Search Results (173)

The current study examines the impact of health claims on consumer preferences and willingness to pay (WTP) for functional snack bars, focusing on anti-inflammatory and antioxidant properties. Through an experimental auction involving 175 participants, this study investigates how providing clear information on product health benefits influences consumer interest and WTP while analysing the role of individual health consciousness (HC) in shaping these preferences. The results indicate that detailed health claims positively affect consumer WTP for functional snack bars compared to standard options. Although both anti-inflammatory and antioxidant claims attract consumer interest, no significant difference in WTP was observed between the two, suggesting similar perceived value for these distinct benefits. However, highly health-conscious consumers demonstrate a stronger preference and WTP for anti-inflammatory options, indicating that HC influences specific health claim valuation. These findings underscore the importance of effective health-related messaging in promoting functional foods and suggest that general health claims may resonate more broadly with consumers than specialised ones. This study’s results enhance the current knowledge on functional foods, especially snack bars, offering valuable insights for manufacturers aiming to implement targeted marketing strategies and public health initiatives focused on promoting healthier dietary choices. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article

Natural rubber, a renewable material with unique properties, is crucial for various products on the modern market. Crepe rubber, a versatile form of natural rubber, is widely used in numerous applications, including footwear soles, medical devices, automotive parts, adhesives, sports equipment, industrial components, musical instruments, and recreational products. Sri Lanka holds a prominent position as a leading producer of premium-quality crepe rubber but faces environmental challenges in its production process. Since previous life cycle assessments (LCAs) in the rubber industry are inadequate to capture the overall environmental impact, the present study attempted to address the gaps by conducting a detailed LCA of a Sri Lankan crepe rubber factory, incorporating a novel index termed the trade-off valuation index (TOVI). The research revealed that fertilizer, water, and electricity use contribute most significantly to crepe rubber production’s environmental impact. To mitigate these impacts, four key improvement options were identified and evaluated through scenario analysis: (1) enhancing fertilizer efficiency, (2) repairing leaky joints and valves, (3) implementing a water reuse system, and (4) installing solar panels. The integration of the TOVI allowed for the prioritization of these options, providing actionable insights for industry stakeholders. This study paves the way for targeted interventions to enhance the sustainability of the natural rubber industry by balancing economic viability with environmental stewardship. Full article

This study investigates how energy retrofitting measures contribute to increasing the market value of multi-family residential buildings within the European real estate market. It examines how energy efficiency improvements, driven by EU decarbonization strategies, enhance the actual and perceived value of these properties. The research employs a dual-methodology approach, integrating the Cost Approach to estimate the financial impact of retrofitting with the Contingent Valuation Method (CVM) to evaluate consumer willingness-to-pay (WTP) for energy-efficient properties. Two real case studies are considered to evaluate the methodology and how the monetary value of buildings is affected by their energy efficiency. The results revealed that buildings subjected to deep energy retrofitting are more attractive to potential buyers, who are willing to pay a premium of 13.5% over properties in pre-retrofit conditions. This underscores the tangible market value increment attributed to energy efficiency enhancements. This study bridges the gap between the quantifiable costs of energy retrofitting and the market valuation, offering a dual perspective by integrating both actual cost analysis and perceived market value. Moreover, this work highlights the correlation between energy retrofit investments and increased market value in the European real estate sector. Full article

Europe faces a situation where housing represents the main savings for most of the population, while the majority of homeowners are seniors aged over 65. The desire to supplement pensions has led to a growing interest in generating income from these savings, with bare ownership emerging as a notable option. This solution makes it possible to transfer the ownership of the home while maintaining usufruct rights for the duration of the owner’s lifetime. This paper examines the status of bare ownership in the city of Rome by web scraping the house offers published on web portals and segmenting those offered as bare ownership. Machine learning analysis based on neural networks and binary logit regression allows for the observation of the particular behavior of the housing supply in bare ownership; it shows the different intrinsic and extrinsic characteristics that determine this Real Estate segment. The findings highlight the development of a growing market strongly influenced by the location of assets. These findings provide valuable insights for both investors and urban planners regarding changes in urban dynamics processes. Full article

