Hasil untuk "Real estate business"

Pierfrancesco De Paola, Francesco Tajani, Mario Ferraro et al.

Real estate redevelopment processes represent an important avenue for achieving sustainable development goals, but at the same time, they pose complex and opaque decision-making problems. Most existing risk assessment tools involve aggregate-scale analysis or require knowledge of extensive project financial data, which is often not yet available under ex-ante evaluation conditions. The aim of the work is to provide a tool that can support stakeholders to recognize high-risk Naples zones in terms of real estate risk investment. This is possible by defining a Spatial Decision Support System (SDSS) for ex-ante risk assessment model that allows to build a Spatial Real Estate Risk Index (ISRR). The proposed model adopts the flexibility of the AHP technique and the GIS system’s abilities in order to provide a georeferenced thematic risk map for the OMI zones of Naples.

Nordine Benkeltoum

This article introduces a metamodel for the Business Model Canvas (BMC) using the Unified Modelling Language (UML), together with a dedicated Domain-Specific Modelling Language (DSML) tool. Although the BMC is widely adopted by both practitioners and scholars, significant challenges remain in formally modelling business models, particularly with regard to explicit specification of inter-component relationships, while preserving the simplicity that characterises the BMC. Addressing this tension between modelling rigour and practical relevance, this research adopts a Design Science Research approach to formally specify relationships among BMC components and to strengthen their theoretical grounding through an adaptation of the V 4 framework. The proposed metamodel consolidates BMC relationships into three core types: supports, determines, and affects, providing explicit semantics while remaining accessible to end users through graphical tooling. The findings highlight that formally specifying relationships significantly improves the interpretability and consistency of BMC representations. The proposed metamodel and tool offer a rigorous yet usable foundation for developing DSML-based BMC tools and for enabling systematic integration of the BMC into widely used software and enterprise modelling environments, thereby bridging business modelling and enterprise architecture practices for both academics and practitioners.

Azian Fatin Umaira Muhamad, Kamal Ernawati Mustafa, Onubi Hilary Omatule et al.

Studies have mentioned the importance of management services in managing high-rise residential buildings. This role also aligns with the rules and regulations mentioned in the Strata Management Act 2013. However, the role of management in influencing the relationship between housing comfort and residential satisfaction also remains incoherent. This article aims to examine the role of management services in mediating the relationship between housing comfort indicators and residential satisfaction. The indicators of housing comfort include indoor air quality, visual comfort, thermal comfort and noise. A survey was conducted to obtain data from residents of medium-cost high-rise residential buildings surrounding the Capital City in Malaysia which are Johor Bharu, Johor, Shah Alam, Selangor and Georgetown, Pulau Pinang. The approach of partial least squares structural equation modelling (PLS-SEM) was used. The results indicate that management services only partially mediate the relationship between indoor air quality and residential satisfaction. The findings contribute to a better understanding of the management services viewpoint on residents of medium housing especially related to housing comfort. Additionally, this study makes a practical contribution in terms of increased awareness for developers, government, housing management, and residents when it comes to housing comfort and management for human well-being in high-rise residential buildings.

Backurs Andris, Laizans Aigars, Latisonoka Ilze

This study investigates the quality management priorities and operational practices of the energy company “Gren”, focusing on how standardised management systems, sustainability principles, and construction project development are integrated into its strategic and day-to-day activities. Using qualitative document analysis, the research examines corporate reports, regulatory frameworks, project descriptions, and relevant ISO standards to identify recurring patterns in how quality, safety, environmental considerations, and risk management are embedded within the company’s operations. The findings reveal five dominant thematic areas: quality management systems, environmental and safety practices, construction project development, sustainability initiatives, and risk management processes. Construction activities and technological modernisation appear as the most prominent themes, reflecting ongoing focus of “Gren” on infrastructure upgrades and energy efficiency improvements. The company’s extensive use of ISO standardisation demonstrates a structured, integrated approach to quality and compliance, while risk management practices show a proactive alignment with environmental and operational challenges. Sustainability, although less frequently referenced, functions as a cross-cutting strategic principle shaping long-term development. The study concludes that the effectiveness in quality management of “Gren” derives from integrating standardised systems with continuous technological advancement and sustainability-oriented project planning. Limitations include reliance on secondary data and the evolving nature of several ongoing construction projects. The findings provide insights relevant to energy companies navigating modernisation, regulatory compliance, and sustainable development objectives.

Cindy Angelina

This research paper presents a novel comparative analysis of carbon taxation and Environmental, Social, and Governance (ESG) regulations within the real estate sectors of Indonesia and Singapore. Through quantitative assessments, the study evaluates the effectiveness of these policies in reducing carbon emissions and promoting sustainable practices in real estate development and management. The analysis reveals significant differences in policy implementation and outcomes between the two nations, offering insights into the efficacy of their respective approaches.

