Hasil untuk "Real estate business"

Faten Ben Bouheni, Manish Tewari, Andrew Salamon et al.

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores.

Felix Weinel, Marcelo Cajias

This study investigates the role of innovation in shaping economic growth across the heterogeneous landscape of the European Union (EU). Through a detailed analysis at the NUTS-3 level, it identifies regions characterized by sustained and above-average growth, thereby providing new insights into the mechanisms through which innovation contributes to regional economic resilience and long-term prosperity. Our methodology employs panel data from the EU27 Member States and the United Kingdom (UK), spanning the period from 2002 to 2022, allowing a more comprehensive understanding of the determinants of regional growth, including Gross Value Added (GVA) across various industries, the unemployment rate, and inflation. Furthermore, the role of innovation as a pivotal force driving economic progress is underscored. The analysis reveals significant differentiation within the cluster, supporting the assumption that innovation plays a crucial role in driving robust and, most notably, sustainable economic growth in a region. The findings further highlight that regions with an industry structure closely resembling the sample average are significantly influenced by innovation. From this, it can be deduced that innovation is a key driver of sustainable growth, especially in regions with the necessary infrastructure, skilled labour, and industry diversification to effectively harness innovative activities.

Nowak Krzysztof Adam, Głuszak Michał

A growing body of empirical evidence suggests that adjustments on the office market are not symmetric. The theoretical framework provides various justifications for the presence of frictions, especially in the case of rent and vacancy rate. In this paper, we investigate the asymmetric reactions of rent and vacancy rate to the demand and supply shocks in the nine major office markets in Poland. In the course of the study we seek answers to two research questions. First, we investigate if the direction (positive or negative) as well as type (demand or supply) of the shocks produce asymmetry in the adjustments of vacancy rate and rent. Secondly, we examine if market conditions and distinction between the long-term and short-term perspective can generate asymmetry in vacancy rate and rent responses to the shocks.

Muczyński Andrzej, Stachowska Sylwia

Housing plays a crucial role in urban development, and social housing management has become a vital aspect of urban management. The social housing sector has experienced significant changes worldwide in recent decades, particularly regarding housing management. In Poland, this sector mainly comprises municipal housing stock, for which public and private housing organizations provide management services appointed by municipalities or public-private homeowners associations. An essential part of these management services includes personnel management, a topic that is rarely discussed in the real estate literature.

Youhong Chen, Debraj Bhattacharjee, Balarko Chaudhuri et al.

This paper demonstrates how Extended Dynamic Mode Decomposition (EDMD), grounded in Koopman operator theory, can effectively identify the main contributor(s) to oscillations in power grids. We use PMU data recorded from a real 0.15 Hz oscillation event in Denmark for post-event analysis. To this end, the EDMD algorithm processed only voltage and current phasors from nineteen PMUs at different voltage levels across the Danish grid. In such a blind-test setting with no supplementary system information, EDMD accurately pinpointed the location of the main contributor to the 0.2 Hz oscillation, consistent with the location of the problematic IBR plant later confirmed by Energinet, where the underlying cause was a control system issue. Conventional approaches, such as the dissipating energy flow (DEF) method used in the ISO-NE OSL tool did not clearly identify this plant. This joint validation with Energinet, reinforcing earlier studies using simulated IBR-dominated systems and real PMU data from ISO-NE, highlights the potential of EDMD-based post-event analysis for identifying major oscillation contributors and enabling targeted SSO mitigation.

Meng Wang, Philipp Görz, Joschua Schilling et al.

Detecting business logic vulnerabilities is a critical challenge in software security. These flaws come from mistakes in an application's design or implementation and allow attackers to trigger unintended application behavior. Traditional fuzzing sanitizers for dynamic analysis excel at finding vulnerabilities related to memory safety violations but largely fail to detect business logic vulnerabilities, as these flaws require understanding application-specific semantic context. Recent attempts to infer this context, due to their reliance on heuristics and non-portable language features, are inherently brittle and incomplete. As business logic vulnerabilities constitute a majority (27/40) of the most dangerous software weaknesses in practice, this is a worrying blind spot of existing tools. In this paper, we tackle this challenge with ANOTA, a novel human-in-the-loop sanitizer framework. ANOTA introduces a lightweight, user-friendly annotation system that enables users to directly encode their domain-specific knowledge as lightweight annotations that define an application's intended behavior. A runtime execution monitor then observes program behavior, comparing it against the policies defined by the annotations, thereby identifying deviations that indicate vulnerabilities. To evaluate the effectiveness of ANOTA, we combine ANOTA with a state-of-the-art fuzzer and compare it against other popular bug finding methods compatible with the same targets. The results show that ANOTA+FUZZER outperforms them in terms of effectiveness. More specifically, ANOTA+FUZZER can successfully reproduce 43 known vulnerabilities, and discovered 22 previously unknown vulnerabilities (17 CVEs assigned) during the evaluation. These results demonstrate that ANOTA provides a practical and effective approach for uncovering complex business logic flaws often missed by traditional security testing techniques.