Beginning with a symmetrical multiple-choice individual as the foundation, I develop a sociophysics model of decision-making. By simplifying the range of choices, the framework incorporates the complex Polytopic fuzzy model to capture nuanced dynamics. This approach enables a deeper analysis of decision-making processes within social systems. Decision-making problems commonly involve uncertainty and complexity, posing considerable challenges for organizations and individuals. Due to their structure and variable parameters, the Einstein t-norm (ETN) and t-conorm (ETCN) offer more elasticity than the algebraic t-norm (ATN) and t-conorm (ATCN). This flexibility makes them commonly effective and valuable in fuzzy multi-attribute decision-making (MADM) problems, where nuanced valuations are critical. Their application enhances the ability to model and analyze vagueness and uncertain information, eventually leading to more informed decision outcomes. The complex Polytopic fuzzy set (CPFS) improves the Polytopic fuzzy set (PFS) and complex fuzzy set (CPFS), allowing for a more precise valuation of attributes in complex (MADM) problems. This study aims to propose a MADM scheme using the ETN and ETCN within the framework of a complex Polytopic fuzzy environment. It begins by presenting the Einstein product and sum operations for complex Polytopic fuzzy numbers (CPFNs) and explores their necessary properties. This method enhances the accuracy and applicability of DM processes in ambiguous environments. Subsequently, three complex Polytopic fuzzy operators with known weighted vectors are developed: the complex Polytopic fuzzy Einstein weighted averaging (CPFEWA) operator, complex Polytopic fuzzy Einstein ordered weighted averaging (CPFEOWA) operator, complex Polytopic fuzzy Einstein hybrid averaging (CPFEHA) operator. Moreover, some substantial properties of the operators are studied. Finally, a method based on novel operators is planned, and a numerical example is provided to prove the practicality and effectiveness of the new proposed methods. Full article

The field of real estate valuations is multivariate in nature. Each property has different intrinsic attributes that have a bearing on its final value: location, use, purpose, access, the services available to it, etc. The appraiser analyzes all these factors and the current status of other similar properties on the market (comparable assets or units of comparison) subjectively, with no applicable rules or metrics, to obtain the value of the property in question. To model this context of subjectivity, this paper proposes the use of a fuzzy system. The inputs to the fuzzy system designed are the variables considered by the appraiser, and the output is the adjustment coefficient to be applied to the price of each comparable asset to obtain the price of the property to be appraised. To design this model, data have been extracted from actual appraisals conducted by three professional appraisers in the urban center of Santa Cruz de Tenerife (Canary Islands, Spain). The fuzzy system is a decision-helping tool in the real estate sector: appraisers can use it to select the most suitable comparables and to automatically obtain the adjustment coefficients, freeing them from the arduous task of calculating them manually based on the multiple parameters to consider. Finally, an evaluation is presented that demonstrates its applicability. Full article

