to a more precise market control and to a better understanding of the local factors affecting current and future rents. For institutional researchers, instead, the findings are essential and might be used as a guide when valuing residential portfolios and forecasting cashflows. Even though this study analyses residential real estate, the results should be of interest to all forms of real estate investments.

Sample size is essential when deriving the asymptotic properties of hedonic models. Whit this study covering more than 570,000 observations, this study constitutes – to the authors’ knowledge – one of the largest data sets used for spatial real estate analysis.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

Real estate is one of the fundamental growth engines for developing economies as it contributes urbanization and infrastructure development. In recent years, Somalia has witnessed massive real estate development in both housing and commercial buildings. The purpose of this study is twofold. First, the study examines the determinants of residential property rents. Second, it investigates whether residential property rents are fairly valued.

This study uses two-stage modeling. A hedonic regression model is used in the first stage, while an artificial neural network is applied in the second stage.

After analysis, this study established that size, location and security of a residential property have a significant influence on its monthly rents. Alternatively, the study identified that residential property rents are not fairly valued in Mogadishu and overvaluation is more frequent than undervaluation.

This implies that Somalia’s real estate industry is more speculative-driven than real demand-driven. Though Somali real estate is an infant industry with huge potentials in the long run, it may end up disastrously following the well-known bubble-then-burst behavior. To avoid such crisis, this study recommends formulating government policies that regulates, supervises and protects the infant real estate industry without undermining the needs of the poor and low-income citizens.

It is assumed that consumers consume games to experience hedonic and utilitarian value. However, there is no conceptual model or empirical evidence that supports this hypothesis in the game context or clarifies whether these consumption values have dual mediated or individual effects. Therefore, the purpose of this research is to model the relationship between hedonic and utilitarian consumption and game purchase and usage.

This research question is answered through two studies. In Study One, qualitative interviews with 18 gamers were implemented to explore the relationship between hedonic and utilitarian consumption and, game purchase and usage behaviour. In Study Two, we surveyed 493 consumers and conducted confirmatory factor analysis and structural equation modelling across four game types to model this relationship.

The paper concludes that hedonic rather than utilitarian consumption positively impacts purchase and usage. Support was also found for the utilitarian‐hedonic dual mediation model (UHDM). Therefore, utilitarian consumption has an indirect causal effect on game purchase or usage through hedonic consumption.

Game development for consumers online, on wireless devices and on consoles should place greater emphasis on the practical implications of hedonic consumption. Attention could be focused on perceived enjoyment, self‐concept, self‐congruity and self‐efficacy as the primary drivers of use and purchase. Practical solutions should also be developed to develop the UHDM effect.

This is the first paper in the game context to explore and model the relationship between hedonic, utilitarian consumption and the UHDM effect on game purchase and usage. This paper is also unique because it provides results across four game groups: all games (ALL), Sports/Simulation/Driving (SSD), Role Playing Game/Massively Multiplayer Online Role‐Playing Game Strategy (RPG), and Action/Adventure/Fighting (AAF).

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

The technology acceptance model (TAM) was developed by Davies et al. to understand the utilitarian benefits of web sites. In recent times, scholars have extended TAM as theoretical underpinning to understand user acceptance of social networking web sites (SNWs) that were hedonic in nature, which seemed inappropriate. The purpose of this paper is to examine the differences in the user acceptance phenomenon of hedonic versus utilitarian SNWs, and highlight that it is essential to provide justice to varying value propositions offered by SNWs.

As the research study was variance based and followed a positivistic paradigm, the authors used survey methodology and collected data through online and offline questionnaires. In total, 181 usable responses were subjected to mediation analysis using structural equation modeling.

The perceived usefulness emerged as a significant mediator in the case of utilitarian SNWs and perceived enjoyment emerged as a significant mediator in the case of hedonic SNWs user acceptance phenomenon.

