Real estate has been a dominant industry in many countries. One problem for real estate companies is determining the most valuable area before starting a new project. Previous studies on this issue mainly focused on market needs and economic prospects, ignoring the impact of natural disasters. We observe that natural disasters are important for real estate area selection because they will introduce considerable losses to real estate enterprises. Following this observation, we first develop a self-defined new indicator named Average Loss Ratio to predict the losses caused by natural disasters in an area. Then, we adopt the existing ARIMA model to predict the Average Loss Ratio of an area. After that, we propose to integrate the TOPSIS model and the Grey Prediction Model to rank the recommendation levels for candidate areas, thereby assisting real estate companies in their decision-making process. We conduct experiments on real datasets to validate our proposal, and the results suggest the effectiveness of the proposed method.
References
[1]
Mauri, M., Rancati, G., Riva, G. and Gaggioli, A. (2024) Comparing the Effects of Immersive and Non-Immersive Real Estate Experience on Behavioral Intentions. Computers in Human Behavior, 150, Article ID: 107996. https://doi.org/10.1016/j.chb.2023.107996
[2]
Baur, K., Rosenfelder, M. and Lutz, B. (2023) Automated Real Estate Valuation with Machine Learning Models Using Property Descriptions. Expert Systems with Applications, 213, Article ID: 119147. https://doi.org/10.1016/j.eswa.2022.119147
[3]
Sandeep Kumar, E., Talasila, V. and Pasumarthy, R. (2021) A Novel Architecture to Identify Locations for Real Estate Investment. International Journal of Information Management, 56, Article ID: 102012. https://doi.org/10.1016/j.ijinfomgt.2019.09.008
[4]
Xiong, X., Guo, H. and Hu, X. (2020) The Housing Demand Analysis and Prediction of the Real Estate Based on the AWGM (1, n) Model. Grey Systems: Theory and Application, 11, 222-240. https://doi.org/10.1108/gs-09-2019-0035
[5]
Jie, Z. and Qi, C.H. (2023) An Equilibrium Analysis of the Impact of Real Estate Price Volatility on Macroeconomics Based on Ant Colony Algorithm. Journal of Combinatorial Optimization, 45, Article No. 70. https://doi.org/10.1007/s10878-023-00995-x
[6]
Lewis, E.J. (2022) A Literature Review on the Marketing Partnership Challenge. International Journal of Smart Education and Urban Society, 13, 1-47. https://doi.org/10.4018/ijseus.299045
[7]
Di Pinto, V. and Rinaldi, A.M. (2018) A Configurational Approach Based on Geographic Information Systems to Support Decision-Making Process in Real Estate Domain. Soft Computing, 23, 2853-2862. https://doi.org/10.1007/s00500-018-3142-9
[8]
Efthymiou, D. and Antoniou, C. (2015) Measuring the Effects of Transportation Infrastructure Location on Real Estate Prices and Rents: Investigating the Current Impact of a Planned Metro Line. EURO Journal on Transportation and Logistics, 3, 179-204. https://doi.org/10.1007/s13676-013-0030-4
[9]
Boston Consulting Group (2023) An Insurance Risk Framework for Climate Adaptation.
[10]
Munich, R.E. (2022) Hurricanes, Cold Waves, Tornadoes: Weather Disasters in USA Dominate Natural Disaster Losses in 2021. https://www.munichre.com/en/company/media-relations/media-information-and-corporate-news/media-information/2022/natural-disaster-losses-2021.html
[11]
Yuan, J. (2022) Real Estate Risk Based on Combination of Factor Analysis and Spatial Statistics. Journal of Computational Methods in Sciences and Engineering, 22, 2007-2021. https://doi.org/10.3233/jcm-226379
[12]
Rabiei-Dastjerdi, H., McArdle, G., Matthews, S.A. and Keenan, P. (2020) Gap Analysis in Decision Support Systems for Real-Estate in the Era of the Digital Earth. International Journal of Digital Earth, 14, 121-138. https://doi.org/10.1080/17538947.2020.1808719
[13]
Zhang, X., Ma, Y. and Wang, M. (2023) An Attention-Based Logistic-CNN-Bilstm Hybrid Neural Network for Credit Risk Prediction of Listed Real Estate Enterprises. Expert Systems, 41, e13299. https://doi.org/10.1111/exsy.13299
[14]
He, W., Zhang, Y., Kong, D., Li, S., Wu, Z., Zhang, L., et al. (2024) Promoting Green-Building Development in Sustainable Development Strategy: A Multi-Player Quantum Game Approach. Expert Systems with Applications, 240, Article ID: 122218. https://doi.org/10.1016/j.eswa.2023.122218
[15]
Wu, S. (2023) Green Building Development for a Sustainable Environment with Artificial Intelligence Technology. International Journal of Computer Applications in Technology, 73, 203-216. https://doi.org/10.1504/ijcat.2023.135590
[16]
Munawar, H.S., Qayyum, S., Ullah, F. and Sepasgozar, S. (2020) Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis. Big Data and Cognitive Computing, 4, Article 4. https://doi.org/10.3390/bdcc4020004
[17]
Battisti, E., Shams, S.M.R., Sakka, G. and Miglietta, N. (2019) Big Data and Risk Management in Business Processes: Implications for Corporate Real Estate. Business Process Management Journal, 26, 1141-1155. https://doi.org/10.1108/bpmj-03-2019-0125
[18]
Hamilton, J. (1994) Time Series Analysis. Princeton University Press.
[19]
Guerra, R.R., Vizziello, A., Savazzi, P., Goldoni, E. and Gamba, P. (2024) Forecasting Lorawan RSSI Using Weather Parameters: A Comparative Study of ARIMA, Artificial Intelligence and Hybrid Approaches. Computer Networks, 243, Article ID: 110258. https://doi.org/10.1016/j.comnet.2024.110258
[20]
Yoon, K. and Hwang, C.L. (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag.
[21]
Pandey, V., Komal and Dincer, H. (2023) A Review on TOPSIS Method and Its Extensions for Different Applications with Recent Development. Soft Computing, 27, 18011-18039. https://doi.org/10.1007/s00500-023-09011-0
[22]
Liu, G. and Yu, J. (2007) Gray Correlation Analysis and Prediction Models of Living Refuse Generation in Shanghai City. Waste Management, 27, 345-351. https://doi.org/10.1016/j.wasman.2006.03.010
[23]
Cai, K. and Wu, L. (2024) Grey Prediction of Carbon Emission and Carbon Peak in Several Developing Countries. Engineering Applications of Artificial Intelligence, 133, Article ID: 108210. https://doi.org/10.1016/j.engappai.2024.108210
[24]
Zhai, S., Li, G., Wu, G., Hou, M. and Jia, Q. (2023) Cooperative Task Allocation for Multi Heterogeneous Aerial Vehicles Using Particle Swarm Optimization Algorithm and Entropy Weight Method. Applied Soft Computing, 148, Article ID: 110918. https://doi.org/10.1016/j.asoc.2023.110918
