Abstract
The valuation of real estate is a fundamental process for the proper functioning of the market and the formulation of public policies. The mother of the traditional methodologies is the comparative method, which is based on data that is often incomplete or unreliable, especially in real estate markets with limited transparency. In contrast, online advertisements offer a wealth of unstructured information, which requires advanced analysis techniques. Even if they reflect price demands rather than real estate’s final market values, they provide a wealth of data that is particularly useful for market transparency. Real estate advertisements are also enriched with photographs and videos of the property. This paper proposes a methodological framework that integrates artificial intelligence techniques (natural language processing and computer vision) to extract structured features from text and photographs of advertisements. The resulting dataset feeds the comparative method of property valuation, applied to thousands of properties. Geographic analysis and neighborhood characteristics of properties enrich the proposed algorithm, covering all the available geodata that affect real estate values. The empirical analysis of apartments in the city of Thessaloniki demonstrates significant improvements in valuation accuracy and the completeness of the characteristics. At the same time, the prediction of the energy class of properties, among other traits that the proposed methodology can accurately calculate, further enriches the valuation process. The work highlights the potential integration of artificial intelligence into modern valuation practice, offering a transparent, scalable, and auditable tool for professionals and policymakers.