Yusen Wu, Xiaotie Deng

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.

Oguntona Olusegun, Aigbavboa Clinton, Akinradewo Opeoluwa

Numerous issues and environmental concerns are attributed to the construction and operation of conventional buildings globally. Dire among these issues are the health impacts of these buildings on their occupants. The study aims to identify the adverse health impacts of conventional buildings on occupants in South Africa based on construction professionals’ perspectives. A field survey was carried out among construction professionals in the Gauteng Province of South Africa to identify the adverse health impacts of conventional buildings on occupants. The study used a simple random sampling method to select participants to avoid sampling bias. A well-structured, closed-ended questionnaire survey was developed and administered to respondents to gather data for the study. The questionnaire comprised twenty-six (26) adverse health impacts identified through an extensive literature review. The collected data from 159 respondents were then subjected to descriptive and inferential analyses using exploratory factor analysis (EFA) methods. The study’s findings showed that sensitivity to odours, daytime dysfunction, and fatigue were the highest-ranked adverse health impacts on conventional buildings’ occupants. The EFA returned five factors that provided a relevant understanding of the adverse health impacts of conventional buildings on occupants: respiratory symptoms, neurological and cognitive effects, general body discomfort, infectious diseases, and sensory sensitivity. In conclusion, the study emphasises the need for attention to the indoor environment and its potential impact on occupants’ health and well-being with evidence that factors in conventional buildings, such as air quality, lighting, noise, temperature, and hygiene practices, play a significant role in influencing occupants’ health outcomes.

Haitong Ma, Zhaolin Ren, Bo Dai et al.

We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decision processes. The spectral decomposition brings about representation that can linearly represent the state-action value function induced by any policies, thus can be regarded as skills. The skill representations are transferable across arbitrary tasks with the same transition dynamics. Moreover, to handle the sim-to-real gap in the dynamics, we propose a skill discovery algorithm that learns new skills caused by the sim-to-real gap from real-world data. We promote the discovery of new skills by enforcing orthogonal constraints between the skills to learn and the skills from simulators, and then synthesize the policy using the enlarged skill sets. We demonstrate our methodology by transferring quadrotor controllers from simulators to Crazyflie 2.1 quadrotors. We show that we can learn the skill representations from a single simulator task and transfer these to multiple different real-world tasks including hovering, taking off, landing and trajectory tracking. Our skill discovery approach helps narrow the sim-to-real gap and improve the real-world controller performance by up to 30.2%.

Alessandro Niro, Michael Werner

Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process mining avoids these limitations by allowing events to be related to different cases. This study proposes a novel framework for anomaly detection in business processes that exploits graph neural networks and the enhanced information offered by object-centric process mining. We first reconstruct and represent the process dependencies of the object-centric event logs as attributed graphs and then employ a graph convolutional autoencoder architecture to detect anomalous events. Our results show that our approach provides promising performance in detecting anomalies at the activity type and attributes level, although it struggles to detect anomalies in the temporal order of events.

Bora Caglayan, Mingxue Wang, John D. Kelleher et al.

NL2SQL (Natural Language to Structured Query Language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark focused on typical NL questions in industrial BI scenarios. We discuss the challenges of constructing a BI-focused benchmark and the shortcomings of existing benchmarks. Additionally, we introduce question categories in our benchmark that reflect common BI inquiries. Lastly, we propose two novel semantic similarity evaluation metrics for assessing NL2SQL capabilities in BI applications and services.

Efim ZUBCO, Silvia ZAHARCO

The agricultural land market, as a specific element of the real estate market, was reestablished in the Republic of Moldova by the end of the last century. During the development of the national agricultural land market, there were different fluctuation trends of the main indicators, which reflect the quantitative aspects of land development: the correlation between demand and supply, market price of agricultural land, sold-purchased land areas, etc. The most important indicator is the market price of agricultural land, which, under the conditions of free land market, is formed based on the correlation between supply and demand for land. The sale-purchase type of business predominates in the transaction structure of the land market. In this context, in order to investigate the issues related to the functioning of the agricultural land market, the number of sale-purchase transactions of agricultural land, the area of agricultural land subject to sale, as well as the average sale-purchase price of agricultural land have been analyzed.

Feiyang Sun, Jan Whittington, Siman Ning et al.

