Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors

Basse, Tobias; Desmyter, Steven; Saft, Danilo; Wegener, Christoph

doi:10.1016/j.irfa.2023.102765

Cited by 6 publications

(3 citation statements)

References 90 publications

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“…The study by Basse et al (2020) utilizes the concept of transfer entropy to examine the relationship between the US National Association of Home Builders Index and the S&P CoreLogic Case-Shiller 20-City Composite Home Price Index. The empirical evidence suggests that the survey data can contribute to predicting US house prices [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers

De Paola

2024

Real Estate

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Accuracy in property valuations is a fundamental element in the real estate market for making informed decisions and developing effective investment strategies. The complex dynamics of real estate markets, coupled with the high differentiation of properties, scarcity, and opaqueness of real estate data, underscore the importance of adopting advanced approaches to obtain accurate valuations, especially with small property samples. The objective of this study is to explore the applicability of the Maximum Entropy Principle to real estate valuations with the support of Lagrange multipliers, emphasizing how this methodology can significantly enhance valuation precision, particularly with a small real estate sample. The excellent results obtained suggest that the Maximum Entropy Principle with Lagrange multipliers can be successfully employed for real estate valuations. In the case study, the average prediction error for sales prices ranged from 5.12% to 6.91%, indicating a very high potential for its application in real estate valuations. Compared to other established methodologies, the Maximum Entropy Principle with Lagrange multipliers aims to be a valid alternative with superior advantages.

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Section: Literature Reviewmentioning

confidence: 99%

Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers

De Paola

2024

Real Estate

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“…On the other hand, some studies have suggested that a decrease in firm value may lead to lower ESG scores. According to [44], firms with low market value are less likely to disclose ESG information. This result supports the negative impact of firm value on ESG score.…”

Section: Firm's Esg Disclosure and Market Valuementioning

confidence: 99%

Analyzing the Dynamic Relationship Between ESG Scores and Firm Value in Chinese Listed Companies: Insights from Cross-Lagged Analysis

Wedajo,

Bhat,

Khan

et al. 2023

Preprint

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The relationship between a company's Environment, Social and Governance (ESG) scores and market value dynamics has been the focus of extensive research. Our study aimed to provide insights into this relationship and its implications for Chinese investors. We used a general Cross-lagged panel model to analyze data from 652 Chinese-listed companies from 2013 to 2019. Our findings indicate that ESG scores have a long-term impact on market value, with a consistently positive correlation between the two. We also discovered that Chinese investors consider ESG factors when evaluating a company's financial health. Companies that prioritize ESG factors are more likely to attract investment. Moreover, the diffusion of ESG information happens slowly, and past ESG performance influences future ESG performance. Thus, maintaining good ESG performance is crucial for long-term sustainability and success. Overall, our study highlights the importance of considering ESG factors when evaluating financial health and making investment decisions, providing valuable insights for firms and investors alike.

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“…From the environmental perspective, the most visible ways of the implementation of a solution involve, amongst others, green building certification (e.g., LEED-Leadership in Energy and Environment Design), energy efficient technologies (e.g., HVAC systems), or sustainable design application [6]. Social aspects that answer the EGS principles can be perceived in community engagement initiatives, a so-called "affordable housing" program or social impact investing [7], while the governance ones usually include a so-called "corporate governance", risk management, or diversity and inclusion issues [8].…”

Section: Introductionmentioning

confidence: 99%

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

Walacik,

Chmielewska

2024

Sustainability

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As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.

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Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors

Cited by 6 publications

References 90 publications

Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers

Real Estate Valuations with Small Dataset: A Novel Method Based on the Maximum Entropy Principle and Lagrange Multipliers

Analyzing the Dynamic Relationship Between ESG Scores and Firm Value in Chinese Listed Companies: Insights from Cross-Lagged Analysis

Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

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