On the uncertainty of real estate price predictions
João Bastos and
Jeanne Paquette
Journal of Property Research, 2025, vol. 42, issue 1, 1-19
Abstract:
Uncertainty quantification associated with real estate appraisal has largely been overlooked in the literature. In this paper, we address this gap by analysing the uncertainty in automated property valuations using conformal prediction, a distribution-free procedure for constructing prediction intervals with valid coverage in finite samples. Through an empirical study of property prices in the San Francisco Bay Area, we find that prediction intervals obtained using conformal quantile regression have exact coverage. In contrast, prediction intervals obtained from non-conformal quantile regressions severely undercover the data. Furthermore, we show that the intervals adapt to various characteristics of the dwellings, which is crucial given the heterogeneous nature of real estate data. Indeed, we observe that larger and older properties, those in both low and high-income neighbourhoods, as well as those on the market for less than one year are more challenging to evaluate.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/09599916.2024.2403998 (text/html)
Access to full text is restricted to subscribers.
Related works:
Working Paper: On the uncertainty of real estate price predictions (2024) 
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:jpropr:v:42:y:2025:i:1:p:1-19
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJPR20
DOI: 10.1080/09599916.2024.2403998
Access Statistics for this article
Journal of Property Research is currently edited by Bryan MacGregor
More articles in Journal of Property Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().