Hedonic Regression Model Research Articles

External costs of high-voltage overhead transmission lines: Evidence from a causal approach in rural France

In light of climatic change and global warming, electricity transmission networks need to be developed for the integration of renewable sources. In North America and Europe, to achieve renewable electricity goals, massive investments in electricity transport infrastructure are planned to connect solar and wind generation sites to the grid network. However, local opposition to new installations of high-voltage overhead transmission lines (HVOTLs) is causing delays and cost overruns. Visual nuisance is an important basis of local stakeholders' opposition to a new project. Estimations of the local economic impact of HVOTLs can help in identifying the external costs of the electricity supply industry to improve both efficiency in electricity pricing and fairness in defining provision for host community compensation. Prior research has provided evidence that existing HVOTLs have negative but not substantial effects on their immediate neighbourhoods. However, these studies have applied a correlational approach. This paper revisits the external effects of HVTOLs, exploring the case of a 225 kV line connecting two medium-size cities (Saint-Privat-d'Allier to Saint-Etienne) in France. We take advantage of the public disclosure in 2009 of undergrounding 8 km out of a total of 87 km lines as a result of a network upgrade project, to develop a difference-in-difference specification of a hedonic regression model, by comparing house sale prices at different distances from existing pylons, before and after the announcement of the decision to upgrade the line. We show that undergrounding was accompanied by an increase of about 38% in the price of treated houses located within 200 m of old pylons. The implied environmental costs of proximity to high-voltage pylons are substantial and their reduction is internalized into house prices as soon as the decision to go underground is announced.

Determinants of Residential Real Estate Property Prices in Ethiopia: The Case of Bahir Dar City

The Ethiopian real estate industry has grown in the previous two decades, especially in the capital city of the country and regional capitals, including Bahir Dar. Real estate prices have doubled and even tripled in a few years because of different factors. Thus, the purpose of this study is to analyse the factors affecting residential real estate prices in Ethiopia, particularly in Bahir Dar City. For this purpose, the study used both purposive and stratified random sampling techniques. Descriptive and inferential statistics were used to analyze the data, and the ordinary least squares method was used to identify the factors that influence residential real estate prices. The hedonic regression model result reveals that R<sup>2</sup> statistics (0.880) indicate that 88% of the variation in residential real estate prices can be explained by variation in the independent variables. Hedonic regression model results revealed that plot size, floor area, number of rooms, age of the house, external wall finishing material, ceiling finishing material, the direction of the house, distance of the property from the main road, and involvement of brokers in the transaction process, were major micro-factors that had a significant effect on residential real estate prices at the 5% level of significance. Furthermore, the interview results show that the challenges faced by real estate developers mostly include loans and aid issues, high-interest rates, mortgage restrictions, the financial capacity of real estate companies, delays in plan approvals, and rising costs of construction materials are among the major challenges affecting investment in real estate. The study suggests that the municipality of Bahir Dar City should provide an adequate supply of land to real estate developers, improve infrastructure development, and federal and regional governments should provide the credit lending mechanism by lowering interest rate especially for low-income class.

Hedonic analysis of attributes and factors affecting small ruminant price in Benin, West Africa

The demand for small ruminants is increasing, but livestock production is not very market oriented. Understanding buyers' preferences allows breeders to orient their production and make more profit. This research aimed to determine the attributes of small ruminants and other specific factors that explain price variations of small ruminants in ordinary period. Twenty-one markets were selected using stratified sampling. Data were collected in these markets on September 2020 from 432 small ruminant transactions between sellers and buyers and analysed with the hedonic regression model. Results showed that the price of goats purchased for slaughter and breeding were respectively 9.4 % and 7.2 % higher than those bought for trade. At the same time, when sheep were purchased for breeding, consumption and catering, their prices were respectively 14.5 %, 10.3 % and 9.4 % higher than those purchased for trade. The attributes weight, age (2–4 years old), sex (female), coat colour (white), body condition (good), breed (sahelian breeds for sheep and djallonke breed for goats) and absence of horn determined significantly and positively the small ruminant's price. In addition to these attributes, the small ruminant prices varied according to type of market and seller, but the buyer had less control over these factors. Thus, it is important to improve attributes preferred by buyers through training for breeders on the control of small ruminant reproduction. These attributes should also be considered when developing strategies to improve the small ruminant sector to transform traditional livestock farming into market-oriented livestock farming. Facilitating access to information through the establishment of functional market information systems will enable breeders to move towards market-oriented livestock systems.

