Different automated valuation modelling techniques evaluated over time

Michael Mayer, Steven Bourassa, Martin Hoesli and Donato Scognamiglio

ERES from European Real Estate Society (ERES)

Abstract: We use a rich data set consisting of 123,000 houses sold in Switzerland between 2004 and 2017 to investigate different automated valuation techniques in settings where the models are updated regularly. We apply six methods (linear regression, robust regression, mixed effects regression, gradient boosting, random forests, and neural networks) to both moving window and extending window models. With respect to the criteria of appraisal accuracy and stability, the preferred methods are robust regression using moving windows, gradient boosting using extending windows, or mixed effects regression for either strategy.

Keywords: automated valuation; Machine Learning; Statistics (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2018-01-01
New Economics Papers: this item is included in nep-big and nep-cmp
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eres.architexturez.net/doc/oai-eres-id-eres2018-40 (text/html)

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:arz:wpaper:eres2018_40

Access Statistics for this paper

More papers in ERES from European Real Estate Society (ERES) Contact information at EDIRC.
Bibliographic data for series maintained by Architexturez Imprints ().