Modern Web Scraping and Data Analysis Tools to Discover Historic Real Estate Development Opportunities

Abstract

The National Parks Service (NPS) is responsible for a database that catalogs every nationally recognized historic building and historic district across the United States. If listed as historic, the property can qualify for both state and federal historic tax credits which can subsidize up to 45% of the Qualified Rehabilitation Expenses (QREs) of the project, depending on the specific state. These tax incentives can significantly increase the financial return profile of these redevelopment projects. However, finding these qualifying sites across the United States is challenging given the size of the NPS database. With over 97,000 rows of static data, the NPS database is unwieldy and difficult to maneuver. Moreover, there is no way to proactively use the tool to find acquisition opportunities.

This thesis project aims to solve this problem by creating an acquisition analytics funnel that aggregates data from multiple online sources and layers it to create a dynamic way to source new historic redevelopment projects. The initial focus area of the thesis is the state of Maine and the subject of the thesis is Historic Tax Credit View (HTC View), a digital data analytics platform conceived built and owned by the author. The platform combines the NPS database with automated web-scraping algorithms to parse publicly available census and market demand data that indicate whether certain markets are of higher investment value than others. Through the development of HTC View, the author and outside partners have raised funds to make a purchase of a historically recognized former Milling Site in Skowhegan, Maine that was originally identified as an opportunity by the platform. The results of this research demonstrate the effectiveness of adopting web-scraping technologies and the usefulness overall to real estate development professionals.