Hasil untuk "Real estate business"

Mintz Mordekhay, Portnov Boris A.

Understanding taxpayer perceptions of a property tax system is essential for developing equitable and transparent fiscal policies that foster public trust. This study explores public attitudes toward Israel’s residential property tax system, also known as Arnona, through an online survey of more than 500 participants, stratified by region of residence, age, income, family status, and homeownership. Although the survey participants were divided regarding the preferred basis for property taxation – a property value - (ad valorem) vs. property size (Arnona) based system, the majority of respondents (~55%) favored the current system, and ~45% supported its value-based alternative. The survey also revealed a wide support for integrating environmental factors into tax assessments, with approximately 75.9% of the survey respondents supporting the idea of incorporating green space access into property tax assessments, and 62.8% of the survey respondents supporting accounting for air quality. Support was even stronger for service-related criteria, with 91.3% of participants supporting the idea of linking the tax rates to the quality of municipal services. However, substantial differences emerged regarding the incorporation of the socioeconomic status of taxpayers into tax calculations. Although 59% of low-income respondents favored this idea, only 33% of highincome respondents supported it. In general, the study underscores the growing public demand for tax models that reflect environmental quality and quality of services, rather than linking property taxes to incomes.

Fahim Ullah, F. Al-turjman

Paolo Rosato, Matteo Galante

Using panel data on 99 Italian provinces in the period between 2005 and 2020, the research investigates the effects of fundamental economic factors on the home sales at the provincial level, in order to build a forecasting model using a non-linear artificial intelligence approach (MLP-Multiple Linear Perceptron neural network). There are multiple objectives to this: (a) to test the hypothesis that national, regional and local fundamentals such as interest rates, income, inflation rate, unemployment and demography affect the activity’s degree of the housing market; (b) to verify the effectiveness of a neural network in describing the dynamics of the real estate market; (c) to build a simulation model capable of predicting the effect of changes in fundamentals, also due to economic policy measures, on the market. Empirical results show that neural networks offer better capabilities than linear models in representing the complex relationships between the economic situation and the real estate market. The study provides useful information for regulators to improve the effectiveness of monetary policy to stabilize real estate markets as well as for stakeholders to draw up scenarios of market development.

Timur Zagidovich Azhimov, Anatoly Alekseevich Demyanko

Экология — это наука, охватывающая существующие связи между человеком и окружающей средой. Экологические связи отличаются не только разнообразием, но и сложностью. Их характерная особенность заключается в том, что многие из них не поддаются изучению традиционными методами. В связи с этим многим видам природных богатств наносится непоправимый ущерб. Строительство наиболее активно вмешивается в сложившиеся природные процессы. К важнейшим нарушениям природной среды в процессе строительства относятся: потери растительного слоя в связи с выполнением больших объемов земляных работ; вырубка зеленых насаждений и леса, нарушения условий развития фауны и флоры, а также верхнего покрова почвы; изменение уровня грунтовых вод, вызывающее подтапливание сельскохозяйственных угодий и населенных мест. В настоящее время уменьшаются нарушения ландшафта, водного и ветрового режима, повреждение почвенного и растительного покрова, загрязнение водного и воздушного бассейнов, атмосферы и почвенного покрова, погашаются шумовые и световые эффекты. В процессе строительства восстанавливаются допущенные нарушения в природной среде путем рекультивации нарушенных земель, сохранения и повторного использования гумусового слоя; необходимых очистных сооружений и воздухоочищающих установок; создания защитных дамб, использования под строительную застройку негодных и болотистых местностей; сохранения природного режима рек и ландшафта; наведения надлежащего порядка на территории, используемой строительными организациями, предприятиями строительной индустрии и промышленности строительных материалов; предотвращения загрязнения территории попутными продуктами и отходами производства.

