The Identification of Seasonality in the Housing Market Using the X13-ARIMA-SEATS Model

• Authors: Łukasz Mach , Ireneusz Dąbrowski , Daria Wotzka , Paweł Frącz

Title variants

PL

Identyfikacja sezonowości na rynku mieszkaniowym przy użyciu modelu X13-ARIMA-SEATS

Abstracts

PL

Cel: W przeprowadzonych badaniach wyznaczono profile sezonowości na rynku mieszkaniowym, co dało możliwość odpowiedzi na dwa zasadnicze pytania: Jaki charakter ma harmoniczna zmienność sezonowości i okresowości badanych składowych procesu budowlanego? Jakie parametry modelu ARIMA optymalnie opisują rynek budowlany? Metodyka: W przeprowadzonych badaniach, wykorzystując model X13-ARIMA-SEATS, dokonano dekompozycji sezonowej w poszczególnych etapach procesu budownictwa mieszkaniowego. Wyniki: Proces badawczy przeprowadzony w celu identyfikacji wahań sezonowych na rynku budownictwa mieszkaniowego wykazał, że można zidentyfikować harmoniczne profile wahań w ujęciu rocznym. Analizę wahań sezonowych przeprowadzono dla każdego z trzech etapów procesu budowy mieszkań, sprawdzając jednocześnie, jak profile te kształtują się dla Polski ogółem oraz dla inwestorów indywidualnych i budujących mieszkania na sprzedaż lub wynajem. Badanie wykazało, że rynek działalności deweloperskiej różni się charakterystyką sezonową od rynku inwestorów indywidualnych. Implikacje i rekomendacje: Wnioski uzyskane z badań mogą stanowić wsparcie w procesie podejmowania decyzji z perspektywy zarówno makro-, jak i mikroekonomicznej. Parametryzacja występujących wahań i uwzględnienie ich w procesie opracowywania prognozy może stanowić przesłankę decyzyjną w realizacji inwestycji deweloperskich. Oryginalność/Wartość: Nowością jest wykazanie, że rynek nieruchomości mieszkaniowych w Polsce charakteryzuje się różnymi parametrami sezonowymi w podziale na rynek inwestorów indywidualnych oraz inwestorów wznoszących mieszkania na sprzedaż lub wynajem.

EN

Aim: In the conducted research, profiles of seasonality in the housing market were determined, which provided an opportunity to answer two fundamental questions: what is the nature of harmonic variation in the seasonality and periodicity of the studied components of the construction process? what parameters of the ARIMA model optimally describe the construction market? Methodology: In the conducted research, using the X13-ARIMA-SEATS model, seasonal decomposition was carried out in the various stages of the housing construction process. Results: The research process conducted to identify seasonal fluctuations in the housing construction market showed that harmonic fluctuation profiles can be identified on an annual basis. An analysis of seasonal fluctuations was carried out for each of the three stages of the housing construction process, while also checking how these profiles function for Poland in general, and for individual investors, and for those building apartments for sale or to rent. The study showed that the market for real estate development activity differs in its seasonal characteristics from that of individual investors. Implications and recommendations: The conclusions obtained from the research can provide support in the decision-making process, both from a macro and microeconomic perspective. Parameterisation of the occurring fluctuations, and taking them into account in the process of developing a forecast can provide decision-making rationale in the implementation of macroprudential and financial stability policies Originality/Value: A novelty is in the demonstration that the residential real estate market in Poland shows different seasonal parameters, divided into the market of individual investors and investors who build apartments for sale or rent.

Keywords

EN

seasonality; real estate market; X13-ARIMA-SEATS

PL

sezonowość; rynek nieruchomości; X13-ARIMA-SEATS

• Publisher: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
• Journal: Econometrics. Ekonometria. Advances in Applied Data Analytics
• Year: 2023
• Volume: 27
• Issue: 4
• Pages: 29-43

Dates

published

2023

Contributors

author

Łukasz Mach

• Opole University, Faculty of Economics, Opole, Poland

author

Ireneusz Dąbrowski

• Warsaw School of Economics, Collegium of Management and Finance, Warszawa, Poland

author

Daria Wotzka

• Opole University of Technology, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole, Poland

