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This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose of this study is to examine the influence of key hotel attributes on the room rates of selected hotels in the Greater Gaborone Region, Botswana.

Using hedonic pricing analysis, the effect of eight attributes collected from 80 standard double rooms on Booking.com in the area was analysed using quantile regression.

The estimated results from quantile regression suggested the importance of the 10th quantile as the best predictor of hotel room price distribution. Overall, the presence of a fitness centre and the availability of meeting and conference facilities were positively significant for the lowest- and premium-priced hotels, respectively.

The study advanced the literature in hedonic pricing models by confirming the applicability of hotel room rate attribute research in unexplored environments.

Hotel managers should be aware of the influence of key attributes, such as meeting and conference space availability and locational factors, on the pricing decisions of room rates in the Greater Gaborone Region. The study also presented opportunities for business-to-business marketing between hotel and tour operators in the region.

The study is one of the few that uses quantile regression in the hedonic pricing analysis of hotel room rates.

The study aims to investigate how attribution theory in the context of pricing strategies can help tourism destinations recover from the negative impacts of the COVID-19 pandemic.

The study adopted a qualitative research design using semi-structured interviews to address the lack of research in this area. Interview participants included tourists and tourism customers. The interview responses were then analyzed using “Nvivo” qualitative data analysis software to identify critical themes regarding applying attribution theory to pricing strategies.

The findings revealed that tourists prefer bundled and hedonic pricing strategies that integrate the service providers' pricing strategies' locus of control, stability and controllability. Tourists do not favor dual pricing strategies unless the reasons for price controllability or stability are justified. Tourists also prefer the controllable pay-what-you-want pricing strategy. Although tourists accept dynamic pricing, certain conditions related to price locus, stability and controllability must be met.

The research shows tourists prefer pricing strategies that give them control and flexibility, like bundled packages and pay-what-you-want models. Service providers should integrate pricing strategies that transparent costs and justify price fluctuations. While dynamic pricing is accepted if necessitated by external factors, tourists are wary of unnecessary price changes. Providers can build trust and satisfaction by explaining pricing rationale and offering controllable options like bundles.

The study contributes to the theory by applying attribution theory to the context of pricing strategies in tourism. It also provides innovative recommendations for tourism managers on how to use pricing strategies after the COVID-19 pandemic. The findings offer new insights that extend beyond previous research.

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 investigate the effects of school quality (SQ) on residential property prices in Johannesburg, South Africa. Previous studies have empirically examined the quality of private and public schools without a standard proxy that is accepted in the literature. As a result, this paper extends the literature to the global south by the effect that SQ has on residential property price changes in the local markets of the City of Johannesburg.

The research adopts the hedonic pricing model to evaluate and quantify the impact that the structural attributes such as erf size; number of bedrooms and bathrooms; and SQ measured by pass rates, sport rankings and quality of facilities have on house prices. A total of 2,763 property transactions covering the Kensington and Observatory areas of the City of Johannesburg over the period 2010 and 2020 were obtained from the deeds registry and used for the empirical analysis.

The study finds that SQ has a positive impact on house prices. When the average pass rate of the model school increases by 1%, all other things being equal, house prices also increase by 1.8%. This suggests that people who live closer to the model school are willing to pay more when the school performance improves. The 1.8% premium this study attributes to a 1% increase in school performance is however generally low when compared to some findings in the literature suggesting that there may be some other important factors that households consider when purchasing their home.

The main contribution is uncovering the relationship between the SQ and residential property prices in the local markets, using Kensington and Observatory in Johannesburg as sampled areas. Due to the presence of reliable and quality of data sets, such studies are not many in the global south and a study of this nature in South Africa is notably not existing in the literature.

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.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

Although wearable health-monitoring technology (WHMT) has become a stimulus for public health, women’s acceptance rate of this technology appears to be low. Thus, this study intends to investigate the factors affecting women’s adoption of WHMT.

The unified theory of acceptance and use of technology–2 model has been used in this study as a research framework that has been extended to include lifestyle and attitude. The proposed extended framework is validated using primary data (n = 314) collected from female respondents using a structured questionnaire; the partial least square-based structural equation modeling technique is subsequently used to test the proposed hypothesis.

The results show that effort expectancy, social influence, price value, habit, attitude and lifestyle have significant positive effects on women’s behavioral intention to use WHMT and accelerate actual usage behavior. Notably, effort expectancy and habit exhibit the largest impact on behavioral intention. However, performance expectancy, facilitating conditions and hedonic motivation are not significantly associated with behavioral intentions.

The findings of this study are important for healthcare practitioners and service providers to comprehensively understand the factors that affect women’s behavioral intentions in line with their actual usage behavior. This insight will help policymakers design viable strategies regarding WHMT to promote its sustainable usage in least developed countries.

This study contributes novelty by using an extended model that links women’s attitudes and lifestyles to their adoption of WHMT. This study also fills the gaps in the existing literature on women’s behavioral intentions in the context of WHMT by showing novel associations in the domain of WHMT uptake.