Artificial Intelligence in real estate services: navigating the tensions between innovation and professionalism

About the Project

Artificial Intelligence (AI) is transforming industries worldwide at an unprecedented pace. From healthcare to finance, sectors are leveraging AI to innovate, improve efficiency, and unlock new opportunities. Yet, despite being one of the largest and most data-intensive industries globally, the real estate services sector has been slower to embrace this technological revolution.

Traditionally reliant on human expertise and often constrained by fragmented or opaque market information, the real estate sector stands at a crossroads: will it continue to rely on established practices, or seize the opportunities that AI, particularly generative AI, can bring? AI has the potential to reshape real estate services in ways that are both exciting and disruptive. Potential applications are many and varied, including automated valuation models (AVMs) providing more accurate and timely property pricing; virtual assistants handling enquiries and scheduling viewings; smart systems managing maintenance and tenant screening; or advanced analytics offering deep market insights at different scales. At the same time, the rise of AI brings challenges, such as concerns about data security, ethical implications, workforce impacts, training requirements, and the risk of undermining the professional principles upon which the sector is built.

This funded PhD will explore these dynamics by critically examining both the opportunities and the challenges of AI adoption in real estate services. The potential of complex adaptive systems perspective will be explored in the research to investigate how the sector can adapt to technological change while safeguarding professional integrity and public trust.

We invite research proposals that will engage with questions such as: How is AI adoption reshaping roles, processes, and decision-making in real estate services? What are the ethical, legal, and social implications of deploying generative AI in property markets? How can robust theoretical frameworks guide the sector in balancing efficiency gains with professional values and client trust? What strategies can help real estate firms and professionals navigate the transition sustainably?

Academic qualifications

Have, or expect to achieve by the time of start of the studentship a first-class honours degree, or a distinction at master level, ideally in Real Estate, Property Studies, Land Economy, Human Geography, Urban Studies, Planning or equivalent with a good fundamental knowledge of Urban studies, real estate, planning.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

• Interest in the intersection of technology and real estate services
• Familiarity with AI/ML concepts and tools
• Knowledge of theories of innovation, organizational change, or complex adaptive systems.
• Ability to conduct independent research.
• Good analytical skills.
• Good problem-solving skills.
• Knowledge of research methods and tools.
• Good written and oral communication skills.
• Only a first-class honours degree, or a distinction at master level in a subject relevant to the PhD project will be considered, or equivalent achievements.

Desirable attributes:

• Practical experience in research or industry will be considered an advantage.

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about:
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.
• The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.
• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use the advertised title as project title

For informal enquiries about this PhD project, please contact Dr Cletus Moobela email C.Moobela@napier.ac.uk