Hedonic Price Research Articles

Is more always better? The returns to alcohol by volume—Evidence from the Austrian “Spirits Trophy 2023”

Abstract Hedonic models that seek to explain the observable variation in wine and beer prices have so far included alcohol by volume (ABV) only as a control variable without paying much attention to the magnitude of the coefficient and without questioning the implicit assumption that the impact of ABV on bottle prices is indeed linear. Using data from the “Austrian Spirits Trophy 2023,” we find the relationship between ABV and bottle prices to be nonlinear, with statistically significant effects observed for linear, squared, and cubic terms of alcohol content. Moreover, we find expert evaluations of spirits to demonstrate a nonlinear relationship with ABV too.

Unveiling housing preferences amidst flood risks: an empirical study in an emerging market

Purpose This study investigates the interplay between flood risk, financial constraints and buyer behavior in the housing market. It aims to discern how flood experience influences housing choices, particularly among financially constrained buyers. Design/methodology/approach Using hedonic pricing models and probit analysis, this research examines the implicit prices of house characteristics and evaluates the determinants of housing choices, considering factors such as flood risk, financial status and past flood experience. Findings The study underscores that lower house prices incentivize buyers to opt for homes in flood-prone areas, thereby increasing their vulnerability to flood risks. Consequently, financial constraints exacerbate this situation, compelling low-income buyers to accept higher flood risks. However, flood experience fosters risk awareness among buyers, encouraging them to steer clear of flood-prone areas, albeit with lesser efficacy among financially constrained individuals. Originality/value This research contributes to the understanding of how flood risk and financial limitations shape housing decisions. It highlights the role of flood experience in influencing buyer behavior and emphasizes the need for targeted policies to mitigate flood-related risks in the housing market, particularly among economically vulnerable populations.

Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model.

Customizing a mass appraisal model for residential property tax assessment: a hedonic regression approach in Bahir Dar city, Ethiopia

PurposeThe paper aims to estimate the house rental values for the purpose of customizing mass appraisals in Bahir Dar City, Ethiopia. It seeks to identify the critical factors affecting the rental values of residential properties and customize a mass appraisal model for such properties. The study focuses on identifying attributes that significantly affect house rental values.Design/methodology/approachThe paper adopted a survey research design, utilizing a survey questionnaire, expert group discussion and document analysis. The data were analyzed using thematic, descriptive and inferential statistical analysis, including correlation and hedonic regression analysis.FindingsAmong the variables included in the model, the number of rooms, availability of schools, land value grading, type of nearest road, housing typology, built-up area, plot area, walling material, traveling cost and fencing materials were the most significant factors for predicting the annual rental value of residential properties in the city.Research limitations/implicationsThe findings of this study will provide valuable insights to tax assessors, property owners and local government authorities, including municipalities, concerning the key determinants of the rental values of residential properties. Besides, these findings will serve as a useful tool for valuers and researchers in the field of property value modeling.Originality/valueThis study represents the first attempt to develop a framework for mass appraisal of residential properties using annual rental values in the Ethiopian context.

A random forests-based hedonic price model accounting for spatial autocorrelation

A random forests-based hedonic price model accounting for spatial autocorrelation

Evaluating objective and perceived ecosystem service in urban context: An indirect method based on housing market

Ecosystem service (ES) evaluation is usually based on the stocks of natural resources and their functions. However, the value of ES in the urban area depends on human activities more than the existence of natural resources. This research implements an indirect market method by integrating hedonic housing price model to assess ES in urban context from both objective (remote sensing) and subjective perspectives (street view image). Machine learning tools are employed to investigate the impacts of objective and perceived ES on housing prices based on a case study in Wuxi, China. The analytical results suggest that the contribution of ES to house prices in Wuxi ranges from 0% to 10.85%. Further investigation found that visible trees are the most important ES factor of housing price, more important than the coverage of green space. We also find that the quality of blue-green spaces might modify the value of ES, while the poor landscape design and water pollution in the central urban area made the values of ES low in the housing market. This study proves that the indirect method based on the housing market is helpful in valuing ES in the urban context. The high importance of perceived blue-green spaces in ES encourages more efforts on landscape design rather than only increasing coverage.

Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices

Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices

An investigation of the impact of energy performance certificate (EPC) ratings on residential property prices in Oxfordshire: a hedonic study

ABSTRACT This paper investigates whether energy performance ratings measured by Energy Efficiency Certificates (EPCs) affect the sales prices of residential properties in Oxfordshire, UK. The study makes use of 186,913 sales transactions that took place between 1995 and 2023. Using a hedonic regression estimation, a nonlinear pattern between energy efficiency levels and the sales prices of dwellings, are observed. The results also show that there is evidence of a significant positive relationship between EPC ratings and detached, semi-detached, terraced houses and flats. In the second part of this study, we examine the effects of age, county and type of dwelling on price premiums. To the best of the authors’ knowledge, this study is the only study, using the most comprehensive literature; and the largest, most up to date available data in analysing the Oxfordshire real-estate market, in terms of energy efficiency and price premiums.

How much are we willing to pay for quality wine? A meta‐analysis and meta‐regression analysis

AbstractThis paper performs a meta‐analysis and a meta‐regression analysis on the price semi‐elasticity of wine quality ratings in the context of estimating hedonic price functions. To do so, we thoroughly search for publications that have estimated hedonic functions in this market, resulting in 223 articles estimating 1595 functions, although not all of them incorporate quality ratings. Additionally, 60 aspects of the different studies are coded, including sample characteristics, incorporated attributes, and other implemented methodological choices. The meta‐analysis is then performed, revealing evidence of selection and publication biases. Taking them into account, the price elasticity is found to be around 5%–6%, remaining robust to several robustness checks. The meta‐regression explores the unexplained heterogeneity in the price elasticities observed in the meta‐analysis. Results indicate that quality ratings are more influential in the context of red wines, and less important in the case of sparkling and organic wines. The nature of the data source matters, but generally not the type of price used except the ones obtained from auction and en primeur sales. The influence of the remaining attributes incorporated in the hedonic function is relatively small, except for other quality aspects, reputation, and environment. The econometric techniques used in the estimation do not affect the effect size. This is also the result found for the type of publication, except for studies published in books. Finally, we find that the most cited studies are those that show a smaller effect.

The Effect of War on Spatial Differentiation of Real Estate Values and Urban Disorder in the Damascus Metropolitan Area

The Syrian war, which commenced in 2011, transformed the Damascus real estate market due to heightened insecurity and tenure disputes. Using the hedonic price models, including 2411 housing transactions over the period 2010–2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus City resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. Our findings show that correlations were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. This study guides decision makers in mitigating severe property value variations across war-affected urban areas by fostering spatial justice in property rights and promoting balanced investment and sustainable real estate development during the post-war recovery phase.

Nature’s Neighborhood: The Housing Premium of Urban Parks in Dense Cities

Urban parks, a core component of urban landscapes, play a crucial role in mitigating the negative effects of rapid urbanization and achieving sustainable urban development. In densely populated urban environments, providing urban parks fairly and efficiently, taking social preferences into account, is an important challenge. In this regard, we take Hangzhou, China, as an example and use a hedonic pricing model with a difference-in-differences estimator to test the effect of different types of urban parks on housing prices, quantify their respective economic values, and reflect homebuyers’ preferences. The findings indicate that the construction of new urban parks leads to an overall increase in the value of the surrounding real estate to some extent. Specifically, the construction of comprehensive parks and large parks significantly enhances the value of nearby housing, while proximity to new medium-sized parks also results in a housing price premium, albeit to a lesser extent. In contrast, community parks and specialized parks have a less significant impact on housing prices. These findings provide valuable insights for equitable urban development and planning, optimizing the allocation of urban parks and determining investment priorities for different types of parks to enhance the sustainability of the urban environment and human well-being.

SUSTAINABLE DEVELOPMENT OF RURAL AREAS IN THE REPUBLIC OF MOLDOVA THROUGH INCREASING THE EFFICIENCY OF THE LAND RESOURCE USE IN THE AGRICULTURAL SECTOR

The evaluation of the agricultural land in the Republic of Moldova represents the main factor in the sustainable rural space development and it emphasizes economic criteria for the efficiency of land resources. The hedonic price in the sales-purchase transactions of parcels in agricultural land may be the scientific innovation and originality of the geographically weighted regression methodology with the spatial approach in the used econometric model. The price of agricultural land used parcels in Maiovca village, Ocnița district were evaluated by using the data from transactions registered in 2022 and the results of the geographically weighted regression stands up the suitable conclusions of the licensed assessors in the field. The autocorrelation coefficient value evaluated by SAR is ρ = 0.072 according to Cobb Douglas model confirms the high quality of the regression techniques carried out and the used spatial methodology can be implemented in cadastral practice for the taxation in the agrarian sector. The geographically weighted regression utility proposed in this methodology has advantage in research area because of the similar evaluation techniques carried out in the countries of the European Union that are adjacent to the Republic of Moldova.

