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A PageRank-Based Algorithm for Real Estate Agent Ranking

1. Kai-Wen Lee, Graduate Student, Computer Science,University of Miami
2. Dr. Vladimir Bugera, Adjunct Faculty, Journalism and Media Management, University of Miami
3. Prof. Sergiy Butenko, Professor, Industrial and Systems Engineering, Texas A&M University
4. Dr. Aidar Vafin, Director, Big Data Realty

Abstract: We applied an analytical method, PageRank, to rank the licensed real estate agents based upon the real estate offers where the agent represents a seller or a buyer of a residential real estate property, and the offers resulted in the property sales with the corresponding parties. We analyzed the resulting ranking by studying patterns and
common characteristics for agents with high and low scores. Our dataset is based on the listing data obtained from Miami Association of Realtors. We used Python programming for data pre-processing and algorithmic ranking.

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