Cookies?
Library Header Image
LSE Research Online LSE Library Services

Land use regulation, homeownership and wealth inequality

Hilber, Christian A. L. ORCID: 0000-0002-1352-495X and Turner, Tracy M. (2024) Land use regulation, homeownership and wealth inequality. CEP Discussion Papers (CEPDP2003). London School of Economics and Political Science. Centre for Economic Performance, London, UK.

[img] Text - Published Version
Download (944kB)

Abstract

We examine the role that housing market regulatory restrictiveness plays in differentially affecting the net wealth of owners and renters over time, and its contribution to wealth inequality. In tightly regulated desirable cities, house prices and rents rise strongly in response to growing demand. Rising prices financially benefit existing homeowners. Rising rents hurt renters. Because credit constraints prevent many households from becoming homeowners, this can lead to growing differences in wealth accumulation between homeowners and renters and, consequently, rising wealth inequality. Employing the confidential version of the Panel Study of Income Dynamics (PSID), we explore to what extent changes in household net wealth can be explained by regulatory restrictiveness and demand shock-induced spatial differences in house price growth. We find that, accounting for sorting, a household with average characteristics that owns instead of rents in a tightly regulated location accumulates 56% more in net wealth between 1999 and 2019. This effect explains 59% of the observed difference in net wealth accumulation between actual owners and renters in these locations, consistent with an observed increase in the Gini-coefficient of wealth inequality during our sample period of 13%. In less regulated metro areas, we do not find a difference in wealth accumulation by homeownership status nor rising wealth inequality. Examining homeowners only and accounting for sorting, our findings suggest that if a homeowner with average characteristics had resided in a tightly rather than loosely regulated metro area, their predicted twenty-year net wealth increase would be 81% higher. We examine transition and timing effects and find theoretically plausible results that the housing boom yielded net wealth changes that varied by regulatory status, but the housing bust did not. We conduct robustness checks that examine the potential endogeneity of initial homeownership, account for unobserved heterogeneity and test for homeowner cash-out/reinvest behavior. In a falsification test, we show that our findings cannot be explained by correlations between local house price growth, a rising college premium and local variation in stock investment behavior. Taken as a whole, our findings imply that expected gains provide powerful financial incentives to existing homeowners in tightly regulated markets to maintain regulatory stringency, further exacerbating housing unaffordability and wealth inequality.

Item Type: Monograph (Discussion Paper)
Official URL: https://cep.lse.ac.uk/_new/publications/discussion...
Additional Information: © 2024 The Author(s)
Divisions: Geography & Environment
Subjects: H Social Sciences > HC Economic History and Conditions
H Social Sciences > HT Communities. Classes. Races
H Social Sciences > HB Economic Theory
JEL classification: R - Urban, Rural, and Regional Economics > R1 - General Regional Economics > R11 - Regional Economic Activity: Growth, Development, and Changes
R - Urban, Rural, and Regional Economics > R2 - Household Analysis > R21 - Housing Demand
R - Urban, Rural, and Regional Economics > R3 - Production Analysis and Firm Location > R31 - Housing Supply and Markets
R - Urban, Rural, and Regional Economics > R5 - Regional Government Analysis > R52 - Land Use and Other Regulations
G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing; Trading volume; Bond Interest Rates
Date Deposited: 24 Feb 2025 10:57
Last Modified: 24 Feb 2025 11:27
URI: http://eprints.lse.ac.uk/id/eprint/126794

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics