AI Integration in Real Estate
Carrington Finney
Professor Dean Smith | Communication Law & Ethics
04/29/2026
Introduction:
Real estate is one of the oldest and most developed careers in human civilization, dating back to 3,000 B.C., and has stayed methodically consistent across time. Although the selling and buying process has certainly changed a lot in the past few thousand years, the necessity and profession of real estate have stayed consistent in nature and need. In recent years, AI is rapidly changing our world, there's no doubt. With the sudden emergence and integration of AI into nearly every facet of everyday life, it is impossible to ignore it. In the real estate realm, recent developments have been made to integrate AI into the formulation of the selling process. As discussed in numerous articles examining the relationship between AI and real estate, AI is no longer available only on a large corporation scale but is becoming accessible to smaller businesses and learning programs. This creates a larger need for AI-fluent individuals in the workforce and overall broadens the use of artificial intelligence in the lives of businesses and consumers. In the real estate world in particular, specific strategies are being implemented to introduce AI in a way that benefits the agents as well as the agency. Yet beneath this promise lies a more complicated reality, one where the professionals AI is designed to serve are often its most conflicted adapters.
As Gardes (2024) discovered in his survey of 310 French real estate agents, the “perceived threat to professional autonomy” is revealed as one of the most significant reasons for hesitation to AI adoption in the real estate industry. Real estate professionals established that they feel that their brand and advantages as a salesperson are built on judgment and relationships with clients, so there is a fear that AI-driven automation will reduce their decision-making authority. This, real estate agents say, dissipates their role to suggest algorithmic recommendations to clients rather than serve as advisors and trusted agents to consumers. Raisch and Krakowski (2021) address this tension, as cited in Gardes’ piece, calling it the augmentation-automation paradox. They define this as the unresolved, lingering question of whether AI will enhance professionals or replace them. Koroleva and Souza (2023) reinforce this paradox from a business standpoint, identifying four categories of risk that real estate firms must now confront: job displacement, return-on-investment uncertainty, technical challenges (including data privacy vulnerability and algorithmic issues), and an overdependence on AI systems that could fail or produce biased answers. Koroleva and Souza warn readers that smaller firms face pressure to create similar output to large firms and that the shortage of AI-trained professionals is driving up implementation costs while leaving companies unable to break even on their AI investments. The use of AI in real estate is already beginning to happen at a staggering rate; it would be impossible to do anything about the implementation of AI in certain forms unless the use of AI were outlawed. Therefore, most firms have to ask themselves the question of adaptation. Do they move forward with AI due to the rapidly changing real estate industry, despite their internal opposition, or keep to a traditional format and risk losing the benefits AI carries? The true problem then is not whether AI will transform the real estate profession, which it is already starting to do, but whether the industry can handle that transformation quickly and responsibly.
The legal and ethical repercussions of AI in real estate are often overlooked when discussing its implementation into the real estate industry. Koroleva and Souza (2023) emphasize that the incorporation of AI raises serious consumer concerns about data privacy, algorithmic bias, and regulatory compliance, noting that mishandling sensitive property and financial data can expose businesses to breaches, legal penalties, and reputational damage. According to Gardes (2024), the opacity of AI algorithms, which he describes as "black boxes" that create internal logic that is invisible to users, creates a fundamental trust problem: when real estate agents are unable to understand or predict how an AI system comes to its conclusions, distrust and anxiety arise in the agent, and the ethical trust AI companies are hoping to gain ultimately fails. This is especially important in a high-stakes industry where AI-generated valuations, investment recommendations, and need-matching for clients carry both significant financial and legal weight. Seagraves and Seagraves (2025) then take this concern to the educational pipeline, arguing that future professionals should be trained not only in how to use AI tools but also in the ethical frameworks that govern their use, such as data privacy, bias mitigation, and the societal impact of automation on employment. AI advocates warn that without this foundation, the next generation of real estate professionals risks using powerful tools they don't fully understand and lack the safeguards to detect errors before they cause harm to themselves or their company’s reputation.
