ARCHIVES

1. B. Yang and B. Cao, “Research on ensemble learning-based housing price prediction model,” Big Geospatial Data and Data Science,
vol. 1, no. 1, pp. 1–8, 2018.
2. J. Q. Guo, S. H. Chiang, M. Liu, C. C. Yang, and K. Y. Guo, “Can machine learning algorithms associated with text mining from
internet data improve housing price prediction performance?” International Journal of Strategic Property Management, vol. 24, no.
5, pp. 300–312, 2020.
3. J. M. Montero, R. Minguez, and G. Fernandez-Aviles, ´ “Housing price prediction: parametric versus semi-parametric spatial
hedonic models,” Journal of Geographical Systems, vol. 20, no. 1, pp. 27–55, 2018.
4. A.R. A. Yakub, M. Hishamuddin, K. Ali, R. B. A. J. Achu, and A. F. Folake, “The effect of adopting micro and macro-economic
variables on real estate price prediction models using ann: a systematic literature,” Journal of Critical Reviews, vol. 7, no. 11, pp.
492–498, 2020.
5. L. Yu, C. Jiao, H. Xin, Y. Wang, and K. Wang, “Prediction on housing price based on deep learning,” International Journal of
Computer and Information Engineering, vol. 12, no. 2, pp. 90–99, 2018.
6. J. Lee and J. P. Ryu, “Prediction of housing price index using artificial neural network,” Journal of the Korea Academia-Industrial
cooperation Society, vol. 22, no. 4, pp. 228–234, 2021.
7. R. Liu and L. Liu, “Predicting housing price in China based on long short-term memory incorporating modified genetic algorithm,”
Soft Computing, vol. 23, no. 22, pp. 11829–11838, 2019.
8. S. Muralidharan, K. Phiri, S. K. Sinha, and B. Kim, “Analysis and prediction of real estate prices: a case of the Boston housing
market,” Issues in Information Systems, vol. 19, no. 2, pp. 109–118, 2018.
9. K. S. Yoon, J. M. Lee, S. J. Ko, H. J. Kim, and J. H. Kim, “Analysing impact of price ceiling system on housing market using machine
learning,” Journal of the Architectural Institute of Korea, vol. 37, no. 8, pp. 221–228, 2021.
10. Y. R. Lin and C. C. Chen, “House price prediction in taipei by machine learning models,” International Journal of Design, Analysis
and Tools for Integrated Circuits and Systems, vol. 8, no. 1, pp. 89–94, 2019.
11. M. Ozdemi̇r, K. Yildiz, and B. Buyuktanir, “Housing price estimation with deep learning: a case study of sakarya Turkey,” Bilecik
Seyh Edebali Universitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 138–151.
12. J. Hong, H. Choi, and W. S. Kim, “A house price valuation based on the random forest approach: the mass appraisal of residential
property in South Korea,” International Journal of Strategic Property Management, vol. 24, no. 3, pp. 140–152, 2020.
13. C. Li, H. Zhu, X. Ye et al., “Study on average housing prices in the inland capital cities of China by night-time light remote sensing
and official statistics data,” Scientific Reports, vol. 10, no. 1, pp. 7732–7750, 2020.
14. J. H. Chen, T. Ji, M. C. Su, H. H. Wei, V. T. Azzizi, and S. C. Hsu, “Swarm-inspired data-driven approach for housing market
segmentation: a case study of Taipei city,” Journal of Housing and the Built Environment, vol. 36, no. 4, pp. 1787–1811, 2021.
15. D. Cao and X. Tian, “Raw anode volume density prediction algorithm based on the genetic algorithm,” SN Computer Science, vol. 3,
no. 5, pp. 354–372, 2022.