Hasil untuk "Real estate business"

Anupam Nanda, Yishuang Xu, Fangchen Zhang

Abstract This paper aims to explore the impact of the COVID-19 pandemic on retail real estate and high street landscape through acceleration of e-commerce and digitalization. The retail business have been evolving over the past several decades, accentuated by the evolution and development of digital technologies. Almost all parts of the world have witnessed the changes in consumer behavior, the nature of retail, and reshaping of the high street landscape due to the e-commerce revolution and continued expansion. Especially due to the COVID-19 pandemic, the retail platforms powered by digital technology had to be adapted quickly, and it is expected to continue to support this change as consumers and retailers adjust to new normalities. Moreover, retail real estate is intricately linked with the retail sector dynamics. While lockdown and social distance rules have devastating impacts on “traditional” retail property sector, it may accelerate the evolution process of multi-channel retail and the channel integration role of physical stores and thus, bring in transformations in urban-retail landscape. It is not necessarily leading to an end of high street stores, but it may have a significant impact on retail real estate business. There remains a lack of understanding of how these changes may pan out with a rigorous academic investigation. To close this knowledge gap, we analyze both the strategy event data of a range of UK retailers as well as the insights from interviews with retail asset manager and landlords using a mixed-method approach. The findings indicate an urgent need for physical shops to reposition the functions of their multi-channel business. Our analysis provide significant insights and highlight several implications for retailers, landlords and, also policy-making units dealing with urban regeneration and local economic development in the post-COVID-19 world.

Julia Manso

Sentiment towards the Chinese real estate sector has deteriorated following the introduction of financing constraints in 2020 with the ''three red lines." Forcing developers to restructure their debt, the policy triggered a cascade of financing troubles, defaults, and reduced housing demand, ultimately culminating in a prolonged real estate crisis. This paper utilizes a network approach in line with Demirer et al. (2018) and Diebold and Yilmaz (2014) to measure daily time-varying connectedness in the stock return volatilities of major Chinese real estate developers throughout the crisis. Focusing on spillover between companies as reflected by market perception, this paper examines how connectedness evolves over time across firms with different regional exposures and state-ownership statuses, filling a gap in the literature to elucidate where property demand and real estate firm trustworthiness have deteriorated most. An event-study analysis of four key moments of the crisis outlines distinct phases of market sentiment: with the introduction of the three red lines, connectedness primarily reflects shared exposure and a uniform shock to the market. Then, the early unrest surrounding Evergrande exposes strong regional differentiation, with firms concentrated in less developed regions receiving significant spillover. By one year into the crisis, previously stable regions receive higher levels of spillover, and there is evidence of a substitution effect towards private developers. Two years into the crisis, the market has much less homogeneity in effects across regions and state-ownership status: major shocks induce minimal network changes, reflecting how investors have already priced in their beliefs. This paper also offers one of the most extensive timelines of the Chinese real estate crisis to date, and a new R package, GephiForR, was created for the network visualization in this paper.

Natalia Dinello, Giorgi Bregadze

Since its independence from the Soviet Union in 1991, Georgia has sought to revive its wine’s international reputation, diversify wine export destinations, and attract Western wine-loving tourists. Although much still needs to be done to decrease dependence on the Russian market and translate the aspirations into reality, Georgia has progressed in pivoting to the West. The country marketed as the Cradle of Wine has prioritized a Western orientation and holds the prospect of becoming a player in the global wine market. The recommendations provided in this paper are intended to solidify this prospect and break down barriers to entering Western wine and tourism markets. Effective promotion of Georgian wines, the cross-fertilization of wine production and wine and food tourism, and the development of domestic skills and international partnerships should go together with balancing various wine and tourist options. Export diversification should also involve complementing Georgian tradition with innovation, building on Western expertise and funding, and emphasizing premium-level wines, including natural wines.

Wassim J. Aloulou, Rahaf Fahad Almarshedi, Shuayyi Sameer Alharbi et al.

This study investigates the relationships between proactive personality, psychological capital, work engagement, work well-being, and innovative work behavior among employees in Saudi public authorities, based on the conservation of resources theory and the job demands-resources model. Using a sequential mediation model, data from 457 public employees were analyzed through structural equation modeling. The results show that a proactive personality and psychological capital significantly predict work engagement, but neither is significantly related to work well-being. Notably, while a proactive personality does not directly impact innovative work behavior, psychological capital does. Additionally, work well-being partially mediates the relationship between work engagement and innovative work behavior. These findings suggest that enhancing psychological capital and fostering engagement are key to promoting innovation. The mediating role of well-being highlights the importance of employee welfare in this process. This study provides practical implications for HR managers in the Saudi public sector and emphasizes strategies for building internal psychological resources. However, as data were collected from a single source, future research should include multiple key informants to enhance generalizability. This study builds on theory by demonstrating how proactive personality and psychological capital jointly stimulate innovative behavior through engagement and well-being, enriching the job demands-resources model with personal resource dynamics in public sector organizations.

