| Title |
Neural network hyperparameter optimization for prediction of real estate prices in Helsinki / |
| Authors |
Kalliola, Jussi Oskari ; Kapočiūtė-Dzikienė, Jurgita ; Damaševičius, Robertas |
| DOI |
10.7717/peerj-cs.444 |
| Full Text |
|
| Is Part of |
PeerJ computer science.. London : PeerJ. 2021, vol. 7, art. no. e444, p. 1-25.. ISSN 2376-5992 |
| Keywords [eng] |
Artificial neural network ; Hyperparameter optimisation ; Prediction model ; Real estate prices |
| Abstract [eng] |
Accurate price evaluation of real estate is beneficial for many parties involved in real estate business such as real estate companies, property owners, investors, banks, and financial institutes. Artificial Neural Networks (ANNs) have shown promising results in real estate price evaluation. However, the performance of ANNs greatly depends upon the settings of their hyperparameters. In this paper, we apply and optimize an ANN model for real estate price prediction in Helsinki, Finland. Optimization of the model is performed by fine-tuning hyper-parameters (such as activation functions, optimization algorithms, etc.) of the ANN architecture for higher accuracy using the Bayesian optimization algorithm. The results are evaluated using a variety of metrics (RMSE, MAE, R2) as well as illustrated graphically. The empirical analysis of the results shows that model optimization improved the performance on all metrics (reaching the relative mean error of 8.3%). |
| Published |
London : PeerJ |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
