A Survey of Methods and Input Data Types for House Price Prediction

Abstract

Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning and tailored methods, which could guide further research.