Hedonic Price Research Articles

Using Co-Ordinate Systems in Hedonic Housing Regressions

Hedonic house price studies typically incorporate information about location by including either a set of dummy variables to represent individual locations called “neighborhoods” or by using a set of distance (or travel time) variables to characterize locations in terms of proximity to amenities and dis-amenities. As an alternative to these, relatively recent research advocates a latitude–longitude co-ordinate system for incorporating distance information into hedonic house price regressions. This study shows that many of the claims made in this research, particularly those referencing the elimination or diminution of “biases of coefficients of non-distance variables”, are given the particulars of the Monte Carlo experiments, not possible to investigate. We further show, both analytically and with our simulations, that there is no omitted variable bias present in their simulations because their randomly generated non-distance variable is uncorrelated with any of the other variables used in their regression models.

Impacts of street tree abundance, greenery, structure and management on residential house prices in New York City

Urban trees have been widely linked to residential house prices. However, most existing studies focus on using tree abundance and greenery measures to evaluate the economic benefits of urban trees, while disregarding the effects of tree structure and tree-related management such as intentional maintenance and stewardship. We hypothesize that tree abundance, greenery, structure, and tree-related management are associated with house prices through offering local benefits that accrue to private homeowners and providing desirable living environments. We take street trees in New York City (NYC) as a case study to test the hypothesis. We first derived street tree canopy cover from high-resolution satellite images, street greenery from Google Street View, and street tree structure (e.g., species diversity, tree size, and tree health) and streetscape management variables (e.g., tree stewardship, tree guards that are a man-made structure to protect trees and enhance tree growth, and sidewalk damages adjacent to street trees) from the 2015–2016 street tree census data in NYC. We then built spatial hedonic price models to examine the associations between the above-mentioned street tree variables and residential house prices, after controlling for a variety of covariates. We found that HPMs that incorporate street tree structure and streetscape management offer stronger explanatory power than the one with only street tree abundance. Among the street tree-related variables we considered, tree canopy cover, green view index, tree stewardship and tree guards are statistically associated with increased house prices, while there are no such relationships for species richness, trees’ health status and streetscape sidewalk damages. More research is needed to understand the potential mechanisms and causal pathways that urban tree abundance, structure, and management affect residential house prices.

Investigating revealed preferences for urban waterway conditions: A hedonic property valuation study

AbstractThis study examines the impact of urban waterway conditions on property market prices. In general, similar revealed preference studies typically focus on identifying the value associated with changes in attributes such as riparian vegetation or water quality. Using an index that classifies waterways based on the vegetation and channel conditions, we analyse both attributes. Our spatial hedonic property price model findings indicate that buyers are willing to pay premiums ranging from 2.7% to 8.5%, depending on vegetation and channel conditions. However, when the proximity to the waterway is accounted for, we found that properties adjacent to the highest‐ranked vegetation and channel conditions attract a higher premium of 12.8%. Overall, the implicit marginal effects for the distance–condition interaction variables indicate that for lower‐ranked waterway conditions, there is a relative aversion to being adjacent to waterways. The results suggest that there are significant gains to be realised from removing concrete‐lined channels and replacing them with stones for banks, or re‐creating unmodified channels, even if there is only limited scope for increasing vegetation.

Analysis of the Effect of Effort Expectancy, Social Influence, Hedonic Motivation, Price Value, Habit, and Flow on MMORPG’s Intention to Play

With more Internet access, MMORPGs have grown in popularity. In Indonesia, the biggest gaming market in Southeast Asia, this genre is less popular. Therefore, studying what inspires Indonesian MMORP users is necessary. The study used a quantitative research design on 221 online gamers who were familiar with MMORPGs but had never played them. The study tested research hypotheses using multivariate linear regression analysis. The study discovered that hedonic motivation, price value, habit, and flow all have a positive impact on one's intention to play MMORPG online games, but effort expectation and social influence have no impact.

Do People Value Environmental Goods? Evidence From the Dutch Housing Market

This article studies the relationship between environmental goods and housing prices in the Netherlands. Applying a hedonic pricing model, we estimate simultaneous correlations between housing prices and air quality, noise pollution, green scenery, and water scenery, demonstrating the importance of including related sets of environmental goods to avoid omitted variable bias. We find that households particularly value noise pollution and green and water amenities in close proximity to their house. Air quality does not appear to substantially impact housing prices, neither do green spaces located more than 200 meters away. These results suggests that households mainly value those environmental goods that are directly experienced. A comparison of our hedonic price results to existing monetary estimates of the health effects of environmental goods indicates that people only partially internalize these health effects.

