Abstract

This thesis addresses a significant gap in real estate valuation models by investigating the economic impact of localized climate conditions and granular geospatial amenities. An abstract summarizes the following: - The main themes, ideas or areas of theory being investigated: This research investigates the integration of localized climate conditions and granular geospatial amenities into machine learning (ML) frameworks for residential real estate valuation. - The background and context of the research: In dynamic urban markets like Dubai, traditional valuation models often rely on broad location labels and structural attributes, overlooking the tangible economic impact of environmental comfort and micro-climates in a hot-arid environment. - The questions which informed data collection: The study sought to determine if modern ML models outperform linear baselines, whether integrating granular geospatial and climate data improves predictive accuracy, and which specific environmental features act as significant price determinants. - The main method(s) used to collect data and the sample: A quantitative approach fused three open data streams: Dubai Land Department transactions (2022–2024), Open-Meteo climate archives, and OpenStreetMap geospatial layers. Three models—Multiple Linear Regression, Random Forest, and XGBoost—were trained and evaluated using a feature ablation study. - A summary of the answers to the research questions: Random Forest achieved the highest accuracy ($R^2$ 0.845), significantly outperforming the linear baseline. The ablation study revealed that geospatial features provided a substantial predictive lift ($R^2$ +0.043), while novel features like distance to coastline and average temperature were identified as top-tier predictors. - The conclusions formed from these results: The study concludes that localized climate and granular geospatial features are not minor amenities but essential, quantifiable drivers of residential value, critical for accurate and resilient valuation in Dubai. - Recommendations for future research and for practice: Valuators are recommended to adopt ensemble ML models and standardly integrate geospatial data, while policymakers can use these findings to economically justify investments in climate-resilient urban infrastructure. Future research should expand this framework to include satellite-derived remote sensing data and different property types.

Library of Congress Subject Headings

Real property--Valuation--United Arab Emirates--Dubai--Mathematical models; Geographic information systems; Climatology--Data processing; Machine learning

Publication Date

12-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Khalid Ezzeldeen

Campus

RIT Dubai

Plan Codes

PROFST-MS

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