Dataset Evaluating Human–Machine Collaboration through a Comparative Analysis of Experts, Machine Learning, and Hybrid Approaches in Real Estate Valuation

It contains expert assessments of newly built apartments sold in Vienna in 2023, collected under three experimental conditions: (i) limited information, (ii) state-of-the-art expert valuation methods, and (iii) collaboration between experts and the ML model. The dataset further includes the corresponding model predictions and ground-truth transaction prices, enabling a systematic comparison of predictive accuracy and task efficiency across valuation strategies.

This dataset was used to analyze the relative strengths of standalone ML models, human expertise, and hybrid human–AI collaboration in residential price prediction, with particular emphasis on accuracy, robustness, and time efficiency. Context and methodology The data set was created to predict of apartment prices 1 to 7 years into the future The data set was used to test of transfer learning capabilities Data collected from apartment ownership transactions, enriched by contextual information from OpenStreetMap.

The features added were selected based on experience with valuation and discussions on potentially relevant factors All personal data were removed from the expert survey and the transaction data Technical details csv-File with raw data; further explanation in ReadMe.txt Python-script to analyse the data: PSFL Licenses Data: CC by 4.0 International Code: PSFL 2.0