Comparative Study on Artificial Intelligence Methods in Housing Price Prediction

Willy Husada, Ambrosius Matthew Junius Reynaldo, Josh Felix Hogianto, Clarissa Arisanti Putri

Abstract


The demand for property, including houses, continues to grow rapidly in Indonesia. The housing price prediction is essential in assisting the stakeholders such as buyers, sellers, and investors to make better decision-making. There are many key factors that influencing the housing prices and it is challenging to identify the most relevant factors. This study provides a comparative analysis of various methods in the housing price prediction that consists of one traditional method, Linear Regression (LR), and three artificial intelligence (AI) methods, including Artificial Neural Network (ANN), Classification and Regression Tree (CART), and Chi-Squared Automatic Interaction Detection (CHAID). The aim is to find the best machine learning method in predicting the housing price in terms of prediction accuracy through the four performance indicators and one combined performance index called the reference index (RI). The main findings of this study is that the AI-based method, the ANN method, has the best accuracy indicated by its highest RI value hence outperforming other methods in predicting the housing prices.


Keywords


Artificial intelligence; housing price; machine learning; prediction

Full Text:

PDF

References


M. Siahaan, “Real estate in Indonesia - statistics & facts,” statista. [Online]. Available: https://www.statista.com/topics/8596/real-estate-in-indonesia/#topicOverview

E. Z. Teoh, W.-C. Yau, T. S. Ong, and T. Connie, “Explainable housing price prediction with determinant analysis,” IJHMA, vol. 16, no. 5, pp. 1021–1045, Aug. 2023, doi: 10.1108/IJHMA-02-2022-0025.

Appraisal Institute, Ed., The appraisal of real estate, Fifteenth edition. Chicago, IL: Appraisal Institute, 2020.

F. Fahirah, A. Basong, and T. Hermansah, “Identifikasi Faktor yang Mempengaruhi Nilai Jual Lahan dan Bangunan pada Perumahan Tipe Sederhana.pdf,” Jurnal SMARTek, vol. 8, no. 4, pp. 251–269.

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, vol. 8, no. 2, pp. 169–188, Jun. 2015, doi: 10.1108/IJHMA-04-2014-0008.

A. Olanrewaju, X. Y. Lim, S. Y. Tan, J. E. Lee, and H. Adnan, “Factors Affecting Housing Prices in Malaysia: Analysis of The Supply Side,” Planning Malaysia Journal, vol. 16, no. 6, pp. 225-235, Sept. 2018, doi: 10.21837/pm.v16i6.477

F. Zulkifli and H. Ismail, “Factors Influencing House Buyer's Decision in Malaysia. Case Study: Sepang, Selangor,” PM, vol. 21, Jul. 2023, doi: 10.21837/pm.v21i27.1293.

M.-Y. Cheng, P. M. Firdausi, and D. Prayogo, “High-performance concrete compressive strength prediction using Genetic Weighted Pyramid Operation Tree (GWPOT),” Engineering Applications of Artificial Intelligence, vol. 29, pp. 104–113, Mar. 2014, doi: 10.1016/j.engappai.2013.11.014.

Hore, Sirshendu et al., “Neural-based prediction of structural failure of multistoried RC buildings,” Structural Engineering and Mechanics, vol. 58, no. 3, pp. 459–473, May 2016, doi: 10.12989/SEM.2016.58.3.459.

M.-Y. Cheng, N.-D. Hoang, and Y.-W. Wu, “Cash Flow Prediction for Construction Project using A Novel Adaptive Time-Dependant Least Square Support Vector Machine Inference Model,” Journal of Civil Engineering and Management, vol. 21, no. 6, pp. 679–688, Jun. 2015, doi: 10.3846/13923730.2014.893906.

A. Saiful, “Prediksi Harga Rumah Menggunakan Web Scrapping dan Machine Learning Dengan Algoritma Linear Regression,” JATISI, vol. 8, no. 1, pp. 41–50, Mar. 2021, doi: 10.35957/jatisi.v8i1.701.