One web site each from hedonic and utilitarian SNWs was considered for the study. Future studies may be conducted by incorporating multiple web sites in each category to further emphasize the findings. Also, future studies might study the user acceptance phenomenon on the theoretical underpinning of the unified theory of acceptance and use of technology, which is much comprehensive than TAM.

There seems to be a notion that SNWs need to be hedonic and offer entertainment value proposition to attract large number of users. However, SNWs with valuable utilitarian value propositions built around appreciable ideas would also be accepted by users as an efficient networking tool.

Research on user acceptance of SNWs has been focused mostly on hedonic SNWs like Facebook and MySpace, while user acceptance of utilitarian SNWs like LinkedIn and Ryze has remained an unexplored domain.

The purpose of this paper is to investigate whether energy efficiency is capitalized in rent and asset value on corporate real estate portfolio. This approach contributes to the research on “green buildings” by using hedonic regression modeling on a portfolio of existing buildings in the French corporate real estate context.

The authors apply hedonic methods on a French corporate real estate portfolio which is composed of industrial, commercial and office buildings.

This model emphasizes two main results: energy efficiency is more capitalized in rent than in asset value and this relationship differs regarding buildings' type.

The model suggests that premium for energy efficiency is stronger for commercial and office buildings than for industrial buildings.

As internet dividends are gradually disappearing, loyalty programs have become the panacea for monetizing traffic, attracting new customers and retaining existing customers. Improving their effectiveness has thus become key to enterprises’ market competitiveness. However, member customers’ hedonic adaptation to this relationship strategy undermines its effectiveness. Based on the hedonic adaptation theory, this study aims to analyze the process of member customers' hedonic adaptation to preferential treatment in loyalty programs and explore the boundary conditions of alleviating this effect.

This study surveyed 271 member customers in China and tested the hypothesized relationships using structural equation modeling and multigroup analysis.

Preferential treatment suffers from hedonic adaptation to member customer engagement and customer gratitude, and customer tenure is a key condition for these effects. Customer gratitude is an intermediary mechanism that explains the hedonic adaptation effect of preferential treatment to member customers engagement. In addition, the structural characteristics of loyalty programs form the boundary condition that alleviates hedonic adaptation. The authors found that high-tier and -payment strategies are more likely to mitigate hedonic adaptation of preferential treatment to customer gratitude.

This study elucidates the factors that influence the effectiveness of preferential treatment and provides constructive insights into customer relationship management and for improving enterprise performance.

The paper aims to address concerns that valuers' choice of depreciation models in their cost approach to value is not sustainable (is incapable of preserving patronage in present and future generations).

The paper draws up conceptual expectations regarding how seven UK and US depreciation models pass or fail four identified sustainability indicators: reliability, consistency, usability and separate treatment of depreciation components. Valuation surveyors in Nigeria were offered as a case study of how valuers in one country respond to such conceptual investigations.

The study found that cross‐sectional models, the breakdown model and hedonic modeling are the depreciation models perceived as most sustainable. However, popular model use follows easiest models rather than most sustainable models.

The paper suggests that the pursuit of sustainability in valuation modeling should involve provision of institutionalized best practice guidance beyond that currently provided so as to assist valuers/appraisers in more sustainable choices.

The paper is probably the first to address both UK and US depreciation models and to assess each using defined sustainability criteria.

Hedonic price modeling (HPM) plays an important role in identifying factors affecting the price of goods or services. Many researchers have extensively used this modeling approach in their research work. However, there has been no bibliometric review that analyses the existing research, which makes use of HPM modeling. This study aims to provide a comprehensive visualized bibliometric and scientometric view of HPM usage in real estate research.

The Scopus database was used to collect the bibliographic data of 269 publications from 1970 to 2019. CiteSpace software was then used to analyze and visualize the data.

The results revealed a significant increase in the annual number of HPM study publications. Core authors, participating countries and representative references have been identified. Research hotspots for the HPM studies were identified.

While the study provides a methodological bibliometric review of HPM literature, the findings provide a very good platform for understanding the important elements of HPM usage in real estate research.