Assessing the speculative dynamics and determinants of residential apartment rentals in Mogadishu, Somalia: A hybrid modeling approach

There has been relatively rapid growth in Somalia's real estate sector recently, resulting in property market booms in major cities of the country. Despite this sector's importance, very little scientific research has been conducted. Thus, this study aims to model the rental value of residential apartments in Mogadishu, Somalia. A hybrid modeling approach is utilized, in which a hedonic regression model was used in the first phase and an Artificial Neural Network (ANN) in the second. After analysis, the study established that an apartment's age, size, value, view, number of toilets, air quality, proximity to CBD, proximity to a university and probability of explosion are key factors determining apartment rental rates in Mogadishu, while floor level, parking space, and school proximity do not have a significant impact on apartment rental rates. Because of speculation, there is a higher likelihood of overvaluation than undervaluation in the housing market, which has profound policy and practical implications. Rather than being driven by real demand, Mogadishu's real estate market is driven by speculation, as overvaluation signals are more evident than undervaluation signals. As one of the world's poorest nations, where 70% of the population lives below the international poverty line, and about 80% of jobs are in the informal sector, extreme speculative activities adversely affect Somalia's economic quality and the living conditions of its citizens. Somali citizens need government policies that facilitate the accessibility, affordability, and adequate availability of decent housing. The study recommends protecting Somalia's real estate sector to attract more investors and boost the country's post-conflict development initiatives. A vital contribution of the study is that it is the first to examine the rental value of residential apartments in Mogadishu systematically. This study contributes significantly to housing economics and real estate development literature.

Housing affordability and housing demand assessment for urban poor in India using the hedonic model

Purpose This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households. Design/methodology/approach A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding. Findings Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R2 > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects. Research limitations/implications This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums. Practical implications All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas. Originality/value This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

Chateau de Montana: Applying Data Analytics to Simulate Room Price of a Repositioned Hotel

Nicolas Dupont, the owner of Chateau de Montana, a struggling (and old) boutique hotel in Crans-Montana Ski Resort, Switzerland, wished to renovate and reposition his family-owned hotel to target higher room rates. Dupont commissioned Olga Mitireva and Yulia Belopilskaya as consultants to assess the proposition. The consultants had to extract cues for the room rate of the repositioned hotel from comparable hotels. However, the room rates varied significantly across similar hotels due to their differing characteristics and locations. It was a cognitive challenge to read the patterns from a few comparable hotels. They collected the data of 200 hotels from similar locations and simulated room prices using hedonic regression models.

On the price dynamics of non-fungible tokens: The 'Bored Apes' case.

We analyse the pattern of daily price of a collection of artistic non-fungible tokens, namely, the "Bored Ape Yacht Club" (BAYC) collectibles, over the first year of their life, from May 2021 to May 2022. Taking a time-series analysis approach, we consider the daily average price, and other variants of daily price index, derived from hedonic regression model. Aesthetic features of the collectibles do matter. At the same time, the price series emerge to be non-stationary, integrated of order 1, with their first difference exhibiting heteroscedasticity and autoregressive variance. Models of ARCH/GARCH class are appropriate to describe the dynamics. Though the price series of BAYC collectibles and their daily movements share many characteristics with the series of financial assets, they do not appear to be related to financial variables from both the crypto- and the real (i.e., not crypto) world.

Konut Alt Piyasalarının Ankara Metropolitan Alanı Örneği Üzerinden Değerlendirilmesi

The housing market consists of heterogeneous products with different housing characteristics. Due to its heterogeneity, the housing market is better analyzed by dividing it into different homogeneous groups, especially submarkets with similar spatial, environmental and physical characteristics, rather than a single homogeneous market. This article discusses the housing submarkets by using the current housing supply in Ankara. In this context, it investigates whether the analysis for the city as a whole reflects a realistic housing price structure. Moreover, it seeks an answer the question of whether socio-economic submarkets based on average household income by neighborhood and spatial submarkets based on location by district are determinant for Ankara. Data were collected from one of Turkey's largest real estate websites on the dwelling units being advertised for sale in the nine central districts of Ankara between January and September 2021. Data were analyzed by hedonic regression model. Empirical results reveal that although a single market does not effectively reflect a realistic price structure, location by districts and average household income by neighborhood do affect the price of housing in each submarket. Each submarket has its own dynamics and the housing price structure in each submarket depends on different variables. For Ankara, spatial submarkets have more explanatory power than socio-economic submarkets.

A comparison of green and conventional buildings in selected office nodes in Gauteng province, South Africa

ABSTRACT This study investigates the economic characteristics of green and conventional office buildings in selected nodes in Gauteng Province in South Africa. We estimated the gross rent, net rent and operating cost per square meter elasticities using hedonic regression models. Contrary to international research, which points to tenants willing to pay rent premiums and enjoy cost savings for green, compared to conventional buildings, our results show that in some nodes, green and conventional buildings of similar quality were not differentiable based on rents and operating costs. In one node, 3-star rated green buildings rented at a premium and their operating costs were higher, compared to conventional buildings of similar quality. Only in one node were 4-star rated green buildings characterized by rent premium and lower operating cost advantages for tenants. Differences in the attractiveness of nodes imply that tenants may forgo the expected cost savings from renting green offices in addition to paying a rent premium if the location is right. Since the dataset is archival, findings may not reflect market conditions caused by the COVID-19 Pandemic. In addition, the relatively limited size of the dataset may inhibit applicability of the insights across the South African office market.

