Prediction of Apartment Price Considering Socio Economic and Crime Rates Factors in DKI Jakarta
DOI:
https://doi.org/10.25008/ijadis.v4i2.1294Keywords:
Machine Learning, Gradient Boosting , Apartment price , Socio Economic , Crime Rates, Prediction, Data MiningAbstract
Investing in real estate properties in Indonesia is highly lucrative due to their consistent appreciation in value. Amongst the various property types, apartments are particularly favored for investment in limited land space. However, determining the value of apartments is often subjective and lacks quantitative measures. To address this issue, this study develops prediction models to predict rental prices and asset value based on apartment specifications, socio-economic factors, and crime rates. Machine learning models employed include Random Forest, Decision Tree, and Gradient Boosting Machine. The findings show Gradient Boosting Machine exhibits the highest accuracy in predicting apartment rental and sale prices, achieving R² values of 0.9230 and 0.8460, respectively. The study also highlights the significant influence of socio-economic factors and crime rates on the performance of the models, contributing between 0.09 and 0.22 with an average of 0.14, as indicated by the improved R² values. This study demonstrate that these models can be valuable tools for real estate investors and professionals seeking quantitative measures to determine the value of apartments. By incorporating apartment specifications, socio-economic factors, and crime rates, the models can provide objective insights into the potential rental income and asset value of apartments.
Downloads
References
BPS, "Kepadatan Penduduk menurut Provinsi (jiwa/km2), 2019-2021," 2021. https://www.bps.go.id/indicator/12/141/1/kepadatan-penduduk-menurut-provinsi.html (accessed Jun. 09, 2023).
E. Rustiadi, A. E. Pravitasari, Y. Setiawan, S. P. Mulya, D. O. Pribadi, and N. Tsutsumida, "Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions," Cities, vol. 111, p. 103000, 2021. https://doi.org/10.1016/j.cities.2020.103000
M. Nethercote and R. Horne, "Ordinary vertical urbanisms: City apartments and the everyday geographies of high-rise families," Environ Plan A, vol. 48, no. 8, pp. 1581-1598, 2016. https://doi.org/10.1177/0308518X16645104
R. A. Komalawati and J. Lim, "Reality of compact development in a developing country: focusing on perceived quality of life in Jakarta, Indonesia," International Journal of Urban Sciences, vol. 25, no. 4, pp. 542-573, 2021. https://doi.org/10.1080/12265934.2020.1803106
R. A. Rahadi, S. K. Wiryono, D. P. Koesrindartoto, and I. B. Syamwil, "Factors influencing the price of housing in Indonesia," International Journal of Housing Markets and Analysis, 2015. https://doi.org/10.1108/IJHMA-04-2014-0008
V. Ceccato and M. Wilhelmsson, "The impact of crime on apartment prices: Evidence from Stockholm, Sweden," Geogr Ann Ser B, vol. 93, no. 1, pp. 81-103, 2011. https://doi.org/10.1111/j.1468-0467.2011.00362.x
C. Ozgur, Z. Hughes, G. Rogers, and S. Parveen, "Multiple linear regression applications in real estate pricing," International Journal of Mathematics and Statistics Invention (IJMSI), vol. 4, no. 8, 2016.
