Abstract

This paper aims to determine the predictive modelling of residential property prices in urban Scotland with the help of an integrated framework in which machine learning methods are applied in combination with detailed socio-economic, health, housing, and geographic indicators. Conventional valuation methods tend to be based on the concept of few structural variables and ignore the effect of multidimensional variables in determining spatial variation in housing markets. To cope with this, the study uses Linear Regression, random forest, Multi-layer perceptron, and XGBoost models, which are assisted by a broad range of feature engineering, outlier management, and data preprocessing. Compared with any other model that was tested, XGBoost performed the best, and its RMSE was around £62,846 with an R2 of 0.739 on the log-transformed target. The model behaviour was analyzed using AI, especially SHAP, to determine the main aspects influencing the price change. The findings show that socio-economic deprivation, health outcomes, accessibility to services, and housing typologies have a great impact on the value of local property. The findings indicate the need to have multidimensional data integration and open machine learning methods in the study of urban housing markets. The paper finds that more equitable and evidence-based decision making could be supported through the use of advanced predictive models in planning, housing policy and resources allocation across the urban areas of Scotland.

Library of Congress Subject Headings

Housing--Prices--Scotland--Forecasting--Data processing; Real property--Prices--Scotland--Forecasting--Data processing; Predictive analytics

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)

Advisor

Sanjay Modak

Advisor/Committee Member

Khalid Ezzeldeen

Campus

RIT Dubai

Plan Codes

PROFST-MS

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