Date of Award

9-2025

Document Type

Thesis

Degree Name

Master of Science in Engineering Management

Department

Department of Systems Engineering and Management

First Advisor

John J. Elshaw, PhD

Abstract

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently emerged as the strongest driver of affordability perceptions, outweighing the effect of income levels.  The findings highlight clear regional disparities: Riyadh’s housing market is shaped by middle- and high-income segments, Jeddah faces persistent affordability pressures, while Dammam reflects a dual-structured economy. While linear models explained only simple relationships, non-linear approaches offered deeper insights into complex dynamics, though cultural and structural factors remained beyond statistical capture.

AFIT Designator

AFIT-ENV-MS-25-S-001

Comments

An embargo was observed for posting this thesis on AFIT Scholar.

Approved for public release. Distribution Statement A, Unlimited Distribution. PA clearance case 88ABW-2025-0726.

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