Hasil untuk "Real estate business"

This paper unveils a new resource for macroeconomic research: a long-run dataset covering disaggregated bank credit for 17 advanced economies since 1870. The new data show that the share of mortgages on banks’ balance sheets doubled in the course of the 20th century, driven by a sharp rise of mortgage lending to households. Household debt to asset ratios have risen substantially in many countries. Financial stability risks have been increasingly linked to real estate lending booms which are typically followed by deeper recessions and slower recoveries. Housing finance has come to play a central role in the modern macroeconomy.

Waszczuk Joanna

In the study, we examine the stability of housing preferences, comparing past – revealed ones and future declarations. The dynamically changing environment after two very significant external shocks, the Covid-19 pandemic and the influx of immigrants after the Russian aggression in Ukraine, may change the factors important for households when making housing decisions. We focus primarily on ownership, location and property type patterns. In this article, we use association analysis to identify the most important patterns of change in meeting housing needs. The results of a questionnaire survey confirmed the important role of location in households’ preferences for purchasing apartments and the significant importance of budget constraints. Moreover, the rules related to moving further from the center and to single-family houses turned out to gain on importance.

Mark Rzepczynski, Wei Feng

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and whether these differences are meaningful. Two leading US HPI choices, the repeat-sale transactional (S&P Case–Shiller) and characteristic-based hedonic (Zillow) indices, although highly correlated, generate different distributions and time-series properties primarily at the city level. The spread between these two HPI choices measures the difference between housing market transaction intensity and a willingness-to-pay characteristic valuation. We find that transactional indices are more volatile, with HPI spreads associated with both macro and local drivers. The transactional index will rise more rapidly in a market with increased buying (positive macro and local market conditions) and fall further in a market with increased selling (negative macro and local market conditions) relative to a hedonic index. A buyer- or seller-biased spread between a transactional and hedonic housing price index (HPI) may impact policy judgments during housing market extremes.

Pavlína Petrová

Nowadays, when digitalization and automation are key trends, technologies such as artificial intelligence are also used in the valuation process itself. Traditional valuation methods are increasingly supplemented with modern tools, including the use of artificial intelligence. In response to this development, it is possible to examine the extent to which these technologies are used in practice and how experts perceive them. This article is based on research and its aim is to map whether companies use automation and artificial intelligence, what specific applications they use and what benefits they perceive from combining modern technologies with traditional valuation procedures. To achieve the set goal, three research questions were formulated. A quantitative research method in the form of a questionnaire survey was used to obtain the necessary data. The data obtained were subsequently analyzed and evaluated using descriptive statistical methods and the set research questions were answered based on the results achieved. The research found that all respondents use modern approaches in the valuation process, such as automation, digitalization and artificial intelligence. Companies use applications such as Valutico, Chat GPT and BgGPT Chat in the business valuation process. Companies perceive the following benefits of integrating digitalization, automation and artificial intelligence into the business valuation process: big data analysis, time savings, accuracy, acceleration of routine calculations, elimination of human errors and reduction of human labor costs.

Mariya Letdin, Dustin Read, Spenser Robinson

This study examined the similarities and differences among university-level real estate education programs in the United States, emphasizing how diverse academic disciplines prepared students to meet industry demands. Semi-structured interviews with academic leaders highlighted both the unique characteristics of different disciplines and their convergence on core competencies. Regardless of whether programs were housed in business, architecture, or urban planning schools, all programs emphasized the importance of financial analysis, market assessment, and communication skills. Additionally, industry feedback played a key role in shaping curricula, ensuring that graduates possessed the technical and interpersonal skills necessary for a rapidly evolving market. The integration of experiential learning opportunities, such as internships, case competitions, and project-based learning, further enhanced students’ preparedness for professional practice. The findings suggested that, despite differences in program focus, there was growing alignment across disciplines to meet the evolving needs of the real estate industry.

Timur Kamilevich Tagirov, Aleksandr Konstantinovich Orlov

В статье рассматриваются ключевые проблемы и перспективы внедрения системы мастер-планирования в градостроительной деятельности. Проанализированы институциональные, правовые и экономические барьеры, препятствующие переходу от ситуативного проектирования к комплексному стратегическому подходу на уровне территорий. Особое внимание уделено вопросам координации между различными уровнями власти, интеграции долгосрочных целевых программ и учету многомерных ограничений — природно-­климатических, инфраструктурных и социальных. На основе сравнительного обзора практик зарубежных и российских городов формулируются практические рекомендации по совершенствованию нормативно-правовой базы, усилению институциональной роли местного самоуправления и созданию финансовых инструментов, стимулирующих комплексное развитие территорий. В заключении представлены прогнозы устойчивого внедрения мастер-планирования при условии системных реформ и предложена дорожная карта пилотного внедрения в российских условиях. Автор дает подробные пояснения и примеры реализации мастер-планирования в разных региональных контекстах, подчеркивая необходимость адаптации международных практик к местным особенностям и правовым реалиям.

