---
title: Real Estate Appraisals: ML vs Traditional Methods
url: https://www.ml-quant.com/papers/repec/spr-gjorer-v-9-y-2023-i-2-d-10-1365-s41056-022-00063-1/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:spr:gjorer:v:9:y:2023:i:2:d:10.1365_s41056-022-00063-1
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1365%2Fs41056-022-00063-1%3Bh%3Drepec%3Aspr%3Agjorer%3Av%3A9%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1365_s41056-022-00063-1
featured: 2023-11-29
citations: unknown
topic: ML & AI Methods
---


# Real Estate Appraisals: ML vs Traditional Methods

ML vs Traditional Methods: Research indicates that XGBoost, a machine learning method, offers the most precise estimates in automated property valuations, suggesting a need for regulators to use multiple methods.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1365%2Fs41056-022-00063-1%3Bh%3Drepec%3Aspr%3Agjorer%3Av%3A9%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1365_s41056-022-00063-1
- Identifier: RePEc:spr:gjorer:v:9:y:2023:i:2:d:10.1365_s41056-022-00063-1
- Released: 2023-11-29
- First featured: Quant Letter No. 27 (2023-11-29): https://www.ml-quant.com/issues/2023-11-29/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

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