The first year of COVID-19 tested the economic resilience of cities, calling into question the viability of density and the essential nature of certain types of services. This study examines built environment and socio-economic factors associated with the closure of customer-facing food businesses across urban areas of Seattle, Washington. The study covers 16 neighborhoods (44 census block groups), with two field audits of businesses included in cross-sectional studies conducted during the peak periods of the pandemic in 2020. Variables describing businesses and their built environments were selected and classified using regression tree methods, with relationships to business continuity estimated in a binomial regression model, using business type and neighborhood socio-demographic characteristics as controlled covariates. Results show that the economic impact of the pandemic was not evenly distributed across the built environment. Compared to grocery stores, the odds of a restaurant staying open during May and June were 24%, only improving 10% by the end of 2020. Density played a role in business closure, though this role differed over time. In May and June, food retail businesses were 82% less likely to remain open if located within a quarter-mile radius of the office-rich areas of the city, where pre-pandemic job density was greater than 95 per acre. In November and December, food retail businesses were 66% less likely to remain open if located in areas of residential density greater than 23.6 persons per acre. In contrast, median household income and percentage of non-Asian persons of color were positively and significantly associated with business continuity. Altogether, these findings provide more detailed and accurate profiles of food retail businesses and a more complete impression of the spatial heterogeneity of urban economic resilience during the pandemic, with implications for future urban planning and real estate development in the post-pandemic era.

Mehrdad Maghsoudi, Navid Nezafati

The rise of information technology has transformed the business landscape, with organizations increasingly relying on information systems to collect and store vast amounts of data. To stay competitive, businesses must harness this data to make informed decisions that optimize their actions in response to the market. Business intelligence (BI) is an approach that enables organizations to leverage data-driven insights for better decision-making, but implementing BI comes with its own set of challenges. Accordingly, understanding the key factors that contribute to successful implementation is crucial. This study examines the factors affecting the implementation of BI projects by analyzing the interactions between these factors using system dynamics modeling. The research draws on interviews with five BI experts and a review of the background literature to identify effective implementation strategies. Specifically, the study compares traditional and self-service implementation approaches and simulates their respective impacts on organizational acceptance of BI. The results show that the two approaches were equally effective in generating organizational acceptance until the twenty-fifth month of implementation, after which the self-service strategy generated significantly higher levels of acceptance than the traditional strategy. In fact, after 60 months, the self-service approach was associated with a 30% increase in organizational acceptance over the traditional approach. The paper also provides recommendations for increasing the acceptance of BI in both implementation strategies. Overall, this study underscores the importance of identifying and addressing key factors that impact BI implementation success, offering practical guidance to organizations seeking to leverage the power of BI in today's competitive business environment.

Melnychenko Oleksandr, Osadcha Tetiana, Kovalyov Anatoliy et al.

The study aimed to examine the impact of inflation on the real estate market using Polish panel data for the last 13 years. It is based on a panel model, where price changes of one square meter of housing are determined as a function in changes of inflation, the central bank’s base rate, dwellings built, as well as new mortgage loans. The quarterly dynamics of the average price of 1 square meter of housing in Poland’s eight largest cities in the 2009-2021 period was studied. This price was modeled and predicted using one of the Box-Jenkins time series models: the Holt-Winter model of exponential smoothing with a damped trend. The forecasting results showed a small (up to 4%) relative error in comparison with the actual data. In addition, the moment (2017) of the price trend change was found. Therefore, piecewise linear regressions with high regression coefficients were used when modeling the impact of inflation changes on the real estate market indicators under consideration. The results obtained provide valuable insight into the relationship of real estate market indicators, allowing consumers to predict available options and make decisions in accordance with their preferences.

Adedayo Ayodeji Odebode, Timothy Tunde Oladokun, Oyeronke Toyin Ogunbayo

The idea of the Integrated Development Scheme (IDS) has received considerable attention in India, Indonesia and in some African countries such as Kenya and Ethiopia. The scheme has led to urban slum upgrading in these countries and has led to notable successes in the provision of common facilities in the rural areas of India. Therefore, given the neglect of rural areas by both private and public sectors, and the need to improve the housing conditions of rural dwellers, this paper examines the benefits of improved livelihoods from the scheme to improving rural housing conditions in Nigeria. A case study of the Rural Development Programme (RUDEP) of Justice Development and Peace Makers' Centre (JDPMC), a non-governmental organisation in Osun State, Nigeria, was conducted. Stratified and purposive sampling was used to select 344 participants/beneficiaries of the programme from 28 active communities out of the 36 communities' coverage by RUDEP. Qualitative and quantitative data obtained from the respondents were analysed using descriptive statistics of percentages and frequency distributions. The results revealed that the RUDEP integrated scheme, which was first initiated with the objective of improving the livelihood of poorer farmers and women that engaged in agricultural-related activities, has also impacted rural housing conditions positively by empowering them to provide facilities that were not initially in place. The paper concluded that IDS could be a viable policy option for improving the condition of rural housing in Nigeria.