Jiaming Wang, Zhe Tang, Zehao Jin et al.

As large language models (LLMs) are widely deployed as domain-specific agents, many benchmarks have been proposed to evaluate their ability to follow instructions and make decisions in real-world scenarios. However, business scenarios often involve complex standard operating procedures (SOPs), and the evaluation of LLM capabilities in such contexts has not been fully explored. To bridge this gap, we propose SOP-Maze, a benchmark constructed from real-world business data and adapted into a collection of 397 instances and 3422 subtasks from 23 complex SOP scenarios. We further categorize SOP tasks into two broad classes: Lateral Root System (LRS), representing wide-option tasks that demand precise selection; and Heart Root System (HRS), which emphasizes deep logical reasoning with complex branches. Extensive experiments reveal that nearly all state-of-the-art models struggle with SOP-Maze. We conduct a comprehensive analysis and identify three key error categories: (i) route blindness: difficulty following procedures; (ii) conversational fragility: inability to handle real dialogue nuances; and (iii) calculation errors: mistakes in time or arithmetic reasoning under complex contexts. The systematic study explores LLM performance across SOP tasks that challenge both breadth and depth, offering new insights for improving model capabilities. We have open-sourced our work on: https://github.com/meituan-longcat/SOP-Maze.

Xiang Zhang, Chenchen Fu, Yufei Cui et al.

Real-time object detection takes an essential part in the decision-making process of numerous real-world applications, including collision avoidance and path planning in autonomous driving systems. This paper presents a novel real-time streaming perception method named CorrDiff, designed to tackle the challenge of delays in real-time detection systems. The main contribution of CorrDiff lies in its adaptive delay-aware detector, which is able to utilize runtime-estimated temporal cues to predict objects' locations for multiple future frames, and selectively produce predictions that matches real-world time, effectively compensating for any communication and computational delays. The proposed model outperforms current state-of-the-art methods by leveraging motion estimation and feature enhancement, both for 1) single-frame detection for the current frame or the next frame, in terms of the metric mAP, and 2) the prediction for (multiple) future frame(s), in terms of the metric sAP (The sAP metric is to evaluate object detection algorithms in streaming scenarios, factoring in both latency and accuracy). It demonstrates robust performance across a range of devices, from powerful Tesla V100 to modest RTX 2080Ti, achieving the highest level of perceptual accuracy on all platforms. Unlike most state-of-the-art methods that struggle to complete computation within a single frame on less powerful devices, CorrDiff meets the stringent real-time processing requirements on all kinds of devices. The experimental results emphasize the system's adaptability and its potential to significantly improve the safety and reliability for many real-world systems, such as autonomous driving. Our code is completely open-sourced and is available at https://anonymous.4open.science/r/CorrDiff.

Ваня Антонова

Устойчивостта е способността на предприятията да преодоляват различни критични моменти и кризисни ситуации във вътрешната и/или външната бизнес среда, да запазват своята жизнеспособност и функционалност и да се адаптират към измененията на средата, поддържайки финансова стабилност. Обект на изследване в статията са секторите „Строителство“ и „Операции с недвижими имоти“, а предмет на изследване са устойчивостта и стабилността на предприятията, опериращи в двата сектора, измерени с показатели като: брой функциониращи предприятия, вкл. по броя на заетите в тях лица, брой активни, „новородени“, оцелели и „умрели“ предприятия, приходи, разходи и ефективност. Целта на автора е да се изследват устойчивостта и стабилността на предприятията, опериращи в секторите „Строителство“ и „Операции с недвижими имоти“ по посочените показатели и на тази основа да се обобщят изводи и изведат препоръки относно способността на предприятията да устояват на различни по сила и характер външни въздействия, да запазват жизнеспособност, функционалност и финансова стабилност. Основни методи, които намират приложение са: методите на анализ и синтез, индукция и дедукция, описание и сравнение. Въз основа на направения анализ са обобщени изводи, очертани са основни проблеми и са предложени мерки за тяхното преодоляване.

Farley Ishaak, Hilde Remøy

In last decades, there is a trend to renew buildings and make them more sustainable. Studies have shown that energy measures (as an aspect of sustainability) often increase the value of real estate. The effect of other sustainability measures on real estate values, however, is unknown. This study examines the relationship between multiple sustainability aspects and the transaction prices of different types of real estate. For this study, we used official data on commercial real estate transactions from the Land Registry Office in the Netherlands and sustainability assessment scores from a Dutch sustainability consultant. In total, 10,652 real estate transaction prices between 2012 and 2023 and corresponding sustainability scores were used to perform regressions and hedonic imputation analyses. The results show that, opposed to energy, other aspects of sustainability often correlate negatively with transaction prices in the lower segment of sustainable real estate. These aspects correlate positively in the upper segment of sustainable real estate.

