Hasil untuk "Real estate business"

Victoria Torres Bosch, Ronny Seiger, Manuela Albert Albiol et al.

The real-time data collection and automation capabilities offered by the Internet of Things (IoT) are revolutionizing and transforming Business Processes (BPs) into IoT-enhanced BPs, showing high potential for improving sustainability. Although already studied in Business Process Management (BPM), sustainability research has primarily focused on environmental concerns. However, achieving a holistic and lasting impact requires a systematic approach to address sustainability beyond the environmental dimension. This work proposes a conceptual model and a structured methodology with the goal of analyzing the potential of IoT to measure and improve the sustainability of BPs. The conceptual model formally represents key sustainability concepts, linking BPM and IoT by highlighting how IoT devices support and contribute to sustainability. The methodology guides the systematic analysis of existing BPs, identifies opportunities, and implements sustainability-aware, IoT-enhanced BPs. The approach is illustrated through a running example from the tourism domain and a case study in healthcare.

Zihao Wang, Junming Zhang

Large Language Models (LLMs) have shown promising potential in business applications, particularly in enterprise decision support and strategic planning, yet current approaches often struggle to reconcile intricate operational analyses with overarching strategic goals across diverse market environments, leading to fragmented workflows and reduced collaboration across organizational levels. This paper introduces BusiAgent, a novel multi-agent framework leveraging LLMs for advanced decision-making in complex corporate environments. BusiAgent integrates three core innovations: an extended Continuous Time Markov Decision Process (CTMDP) for dynamic agent modeling, a generalized entropy measure to optimize collaborative efficiency, and a multi-level Stackelberg game to handle hierarchical decision processes. Additionally, contextual Thompson sampling is employed for prompt optimization, supported by a comprehensive quality assurance system to mitigate errors. Extensive empirical evaluations across diverse business scenarios validate BusiAgent's efficacy, demonstrating its capacity to generate coherent, client-focused solutions that smoothly integrate granular insights with high-level strategy, significantly outperforming established approaches in both solution quality and user satisfaction. By fusing cutting-edge AI technologies with deep business insights, BusiAgent marks a substantial step forward in AI-driven enterprise decision-making, empowering organizations to navigate complex business landscapes more effectively.

Saso Nikolovski, Pece Mitrevski

In today's corporate landscape, particularly where operations rely heavily on information technologies, establishing a robust business continuity plan, including a disaster recovery strategy, is essential for ensuring swift recuperation following outages. This study presents a comparative analysis of recovery solutions, focusing on systems that operate partially or entirely within cloud environments and assessing their reliability in fulfilling organizational roles securely and dependably. Two such systems were deployed and evaluated in a real-world production setting. Key performance and reliability metrics were identified using simulation software to enhance these systems, alongside a System Dynamics analysis conducted for each. This work proposes a comprehensive framework for selecting and maintaining data protection and recovery solutions within organizational structures, outlining criteria for aligning chosen approaches with operational needs while adhering to predetermined timelines specified in business continuity and disaster recovery plans. The resulting analysis and findings offer actionable insights to guide decision-making when selecting appropriate recovery concepts.

Karp Piotr, Wolski Rafał

This study examines the efficiency of housing real estate investments in Poland as an inflation hedge, with a particular focus on the influence of exchange rate and inflation measured by the HICP index on housing property prices. Aim: to determine whether housing real estate investment can be inflation hedge in both low and high inflation environments. Methodology: The analysis applies threshold cointegration techniques to identify regimes of high and low inflation and assess the long-term and short-term relationships between the variables. Findings: The results suggest that the variables used in the study can be effectively analyzed using threshold cointegration analysis. The findings indicate that investment in housing real estate can serve as a strong capital hedge, particularly during periods of low inflation. Implications: The study provides important insights for examining the relationship between housing real estate investments, inflation, and exchange rates. The analysis allows for the identification of distinct regimes of high and low inflation, which can have significant implications for understanding the dynamic relationships between the inflation, exchange rate and real estate prices. The results suggest that real estate can be a valuable asset in a diversified portfolio, particularly during periods of low inflation, as it can serve as a hedge against loss of value. Originality/value: The research delves into the nuances of the relationship between inflation and housing real estate, examining the potential for housing real estate to serve as an effective hedge against inflation and the exchange rate as a factor that may influence its performance in low and high inflationary contexts.

