Computer Information Systems and Engineering Technology


Evinn Wheeler



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Evinn Wheeler, CIS 494: Undergraduate Research in CIS
Faculty Mentor: Professor Sarbani Banerjee, Computer Information Systems

There are many real estate investors looking to invest in off-market properties. Many investors have trouble trying to maintain their current properties, as well as finding new good deals, all at once. This research project will help investors to search for these deals and offer them all the information they need to secure a great deal. American homeowners go through foreclosures and lose their homes every day. Instead of their losing everything, the current project aspires to connect them with investors to sell their home; at the same time, the data will offer buyers an opportunity to make a great return on investment. This project was built with Python programming language. The Python modules used in this project Pandas, NumPy, Matplotlib, and Altair. The data input will comes from several database sources, including Buffalo City, Erie County Surrogate, Zillow, and Prop Stream Data. These databases contain data related to property and tax records, mortgage information, foreclosure and probate lists, high-equity lists, projected home prices, the actual repair value of the home, and more. The project then cross-references all these lists and corresponding values to predict the likelihood of a homeowner facing foreclosure, possible sales price range, and the profit percentage of each home. This web-based project may be accessed by investors through the Google cloud platform. My presentation includes key aspects of the project, as well as a presentation of its software design.

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Computer Sciences | Engineering

Off-Market Real Estate Investing: Exploring Data Analytics
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