Shawn Berry

The use of online reviews to aid with purchase decisions is popular among consumers as it is a simple heuristic tool based on the reported experiences of other consumers. However, not all online reviews are written by real consumers or reflect actual experiences, and present implications for consumers and businesses. This study examines the effects of fake online reviews written by artificial intelligence (AI) on consumer decision making. Respondents were surveyed about their attitudes and habits concerning online reviews using an online questionnaire (n=351), and participated in a restaurant choice experiment using varying proportions of fake and real reviews. While the findings confirm prior studies, new insights are gained about the confusion for consumers and consequences for businesses when reviews written by AI are believed rather than real reviews. The study presents a fake review detection model using logistic regression modeling to score and flag reviews as a solution.

Qiao Feng, Yuanwang Yang, Yebin Liu et al.

We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an efficient 3D representation by learning the Fourier series. The core of FOF is to factorize a 3D occupancy field into a 2D vector field, retaining topology and spatial relationships within the 3D domain while facilitating compatibility with 2D convolutional neural networks. Such a representation bridges the gap between 3D and 2D domains, enabling the integration of human parametric models as priors and enhancing the reconstruction robustness. Based on FOF, we design a new reconstruction framework, FOF-X, to avoid the performance degradation caused by texture and lighting. This enables our real-time reconstruction system to better handle the domain gap between training images and real images. Additionally, in FOF-X, we enhance the inter-conversion algorithms between FOF and mesh representations with a Laplacian constraint and an automaton-based discontinuity matcher, improving both quality and robustness. We validate the strengths of our approach on different datasets and real-captured data, where FOF-X achieves new state-of-the-art results. The code has already been released for research purposes at https://cic.tju.edu.cn/faculty/likun/projects/FOFX/index.html.

Weerakoon Thilina Ganganath, Wimalasena Sulaksha, Fedotova Kristine

The construction industry is a critical sector in the nation’s economic growth, accounting for a sizable share of GDP growth. However, it is the most vulnerable industry to a financial depression, whether local or worldwide. The present economic crisis has had an impact on the Sri Lankan construction industry, with more than half a million employees lost their jobs in the previous year. Many major construction firms have either paused or abandoned their projects and shifted to overseas construction. Therefore, understanding the consequences of financial crises regarding construction initiatives in Sri Lanka, as well as anticipated post-crisis growth paths in this sector, necessitates a thorough examination. The goal of this study is to extensively examine the consequences of the 2022 economic crisis on Sri Lankan construction projects and investigate remedies that might start a post-crisis rebound. The study utilized a mixed-method approach, combining quantitative and qualitative research methods. Purposive sampling was used to choose construction industry participants from various backgrounds in order to get a varied range of perspectives. The findings of this study not only emphasize the negative consequences of the crisis but also reveal prospects for development within the industry. The article offers construction professionals and other industry stakeholders useful insights about the foreseeable future of the country’s construction sector. The research looks at prospective growth areas such as the development of infrastructure, sustainable construction strategies, and the usage of emerging technology. The findings of the research can help to ensure that the sector has a robust and productive future.

Cellmer Radosław

Points of Interest (POI) are an inherent element of the urban landscape, and their number and density reflect, among other things, the degree of urbanization and the city’s spatial structure. The very presence of POI in the closest vicinity of a residential property may indirectly or directly affect housing value. This paper aims to demonstrate the usefulness of POI density information of different categories in assessing the quality of a property’s immediate surroundings. While the mere presence of POIs in the nearest neighborhood may affect real estate prices, the influence of specific categories may not necessarily be positive. Therefore, the study attempted to classify POIs and determine their importance in the price formation process using spatial regression models. The results indicate that a high density of POIs in the immediate area is a stimulant for housing prices. The detailed analysis indicated that only some POI categories might be related to transaction prices, while in certain situations, some POI categories may negatively impact prices.

Lioara-Veronica Pasc, Camelia-Daniela Hategan

The growing expenses, dependence on IT for business operations, and growing requirements regarding related party transaction (RPT) reporting impose the need for increased attention to this area. The paper’s objective is to examine the nature of RPTs, identified by auditors as a key audit matter (KAMs), challenges and solutions to problems related to risk management, and the detection of factors affecting audit quality. The research methodology is qualitative, with an analysis of the level of disclosure of KAMs reported by auditors from the Related Parties category, grouped by type of auditors, their opinion, year, country, and fields of activity. Data were collected from the Audit Analytics database and filtered by category KAM: Related parties, period 2013–2021. The selection resulted in 111 companies reporting 248 KAMs related to RPTs, from which most were reported in 2017–2019. Of these, nearly two-thirds were reported by auditors from the Big4 category. Most KAMs were reported by companies in the U.K., Germany, and France, and the industries with the most KAMs were finance, insurance, and real estate. In conclusion, there are factors that can affect audit quality due to the reporting of RPTs, but by identifying them, the audit process can be better managed, thus increasing its efficiency.