N. N. Ghosalkar and S. N. Dhage, "Real estate value prediction using linear regression," in 2018 fourth international conference on computing communication control and automation (ICCUBEA), IEEE, 2018, pp. 1-5. https://doi.org/10.1109/ICCUBEA.2018.8697639
A. Abdulhafedh, "Incorporating multiple linear regression in predicting the house prices using a big real estate dataset with 80 independent variables," Open Access Library Journal, vol. 9, no. 1, pp. 1-21, 2022. https://doi.org/10.4236/oalib.1108346
S. Levantesi and G. Piscopo, "The importance of economic variables on London real estate market: A random forest approach," Risks, vol. 8, no. 4, p. 112, 2020. https://doi.org/10.3390/risks8040112
R. Sawant, Y. Jangid, T. Tiwari, S. Jain, and A. Gupta, "Comprehensive analysis of housing price prediction in pune using multi-featured random forest approach," in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, 2018, pp. 1-5. https://doi.org/10.1109/ICCUBEA.2018.8697402
J. Hong, H. Choi, and W. 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. https://doi.org/10.3846/ijspm.2020.11544
S. Levantesi and G. Piscopo, "The importance of economic variables on London real estate market: A random forest approach," Risks, vol. 8, no. 4, p. 112, 2020. https://doi.org/10.3390/risks8040112
K. C. Lam, C. Y. Yu, and C. K. Lam, "Support vector machine and entropy based decision support system for property valuation," Journal of property research, vol. 26, no. 3, pp. 213-233, 2009. https://doi.org/10.1080/09599911003669674
X.-J. Wang, G.-T. Zeng, K.-X. Zhang, H.-B. Chu, and Z.-S. Chen, "Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing.," Int. J. Comput. Intell. Syst., vol. 13, no. 1, pp. 153-166, 2020. https://doi.org/10.2991/ijcis.d.200129.001
W. Li, Y. Zhao, W. Meng, and S. Xu, "Study on the risk prediction of real estate investment whole process based on support vector machine," in 2009 Second International Workshop on Knowledge Discovery and Data Mining, IEEE, 2009, pp. 167-170. https://doi.org/10.1109/WKDD.2009.40
W. K. O. Ho, B.-S. Tang, and S. W. Wong, "Predicting property prices with machine learning algorithms," Journal of Property Research, vol. 38, no. 1, pp. 48-70, 2021. https://doi.org/10.1080/09599916.2020.1832558
R. Yang et al., "Big data analytics for financial Market volatility forecast based on support vector machine," Int J Inf Manage, vol. 50, pp. 452-462, 2020. https://doi.org/10.1016/j.ijinfomgt.2019.05.027
M. Renigier-Bi?ozor, A. Chmielewska, M. Walacik, A. Janowski, and N. Lepkova, "Genetic algorithm application for real estate market analysis in the uncertainty conditions," Journal of Housing and the Built Environment, vol. 36, no. 4, pp. 1629-1670, 2021. https://doi.org/10.1007/s10901-020-09815-8
V. Del Giudice, P. De Paola, and F. Forte, "Using genetic algorithms for real estate appraisals," Buildings, vol. 7, no. 2, p. 31, 2017. https://doi.org/10.3390/buildings7020031
S. M. Muneer, M. B. Alvi, and M. A. Rasool, "Genetic Algorithm Based Intelligent System for Estate Value Estimation," International Journal of Computational and Innovative Sciences, vol. 1, no. 1, 2022.
A. Singh, A. Sharma, and G. Dubey, "Big data analytics predicting real estate prices," International Journal of System Assurance Engineering and Management, vol. 11, pp. 208-219, 2020. https://doi.org/10.1007/s13198-020-00946-3
E. A. Antipov and E. B. Pokryshevskaya, "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," Expert Syst Appl, vol. 39, no. 2, pp. 1772-1778, 2012. https://doi.org/10.1016/j.eswa.2011.08.077
A. A. Neloy, H. M. S. Haque, and M. M. Ul Islam, "Ensemble learning based rental apartment price prediction model by categorical features factoring," in Proceedings of the 2019 11th International conference on machine learning and computing, 2019, pp. 350-356. https://doi.org/10.1145/3318299.3318377
R. Monika, "House Price Forecasting Using Machine Learning Methods," Turkish Journal of Computer and Mathematics Education (TURCOMAT), vol. 12, no. 11, pp. 3624-3632, 2021.
M. Tekin and I. U. Sari, "Real Estate Market Price Prediction Model of Istanbul," Real Estate Management and Valuation, vol. 30, no. 4, pp. 1-16, 2022. https://doi.org/10.2478/remav-2022-0025
A. Ya?mur, M. Kayaku?, and M. Terzio?lu, "House price prediction modeling using machine learning techniques: a comparative study," Aestimum, vol. 81, 2022. https://doi.org/10.36253/aestim-13703
Y. Kang et al., "Understanding house price appreciation using multi-source big geo-data and machine learning," Land use policy, vol. 111, p. 104919, 2021. https://doi.org/10.1016/j.landusepol.2020.104919
G. Liu, "Research on prediction and analysis of real estate market based on the multiple linear regression model," Sci Program, vol. 2022, pp. 1-8, 2022. https://doi.org/10.1155/2022/5750354
V. Ceccato and M. Wilhelmsson, "Do crime hot spots affect housing prices?," Nordic journal of criminology, vol. 21, no. 1, pp. 84-102, 2020. https://doi.org/10.1080/2578983X.2019.1662595
G. E. Tita, T. L. Petras, and R. T. Greenbaum, "Crime and residential choice: a neighborhood level analysis of the impact of crime on housing prices," J Quant Criminol, vol. 22, pp. 299-317, 2006. https://doi.org/10.1007/s10940-006-9013-z
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 David Noorcahya, Achmad Pratama Rifai, Agus Darmawan, Wangi Pandan Sari
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.