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Kong, H.; Shen, J.; Zhao, Y., and Sun, Q., 2022. Assessment of sea-area benchmark pricing: Using Chinese aquaculture to evaluate and revise the price structure of resources. Journal of Coastal Research, 38(5), 925–936. Coconut Creek (Florida), ISSN 0749-0208. China has implemented a system of paid use of sea areas and has in recent years promoted their market-oriented allocation to ameliorate the efficiency and to realize their intensive and economical use. Estimations of the sea-area benchmark price can reveal the differences in sea areas, quality, and efficiency. This study introduces a framework to assess the benchmark price of the sea areas, applied to open aquaculture in Lianjiang County (Fujian Province, China). The assessment results show that the sea area used for bottom-sowing aquaculture in Lianjiang County was classified as Grade 2, whereas the sea area used for raft aquaculture and net cage aquaculture was classified as Grade 4. Upon comprehensive consideration of the theoretical value and the actual demand for sea-area management, the benchmark price of three types of open aquaculture sea areas is evaluated. The benchmark prices for bottom-sowing culture sea areas were found to be RMB 65–117/mu/year; for raft culture sea areas, they were RMB 20–252/mu/year; and for net cage culture, sea areas were RMB 100–800/mu/year. Overall, this study constructed a revised system of sea-area benchmark pricing, considering the natural conditions of the sea area, policy factors, transportation and infrastructure, culture conditions, and development degree. These results could be used as the guiding prices of the government to transfer sea-area resources and could ensure the realization of the economic interests of the government as owner of the sea area. Furthermore, this approach can provide a novel way to evaluate sea-area prices in other coastal areas.

Consumers' price evaluations are influenced by the left-digit bias, wherein consumers judge the difference between $4.00 and $2.99 to be larger than that between $4.01 and $3.00, even though the numeric differences are identical. This research examines when and why consumers are more likely to fall prey to the left-digit bias. The authors propose that the left-digit bias is stronger in stimulus-based price evaluations, wherein people see the focal price and the reference price side by side, and weaker in memory-based price evaluations, wherein people have to retrieve at least one price from memory. This is because in stimulus-based price evaluations, people tend to rely on perceptual representations of prices without rounding them. In memory-based price evaluations, they rely more on conceptual representations, which makes them more likely to round the prices. Results from six studies—five experiments and a scanner panel study—support the hypothesis that the left-digit bias is stronger in stimulus-based evaluations. These results inform managers about when to use left-digit pricing and characterize fundamental differences between stimulus-based and memory-based evaluations.

A heated discussion has recently broken out in Europe about the price of Zolgensma, ‘the most expensive drug ever’. The National Institute for Health and Care Excellence (NICE) approved Zolgensma in March this year, which is set to become the most expensive treatment ever approved by NICE. Zolgensma is a gene therapy medicine for treating spinal muscular atrophy (SMA), a serious and rare condition of the nerves that causes muscle wasting and weakness [1]. It is estimated that the drug will cost approximately €1.9 million per course of treatment [2]. Patients with SMA have a defect in a gene known as SMN1, which the body needs to make a protein essential for the normal functioning of nerves that control muscle movements. Zolgensma is a gene therapy containing a functional copy of this gene which, after injection, passes into the nerves from where it provides the correct gene to make enough of the protein and, thereby, restore nerve function [1]. At first impression, the price level of Zolgensma raises many understandable questions, because €1.9 million sounds exorbitantly high in the public domain (often driven by emotions and lack of specialised knowledge of the costs and risk of the development of a new pharmaceutical). However, there are many factors that may justify NICE’s decision to approve the intervention for use. In the Netherlands, since the debate in 2013 about the high price of medicines for Fabry and Pompe diseases, ‘expensive’ medicines are increasingly only reimbursed after tough price negotiations with the Ministry of Health [3]. This usually concerns medicines for the treatment of rare diseases, the so-called ‘orphan drugs’ such as Zolgensma. For example, it is estimated that only one in 11,000 children is born with SMA [4]. These price negotiations have since become a permanent and important part of the market access process for new ‘expensive’ orphan drugs, where expenditure weighed against patient suffering, a difficult and ethically difficult task for all parties [3].

Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.

