Author(s): Justin Contat, and William D. Larson
*Revised July 2023
Abstract: The major issue which we address in this paper is the one-size-fits-all nature of the typical city-level house price index. In this vein, we make two contributions. First, we develop a new algorithm to ensure feasible estimation of geographically granular repeat-sales house price indices in cases of low transactions counts. This facilitates the estimation of a balanced panel of 63,122 Census tract-level repeat-sales house price indices (2010 definitions) at an annual frequency between 1989 and 2021, which we release alongside this paper. Second, we use these indices to estimate city-level house price indices that are robust to heterogeneous submarket appreciation and non-random sampling, two issues that confound classic approaches. Numerical simulations show this algorithm uncovers population indices even when house prices, quantities, and transaction sampling vary across locations and over time. This approach can be used in a flexible manner to calculate canonical price indices such as Lowe and Laspeyres, and more tailored summary indices on a variety of topics, including collateral valuation, climate risk assessment, or tracking changes to minority housing wealth over time.
The supertract-based census tract HPIs constructed in this staff working paper are available below. Our FAQs address common questions about the indices. Please cite this working paper when using the supertract HPIs. The data files can be downloaded in a compressed format saved in the following file types:
A revised version of this paper has undergone external peer-review and has been accepted for publication in Real Estate Economics (forthcoming).
Related papers: FHFA Working Paper 16-01: Local House Price Dynamics: New Indices and Stylized Facts