Hasil untuk "Real estate business"

Marlon Dumas, Fredrik Milani, David Chapela-Campa

Since the early 90s, the evolution of the Business Process Management (BPM) discipline has been punctuated by successive waves of automation technologies. Some of these technologies enable the automation of individual tasks, while others focus on orchestrating the execution of end-to-end processes. The rise of Generative and Agentic Artificial Intelligence (AI) is opening the way for another such wave. However, this wave is poised to be different because it shifts the focus from automation to autonomy and from design-driven management of business processes to data-driven management, leveraging process mining techniques. This position paper, based on a keynote talk at the 2025 Workshop on AI for BPM, outlines how process mining has laid the foundations on top of which agents can sense process states, reason about improvement opportunities, and act to maintain and optimize performance. The paper proposes an architectural vision for Agentic Business Process Management Systems (A-BPMS): a new class of platforms that integrate autonomy, reasoning, and learning into process management and execution. The paper contends that such systems must support a continuum of processes, spanning from human-driven to fully autonomous, thus redefining the boundaries of process automation and governance.

Simon Popelier, Matthieu X. B. Sarazin, Maximilien Bohm et al.

The Sales Comparison Approach (SCA) is one of the most popular when it comes to real estate appraisal. Used as a reference in real estate expertise and as one of the major types of Automatic Valuation Models (AVM), it recently gained popularity within machine learning methods. The performance of models able to use data represented as sets and graphs made it possible to adapt this methodology efficiently, yielding substantial results. SCA relies on taking past transactions (comparables) as references, selected according to their similarity with the target property's sale. In this study, we focus on the selection of these comparables for real estate appraisal. We demonstrate that the selection of comparables used in many state-of-the-art algorithms can be significantly improved by learning a selection policy instead of imposing it. Our method relies on a hybrid vector-geographical retrieval module capable of adapting to different datasets and optimized jointly with an estimation module. We further show that the use of carefully selected comparables makes it possible to build models that require fewer comparables and fewer parameters with performance close to state-of-the-art models. All our evaluations are made on five datasets which span areas in the United States, Brazil, and France.

Nikita Aleksandrovich Zubakhin, Vera Alexandrovna Akristinii

Освещаются вопросы особенностей проведения исследования объектов капитального строительства, обладающих признаками самовольной реконструкции в рамках судебной строительно-технической экспертизы. В настоящий момент из-за отсутствия в законодательстве указанного понятия многими неоднозначно трактуются требования к строительно-монтажным работам (СМР), связанным с различными вариантами изменения объектов недвижимости. Нередко реконструкцией оборачиваются результаты, полученные в рамках капитального ремонта зданий, реставрации, перепланировки или переустройства помещения. С другой стороны, проект, изначально заявленный и согласованный как реконструкция, может преобразоваться в создание новых объектов капитального строительства. В любом случае неправомерное использование земельных участков, действия в отсутствии на то технической или разрешительной документации, а также нарушение предъявляемых к СМР требованиям — все это признаки самовольности. Самоуправство в области строительства и реконструкции чревато множеством негативных последствий, начиная от нарушения прав третьих лиц, заканчивая возникновением угрозы жизни и здоровью людей. Сложность вопроса отнесения СМР к тому или иному процессу, а также комплексность регулирования в строительстве являются причиной назначения строительно-технической экспертизы для исследования самовольных построек. Судебная практика показывает отсутствие единых методических рекомендаций к экспертизе самовольной реконструкции и ситуативность действий сведущих лиц, неверную ориентацию на алгоритмы исследования самовольных построек, большое количество ошибок и неточностей в заключениях. Также особенно затруднительной представляется преобразовательная задача, которая должна разрешаться экспертами для помощи суду в принятии решения о дальнейшей судьбе самовольно реконструированного объекта.