author

Paweł Frącz

• Opole University, Faculty of Economics, Opole, Poland

References

• Breuer, W., and Steininger, B. I. (2020). Recent Trends in Real Estate Research: A Comparison of Recent Working Papers and Publications Using Machine Learning Algorithms. Journal of Business Economics, 90, 963-974. https://doi.org/10.1007/ s11573-020-01005-w
• Box, G. E. P., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M. (2016).Time Series Analysis. Forecasting and Control (5th Edition). John Wiley & Sons.
• Coskun, E. A. (2022). The Effect of Heuristics and Biases on Real Estate Valuation and Cyclicality. In M. d'Amato, Y. Coskun (Eds.), Property Valuation and Market Cycle. Springer. https://doi.org/10.1007/978-3-031-09450-7_8
• Del Giudice, V., De Paola, P., and Forte, F. (2017). Genetic Algorithms for Real Estate Market Analysis. In O. Gervasi, et al., Computational Science and Its Applications - ICCSA 2017 - 17th International Conference, Trieste, Italy, July 3-6, 2017, Proceedings, Part III. Volume 10406 of Lecture Notes in Computer Science, 467-477, Springer. https://doi.org/10.1007/ 978-3-319-62398-6_33
• Dong, H., Guo, X., Reichgelt, H. et al. (2020). Predictive Power of ARIMA Models in Forecasting Equity Returns: A Sliding Window Method. Journal of Asset Management, 21, 549-566. https://doi.org/10.1057/s41260-020-00184-z
• Findley, D. F., Lytras, D. P., and Maravall, A. (2016), Illuminating ARIMA Model-based Seasonal Adjustment with Three Fundamental Seasonal Models. SERIEs, 7, 11-52.
• Friedman, W. A. (2018). Wesley Mitchell's Business Cycles after 100 Years. New Perspectives on the History of Political Economy. Springer International Publishing, https://doi.org/10.1007/978-3-319-58247-4_10
• Gomez, V., and Maravall, A. (2001a). Automatic Modelling Methods for Univariate Series. In D. Peña, G. Tiao and R. Tsay, (Eds.), A Course in Time Series Analysis, Wiley Series (pp. 171-201). John Wiley & Sons.
• Gomez, V., and Maravall, A. (2001b). Seasonal Adjustment and Signal Extraction in Economic Time Series. In D. Peña, G. Tiao, and R. Tsay (Eds.). A Course in Time Series Analysis (pp. 202-246). Wiley Series.
• Hoyt, H. (1933). One Hundred Years of Land Values in Chicago. University of Chicago Press.
• Jadevicius, A., and Huston, S. (2015). ARIMA Modelling of Lithuanian House Price Index. International Journal of Housing Markets and Analysis, 8(1), 135-147. https://doi.org/10.1108/IJHMA-04-2014-0010
• Kabaivanov, S., and Markovska, V. (2021). Artificial Intelligence in Real Estate Market Analysis. (AIP Conf. Proc. 2333, 030001).
• Kuang, P. (2014). A Model of Housing and Credit Cycles with Imperfect Market Knowledge. European Economic Review, Elsevier, (70), 419-437. https://doi.org/10.1016/j.euroecorev.2014.06.013
• Lambertini, L., Mendicino, C., and Punzi, M. T. (2013). Expectation-driven Cycles in the Housing Market: Evidence from Survey Data. Journal of Financial Stability, 9(4), 518-529. https://doi.org/10.1016/j.jfs.2013.07.006
• Lengwiler, Y. (2022). X-13 Toolbox for Seasonal Filtering. MATLAB Central File Exchange. MATLAB Central File Exchange, 29 September.
• Mitchell, W. C. (1927). Business Cycles: The Problem and Its Setting. National Bureau of Economic Research, Incorporated.
• Peter, N. J., Okagbue, H. I., Obasi, E. C. M., and Akinola, A. O. (2020). Review on the Application of Artificial Neural Networks in Real Estate Valuation. International Journal of Advanced Trends in Computer Science and Engineering, 9(3), 2918-2925. https://doi.org/10.30534/ijatcse/2020/66932020
• Pyhrr, S., Roulac, S., and Born, W. (1999). Real Estate Cycles and Their Strategic Implications for Investors and Portfolio Managers in the Global Economy. Journal of Real Estate Research, 18(1), 7-68.
• Quigley, J. M. (1999). Real Estate Prices and Economic Cycles. International Real Estate Review, 2.
• Sherman, H. (2001). The Business Cycle Theory of Wesley Mitchell. Journal of Economic Issues, Association for Evolutionary Economics, 35(1), 85-97. https://doi.org/10.1080/00213624.2001.11506341
• Shiskin, J., Young, A., and Musgrave, J. (1967). The X-11 Variant of the Census Method II Seasonal Adjustment Program, No. 15.
• Sommervoll, D. E., Borgersen, T. A., and Wennemo, T. (2010). Endogenous Housing Market Cycles. Journal of Banking and Finance, 34(3), 557-567. https://doi.org/10.1016/j.jbankfin.2009.08.021
• Sun, Y. (2019). Real Estate Evaluation Model Based on Genetic Algorithm Optimized Neural Network. Data Science Journal, 18(1), 36. https://doi.org/10.5334/dsj-2019-036
• Tse, R. Y. C. (1997). An Application of the ARIMA Model to Real Estate Prices in Hong Kong. Journal of Property Finance, 8(2), 152-163. https://doi.org/10.1108/09588689710167843
• Yasnitsky, L. N., Yasnitsky, V. L., and Alekseev, A. O. (2021). The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time. Complexity, 2021, Article ID 5392170. https://doi.org/10.1155/2021/5392170
• Yépez, C. A. (2018). Financial Intermediation and Real Estate Prices Impact on Business Cycles: A Bayesian Analysis. North American Journal of Economics and Finance, 45, 138-160. https://doi.org/10.1016/j.najef.2018.02.006
• Żelazowski, K. (2017). Housing Market Cycles in the Context of Business Cycles. Real Estate Management and Valuation. De Gruyter Open Ltd, 25(3), 5-14. https://doi.org/10.1515/remav-2017-0017

Identifiers

DOI

10.15611/eada.2023.4.03

Biblioteka Nauki

28407797