Hedonic Approach to Vertical Residential Rentals in the Brazilian Amazon: The Case of Belém, Pará

This study set out to identify and analyze the factors that influence the formation of rental prices for residential apartments in the city of Belém. The approach adopted was based on the hedonic price theory, which considers that the rental price of an apartment reflects the implicit prices of its attributes—structural, locational, and neighborhood quality. The sample used consisted of 259 observations, corresponding to the rental advertisements of the representative apartments in each building. The ordinary least squares (OLS), spatial lag model (SLM), and geographically weighted regression (GWR) techniques were used in the statistical analysis in this study. The results of the OLS model showed statistical significance between the attributes analyzed and the rental price of the apartments. In turn, the SLM indicated that the structural attributes have an impact on the rental prices of neighboring apartments, configuring a contagion effect in the real estate market. The GWR model showed that there was no spatial heterogeneity in the effects of the determinants on apartment rental prices throughout the sample.

Competing to Sell the Reference Product

In a sequential model of vertical product differentiation in which consumers are loss-averse, I analyse how firms compete to sell the reference product when they set prices. I find that there are two subgame perfect equilibria: one where the reference point for all consumers is the higher-quality product; and the other where the reference point is the lower-quality product. However, applying the risk-dominance criterion, I obtain that the sole risk-dominant equilibrium is for the higher-quality firm to sell the reference product. Since the hedonic price of the higher-quality product is the highest, consumers do not suffer any psychological disutility in the risk-dominant equilibrium.

Willingness to pay for flood risk mitigation among residents living near river’s confluence in Yogyakarta, Indonesia

Both property buyers and owners are concerned about the impact of flooding on residential property price. Although several studies have investigated the possibility of property devaluation in flood-prone areas, there are still few that have looked into the effects of flooding on residential properties in Sriharjo, Imogiri, Indonesia. This study aims to examined housing characteristics such as structure, neighborhood, and environment on residential property value. We also created an interaction variable between flood and structural attributes to see whether the impact of flood inundation on property value varies depending on the age of the building, the size of the building area, and the number of bedrooms. Using the hedonic pricing model, we found that flooding reduces the market value of residential property value. The characteristics of property like land square, building square, building age, and number of rooms have influence on property price. The characteristics of neighborhoods are also influence to property price such as distance to city, distance to main road, distance to school and distance to the river. Only one environmental characteristic which influence to property price is height of flood inundation. We discovered that, on average, a rise in the amount of flood inundation decreased property prices in the research region by.015% by using a two-stage estimating approach to evaluate these associations. We calculated IDR 698,500 as the marginal implicit cost of reducing flood inundation. According to subsequent estimates, homeowners would be ready to pay an extra 0.685 percent for a decrease in flood inundation. The study’s findings will help property owners better understand the elements that contribute to property depreciation due to floods. This paper also suggests flood insurance plans since flooding is a significant issue for real estate owners.

Cost-benefits analysis of noise abatement measures in the port of Halifax, Nova Scotia, Canada

Impacts of anthropogenic noise on human beings range from physiological to phycological and include direct damage to auditory organs, to elevated stress and annoyance, to impairments in mental development. Maritime transportation operations are also recognized as a major contributor of anthropogenic noise to nearby communities. The Port of Halifax, Nova Scotia, Canada may be a contributor to these concerns because of its noise emitting operations and it being near densely populated areas. This study examines the cost and benefits of implementing noise abatement measures to reduce potential impacts on residents by limiting road and rail traffic and cargo handling noise from Port operations. The upfront investment costs of noise abatement measures are estimated and compared to the associated human health and wellbeing benefits of reducing noise exposures in exposed residential areas. The hedonic price method was used to translate the benefits of reducing noise exposures in monetary terms. Geospatial analysis was conducted to analyze extent of dwellings exposed to unsafe levels of noise before and after the implementation of abatement measures. Total costs were calculated at $6,914,318 CAD and total benefits at $4,127,870 CAD. This equates to a return of investment of $-2,786,448 CAD. Although this may appear to be an unfavourable investment for the Port of Halifax, this study presents opportunities for improvements and recommendations for noise abatement in port operations.