Daniela Tavano, Francesca Salvo, Marilena De Simone et al.

The real estate sector is steadily moving towards zero-emission buildings, driven by EU policies to achieve near-zero energy (NZEB) buildings by 2050. In Italy, more than 70% of residential buildings fall into the lower energy classes, and this mainly affects low-income households. As a result, the decarbonisation of the real estate sector presents both technical and socio-economic obstacles. Building on these premises, this study introduces the Retrofit Optimization Problem (ROP), a methodological framework adapted from the Multidimensional Knapsack Problem (MdKP). This method is used in this study to conduct a qualitative analysis of accessibility to retrofit between different socio-economic groups, integrating constraints to simulate restructuring capacity based on different incomes. The results show significant disparities: although many retrofit strategies can meet regulatory energy performance targets, only a small number are financially sustainable for low-income households. In addition, interventions with the greatest environmental impact remain inaccessible to vulnerable groups. These preliminary results highlight important equity issues in the energy transition, indicating the need for specific and income-sensitive policies to prevent decarbonisation efforts from exacerbating social inequalities or increasing the risk of assets being stranded in the housing market.

Chenya Huang, Zhidong Li, Fang Chen et al.

Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and techniques, along with their evaluation methods, under the framework of these two research questions. Furthermore, specific application domains are summarized. Finally, we present insights into future research directions including multimodal complementarity, technology and modality contribution.

Oleh Pastukh, Viktor Khomyshyn

In recent years, machine learning (ML) techniques have become a powerful tool for improving the accuracy of predictions and decision-making. Machine learning technologies have begun to penetrate all areas, including the real estate sector. Correct forecasting of real estate value plays an important role in the buyer-seller chain, because it ensures reasonableness of price expectations based on the offers available in the market and helps to avoid financial risks for both parties of the transaction. Accurate forecasting is also important for real estate investors to make an informed decision on a specific property. This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values. The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R^2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model. The Gradient Boosting Regressor model provides the highest accuracy, the Extra Trees Regressor, Hist Gradient Boosting Regressor and Random Forest Regressor models give good results. In general, ensemble machine learning techniques can be effectively used to solve real estate valuation. This work forms ideas for future research, which consist in the preliminary processing of the data set by searching and extracting anomalous values, as well as the practical implementation of the obtained results.

Francesca Salvo, Daniela Tavano

The Market Comparison Approach (MCA) is the most widely utilized method for assessing a property’s market value. This approach entails comparing the property to a selection of similar properties with known sale prices. It operates on the premise that the market assigns a property’s price in a manner akin to how it prices comparable properties. The accuracy of the MCA is closely linked to the quality of the process used for selecting these comparable properties; the closer the similarities and the more reliable the sale prices, the higher the precision of the assessment. The valuer’s objective is to identify the optimal combination of comparable sales that meet the standards of similarity and reliability. This study introduces a systematic procedure for selecting the most suitable comparable sales, employing quantitative metrics to evaluate both property similarity and the transparency and dependability of their sale prices. The methodology leverages the «Ideal Point Method» operational principles to classify and select the most appropriate comparables. This ensures a thorough and replicable process in choosing reference properties for accurate market value assessment. This research underscores the critical significance of selecting comparable properties, as this choice can substantially influence the diversity of valuation outcomes. With proper evaluation criteria, interpreting and justifying these results can prove easier. The findings from case studies indicate that the MCA demonstrates enhanced effectiveness when applied to a carefully curated selection of comparables based on specific criteria, as evidenced by minimal discrepancies between adjusted prices and reduced forecasting error margins.Additionally, the analysis shows a notable decline in the model’s accuracy as the dataset size increases, attributed to the inclusion of less suitable comparable properties in the valuation process.

Gluszak Michal, Trojanek Radoslaw

In recent years, a lot of empirical effort has been made to search for potential nonlinear responses of house prices to various demand and supply factors. This paper examines Poland's heterogeneous regional housing market reactions to key economic variables from 2000 to 2022. The study raises two research questions related to the asymmetric adjustment of housing markets to selected demand and supply shocks.