Anshika Rawal, Abhijeet Kumar, Mridul Mishra

Extracting useful signals or pattern to support important business decisions for example analyzing investment product traction and discovering customer preference, risk monitoring etc. from unstructured text is a challenging task. Capturing interaction of entities or concepts and association mining is a crucial component in text mining, enabling information extraction and reasoning over and knowledge discovery from text. Furthermore, it can be used to enrich or filter knowledge graphs to guide exploration processes, descriptive analytics and uncover hidden stories in the text. In this paper, we introduce a domain independent pipeline i.e., generalized framework to enable document filtering, entity extraction using various sources (or techniques) as plug-ins and association mining to build any text mining business use-case and quantitatively define a scoring metric for ranking purpose. The proposed framework has three major components a) Document filtering: filtering documents/text of interest from massive amount of texts b) Configurable entity extraction pipeline: include entity extraction techniques i.e., i) DBpedia Spotlight, ii) Spacy NER, iii) Custom Entity Matcher, iv) Phrase extraction (or dictionary) based c) Association Relationship Mining: To generates co-occurrence graph to analyse potential relationships among entities, concepts. Further, co-occurrence count based frequency statistics provide a holistic window to observe association trends or buzz rate in specific business context. The paper demonstrates the usage of framework as fundamental building box in two financial use-cases namely brand product discovery and vendor risk monitoring. We aim that such framework will remove duplicated effort, minimize the development effort, and encourage reusability and rapid prototyping in association mining business applications for institutions.

Stéphane Aroca-Ouellette, Ian Berlot-Attwell, Panagiotis Lymperopoulos et al.

Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains, such as business management, require optimizing an open-ended and multi-faceted objective, actively learning environment dynamics from sparse experience, planning over long horizons in stochastic settings, and reasoning over spatial information. Yet existing human--AI benchmarks isolate subsets of these capabilities, limiting our ability to assess holistic decision-making competence. We introduce Mini Amusement Parks (MAPs), an amusement-park simulator designed to evaluate an agent's ability to model its environment, anticipate long-term consequences under uncertainty, and strategically operate a complex business. We provide human baselines and a comprehensive evaluation of state-of-the-art LLM agents, finding that humans outperform these systems by 6.5x on easy mode and 9.8x on medium mode. Our analysis reveals persistent weaknesses in long-horizon optimization, sample-efficient learning, spatial reasoning, and world modelling. By unifying these challenges within a single environment, MAPs offers a new foundation for benchmarking agents capable of adaptable decision making. Code: https://github.com/Skyfall-Research/MAPs

Adam McCabe, Matthew H. Chequers

Organizations generate vast amounts of interconnected content across various platforms. While language models enable sophisticated reasoning for use in business applications, retrieving and contextualizing information from organizational memory remains challenging. We explore this challenge through the lens of entropy, proposing a measure of entity entropy to quantify the distribution of an entity's knowledge across documents as well as a novel generative model inspired by diffusion models in order to provide an explanation for observed behaviours. Empirical analysis on a large-scale enterprise corpus reveals heavy-tailed entropy distributions, a correlation between entity size and entropy, and category-specific entropy patterns. These findings suggest that not all entities are equally retrievable, motivating the need for entity-centric retrieval or pre-processing strategies for a subset of, but not all, entities. We discuss practical implications and theoretical models to guide the design of more efficient knowledge retrieval systems.

Kexin Zhu, Yang Han

The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.

S. R., Swamy Perumandla, S. Bhattacharyya

Purpose The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable. Design/methodology/approach A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade. Findings This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns. Research limitations/implications This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories. Practical implications The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics. Originality/value Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.

