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

• URL: http://arxiv.org/abs/2309.00855v3
• Date: Thu, 14 Sep 2023 09:24:28 GMT
• Title: DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal
• Authors: Wei-Wei Du, Wei-Yao Wang, Wen-Chih Peng
• Abstract summary: 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 for equipping the real estate representations with prior domain knowledge. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines.
• Score: 15.404630852751547
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Learning to Discover Knowledge: A Weakly-Supervised Partial Domain Adaptation Approach [20.899013563493202]
  Domain adaptation has shown appealing performance by leveraging knowledge from a source domain with rich annotations. For a specific target task, it is cumbersome to collect related and high-quality source domains. In this paper, we propose a simple yet effective domain adaptation approach, termed as self-paced transfer classifier learning (SP-TCL)
  arXiv  Detail & Related papers  (2024-06-20T12:54:07Z)
• Real Estate Property Valuation using Self-Supervised Vision Transformers [2.1320960069210475]
  We propose a new method for property valuation that utilizes self-supervised vision transformers. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data.
  arXiv  Detail & Related papers  (2023-01-31T21:54:15Z)
• Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal [6.14249607864916]
  We propose a novel Neighbor Relation Graph Learning Framework (ReGram) for real estate appraisal. ReGram incorporates the relation between target transaction and surrounding neighbors with the attention mechanism. Experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-23T08:20:19Z)
• Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach [14.344142985726853]
  We study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can learn to adapt the sensitive attributes from a source domain for fair classification in the target domain.
  arXiv  Detail & Related papers  (2022-06-08T02:53:18Z)
• MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal [57.28018917017665]
  We propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. By acquiring and integrating multi-trivial urban data, we first construct a rich feature set to comprehensively profile real estate from multiple perspectives. An evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronouslytemporal dependencies among real estate transactions.
  arXiv  Detail & Related papers  (2021-07-12T03:51:44Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer [137.36099660616975]
  Unsupervised adaptation adaptation (UDA) aims to transfer knowledge from a related but different well-labeled source domain to a new unlabeled target domain. Most existing UDA methods require access to the source data, and thus are not applicable when the data are confidential and not shareable due to privacy concerns. This paper aims to tackle a realistic setting with only a classification model available trained over, instead of accessing to the source data.
  arXiv  Detail & Related papers  (2020-12-14T07:28:50Z)
• Active Feature Acquisition with Generative Surrogate Models [11.655069211977464]
  In this work, we consider models that perform active feature acquisition (AFA) and query the environment for unobserved features. Our work reformulates the Markov decision process (MDP) that underlies the AFA problem as a generative modeling task. We propose learning a generative surrogate model ( GSM) that captures the dependencies among input features to assess potential information gain from acquisitions.
  arXiv  Detail & Related papers  (2020-10-06T02:10:06Z)
• Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market [75.28009817291752]
  We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market.
  arXiv  Detail & Related papers  (2020-08-12T07:32:16Z)
• Towards Inheritable Models for Open-Set Domain Adaptation [56.930641754944915]
  We introduce a practical Domain Adaptation paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. We present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data.
  arXiv  Detail & Related papers  (2020-04-09T07:16:30Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.