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1 – 10 of over 7000Wakuo Saito and Teruo Nakatsuma
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Elena Shakina, Iuliia Naidenova and Angel Barajas
Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible…
Abstract
Purpose
Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies obtaining their shadow prices.
Design/methodology/approach
The paper adapts a hedonic-pricing methodology developed primarily for markets in real estate and secondhand cars to define how much intangibles may contribute to companies' market value. A certain calibration of the original tool has been developed to make this methodology appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of the intangible resources which are not reflected on the balance sheet.
Findings
The results of this study demonstrate that hedonic pricing with a self-selection correction generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect of a low endowment is even more evident for the baseline model. This model shows consistent negative shadow prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced models; human capital, as measured by the qualification of top management and investments in employees, has likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large degree in companies' market value.
Practical implications
This paper brings new opportunities to obtain the monetary value of intangible resources based on estimated market prices of a corporation's resource portfolio. These prices may be used for several purposes – for example, benchmarking for performance management, capital budgeting or knowledge-management practices. Moreover, by having methodological value, this study opens ways to evaluate any other intangibles which are not explicitly discussed in the empirical test of this particular study.
Originality/value
This study primarily contributes to the methodological advancement of evaluation of corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for specific intangible resources which makes the contribution of this study unique for the existing literature, both within resource-based and value-based views.
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Margarita M. Lenk, Elaine M. Worzala and Ana Silva
Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive performance…
Abstract
Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive performance evidenced from both techniques, which contradicts some of the earlier studies which support a position of artificial neural network superiority. Demonstrates that at least 18 per cent of the “normal” property predictions and over 70 per cent of the “outlier” property predictions contained valuation errors greater than 15 per cent of the actual sales price. The combination of these substantial errors and the model‐optimization costs incurred motivate a message of caution before artificial neural networks are adopted by the real estate valuation and/or lending industries.
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The aim of this education briefing is to comment upon how basic hedonic pricing models for the valuation of property can be expanded and developed. In this case, the briefing…
Abstract
Purpose
The aim of this education briefing is to comment upon how basic hedonic pricing models for the valuation of property can be expanded and developed. In this case, the briefing illustrates the use of the new economic approach to the analysis of housing markets, namely the search-and-matching models.
Design/methodology/approach
This education briefing discusses the connection of two important economic theories: the hedonic price theory and the search-and-matching theory.
Findings
This education briefing gives an example of a (non-linear) form of the hedonic price function.
Practical implications
In cases of mass appraisals, hedonic pricing models can provide a broad indication of value across submarkets and this education briefing demonstrates a theoretical model that can be used to provide a theoretical groundwork for the use of a concave hedonic price function in empirical estimates.
Originality/value
This education briefing shows how basic hedonic pricing models can be enhanced by a search-and-matching approach to determine property values.
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Sherif Roubi and Ashraf Ghazaly
The purpose of this paper is to focus on inter‐neighbourhood variation in the rental apartment market in Greater Cairo, Egypt, and its potential influence on property prices and…
Abstract
Purpose
The purpose of this paper is to focus on inter‐neighbourhood variation in the rental apartment market in Greater Cairo, Egypt, and its potential influence on property prices and performance of hedonic pricing models.
Design/methodology/approach
The paper delves into the issue of whether comparables from different neighbourhoods are homogeneous enough to be aggregated in hedonic pricing models. This paper extends the research on rental‐property market segmentation by investigating the existence of apartment submarkets determined by neighbourhoods.
Findings
Results show that parameters are unstable across neighbourhoods and Chow test provides further support for utilising spatial hedonic pricing models.
Originality/value
This paper provides further support for spatial hedonic pricing models using empirical evidence from Greater Cairo, Egypt. The paper finds that explanatory and predictive powers of hedonic pricing models are improved when separate hedonic equations are estimated for each neighbourhood in Greater Cairo. The paper does not provide an elaborate solution for implementing spatial models in Greater Cairo but rather supports the notion that one has to be developed.
Funlola Famuyiwa and Gabriel Kayode Babawale
– The purpose of this study is to examine the relationship and pricing effects of physical infrastructure on house rents using the hedonic technique.