Trong Phuong Tran, Nguyen Tran Tuan

Land use is increasingly influencing ecosystem services (ES) in Vietnam. However, in Vietnam, there have not been many studies addressing the quantification of ecosystem service values (ESV) as well as the relationship between land use conversion and ES from a spatial perspective. This study seeks to address this deficiency by analyzing the land use changes (LUC) and the ESV in 14 provinces within Vietnam’s NMR The study used several indicators to assess LUC. These indicators include S (dynamic index of a given land-use category), I (integrated land-use level), LUI (land-use intensity), LUM (mixed land-use entropy index), and LUD (land-use diversity). Furthermore, the study considers the ESV _aver (average ecosystem service value) index. The research findings indicate that the shrub underwent the most significant changes in nearly every stage across all 14 localities. With the LUI index, the period 1990–1995 shows that there are localities with the highest level of adaptation, with adaptation levels ranging from 4.15 to 8.74 in three western provinces of the region. The LUD and LUM indexes reveal a high level of complexity in land use types during the periods 2000–2005 and 2015–2020. This shows that the level of diversity in land use in most localities is increasing day by day. Meanwhile, in the NMR region, the ESV shows a slight increase in the first 5 years of study, followed by a series of gradual decreases until 2020, to 67,647.56 million USD/year. During its initial rise, the ESV _aver index exhibited a similar trend. From 1995 to 2005, it dropped sharply. The results of this study will enable researchers and decision-makers to formulate effective policies in the future.

L.A. Fredrick, C. Lucian, J. Urassa

This study investigates the impact of land formalization projects on women’s tenure security in Pangani Ward, Coastal Region, Tanzania, while also considering the broader global implications for women’s land rights. Despite progressive legislation that guarantees equal land rights for women, systemic challenges rooted in socio-cultural norms and economic constraints persist. Using a mixed-methods approach, the research combines qualitative and quantitative data from semi-structured interviews, focus group discussions, and surveys to assess women’s experiences with land formalization. The findings indicate that land formalization has had positive effects, including enhanced tenure security through legal recognition, improved credit access, and reduced land conflicts. However, the study also reveals significant barriers to women’s full engagement in the process, such as declining participation rates, socio-economic inequalities, and deeply ingrained gender biases that limit their ability to exercise land rights fully. These findings are relevant to Tanzania and offer insights into the challenges and opportunities for land rights formalization in other regions globally. The study highlights the need for targeted policy interventions, including increasing women’s participation in formalization processes, providing financial support, and addressing socio-cultural barriers. By examining Tanzania’s experience, this research contributes to a broader global conversation on the intersections of legal frameworks, cultural norms, and economic empowerment, urging a more inclusive approach to land formalization that can support women’s long-term tenure security and improved livelihood outcomes worldwide.

Sen Wang, Dong Li, Shao-Yu Huang et al.

When optimizing real-time systems, designers often face a challenging problem where the schedulability constraints are non-convex, non-continuous, or lack an analytical form to understand their properties. Although the optimization framework NORTH proposed in previous work is general (it works with arbitrary schedulability analysis) and scalable, it can only handle problems with continuous variables, which limits its application. In this paper, we extend the applications of the framework NORTH to problems with a hybrid of continuous and discrete variables. This is achieved in a coordinate-descent method, where the continuous and discrete variables are optimized separately during iterations. The new framework, NORTH+, improves around 20% solution quality than NORTH in experiments.

Alexander Stevens, Jari Peeperkorn, Johannes De Smedt et al.

