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This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

This study aims to examine the market structure and the factors influencing the price of fuelwood in the Dormaa Municipal in the Bono region of Ghana.

A total of 200 fuelwood harvesters, 20 wholesalers and 20 retailers were sampled by using probability and non-probability sampling methods. Gini coefficient was used to analyse the market structure, whereas quantile regression was used to analyse the factors influencing the pricing of fuelwood.

The study results indicated that the fuelwood harvesters’ market is less concentrated, with a Gini coefficient of 0.22, likewise the fuelwood intermediaries’ market, with Gini coefficients of 0.22 and 0.32 for wholesalers and retailers, respectively. The price of fuelwood decreased when sold through the retailer and wholesaler outlets, but the price increased when sold via the end-user outlet. Less smoky fuelwood species attracted higher prices, whereas easy-to-light fuelwood species were sold at lower prices. Furthermore, fuelwood from Perpewa (Celtis zenkeri) and Acacia (Senna siamea) species received the highest prices in the market. It is recommended that fuelwood harvesters establish woodlots with acacia (Senna siamea), especially and Perpewa (Celtis zenkeri), both of which emit less smoke and have high calorific value with fast rotation period. This will ensure fuelwood availability and offer better prices to the harvesters, as such species command high prices in the market.

There is paucity or near unavailability of literature on the market structure and the influence of the hedonic attributes on different quartile prices of fuelwood; the result of this study provides the foundational springboard for future studies on fuelwood marketing.

This paper aims to model the price of cellar tours using a hedonic pricing approach. The authors analyze the complex relationship between the price of an add-on (here, cellar tours) and the price of the reference product (here, wine).

Thanks to a large database containing information on about 1,000 winery experiences, the authors regress the price of cellar tours on wine prices and on a broad set of objective characteristics that are (1) tour specific and (2) common to all tours offered by the winery. These exogenous controls include the type and style of experience offered, amenities and winemaking characteristics.

The authors show that the price of cellar tours follows the price of the most expensive wine sold by the winery, which is a proxy for reputation. The authors find that one of the main determinants of cellar tour prices is visit length: wineries charge more for longer experiences. The number of wines tasted during the visit also increases the price. Prices are higher in places where there is a high level of wine tourism activity, which might be a sign of authenticity.

Wine producers in different countries need to gain insights on how to price cellar tours, which are composite goods. The results can help practitioners price their winery experience according to common practices in different wine regions. The results may also be of interest to professionals in the tourism sector who are in charge of the pricing of by-products (e.g. tee-shirts, books, etc.), or for luxury fashion labels extending their brand in the catering industry with cafes and restaurants.

To the best of the authors’ knowledge, this paper is the first empirical analysis that examines the complex relationship between the price of an add-on and the price of the reference product in the context of wine tourism.

This paper aims to explore the relationship between market pricing and design quality within the development industry. Currently, there is a lack of research that examines real estate at the property level. Development quality is widely believed to have diminished over the past decades, while many investors seem uninterested in the design process. The study aims to address these issues through a pricing model that integrates design attributes. It is hoped that empirical findings will invite broader stakeholder interest in the design process.

The research establishes a framework for assessing spatial compliance across residential developments within London. Compliance is assessed across ten boroughs, with technical space guidelines used as a proxy for design quality. Transaction prices and spatial assessments are aligned within a hedonic pricing model. Empirical findings are used to establish whether undermining spatial standards presents a significant development risk.

Findings suggest a relationship between sale time and unit size, with “compliant” units typically transacting earlier than “non-compliant” units. Almost half of the 1,600 apartments surveyed appear to undermine technical guidelines.

It is suggested that an array of design attributes be explored that extend beyond unit size. Additionally, future studies may consider the long-term implications of design quality via secondary transaction prices.

Practical implications include the development of a more scientific approach to design valuation. This may enhance the position of product design management within the development industry and architectural services.

Social implications may include improvement in residential design.

An innovative approach combines a thorough understanding of both design and economic principles.

This paper aims to examine the impact of coastline on the rental value of residential property in proximity to the coastline, using the hedonic pricing model from two perspectives. First, Model 1A–C accounted for estimating the influence of coastal amenities while controlling for other housing attributes influencing rent. Second, Model 2A–C accounted for the interaction between coastal amenities/disamenities and other housing attributes influencing rent.

A survey approach was adopted for the data collection process. For both models, property values were measured in proximity to coastline using 0–250 m, 251–500 m and 0–500 m.

