Abstract

The Dubai Emirate is a burgeoning and vibrant region that has gained significant attention in the real estate industry due to its rapid development. With an increase in demand for land, the region has experienced a substantial surge in land prices. Therefore, precise land price prediction has become paramount for real estate investors, developers, and government officials. The purpose of this study was to employ analytics and machine learning techniques to accurately forecast land prices in the Dubai Emirate.

The study employed a dataset that included influencing factors such as, location, amenities, infrastructure, size, and other variables that affect land pricing. The dataset underwent preprocessing step, and the necessary methods were used to fill in any missing values. To understand the data distribution and correlations between variables, a comprehensive exploratory data analysis was performed in a way to . predict land values. The dataset was split into training and testing sets, and a variety of machine learning methods, including XGBoost Tree, Linear-AS (Linear Auto-Stacking), XGBoost Linear, Generalized Linear, and Least Squares Support Vector Machine Models, were used.

The study's findings showed that the XGBoost Tree model beat other models on the testing set in terms of accuracy (0.948%), feature selection, and random tree performance metrics. The study emphasizes the potential for using analytics and machine learning approaches to precisely anticipate land prices and offers insightful information about the variables that influence land prices in the Dubai Emirate.

To sum up, the study's findings showed how analytics and machine learning approaches can be used to accurately predict land values in the Dubai Emirate. The findings give real estate investors, developers, and government officials insightful information about the region's land market and its possibilities for investment. For the real estate sector's strategic decision-making, risk management, and financial planning, accurate land price prediction is essential in the Dubai Emirate.

Publication Date

5-15-2023

Document Type

Master's Project

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research (Dubai)

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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