Antczak-Stępniak Agata, Załęczna Magdalena

In many countries struggling with a shortage of affordable housing, one available instrument is to increase the housing stock at the disposal of local authorities using commercial, residential projects. This solution is called the Inclusionary Housing (IH) model, which creates a mix of residents with different rights to housing and who belong to various social groups. The developer should “pay in apartments” when a residential project benefits primarily from planning and spatial privileges given by the public actor. This instrument has advantages and disadvantages, and its success depends on local social, cultural, and economic conditions. The authors decided to investigate the possible effects of introducing the IH model in Poland considering the results of the application of the Act of July 5, 2018, on facilitating the preparation and implementation of housing investments and accompanying investments (Lex Developer Act). The authors aim to determine the number of apartments that developers could potentially offer to public actors in cities where privileged decisons were applied most often. The research was conducted using methods such as a critical literature review, research of legal acts, analysis of local source data, case studies, and a comparative analysis. The results allow conclusions regarding local housing needs.

Raziyeh Moghaddas, Farinaz Tanhaei, Maryam Al Moqbali et al.

The incorporation of machine learning (ML) approaches into business intelligence (BI) results in a great impact on fields that require predictive analysis, such as the real estate market. An accurate prediction of housing prices can provide benefits to stakeholders such as developers, investors, and policy planners. This study aims to explore the application of ML techniques to property valuation by creating a dataset based on real-world house price data collected from various areas in Muscat, Oman. Several ML models, including Linear Regression, Ridge Regression, Gradient Boosting, Random Forest, and Support Vector Regression, were applied and examined on the created dataset to estimate the house prices. Besides, hyperparameter tuning is used for each model in order to improve their predictive accuracy. Finally, we assessed the performance of each model using standard evaluation metrics, i.e., Mean Absolute Error (MAE), Mean Squared Error (MSE), and the R-squared (R²) score. The findings of this research work provide a comparative analysis of model efficiency that highlights both the capabilities and limitations of each model. This study demonstrates the practical power of ML techniques in real-state analytics and its wider applicability in improving BI systems subsequently.

Ankolika De

This study examines how WhatsApp has evolved from a personal communication tool to a professional platform, focusing on its use by small business owners in India. Initially embraced in smaller, rural communities for its ease of use and familiarity, WhatsApp played a crucial role in local economies. However, as Meta introduced WhatsApp Business with new, formalized features, users encountered challenges in adapting to the more complex and costly platform. Interviews with 14 small business owners revealed that while they adapted creatively, they felt marginalized by the advanced tools. This research contributes to HCI literature by exploring the transition from personal to professional use and introduces the concept of Coercive Professionalization. It highlights how standardization by large tech companies affects marginalized users, exacerbating power imbalances and reinforcing digital colonialism, concluding with design implications for supporting community-based appropriations.

Di Liao, Ruijia Liang, Ziyi Ye

With the deepening of digital transformation, business process optimisation has become the key to improve the competitiveness of enterprises. This study constructs a business process optimisation model integrating artificial intelligence and big data to achieve intelligent management of the whole life cycle of processes. The model adopts a three-layer architecture incorporating data processing, AI algorithms, and business logic to enable real-time process monitoring and optimization. Through distributed computing and deep learning techniques, the system can handle complex business scenarios while maintaining high performance and reliability. Experimental validation across multiple enterprise scenarios shows that the model shortens process processing time by 42%, improves resource utilisation by 28%, and reduces operating costs by 35%. The system maintained 99.9% availability under high concurrent loads. The research results have important theoretical and practical value for promoting the digital transformation of enterprises, and provide new ideas for improving the operational efficiency of enterprises.

Tassilo Klein, Clemens Biehl, Margarida Costa et al.

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.

Vanags Janis, Jansons Leo, Geipele Ineta et al.