Despite its obstacles, the ultimate goal of AI in real estate is to make the industry “more precise, accessible, and strategic." Kabaivanov and Markovska (2021) established the foundation for AI's value in this space by pointing out the profession's weaknesses, such as high transaction costs, information asymmetries, and lengthy closing timelines, making AI a perfect model for quick automated solutions. Kabaivanov and Markovska propose a three-stage valuation model to show how machine learning could better calculate the processing of complex data and calculate qualitative data such as neighborhood desirability, which traditional models fail to accurately analyze. Morgan Stanley Research (2025) places a figure on the expanding potential of AI, projecting $34 billion in operating revenue. According to their analysis of 162 investment trusts and commercial real estate firms, AI has the potential to automate up to 37% of tasks in the property management, sales, administration, and maintenance realms by 2030. Rossini (2000) wrote very early at the start of this development and predicted much of AI's trajectory. He argued that systems that combine human reasoning with computerized power would eventually outperform any single technique used alone. Together, these sources demonstrate that AI's goal and purpose in real estate is not to replace human expertise but to supplement it. In doing so, this frees agents from routine data processing so they can focus on the relationship-driven work that computers can’t duplicate.
The methods in which AI is being implemented reflect both the scope of its capabilities in the industry, but also highlights the unevenness in implementation on a firm-to-firm scale. As far as the capabilities of AI go, Kabaivanov and Markovska (2021) describe multi-step learning models that generate more accurate market forecasts and valuations than conventional methods. AI models use granular property-level data and global economic indicators to calculate the value, which would take humans much longer to average. Even in its early stages, AI was intended to be used as a support tool rather than a replacement for professionals. Rossini (2000) documented an earlier but similar method to Kabaivanov and Markovska: a seven-step expert system prototype that guides practitioners through data collection, sales identification, neural network modeling, and weighted valuation. Koroleva and Souza (2023) describe how language processing tools are currently being integrated into investment workflows to track market data and automatically optimize rental pricing, whereas predictive models usually look at employment growth trends and construction permit activity to identify opportunities. What is most pressing, especially for those going into a real estate profession, is, as Seagraves and Seagraves (2025) detail, how the use of AI is infiltrating education itself. Universities are starting to incorporate automated valuation models, AI chatbots, and detailed simulations into undergraduate real estate curriculum. This ensures that the students entering the workforce are taught from the start to use, ethically oversee, and question the systems that will define their careers rather than discovering them with no prior experience.
Annotated Bibliography:
Claude AI
Kabaivanov, S., & Markovska, V. (2021). Artificial intelligence in real estate market analysis. AIP Conference Proceedings, 2333(1), 030001. https://doi.org/10.1063/5.0041806
In this paper, Kabaivanov and Markovska examine how artificial intelligence can deepen understanding of real estate market dynamics. The authors argue that real estate has long been a significant investment vehicle, with a wide array of financial instruments tied to property assets, making it critically important for both investors and intermediaries. They note that the real estate sector is particularly prone to high transaction costs, greater information asymmetries compared to stock markets, and longer delays in completing transactions — challenges that make automated, data-driven approaches especially valuable. To address these issues, the authors propose and test a three-stage model designed to support real estate valuation and market forecasting, one that accounts for both broad global economic factors and the specific individual characteristics that influence property prices. Each stage of the model applies a different combination of AI and machine learning methods, enabling the automation of market data processing and the evaluation of how qualitative factors shape property valuations.
The paper demonstrates that AI functions as a strategic tool for enhancing comprehension of real estate market dynamics, with the three-stage framework allowing analysts to move beyond simple price comparisons toward a more holistic, data-integrated approach. Later research building on this work has found that AI is primarily applied to price forecasting in the real estate sector, while affirming the original authors' conclusion that AI applications hold the potential to provide meaningful competitive advantages in real estate investment. Broadly, the paper makes the case that machine learning methods can automate the processing of large, complex datasets and quantify the influence of hard-to-measure qualitative variables — such as neighborhood desirability or aesthetic features — that traditional valuation models tend to overlook. For students and researchers studying the intersection of AI and real estate, this article provides a foundational methodological framework and serves as an early, frequently cited contribution to the growing body of literature on algorithmic property valuation and market forecasting.
Rossini, P. (2000). Using expert systems and artificial intelligence for real estate forecasting. Paper presented at the Sixth Annual Pacific-Rim Real Estate Society Conference, Sydney, Australia.