David Oluwatofunmi AKINWAMIDE, Jonas Hahn

The demand for quality of service among practicing firms of real estate to achieving customers’ satisfaction has become the major concern of real estate professional bodies. However, customers’ satisfaction is influenced by their emotional needs in decision making. Therefore, this study develops a conceptualized model for measuring service quality among practicing firms of real estate with the application of emotional intelligence. To assess customers’ satisfaction level, the knowledge gap between perception of real estate firms on expectation of customers and the actual customers’ expected service in the Lagos property market in Nigeria were analyzed. A random selection of 100 members of Nigerian Estate Surveyors and Valuers with a total of 400 real estate customers was purposively selected and administered with a structured questionnaire, However, only 85 retrieved questionnaires from real estate firms and a total of 362 real estate customers responded and utilized for analyzing the data. Knowledge gap analysis model with weighted mean score was employed for data analysis. Findings depicted that real estate firms have a basic knowledge barrier on the adoption of emotional intelligence (with more emphasis on self-awareness and social skills determinants) as an instrument of real estate service quality to satisfy customers’ emotional needs in delivery of service among the practitioners. Therefore, practitioners in the real estate firms need to improve on their knowledge of emotional intelligence as an instrument of real estate service quality to enhance customers’ satisfaction on emotional needs.

Ilija Radosavovic, Tete Xiao, Stephen James et al.

In this work, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our visual representations are pre-trained via a masked autoencoder (MAE), frozen, and then passed into a learnable control module. Unlike prior work, we show that the pre-trained representations are effective across a range of real-world robotic tasks and embodiments. We find that our encoder consistently outperforms CLIP (up to 75%), supervised ImageNet pre-training (up to 81%), and training from scratch (up to 81%). Finally, we train a 307M parameter vision transformer on a massive collection of 4.5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.

Mehmet Emre Çamlibel, Levent Sümer, Ali Hepşen

The purpose of this research is to give an insight into the Turkish real estate investment funds (T-REIFs) by comparing their risk-return performances with the main benchmark investment tool Istanbul Stock Exchange-100 (BIST-100) Index. This study evaluated the performance of T-REIFs in four different periods between January 2017 and December 2020 (2017m1–2017m12, 2018m1–2018m12, 2019m1–2019m12 and 2020m1–2020m12) including the Coronavirus Disease (Covid-19) period by applying the Sharpe and Treynor ratios. In a well-diversified portfolio both ratios give the same results, but in the presence of non-systematic risk and the portfolio is poorly diversified, the Treynor ratio is a better indicator than the Sharpe ratio. The findings of this study show that rankings of Sharpe and Treynor ratios may differ for each period. These results also support the fact that the portfolios of funds in the Turkish real estate market are not well diversified. By providing corporate tax exemptions, and by enabling the investors to diversify their investments and reduce their risks, real estate investment funds are important alternatives to direct real estate investments in Turkey. In that context, being one of the pioneer studies in this niche and a new topic in emerging markets, analyzing the return performances of T-REIFs and comparing them with the returns of the BIST-100 index is aimed to contribute to literature as well as provide insight to investors who may consider investing in the Turkish real estate capital market instruments.

Ataguba Joseph Obaje

While the use of simple deterministic models to calculate rental value growth (RVGrowth) rate of reversionary freeholds across epochs prior to upward rent review appears illusive, literature evidence of the synthesis between short-cut DCF valuation and Solver tools in a spreadsheet does not constitute an exhaustive list of solutions. This study examined alternative spreadsheet and iteration tools that can determine RVGrowth rate of freehold investment properties across rent review epochs. With recourse to a hypothetical case of a freehold investment property, this experimental study identified the mathematical composition of RVGrowth in an explicit DCF framework, performed short-cut DCF Valuation and equivalent yield calculation at specific epochs prior to and including the full reversion; as well as using Goal Seek to calculate RVGrowth across all epochs prior to- and including the full reversion. Excel® Solver and Goal Seek, as well as the graphing/root-solving tool in Kyplot® were found to feasibly produce identical results for RVGrowth rate. This is among the limited studies that identified and researched the veracity of alternative tools for RVGrowth rate iteration. The value of this study is the awareness of alternative analytical tools avail freehold investors who desire knowledge of RVGrowth rate when making purchase-, hold-, and sales decisions.