Theses and Dissertations

Date of Award

8-1-2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Marzieh Ayati

Second Advisor

Zhixiang Chen

Third Advisor

Dongchul Kim

Abstract

The aim of this thesis is to apply exploratory data analysis and machine learning (ML) to answer critical questions in real estate development within the Rio Grande Valley (RGV). The real estate development sector is highly dynamic and relies on multiple integrated systems and planning to bring new homes to the market. Customer Relationship Management (CRM) software and real estate listing services, such as Redfin, are utilized to manage the customer lifecycle and analyze the local market, respectively. Two of the most crucial aspects of the customer lifecycle are the first home tour and deal closing. This paper explores the data within the HubSpot CRM for first home tours and investigates the feasibility of predicting deal closing using ML. Another popular topic in real estate development is predicting the price of a home based on a specific market. Various features play a key role in the pricing of a home such as the location, lot size, number of bedrooms, number of baths, etc. This paper investigates the data that is available for each home in Redfin and uses this data to predict the prices of homes in the RGV area using ML.

Comments

Copyright 2024 Mathew C. Sosa. https://proquest.com/docview/3115240666

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