Maksim Olegovich Kuzmich, Valentin Valentinovich Volgin

В статье рассмотрены вопросы интеграции информационного моделирования для улучшения эксплуатационных качеств зданий, базовые понятия жизненного цикла зданий, современные подходы к управлению зданием и повышению эксплуатационных качеств на различных этапах ЖЦ. В современных условиях технология информационного моделирования (ТИМ) становится главным инструментом, который позволяет улучшить эксплуатационные характеристики объектов. Параметры эксплуатационных качеств здания (ПЭК) — характеристики здания, показывавшие оценку функционирования объекта в соответствии с установленными/заданными критериями. Рассмотрены современные подходы к внедрению информационного моделирования в эксплуатационные процессы, такие как: использование цифровых двойников, электронная паспортизация, внедрение программных решений. Также подробно обсуждаются перспективы применения ТИМ для более точного планирования профилактических работ, оперативного выявления и устранения неисправностей, что приводит к значительному снижению затрат на этапе эксплуатации. Важно уточнить, что строительство с каждым годом совершенствуется и становится быстрым, это приводит к увеличению числа вводимых в эксплуатацию объектов недвижимости. Главной причиной такого быстрого темпа роста обусловлена интеграция ТИМ в этапы жизненного цикла объектов строительства. Другая сторона, которая является негативной и нуждается в значительной и глубокой проработке, а также в свежих идеях — это колоссально высокие затраты на этапе эксплуатации, которые проблематично предсказать в большинстве случаев. Недоработки на предыдущих этапах активизируются и проявляются более ярко на этапе эксплуатации. ТИМ является важным процессом на всех этапах ЖЦ, на этапе эксплуатации ценность данной технологии заключается в данных, которые несут в себе актуальную информацию о проекте, и в повышенном уровне интеграции работы с ними.

Мария Армянова

Ефективното управление на ресурсите на градовете подпомага решаването на съвременните проблеми, като глобалното затопляне, нарастването на населението и изчерпването на ресурсите. Затова основна тенденция е създаването на интелигентни градове (ИГ), в които съвременни технологии си сътрудничат, за да създадат ползи за гражданите и да предложат решение за част от тези проблеми. Разработването на ИГ се базира на информацио­нни технологии, като Интернет на нещата, безжичните мрежи, виртуалната реалност и 3D моделирането, обработката на големи данни и изкуствения интелект. Целта на доклада е да се определят и систематизират предизвикателствата пред създаването на ИГ и пред възможностите за използването му. Преодоляването на предизвикателствата става с въвеждането на подходящи мерки. В доклада се систематизира ролята и мястото на ИГ, разгледани са технологиите за ИГ и предизвикателствата пред прилагането им, определени са и предизвикателства, свързани с етични, психологически и правни въпроси. Познаването на предизвикателствата и възможностите за преодоляването им, позволява да се създаде по-сигурен и надежден ИГ, който да се ползва с доверието на обществото.

Kandel Shankar, Sigdel Sandesh

Construction projects rely on the precise interplay of time and cost to achieve success. This study explores this critical relationship, exploring how contract size influences these vital metrics and proposes data-driven mitigation strategies to ensure projects resonate with optimal performance. This study employs Bromilow’s time-cost model to investigate the relationship between project duration and expenditure. A statistically significant positive correlation was found, with a linear regression model showing a strong association (R² = 0.836) between the two variables. The analysis also reveals a more detailed role for contract size, suggesting a potentially limited influence on time and cost variations. Several factors can disrupt the harmony of project execution, thereby impeding its performance concerning both cost and time. Key factors contributing to this disruption include the unreliability of contractors, inadequacies in site management, breakdowns in communication channels, delays in decision-making processes, and constraints within budgetary allocations. The identification and subsequent mitigation of these diverse challenges emerge as pivotal tasks in ensuring the smooth arrangement of project endeavours. Bromilow’s model provides a path to a more harmonious performance. It unveils a logarithmic correlation between project cost and duration, indicating a constant value of 24.35 days for projects exceeding one million Nepalese rupees (NPR). This underscores the substantial influence of project scale on time, emphasizing the necessity for management approaches based on project size. To achieve a truly masterful performance, the study advocates for a holistic management approach. This includes proactive decision-making, establishing clear and effective communication channels that resonate with all stakeholders, and maintaining meticulous financial oversight to ensure accurate budgeting and efficient resource allocation. By implementing these strategies, project teams can navigate the complexities of construction with greater ease, optimizing both time and cost performance. Ultimately, they can turn projects into symphonies of success, ensuring their completion on time, within budget, and to the highest quality.

Sergei Igorevich Beliakov, Alexander Andreevich Solovyov, Igor Romanovich Remizov

