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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.

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.

Information was obtained in interviews with Richard Nagel in Winter/Spring 2022. This information was supplemented by material from secondary sources. The only information that was disguised were the real names for Bob Crater, Tim Landy, Jane Tolley and Mary Nagel.

The case was classroom tested in Summer 2022. The responses from students helped to shape the writing of the case.

Richard Nagel, the owner of the RE/MAX Elite real estate agency in Monmouth Beach, New Jersey, has just learned that one of his agents, Tim Landy, quit and left the industry. Tim was a young real estate agent and Richard had spent considerable time training him. Tim was motivated and he worked hard to prospect for business, but he showed that he was experiencing difficulty closing on his sales. Richard decided to recommend that Tim work with another agent, Bob Crater, as Bob was an experienced salesman but was not doing the up-front prospecting that Tim was doing. Richard suggested two different strategies to the two agents – a pairing up arrangement and peer-to-peer learning. The outcome that Richard envisioned was that both of the struggling salesmen would benefit from either of these strategies, but Bob refused to collaborate.

Tim’s quitting was characteristic of an ongoing problem with employee retention that Richard had been experiencing as a manager in recent years. This problem caused Richard to think about how he recruited his real estate agents, how he developed them through coaching and how he motivated them so that they would stay happy in their job and not leave. He recognized the importance of thoroughly examining his retention strategy within the next 12 months so that he could better manage the problem and strengthen the productivity of his real estate agency.

The case is intended for an undergraduate course in human resources management, as it deals directly with recruiting, coaching and retaining employees.

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.

The study aims to assess the effectiveness of prevailing methods for quantifying physical climate risks. Its goal is to evaluate their utility in guiding financial decision-making within the real estate industry. Whilst climate risk has become a pivotal consideration in transaction and regulatory compliance, the existing tools for risk quantification frequently encounter criticism for their perceived lack of transparency and comparability.

We utilise a sequential exploratory mixed-methods analysis to integrate qualitative aspects of underlying tool characteristics with quantitative result divergence. In our qualitative analysis, we conduct interviews with companies providing risk quantification tools. We task these providers with quantifying the physical risk of a fictive pan-European real estate portfolio. Our approach involves an in-depth comparative analysis, hypothesis tests and regression to discern patterns in the variability of the results.

We observe significant variations in the quantification of physical risk for the pan-European portfolio, indicating limited utility for decision-making. The results highlight that variability is influenced by both the location of assets and the hazard. Identified reasons for discrepancies include differences in regional databases and models, variations in downscaling and corresponding scope, disparities in the definition of scores and systematic uncertainties.

The study assists market participants in comprehending both the quantification process and the implications associated with using tools for financial decision-making.

To our knowledge, this study presents the initial robust empirical evidence of variability in quantification outputs for physical risk within the real estate industry, coupled with an exploration of their underlying reasons.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

This paper aims to empirically investigate the effect of facility–maintenance service quality on tenants’ satisfaction and their subsequent willingness to pay higher rent in the National Capital Region (NCR), India.

The data for this study was collected from 1,692 tenants in NCR, India. SmartPLS4.0 was used to analyze the data using structured equation modeling.

The study findings indicate that all parameters of facility–maintenance service quality (tangibles, service personnel quality and empathy) positively impact tenants’ satisfaction. Further, satisfied tenants are willing to pay higher rentals. In addition, customer satisfaction partially mediates the relationship between facility–maintenance service quality and willingness to pay higher rent.

The study extends evidence-based research in the service industry to provide empirical evidence that facility–maintenance service quality positively impacts customer satisfaction in real estate settings in emerging markets (India). This research will guide future researchers to explore other dimensions to support evidence-based research in real estate settings.

Based on the data collected online after personal interaction in residents’ meetings, the study findings provide significant insights for stakeholders such as policymakers, practitioners, landlords, associations and builders. With rising housing demand because of rural migrations toward urban or metro locations coupled with the government’s inability to expand the infrastructure simultaneously, the government has enhanced the role of public–private partnership (PPP) in housing development. The findings will help policymakers incorporate the service angle into key performance indicators in PPP contracts. Additionally, with rising competition in the housing sector, understanding these factors will help landlords and resident associations improve service quality standards, thus enhancing the residential societies’ word-of-mouth publicity and attracting high-paying residents.

To the best of author’s knowledge, this is a pioneer study to empirically investigate the impact of facility–maintenance service quality standards on tenants’ satisfaction and willingness to pay higher rent in a residential setting in India.

This study attempts to find the response of the real estate market to economic changes by identifying cause-effect relationships between mortgage, residential investment, and Saudi employment.

A quantitative approach to analytically examine the relationship among the variables. To find out the impact of investment, mortgage and Saudi employment on the Saudi real estate growth from 1970 to 2019. All data sets were obtained from the General Authority for Statistics (GAST), Saudi Central Bank (SAMA) and World Bank Group.

This study reveals a positive relationship between the mortgage and GDP in the Saudi Arabian real estate market. The same results for employment and investment; both have a positive effect on the GDP of the real estate market.

Analyzing the impact of real estate financing on various industries and the extent to which it is related to employment and unemployment rates is essential for future research. Moreover, this research can be applied to different countries and compared based on similarities and differences in implementing mortgage-related policies.

The government must encourage investment in various ways and establish a stable structure that ensures market stability and finds a balance between supply and demand.

This study reflects the importance of real estate financing not only to individuals and governments but also to investors and business workers, and it is essential to analyze the impact of real estate financing on various industries, as well as the extent to which it is related to employment and unemployment rates. This research can be applied to different countries and compared based on similarities and differences in the implementation of mortgage-related policies.

This study contributes to testing this study’s hypothesis: that mortgage positively impacts the real estate market of Saudi Arabia.

Many studies have analysed the impact of various variables on the ability of companies to raise capital. While most of these studies are sector-agnostic, literature on the effects of macroeconomic variables on sectors that established over the last 20 years like property technology and financial technology, is scarce. This study aims to identify macroeconomic factors that influence the ability of both sectors and is extended by real estate variables.

The impact of macroeconomic and real estate related factors is analysed using multiple linear regression and quantile regression. The sample covers 338 observations for PropTech and 595 for FinTech across 18 European countries and 5 deal types between 2000–2001 with each observation representing the capital invested per year for each deal type and country.

Besides confirming a significant impact of macroeconomic variables on the amount of capital invested, this study finds that additionally the real estate transaction volume positively impacts PropTech while the real estate yield-bond-gap negatively impacts FinTech.

For PropTech and FinTech companies and their investors it is critical to understand the dynamic with mac-ro variables and also the real estate industry. The direct connection identified in this paper is critical for a holistic understanding of the effects of measurable real estate variables on capital investments into both sectors.

The analysis fills the gap in the literature between variables affecting investment into firms and effects of the real estate industry on the investment activity into PropTech and FinTech.

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.