Real Estate Price Prediction: A Comprehensive Review

Abstract: The document below outlines the execution of a price prediction project for housing and real estate markets. A multitude of algorithms have been employed to enhance the accuracy of predictions. Various researchers have undertaken this project and implemented algorithms such as hedonic regression, artificial neural networks, AdaBoost, and J48 tree, which are deemed as the optimal models for price prediction. These models serve as the foundation, and with the aid of sophisticated data mining tools, algorithms like random forest, gradient boosted trees, multilayer perceptron, and ensemble learning models are utilized to achieve a higher rate of prediction accuracy. The results and evaluation of these models, facilitated by machine learning and advanced data mining tools like Weka and Rapid Miner, significantly impact price prediction.

Real Estate Price Prediction: A Comprehensive Review

Abstract: The document below outlines the execution of a price prediction project for housing and real estate markets. A multitude of algorithms have been employed to enhance the accuracy of predictions. Various researchers have undertaken this project and implemented algorithms such as hedonic regression, artificial neural networks, AdaBoost, and J48 tree, which are deemed as the optimal models for price prediction. These models serve as the foundation, and with the aid of sophisticated data mining tools, algorithms like random forest, gradient boosted trees, multilayer perceptron, and ensemble learning models are utilized to achieve a higher rate of prediction accuracy. The results and evaluation of these models, facilitated by machine learning and advanced data mining tools like Weka and Rapid Miner, significantly impact price prediction

개발 호재가 아파트 분양 프리미엄에 미치는 영향

1. CONTENTS
 (1) RESEARCH OBJECTIVES
 The purpose of this study is to examine how significant the construction of Samsung Electronics' Pyeongtaek Campus and the SRT Pyeongtaek-Jije Station, which are favorable developments in the Pyeongtaek area, affect the premium of apartment sales rights.
 (2) RESEARCH METHOD
 Hedonic price model was used for the factors affecting the actual transaction price, and multiple regression analysis was applied to find out the factors affecting the premium of the actual transaction right.
 (3) RESEARCH FINDINGS
 Model 1 (related to SRT Pyeongtaek Jije Station) empirical analysis showed that variables such as the number of floors, 2021 contract rankings, household parking, interest rates, exchange rates, and Pyeongtaek apartment sales price index had a positive (+) effect. The distance from the SRT Pyeongtaekji Station, a characteristic of favorable development, was found to have the second-largest influence on the dependent variable among the variables.
 Model 2 (related to Samsung Electronics' Pyeongtaek Campus) empirical analysis showed that the number of floors, 2021 contract ranking, exclusive area, number of parking lots by generation, interest rate, exchange rate, Pyeongtaek apartment sales price index had a positive (+) effect. Whether it was completed in 2018, the distance to Samsung Electronics' Pyeongtaek campus, the distance to the school, distance to commercial facilities, and the overall stock index were significant and had a negative (-) effect. The distance from Samsung Electronics' Pyeongtaek campus, a characteristic of favorable development, was found to have the greatest influence on the dependent variable among the variables.
 2. RESULTS
 The results of the study provide basic data that can be used to predict the real estate market when developing station areas around high-speed railway stations by grasping the impact of the opening of SRT Pyeongtaek Jije Station on the premium of the apartments around the area. This is expected to be useful data in grasping the degree of change in the value of local real estate assets and analyzing the development effect when constructing additional transportation facilities in the future

머신러닝과 통계 모형을 이용한 미술품 가격 예측

The art market is comprised of complex cultural, economic, and social characteristics and has recently received a lot of attention. Art transactions are carried out in a variety of ways, of which art auctions account for a large portion. We collected auction data on Post-War and Contemporary Art artists born after 1920 from Artnet and implemented an art price prediction model. In addition to the hedonic price model commonly used in existing studies, price prediction performance was compared using various machine learning models such as regression model, random effects model, regularization model, tree-based model, and SVM. To compare various machine learning models, RMSE and MAE were compared using two cross-validation methods. As a result, the nonlinear random effects model using author information showed the good performance. The impact of explanatory variables on price was analyzed using the optimal model. In addition, we were able to obtain price curves of various patterns according to the passage of time for each author. Among machine learning models that do not use author information, Bagging showed the best performance. As a result of measuring the importance of variables through Bagging, it was found that the size of the work had the most influence in predicting the price, followed by the artist's year of birth, the method of painting, the material with which the painting was painted, and the year of production.