Weijia Zhang, Jindong Han, Hao Liu et al.

Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. Nonetheless, deep learning is data-hungry, and thus it may not be trivially applicable to enormous small cities with limited data. To this end, we propose Meta-Transfer Learning Powered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance. Specifically, by modeling the ever-growing real estate transactions with associated residential communities as a temporal event heterogeneous graph, we first design an Event-Triggered Temporal Graph Network to model the irregular spatiotemporal correlations between evolving real estate transactions. Besides, we formulate the city-wide real estate appraisal as a multi-task dynamic graph link label prediction problem, where the valuation of each community in a city is regarded as an individual task. A Hypernetwork-Based Multi-Task Learning module is proposed to simultaneously facilitate intra-city knowledge sharing between multiple communities and task-specific parameters generation to accommodate the community-wise real estate price distribution. Furthermore, we propose a Tri-Level Optimization Based Meta- Learning framework to adaptively re-weight training transaction instances from multiple source cities to mitigate negative transfer, and thus improve the cross-city knowledge transfer effectiveness. Finally, extensive experiments based on five real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.

Rebekah Rousi, Hooman Samani, Niko Mäkitalo et al.

Business and technology are intricately connected through logic and design. They are equally sensitive to societal changes and may be devastated by scandal. Cooperative multi-robot systems (MRSs) are on the rise, allowing robots of different types and brands to work together in diverse contexts. Generative artificial intelligence has been a dominant topic in recent artificial intelligence (AI) discussions due to its capacity to mimic humans through the use of natural language and the production of media, including deep fakes. In this article, we focus specifically on the conversational aspects of generative AI, and hence use the term Conversational Generative artificial intelligence (CGI). Like MRSs, CGIs have enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business. From a business perspective, cooperative MRSs alone, with potential conflicts of interest, privacy practices, and safety concerns, require ethical examination. MRSs empowered by CGIs demand multi-dimensional and sophisticated methods to uncover imminent ethical pitfalls. This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model.

Yu Zhao, Haoxiang Gao, Jinghan Cao et al.

Real estate sales contracts contain crucial information for property transactions, but manual data extraction can be time-consuming and error-prone. This paper explores the application of large language models, specifically transformer-based architectures, for automated information extraction from real estate contracts. We discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis. We generated synthetic contracts using the real-world transaction dataset, thereby fine-tuning the large-language model and achieving significant metrics improvements and qualitative improvements in information retrieval and reasoning tasks.

Navid Madani, Anusha Bagalkotkar, Supriya Anand et al.

In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure it can be used without perpetuating discriminatory practices like steering and redlining, which have historically plagued the real estate industry in the United States. Building on prior work, we present a method for generating a synthetic general instruction-following dataset, along with safety data. Through extensive evaluations and benchmarks, we fine-tuned a llama-3-8B-instruct model and demonstrated that we can enhance it's performance significantly to match huge closed-source models like GPT-4o while making it safer and more compliant. We open-source the model, data and code to support further development and research in the community.

Olli Vigren, A. Kadefors, Kent Eriksson

Purpose The purpose of this paper is to increase the knowledge of real estate firms’ capabilities to innovate and, consequently, their capacity to absorb new innovations and benefit from digital technologies in an ecosystem context. Design/methodology/approach The results are based on 32 interviews with representatives of Swedish real estate owners, real estate owner industry associations and suppliers of digital technology to real estate owners. The data are interpreted using theories on absorptive capacity (i.e. the capacity to absorb new innovations), innovation capabilities and innovation ecosystems. Findings The real estate owners, technology suppliers and real estate owner industry associations have expanded their innovation capabilities and reshaped their innovation ecosystem by initiating a number of different digitalization activities; for example, the development of new IT systems, digital platforms, services and business models. The absorptive capacity has been improved as the organizations have changed routines and structures related to innovation, and they have taken on new roles related to digitalization and innovation, making them better able to absorb new innovations. Also, this paper identifies several drivers and obstacles to digitalization in the real estate sector. Research limitations/implications The increased capabilities related to digitalization can lead to better absorptive capacity on an individual firm level, which can contribute to the overall development of these firms in a longer-term. Also, new capabilities may lead to better absorptive capacity in the real estate sector at large, as firms may benefit from each other’s capabilities through collaboration. The limitations are that this study does not interview tenants or facility management firms and that the findings represent the context of the Swedish real estate market. Originality/value This paper investigates innovation capabilities, absorptive capacity and innovation ecosystems of real estate owners, their technology suppliers and real estate owner industry associations on the organizational level and on the sector level, into which there is little previous research. Also, this paper highlights the novelty of digitalization as a phenomenon in the sector.

