Application of the ARIMA Method for Real Estate Valuation Forecasting
DOI:
https://doi.org/10.61347/psa.v1i1.56Keywords:
ARIMA, data mining, forecasting, real estate, time seriesAbstract
The analysis of data is one of the main aspects to consider for decision-making in social and business contexts. The real estate market is a crucial and supportive component for the economies of countries worldwide. Analyzing the variation of real estate prices through time series can help investors identify better opportunities. This research aims to apply the ARIMA method for real estate appraisal forecasting. A quantitative approach with a non-experimental research design is used. For data mining, the KDD process is applied in conjunction with the ARIMA process for forecasting. The main results include a cleaned database, which is subsequently transformed into a stationary series to adjust the model and apply the forecast to the case study in the city of Riobamba, Ecuador. Data are presented with confidence intervals of 80% and 95%. The procedure detailed in this study can be applied to any time series prediction context in the real estate sector.
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References
Afeef, M., Ihsan, A., & Zada, H. (2018). Forecasting Stock Prices through Univariate ARIMA Modeling. 13, 130-142.
Al-Marwani, H. A. (2014). An approach to modeling and forecasting real estate residential property market [Doctoral thesis]. http://bura.brunel.ac.uk/handle/2438/10005
Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock Price Prediction Using the ARIMA Model. 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 106-112. https://doi.org/10.1109/UKSim.2014.67 DOI: https://doi.org/10.1109/UKSim.2014.67
Awe, O., Okeyinka, A., & Fatokun, J. O. (2020). An Alternative Algorithm for ARIMA Model Selection. 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), 1-4. https://doi.org/10.1109/ICMCECS47690.2020.246979 DOI: https://doi.org/10.1109/ICMCECS47690.2020.246979
Bartlett, M. (2022). Modelización de Series Univariantes: (S)ARIMA | Series de Tiempo. https://bookdown.org/victor_morales/SeriesdeTiempo/modelizaci%C3%B3n-de-series-univariantes-sarima.html
Bodero-Poveda, E., Morales-Alarcón, C., Congacha-Aushay, A. E., & Ramos-Araujo, C. E. (2022). Técnicas de minería de datos para el análisis de la plusvalía inmobiliaria. Dominio de las Ciencias, 8(1), Article 1. https://dominiodelasciencias.com/ojs/index.php/es/article/view/2531
Box, G. E. P., & Jenkins, G. M. (1976). Time Series Analysis: Forecasting and Control. Holden-Day.
Castán-Lascorz, M. A., Jiménez-Herrera, P., Troncoso, A., & Asencio-Cortés, G. (2022). A new hybrid method for predicting univariate and multivariate time series based on pattern forecasting. Information Sciences, 586, 611-627. https://doi.org/10.1016/j.ins.2021.12.001 DOI: https://doi.org/10.1016/j.ins.2021.12.001
Cejas, M., & Romero, M. (2020). Negociación e inversión: Binomio clave en el sector comercial de Riobamba—Ecuador. Editorial CIDE. https://repositorio.cidecuador.org/bitstream/123456789/230/1/negociacion-e-inversion-binomio-clave-en-el-sector-comercial-de-riobamba-ecuador.pdf
Del Giudice, V., De Paola, P., & Forte, F. (2017). Using Genetic Algorithms for Real Estate Appraisals. Buildings, 7(2), Article 2. https://doi.org/10.3390/buildings7020031 DOI: https://doi.org/10.3390/buildings7020031
Durán, A. (2019). Time series data mining: Preprocessing, analysis, segmentation and prediction. Applications [Universidad de Córdoba, UCOPress]. http://helvia.uco.es/xmlui/handle/10396/18590
Ghysels, E., Plazzi, A., Valkanov, R., & Torous, W. (2013). Chapter 9—Forecasting Real Estate Prices. En G. Elliott & A. Timmermann (Eds.), Handbook of Economic Forecasting (Vol. 2, pp. 509-580). Elsevier. https://doi.org/10.1016/B978-0-444-53683-9.00009-8 DOI: https://doi.org/10.1016/B978-0-444-53683-9.00009-8
González, M. (2016). Clasificación semi-supervisada de series temporales [Tesis Doctoral, Universidad de Granada]. https://digibug.ugr.es/handle/10481/44303
Han, S., Ko, Y., Kim, J., & Hong, T. (2018). Housing Market Trend Forecasts through Statistical Comparisons based on Big Data Analytic Methods. Journal of Management in Engineering, 34(2), 04017054. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000583 DOI: https://doi.org/10.1061/(ASCE)ME.1943-5479.0000583
Kabaivanov, S., & Markovska, V. (2021). Artificial intelligence in real estate market analysis. AIP Conference Proceedings, 2333, 030001. https://doi.org/10.1063/5.0041806 DOI: https://doi.org/10.1063/5.0041806
Kang, J., Lee, H. J., Jeong, S. H., Lee, H. S., & Oh, K. J. (2020). Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence. Sustainability, 12(7), Article 7. https://doi.org/10.3390/su12072899 DOI: https://doi.org/10.3390/su12072899
Knoll, K., Schularick, M., & Steger, T. (2017). No Price Like Home: Global House Prices, 1870-2012. American Economic Review, 107(2), 331-353. https://doi.org/10.1257/aer.20150501 DOI: https://doi.org/10.1257/aer.20150501
Kok, N., Koponen, E.-L., & Martínez-Barbosa, C. A. (2017). Big Data in Real Estate? From Manual Appraisal to Automated Valuation. The Journal of Portfolio Management, 43(6), 202-211. https://doi.org/10.3905/jpm.2017.43.6.202 DOI: https://doi.org/10.3905/jpm.2017.43.6.202
Lizarazu-Alanez, E., Villaseñor-Alva, J. A., Lizarazu-Alanez, E., & Villaseñor-Alva, J. A. (2007). Efectos de rompimientos bajo la hipótesis nula de la prueba dickey-fuller para raíz unitaria. Agrociencia, 41(2), 193-203.