Findings revealed that property rental value increases as we move away from the coastline when disamenities are not controlled. The results suggested that for a mean-priced home (N2,941,029 or $8,170) at the mean distance from the coastline (301.83 m), a 1% increase in distance from the coastline would result in a 0.001% or N9.77 ($0.03) increase in rental value.

The implication to real estate valuers is that varying premiums should be considered when valuing a property depending on the distance to the coastline while considering other housing attributes.

This research introduces a novel approach to the hedonic model for determining property values in proximity to coastal environment by estimating the influence of coastal amenities while controlling for other housing attributes influencing rent, on the one hand, and accounting for the interaction between coastal amenities/disamenities and other housing attributes influencing rent, on the other.

Global warming exacerbates sea level rise and extreme weather events that cause severe flooding, resulting in lost productivity and property damage. To reduce the impact of flooding, residents are avoiding purchasing homes in high-risk areas. There are numerous studies on the relationship between flood hazards and housing prices in developed countries, but few in developing countries. Therefore, this study aims to investigate the relationship between flood hazards and housing prices in Hat Yai, Songkhla, Thailand.

This study uses spatial-lag, spatial error and spatial autoregressive lag and error (SARAR) models to analyze the effect of flood risk on property prices. The main analysis examines the degree of flood risk and housing rental prices from our survey of 380 residences. To test the robustness of the results, the authors examine a different data set of the same samples by using the official property valuation from the Ministry of Finance and the flood risk estimated by the Southern Natural Disaster Research Center.

The SARAR model was chosen for this study because of the occurrence of spatial dependence in both dependent variable and the error term. The authors find that flood risk has a negative impact on property prices in Hat Yai, which is consistent with both models.

This study is one of the first to use spatial econometrics to analyze the impact of flood risk on property prices in Thailand. The results of this study are valuable to policymakers for benefit assessment in cost–benefit analysis of flood risk avoidance or reduction strategies and to the insurance market for pricing flood risk insurance.

This study aims to provide a comprehensive overview of the academic landscape in wine economics and business research over the past decades, capturing and analysing the literature through rigorous bibliometric methodologies. The study is intended as a foundational resource for academics, policymakers and industry stakeholders interested in the evolving scholarly discourse within the wine industry.

The authors analyse data from over 3,200 papers in the field of wine economics and business published between 1990 and 2022, sourced from Scopus. Various bibliometric indicators are applied, including publication and citation counts, and methods like keyword and co-citation analyses were used to map out the thematic and intellectual landscape.

The study reveals the escalating global relevance of wine economics and business research and identifies prominent papers and authors, influential countries and leading journals. The analysis reveals a dynamic shift in academic focus. Initially concentrating on foundational inquiries in the 1990s, research evolved to encompass complex themes such as e-commerce, wine tourism, sustainability and global crises. The study emphasises the adaptability and resilience of the wine supply chain and anticipates future research areas.

This study presents a comprehensive bibliometric analysis of the expanding body of research in wine economics and business, using data from over 3,200 documents published between 1990 and 2022. It uniquely combines different advanced bibliometric tools to provide a multifaceted overview of wine economics and business research.

The global automobile industry is striving towards a sustainable future. Emerging countries including India are gearing up for the revolution. Considering the key role of customer acceptance in the success of any technological shift, the study endeavors to ascertain the catalysts accelerating the adoption of Electric Two-Wheelers (E2W) in India by leveraging an extended Unified Theory of Acceptance and Use of Technology-2 model. The same would assist Electric Vehicle (EV) stakeholders in directing their efforts toward pivotal aspects having the potential to significantly bolster E2W penetration.

Data was collected using convenience sampling technique from 1,254 electric two-wheeler owners across four Indian states and analyzed using Structural Equation Modelling.

Performance Expectancy, Price Value and Hedonic Motivation have a significant influence on purchase intention leading to actual buying behavior. Effort Expectancy, Social Influence, habit value and facilitating conditions were insignificant. Pro-Environmental Approach and Government Support significantly impact adoption intention and behavior respectively in addition to model predictors thus supporting the study’s novelty. Purchase intention proved to influence Actual Buying Behavior. Synergized efforts of EV stakeholders towards performance innovation, cost-effectiveness, improved infrastructure and information diffusion on sustainability and user-friendliness could aid in achieving transition to green mobility.

The study predominantly intends to address the intention–behavior gap related to electric two-wheelers in India. Also, two additional constructs, government support and pro-environmental approach, were incorporated resulting in a novel research framework that aims to test their nuanced ability to impact the model predictors.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.