Luybov V. Gajkova, Oleg E. Izotov

Рассматривается управление жилой недвижимостью и прилегающей территории на основе системы управления инцидентами управляющей компании, как одного из способов регулирования функций эксплуатации, технического и санитарного содержания многоквартирных домов в условиях рыночной экономики. The management of residential real estate and adjacent territory on the basis of the incident management system of the management company as one of the ways to regulate the functions of operation, technical and sanitary maintenance of apartment buildings in a market economy is considered. Purpose: to identify the problems that determine the process of working with consumer appeals to the management company, in order to work out the methodological foundations for building an incident management system in the management company.Methodology: the dialectical method is used as a general scientific method of cognition; techniques and tools of system, comparative analysis and generalization. Results: analyzed the business process, identified business problems relating to the current process of working with customer complaints to the management company, built a strategic map of the management company. Scope of results: heads of management companies when addressing issues of determining the optimal relationship with consumers to reduce the risk of poor-quality work.

Roman Evgenyevich Abdalov, Kirill Yurevich Kulakov

The scientific paper is devoted to the issues of pre-project marketing research and its impact on the quality of investment and construction project (ICP) and the quality of finished construction products. In modern conditions of economic crisis and increased competition of goods and services marketing plays a special role in all spheres of activity, including the construction industry. The authors note that not all organizations in their practical activities pay due attention to pre-project marketing research. In order to develop the methods of pre-project marketing research already used in practice by marketing departments and to form new unique approaches in organizations, the authors have developed a conceptual model of pre-project marketing research. The author's conceptual model is aimed at improving the effectiveness of pre-project marketing research — obtaining information that meets the established requirements. The article presents these requirements, in particular, the requirement for completeness of information is considered in the most detail, negative consequences of information insufficiency and excess for the project and its participants are outlined, methods of ensuring completeness of information are proposed. Attention is paid to the high value of marketing information obtained at the pre-project stage of a project for decision-making at subsequent stages of ICP and for building a marketing strategy within an organization's project activities. Based on the analysis, the authors provide a logical justification for the positive impact of pre-project marketing research on the quality of individual processes and activities within the life cycle of ICP and, ultimately, on the consumer and production quality of finished construction products.

Sumin Han, Youngjun Park, Sonia Sabir et al.

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.

Mahdieh Yazdani, Maziar Raissi

The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.

Daniel Greene

Who owns the internet? It depends where you look. The physical assets at the core of the internet, the warehouses that store the cloud’s data and interlink global networks, are owned not by technology firms like Google and Facebook but by commercial real estate barons who compete with malls and property storage empires. Granted an empire by the US at the moment of the internet’s commercialization, these internet landlords shaped how the network of networks that we call the internet physically connects, and how personal and business data is stored and transmitted. Under their governance, internet exchanges, colocation facilities, and data centers take on a double life as financialized real estate assets that circle the globe even as their servers and cables are firmly rooted in place. The history of internet landlords forces a fundamental reconsideration of the business model at the base of the internet. This history makes clear that the internet was never an exogenous shock to capitalist social relations, but rather a touchstone example of an economic system increasingly ruled by asset owners like landlords.

A. Małkowska, Małgorzata Uhruska

Research background: Based on the literature, several ways of assessing the conduct of business and a number of factors influencing the growth and development of the companies can be identified. However, the diversity of business entities and their business environment raises the importance of considering the unique nature of the industry in the selection of performance measures. Our research focuses on real estate valuation firms that provide information and consulting services to real estate markets. Purpose of the article: As professional practice shows, there are different business models for property valuation. These businesses differ in their organisational and legal form and the type of valuations performed, the type of client served, or the scope of services provided. The main purpose of the research is to identify factors that significantly affect the development odds of valuation companies in Poland, especially the growth of income. Methods: The study was based on data collected from the survey of Polish real estate valuers. The analysis was conducted on a sample of 277 professionals who own valuation companies and were certified no later than the end of 2014. A quantitative analysis using a logistic regression model was conducted to identify the factors that influence the prospects for valuation business growth. Findings & value added: The research confirms the relationship between the way of conducting real estate valuation activities and its development chances. The most important factors are a spatial and substantive range of services, cooperation and employment, and clients' profile. Demographic characteristics were also found to be significant. Although the results presented here are based on data from the real estate valuation industry, their relevance is much broader. The findings provide a better understanding of the factors that influence the performance and success of SMEs, particularly in the information and consulting industry.