В условиях высокой ключевой ставки и, как следствие, сниженного спроса на недвижимость актуальным является вопрос обеспечения коммерческой эффективности деве­лоперских проектов. На основе проведенного анализа деятельности крупных девелоперских компаний в текущих кризисных условиях выявлены ключевые направления оптимизации проектной деятельности в актуальном негативном макроэкономическом контексте. Они могут быть систематизированы как на основе иерархии: уровень портфеля проектов и уровень проекта, так и по отдельным значимым критериям. В целях обоснования и иллюстрации выявленных тенденций и закономерностей в статье приведены обработанные статистические данные портала ЕРЗ.РФ по группе компаний ФСК. В результате анализа сделан вывод, что при выборе конкретного направления адаптации и развития необходимо учитывать множество разнородных факторов, в том числе целевые эффекты, отраслевую специфику девелопмента, текущие стадии реализуемых проектов, необходимость затрат на реализацию адаптационных и оптимизационных мероприятий, продолжительность горизонта планирования, степень влияния мероприятий на репутацию девелоперской компании в бизнес-среде и другое. Проведение всесторонней оценки результативности принимаемых в настоящее время решений будет возможно, как минимум, в перспективе 1–3 года, однако уже сейчас можно отметить, что рынок в целом адаптировался к текущим условиям и пребывает в состоянии относительной устойчивости при общей тенденции к снижению показателей деловой активности и среднесрочной стагнации.

Margot Geerts, Manon Reusens, Bart Baesens et al.

The real estate market is vital to global economies but suffers from significant information asymmetry. This study examines how Large Language Models (LLMs) can democratize access to real estate insights by generating competitive and interpretable house price estimates through optimized In-Context Learning (ICL) strategies. We systematically evaluate leading LLMs on diverse international housing datasets, comparing zero-shot, few-shot, market report-enhanced, and hybrid prompting techniques. Our results show that LLMs effectively leverage hedonic variables, such as property size and amenities, to produce meaningful estimates. While traditional machine learning models remain strong for pure predictive accuracy, LLMs offer a more accessible, interactive and interpretable alternative. Although self-explanations require cautious interpretation, we find that LLMs explain their predictions in agreement with state-of-the-art models, confirming their trustworthiness. Carefully selected in-context examples based on feature similarity and geographic proximity, significantly enhance LLM performance, yet LLMs struggle with overconfidence in price intervals and limited spatial reasoning. We offer practical guidance for structured prediction tasks through prompt optimization. Our findings highlight LLMs' potential to improve transparency in real estate appraisal and provide actionable insights for stakeholders.

Iqbal H. Jebril, M. Al-Zaqeba, H. Al-khawaja et al.

The integration of blockchain technology into estate governance has the potential to revolutionize transparency, efficiency, and security in estate management. Traditional governance structures often grapple with inefficiencies, lack of transparency, and security issues in estate management. This paper comprehensively explores the impact of blockchain on estate governance, and then risk management and business sustainability. This research centers on the role of risk management on business sustainability to mediate and moderate the effect of estate governance on business sustainability. The results indicate that effective real estate governance positively affects risk management practices in real estate. However, both real estate governance and risk management contribute to business sustainability. Moreover, there are still gaps in the literature that require further investigation. Where policymakers and practitioners can develop informed strategies to strengthen governance structures, mitigate risks, and promote sustainable practices in real estate; Thus, promoting long-term success and resilience in the real estate industry. It is worth noting that future research should focus on empirical testing of the proposed hypotheses to provide a better understanding of these dynamics and their implications for risk management that can affect business sustainability.

Visar Hoxha

Purpose The purpose of this study is to carry out a comparative analysis of four machine learning models such as linear regression, decision trees, k-nearest neighbors and support vector regression in predicting housing prices in Prishtina. Design/methodology/approach Using Python, the models were assessed on a data set of 1,512 property transactions with mean squared error, coefficient of determination, mean absolute error and root mean squared error as metrics. The study also conducts variable importance test. Findings Upon preprocessing and standardization of the data, the models were trained and tested, with the decision tree model producing the best performance. The variable importance test found the distance from central business district and distance to the road leading to central business district as the most relevant drivers of housing prices across all models, with the exception of support vector machine model, which showed minimal importance for all variables. Originality/value To the best of the author’s knowledge, the originality of this research rests in its methodological approach and emphasis on Prishtina's real estate market, which has never been studied in this context, and its findings may be generalizable to comparable transitional economies with booming real estate sector like Kosovo.

Stéphane C. K. Tekouabou, Ș. Gherghina, E. Kameni et al.