In predictive process monitoring, predictive models are vulnerable to adversarial attacks, where input perturbations can lead to incorrect predictions. Unlike in computer vision, where these perturbations are designed to be imperceptible to the human eye, the generation of adversarial examples in predictive process monitoring poses unique challenges. Minor changes to the activity sequences can create improbable or even impossible scenarios to occur due to underlying constraints such as regulatory rules or process constraints. To address this, we focus on generating realistic adversarial examples tailored to the business process context, in contrast to the imperceptible, pixel-level changes commonly seen in computer vision adversarial attacks. This paper introduces two novel latent space attacks, which generate adversaries by adding noise to the latent space representation of the input data, rather than directly modifying the input attributes. These latent space methods are domain-agnostic and do not rely on process-specific knowledge, as we restrict the generation of adversarial examples to the learned class-specific data distributions by directly perturbing the latent space representation of the business process executions. We evaluate these two latent space methods with six other adversarial attacking methods on eleven real-life event logs and four predictive models. The first three attacking methods directly permute the activities of the historically observed business process executions. The fourth method constrains the adversarial examples to lie within the same data distribution as the original instances, by projecting the adversarial examples to the original data distribution.

Lars Ackermann, Martin Käppel, Laura Marcus et al.

The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.

Ana Lavalle, Alejandro Maté, Juan Trujillo et al.

Information visualization plays a key role in business intelligence analytics. With ever larger amounts of data that need to be interpreted, using the right visualizations is crucial in order to understand the underlying patterns and results obtained by analysis algorithms. Despite its importance, defining the right visualization is still a challenging task. Business users are rarely experts in information visualization, and they may not exactly know the most adequate visualization tools or patterns for their goals. Consequently, misinterpreted graphs and wrong results can be obtained, leading to missed opportunities and significant losses for companies. The main problem underneath is a lack of tools and methodologies that allow non-expert users to define their visualization and data analysis goals in business terms. In order to tackle this problem, we present an iterative goal-oriented approach based on the i* language for the automatic derivation of data visualizations. Our approach links non-expert user requirements to the data to be analyzed, choosing the most suited visualization techniques in a semi-automatic way. The great advantage of our proposal is that we provide non-expert users with the best suited visualizations according to their information needs and their data with little effort and without requiring expertise in information visualization.

Olena Dobrovolska, Nazar Fenenko

The real estate market in Ukraine, particularly in Kyiv, has experienced significant fluctuations due to the ongoing conflict and associated economic challenges. This research focuses on forecasting trends within the market, with a specific emphasis on price dynamics and the factors influencing these shifts. The actuality of this study stems from the pressing need to understand how the war, macroeconomic instability, and demographic changes impact real estate demand, investment patterns, and property prices. Given the rapid shifts in market dynamics and the complex interplay between economic variables, accurate forecasting is crucial for stakeholders such as investors, policymakers, and analysts. The methodology of this study combines traditional and advanced analytical tools to provide comprehensive and accurate forecasts. A dual-approach forecasting model was employed, using both the Brown-Mayer method implemented in Excel and advanced functionality provided by Statgraphics software. The Brown-Mayer method, which relies on exponential smoothing, allowed for the analysis of basic trends and seasonality in the time series data. Statgraphics, with its capacity to consider complex changes in market dynamics, provided more precise forecasts. The study involved data preparation, anomaly detection, correction, and checks for stationarity to ensure that the forecasts were not distorted by irregularities or missing data. Data were derived from key indicators like the Ukrainian Index of Retail Deposit Rates (UIRD3M), price changes in construction, the National Bank of Ukraine's key interest rate, and the average prices of primary real estate in Kyiv from 2018 to 2023. The findings indicate that Statgraphics was more accurate in forecasting real estate trends in Kyiv than the Brown-Mayer method. The confidence intervals generated by Statgraphics aligned closely with observed price trends, while the Brown-Mayer method showed a larger deviation due to its simpler approach to trend smoothing. The study revealed that external factors such as war-induced economic instability, high interest rates, inflation, and currency devaluation slowed the growth rates of real estate prices, particularly in urban centers like Kyiv. Moreover, the demand for real estate shifted towards rental properties and commercial spaces, particularly warehousing, reflecting changes in business operations and population migration to safer regions. The discussion emphasizes the importance of employing multiple methodologies to enhance forecast reliability. The research underlines that while simpler methods like the Brown-Mayer model are useful for general trends, advanced software tools like Statgraphics are more effective for accurate market prediction in volatile environments. Additionally, the study recommends a holistic approach to future forecasting models by incorporating a wider range of variables and indicators. This would improve model robustness and predictive power, particularly in the context of geopolitical and macroeconomic disruptions. This study contributes to the understanding of real estate market forecasting, providing valuable insights for stakeholders aiming to navigate the complexities of a rapidly changing market landscape in Ukraine. The research highlights the need for adaptive forecasting methods that can account for market volatility and external shocks.

