Integrating spatial econometrics, machine learning, and GIS for predictive housing price modelling

About the Project

Housing markets are inherently spatial, shaped by location, neighbourhood characteristics, accessibility, and environmental factors. While traditional econometric models are useful for identifying relationships, these are often inadequate when it comes to capturing the non-linearities and complex interactions surrounding the dynamics of the markets. However, with the providence of technological advancements, such as machine learning, there are opportunities for strong predictive performance. This PhD will explore novel integration of spatial econometrics, machine learning, and GIS-based spatial analysis to develop housing price models that are both highly predictive and interpretable. It will incorporate spatial econometrics to account for spatial dependence and heterogeneity across regions, while taking advantage of machine learning to uncover non-linear and complex patterns that traditional models fail to achieve. In addition, GIS will provide spatial visualisation, neighbourhood context, and geostatistical inputs for more advanced understanding of the markets. The contribution lies in advancing methodological innovation for urban studies, bridging the gap between explainability and predictive accuracy. It is anticipated that the resulting models will go beyond mere improvement in price forecasting but also generate policy-relevant insights into how accessibility, environmental risks, amenities, and socio-demographic factors shape housing prices.

Supervisors: Dr Cletus Moobela, Dr Temidayo Osunsanmi

Applications accepted all year round

Self-Funded PhD Students Only

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, Geography, urban planning, urban studies, Economics / Econometrics. or equivalent with a good fundamental knowledge of Real estate, urban studies, Economics.

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:

• Data Science / Machine Learning / Computer Science
• Ability to conduct independent research.
• Good analytical skills.
• Good problem-solving skills.
• Knowledge of research methods and tools.
• Good written and oral communication skills.
• Interest in housing, real estate, or urban policy, and enthusiasm for interdisciplinary research
• 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