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To enhance the appeal of residential real estate listings and captivate online customers, clean and visually convincing indoor scenes are highly desirable. In this research, we introduce an innovative image inpainting model designed to seamlessly replace undesirable elements within images of indoor residential spaces with realistic and coherent alternatives. While Generative Adversarial Networks (GANs) have demonstrated remarkable potential for removing unwanted objects, they can be resource-intensive and face difficulties in consistently producing high-quality outcomes, particularly when unwanted objects are scattered throughout the images. To empower small- and medium-sized businesses with a competitive edge, we present a novel GAN model that is resource-efficient and requires minimal training time using arbitrary mask generation and a novel half-perceptual loss function. Our GAN model achieves compelling results in removing unwanted elements from indoor scenes, demonstrating the capability to train within a single day using a single GPU, all while minimizing the need for extensive post-processing. [ABSTRACT FROM AUTHOR]

The authors evaluate listed and unlisted real estate asset classes from the perspective of an investor holding a diversified equity and fixed income portfolio. They analyze the exposures of returns to fundamental drivers of equity and fixed income returns: expected cash flows, inflation, real interest rates, and risk premiums. Listed and unlisted real estate share similar exposures to drivers, which is not picked up by traditional spanning regression approaches. They find that both segments of real estate hedge inflation risk, in contrast to broad equities and nominal bonds. In addition, listed real estate has a higher exposure to transitory risk premium shocks. These findings help explain the higher correlations between listed and unlisted real estate at longer horizons, and lower long-horizon correlations between real estate and the broad equity market. [ABSTRACT FROM AUTHOR]

In many areas of life, uncertainties exist, and decisions under these uncertainties require mathematical data. This study evaluates the investment suitability of real estate companies listed on Borsa Istanbul (BIST) by integrating neutrosophic sets with AHP and TOPSIS methods. The aim is to help investors minimize risks and make optimal decisions in highly uncertain environments. The study defines neutrosophic sets and presents an integrated neutrosophic AHP and TOPSIS model. The past performance of BIST-listed real estate companies was analyzed for investment suitability. Three stock market analysts indicated the importance of various criteria and expressed company statuses using verbal terms, which were converted into neutrosophic scores. Criteria importance weights were calculated with neutrosophic AHP, and company statuses were evaluated with neutrosophic TOPSIS, resulting in a ranking of companies. The study's uniqueness lies in significantly accounting for uncertainty to minimize risks, allowing decision-makers to use unrestricted verbal expressions for uncertainty. This method considers external factors crucial to the stock market, offering a new approach to minimize risks from uncertainties. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this research paper is to analyze the connection between ESG performance and financial performance within the real estate sector. By focusing on ESG ratings and pillar scores as proxies for ESG performance, the study investigates how these factors impact both profitability and market indicators. Design/methodology/approach: With data sourced from over 680 publicly listed real estate companies, the research employs a fixed effects regression model to analyze the findings. By utilizing this method, the study can assess the impact of governance, environmental and social factors on both the accounting and market performance of real estate companies. Findings: The outcomes of this study underscore a link between sustainability, particularly environmental aspects and financial performance. However, the study also reveals a contrasting result: governance factors are associated with adverse financial outcomes. Nevertheless, it is important to highlight the limitations as the results present a mixed picture with limited significant findings. Practical implications: Companies should prioritize improvements in environment to boost profitability, while they should carefully consider the costs and benefits associated with enhancing their governance structure. Originality/value: By focusing on this industry and adopting a global perspective, the study addresses a gap in the literature. The research's innovative approach to utilizing ESG ratings and pillar scores as proxies for ESG performance enhances its originality. Furthermore, the research's identification of the differing impacts of environmental and governance factors on financial outcomes add novel perspectives to the discourse. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Palynology of the uppermost Cretaceous to lowermost Paleocene Deccan volcanic associated sediments of the Mandla Lobe, Central India." The authors of the article have made revisions to the plate images that were originally published. The purpose of this notice is to provide accurate and updated information to readers. The document includes a list of slide numbers and abbreviations for various pollen samples, each representing different types of pollen. The revised images include a range of pollen types such as Malvacipolloides intertrappea, Mulleripollis bolpurensis, Paleosantalaceaepites miocenicus, and many others. [Extracted from the article]

