Analysis of the Housing Market Dynamics Using NARX Neural Network
DOI:
https://doi.org/10.26034/lu.akwi.2024.5947Keywords:
NARX model, Nonlinear Autoregressive Exogenous Model, real estate forecast, machine learning, time series predictionAbstract
This study employs a Nonlinear Autoregressive with eXogenous inputs (NARX) neural network to model the dynamics of the housing construction market in Poland, with a distinction made between segments of developers and individual investors. The dataset under analysis contains the 19-year data corresponding to the numbers of housing units approved for construction, under construction, and completed. The NARX model was calibrated thoroughly to suit unique characteristics of the data, with an emphasis put on the hidden layer size and delay parameters, to capture the estate market's nonlinear trends. Results show a very high efficiency of NARX models and highlight distinct patterns and dynamics in the housing completion, construction starts, and permit issuance between the two market segments. These variations are vital for understanding the distinct forces and trends shaping the developers’ and individual investors’ markets in the Polish housing sector. Findings of the analysis provide valuable insight into the nanced functioning of these market segments.
References
W. Breuer and B.I. Steininger. Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms. J. of Business Economics, 90(7): 963–974, 2020. https://doi.org/10.1007/s11573-020-01005-w.
G. de Bondt, A. Gieseck, and M. Tujula. Household Wealth and consumption in the euro area. ECB Economic Bulletin, 1, 2020.
P. Frącz, I. Dąbrowski, D. Wotzka, D. Zmarzły, and Ł. Mach. Identification of differences in the seasonality of the developer and individual housing market as a basis for its sustainable development. Buildings, 13(2), 2023. https://doi.org/10.3390/buildings13020316.
L. Gabrielli, A.G. Ruggeri, and M. Scarpa. Using Artificial Neural Networks to uncover real estate market transparency: The market value. In ICCSA’2021, pages 183–192, 2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-86979-3_14.
J.E. Gomez-Gonzalez, J. Hirs-Garzón, S. Sanin-Restrepo, and J.M. Uribe. Financial and macroeconomic uncertainties and real estate markets. Eastern Economic Journal, 50(1): 29–53, 2024. https://doi.org/10.1057/s41302-023-00263-0.
A. Grybauskas, V. Pilinkienė, and A. Stundžienė. Predictive analytics using Big Data for the real estate market during the COVID-19 pandemic. Journal of Big Data, 8(1): 105, 2021. https://doi.org/10.1186/s40537-021-00476-0.
GUS. Główny Urząd Statystyczny – Publications, 2023. https://stat.gov.pl/publikacje/publikacje-a-z.
L. Hong. The dynamic relationship between real estate investment and economic growth: Evidence from Prefecture City Panel Data in China. IERI Procedia, 7: 2–7, 2014. https://doi.org/10.1016/j.ieri.2014.08.002.
I.I. Kakulu. Qualitative research strategies and data analysis methods in real estate research – an innovative approach using the BB Model. In Estate Management Department Workshop, 2014.
J.H. Lang, M. Behn, B. Jarmulska, and M. Lo Duca. Real estate markets, financial stability and macroprudential policy. Macroprudential Bulletin, European Central Bank, 2022.
MathWorks Documentation – Design Time Series NARX Feedback Neural Networks, https://www.mathworks.com/help/deeplearning/ref/narnet.html (access date: 08.01.2024).
S.W. Shao, X. Huang, L.X. Xiao, and H. Liu. Exploring the impact of real estate policy on real estate trading using the time series analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42: 1281–1287, 2020. https://doi.org/10.5194/isprs-archives-xlii-3-w10-1281-2020.
Statista. Real estate market in Poland – statistics & facts, https://www.statista.com/topics/7893/real-estate-market-in-poland/#topicOverview (access date: 25.03.2024).
D. Wotzka, G. Suchacka, Ł. Mach, P. Frącz, J. Foltys, and I. Maniu. Time series prediction for the housing construction market with the use of NARNET. In Proc. ECMS’2023, pages 284–290, 2023. ECMS. https://doi.org/10.7148/2023-0284.
W. Xiong. The impact of the real estate market on labor productivity. In Journal of Education, Humanities and Social Sciences, 14: 50–53, 2023. https://doi.org/10.54097/ehss.v14i.8795.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Daria Wotzka, Grażyna Suchacka, Paweł Frącz, Łukasz Mach, Marzena Stec, Joachim Foltys
This work is licensed under a Creative Commons Attribution 4.0 International License.