Mira, S. (2019). Digitalización y tendencias del sector inmobiliario [Master thesis, Universitat Politècnica de Catalunya]. https://upcommons.upc.edu/handle/2117/172286
Mittal, R., Kumar, P., Mittal, A., & Malik, V. (2021). Developing an Evaluation Model for Forecasting of Real Estate Prices. En A. Choudhary, A. P. Agrawal, R. Logeswaran, & B. Unhelkar (Eds.), Applications of Artificial Intelligence and Machine Learning (pp. 619-627). Springer. https://doi.org/10.1007/978-981-16-3067-5_46 DOI: https://doi.org/10.1007/978-981-16-3067-5_46
Moffat, I. U., & Akpan, E. A. (2019). White Noise Analysis: A Measure of Time Series Model Adequacy. Applied Mathematics, 10(11), Article 11. https://doi.org/10.4236/am.2019.1011069 DOI: https://doi.org/10.4236/am.2019.1011069
Muhammad, A. P., Wibowo, W., & Prastuti, M. (2022). Forecasting of analytic residential price index using ARIMA Box-Jenkins. AIP Conference Proceedings, 2668(1), 070019. https://doi.org/10.1063/5.0116915 DOI: https://doi.org/10.1063/5.0116915
Pai, P.-F., & Wang, W.-C. (2020). Using Machine Learning Models and Actual Transaction Data for Predicting Real Estate Prices. Applied Sciences, 10(17), Article 17. https://doi.org/10.3390/app10175832 DOI: https://doi.org/10.3390/app10175832
Papastamos, D., Matysiak, G., & Stevenson, S. (2015). Assessing the accuracy and dispersion of real estate investment forecasts. International Review of Financial Analysis, 42, 141-152. https://doi.org/10.1016/j.irfa.2015.01.014 DOI: https://doi.org/10.1016/j.irfa.2015.01.014
Pohl, M., Staegemann, D. G., & Turowski, K. (2022). The Performance Benefit of Data Analytics Applications. Procedia Computer Science, 201, 679-683. https://doi.org/10.1016/j.procs.2022.03.090 DOI: https://doi.org/10.1016/j.procs.2022.03.090
Racine, J. S. (2019). Reproducible Econometrics Using R. Oxford University Press. https://doi.org/10.1093/oso/9780190900663.001.0001 DOI: https://doi.org/10.1093/oso/9780190900663.001.0001
Rahmer, B. de J., Garzón Saénz, H., & Solana Garzón, J. (2022). Aplicación de modelos de difusión y de series temporales para pronóstico de demanda agregada. Revista de ciencias sociales, 28(3), 142-159.
Rojas, F. M., & Gomez, C. (2014). Funcionalidades de la minería de datos. Ingeniería y Región, 12, 31-40. https://doi.org/10.25054/22161325.728 DOI: https://doi.org/10.25054/22161325.728
Shinde, N., & Gawande, K. (2018). Survey on predicting property price. 2018 International Conference on Automation and Computational Engineering (ICACE), 1-7. https://doi.org/10.1109/ICACE.2018.8687080 DOI: https://doi.org/10.1109/ICACE.2018.8687080
Sofía, F., & Adrián, J. (s. f.). La analítica de datos como ventaja competitiva en las organizaciones.
Stellwagen, E., & Tashman, L. (2013). ARIMA: The Models of Box and Jenkins. Foresight: The International Journal of Applied Forecasting, 30, 28-33.
Stevenson, S. (2007). A comparison of the forecasting ability of ARIMA models. Journal of Property Investment & Finance, 25(3), 223-240. https://doi.org/10.1108/14635780710746902 DOI: https://doi.org/10.1108/14635780710746902
Verma, P., Reddy, S. V., Ragha, L., & Datta, D. (2021). Comparison of Time-Series Forecasting Models. 2021 International Conference on Intelligent Technologies (CONIT), 1-7. https://doi.org/10.1109/CONIT51480.2021.9498451 DOI: https://doi.org/10.1109/CONIT51480.2021.9498451
Wang, D., & Li, V. J. (2019). Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review. Sustainability, 11(24), Article 24. https://doi.org/10.3390/su11247006 DOI: https://doi.org/10.3390/su11247006
Wang, F., Zou, Y., Zhang, H., & Shi, H. (2019). House Price Prediction Approach based on Deep Learning and ARIMA Model. 2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 303-307. https://doi.org/10.1109/ICCSNT47585.2019.8962443 DOI: https://doi.org/10.1109/ICCSNT47585.2019.8962443
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Copyright (c) 2023 Elba María Bodero Poveda, Katherine Alexandra Guerrero Morejón
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