Tomal Mateusz

The endowment effect is one of the key behavioral biases causing friction in the housing market. It results in sellers’ offer prices being inflated relative to buyers’ bid prices. Although this effect has been confirmed in many studies, little is known about how it can be reduced or eliminated. Therefore, this article assesses the impact of behavioral interventions on the intensity of the endowment effect using the Polish housing market as a case study. The research was based on a lab-in-the-field experiment, in which a hypothetical transaction in the secondary sales housing market was simulated and the recruited respondents were randomly divided into sellers and buyers. The endowment effect was measured by the gap between the average value of minimum prices for which sellers would be willing to sell a dwelling (WTA) and the average value of maximum prices that buyers would be willing to pay to acquire that dwelling (WTP). The results show that the endowment effect significantly decreases but does not disappear after the application of behavioral interventions. The latter consists of highlighting relevant information about the market price of a property and visualizing it graphically. Specifically, before the intervention, the WTA-WTP gap was 7.01%, and after 2.48%.

Frank Nyanda, Henry Muyingo, Mats Wilhelmsson

The housing market in Dar es Salaam, Tanzania, is expanding and with it a need for increased market transparency to guide investors and other stakeholders. The objective of this paper is to evaluate machine learning (ML) methods to appraise real estate in formal and informal housing markets in this nascent market sector. Various advanced ML models are applied with the aim of improving property value estimates in a market with limited access to information. The dataset used included detailed property characteristics and transaction data from both market types. Regression, decision trees, neural networks, and ensemble methods were employed to refine property appraisals across these settings. The findings indicate significant differences between formal and informal market valuations, demonstrating ML’s effectiveness in handling limited data and complex market dynamics. These results emphasise the potential of ML techniques in emerging markets where traditional valuation methods often fail due to the scarcity of transaction data.

Mohd Don Omar, Qaisar Ali

The recent traction in strata title living (STL) has addressed legal, regulatory, and governance issues to a certain extent. Despite addressing these underlying issues, the acceptance of STL remains low due to the low understanding of the features shaping residents’ perceptions and expectations. Accordingly, this study aims to explore the features of STL and examine how they contribute to enhancing the acceptance of STL (ASTL). Considering STL as an innovative living arrangement, we use the diffusion of innovation (DOI) theory to categorize the factors affecting 201 Bruneian residents’ perceptions of STL. The data collection was done through a self-administrated survey questionnaire and the collected data was analyzed using SPSS software. The findings reveal that perceptions of resource facilitation, relative advantages, and compatibility features of STL have the highest effect on ASTL. Whereas, perceptions of trialability, complexity, and observability have the lowest effect on ASTL. This study contributes to developing commercialization strategies for ST properties by understanding the features affecting the perceptions of ST residents.

Mikhail Alexandrovich Lunyakov, Andrey Andreevich Kirpichenkov

Одним из важнейших направлений социально-экономической политики каждого государства является обеспечение граждан безопасными и комфортными жилищными условиями, которые бы отвечали требованиям не только актуальных норм и правил, но и сложившимся требованиям современного общества. С целью улучшения жилищных условий и обновления ветхого и аварийного жилья ведется программа реновации жилой застройки. Одной из особенностей представленного во многих городах Российских Федерации жилого фонда является большое и, зачастую, преобладающее количество жилых домов, построенных в период с 1958 по 1967 гг., в период индустриального домостроения. В соответствии с действующими нормативными актами срок эксплуатации данных домов составляет до 70 лет при условии своевременного обслуживания. Это означает, что в ближайшие 10–15 лет общее состояние жилищного фонда может существенно ухудшиться и произойдет резкое увеличение ветхого и аварийного жилья. С целью не допустить массового появления аварийных зданий, для которых на данном этапе их жизненного цикла капитальный ремонт нецелесообразен, реализуется программа реновации жилой застройки. Главной задачей данной программы является модернизация существующего жилищного фонда и обновление среды жизнедеятельности с целью приведения его в соответствие со стандартами качества, обеспечивающими комфортные условия проживания. В реализации программы реновации жилой застройки задействованы многие участники: от государственных структур до локальных подрядных организаций. Такой широкий круг участников требует максимально слаженной взаимной работы для достижения всех поставленных задач. Но, как показала практика, не всегда участники действуют слаженно, что приводит к различным последствиям, выраженным в существенном увеличении стоимости или сроков строительства. Тем самым необходима тщательная проработка действующего порядка взаимодействия участников с целью выявления возможных проблем и анализа для совершенствования механизма управления и взаимодействия участников реализации программы.