Forecasting residential real estate prices and energy usage in Flanders

Purpose Currently, residential forecasting literature is limited with few to no forecasting papers that consider residential prices and energy consumption for the selling and rental market within a single study. The purpose of this paper is to study the prices and energy usage for both markets by combining an explanatory model with the predictions of a predictive model, which will lead to a better understanding of the forecasting results. Design/methodology/approach This study presents an econometric comparison between 508,612 selling properties and 174,007 rental properties transacted in Flanders between January 2019 and December 2023. The authors study their prices and energy usage by combining the causal results of a hedonic price model (HPM) with the predictions of an autoregressive integrated moving average model (ARIMA). Findings The authors find that both markets are extremely sensitive to changes in related factors, whereby selling prices are primarily influenced by macroeconomic factors, but rental prices and especially energy usage are also sensitive to market standards. Policies that target energy efficiency can therefore steer its future trend. In addition, in line with previous literature, the HPM shows an increasingly strong relationship between residential prices and energy efficiency over the years, indicating that these steering energy policies also indirectly monitor residential prices. Originality/value This study underscores the importance of integrating the results of an explanatory model with a predictive model. It demonstrates that combining them provides a more comprehensive understanding which is helpful for policymakers, real estate professionals and investors during their decision-making process.

An approach for constructing spatially paired pseudo repeat-sales housing price indices in China

AbstractConstructing a reliable housing price index is crucial for accurately reflecting housing price dynamics and enhancing transparency in the housing market. However, existing methods for constructing housing price indices often confront many challenges, such as omitted variable issues in the hedonic model and constrained samples used in the repeat-sales model. To address these challenges, this study identifies a spatially paired relationship between complexes and develops a Spatially Paired Pseudo Repeat-Sales model to construct the housing price index. This approach offers two significant advantages: first, it enlarges the sample size used in the repeat-sales model at least 1.8 times; second, it effectively estimates the effects of spatial dependency and physical housing factors on prices while mitigating the impacts of unobservable factors through differentiation. The findings of this paper suggest that using the spatially paired pseudo repeat-sales model can significantly improve the estimation of housing price volatility, by approximately 13%. Moreover, the constructed housing price index model demonstrates significant robustness, even when the spatial weight settings are altered. This research provides convincing evidence of improved housing market transparency for stakeholders, including governments, institutional developers, and individual investors. Additionally, the constructed model can inform other similar research in housing price index construction by providing a spatially pairing perspective on housing complexes.

A novel approach to commercial property valuation: Successive term indexing and its competitive implications

The development of innovative valuation models in the commercial real estate sector is crucial for enhancing competitiveness, as they provide stakeholders with the accuracy and adaptability needed to navigate and capitalize in an increasingly complex market landscape. This paper aims to improve initial valuations provided by real estate agents by updating their timeliness while also enhancing their accuracy. Leveraging statistical techniques based on hedonic regression, it introduces a novel mechanism, successive term indexing, enabling the reassessment of widely varying commercial properties with initial assessments that are at least one term old, where a term typically encompasses one year. The model's novelty lies in its approach to indexing individual property characteristics across two successive terms, using a k-means algorithm to categorize all numerical variables and stepwise selection. The former enables differing index values for varying sizes, while the latter allows dynamic evaluation of the significance of regressors in time. Applied to data from a banking institution, the model showcases strong predictive accuracy with unique reassessment ratios ranging from -3.8% to 5.2% for 2022. With its nuanced analysis of market dynamics and indexing capabilities, the mechanism intersects the elements of PPIs and AVMs, presenting a significant methodological advancement and a practical, simple-to-use tool for valuation.