Cai Yang, Lexing Xie, Siqi Wu

News media is often referred to as the Fourth Estate, a recognition of its political power. New understandings of how media shape political beliefs and influence collective behaviors are urgently needed in an era when public opinion polls do not necessarily reflect election results and users influence each other in real-time under algorithm-mediated content personalization. In this work, we measure not only the average but also the distribution of audience political leanings for different media across different countries. The methodological components of these new measures include a high-fidelity COVID-19 tweet dataset; high-precision user geolocation extraction; and user political leaning estimated from the within-country retweet networks involving local politicians. We focus on geolocated users from eight countries, profile user leaning distribution for each country, and analyze bridging users who have interactions across multiple countries. Except for France and Turkey, we observe consistent bi-modal user leaning distributions in the other six countries, and find that cross-country retweeting behaviors do not oscillate across the partisan divide. More importantly, this study contributes a new set of media bias estimates by averaging the leaning scores of users who share the URLs from media domains. Through two validations, we find that the new average audience leaning scores strongly correlate with existing media bias scores. Lastly, we profile the COVID-19 news consumption by examining the audience leaning distribution for top media in each country, and for selected media across all countries. Those analyses help answer questions such as: Does center media Reuters have a more balanced audience base than partisan media CNN in the US? Does far-right media Breitbart attract any left-leaning readers in any countries? Does CNN reach a more balanced audience base in the US than in the UK?

Robin Hirt, Niklas Kühl, Dominik Martin et al.

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.

Amin Beheshti, Jian Yang, Quan Z. Sheng et al.

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, supporting evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process augmentation, automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

Aaron Friedrich Kurz, Timotheus Kampik, Luise Pufahl et al.

In order to better facilitate the need for continuous business process improvement, the application of DevOps principles has been proposed. In particular, the AB-BPM methodology applies AB testing and reinforcement learning to increase the speed and quality of improvement efforts. In this paper, we provide an industry perspective on this approach, assessing requirements, risks, opportunities, and more aspects of the AB-BPM methodology and supporting tools. Our qualitative analysis combines grounded theory with a Delphi study, including semi-structured interviews and multiple follow-up surveys with a panel of ten business process management experts. The main findings indicate a need for human control during reinforcement learning-driven experiments, the importance of aligning the methodology culturally and organizationally with the respective setting, and the necessity of an integrated process execution platform.

Cristiano André da Costa, Uélison Jean Lopes dos Santos, Eduardo Souza dos Reis et al.

In this article, we provide an overview of the latest intelligent techniques used for processing business rules. We have conducted a comprehensive survey of the relevant literature on robot process automation, with a specific focus on machine learning and other intelligent approaches. Additionally, we have examined the top vendors in the market and their leading solutions to tackle this issue.

Svetlana Mikhailovna Borozdina, Daler Zarifovich Iskandarov

Статья посвящена изучению и формированию показателей, характеризующих процесс интеграции предприятий инвестиционно-строительной сферы (ИСС) и особых экономичес­ких зон (ОЭЗ) при реализации инвестиционно-строительных проектов (ИСП). Решение этой задачи заключается в исследовании внешней и внутренней среды, окружающей субъекты взаимодействия, представленной факторами мезосреды и микросреды. Ключевым подходом, используемым для синтеза показателей, является анализ информационных потоков, учитывающих перспективы реализации ИСП на территории ОЭЗ и оценку эффективности участия предприятия ИСС в возведении объекта строительства, основанного на анализе финансово-хозяйственной деятельности изучаемого объекта (предприятия ИСС) и расчете доходности и рентабельности инвестиционных вложений, отражающих вероятность осуществления процесса интеграции ОЭЗ и предприятий ИСС. Применяемый комплексный мониторинг информационной среды воздействия сущностей различной природы возникновения позволяет разработать показатели, которые отражают качественные и количественные характеристики предмета исследования, связанного с выявлением внешних и внутренних эффектов, способствующих интеграции — экстерналий и интерналий. Определение характера влияния внешних и внутренних эффектов дает возможность спрогнозировать величину выгод и издержек для сторон взаимодействия, демонстрируя сильные и слабые стороны возможной интеграции предприятий ИСС и ОЭЗ при реализации ИСП. Результаты мониторинга представляются в виде матриц абсолютных и относительных показателей, которые необходимо привести к единой размерности. Авторы статьи предлагают применять разработанную систему показателей для осуществления целей стратегического развития, изложенных в программных документах по совершенствованию строительной отрасли, связанной с решением проблемы диспропорции размещения ОЭЗ, которые необходимы для реализации технически сложных и уникальных ИСП.