The housing business is at a time of extraordinary growth. The COVID-19 epidemic pushed interest rates to all-time lows, resulting in a $4 trillion buy and refinancing mortgage boom in 2021. Afterwards, rapid inflation not seen in 30 years ensued, prompting the Federal Reserve to intervene, virtually doubling interest rates and resulting in a predicted $2.4 trillion in mortgage origination volume in 2022.
Throughout this turbulence, one constant has remained: a chronic and apparently intractable shortage of single-family, 1-4-unit entry-level houses for sale. According to CoreLogic, the US will face a 1.2 million-unit shortfall affecting low-income households. As such, there is an urgent need to bring more equity to homeownership, specifically for these households.
Comprehensive property data provides unique insights that enable lenders to effectively serve their clients while also addressing the most difficult modern public policy concerns confronting the sector. Using data science as the cornerstone of the industry may work as a catalyst for bringing industry participants together, generating insights and actions that will assist address the urgent shortage of entry-level homes for purchase by LMI homebuyers. With data science, stakeholders can utilize several data sources to implement innovative techniques and models to form new business knowledge, which drives effective and smarter business decisions.
One strategic way of doing this is the development of an evidence-based analysis platform by CoreLogic and the Mortgage Bankers Association (MBA). This platform uses common Business Intelligence resources, with data, analytics, and machine learning models viewed as accurate and objective by stakeholders. The platform is able to rank neighborhoods that meet the collective requirements, including affordability to Low-Middle Income owner occupants, cost to construct, quality of life, and the possibility of a positive economic rate of return. To read more on this, click here.