Exploring the Impact of Multimodal Access on Property and Land Economies in Shanghai’s Inner Ring Districts: Leveraging Advanced Spatial Analysis Techniques

This study explores the impact of accessibility on property pricing and land economies by advanced spatial analysis techniques, focusing on Shanghai as a representative metropolis. Despite the impact of metro systems on residential property values, which has been frequently assessed, a research gap exists in understanding this phenomenon in Asian, particularly Chinese, urban contexts. Addressing this gap is crucial for shaping effective urban land use policy and improving the land economy rationally in China and similar settings facing urban challenges. To assess the impact of metro station accessibility on property prices in Shanghai, with extensive rail transit, and to deeply explore the overall impact of land value varieties driven by metro on urban development, we conducted a comprehensive analysis, with discussion about future aspirations for land planning and management along with landscape and facility design, and measures to improve land economy. The procedures involved creating neighborhood centroids to represent accessibility and using the Euclidean distance analysis to determine the shortest paths to metro stations. Our evaluation incorporated a hedonic pricing model, considering variables like neighborhood characteristics, housing attributes, and socio-economic factors. Advanced spatial analysis encompassing Ordinary Least Squares (OLS) regression and XGBoost analysis were employed to explore spatial effects, and Geographically Weighted Regression (GWR) helped examine spatial patterns and address autocorrelation challenges. Results revealed a negative association between distance to metro station and property prices, indicating a non-linear and spatially clustered relationship and heterogeneous spatial pattern. We dissected the non-linear results in detail, which complemented the conclusion in existing research. This study provides valuable insights into the dynamic interplay between metro accessibility and housing market behaviors in a significant Asian urban context, offering targeted suggestions for urban planners and governors to decide on more reasonable land use planning and management strategies, along with landscape and infrastructure design, to promote not only the healthy growth of the real estate market but also the sustainable urban development in China and similar regions.

Hedonic analysis of willingness to pay for dam removal: evidence from Plymouth, Massachusetts

This study investigates how the removal of small dams, and subsequent river restoration investments, affect residential housing prices in Plymouth, Massachusetts, selected for its unusually high density of dam removals. From 2002 to 2019, 5 dams within 1.05 miles of each other have been removed. Following removal, riverine restoration was completed, leading to improved public safety for downstream infrastructure, fish passage, and better water quality. Yet, contention exists in local government decision-making from fear of loss of property values of homes that are no longer classified as “waterfront.” To examine that charge, we evaluate sale prices of 9,710 single-family homes from 1997 to 2021, in a comparative estimation of a hedonic regression and a difference in differences model. The results indicate that the price of homes sold in Plymouth after dam removal increased, with no impact on property values of waterfront and properties within 1 mile of riverine restoration locations. For properties within a 5-mile radius of a former dam, we observe comparative difference in price of a fraction of a percent for lots around 2 of the dams, in relation to all home lots in Plymouth. We explain that this difference is due to property-specific features, rather than the impact of dam removal. The implications are that dam removal would not negatively impact waterfront property values, while it may translate into an overall environmental price premium for the community at large.

Understanding Factors That Affect Social Media Advertisement Adoption for Small Medium Enterprises in Indonesia the Case of Facebook Advertisement

Advancement of technology in the field of marketing has been one significant factor in assisting Small Medium Enterprises (SMEs) to increase their competitiveness. Meta, as a technological company, provide Facebook advertisement as a mean of digital social media advertising. One of the challenges that Meta faced is to attract more Facebook ads adoption especially in the Small Medium Enterprise sector. This research objective is to understand the factors that affect social media advertisement adoption, in this case Facebook advertisement, for small medium enterprises in Indonesia. There are eight variables that are believed to influence customer intention to adopt Facebook advertisement which are performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation, price value, perceived technology security and self-efficacy. The main data was collected by an online survey and 350 SMEs participated in it. The respondents are SMEs that operate in Indonesia and either have not use Facebook advertisement service or do not use the service anymore. The sample was gathered using non-probability convenience sampling and the data was analysed using structural equation model technique. The research found that Self-efficacy is the highest influencing variable that are statistically significant to influence SMEs intention to adopt Facebook advertisement. Effort expectancy and social influence are found to be statistically significant to influence SMEs intention to adopt Facebook advertisement. Hedonic motivation and price value factor are found to be statistically significant to influence intention to adopt Facebook ads for SMEs that have use the service but choose not to use it anymore. In the contrary, the factor of performance expectancy, facilitating condition and perceived technology security are found to be not statistically significant to influence intention to adopt Facebook ads among Indonesia SMEs. The research suggests Meta to prioritize an effort to increase SMEs self-efficacy in using Facebook advertisement by giving free training and certification. Other suggestions are to partner with relevant key opinion leaders (KOL)/communities, creating engagement with SMEs through events and giving monetary incentives such as discount coupons. For future research, researcher suggest different/smaller respondent demography and/or different social media advertising platform. Research with respondent that are actively using Facebook advertising service could also give additional insight to the study.

