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Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach

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Bibliographic Details
Main Authors: Juergen, Deppner, Benedict von, Ahlefeldt-Dehn, Eli, Beracha
Format: Book
Language:English
Published: Springer 2023
Subjects:
NCREIF Property Index
machine learning algorithms
Online Access:https://link.springer.com/article/10.1007/s11146-023-09944-1
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7813
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spelling oai:localhost:PNK-78132023-04-12T03:49:25Z Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach Juergen, Deppner Benedict von, Ahlefeldt-Dehn Eli, Beracha NCREIF Property Index machine learning algorithms CC BY In this article, we examine the accuracy and bias of market valuations in the U.S. commercial real estate sector using properties included in the NCREIF Property Index (NPI) between 1997 and 2021 and assess the potential of machine learning algorithms (i.e., boosting trees) to shrink the deviations between market values and subsequent transaction prices. Under consideration of 50 covariates, we find that these deviations exhibit structured variation that boosting trees can capture and further explain, thereby increasing appraisal accuracy and eliminating structural bias. The understanding of the models is greatest for apartments and industrial properties, followed by office and retail buildings. This study is the first in the literature to extend the application of machine learning in the context of property pricing and valuation from residential use types and commercial multifamily to office, retail, and industrial assets. 2023-04-12T03:49:25Z 2023-04-12T03:49:25Z 2023 Book https://link.springer.com/article/10.1007/s11146-023-09944-1 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7813 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic NCREIF Property Index
machine learning algorithms
spellingShingle NCREIF Property Index
machine learning algorithms
Juergen, Deppner
Benedict von, Ahlefeldt-Dehn
Eli, Beracha
Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
description CC BY
format Book
author Juergen, Deppner
Benedict von, Ahlefeldt-Dehn
Eli, Beracha
author_facet Juergen, Deppner
Benedict von, Ahlefeldt-Dehn
Eli, Beracha
author_sort Juergen, Deppner
title Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
title_short Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
title_full Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
title_fullStr Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
title_full_unstemmed Boosting the Accuracy of Commercial Real Estate Appraisals: An Interpretable Machine Learning Approach
title_sort boosting the accuracy of commercial real estate appraisals: an interpretable machine learning approach
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s11146-023-09944-1
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7813
_version_ 1762999678213816320
score 8.891145

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