This paper, presented by Peter Rossini of the University of South Australia, explores how expert systems (ES) and artificial intelligence (AI) — particularly artificial neural networks (ANN) — can be applied to real estate forecasting. Rossini begins by challenging the assumption that more sophisticated forecasting methods automatically yield better results, citing the landmark work of Makridakis et al. (1982), whose study of over 1,000 time series found that simpler methods could perform just as well as complex ones. From this foundation, Rossini argues that a well-designed system, rather than any single advanced technique, is the true driver of accurate forecasting outcomes. The paper then distinguishes between conventional program systems, which focus purely on computational output, and expert systems, which attempt to replicate how human experts reason and solve problems. ANNs are identified as the most widely used AI technique, functioning similarly to the human brain through interconnected neurons, and are shown through multiple business studies to generally produce lower forecasting errors than traditional regression methods. Rossini also cautions, however, that ANNs risk being misapplied by practitioners who lack statistical grounding, potentially producing misleading conclusions in real estate contexts.
In the second half of the paper, Rossini broadens the discussion beyond mass appraisal — the area where AI had been most heavily researched at the time — to highlight underexplored applications such as qualitative forecasting, lease documentation preparation, development cost estimation, and property management. He then presents a detailed case study: a seven-step residential valuation prototype system developed at the University of South Australia. The system walks users through collecting property data, identifying comparable sales from a regularly updated database, building a regression or neural network model of the market, quantifying value adjustments, selecting the most comparable sales using nearest-neighbor techniques, applying adjustments, and ultimately producing a weighted mean value estimate. A notable strength of this system is its educational value — by requiring users to consciously work through every step, including decisions that experienced valuers might instinctively shortcut, the system serves as both a practical tool and a training instrument for novice practitioners. Rossini concludes that AI and expert systems hold considerably more potential for real estate than the existing literature had yet explored, and that hybrid approaches combining computational power with structured expert knowledge represent the most promising path forward.
Koroleva, O., & Souza, L. A. (2023). New risk-management considerations for the real estate industry in the era of artificial intelligence. Global Studies on Economics and Finance, 1(2). https://doi.org/10.15678/GSEF.2023.01.02
Koroleva and Souza examine how the rapid adoption of artificial intelligence is fundamentally transforming the real estate investment industry, not only in terms of operational efficiency but also in the organizational structures and risk frameworks companies must now navigate. The authors begin by establishing that AI, machine learning, and big data analytics have become widely accessible tools — no longer the exclusive domain of large institutions — and are now reshaping how investors, tenants, and property managers interact with real estate assets. Key AI applications highlighted include Natural Language Processing tools that track hyper-local market data to automatically optimize rental pricing in real time, as well as predictive models capable of identifying promising acquisition targets by analyzing employment growth trends, construction permit activity, and neighborhood-level economic indicators. The authors also note that the same analytical logic applied to REIT financial data can be used to construct high-performing investment portfolios. Taken together, these capabilities represent a significant shift in how value is created and managed across the industry, compelling real estate firms to adopt a hybrid "proptech" approach that blends traditional property management with technology-driven innovation. The authors argue that to remain competitive, firms must restructure to operate more like technology companies — flattening hierarchies, appointing Chief Technology Officers, embracing location-independent work cultures, and prioritizing agility and customer-centricity.
The second half of the paper turns to the risks that accompany this transformation, which the authors organize into four categories: job displacement, ROI uncertainty, technical challenges, and dependency on AI. On the workforce side, the authors caution that while AI can create new opportunities, it also threatens to erode human decision-making skills over time and exacerbate economic inequality among workers without access to relevant technical training. Regarding ROI, the authors note that while top-performing firms can achieve strong returns on AI investments, many businesses struggle to break even, partly due to a widespread shortage of skilled AI professionals and the significant ongoing costs of maintaining and updating AI systems. Technical challenges such as data privacy vulnerabilities, algorithmic opacity, and poor data quality further complicate adoption. Finally, the authors warn against over-reliance on AI systems that may fail or produce biased outputs without adequate human oversight. Their overarching conclusion is that the most important transformation in real estate will not come from the technology itself, but from a fundamental shift in leadership culture — one that prioritizes self-leadership, transparent communication, and collaborative problem-solving to responsibly integrate AI into everyday operations.