G. Morri, Federico Colantoni, Antonio Maria De Paolis

PurposeThe central aim of this study is to examine the relationship between ESG metrics and financial outcomes in the real estate industry, honing in on particular sectors and geographical areas. Utilizing ESG ratings and pillar scores as indicators of sustainability performance, this research endeavors to discern their effects on measures of profitability and market performance.Design/methodology/approachDrawing on a dataset encompassing more than 200 publicly listed companies in the real estate sector, this research utilizes a fixed effects regression model and instrumental variables to scrutinize the data. This approach enables a thorough evaluation of how governance, environmental and social dimensions influence the financial and market outcomes of these entities.FindingsThe research reveals a complex relationship between ESG factors and financial performance, defying any simplistic, universal application. The connection is marked by diversity, deeply influenced by the unique aspects of each real estate industry segment and the particularities of regional markets. Specifically, the environmental aspect often corresponds with an increase in ROA, yet this pattern is not consistent throughout all cases. On the other hand, the social aspect is frequently associated with diminished performance indicators, while the influence of governance factors varies, affecting financial outcomes less predictably.Originality/valueWith its pioneering methodology, the research delves into the granular impacts of ESG factors within individual real estate sectors and specific countries. Insights into the Real Estate Rental, Development and Operations sector as well as firms operating in Oceania, extend the conversation in an area of ESG literature that has been relatively uncharted. Moreover, the study’s illumination of how environmental, social and governance elements distinctly influence financial results injects fresh viewpoints into the ongoing dialogue on sustainable business practices.

J. A. Numan, Izham Mohamad Yusoff

Abstract Real estate appraisal, also known as property valuation, plays a crucial role in numerous economic activities and financial decisions, such as taxation assessment, bank lending, and insurance, among others. However, the current methods used in real estate appraisal face several challenges related to fundamental aspects such as accuracy, interpretation, data availability, and evaluation metrics. Therefore, the purpose of this research is to identify the current status of real estate appraisal methods, highlighting challenges and providing guidance for scholars to undertake further research in addressing them. The methodology retrieves the most recent papers published in the Scopus database over the past five years, covering the period from 2019 to the end of 2023, with an emphasis on empirical studies. These retrieved papers serve as references to capture the current status of real estate appraisal methods. The research findings confirm a clear trend towards increased utilization of artificial intelligence techniques, especially machine learning, but with unfinished work regarding related challenges. Artificial intelligence techniques enhance the accuracy of real estate appraisal, paving the way for improved decision support systems in business, financial, and economic sectors.

Nikolay Igorevich Korolev

The investment process of construction and operation of real estate objects constantly requires taking into account the interests of all its participants and the need to establish stable relationships between them. In the context of the implementation of such processes, various organizational and technological situations arise, when the nature of their functioning and development sharply changes, which determines the shift of interests of each of the sections of the investment process, as well as the directions of their functioning and development in the housing market, taking into account the accumulated experience and potential. This determines the need to form such a system of relationships between participants in the investment process that will ensure minimum risks in the creation of the final product, aimed at stabilizing the entire construction industry and processes, reducing the influence of the external and internal environment. The research methods used in the work are theoretical analysis, empirical study with subsequent generalization and systematization of the obtained data, in addition, the main scientific approaches were used: “dialectical”, “systemic”, “dynamic”, “variant”, “balance” and “modelling”. The object of the study is the main stages of the investment process, including the stages of pre-design, design, construction and operation, as well as participants in these processes. The procedure for forming a system of relationships between participants in the investment process based on the use of basic investment principles in order to increase the effectiveness of this system in various organizational and technological situations and the influence of various types of risk is considered [1, 2]. The use of various mechanisms in the field of rational forms of organization of material production in the activities of enterprises for their effective management and construction of real estate at the stages of the life cycle will increase the stability and reliability of their work under the influence of risks and uncertainty factors of construction production.

