This project aims to build a machine learning regression model to predict the market prices of fine stringed instruments, including violins, violas, cellos, bows, etc. The goal is to estimate fair market values based on key attributes such as maker, year of construction, instrument type, origin, and provenance proxies.

The model will be trained on high-quality historical auction data collected from Tarisio’s Cozio Archive. This database provides real realized sale prices, detailed maker information, temporal trends, and condition-related notes, offering reliable ground truth for market-based valuation.

In the context of insurance/insurtech, accurate valuation can reduce barriers to insurance access. Many customers lack recent receipts or find professional appraisals time-consuming and costly, which causes them to skip coverage or underinsure. Our model delivers instant, data-driven price benchmarks to guide users toward realistic values upfront. It helps overcome appraisal variability and value changes over time, ultimately supporting better underwriting decisions and reducing under- or over-insurance issues.