Behavioral pricing extends traditional price theory by exploring consumers’ reactions to prices from a psychological perspective. With the constant and substantial evolution of this research field, we review the progress on the state of knowledge on consumers’ processing of price information and price behavior. To this end, we develop a framework to classify the advances made in behavioral pricing research during the past decade. We discuss conceptual developments, the contribution of the adoption of new theories, and new relationships and pricing phenomena. We show that several concepts have undergone conceptual developments (e.g., price search) while other concepts are new to the literature (e.g., participative pricing mechanisms). Regarding theoretical developments, the adoption of theories from other disciplines has contributed to enhance understanding of price-related effects. Finally, new pricing phenomena, such as the zero price effect or the placebo effect of price promotions, challenge the traditional view of consumers’ response to price information. Furthermore, a number of recent empirical findings contradict existing knowledge on price-related concepts and phenomena. Thus, it is of prime importance to integrate the latest findings with prior literature. From the key findings in the literature, we derive directions for future research.

Index-level price-to-rent (PTR) ratios are a widely used metric for analyzing housing markets, employed by both real estate practitioners and policymakers. This article seeks to improve the contextualization of observed PTR values by examining the interplay between these ratios and macroeconomic and housing-market developments in a non-linear framework. We analyze historical data on housing prices, rents and macroeconomic developments from 18 advanced economies, spanning from 1870, using Boosted Regression Trees and explainable machine learning techniques. As a precursor to this analysis, we also present the empirical distribution of the price-to-rent ratio and the implied housing risk premia across all years and countries.

Spatial heterogeneity analysis of housing prices, in general, is crucial for maintaining high-quality economic development in China, especially in the post-COVID-19 pandemic context. Previous studies have attempted to explain the associated geographical evolution by studying the spatial non-stationary continuous heterogeneity; however, they ignored the spatial discrete heterogeneity caused by natural or policy factors, such as education, economy, and population. Therefore, in this study, we take Beijing as an example and consider educational factors in order to propose an improved local regression algorithm called the regionally geographically weighted regression affected by education (E-RGWR), which can effectively address spatial non-stationary discrete heterogeneity caused by education factors. Our empirical study indicates that the R2 and R2adj values of E-RGWR are 0.8644 and 0.8642, which are 10.98% and 11.01% higher than those of GWR, and 3.26% and 3.27% higher than those of RGWR, respectively. In addition, through an analysis of related variables, the quantitative impacts of greening rate, distance to market, distance to hospitals. and construction time on housing prices in Beijing are found to present significant spatial discrete heterogeneity, and a positive relationship between school districts and housing prices was also observed. The obtained evaluation results indicate that E-RGWR can explain the spatial instability of housing prices in Beijing and the spatial discrete heterogeneity caused by education factors. Finally, based on the estimation results of the E-RGWR model, regarding housing prices in Beijing, we analyze the relationships between enrollment policy, real estate sales policy, and housing prices, E-RGWR can provide policy makers with more refined evidence to understand the nature of the centralized change relationship of Beijing’s housing price data in a well-defined manner. The government should not only carry out macro-control, but also implement precise policies for different regions, refine social governance, promote education equity, and boost the economy.

A review of literature during calendar year 2014 focused on environmental policies and sustainable development, and economic policies. This review is divided into these sections: sustainable development, irrigation, ecosystems and water management, climate change and disaster risk management, economic growth, water supply policies, water consumption, water price regulation, and water price valuation.

Consumers often provide different evaluations of absolute and percentage descriptions of the same quantity. Prior research has attributed this to two factors: selection of distinct reference contexts and differential cognitive difficulty. However, in a preliminary study, we show that discrepancies in consumer evaluations of absolute and percentage quantities can arise even when these two factors are held constant. A series of studies provides evidence that (1) this effect is rooted in automatic, nonverbal associations between numerical stimuli and analogue magnitude coding and (2) the influence of analogue magnitude codes manifests across different kinds of quantities, different evaluations, and different processing modes.

Purpose With a view to the unconventional monetary policy measures implemented in the euro area in recent years, this study aims to investigate whether the recent house price increases in Germany are signals of an incipient overheating of the German housing market. Design/methodology/approach This paper presents a valuation measure for residential property based on a large and exhaustive regional panel data set for Germany. The fitted house prices from a panel regression at the district level, taking into account spatial spillovers, are taken as a measure of the fundamental equilibrium house prices, which can be aggregated for various regional subsets. Findings The estimation results suggest that apartment prices over the past years substantially exceeded the fundamental price suggested by the model, in particular in the big cities. Single-family houses appear to be markedly overvalued mainly in the cities. The low level of interest rates in recent years appears to have contributed to the emergence of misalignments. Originality/value Exploiting the variation across local housing markets, the estimation approach provides value-add for the estimation of house price valuation results in various regional subsets, as conventional time-series approaches to valuing property are subject to severe data limitations in the case of Germany.