The composition of the housing market is shaped by the social dimensions of buyer heterogeneity, prompting households to prioritize housing development to fulfill their needs efficiently. Both quantitative and qualitative dimensions of housing heterogeneity in transactions stem from the different characteristics, needs, and incomes of residents in different areas. The relevance of this research lies in understanding the social dimensions driving housing diversity among buyers and sellers. In a market economy, meeting the evolving needs of market participants is crucial. Consequently, stakeholders in the housing market focus on understanding buyer needs, changing trends, and adapting to the heterogeneity of the housing options. The housing market, characterized by significant information asymmetry, underscores the importance of comprehensively studying the social dimensions of housing diversity, particularly its impact on market value and transaction prices. Viewing households as heterogeneous social systems highlights the dominance of the social dimension in the housing market, necessitating a comprehensive exploration of its quantitative and qualitative aspects. Findings can inform managerial decisions to mitigate information asymmetry, improve housing availability, stabilize prices, and improve the market value of properties.

Andrey Andreevich Kirpichenkov

Реализация программы реновации жилой застройки является одной из важнейших программ, ориентированных на улучшение социально-экономического благосостояния регионов Российской Федерации. Реализация данной программы предполагает восстановление и обновление сложившейся жилой застройки с целью обеспечения граждан комфортными и безопасными жилищными условиями. С целью сокращения количества аварийных и ветхих жилых домов предусмотрен комплекс мер по восстановлению технического состояния, сокращению физического износа или в случае, если восстановление не является целесообразным, то проведение работ по ликвидации существующих жилых домов и реализации проектов нового строительства. Опыт реализации программы реновации жилой застройки на территории московского региона, где данная программа действует с 2017 г., показывает, что выбор стратегии реализации проекта не всегда в полной мере учитывает все исходные параметры, которыми обладает земельный участок или объект, попадающий в адресную программу реновации. Данная особенность может приводить к снижению эффективности реализации проекта и повышению рисков увеличения стоимости проекта, увеличению сроков строительства и иным негативным последствиям. В связи с тем, что программа реновации жилой застройки реализуется за счет федерального бюджета, а также программа предполагает своевременное планомерное волновое переселение граждан, то сокращение вероятности возникновения отставания в сроках реализации и превышение первоначального бюджета являются одними из самых существенных негативных аспектов при реализации программы реновации жилой застройки. С целью оценки исходных параметров объектов, а также определения результирующей эффективности проекта применение экономико-математической модели позволит в достаточной мере сформировать ряд оцениваемых параметров, которые представляют наибольшую значимость для реализуемых проектов реновации, провести их анализ и на их основе сформировать расчетным образом результирующий показатель эффективности проекта, который впоследствии ляжет в основе определения наиболее оптимальной стратегии реализации проекта с учетом его особенностей.

Oza Bhavik Manish, Sanchaniya Rashmi Jaymin, Kundziņa Antra et al.

This systematic literature review examines the challenges and barriers to property reuse for social housing development, a strategy increasingly recognized for its potential to address housing shortages while promoting sustainable urban development. The study synthesizes findings from a comprehensive analysis of peer-reviewed articles, policy documents, and grey literature, identifying key obstacles across technical, economic, regulatory, social, and environmental domains. Our review reveals that while property reuse offers significant opportunities for creating affordable and sustainable housing solutions, it is hindered by complex interplays of structural limitations, financial constraints, regulatory hurdles, community resistance, and environmental concerns. The findings highlight the multifaceted nature of barriers to adaptive reuse in the context of social housing, emphasizing the need for integrated approaches to overcome these challenges. This review contributes to the existing body of knowledge by providing a holistic understanding of the obstacles faced in property reuse projects for social housing and by identifying gaps in current research. The paper concludes with recommendations for policy reforms, innovative financing models, and community engagement strategies to facilitate successful property reuse initiatives. These insights are valuable for policymakers, urban planners, and housing developers seeking to implement effective property reuse strategies for social housing development.

Rosen Nikolaev, Tanka Milkova

The availability of free funds in the economy is usually associated with the investment process. In the specialized literature, various methods for assessing investments are known, but one of the fundamental methods of assessing investments is related to the calculation of the NPV (Net Present Value) indicator. According to the generally accepted rule, an investment is profitable if there is an NPV greater than zero. A negative NPV indicates that the investment is loss-making and should be rejected. However, this rule is derived under certain conditions. This paper shows a peculiarity in the assessment of investments related to the fact that in conditions of recession there are situations in which an investment with a negative NPV can be accepted. The article uses financial mathematics methods, in particular formulas for accruing interest and discounting future cash flows. Three options are considered in the presence of free cash – to keep it at home, to deposit it on a bank and to invest. Through concrete examples, it has been shown that in conditions of low deposit interest rates and high bank account service fees, it is possible that an investment with a negative NPV would be preferable, which refutes the generally accepted claim in theory.

