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This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

TikTok social media app has become one of the most popular forms of leisure and entertainment activities, but how hedonic consumption experiences (comprising fantasy, escapism, enjoyment, role projection, sensory, arousal and emotional involvement) of the TikTok app determine users' intention to use the app and its resulting impact on the actual usage behavior remains limited in the information systems literature, especially featuring the hedonic consumption perspective in entertainment industry.

This study employs uses & gratification theory to answer the “why” via predicting the role of hedonic consumption experiences that serve as gratifications to trigger technology acceptance behavior (especially, in form of users' behavioral intention to use the TikTok app and its further impact on usage behavior). This study utilizes the partial least squares-structural equation modeling approach to perform data analyses on 258 TikTok app users.

Our results provide a strong support such that users' playful consumption experiences (i.e. escapism, role projection, arousal, sensory experience and enjoyment) positively influence their intention to use the TikTok app and its resultant effect on users' actual usage of the app. In contrast, fantasy and emotional involvement fail to influence users' intention to use the TikTok app.

To the best of our knowledge, our investigation is one of the first studies to apply the hedonic consumption experiences as potential gratifications that derive users' intention and its subsequent influence on the actual usage of the TikTok app. Our study results would assist marketing and brand managers to redefine approaches and tactics to create effective strategies that implement essential determinants to increase behavioral intention among entertainment service providers.

The purpose of this study is to analyse the trends regarding housing segregation over the past 10–20 years and determine whether housing segregation has a spillover effect on neighbouring housing areas. Namely, the authors set out to determine whether proximity to a specific type of segregated housing market has a negative impact on nearby housing markets while proximity to another type of segregated market has a positive impact.

For the purposes of this paper, the authors must combine information on segregation within a city with information on property values in the city. The authors have, therefore, used data on the income of the population and data on housing values taken from housing transactions. The case study used is the city of Stockholm, the capital of Sweden. The empirical analysis will be the estimation of the traditional hedonic pricing model. It will be estimated for the condominium market.

The results indicate that segregation, when measured as income sorting, has increased over time in some of the housing markets. Its effects on housing values in neighbouring housing areas are significant and statistically significant.

A better understanding of the different potential spillover effects on housing prices in relation to the spatial distribution of various income groups would be beneficial in determining appropriate property assessment levels. In other words, awareness of this spillover effect could improve existing property assessment methods and provide local governments with extra information to make an informed decision on policies and services needed in different neighbourhoods.

On housing prices emanating from proximity to segregated areas with high income differs from segregated areas with low income, policies that address socio-economic costs and benefits, as well as property assessment levels, should reflect this pronounced difference. On the property level, positive spillover on housing prices near high-income segregated areas will cause an increase in the number of higher income groups and exacerbate segregation based on income. Contrarily, negative spillover on housing prices near low-income areas might discourage high-income households from moving to a location near low-income segregated areas. Local government should be aware of these spillover effects on housing prices to ensure that policies intended to reduce socioeconomic segregation, such as residential and income segregation, produce desirable results.

Furthermore, a good estimation of these spillover effects on housing prices would allow local governments to carry out a cost–benefit analysis for policies intended to combat segregation and invest in deprived communities.

The main contribution of this paper is to go beyond the traditional studies of segregation that mainly emphasise residential segregation based on income levels, i.e. low-income or high-income households. The authors have analysed the spillover effect of proximity to hot spots (high income) and cold spots (low income) on the housing values of nearby condominiums or single-family homes within segregated areas in Stockholm Municipality in 2013.

This study aims to measure the occurrence of gentrification and to relate gentrification with housing values.

The authors have used Getis-Ord statistics to identify and quantify gentrification in different residential areas in a case study of Stockholm, Sweden. Gentrification will be measured in two dimensions, namely, income and population. In step two, this measure is included in a traditional hedonic pricing model where the intention is to explain future housing prices.

The results indicate that the parameter estimate is statistically significant, suggesting that gentrification contributes to higher housing values in gentrified areas and near gentrified neighbourhoods. This latter possible spillover effect of house prices due to gentrification by income and population was similar in both the hedonic price and treatment effect models. According to the hedonic price model, proximity to the gentrified area increases housing value by around 6%–8%. The spillover effect on price distribution seems to be consistent and stable in gentrified areas.

A few studies estimate the effect of gentrification on property values. Those studies focussed on analysing the impacts of gentrification in higher rents and increasing house prices within the gentrifying areas, not gentrification on property prices in neighbouring areas. Hence, one of the paper’s contributions is to bridge the gap in previous studies by measuring gentrification’s impact on neighbouring housing prices.

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.

Improving the competitiveness of East Africa's rice industries necessitates increased and viable production of rice of the quality desired by consumers. This paper aims to understand consumer preferences for rice quality attributes in Uganda and Kenya to inform the countries' rice breeding programs and value chain development interventions.

Rice samples are obtained from retail markets in various districts/counties across the two countries. The samples are analyzed in a grain quality laboratory for the rice's physicochemical characteristics and the resulting data are used to non-parametrically estimate hedonic price functions. District/county dummies are included to account for potential heterogeneity in consumer preferences.

Ugandan consumers are willing to pay a price premium for rice with a relatively high proportion of intact grains, but the consumers discount chalkiness. Kenyan consumers discount high amylose content and impurities. There is evidence of heterogeneity in consumer preferences for rice in Mbale, Butaleja and Arua districts of Uganda and in Kericho and Busia counties of Kenya.

The study makes a novel contribution to the literature on consumer preferences for rice in East Africa by applying a hedonic pricing model to the data generated from a laboratory analysis of the physicochemical characteristics of rice samples obtained from the market. Rather than base our analysis on consumers' subjective sensory assessment of the quality characteristics of rice, standard laboratory methods are used to generate the data, which enables a more objective assessment of the relationship between market prices and the quantities of attributes present in the rice samples.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

Weaknesses in the grades and standards system in low-income countries across Sub-Saharan Africa undermine the transparency of agricultural markets. In the Democratic Republic of the Congo (DRC), Ghana and Mozambique rice is predominately sold in open bags and if rice price does not reflect its quality, then inefficiencies may lead to consumer welfare losses. Importantly, it is possible that impoverished communities are priced out of the market due to inflated and inefficient prices. The objective of this study is to examine determinates of rice price by estimating the impact of selected rice quality attributes on rice prices in Democratic Republic of the Congo, Ghana and Mozambique.

We collected 363 rice samples from open air markets in Bukavu (DRC), Nampula (Mozambique) and across Ghana in 2019. Each rice sample was analyzed in a food science lab for the quality attributes: percentage of chalk and brokens, chalk impact, length and length-to-width ratio. We used multiple regression analysis to estimate if and to what extent quality attributes were the drivers of price.

Findings suggest that there are irregularities in the Ghanaian market for broken rice and that regardless of quality, imported rice is priced higher than domestic rice. In the DRC and Mozambique, our results indicate price is driven by length and length-to-width ratio in the former and length-to-width ratio in the latter.

Rice samples were purchased from market vendors and thus consumer preferences for attributes were not revealed.

These results provide valuable insight to policymakers regarding the need for proper labeling and regulation of open bag rice sales in an effort to increase consumer welfare and improve food security.