用图像评估产品质量，精准预测房价和售出速度。
Measuring Product Quality Using Images: The CLIP Q-Score and an Application to Real Estate
- 基于CLIP模型从图片提取客观质量评分
- 在50万张房产图上验证，能准确预测房价与租售周期
- 开源工具适合房地产、视觉评估等场景
CLIP Q-score是一种新颖、安全、完全可复现且计算高效的视觉数据质量度量方法，利用对比语言-图像预训练模型提取客观产品品质指标。本文介绍该技术，并在某在线平台约50万张房产图像数据上进行了广泛应用。我们开源的度量指标与大语言模型评估结果一致，对房屋市场售价和租金具有强大预测能力。此外，更高的CLIP Q-score与更好流动性（缩短挂牌时间）相关，尤其体现在出售类房产中。
原文摘要 · Abstract (English)
The CLIP Q-score is a novel, safe, fully reproducible, and computationally efficient method for extracting objective product quality metrics from visual data using contrastive language-image pre-training. We introduce the technique and provide an extensive application to real estate data from an online platform ($\sim500,000$ images). Our open-source metric aligns with LLM assessments and proves to be a powerful predictor of housing market prices for both sales and rentals. We also show that a higher CLIP Q-store is associated with better liquidity (reduced time on the market), especially for properties on sale.
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