Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach

Authors

  • Jingwen Yang

DOI:

https://doi.org/10.62051/ijcsit.v3n1.32

Keywords:

Predictive analytics, Investment strategies, Real estate market, Machine learning models, Neural networks

Abstract

The study investigates the application of advanced predictive analytics in formulating investment strategies for the international real estate market. Utilizing extensive datasets, including real estate transaction records, economic indicators, and market reports, covering over ten years of data from 2010 to 2020 across multiple regions, we implemented predictive models such as linear regression, decision trees, random forests, support vector machines (SVM), neural networks, and gradient boosting machines (GBM). The results indicate that AI and machine learning models significantly outperform traditional statistical methods in forecasting market trends. Specifically, the neural network model achieved an R² of 0.822, while the random forest model attained an R² of 0.804, compared to an R² of 0.751 for the traditional linear regression model. Performance varied across regions and property types; for instance, the neural network model's MAE and RMSE in North America were 17,500 and 26,800, respectively, whereas in the Asia-Pacific region, the MAE and RMSE were 20,100 and 29,800. Additionally, these models resulted in an average reduction of 12.5% in operational costs and an 18.3% improvement in customer satisfaction. This study systematically integrates and compares multiple advanced predictive models, demonstrating that data-driven investment strategies offer significant competitive advantages in the real estate sector. These findings provide robust evidence supporting the use of predictive analytics to optimize investment decisions and highlight the transformative impact of these technologies on the real estate industry.

References

Park, B., & Bae, J. K. (2015). Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data. Expert systems with applications, 42(6), 2928-2934.

Yao, Y. (2024). Application of Artificial Intelligence in Smart Cities: Current Status, Challenges and Future Trends. International Journal of Computer Science and Information Technology, 2(2), 324-333.

Yao, Y. (2024). Digital Government Information Platform Construction: Technology, Challenges and Prospects. International Journal of Social Sciences and Public Administration, 2(3), 48-56.

Djenouri, D., Laidi, R., Djenouri, Y., & Balasingham, I. (2019). Machine learning for smart building applications: Review and taxonomy. ACM Computing Surveys (CSUR), 52(2), 1-36.

Lin, L., Wang, F., Xie, X., & Zhong, S. (2017). Random forests-based extreme learning machine ensemble for multi-regime time series prediction. Expert Systems with Applications, 83, 164-176.

Zhang, Y., Yang, K., Wang, Y., Yang, P., & Liu, X. (2023, July). Speculative ECC and LCIM Enabled NUMA Device Core. In 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) (pp. 624-631). IEEE.

Lin, S., Zheng, H., Han, B., Li, Y., Han, C., & Li, W. (2022). Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. Acta Geotechnica, 17(4), 1477-1502.

Xia, Y., Liu, S., Yu, Q., Deng, L., Zhang, Y., Su, H., & Zheng, K. (2023). Parameterized Decision-making with Multi-modal Perception for Autonomous Driving. arXiv preprint arXiv:2312.11935.

Yu, L., Wang, S., & Lai, K. K. (2009). A neural-network-based nonlinear metamodeling approach to financial time series forecasting. Applied Soft Computing, 9(2), 563-574.

Padhi, D. K., Padhy, N., Bhoi, A. K., Shafi, J., & Ijaz, M. F. (2021). A fusion framework for forecasting financial market direction using enhanced ensemble models and technical indicators. Mathematics, 9(21), 2646.

Qiu, L., & Liu, M. (2024). Innovative Design of Cultural Souvenirs Based on Deep Learning and CAD.

Menin Machado, G. A. B. R. I. E. L. A. (2018). Big data analytics for real estate asset management.

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial marketing management, 69, 135-146.

Liu, M., & Li, Y. (2023, October). Numerical analysis and calculation of urban landscape spatial pattern. In 2nd International Conference on Intelligent Design and Innovative Technology (ICIDIT 2023) (pp. 113-119). Atlantis Press.

Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986.

Venkatesan, S., Lim, J., Ko, H., & Cho, Y. (2022). A machine learning based model for energy usage peak prediction in smart farms. Electronics, 11(2), 218.

Subbiah, S. S., & Chinnappan, J. (2021). Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review. Ingénierie des Systèmes d'Information, 26(1).

Downloads

Published

15-06-2024

Issue

Section

Articles

How to Cite

Yang, J. (2024). Data-Driven Investment Strategies in International Real Estate Markets: A Predictive Analytics Approach. International Journal of Computer Science and Information Technology, 3(1), 247-258. https://doi.org/10.62051/ijcsit.v3n1.32

Similar Articles

1-10 of 122

You may also start an advanced similarity search for this article.