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Using Machine Learning Algorithms to Predict Property Prices
Armon S. | Summer 2022

I decided to investigate the connection between AI and real estate, and how machine learning algorithms can be effective in determining property prices, analyzing a dataset on AirBnb listings in New York City from 2016.

While choosing a research topic, I wanted to explore a subject that related to troubles I had experienced in my daily life. After witnessing my parents struggle with purchasing a home in South Carolina, I decided to investigate the connection between AI and real estate, and how machine learning algorithms can be effective in determining property prices. I analyzed a dataset on AirBnb listings in New York City from 2016, which included characteristics such as location, availability, and interior/exterior features. After creating linear regression, decision tree, random forest, and neural network models, I calculated their respective MAEs to determine which was most effective in predicting prices. Then, I iterated through different values for their hyperparameters to study how I could increase their accuracies. After completing this step, I found that the neural network model was most effective in predicting real estate prices.

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Armon S.
Bryce Johnson
Computer Science Stanford Alum, Industry Data Analytics and Business Consultant at Oliver Wyman

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