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dc.contributor.authorLorenz, F
dc.contributor.authorWillwersch, J
dc.contributor.authorCajias, M
dc.contributor.authorFuerst, F
dc.date.accessioned2022-06-29T19:46:51Z
dc.date.available2022-06-29T19:46:51Z
dc.date.issued2022-06-26
dc.identifier.issn1080-8620
dc.identifier.otherreec12397
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338527
dc.description.abstractWhile Machine Learning (ML) excels at predictive tasks, its inferential capacity is limited due to its complex non-parametric structure. This paper aims to elucidate the analytical behavior of ML through Interpretable Machine Learning (IML) in a real estate context. Using a hedonic ML approach to predict unit-level residential rents for Frankfurt, Germany, we apply a set of model-agnostic interpretation methods to decompose the rental value drivers and plot their trajectories over time. Living area and building age are the strongest predictors of rent, followed by proximity to CBD and neighborhood amenities. Our approach is able to detect the critical distances to these centers beyond which rents tend to decline more rapidly. Conversely, close proximity to hospitality facilities as well as public transport is associated with rental discounts. Overall, our results suggest that IML methods provide insights into algorithmic decision-making by illustrating the relative importance of hedonic variables and their relationship with rental prices in a dynamic perspective.
dc.languageen
dc.publisherWiley
dc.subjectORIGINAL ARTICLE
dc.subjectORIGINAL ARTICLES
dc.subjectblack box
dc.subjecthedonic modeling
dc.subjectinterpretable machine learning
dc.subjectrental estimation
dc.subjectresidential real estate
dc.titleInterpretable machine learning for real estate market analysis
dc.typeArticle
dc.date.updated2022-06-29T19:46:50Z
prism.publicationNameReal Estate Economics
dc.identifier.doi10.17863/CAM.85940
dcterms.dateAccepted2022-05-06
rioxxterms.versionofrecord10.1111/1540-6229.12397
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.orcidLorenz, F [0000-0002-9795-7700]
dc.contributor.orcidWillwersch, J [0000-0003-3102-9044]
dc.contributor.orcidCajias, M [0000-0002-0777-7459]
dc.contributor.orcidFuerst, F [0000-0003-2780-938X]
dc.identifier.eissn1540-6229
cam.issuedOnline2022-06-26


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