EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation

• URL: http://arxiv.org/abs/2503.12344v1
• Date: Sun, 16 Mar 2025 03:49:52 GMT
• Title: EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation
• Authors: Wei-Wei Du, Yung-Chien Wang, Wen-Chih Peng,
• Abstract summary: We propose an LLM- Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS.<n>It provides the customizable missing value imputation technique, and addresses the opaqueness of prediction.<n>We generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.
• Score: 9.741952588343086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these challenges, we propose an LLM-Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS, which provides the customizable missing value imputation technique, and addresses the opaqueness of prediction by providing the feature-wise explanation generated by LLM. The dynamic nearest neighbor search finds similar properties depending on different application scenarios by property configuration set by users (e.g., house age as criteria for the house in rural areas, and locations for buildings in urban areas). Motivated by the human appraisal procedure, we generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.

Related papers

• FADE: Why Bad Descriptions Happen to Good Features [14.00042287629001]
  We introduce FADE: Feature Alignment to Description Evaluation.<n>FADE is a scalable framework for evaluating feature-description alignment.<n>We apply FADE to analyze existing open-source feature descriptions, and assess key components of automated interpretability pipelines.
  arXiv  Detail & Related papers  (2025-02-24T09:28:35Z)
• Latent Factor Models Meets Instructions:Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• Modeling the Feedback of AI Price Estimations on Actual Market Values [0.0]
  Public availability of Artificial Intelligence generated information can change the markets forever. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, is validated in simulations.
  arXiv  Detail & Related papers  (2024-03-13T03:44:13Z)
• Introducing User Feedback-based Counterfactual Explanations (UFCE) [49.1574468325115]
  Counterfactual explanations (CEs) have emerged as a viable solution for generating comprehensible explanations in XAI. UFCE allows for the inclusion of user constraints to determine the smallest modifications in the subset of actionable features. UFCE outperforms two well-known CE methods in terms of textitproximity, textitsparsity, and textitfeasibility.
  arXiv  Detail & Related papers  (2024-02-26T20:09:44Z)
• HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition [92.17397504834825]
  HD-Eval is a framework that iteratively aligns large language models evaluators with human preference. HD-Eval inherits the essence from the evaluation mindset of human experts and enhances the alignment of LLM-based evaluators. Extensive experiments on three evaluation domains demonstrate the superiority of HD-Eval in further aligning state-of-the-art evaluators.
  arXiv  Detail & Related papers  (2024-02-24T08:01:32Z)
• Improving Real Estate Appraisal with POI Integration and Areal Embedding [2.977678192707268]
  This study focuses on two pivotal challenges in real estate appraisal methods. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal.
  arXiv  Detail & Related papers  (2023-11-20T14:48:09Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• 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)
• Visual Perception of Building and Household Vulnerability from Streets [0.294944680995069]
  In developing countries, building codes often are outdated or not enforced. A large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. We propose an evaluation framework that is cost-efficient for first capture and future updates.
  arXiv  Detail & Related papers  (2022-05-28T15:35:47Z)
• 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)

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.