A. Odebode, O. T. Ogunbayo, A. Obayomi

PurposeTechnological disruption has transformed the traditional ways of doing business in the real estate sectors. As a result of the new business realities, technology has become an integral part of the real estate business. However, due to the significant barrier to the incorporation of the technology among modern real estate start-ups, there is a need to assess the adoption and willingness to use property management software.Design/methodology/approachThe study employed an exploratory research design. The study adopts a total enumeration of real estate start-up firms in Lagos, Nigeria, to ensure true representations among the respondents and reduce sampling errors. The data obtained were analyzed using descriptive and inferential statistics.FindingsThe study revealed that the majority of the respondents are aware of the identified property management software but tenant verification software recorded the highest level of awareness and usage. The finding also revealed that the association between the availability of staff competence, practicality of the software, ease of use, data ownership and copyright, financial resources, future-proof technology track, Internet connection, perceived benefits and productivity and branding are statistically significant in influencing the level of adoption among the respondents.Research limitations/implicationsThe researchers had initial challenges with the attitude of respondents to willingness and timely disbursement of information which was later resolved by explaining the significance for the study. The findings of the research will be useful and serve as an eye opener to practitioners, the conventional real estate surveying and valuation firms, to relevance of software technology in enhancing their operations and efficiency, while it can also boost the academic curriculum.Practical implicationsThe knowledge about the adoption of property management software will equip real estate tech start-ups with the right information.Originality/valueThe paper is significant because the ultimate goal of this study is to document the empirical investigation on the level of adoption and application of emerging software among real estate tech start-ups in the Nigerian property market to facilitate the efficiency and delivery of property management services.

Eugeniy Borisovich Balashov

Актуальность обеспечения устойчивого функционирования жилищно-коммунального хозяйства (ЖКХ) ставит задачу исследования его состояния и проблем функционирования с выработкой стратегических подходов к эффективной модели трансформации и ускорения модернизации объектов инженерно-коммунальной инфраструктуры. Показано, что наиболее существенным является влияние на состояние ЖКХ уровня потенциала различных типов городов, взаимоувязанных с деятельностью предприятий и организаций, демографическими формами расселения и средой жизнедеятельности. Доказано, что крупнейшие города-миллионники обладают достаточно высоким потенциалом для дальнейшего развития ЖКХ, в связи с этим актуальной задачей является определение моделей и драйверов их перспективного развития. На основе анализа генезиса развития современной системы ЖКХ выделены достигнутые определенные структурные сдвиги в решении приоритетных задач в сфере ЖКХ по дальнейшей реализации программ переселения граждан из непригодного жилья, региональных программ капитального ремонта общего имущества в многоквартирных домах, реализации целевых муниципальных программ модернизации коммунальных сетей, принятых нормативно-правовых документов, создающих условия для использования механизмов государственно-муниципально-частного партнерства, роста инвестиций и возведения инфраструктурных объектов. Рассмотрены характерные особенности жилищно-коммунального хозяйства и определен комплекс современных проблем и факторов, негативно влияющих на качество деятельности организаций и предприятий ЖКХ. Выявлены параметры технического состояния жилищно-коммунальной инфраструктуры и надежности функционирования системы ЖКХ регионов и ряда крупнейших городов. Обоснованы ключевые тренды стратегии развития ЖКХ, позволяющие улучшить состояние инфраструктурного потенциала ЖКХ и обеспечить опережающее развитие инфраструктуры комфортных условия проживания и качество жизнедеятельности населения.

Sahiba Khan, Ranjit Singh, H. Kent Baker et al.

This study examines significant topics and customer sentiments conveyed in reviews of P2P lending applications (apps) in India by employing topic modeling and sentiment analysis. The apps considered are LenDenClub, Faircent, i2ifunding, India Money Mart, and Lendbox. Using Latent Dirichlet Allocation, we identified and labeled 11 topics: application, document, default, login, reject, service, CIBIL, OTP, returns, interface, and withdrawal. The sentiment analysis tool VADER revealed that most users have positive attitudes toward these apps. We also compared the five apps overall and on specific topics. Overall, LenDenClub had the highest proportion of positive reviews. We also compared the prediction abilities of six machine-learning models. Logistic Regression demonstrates high accuracy with all three feature extraction techniques: bag of words, term frequency-inverse document frequency, and hashing. The study assists borrowers and lenders in choosing the most appropriate application and supports P2P lending platforms in recognizing their strengths and weaknesses.

Diabul Haque

The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, covering all municipalities from 2005 to the present, using a dataset of over 5 million transactions. This core dataset was enriched with economic factors spanning decades, allowing for price trajectory predictions. The findings show that alternative data variables can indeed forecast real estate performance effectively. Investment signals based on these variables yielded notable returns with low volatility. For example, the net migration ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5. Taxable income growth and new dwellings ratio also performed well, with annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of 0.9), respectively. When combined with transformer models to predict risk-adjusted returns 4 years in advance, the model achieved an R-squared score of 0.28, explaining nearly 30% of the variation in future municipality prices. These results highlight the potential of alternative data variables in real estate investment. They underscore the need for further research to identify more predictive factors. Nonetheless, the evidence suggests that such data can provide valuable insights into real estate price drivers, enabling more informed investment decisions in the Japanese market.