We rely on novel textual analysis of real estate listings and identify renovated dwellings in a dataset of Norwegian transactions to estimate the renovation premium in an urban housing market. The renovation premium is estimated in a hedonic framework by classical regression approaches and a random forest algorithm. The strength of the latter is that it allows for a more complex interplay between the renovation premium and explanatory variables. We estimate a significant positive renovation premium of 5–7 percent for renovated dwellings and a negative premium of 9–10 percent for unmaintained/neglected dwellings. These averages mask significant variations in these premiums over time, particularly, a counter-cyclical effect. Omitting renovation information also has implications for estimated short-term house price growth. Unmaintained dwellings tend to transact more in the fourth quarter, indicating that parts of the seasonal price variation reported in the literature are due to compositional variation with respect to renovation. This composition effect bias price movement estimates downward, if uncontrolled for, as unmaintained dwellings transact at significantly lower prices. [ABSTRACT FROM AUTHOR]

With the ongoing advancement of big data technology and the increasing uncertainty in the economic environment, enterprises are encountering heightened financial risks. Consequently, timely and accurate financial risk warnings have become a critical focus for both managers and investors. This article examines the financial risk warning mechanisms for real estate listed companies, utilizing the BP neural network model to assess and analyze these risks. The research involves several key steps: First, financial data from real estate listed companies is collected and organized. Second, drawing on financial management theories, factor analysis identifies six critical indicators--development capability, debt repayment ability, profitability, cash flow capability, operational efficiency, and stability--as determinants of corporate financial risk. Subsequently, the BP neural network model is employed to iterate and analyze the financial risks of these companies, ultimately providing early warning signals. The model's prediction accuracy reaches 85.31%, demonstrating its effectiveness in early financial risk detection. In summary, within the realm of modern big data technology, the BP neural network model offers significant advantages for financial risk warning in enterprises, providing valuable theoretical insights and practical guidance for similar organizations. [ABSTRACT FROM AUTHOR]

Tax aggressiveness is an activity or reconstruction effort carried out with the aim of maximizing contributions to the company through tax management strategies. This study aims to analyze the effect of capital intensity, inventory intensity, profitability, and leverage on tax aggressiveness. The population studied was property and real estate companies listed on the Indonesia Stock Exchange from 2019 to 2022. Sampling using a purposive sampling method. The number of samples as many as 61 companies. Data collection techniques through documentation techniques using secondary data sources. Data analysis using multiple linear regression using statistical Application Program tools: Statistical Product and Service Solutions (SPSS). The results of this study showed that capital intensity, and profitability affect tax aggressiveness, while inventory intensity, and leverage did not affect tax aggressiveness. [ABSTRACT FROM AUTHOR]

With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market. [ABSTRACT FROM AUTHOR]

Houses, land, and property are vital human requirements, with increasing demands year after year. This is mostly due to Indonesia's favorable population growth and economic development, which leads the demand for land and property to be even higher. However, this industry also experienced difficulties during the COVID-19 pandemic, which affected macroeconomic conditions. Therefore, this study aims to reinvestigate the role of internal and external factors on the capital structure and firm value of property and real estate firms listed on the IDX for the 2017-2021 period. In this study, the partial least squares structural equation modeling was used for the data analysis, whereas purposive sampling was used for company selection. The results showed that internal factors had a significant effect on capital structure and firm value, whereas external factors did not significantly impact capital structure and firm value. Lastly, capital structure is also proven to have no significant effect on firm value. These findings validate the signaling theory, where internal factors managed by the firm might produce a positive signal in the form of the firm's future prospects so that it can become essential information for stakeholders. This study contributes to helping policymakers with various considerations and sets realistic expectations about the role of internal and external factors on the capital structure and firm values. [ABSTRACT FROM AUTHOR]

