Determinants of county-level rent levels: a Bayesian model averaging approach

Richard Cebula () and James William Saunoris

Applied Economics Letters, 2024, vol. 31, issue 16, 1565-1568

Abstract: This paper uses Bayesian Model Averaging (BMA) to account for model uncertainty and to identify the robust determinants of county-level rent levels for a panel of 111 counties and independent cities in Virginia from 2008 to 2017. Of the fifteen determinants tested, the results show that single-family home prices, per capita education spending, and educational attainment are robust and positively associated with rent levels, while unemployment rate, population density, labour force participation, and the percent of the population that is Latino are robust and negatively associated with rent levels.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13504851.2023.2203449 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:apeclt:v:31:y:2024:i:16:p:1565-1568

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEL20

DOI: 10.1080/13504851.2023.2203449

Access Statistics for this article

Applied Economics Letters is currently edited by Anita Phillips

More articles in Applied Economics Letters from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().