Association for Public Policy Analysis & Management

Housing prices are cyclical. Ed Leamer famously said that housing isthe business cycle. This view masks even greater volatility at the local level. We all know that housing prices followed a particularly severe boom-and-bust trajectory from 2000 to 2010, but which houses experienced the sharpest rise and fall -- and in which parts of the country? Though some research has hinted at the answers to these questions, they remain largely unresolved because the literature has focused on housing prices in aggregate. This paper analyzes the entire distribution, breaking down the change in housing prices into quantiles.

Specically, I apply the unconditional quantile regression pioneered by Firpo, Fortin, and Lemieux (2009) and recently advanced by Bento, Gillingham, and Roth (2017) to reveal how the distribution of house prices shifts over time in response to a variety of factors suggested by economic theory. Using a private database of millions of housing transactions from Zillow, I test how monetary policy shocks, credit supply, land-use regulations, structural housing characteristics, neighborhood characteristics, and time and county fixed effects affect the distribution of house prices in the United States from the 1970 to today.

Do these factors aect all house prices equally? Or do they affect some quantiles more than others? Who is experiencing these shocks most acutely? At its core, this approach can help us understand why the housing bubble popped up in some places but not in others; and who benefited the most, who suffered the most, and why.

Finally, I estimate the impact of these effects on overall wealth inequality by calculating the change in the user cost of housing along the distribution and the resulting change in the access to homeownership in each market -- with policy implications for the role of the housing market in social equity and financial stability going forward.