Uncovering the Origins of Federal Housing Data
In the 1920s, housing was seen as a potential countercyclical force to the boom and bust business cycle, with timely housing data the key to unleashing this potential. While this did not pan out as hoped, today's housing data landscape traces its roots to these efforts.
With each passing day, data and research are becoming more enmeshed in the work of policymakers. Accurate, timely statistics are critical for understanding the state of the world, building effective policy solutions, and monitoring outcomes. At the federal level, the quantification of the real estate and construction sectors traces its roots back a century, argues Tommy Shay Hill, a lead economist at Fannie Mae and former Meyer Fellow at the Joint Center for Housing Studies (JCHS). On March 31, 2023, JCHS hosted Hill’s lecture, “How to See the Housing Sector: The Roots of the US Housing Data Ecosystem.” In his talk, Hill explained the impetus for the federal government to provide national housing data, the aspirations fueling that effort, and how the legacy of the 1920s carries forth to the contemporary period.
The Housing Challenges of the 1920s
Although privately run efforts to collect housing-related data, such as construction statistics, had been underway at a local level for decades, it was not until the 1920s, Hill says, that federal efforts truly gained widespread support. Then, as now, the United States was experiencing a severe nationwide housing shortage, as a result of World War I and the subsequent recession. At the time, home prices remained out of reach for many Americans, and inflation eroded overall spending power.
The housing sector in the early 1920s reflected a paradox: contrary to classical economic theory, the era’s high housing prices were not generating enough housing construction to ease the housing shortage. Economists and businesses interpreted this situation as a market failure in need of correction, but they believed that the federal government should avoid taking too large of a role in correcting the market, which they considered an improper overreach of government power. Commerce Secretary Herbert Hoover, who spearheaded federal housing data efforts at the time, exemplified the view that the government's proper role lay in disseminating information and best practices that could gently nudge the housing industry toward socially desirable ends. Hoover believed that “the stability and soundness of business can be greatly enhanced and that vicious speculation [in the housing sector] can be curtailed by a more adequate information service.”
At the time, says Hill, housing was seen as a key component of the U.S. economy that had countercyclical potential. Reliable and comprehensive national housing statistics would empower developers and financiers to take advantage of low labor and material costs during economic recessions to build up the nation’s housing supply while providing investments that would spur a return of economic growth.
A New Understanding of the Housing Sector
The increasing sophistication of housing data did not live up to the market-correcting hopes of its early advocates. In practice, Hill argues, the development of increasingly comprehensive housing data allowed construction and housing finance firms to grow and work at ever larger geographic scales. Unfortunately, the development firms that grew largest on the strength of these new, statistics-backed market analyses ended up fueling a real estate bubble that eventually collapsed, which served as one of the triggers of the Great Depression. According to Hill, data on real estate and housing production have “had a tendency to inflate the bubble dynamics of the real estate market precisely by making it easier to commoditize property.”
The new statistics helped reveal the full reach of construction-related economic activity — not simply the direct activity of building but all of construction’s inputs, including industries such as lumber and other building materials, architecture and engineering, and other building trades. Previously, these activities had been viewed as distinct economic units rather than components of a single productive system. Hill reports that, in one of the first published graphs of national housing data, the authors reflect this emerging understanding not only by attempting to show the cost of housing construction but also by tying that cost to the underlying costs of material and labor. These improved housing statistics had additional effects on the evolution of urban development in the United States, including the adoption of zoning codes (the U.S. Department of Commerce published a “zoning primer” in 1922 based on housing data that served as a reference for many early zoning codes) and the professionalization and consolidation of real estate developers and financiers.
Better data also encouraged standardization in the construction industry, helping to achieve an early goal of efficiency. New industrywide organizations emerged to help coordinate production. For the first time, housing could be understood as an economic outcome of a process embedded in other economic realities, such as interest rates and input costs.
By the 1930s, the belief that data alone could result in a self-regulating construction industry that was immune to bubbles was revealed to be untenable as mortgage lending and real estate markets collapsed. The data collected in the 1920s, however, provided researchers in the 1930s with important insights into the causes of the economic collapse. These insights, says Hill, helped set the stage for the creation of the Federal Housing Administration, one of the nation’s first federal attempts to leverage government power to counterbalance the failures of a laissez-faire approach to the urban development market. Today, a data-centered approach with complementary roles for the private and public sectors remains a lasting legacy of the drive for quantification that began in the housing sector a century ago.