Abstract
Purpose
The purpose of this study is to examine the relationship and pricing effects of physical infrastructure on house rents using the hedonic technique.
Design/methodology/approach
Primary data are derived through a questionnaire survey and secondary data from existing literature. Sampling data on 211 detached residential buildings with a range of physical infrastructure attributes within Lekki Phase 1 area of Lagos are analysed with the hedonic regression technique.
Findings
Results reveal significant impacts and a range of price premium estimates of physical infrastructure on house rents in the study area.
Originality/value
The study suggests a nouvelle and contextualized approach for sustainable infrastructure delivery, improvement and maintenance. Appropriate pricing will help to guide and support physical infrastructure development and sustainability. When tailored in line with market support, achievable pricing can be attained in setting land-based user charges and tariffs for cost recovery on projected developments and reform. Results from empirical market evidence also provide demand and viability indicators that offer invaluable blueprints, by which governments, policy/decision makers, investors, town-planning authorities and other stakeholders can take sustainable decisions based on priority, in the face of budgetary constraints – a significant characteristic of the Nigerian economy.
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Michael J. McCord, Sean MacIntyre, Paul Bidanset, Daniel Lo and Peadar Davis
Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become…
Abstract
Purpose
Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.
Design/methodology/approach
A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).
Findings
The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.
Originality/value
Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.
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Walid Marrouch and Nagham Sayour
This study aims to examine the impact of local air pollution on housing prices in Lebanon.
Abstract
Purpose
This study aims to examine the impact of local air pollution on housing prices in Lebanon.
Design/methodology/approach
The authors apply a hedonic pricing approach using a unique data set from Lebanon. To account for non-linearities in pricing, the authors use three different functional regression forms for the hedonic model approach. The authors also deal with potential omitted variable bias by estimating a hedonic frontier specification.
Findings
The authors find that, in all specifications, air pollution negatively and significantly affects housing prices. The estimated marginal willingness to pay for a one microgram per cubic meter change in particulate matter (PM10) concentration ranges between 2.88% and 3.18% of mean housing prices. The authors also provide evidence of a negative pricing gradient away from the city center, landing support for the monocentric urban development hypothesis.
Research limitations/implications
Given the lack of a data set linking household socioeconomic characteristics with housing data, the authors only consider the first-stage hedonic model.
Practical implications
The proposed hedonic pricing regression approximates a housing pricing equation that can be used by policymakers.
Social implications
The findings suggest that pollution is a significant factor in household behavior in Lebanon.
Originality/value
This paper adds to the scant literature studying the effects of air pollution on housing prices in developing countries. To the best of the authors’ knowledge, this is the first paper to study the impact of pollution on housing prices in a country in the Middle East and North Africa Region.
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David P. Lorenz, Stefan Trück and Thomas Lützkendorf
The basic purpose of this paper is to explore the relationship between the sustainability of construction on the one hand and market value, worth and property investment…
Abstract
Purpose
The basic purpose of this paper is to explore the relationship between the sustainability of construction on the one hand and market value, worth and property investment performance on the other hand. This paper aims to analyse price movements and price differences caused by different property characteristics.
Design/methodology/approach
Based on the estimated log‐linear hedonic regression model, a hedonic price index is calculated. Price movements subject to different property characteristics are examined by constructing various conditional hedonic price indexes.
Findings
The results reveal that, high‐quality flats or flats within preferred locations clearly outperform their competitors in terms of price stability during an overall market downturn. However, it is also shown that contemporary building descriptions or specifications of transactions within property databases are not yet sufficient and need to be widened to meet forthcoming challenges. Therefore, an “integrated building performance approach” is introduced and a proposal for the step‐wise improvement of building descriptions is made.
Practical implications
The paper shows that efforts need to be undertaken by the property profession in combining and transferring financial performance data along with information that is indicative of a building's contribution to sustainable development.
Originality/value
The paper offers insights into the relationship between the sustainability of construction and market value.
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Amirhosein Jafari and Reza Akhavian
The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of…
Abstract
Purpose
The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes.
Design/methodology/approach
A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity.
Findings
Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics.
Research limitations/implications
An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation.
Practical implications
The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors.
Social implications
Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices.
Originality/value
Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.
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