Michel Kunkler, Felix Schumann, Stefanie Rinderle-Ma

Business Process Management and Operations Research are two research fields that both aim to enhance value creation in organizations. While Business Process Management has historically emphasized on providing precise models, Operations Research has focused on constructing tractable models and their solutions. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. In particular, it analyzes how business process models have been conceptualized as mathematical models and which optimization techniques have been applied to these models. Results indicate a strong focus on resource allocation and scheduling problems. Current approaches often lack support of the stochastic nature of many problems, and do only sparsely use information from process models or from event logs, such as resource-related information or information from the data perspective.

Orlenys López-Pintado, Serhii Murashko, Marlon Dumas

Simulation is a common approach to predict the effect of business process changes on quantitative performance. The starting point of Business Process Simulation (BPS) is a process model enriched with simulation parameters. To cope with the typically large parameter spaces of BPS models, several methods have been proposed to automatically discover BPS models from event logs. Virtually all these approaches neglect the data perspective of business processes. Yet, the data attributes manipulated by a business process often determine which activities are performed, how many times, and when. This paper addresses this gap by introducing a data-aware BPS modeling approach and a method to discover data-aware BPS models from event logs. The BPS modeling approach supports three types of data attributes (global, case-level, and event-level) as well as deterministic and stochastic attribute update rules and data-aware branching conditions. An empirical evaluation shows that the proposed method accurately discovers the type of each data attribute and its associated update rules, and that the resulting BPS models more closely replicate the process execution control flow relative to data-unaware BPS models.

Ivan Huljak, Reiner Martin, Diego Moccero et al.

ABSTRACT We estimate the impact of changes in non-performing loan (NPL) ratios on aggregate banking sector variables and the macroeconomy by estimating a panel Bayesian VAR model for twelve euro area countries. The main findings are as follows: i) An impulse response analysis shows that an exogenous increase in the change in NPL ratios tends to depress bank lending volumes, widens bank lending spreads and leads to a fall in real GDP growth and residential real estate prices; ii) A forecast error variance decomposition shows that shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that experienced a large increase in NPL ratios during the recent crises; and iii) A three-year structural out-of-sample scenario analysis suggests that reducing banks’ NPL ratios can produce significant benefits in terms of improved macroeconomic and financial conditions.

Anna Mikhaylovna Krylova

The topic of methodological peculiarities of planning the cost of housing and communal services is relevant in light of the need to increase the efficiency of managing housing and communal services by optimizing costs and reducing management expenses. The government tariff policy on organizations providing housing and communal services is one of the main directions of the Russian federation modern policy. The growth rate (changes) of regulated prices (tariffs) of natural monopolies and organizations in the housing and communal services sector is an initial internal factor in the forecast of socio-economic development of Russia; and the growth rate (changes) of prices specified in this forecast is a reference point for regulatory bodies when setting maximum tariffs for housing and communal services in the Russian regions and tariffs for organizations' services for consumers. Currently, there is an imbalance of economic interests of providers of housing and utilities services and consumers, which can be identified as one of the essential principles of organizing economic relations with regulated pricing within the government's tariff policy. With continuously increasing cost of resources, increasing demands from consumers, as well as the renovation of existing service implementation systems by resource supplying organizations, cost management and planning are an integral part of the activities of housing and communal services organizations. Proper planning of services prime cost allows not only to optimize the budgets of resource supplying organizations by reducing expenses but also increasing the quality and reliability of provided services, leading to an improvement in the standard of living and a decrease in the growth rate of tariffs for housing and communal services. Therefore, the study of the methodological peculiarities of housing and communal services prime cost planning can be useful for optimizing financial resources, improving service quality, and enhancing the overall management of the housing complex.

Skovajsa Štěpán

A huge effort has already been made to prove the existence of housing market segments, as well as how to utilize them to improve valuation accuracy and gain knowledge about the inner structure of the entire superior housing market. Accordingly, many different methods on the topic have been explored, but no universal framework is yet known. The aim of this article is to review some previous studies on data-driven housing market segmentation methods with a focus on clustering methods and their ability to capture market segments with respect to the shape of clusters, fuzziness and hierarchical structure.