Extend UTAUT2 Model to Analyze User Behavior of China Construction Bank Mobile App

The outbreak of COVID-19 has been accelerating the digital transformation of financial institutions all over the world. Mobile banking APPs become an important channel to provide financial services in the Internet and Big Data era. Factors that restrict its development have attracted the close attention of scholars and practitioners around the world. Different from a general APP software, mobile banking APPs naturally have some financial attributes which set high requirements for personal privacy protection and other security issues. For this reason, this paper extends the original UTAUT2 model by incorporating a new constructor: perceived risks. The study carefully selects China Construction Bank’s mobile banking app (short as “the CCB APP”), as she is the biggest bank in China and has good novelty and representativeness in financial technology. This paper constructs an extended UTAUT2-based model using questionnaires and structural equation modeling the user behavior of CCB APP and tests the influencing factors of the proposed model through empirical analysis. Similar to other studies using the UTAUT2 model, the findings of this paper indicate that performance expectations, effort expectations, social influence, facilitators, and habits, all have a positive impact on CCB APP users’ intentions. Their intention to use was a mediating variable of user behavior, while facilitators directly influenced CCB users’ behavior. However, the study finds that perceived risk negatively affects the CCB users’ intention while hedonic motivation and price value do not significantly affect users’ decision-making process. Based on these findings, the misdirection should to be avoided in regard to financial APP development by financial institutions is discussed and the future research direction concerning mobile banking app adoption is illustrated.

Food and fraud: On the Codfather and harvest mislabeling

AbstractWe study incentives to mislabel output in multispecies fisheries managed by tradeable catch shares. Firms can save quota costs by misreporting species harvested, as seen in criminal proceedings against the Codfather. We show that price elasticities for the abundant species reported in the cheating will be higher for compliant than non‐compliant firms. Using data from the New England groundfish fishery, we test this prediction using a latent class hedonic price equation to identify compliant versus non‐compliant firms. We then estimate multi‐output technologies for compliant and noncompliant firms to identify differences in substitution possibilities. Our empirical results align with theoretical expectations and are robust to placebo tests.

The influence of urban green and recreational areas on the price of housing in Zagreb

Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

Gobi wind blows housing price away: Willingness to pay for clean air in China

Economists often reply on housing market to investigate the willingness to pay for clean air, while potential endogeneity concerns pose challenges. This paper proposes an identification strategy based on the Gobi Desert dust storm, providing suitable instrumental variable for hedonic models at the house-day level in East Asia. We exploit within-Beijing and over-time variation in air pollution caused by dust storm and find that after addressing endogeneity issues, the negative impact of air pollution on housing prices strengthened nearly fivefold. Buyers in Beijing are willing to pay an additional 7.9% in housing prices for a decrease of PM10 by 100 μg/m³, indicating that the benefits of air pollution improvement in Beijing over the past decade are more than 1.5 trillion yuan in the housing market. This paper also reveals that an increase in air pollution leads sellers to exhibit a higher willingness to sell, reflecting in lower listing prices, heightened bargaining power for buyers, and a shorter transaction cycle.

Analyze Factors Affecting Room Pricing of Accommodation in Luang Prabang Province

The study of “Analyze Factors Affecting Room Pricing of Accommodation in Luang Prabang Province”. The objectives of the study are the overview Luang Prabang tourism, hotel and guesthouse statistics, in particular to investigate the way which room rates are determined. Data was collected from Agoda website by survey in May 2108. The sample size is 204 hotel and guesthouse that consists of 1,326 observations. Based on Hedonic Pricing Model for estimation and data analyzing for Ordinary Least Square (OLS) by Stata 13. Our result of finding included that there were 643,319 tourists (2016) from domestic and foreign countries. The total number of hotels and guesthouses accounted for 415 (2016) which includes 65% and 35% of domestic and foreigner firms respectively. The room rates of hotel and guesthouse are between $ 2 and $ 400 per night. The results illustrate that the factors determined the hotel and guesthouse room rates in Luang Prabang are 17 variables; for instance, colonial style, deluxe room, river view, room size, number of rooms, guest limited, bed king, wooden parquet floor, free cancellation, car park, front desk 24 hours, swimming pool, and massage. Which these variables are most statistically significant. Furthermore, the symbol of variables is similar on our prediction; expecting some variables are the heritage site area, Wi-Fi and 24 hours security which is not statistically significant. In addition, Free airport transfer service is statistically significant; apart from, the symbol of variable is not the same on our prediction.