Aggregated Housing Price Predictions with No Information About Structural Attributes—Hedonic Models: Linear Regression and a Machine Learning Approach

A number of studies have shown that, in hedonic models, the structural attributes of real property have a greater influence on price than external attributes related to location and the immediate neighbourhood. This makes it necessary to include detailed information about structural attributes when predicting prices using regression models and machine learning algorithms and makes it difficult to study the influence of external attributes. In our study of asking prices on the primary residential market in Warsaw (Poland), we used a methodology we developed to determine price indices aggregated to micro-markets, which we further treated as a dependent variable. The analysed database consisted of 10,135 records relating to 2444 residential developments existing as offers on the market at the end of each quarter in the period 2017–2021. Based on these data, aggregated price level indices were determined for 503 micro-markets in which primary market offers were documented. Using the analysed example, we showed that it is possible to predict the value of aggregated price indices based only on aggregated external attributes—location and neighbourhood. Depending on the model, we obtained an R2 value of 75.8% to 82.9% for the prediction in the set of control observations excluded from building the model.