Sid Bhatia, Samuel Gedal, Himaya Jeyakumar Grace Lee et al.

This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market.

Shashank Joshi, Arhan Choudhury

Blockchain technology is one of the key technologies that have revolutionized various facets of society, such as the banking, healthcare, and other critical ecosystems. One area that can harness the usage of blockchain is the real estate sector. The most lucrative long-term investment is real estate, followed by gold, equities, mutual funds, and savings accounts. Nevertheless, it has administrative overheads such as lack of transparency, fraud, several intermediaries, title issues, paperwork, an increasing number of arbitrations, and the lack of liquidity. This paper proposes a framework that uses blockchain as an underlying technology. With the aid of blockchain and the suite of tools, it supports many of these problems that can be alleviated in the real estate investment ecosystem. These include smart contracts, immutable record management, tokenization, record tracking, and time-stamped storage. Tokenization of real estate lowers the entry barrier by fixing liquidity and interoperability and improving the interaction between various stakeholders.

Iván Belenky

The Argentinian real estate market presents a unique case study characterized by its unstable and rapidly shifting macroeconomic circumstances over the past decades. Despite the existence of a few datasets for price prediction, there is a lack of mixed modality datasets specifically focused on Argentina. In this paper, the first edition of ARED is introduced. A comprehensive real estate price prediction dataset series, designed for the Argentinian market. This edition contains information solely for Jan-Feb 2024. It was found that despite the short time range captured by this zeroth edition (44 days), time dependent phenomena has been occurring mostly on a market level (market as a whole). Nevertheless future editions of this dataset, will most likely contain historical data. Each listing in ARED comprises descriptive features, and variable-length sets of images.

Pauli Autio, Lauri Pulkka, S. Junnila

PurposeThe aim of this paper is to introduce a framework that helps to identify strategic themes on which real estate investors form their strategies. A holistic approach to strategic management in real estate management has enjoyed popularity in corporate real estate research, while similar research has been lacking from the investor-based real estate management.Design/methodology/approachThe research design consists of two main parts: 1) formulating propositions based on existing literature and 2) attempting to validate the propositions through a qualitative interview study with major real estate owners in Finland.FindingsThe main finding is that the current real estate investors reflect the transient nature of competitive advantages and assess their strategies accordingly. The companies consider the traditional profitability and revenue growth aspects of their business but also a more long-term future growth dimension. As an outcome, the investors base their strategies on eight strategic themes which are “Innovation”, “ESG”, “Marketing and sales”, “Financial management”, “Leasing management and tenant satisfaction”, “Competitive environment and portfolio management”, “Outsourcing and strategic partnerships” and “Cost and operation optimization”.Research limitations/implicationsThis paper opens opportunities for future research concerning different strategies in real estate investment business and their impacts.Practical implicationsThe presented framework provides support for real estate investors to create real estate management strategy or to evaluate their current strategy and to recognize operational actions and decisions that are relevant for their strategy.Originality/valueThis paper provides an extension to corporate real estate (CRE) literature by showing that the CRE theories are adaptable to real estate investment and provide value for their strategic management. This paper also contributes to real estate investment literature by providing a well-founded and empirically contested strategic management framework, the IREM framework, for identifying strategic themes on which real estate investors form their strategies.

N. S. Nikitina

In this paper, we have constructed a VAR model to identify and assess the impact of real interest rate shocks, real estate demand, oil prices, uncertainty, and aggregate business activity on residential real estate prices in Russia. The relevance of the research is due to the following: the dynamics of real estate prices determines the consumer and investment behavior of households, and serious fluctuations in real estate prices lead to adverse consequences in many areas of life, so more and more researchers are asking questions about the presence of bubbles in the real estate market, which can be dangerous to the stability of the economy. In addition, a sharp increase in the cost of housing in Russia in 2020 is an open question for researchers. Our goal is to determine what factors caused the rise in real estate prices in Russia in the time interval from the Q1 of 2000 to the Q2 of 2022. A VAR model with a Cholesky decomposition was used for the evaluation. Several specifications were considered with the inclusion of the real oil price as an exogenous variable and a set of endogenous variables: real GDP, real interest rate, uncertainty index and housing price index. The main conclusion of the paper is that the housing market is sensitive to identified macroeconomic shocks, and a decrease in the interest rate leads to an increase in demand and real estate prices. The estimate of the long-term elasticity of housing prices for oil prices was 0.35, the dynamics of oil prices explained a significant proportion of the variation in real estate prices, but the predominant role in housing price fluctuations is given to housing demand shocks. The housing demand shocks in Russia itself had a negligible impact on GDP.