This research aims to empirically test the relationship between tax avoidance, related party transactions and intellectual capital on dividend policy. Furthermore, this research also tests managerial ownership in moderating tax avoidance, related party transactions and intellectual capital on dividend policy. This type of research is a type of quantitative research using secondary data. The population in this study are real estate companies listed on the Indonesian Stock Exchange (BEI) during 2018-2022. The sampling technique in this research used a purposive sampling technique. Analysis of research data uses moderate regression analysis. The results of this research show that (1) tax avoidance influences dividend policy, (2) related party transactions have a positive influence on dividend policy, (3) intellectual capital influences dividend policy, (4) managerial ownership weakens the relationship between tax avoidance and policy. dividends, (5) Good corporate governance can moderate and strengthen the influence of related party transactions on cash dividends, (6) moderate managerial ownership and strengthen intellectual capital on dividend policy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has disrupted economies and industries worldwide, including the real estate sector. This study aims to assess the effects of the pandemic on commercial real estate prices in the Malaysian market. By examining variations in property types and considering key factors influencing pricing, the research contributes to a better understanding of the pandemic's impact on the real estate market. To analyze the effects of the COVID-19 pandemic on commercial real estate prices, a mixed-method approach was employed. The study combines data from direct real estate indices, which provide insights into property prices based on transaction data, and listed real estate, which includes publicly traded real estate investment trusts (REITs). By utilizing both sources, a comprehensive analysis of the market is achieved. The sample for this study consists of commercial real estate properties in the Malaysian market. It includes properties from various sectors, such as retail, hospitality, and office buildings. The sample is representative of the overall market and captures the different property types affected by the pandemic. The analysis begins by comparing direct real estate indices to highlight the limitations and potential biases associated with using these indices. It then examines the variations in commercial real estate prices during the COVID-19 outbreak, focusing on the different property types. Statistical techniques, such as regression analysis and trend analysis, are employed to identify patterns and quantify the impact on commercial real estate prices. The study's main findings reveal that the retail and hospitality sectors experienced the most significant impact on commercial real estate prices during the COVID-19 pandemic. These sectors witnessed a substantial decline in property values due to restrictions, lockdown measures, and reduced consumer demand. Office buildings, although moderately affected, also experienced some decline in prices. This research contributes to the existing literature on the effects of the COVID-19 pandemic on commercial real estate prices, specifically in the Malaysian market. By combining data from direct and listed real estate sources, the study provides a comprehensive understanding of the variations in property prices across different sectors. The findings offer valuable insights for real estate investors, policymakers, and industry professionals in adapting to the changing market conditions and making informed decisions regarding commercial real estate investments. In conclusion, this article sheds light on the effects of the COVID-19 pandemic on commercial real estate prices in the Malaysian market. The research methodology, which combines data from direct and listed real estate, allows for a comprehensive analysis of property variations among different sectors. The findings emphasize the significant impact on the retail and hospitality sectors, while showing office buildings to have been moderately affected. It also provides important insights for stakeholders in the real estate industry, enabling them to make informed decisions and develop appropriate strategies. [ABSTRACT FROM AUTHOR]

This study aims to analyze and determine the effect of profitability as measured by return on assets, solvency as measured by debt to equity ratio to company value measured by price book value. This research uses quantitative methods with secondary data sources in the form of the company's annual financial statements. The population of this study is property and real estate companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2022 period. The sampling technique used purposive sampling, and a sample of 37 companies was obtained. The data analysis technique uses multiple linear regression with the SPSS 25 program. The results showed that the return on assets and debt-to-equity ratio had no effect on the value of the company. [ABSTRACT FROM AUTHOR]

Although extensive research has examined the credit risk of real estate enterprises, the relationship between the political connection of real estate enterprises and these enterprises' credit risk has not been formally studied. Using the panel data of 123 real estate listed companies in the Chinese stock market from 2008 to 2021, this paper finds a significant positive correlation between the political connection of private real estate listed companies and their credit risk. This phenomenon is attributed to the excessive debt that benefits from political connections since it may raise the credit risk of any real estate firm. Interestingly, considering that 2013 is the first year of China's Internet finance era, we find that the popularity of Internet finance and other decentralized lending financing channels may enhance the impact of political connections on real estate credit risk. Our findings provide new micro evidence for the influencing factors and mechanism of credit risk of real estate enterprises during the recent "credit crisis" in the real estate market in China. [ABSTRACT FROM AUTHOR]

