过去15年来，应对城市衰败问题日益受到关注。评估城市衰败对于指导城市规划、确定修复目标及保障公共健康至关重要，然而传统住宅衰败调查因人力成本高、周期长而难以大规模维持。本研究提出了一种基于开源大型视觉-语言模型（Large Vision-Language Models, LVLMs）的多视角住宅衰败估算可扩展框架。通过结构化提示词引导模型评估房屋属性，包括屋顶完整性、墙体损坏以及破损或封堵的开口，从而生成二元判定及失修概率估计。为评估这些视觉判定的性能，我们将多个模型的专业人工标注结果进行了对比，其中包括基于XGBoost的集成堆叠方法和加权评分系统。结果表明：(i) 多街道视角有助于提高准确性；(ii) 不同的大型视觉-语言模型在推理能力上各具优势；(iii) 集成学习器优于单个基础模型，增强了在所有住宅条件及衰败评估中的鲁棒性。该方法的应用可实现对住房存量状况的低成本跟踪与管理，为传统衰败调查提供了一种可定期更新的补充手段。

Addressing urban blight has seen increased focus in the past 15 years. Assessing urban blight is essential for guiding urban planning, targeting rehabilitation, and safeguarding public health, yet traditional residential blight surveys are difficult to maintain at scale due to the labor-intensive cost and long-term cycle. This study introduced a scalable framework for estimating residential blight using open-source large vision-language models on multiple views. Structured prompts guided models to evaluate housing attributes, including roof integrity, wall damage, and broken or boarded openings, producing both binary assessments and probabilistic estimates of disrepair. To evaluate the performance of these visual assessments, we compared professional human annotations of these features across several models, including an ensemble stacking approach based on XGBoost and a weighted scoring system. Results showed that (i) multiple street views can contribute to the improvement of accuracy, (ii) large vision-language models have different strengths of inference, (iii) the ensemble learner outperforms individual base models, enhancing robustness across all residential conditions and blight assessment. The practical application of the method allows low-cost tracking and management of housing stock conditions, providing a regularly updatable complement to traditional blight surveys.