Seagraves, C., & Seagraves, P. (2025). Revolutionizing real estate education: Integrating AI and LLMs into undergraduate curriculum. University of Tulsa; Middle Tennessee State University. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6244538
This paper, authored by Cayman Seagraves of the University of Tulsa and Philip Seagraves of Middle Tennessee State University, argues that integrating Artificial Intelligence and Large Language Models into undergraduate real estate curricula is no longer optional but essential for preparing students to compete in a technology-driven marketplace. The authors begin by tracing the real estate industry's digital evolution — from internet-based listings and electronic Multiple Listing Services to Automated Valuation Models, virtual reality property tours, and electronic signatures — establishing a trajectory that makes AI the next logical step in the sector's transformation. The paper identifies five key application areas where AI is already reshaping professional practice: market analysis and investment strategy, property valuation and appraisal, real estate law and ethics, customer service and relationship management, and sustainable development. Crucially, the authors argue that this technological shift demands a parallel transformation in pedagogy, moving away from traditional lecture-based instruction toward simulation-based, data-driven, and interactive learning environments. They also examine how AI stands to affect real estate careers, noting that routine administrative roles, basic data reporting, and repetitive customer service functions face the greatest risk of automation, while agents, developers, appraisers, and legal professionals who leverage AI tools stand to have their roles meaningfully enriched.
The second half of the paper focuses on the practical and ethical dimensions of embedding AI and LLMs into real estate education. The authors outline specific pedagogical strategies — such as integrating AI tools into homework assignments, using LLMs to generate legal documents and property descriptions for student practice, and deploying AI-driven chatbots as around-the-clock teaching assistants — all of which are designed to build technological fluency alongside critical thinking. Ethical considerations receive substantial attention, particularly the need for transparency when educators use AI to create course materials, and the challenge of distinguishing legitimate AI assistance from academic dishonesty when students use these tools in their own work. The authors also raise concerns about equity, warning that access to premium AI platforms like Microsoft CoPilot or paid OpenAI models creates a meaningful competitive divide among students from different socioeconomic backgrounds and institutions with varying resources. Their conclusion calls for collaboration among educators, technology developers, and industry professionals to build a future-proof curriculum — one that balances technical AI proficiency with ethical awareness, ensuring graduates are prepared not just to use these tools, but to lead responsibly in an industry increasingly defined by them.
Gardes, N. (2024). Ally or adversary? AI and the perceived threat to autonomy in services—The case of real estate professions [Preprint]. SSRN. https://ssrn.com/abstract=5042576
This preprint study by Nathalie Gardes of the University of Bordeaux investigates why real estate professionals resist or embrace artificial intelligence, arguing that emotional and motivational factors — rather than purely practical considerations — are the primary drivers of AI adoption behavior. The paper is grounded in Lazarus's cognitive appraisal theory, which holds that individuals evaluate new situations through a multi-stage emotional process before deciding how to respond. Gardes applies this framework to the specific context of real estate agents, a professional group for whom autonomy — defined as control over one's own work conditions, processes, and decision-making — is deeply tied to professional identity and competitive advantage. The study also engages with the augmentation-automation paradox outlined by Raisch and Krakowski (2021), which captures the tension between AI as a tool that enhances human capabilities versus AI as a force that replaces them. To test her model, Gardes surveyed 310 French real estate agents and analyzed the data using structural equation modeling, examining relationships among intrinsic motivation, perceived autonomy threat, performance expectations, perceived effort, emotional responses, and willingness to adopt AI. The results confirmed that intrinsic motivation significantly increases performance expectations and reduces the perceived effort of using AI tools, while the perceived threat of losing professional autonomy significantly dampens performance expectations. Notably, social influence — the pressure from peers or industry leaders — had no statistically significant effect on either performance expectations or perceived effort, a finding that diverges from prior technology adoption research and suggests that real estate agents evaluate AI primarily on its personal and practical merits rather than on conformity to group norms.
The study's most consequential finding is that emotional responses serve as the critical mediating link between agents' cognitive appraisals and their actual willingness to adopt AI. Agents who anticipate that AI will augment their capabilities tend to develop positive emotions—enthusiasm, confidence, and optimism—which in turn drive adoption. Conversely, agents who feign enthusiasm, automate, and diminish their roles generate negative emotions such as anxiety and resentment, rooted partly in what the paper describes as the "black box" problem: the opacity of AI algorithms, which makes agents distrust systems they cannot understand or predict. Gardes draws several managerial recommendations from these findings, urging real estate firms to reframe AI as a collaborator rather than a competitor; invest in transparent and explainable AI systems; involve agents in the design of AI tools to preserve their sense of ownership; and provide targeted training that builds both technical competence and emotional intelligence. The paper concludes that successful AI integration in real estate depends less on the technology itself and more on cultivating organizational cultures of trust, transparency, and genuine respect for professional autonomy — making this study a valuable contribution to the growing body of research on the human dimensions of AI adoption in property markets.