Urban park green spaces have the functions of improving the urban ecological environment and providing recreational services, and at the same time, they have a certain effect on the value of the surrounding residential property. To quantitatively assess the value of the landscape premium function of park green space, many scholars have carried out research exploration and adopted a variety of methods (such as the contingent valuation method (CVM), travel cost method (TCM) and hedonic price method (HPM)), which have developed from simple theoretical models with single factors to complex empirical models with multiple factors. Among them, the hedonic price method has become the mainstream research method, and in recent years, it has been widely adopted in combination with GIS technology. In terms of research objects, single park green space or multiple park green spaces in large cities are the main focus, while there are fewer studies on park green spaces in built-up areas of small and medium-sized cities. In terms of research content, there are more studies on the value-added coefficient of landscape premium and influence distance, and there are fewer studies on the total value of landscape premium. This article aims to calculate the total landscape premium value of all park green spaces in the built-up areas of small and medium-sized cities, proposing a complete and operable accounting system for the functional value of park green space landscape premiums by combining GIS with a hedonic pricing model and remote sensing image interpretation methods. For the first time, a method for interpreting the height of residential buildings within the benefit range of landscape premium through remote sensing images is proposed, and then the floor area ratio of residential plots is estimated, so as to estimate the total area of actual beneficial buildings. Therefore, this paper takes Chifeng City, a small and medium-sized city, as a case study, and empirically demonstrates the assessment of the landscape premium function of parks and green spaces in the built-up area of Chifeng City by using this accounting system. Research shows that this method has certain feasibility, not only calculating the total value of landscape premium but also addressing the issue in existing studies where all areas within the potential range of landscape premium function are counted as appreciated areas, leading to an overestimation of the premium. It further advances the accuracy of accounting for the value of landscape premium function of urban park green space and provides theoretical reference for the planning and construction of urban park green space. Full article

This study examines the influence of sustainability certifications on the real estate market, particularly highlighting the advantages they offer compared to uncertified buildings and their recognition within the industry. A survey targeting various industry professionals garnered ninety responses, predominantly from the real estate sector. The survey explored the respondents’ awareness and perceived benefits of sustainability certifications, their priority areas within sustainability, and the relevance of these certifications across different real estate sectors. The analysis also compared the additional costs and operational savings of certified versus uncertified buildings. Among the certifications, LEED and BREEAM were the most recognized. The primary benefits associated with these certifications included enhanced corporate image, improved health and well-being, increased building value, and higher rental yields. We estimated a valuation and rent premium for certified buildings, noting that these premiums were more pronounced among respondents who were younger, had less professional experience, and were from the property sector. The office market was identified as the segment placing the highest importance on sustainability certifications. Additionally, the LiderA evaluation system’s weighting closely aligned with the respondents’ sustainability priorities. This study concludes that while sustainability certifications incur a cost premium, this is outweighed by the appreciation in building value, rental advantages, and operational cost savings. Full article

Constructed wetlands (CWs) are man-made ecosystems that mimic the properties of natural wetlands. They are being utilized to treat various types of wastewater, from domestic to agricultural, municipal, commercial, and industrial effluents. Despite their economic viability and environmental benefits, their widespread adoption is challenged with several uncertainties, including public support, technology learning, and the impacts of climate change. This study proposes a valuation framework that considers these uncertainties to analyze the feasibility of CWs. Using existing CWs in the Philippines as a case, this study employs the real options approach to (1) evaluate the feasibility of CW projects using cost–benefit analysis, (2) calculate the value of postponing decisions to implement CWs projects using real options analysis, and (3) identify the optimal investment decisions for CWs considering the opportunity costs of waiting and uncertainties in public support and the impacts of climate change. Results found that the project is feasible with a net present value of USD 88,968. Yet, the real options value at USD 208,865 indicates that postponing the project may be a more optimal decision. Considering the cost of waiting, the valuation identified the threshold at 5.56% to immediately implement the project. The calculated values increase with uncertainty in public support but decrease with uncertainty in climate change’s impacts. Yet, these uncertainties prolong the decision to implement CW projects until they are resolved. The findings from this case study provide a basis for recommendations to support the adoption of CWs as nature-based water treatment for a more sustainable future. Full article