Artem Pugachov, Lesya Lyuta, Svitlana Yanovskaya

The purpose of the work is to determine the characteristics of the subjective economic well-being of entrepreneurs during the war in Ukraine. Ukrainian entrepreneurs have been working in conditions of full-scale war for more than two years. A catastrophic situation of uncertainty hinders the conduct of business, but despite this, specialists determine an increase in economic activity. Entrepreneurs working in Ukraine (N=50), aged 23 to 55, were involved in the study. The experience of entrepreneurial activity ranged from 7 to 22 years. Areas of business activity: trade, sale and lease of real estate, agronomy, construction, freight transportation, furniture production. Questionnaires, the method of incomplete sentences (modified by us), the questionnaire of subjective economic well-being (V.A. Khashchenko) and the methods of mathematical statistics were chosen as research methods. It was determined that the subjective economic well-being of the studied entrepreneurs has a moderate level of expressiveness. At the same time, there is a noticeable tendency for men to grow in negative experiences caused by a lack of finances. And for women, a positive assessment of the current well-being of the family is characteristic. It should also be said that women entrepreneurs, compared to men, experience significantly higher economic optimism, current family well-being, and overall economic well-being. Economic optimism is higher among entrepreneurs with higher education than among those with only secondary education. Not married entrepreneurs evaluate the current well-being of their own family more positively than married ones; but no differences were found in the level of subjective economic well-being depending on the presence of children, as well as depending on objective indicators and their subjective assessment by entrepreneurs of the amount of financial profit.

Radomir Yurievich Simionov, Timur Zagidovich Azhimov

Строительство относится к одной из ресурсоемких отраслей экономики, что делает актуальной проблему организации ресурсообеспечения строительного производства. Анализ зарубежной и отечественной практики управления цепями поставок показал, что логистические издержки имеют высокую долю в затратах организаций независимо от их отраслевой принадлежности. Существенна доля транспортных расходов. С учетом динамики бизнес-среды необходимо постоянное совершенствование механизма управления цепями поставок, в том числе с учетом региональных и отраслевых аспектов деятельности предприятий. Рассматривая специфику строительства с позиций ресурсного обеспечения, можно сделать вывод, что логистические проблемы возрастают из-за недостаточной интегрированности участников строительства, неравномерности потребления отдельных видов ресурсов, а также высокого влияния и на процессы поставок, и на строительное производство природно-климатических условий. Каждый участник цепи поставок имеет риски, и они влияют на деятельность всех субъектов цепи, усложняя процессы управления. В управлении цепями поставок нашла широкое применение SCOR-модель, ориентированная на ключевые бизнес-процессы организации. Предлагаемые в модели метрики имеют иерархическую систему и ориентированы на отдельные подсистемы цепи поставок, управленческие уровни контроля, создают информационную основу для принятия эффективных управленческих решений. Авторами предлагается система показателей, отражающих специфику строительства, применительно к подсистемам SCOR-модели: надежность поставок, ресурсоотдача, управление логистическими затратами и эффективность управления логистическими активами. Система может эффективно применяться при организации управленческого учета по центрам затрат, достоверно отражающего логистические затраты по объектам строительства, процессам, видам работ, потребляемым ресурсам, поставщикам ресурсов.

Yingxuan Yang, Qiuying Peng, Jun Wang et al.

In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get feedback from the environment so as to accomplish the given tasks in an autonomous manner. Besides the environment-interaction property, the LLM agents can call various external tools to ease the task completion process. The tools can be regarded as a predefined operational process with private or real-time knowledge that does not exist in the parameters of LLMs. As a natural trend of development, the tools for calling are becoming autonomous agents, thus the full intelligent system turns out to be a LLM-based Multi-Agent System (LaMAS). Compared to the previous single-LLM-agent system, LaMAS has the advantages of i) dynamic task decomposition and organic specialization, ii) higher flexibility for system changing, iii) proprietary data preserving for each participating entity, and iv) feasibility of monetization for each entity. This paper discusses the technical and business landscapes of LaMAS. To support the ecosystem of LaMAS, we provide a preliminary version of such LaMAS protocol considering technical requirements, data privacy, and business incentives. As such, LaMAS would be a practical solution to achieve artificial collective intelligence in the near future.

Zhipeng Ma, Bo Nørregaard Jørgensen, Michelle Levesque et al.

Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and internet of things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.

Nimrod Busany, Ethan Hadar, Hananel Hadad et al.