A study on using robust hedonic regression implementation

This article aims to determine the features affecting the price of a product with the hedonic regression model and to estimate the contribution of each feature to the price by using robust regression estimation methods. For the analysis, the price and feature information of the laptop product group were obtained from the big data source by using the web scraping method. Four alternatives of the hedonic regression model are used to determine the features affecting the price of the laptops. The contribution of each feature to the laptop price is estimated by using the robust (Huber M-estimator) estimation method and the Ordinary Least Squares (OLS) estimation method, and the resulting estimates are compared for both methods. In the framework of the data set used in the study, it is observed that the effective model is the Logarithmic Robust Hedonic Regression Model.

The impact of energy certificates on sales and rental prices: a comparative analysis

PurposeThe current literature on energy certificates shows that Energy Performance Certificate labels have an important effect on real estate prices. However, interestingly, the limited studies that address the rental market find significantly lower price premiums than the sales market. The purpose of this paper is to add to this literature, by doing a comparative analysis of price premiums in the sales and rental market in Flanders (Belgium).Design/methodology/approachThis study uses a hedonic regression model to analyze 177,670 real estate listings between 2016 and 2021. The data is provided by Immoweb – the largest online real estate platform in Belgium. The data set was divided in sold and rented properties: the authors evaluated 126,217 sales listings and 51,453 rent listings.FindingsThe results confirm that energy efficient properties generate a price premium, but that this premium is significantly larger in the sales market than in the rental market. In addition, the findings indicate that both investors and landlords could benefit strongly from renovating dwellings – especially when renovating from an F label to an A label.Originality/valuePrevious research focuses strongly on the sales market, although in many countries the rental market is similar in size and responsible from much energy consumption. Interestingly, the few studies that are addressing the rental market, find singificantly smaller price premiums than in the sales market. The findings add to this literature tradition and offer a comparative analysis of price premiums in the sales and rental market in Flanders. This allows us to not only show the similarities between both markets but also highlight the differences – creating valuable insights for academia, governments and real estate professionals.

Potential impact of property management on the market value of dwellings in multi-owned housing

The expanding role of property management services and the increasing share of multi-owned housing in many housing markets raises issues concerning the impact of such services on property value in this sector. The presented study investigated the potential impact of property management on the market value of dwellings in multi-owned housing developments. This impact was identified based on estimated implicit values of the extracted common property attributes dependent on property management services. An original research methodology tailored to the institutional arrangement in multi-owned housing developments was used, involving the estimation of hedonic regression models. Empirical research was conducted on representative samples of multi-owned housing developments in the housing market of the city of Olsztyn, Poland. The results showed that common property attributes such as building condition, building accessibility, cleanliness and orderliness, and land development significantly influenced dwelling prices and thus their market value. In extreme cases, the cumulative potential impact of property management services estimated on this basis can reach up to ca. 30% of the dwellings’ value. These findings have far-reaching policy and practical implications for urban development policies, including the built environment, land use and local housing, as well as for housing market investors and developers, homeowners associations, dwelling owners and property managers. Hence, it is recommended to expand research on this topic to other housing markets functioning in different institutional arrangements.

Tackling the Accuracy-Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal

Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows extending the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this article, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning, which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.

Pricing Strategy for a Smart-Tourist Area: Does Location Matters?

This study aims to investigate the effect of hotel characteristics, particularly their locations, on room pricing strategy for hotels in the Bogor Area, Indonesia. The author applies the quantile hedonic regression model on a dataset of Bogor Hotels collected through a travel agent’s website. A total of 194 hotels were collected and used as data samples. Our findings suggest that locations near tourist attractions are a significant factor in explaining hotel room prices in the Bogor area, while in contrast, the city center location is not. In addition, we also find that hotel room size and stars-awarded have significant positive effects on room rates. The outcomes of this study advocate for the continuation of fine-tuning the existing pricing strategy adopted by hoteliers to optimize revenue.

A heuristic for incorporating ancillaries into air choice models with personalization (part 2: integrated multinomial logit and hedonic regression models)

A heuristic for incorporating ancillaries into air choice models with personalization (part 2: integrated multinomial logit and hedonic regression models)

Look before you leap: Comparison and profiles of hotel price determinants in four European markets

Pricing in the hospitality industry moves between adapting to a global demand and the need to manage locally. This double-edged challenge requires a managerial response based on flexibility and variety but one which is constrained by resources and competitive conditions. Since the sensitivity of each determinant may be different across types of hotels and countries, how hotel managers reach their compromises between determinants and countries remains an unsettled issue. Based on cross-nation methodology, we carry out a comparative analysis of price determinants from hotels in four main international tourist countries. The set of hypotheses developed are tested by estimating a quantile hedonic regression model with data from hotels in four countries. Results indicate that outcomes of pricing decisions differ by the country-of-operation, yielding a managerial profile per country. Also, the study estimates the contribution of the country to hotel pricing.

Price predictors in an extended hedonic regression framework: An application to wholesale cattle markets in Ethiopia

AbstractLivestock markets influence income generation for producers, but also accessibility and affordability of highly nutritious animal‐sourced foods for consumers. Despite their importance, the functioning of livestock markets in lower‐income countries is poorly understood and rarely studied compared to more developed countries. This study analyzes wholesale cattle markets in Ethiopia using a uniquely rich large‐scale dataset covering both prices and cattle characteristics in 39 markets (in both highland and lowland areas) over a 10‐year period, and hedonic regression models structured to understand both cattle price formation and seasonal and secular price dynamics. We show that cattle prices are influenced by a wide range of factors, including proxies for meat quality, religious fasting practices, climate‐based seasonality but also climate shocks and availability of grazing land, competition from animal traction services, and rising consumer incomes. However, the implied effects of these factors are often significantly different in highland mixed crop‐livestock areas compared to agro‐pastoralist lowland areas, emphasizing the dualistic nature of cattle markets in Ethiopia. The analyses help inform the systemic challenges that Ethiopia will need to overcome to meet rising demand for beef products in the face of sustained income and population growth, as well as the adverse effects of climate change.

Housing demand of urban fringe residents and underlying causes: example of Ankara, Türkiye

Purpose This study aims to explore the housing demand of urban fringe residents in southwest Ankara. Two subquestions were developed: What are the respondents’ perceptions of Ankara city center and which characteristics do they prioritize for living in the urban fringe? Data were collected through a face-to-face household survey, and a hedonic regression model was developed based on responses.Design/methodology/approach Increasing housing demand, lifestyle change and faulty housing policies in Ankara have triggered urban sprawl along fringe areas, which causes several urban problems. Considering that urban sprawl is related to housing demand, it is essential to examine the structure of housing demand and the preference to live near the urban fringe.Findings According to the survey results, security, crime, noise pollution, traffic congestion and parking problems that reduce the welfare of Ankara city center encouraged expansion toward the rural–urban fringe, in addition to low-quality or traditional housing attributes. The urban core became unattractive to the respondents for being insecure, chaotic and down-market. The hedonic model showed that seven variables, all related to housing characteristics, best explain the housing demand in the area. Socioeconomic status and lifestyle were found to be associated with the desire to live on the urban fringe, also indicating the snob effect.Originality/value The authors propose taking domain-specific housing demand patterns in the spatial planning assumptions and housing policies into consideration for a well-governed urban development in Ankara. Making the city center more appealing through rehabilitation should be preferable rather than limiting demand on the urban fringe with a strict intervention in housing supply.

A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in Amsterdam and Warsaw

A hedonic approach is typically performed to identify housing rental or sales price determinants. However, standard hedonic regression models disregard spatial autocorrelation of prices and heterogeneity of housing preferences across space and over price segments. We developed a spatial autoregressive geographically weighted quantile regression (GWQR-SAR) to address these shortcomings. Using data on the determinants of residential rental prices in Warsaw (Poland) and Amsterdam (The Netherlands) as case studies, we applied GWQR-SAR and rigorously compared its performance with alternative mean and quantile hedonic regressions. The results revealed that GWQR-SAR outperforms other models in terms of fitting accuracy. Compared with mean regressions, GWQR-SAR performs better, especially at the tails of the dependent variable distribution, where non-quantile models overestimate low rent values and underestimate high ones. Policy recommendations for the development of private residential rental markets are provided based on our results, which incorporate spatial effects and price segment requirements.

A Bayesian dynamic hedonic regression model for art prices

Traditional ordinary least squares (OLS) regressions applied to hedonic pricing models assume that, when using time series, the estimated coefficients for each of the attributes remain constant. We propose a Bayesian dynamic estimation of the hedonic regression model in which the estimated coefficients can be time-varying, demonstrated with an application of art prices. Our dynamic linear regression model overcomes the problems associated with traditional rolling-window based OLS (which represent ad hoc approximations to dynamic estimation), such as under or over-estimation of parameter values and non-adaptive window sizes to account for time-variability. Using a sample of 27,124 paintings sold at auction from 63 Pop-artists (2001–2013), we demonstrate that the estimated coefficients associated with commonly used art attributes fluctuate noticeably through time, and that certain types of artworks and artists might be regarded as “safer” investments (as their art experiences smaller maximum drawdowns), based on price dynamics during the financial crisis (2008–09).