A. R. Hutami, “Aplikasi Neural Network untuk Prediksi Harga Rumah di Yogyakarta Menggunakan Backpropagation,” Universitas Islam Indonesia, Yogyakarta, 2018.

P. Choirunisa, “Implementasi Artificial Inteligence Untuk Memprediksi Harga Penjualan Rumah Menggunakan Metode Random Forest dan Flask,” Universitas Islam Indonesia, Yogyakarta, 2020.

K. M. A. Hossain and M. Lachemi, “Strength, durability and micro-structural aspects of high performance volcanic ash concrete,” Cement and Concrete Research, vol. 37, no. 5, pp. 759–766, May 2007, doi: 10.1016/j.cemconres.2007.02.014.

S. A. A. Karim and N. F. Kamsani, Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model: Case Study in Perak River, Malaysia. in SpringerBriefs in Water Science and Technology. Singapore: Springer Singapore, 2020. doi: 10.1007/978-981-15-3485-0.

S. A. Eslamian, S. S. Li, and F. Haghighat, “A new multiple regression model for predictions of urban water use,” Sustainable Cities and Society, vol. 27, pp. 419–429, Nov. 2016, doi: 10.1016/j.scs.2016.08.003.

P. Pandit, P. Dey, and K. N. Krishnamurthy, “Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models,” SN COMPUT. SCI., vol. 2, no. 2, p. 76, Apr. 2021, doi: 10.1007/s42979-021-00473-3.

A. Yağmur, M. Kayakuş, and M. Terzioğlu, “House price prediction modeling using machine learning techniques: a comparative study,” Aestimum, vol. 81, Feb. 2023, doi: 10.36253/aestim-13703.

Y. Wu and J. Feng, “Development and Application of Artificial Neural Network,” Wireless Pers Commun, vol. 102, no. 2, pp. 1645–1656, Sep. 2018, doi: 10.1007/s11277-017-5224-x.

H. Yoon, S.-C. Jun, Y. Hyun, G.-O. Bae, and K.-K. Lee, “A

comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer,” Journal of Hydrology, vol. 396, no. 1–2, pp. 128–138, Jan. 2011, doi: 10.1016/j.jhydrol.2010.11.002.

K. A. Grajski, L. Breiman, G. V. Di Prisco, and W. J. Freeman, “Classification of EEG Spatial Patterns with a Tree-Structured Methodology: CART,” IEEE Trans. Biomed. Eng., vol. BME-33, no. 12, pp. 1076–1086, Dec. 1986, doi: 10.1109/TBME.1986.325684.

J.-S. Chou, C.-F. Tsai, A.-D. Pham, and Y.-H. Lu, “Machine learning in concrete strength simulations: Multi-nation data analytics,” Construction and Building Materials, vol. 73, pp. 771–780, Dec. 2014, doi: 10.1016/j.conbuildmat.2014.09.054.

M. Milanović and M. Stamenković, “CHAID Decision Tree: Methodological Frame and Application,” Economic Themes, vol. 54, no. 4, pp. 563–586, Dec. 2016, doi: 10.1515/ethemes-2016-0029.

M. Gunduz and I. Al-Ajji, “Employment of CHAID and CRT decision tree algorithms to develop bid/no-bid decision-making models for contractors,” ECAM, vol. 29, no. 9, pp. 3712–3736, Nov. 2022, doi: 10.1108/ECAM-01-2021-0042.

M. Abdar, M. Zomorodi-Moghadam, R. Das, and I.-H. Ting, “Performance analysis of classification algorithms on early detection of liver disease,” Expert Systems with Applications, vol. 67, pp. 239–251, Jan. 2017, doi: 10.1016/j.eswa.2016.08.065.




DOI: http://dx.doi.org/10.12962%2Fj20861206.v40i2.22747

Refbacks

  • There are currently no refbacks.



Journal of Civil Engineering is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats