DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal

Wei Wei Du, Wei Yao Wang, Wen Chih Peng

研究成果: Conference contribution同行評審

摘要

The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.

原文English
主出版物標題CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
發行者Association for Computing Machinery
頁面4552-4558
頁數7
ISBN(電子)9798400701245
DOIs
出版狀態Published - 21 10月 2023
事件32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom
持續時間: 21 10月 202325 10月 2023

出版系列

名字International Conference on Information and Knowledge Management, Proceedings

Conference

Conference32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
國家/地區United Kingdom
城市Birmingham
期間21/10/2325/10/23

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