Search results

This paper aims to provide a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and Large Language Models (LLMs), such as GPT-4, on the real estate industry. It explores how these technologies are reshaping various aspects of the sector, from market analysis and valuation to customer interactions and evaluates the balance between technological efficiency and the preservation of human elements in business.

The study is based on an analysis of the strengths and weaknesses of AI as a technology in applications for real estate. It uses this framework to assess the potential of this technology in different use cases. This is supplemented by an emerging literature on the topic, practical insights and industry expert opinions to provide a balanced perspective on the subject.

The paper reveals that AI and LLMs offer significant benefits in real estate, including enhanced data-driven decision-making, predictive analytics and operational efficiency. However, it also uncovers critical challenges, such as potential biases in AI algorithms and the risk of depersonalising customer interactions.

The paper advocates for a balanced approach to adopting AI, emphasising the importance of understanding its strengths and limitations while ensuring ethical usage in the diverse and complex landscape of real estate.

This work stands out for its balanced examination of both the advantages and limitations of AI in real estate. It introduces the novel concept of the “jagged technological frontier” in real estate, providing a unique framework for understanding the interplay between AI and human expertise in the industry.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The purpose of this briefing is to highlight the critical importance of negotiation skills in the everyday lives of real estate professionals. It delves into how negotiators must improve their negotiations skills given the negotiation-intensive nature of real estate. It also helps to handle common pitfalls and challenges in negotiations, particularly in the increasingly volatile, uncertain, complex and ambiguous (VUCA) reality of the real estate industry. The briefing offers strategic insights for preparation and negotiation aimed at improving any real estate negotiator’s average performance.

The expert opinion piece combines a literature review on negotiation strategies with practical insights. It addresses the observed under appreciation of negotiation theory and skill, reflecting on real-world real estate negotiations. The goal is to enhance the use and recognition of negotiation theory in the real estate industry. The approach merges theoretical analysis with practical application, offering actionable recommendations to improve negotiation outcomes.

The negotiation-intensive real estate industry and the transformative impact of VUCA challenges on real estate professionals’ ability to adapt and continuously negotiate successful deals clashes with many real estate’s professional or fixed mind-set over negotiation historically being an art or a talent and mostly being stuck with win-lose strategies. Instead, negotiation is a science that can be learned and deliberately improved to counter stress-induced or fear-based responses that lead negotiators toward suboptimal negotiation strategies, such as win-lose or naive win-win. However, these dynamics are preventable. Well-equipped and well-prepared value win-win negotiators can adopt a growth mind-set, study modern negotiation advice and frameworks to thrive in the negotiation-rich real estate industry and convert even VUCA challenges into an amazing source of value.

Real estate professionals can become more aware of which and how current obstacles and poor choices negatively contribute to their negotiation performance. It contrasts win-lose and win-win strategic frameworks to enable real estate professionals to become more sophisticated when choosing their negotiation strategies. The briefing also helps real estate professionals expand their negotiation repertoire towards improved strategic flexibility when managing the evolving real estate profession reality and challenges.

The originality and value of the briefing lie in its comprehensive approach to addressing the negotiation challenges faced by real estate professionals. It offers a holistic view of real estate negotiation, advocating for a paradigm shift from traditional win-lose tactics to a collaborative, value win-win approach. The briefing integrates modern negotiation theory and emphasises ethical practices, providing practical strategies and best practices for professionals to improve their skills and adapt to industry changes. By empowering real estate professionals with knowledge and tools to navigate negotiations effectively, the briefing contributes to the overall success and professionalism of the industry.

Sustainability has become an integral part of the real estate industry, alongside advancing globalization and demographic development. Due to real estate's influence on greenhouse gas emissions throughout its life cycle, both the regulatory and legal requirements concerning the sustainability of real estate are growing and, as a result of social responsibility, the interest of tenants and investors in sustainable real estate. However, criteria for measuring the ecological sustainability of a real estate investment in the purchase process in order to reduce the risk of including “stranded assets” in the portfolio are missing. This paper aims to address the need to integrate the issue of carbon stranding into existing sustainability rating tools.

Existing tools are examined based on defined criteria to determine whether they are suitable for purchasing a property before suitable tools for purchase are compared. Strengths and weaknesses are identified, which are to be remedied with the scoring tool. Taxonomy regulation is integrated into the existing valuation basis as a legal regulation.

The result is a scoring tool that enables real estate companies to measure and evaluate the ecological sustainability performance of a property during the acquisition process, taking into account the three aspects of sustainability and considering them when determining an appropriate purchase price in line with market conditions. Moreover, the developed tool helps to minimize the risk of acquiring a stranding asset.

The environmental, social and governance (ESG) framework employed in this study does not incorporate governance considerations. While the analysis extensively evaluates the building's environmental and social aspects, it does not extend to examining the governance practices of the companies involved. Thus, the assessment is confined solely to the physical attributes of the property without accounting for broader corporate governance factors.

The developed scoring tool represents a valuable tool for the real estate industry, offering insights into sustainability performance during property acquisitions and providing a structured framework for decision-making. By addressing both certification and taxonomy regulation requirements, the tool contributes to the industry's evolution toward more sustainable and environmentally responsible real estate practices.

In response to the growing importance of sustainability in the real estate industry, this paper introduces a novel scoring tool for evaluating the sustainability of real estate investments during the acquisition process.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

ESG (Environment, Social, Governance) has taken on increased importance in recent years for all stakeholders, with the S dimension now taking on a stronger focus in the real estate space. This paper proposes a new metric to be used in the S space to assess improvements in aspects such as gender equality and cultural diversity in real estate. It adds to the S metrics currently available to see the more effective delivery of the S dimension into real estate investment decision-making.

A new S metric in ESG is proposed and validated. Using this metric, examples regarding gender equality and cultural diversity are assessed among leading real estate players in Australia. This S metric is assessed over a number of time periods to demonstrate the improvements in gender equality and cultural diversity in these major real estate players.

This new S metric is seen to be highly effective and robust in capturing the changes in various aspects of the S dimension in ESG in the real estate space today; particularly concerning gender equality and cultural diversity. It is clearly able to demonstrate the significant changes in increased participation of women at the more senior leadership levels by leading players in the real estate space.

With ESG becoming a critical issue in the real estate sector, issues involved in the S space will take on increased significance going forward. This is critical, as the elements of the S dimension such as gender equality and cultural diversity are important aspects for an effectively functioning real estate industry. The S metric developed in this paper can be used for benchmarking purposes over time, as well as between real estate players, between sub-sections within a real estate organisation, and comparing against other industry sectors. It is also relevant in all organisations, and is not just limited to the real estate sector. Additional metrics in the S space are an important development to further empirically assess the effective delivery of the S dimension of ESG in the real estate sector and more broadly.

This paper specifically proposes this new S metric in ESG in the real estate industry. This is a key issue for the real estate industry going forward at all levels, as it will facilitate a more diverse real estate industry and more effective real estate investment decision-making. This S metric is applicable in all organisational sectors where the S dimension of ESG is important.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.

Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.

The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.

To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.