Cymbidium goeringii (Rchb. f.) is a traditional Chinese flower with highly valued biological, cultural, and artistic properties. However, the valuation of Rchb. f. mainly relies on subjective judgment, lacking a standardized digital evaluation and grading methods. Traditional grading methods solely rely on unimodal data and are based on fuzzy grading standards; the key features for values are especially inexplicable. Accurately evaluating Rchb. f. quality through multi-modal algorithms and clarifying the impact mechanism of key features on Rchb. f. value is essential for providing scientific references for online orchid trading. A multi-modal Transformer for Rchb. f. quality grading combined with the Shapley Additive Explanations (SHAP) algorithm was proposed, which mainly includes one embedding layer, one UNet, one Vision Transformer (ViT) and one Encoder layer. A multi-modal orchid dataset including images and text was obtained from Orchid Trading Website, and seven key features were extracted. Based on petals’ RGB segmented from UNet and global fine-grained features extracted from ViT, text features and image features were organically fused into Transformer Encoders throughout concatenation operation, a 93.13% accuracy was achieved. Furthermore, SHAP algorithm was utilized to quantify and rank the importance of seven features, clarifying the impact mechanism of key features on Rchb. f. quality and value. This multi-modal Transformer with SHAP algorithm for Rchb. f. grading provided a novel idea to represent the explainable features accurately, exhibiting good potential for establishing a reliable digital evaluation method for agricultural products with high value. Full article

Property valuation services using artificial intelligence (AI) have been developed, with more than 20 services available in Japan. However, since their algorithms and training data are not publicly available, the extent of variations in the AI property valuations among these services is not clear. This study focuses on five services and uses a sample of 4295 valuations for 859 condominium units in six popular residential areas in Tokyo. (1) Multiple comparison tests of the AI property valuations among the services are conducted to confirm their statistical significance and to examine the extent of the variations. (2) The business models of each service are compared to examine the factors contributing to these variations. The results showed that the average variation in the AI property valuations was 10.6%, which was larger than the variations observed in traditional property valuations. It was also found that the valuation groups, categorized as high or low, varied based on the business models of the service providers. These results indicate that it is necessary to promote the healthy development of AI property valuation by establishing guidelines, such as requiring the AI property valuation services to ensure fair prices or disclosing their algorithms and data. Full article

This study aimed to predict residential apartment prices in Latvia and Brazil using algorithms from machine learning, fuzzy logic, and civil engineering principles, with a focus on overcoming multicollinearity challenges. To explore the market dynamics, we conducted four initial experiments in the central regions of Riga and Jelgava (Latvia), as well as São Paulo and Niterói (Brazil). Data were collected from real estate advertisements, supplemented by civil engineering inspections, and analyzed following international valuation standards. The research integrated human decision-making behavior with machine learning and the Apriori algorithm. Our methodology followed five key stages: data collection, data preparation for association rule mining, the generation of association rules, fuzzy logic analysis, and the interpretation of model accuracy. The proposed method achieved a mean absolute percentage error (MAPE) that ranged from 5% to 7%, indicating strong alignment with market trends. These findings offer valuable insights for decision making in urban development, particularly in optimizing renovation priorities and promoting sustainable growth. Full article

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R² score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R² and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector. Full article

The concept of fair value, defined by the valuation of assets and liabilities at their current market worth, remains central to the International Financial Reporting Standards (IFRS) and has persisted despite critiques intensified by the 2008 financial crisis. This valuation method continues to be prevalent under both IFRS and the US Generally Accepted Accounting Principles (GAAP). The adoption of IFRS has notably enhanced the role of accounting in information analysis, vital for owners who prioritize both secure accounting practices and reliable data for strategic management decisions. Real estate, a significant business asset, has long been a focal point in accounting discussions, prompting extensive research into the applicability and effectiveness of various accounting standards. These investigations assess the adaptability of standards based on property type, utility, and valuation techniques. However, the challenge of accurately determining the fair value of real estate remains unresolved, signifying its importance not only in the corporate manufacturing realm but also among development companies striving to manage property values efficiently. This study addresses the challenge of accurately determining the fair market value of real estate in Kazakhstan, leveraging a multi-methodological approach that encompasses statistical models, regression analysis, data visualization, neural networks, and particularly, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The integration of these diverse methodologies not only enhances the robustness of real estate valuation but also introduces new insights into effective asset management. The findings suggest that ANFIS provides superior precision in real estate pricing, demonstrating its potential as a valuable tool for strategic management and investment decision-making. Full article