Predicting owner-occupied housing values using machine learning: an empirical investigation of California census tracts data

Prodosh E. Simlai

Journal of Property Research, 2021, vol. 38, issue 4, 305-336

Abstract: In this paper, we introduce machine-learning (ML) methods to evaluate one of the key concepts of real estate analysis – the prediction of housing prices in the presence of a large number of covariates. We use several supervised ML tools that are based on regularisation methods – notably Ridge, LASSO, and Elastic Net regressions – and discuss their relative performance in comparison to conventional OLS-based methods. Our empirical results show that the supervised ML methods provide a comprehensive description of the determinants of owner-occupied housing values in the census tracts of California. We find that, compared to the familiar worlds of OLS and WLS, the Ridge, LASSO, and Elastic Net regressions provide relatively better out-of-sample predictions. Among the benefits of shrinkage-based ML methods are their ability to resolve such issues as variable selection and overfitting.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/09599916.2021.1890187 (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:jpropr:v:38:y:2021:i:4:p:305-336

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJPR20

DOI: 10.1080/09599916.2021.1890187

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 ().