AI-synthesized results on the effects of AI in real estate:
1. Automation and Labor Efficiency
There is a clear overlap in how AI is expected to transform the workforce by automating repetitive tasks and reducing labor hours:
Sector-Wide Automation: AI has the potential to automate approximately 37% of tasks within the real estate sector, particularly in property management and office administration.
Measurable Labor Reduction: Practical applications of AI in on-site staffing and property management have already demonstrated a 30% reduction in labor hours.
Operating Efficiencies: This automation is projected to generate roughly $34 billion in operating efficiencies by the year 2030.
2. The Evolution of Consumer "Discovery"
Both the advertising and real estate findings point toward a fundamental shift in how information is found and consumed:
The "AI Attention Stack": Consumers are transitioning from traditional link-based search engines to AI interfaces (like ChatGPT and Gemini) that synthesize answers.
Conversational Advertising: As a result, 53% of organizations are already shifting budgets toward conversational advertising formats to meet consumers within these new AI layers.
Growth in AI-Driven Shopping: Usage of generative AI for shopping-related activities grew by 35% in 2025, marking it as the new default layer for digital discovery.
3. Enhanced Risk Management and Forecasting
Overlap exists in how AI is used to provide "early warnings" and detect anomalies that human analysts might miss:
Early Warning Systems: AI tools are being deployed as monitoring systems to detect market volatility and turbulence, which is vital for the long lead times of real estate transactions.
Pattern Discovery: Neural networks are used to analyze financial time series and identify "outliers" or anomalies that indicate disruptive market events.
Identifying Specific Risks: Beyond market trends, AI is now used to track cash flow stability, regulatory shifts, and even climate change impacts on investments.
4. Valuation and Strategic Decision-Making
The data shows a consistent move toward more complex, multi-factor models for making high-stakes decisions:
Multi-Stage Valuation: New models for property valuation use AI to simultaneously account for global economic factors and hyper-local property characteristics.
Expert System Improvement: AI improves the selection of "comparable" properties by using nearest-neighbor techniques, which significantly reduces error margins compared to traditional manual "comping."
5. Professional Role Convergence
The research concludes that these advancements are forcing a merger of skills across industries:
Strategic AI Orchestration: Both real estate and communication professionals are moving away from manual data processing toward roles as "AI managers."
The "Human-in-the-Loop": While AI handles lead nurturing and data entry, the human role is pivoting toward "high-touch" relationship management and ethical oversight.
Conclusion:
As someone who genuinely believes that AI serves as a devalued supplement of human creation, I was surprised to see numerous positives in the implementation of AI in real estate. Although I don’t want AI to take my job, researching and reading about the use of AI in the profession I most aspire to be in has empowered me to begin to embrace AI for what it is when used for real estate: a tool to enhance and ease the profession rather than supplement it. Going into my research, I heavily assumed that most papers would blindly advocate for AI, but I was misproven several times. Balancing the positives of AI usage with the ethical concern both clients and agents face proves to be incredibly challenging, but if the methodology is properly taught, AI can be incredibly rewarding for the industry.
I’ve always wanted to go into real estate but have always felt an internal struggle as to whether or not to work for a big firm. As much as a small-owned business appeals to me, I couldn’t handle every step of the selling process by myself in a considerable amount of time and be able to make a full-time income, especially in rural NC. Larger firms usually hire people to take care of the “long, boring, number-packed” part of the job, eliminating the need for agents to perform long, extensive forms. This only eliminates part of the issue for real estate agents, as they have to be well-versed in the law and codes of the area, as well as spend much of their time on finding information relevant to each client and remembering such information. Even so, agents usually spend most of their day prospecting and qualifying clients, which can take hours of extensive research.
AI reframes the role of the salesperson, performing certain data-intensive tasks in order to increase efficiency while decreasing the amount of effort required and the longevity of probing and comparing data. Ultimately, this saves a lot of time and resources for agents, and it’s supposed to allow them to spend more time building meaningful relationships with clients. This framework means the world to me because the primary reason for my going into sales is the relationship-based aspect of it. As a salesperson, my main goal is to reinforce the buyer and help them find what they need most; AI allows agents to do more of that and less of the grunt work of running numbers and filling out paperwork. Of course, some firms have taken this to the extreme. An incredibly popular realtor just debuted a Netflix show that details his company's efforts to integrate AI into the framework of their business. He states that his employees have access to AI language models that can synthesize calls in order to find statistically what AI believes the customer would want the most. In doing this, consumers are worried that AI, if taken too far, will completely erase the ability for them to express their needs because the AI will have it computed for them in advance. Likewise, realtors and students, myself included, worry that if they assume what a consumer would want based off of formulated AI results, they will ultimately harm the trust between a client and a realtor.
Given the framework that the academic articles provided, I am able to thread together a thin line of ethical responsibility and AI use in real estate. I have rationalized that if AI generalizes a consumer's needs, miscalculates a number, or AI texts them automatically because the salesperson forgets to do so, we lose the authenticity and the true nature of what makes sales so important. If we use AI extensively, and moreeso in the place of the salesperson, it eliminates the need for the salesperson. A recent study found that in the sales profession, if a salesperson lacks finding value in their profession, their productivity goes down and hurts their overall sales. In the case of these AI-forward firms, I personally believe they take it a step too far that devalues the need for a salesperson, and therefore, while they may have boosted productivity, the trust of the consumers and the spirit of the firm dissipate. Despite these specific firms, this project has revealed the good that AI can bring to the profession, despite adaptational delays. In both itemizing and speeding up the process of data crunching, AI is able to take some of the stress off of the salesperson's back and allow them to focus more on those relationships the sales profession relies on to continue to grow. Additionally, AI is seen as a progressive tool for qualifying and prospecting customers; AI’s seemingly limitless accessibility to information allows the salesperson to get a more detailed and prepared brief to introduce their relationship.
AI is coming. Whether we want to or not, AI is seeping into nearly every career. Jobs will be lost, but jobs will also be gained from this technological shift, despite our wishes. It appears that the best thing the working class can do is adapt to it and learn how to use it. Regardless of how realtors feel about AI coming into the field, it's here. Therefore, in order to get above the curve, learning and implementing it is necessary. In preparing the next generation of realtors to enter a world of AI, we are doing what not only is necessary for the curve of growth, technology, and the rate of investment in AI tools in the real estate sector (I also discovered lack of knowledge and incorrect use are the main reasons most smaller firms don’t profit from the installation costs - mindblowing!), but we are also creating more versed realtors who are able to use an array of tools to best fit the needs of their clients. When we stop framing AI as being against us, we are finally able to see what it can do for us.
Sources:
Non-Academic Sources
Derow, R., Kuron, J., Reiner, J., & Abraham, M. (2026, January 19). How AI is reshaping advertising for the first time in a decade. BCG X: The Multiplier. https://www.bcg.com/x/the-multiplier/how-ai-is-reshaping-modern-advertising
Morgan Stanley Research. (2025, July 2). How AI is reshaping real estate. Morgan Stanley Insights. https://www.morganstanley.com/insights/articles/ai-in-real-estate-2025
Academic Sources
Sutherland, K. E. (2025). Artificial intelligence for strategic communication. Palgrave Macmillan. https://doi.org/10.1007/978-981-96-2575-8
Osei-Mensah, B., Asiamah, E. O., & Sackey, R. (2023). Strategic communication and artificial intelligence: Reviewing emerging innovations and future directions. Archives of Business Research, 11(1), 85–102. https://doi.org/10.14738/abr.111.13616
Morapeli, S., & Khemisi, M. (2024). The influence of artificial intelligence on the strategic communication industry. Communicare: Journal for Communication Studies in Africa, 43(1), 48–58. https://doi.org/10.36615/jcsa.v43i1.1432
Kabaivanov, S., & Markovska, V. (2021). Artificial intelligence in real estate market analysis. AIP Conference Proceedings, 2333(1), 030001. https://doi.org/10.1063/5.0041806
Rossini, P. (2000). Using expert systems and artificial intelligence for real estate forecasting. Paper presented at the Sixth Annual Pacific-Rim Real Estate Society Conference, Sydney, Australia. https://www.researchgate.net/publication/255480942
Koroleva, O., & Souza, L. A. (2023). New risk-management considerations for the real estate industry in the era of artificial intelligence. Global Studies on Economics and Finance, 1(2). https://www.sandermanpub.net/uploads/20231128/1629645262306fd774337a7d66a86112.pdf
Seagraves, C., & Seagraves, P. (2025). Revolutionizing real estate education: Integrating AI and LLMs into undergraduate curriculum [Working paper]. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6244538