A study on factors affecting Japanese consumer’s behavior toward 5G smartphone purchase

PurposeThe purpose of this study is to analyze the determinants influencing Japanese consumers’ behavior toward the purchase of 5G smartphones.Design/methodology/approachEmpirical data was collected through an online survey of 320 Japanese respondents from ages 20 to 59. Data was analyzed by exploratory factor analysis, confirmatory factor analysis and multiple regression analysis.FindingsThe results indicate that hedonic value, user interface and price value (PV) positively influence attitude toward 5G smartphone purchase, while utilitarian value does not. In addition, the mediation effect of social influence (SI) is found to increase those positive effects. Male and female consumers displayed differences in the determinants, showcasing the effect of gender on attitude and behavior.Originality/valueTo the best of the authors’ knowledge, this study is the first research to analyze Japanese consumers’ behaviors’ toward 5G smartphone purchase. Past research for 3G/4G smartphones supported that usefulness positively influenced consumers’ behavior toward smartphones, so usefulness of 5G (e.g. high speed) was expected to be a factor to influence consumer behaviors’ toward 5G smartphones. However, this research reveals that a simple tagline emphasizing such usefulness may not be convincing enough to promote the 5G smartphones to Japanese consumers. Alternatively, the research indicates that introducing the concepts of joyfulness, improved HV and greater PV of the 5G smartphone could attract more consumers in Japan. In addition, SI could enhance the perception of the various benefits of 5G smartphones.

Do people value farmers markets: A spatial hedonic pricing model approach

Abstract Farmers markets can generate positive externalities by improving food access and negative externalities through pollution. The presence of both may influence people’s willingness to pay (WTP) for living nearby. This study employs spatial hedonic pricing models to estimate the WTP for living near farmers markets in Edmonton, Canada. We find an inverted U-shaped relationship between proximity to a farmers market and property values. Our results suggest that local governments might consider the economic impact of building new or relocating existing farmers markets on residential housing values, alongside the benefits of improved access to high-quality food sources.

Urban Disparities in Energy Performance Premium Prices: Towards an Unjust Transition?

In recent years, numerous studies have explored how energy and environmental performance impact property values. Superior energy efficiency is the basis for value disparities in real estate markets. However, measurements of these variations vary significantly. This research aims to investigate the relationship between market size and vitality and market value differences. This has significant implications for the nature of the energy transition, potentially determining fairness or inequality. The study considers the real estate market in six Italian cities: three metropolitan (Milan, Turin, and Florence) and three medium-sized cities (Padua, Mestre, and Bergamo). The sample includes 2935 properties. In metropolitan cities, hedonic pricing models confirm the relevance of energy performance in market value formation, highlighting a potential depreciation in property values by up to 30% between properties belonging to the highest energy class (A) compared to the lowest (G), and 14% between class D and G. Such premium gaps are halved in medium-sized cities. Conclusions foresee a scenario of socially and economically unjust transition that must be considered in policies aimed at improving the energy efficiency of existing buildings, with a specific concern for the nature and characteristics of the real estate markets involved.

The Economic Effects of Stormwater Best Management Practices (BMPs) on Housing Sale Prices in Washington, D.C.

This study aims to investigate the economic effects of stormwater best management practices (BMPs) on housing sale prices in Washington, D.C., BMPs play a significant role in mitigating multiple threats, such as water pollution, soil erosion, and property damage. While studies on the economic value of BMPs were limited, literature addresses that housing sale prices can be affected by nearby stormwater BMPs. This study addresses the following research questions: Do stormwater BMPs positively impact housing sale prices? How do proximity and number of structural BMPs affect the housing sale prices? We used the hedonic pricing method by applying multiple linear regression models to determine whether a set of independent variables significantly improved the models. Our primary findings indicate that BMPs have positive, negative, or no effects on housing sale prices. The proximity of BMPs inside of parks increased housing sale prices in all buffers. In contrast, the proximity of BMPs outside of parks and impervious roads decreased housing sale prices in all buffers. Percent tree canopy coverage negatively linked to a 50 m buffer and had no relationship with other buffers on housing sale prices. This study implies that BMPs impact housing prices and can be improved by landscape architects, policymakers, and stakeholders.