Evaluating the potential of neural discrete choice model with siamese networks for a real estate sales forecasting problem

The paper considers the problem of reproducing the process of purchasing real estate, the solution of which will allow testing both existing and future dynamic pricing algorithms, building predictions of buyers' preferences and forming a demand curve. As a solution, it is proposed to use an approach based on the use of discrete choice models, which are widely represented in the economic literature and have a wide range of applications in the field of studying consumer behavior and preferences in competitive markets. This paper presents a new discrete choice model that uses a neural network to form the utility of a real estate object. An approach to training the model through Siamese neural networks is proposed. The article also proposes a non-standard architecture of the main neural network, which allows avoiding the loss of convergence during its training. The paper simulates the process of purchasing real estate using classical models based on logistic regression with random coefficients and using a neural network model, and compares them. As a result of numerical experiments, a noticeable advantage of the proposed neural network approach is shown. Using a permutation test, the statistical significance of the obtained results is proved.

1. McFadden D. Economic Choices. American Economic Review. 2001;91(3):351–378. https://doi.org/10.1257/aer.91.3.351

2. Sifringer B., Lurkin V., Alahi A. Enhancing Discrete Choice Models with Neural Networks. In: 18th Swiss Transport Research Conference (STRC 2018), 16–18 May 2018, Monte Verità, Switzerland. 2018. pp. 1–13.

3. Jeng J.-M., Fesenmaier D.R. A Neural Network Approach to Discrete Choice Modeling. Journal of Travel & Tourism Marketing. 1996;5(1-2):119–144. https://doi.org/10.1300/J073v05n01_08

4. Haj-Yahia Sh., Mansour O., Toledo T. Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models. arXiv. URL: https://arxiv.org/abs/2306.00016 [Accessed 20th December 2024].

5. Aouad A., Désir A. Representing Random Utility Choice Models with Neural Networks. arXiv. URL: https://arxiv.org/abs/2207.12877 [Accessed 20th December 2024].

6. Yang Ya., Zhai P. Click-Through Rate Prediction in Online Advertising: A Literature Review. Information Processing & Management. 2022;59(2). https://doi.org/10.2139/ssrn.4036054

7. Guo Ya., Wang M., Li X. An Interactive Personalized Recommendation System Using the Hybrid Algorithm Model. Symmetry. 2017;9(10). https://doi.org/10.3390/sym9100216

8. Craparotta G., Thomassey S., Biolatti A. A Siamese Neural Network Application for Sales Forecasting of New Fashion Products Using Heterogeneous Data. International Journal of Computational Intelligence Systems. 2019;12:1537–1546. https://doi.org/10.2991/ijcis.d.191122.002

9. Van Cranenburgh S., Garrido-Valenzuela F. Computer vision-enriched discrete choice models, with an application to residential location choice. arXiv. URL: https://arxiv.org/abs/2308.08276 [Accessed 20th December 2024].

10. Ruan G., Kirschen D.S., Zhong H., Xia Q., Kang C. Estimating Demand Flexibility Using Siamese LSTM Neural Networks. IEEE Transactions on Power Systems. 2021;37(3):2360–2370. https://doi.org/10.48550/arXiv.2109.01258

11. Train K.E. Discrete Choice Methods with Simulation. Cambridge; New York: Cambridge University Press; 2009. 388 p. https://doi.org/10.1017/CBO9780511805271

12. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks.1991;4(2):251–257. https://doi.org/10.1016/0893-6080(91)90009-T

13. Dong G., Kweon Y., Park B.B., Boukhechba M. Utility-Based Route Choice Behavior Modeling Using Deep Sequential Models. Journal of Big Data Analytics in Transportation. 2022;4(2-3):119–133. https://doi.org/10.1007/s42421-022-00058-3

14. Wang F., Ross C.L. Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model. Transportation Research Record: Journal of the Transportation Research Board. 2018;2672(47). https://doi.org/10.1177/0361198118773556