Thuy P. Le, Phe H. Hoang, Linh X. Nguyen et al.

Urban Quality of Life (UQoL) is the main objective of sustainable development in the urban context. It is now widely recognised as a multidimensional concept. The satisfaction provided by the elements related to accommodation, such as housing and land, greatly contributes to the satisfaction with quality of life. Meanwhile, the UQoL also contributes to housing and land prices in cities. Our review shows that most current studies on this interrelationship are limited to several dimensions of UQoL and their impact on housing or land prices. This article will fill the gap by using the land price as an input for calculating a UQoL index from the viewpoint of the Status-Quality Trade-Off theory and ecosystem services. A case study was conducted in Cau Giay District, Hanoi, Vietnam, to create a map of the UQoL index and investigate the interrelationship UQoL – land price. In an ideal condition, this interrelationship should be positive (high/low UQoL index – high/low land price). However, this research revealed two other negative scenarios: “high UQoL index – low land price”, and “low UQoL index – high land price”. These negative scenarios can bring many business opportunities and therefore be interesting for stakeholders in the real estate market.

Lyudmila Anatolyevna Oparina, Yuliya Dmitrievna Obukhova

The purpose of the paper is a system representation of the factors influencing the formation of the carbon footprint value formed during the life cycle of capital construction objects. The main methods of work are analysis and systematization. The results of the work are presented in the form of a matrix of factors. The use of this matrix makes it possible to make more accurate calculations of the carbon footprint value formed during the life cycle of capital construction objects, as well as to develop databases for the calculation of the identified factors.

Christoph Blumschein, Ilja Behnke, Lauritz Thamsen et al.

When IP-packet processing is unconditionally carried out on behalf of an operating system kernel thread, processing systems can experience overload in high incoming traffic scenarios. This is especially worrying for embedded real-time devices controlling their physical environment in industrial IoT scenarios and automotive systems. We propose an embedded real-time aware IP stack adaption with an early demultiplexing scheme for incoming packets and subsequent per-flow aperiodic scheduling. By instrumenting existing embedded IP stacks, rigid prioritization with minimal latency is deployed without the need of further task resources. Simple mitigation techniques can be applied to individual flows, causing hardly measurable overhead while at the same time protecting the system from overload conditions. Our IP stack adaption is able to reduce the low-priority packet processing time by over 86% compared to an unmodified stack. The network subsystem can thereby remain active at a 7x higher general traffic load before disabling the receive IRQ as a last resort to assure deadlines.

Lorenzo Bonazzi

We present a characterization of the finite groups in which all real classes have prime powers size.

Munshifwa Ephraim K.

The sales comparison is the most common and universally accepted method in valuation. Although the theoretical entry point of the method is the same across most continents, its application in practice is varied and often determined by local circumstances. This often necessitates the modification of the method. For instance, while Zambian valuation practice uses this method in residential valuation, its application goes beyond the basic valuation model, incorporating a less known technique called the “reduced floor area (RFA)” technique. The RFA technique is a form of relative importance (weight) concept which assesses ancillary buildings on site relative to the main use; for residential properties this is the main house on site. Despite its obscurity in valuation literature, practitioners find its use acceptable within the dictates of local circumstances. Nonetheless, the lack of documentation means knowledge on the technique is transmitted verbally from senior valuers to graduates, and its application is not consistent across the profession, contributing to variances in the assessed values. This necessitates detailed scrutiny of the technique. Data for the study was collected from the Valuation Surveyors Registration Board (VSRB), a statutory body responsible for licensing valuers and regulating valuation practice. This is the first time the RFA technique is being discussed in a scholarly article.