Eliciting requirements for Business Intelligence (BI) systems remains a significant challenge, particularly in changing business environments. This paper introduces a novel AI-driven system, called AutoBIR, that leverages semantic search and Large Language Models (LLMs) to automate and accelerate the specification of BI requirements. The system facilitates intuitive interaction with stakeholders through a conversational interface, translating user inputs into prototype analytic code, descriptions, and data dependencies. Additionally, AutoBIR produces detailed test-case reports, optionally enhanced with visual aids, streamlining the requirement elicitation process. By incorporating user feedback, the system refines BI reporting and system design, demonstrating practical applications for expediting data-driven decision-making. This paper explores the broader potential of generative AI in transforming BI development, illustrating its role in enhancing data engineering practice for large-scale, evolving systems.

Akshita Singh, Shailendra Kumar, Utkarsh Goel et al.

Psychological aspects of human nature cause behavioural biases and can lead to decisions that differ from what is expected based solely on rational analysis. The effects of behavioural biases on financial markets like stocks and mutual funds have been studied previously, but real estate has yet to receive much attention. The existing works in the real estate domain have focused on different biases, but no study has examined the works already done to provide concise documentation of these past works. Thus, this article is an earnest attempt to fill that gap. This paper reviews the articles which were sourced from Scopus and the Web of Science database, published between 1980 and 2022. The PRISMA model led to the inclusion of 86 articles for the review. Analysis revealed that anchoring bias, loss aversion, and herding bias have been studied extensively. On the other hand, biases like gambler’s fallacy, familiarity bias, framing bias, home bias, confirmation bias and mental accounting have been less explored. The paper identifies the substantial gaps in the existing studies, giving avenues for future exploration. The key ones are, firstly only a few biases have been studied extensively and many biases are less explored, particularly using primary data. This provides a vast available space for future work. Secondly, studies in developing countries are fewer, which needs to be addressed. Lastly, studies need to explore the interplay of different biases to create a more robust model that can explain the effect of these biases. The paper gives a conceptual understanding of different biases and what factors affect them. Also, it will help policymakers strategize their business and mitigate the negative effects of biases.

Zhengrui Jiang, Arun Rai, Hua Sun et al.

This study highlights the critical function that digital real estate platforms, like Zillow, serve in facilitating effective property transactions. They do this by transmitting vital property information from sellers to buyers, thereby enriching the value of offline deals. Our findings indicate that Zillow, as a source of information, is incredibly valuable for properties that deviate significantly from their neighborhood’s average value, either above or below. It’s particularly useful in conveying experiential details through images and textual descriptions. For potential buyers, Zillow is a trustworthy source of property information for estimating property value, especially when alternative sources of information are limited. This study underscores the necessity for sellers and their agents to effectively represent property information online, considering its significant impact on sale prices. This is especially true for unique properties and properties with notable experiential elements. Furthermore, our study suggests that real estate professionals need to modify their business practices to take full advantage of digital platforms and provide superior services to their clients. Finally, digital real estate platforms can use these insights to enhance their platform design by focusing on the collection and display of significant information, ultimately increasing the value provided to both buyers and sellers of properties.

Benedikt Gloria, Ben Höhn

We present a deep learning model estimating carbon dioxide equivalent (CO2e) emissions in the real estate sector. The model, which utilizes convolutional neural networks (CNNs) and image classification techniques, is designed to estimate CO2e emissions based on publicly available images of buildings and their corresponding emissions. Our findings show that the model has the ability to provide reasonably accurate estimations of CO2e emissions using images as the sole input. Notably, incorporating primary energy sources as additional input further improves the accuracy up to 75%. The creation of such a model is particularly important in the fight against climate change, as it allows for transparency and fast identification of buildings, contributing significantly to CO2e emissions in the building sector. Currently, information on emission intensity in the real estate sector is scarce, with only a few countries collecting and providing the required data. Our model can help reduce this gap and provide valuable insights into the carbon footprint of the real estate sector.