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Purpose: The aim of this study is to analyze the influence mechanism of real estate enterprises' status on debt default risk and explore the heterogeneity effect of the characteristics of enterprises. Design/methodology/approach: Against the background of the "three red lines" regulation of the financing of real estate enterprises and the COVID-19 pandemic, the authors select 123 real estate enterprises listed on China's Shanghai and Shenzhen A-shares markets from the first quarter of 2021 to the second quarter of 2022 as a research sample. The social network analysis method and Z-score financial risk early warning model are used to measure real estate enterprises' status and debt default risk. The authors construct a panel regression model to analyze how the status of real estate enterprises influences their debt default risk. Findings: The results show that the status of real estate enterprises negatively and significantly affects their debt default risk. Economic policy uncertainty and financing constraints play negative moderating and mediating roles, respectively. Further research has found that the effect of real estate enterprises' status on debt default risk is characterized by heterogeneity in equity characteristics, i.e. it is significant in the sample of nonstate-owned enterprises but not in the sample of state-owned enterprises. Practical implications: It is helpful for real estate enterprises to attach importance to the value of social networks, and the authors provide policy suggestions for real estate enterprises to constantly improve their risk management systems. Originality/value: Using economic policy uncertainty as the moderating variable and financing constraints as the mediating variable, the authors analyze how the status of real estate enterprises influences debt default risk, which contributes to a better understanding of the formation of the debt default risk of real estate enterprises. [ABSTRACT FROM AUTHOR]

Financial distress is a critical phenomenon in a company that has significant implications for the business itself, employees, investors, and creditors, and can also impact a country's economy. Predicting a company's financial distress, including property and real estate companies, becomes one crucial thing to be studied. Support Vector Machine (SVM) is said to be one of the most powerful model for prediction among other methods. However, it is difficult to determine the parameters of the SVM model. Thus, the SVM parameters must be improved for higher accuracy. This research aims to increase the performance of SVM in financial distress prediction of property and real estate companies. Particle Swarm Optimization (PSO) is used to optimize the previous model. The PSO approach takes cues from how a group of insects or birds interacts to maintain life. The PSO algorithm uses a population of random particles as points initialized in a D-dimensional search space. Each particle modifies its direction using the best experience it discovers (p-best) and the best value discovered by all other members (g-best) to arrive at the ideal outcome. As a result, throughout the search process, particles will move through multidimensional space to more advantageous locations. This research showed that SVM produced the greatest accuracy at 80.47% while when the PSO method was implemented in the SVM model, the accuracy increased to 83.16%. It can be concluded that the PSO method successfully optimized the parameters and increased the accuracy of SVM in the financial distress prediction for property and real estate companies listed on the Indonesian Stock Exchange. [ABSTRACT FROM AUTHOR]

Purpose: In periods of economic distress, expectations for businesses change and there is a heightened need for reporting quality. This study investigates the impact of crises on earnings management in the real estate sector. Design/methodology/approach: The data consisted of financial statements from 2005 to 2021 from real estate firms listed on 10 European stock exchanges. Estimated discretionary accruals from four standard accruals models were used as a proxy for earnings management, using cross-sectional industry and firm fixed effects models. The authors examined earnings management during three crises: the financial crisis (2008–2009), the debt crisis (2011–2012) and the COVID-19 pandemic (2020–2021). Findings: The results showed less earnings management during the COVID-19 crisis and more earnings management during the financial crisis, though with slightly weaker evidence. The authors did not find significant evidence of earnings management related to the debt crisis. These results suggest that stakeholders in the real estate sector should be extra vigilant in crisis periods. Originality/value: This study is the first to investigate earnings management in European real estate firms, focusing on the impact of crises. [ABSTRACT FROM AUTHOR]

The article discusses a recent decision by the Administrative Appeals Tribunal (AAT) regarding an individual taxpayer's deductible loss on the purchase and sale of a residential property. Topics include the AAT's analysis of Mrs. Bowerman's motivations, the application of Myer Emporium principles, and considerations related to the income year for claiming the deduction.

This study aims to provide the users with formulas for petrol station value estimation activities in Turkey. Firstly, 432 advertisements were used to estimate the asking price of the real estate listings dataset. Secondly, data obtained from 239 valuation reports were used to estimate value. The data were analysed with regression analysis, the variables were found to explain 68.7% of the asking price in the advertisement data and 90.7% of the value in the valuation reports. The results indicated that users could estimate the value of a petrol station by using some of the advertisement data or valuation reports in the estimation. This study can help valuers, especially in cases where access to the actual sales data is required for the market approach, the data needed for the cost approach, and the time needed for value estimation are limited. Our findings indicate that the share of ancillary revenues in the total revenues of petrol stations increased. In Turkey, the revenues of petrol stations, and the revenues from sales of petroleum products increased by 189%, and ancillary revenues by 326% from 2016 to 2021. Moreover, the value of petrol stations belong to one of the five major dealers increase by 24.4%. [ABSTRACT FROM AUTHOR]