Deciphering Housing Market Trends: Forecasting Total Housing Sales in Turkey

Year 2024, Volume: 6 Issue: 1, 113 - 127, 30.06.2024

Ahmet Akusta

Abstract

This paper uses the SARIMA (Seasonal Autoregressive Integrated Moving Average) model to analyze monthly real estate sales in Turkey and forecast future sales. Monthly real estate sales data provided by the Turkish Statistical Institute (TurkStat) between 01/2013 and 03/2023 are used. The data set is divided into two phases: the training phase (85%) and the estimation phase (15%). Data analysis is performed using differencing and z-score normalization. Seasonal decomposition techniques are used to identify seasonal, trend and residual components in the data set and the ADF test is used to verify that the data are stationary. The SARIMAX model is evaluated using information criteria (AIC, BIC, HQIC) and estimation accuracy (MAPE, MSE, MAE) and is determined as the most appropriate model. The AIC, BIC and MSE values of the model indicate that the model has high predictive ability. The fit between the model's forecasting results and actual home sales is evaluated graphically and statistically, and it is found that the model accurately captures seasonal effects and reliably predicts future sales. This study provides an important source of information for stakeholders in the real estate market, economists, policy makers and investors.

Keywords

Housing Sales, Machine Learning, Financial Modeling, Financial Forecasting, Seasonal Disaggregation

Konut Piyasası Trendlerinin Çözümlenmesi: Türkiye'de Toplam Konut Satışlarının Tahmini

Abstract

Bu çalışma, Türkiye'deki aylık gayrimenkul satışlarını analiz etmek ve gelecekteki satışları tahmin etmek amacıyla SARIMA (Sezonluk Otoregresif Entegre Hareketli Ortalama) modelini kullanmaktadır. Türkiye İstatistik Kurumu (TÜİK) tarafından sağlanan 01/2013-03/2023 tarihleri arasındaki aylık konut satış verileri kullanılmıştır. Veri seti, modelin eğitim aşaması (%85) ve tahmin aşaması (%15) olmak üzere ikiye ayrılmıştır. Fark alma ve z- score normalizasyonu kullanılarak veri analizi yapılmıştır. Mevsimsel ayrıştırma teknikleriyle veri setindeki mevsimsel, trend ve artık bileşenler belirlenmiş ve ADF testi ile verinin durağan hale geldiği doğrulanmıştır. SARIMAX modeli, bilgi kriterleri (AIC, BIC, HQIC) ve tahmin doğruluğu (MAPE, MSE, MAE) kullanılarak değerlendirilmiş ve en uygun model olarak belirlenmiştir. Modelin AIC, BIC ve MSE değerleri, modelin tahmin yeteneğinin yüksek olduğunu göstermektedir. Modelin tahmin sonuçları ile gerçekleşen konut satışları arasındaki uyum, grafiksel ve istatistiksel olarak değerlendirilmiş ve modelin mevsimsel etkileri doğru bir şekilde yakaladığı ve gelecekteki satışları güvenilir bir şekilde tahmin edebildiği görülmüştür. Bu çalışma, gayrimenkul piyasasındaki paydaşlar, ekonomistler, politika yapıcılar ve yatırımcılar için önemli bir bilgi kaynağı sunmaktadır.

Keywords

Konut Satışları, Makine Öğrenmesi, Finansal Modelleme, Finansal Tahmin, Mevsimsel Ayrıştırma

References

• Abhyankar, A. ve Singla, H. K. (2021). Comparing Predictive Performance of General Regression Neural Network (GRNN) and Hedonic Regression Model for Factors Affecting Housing Prices in “Pune-India”. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/ijhma-01-2021-0003
• Akin, O., Marin, J. J. ve Peydró, J.-L. (2016). Anticipating the Financial Crisis: Evidence From Insider Trading in Banks. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2779048
• Alm, J., Buschman, R. D. ve Sjoquist, D. L. (2011). Rethinking Local Government Reliance on the Property Tax. Regional Science and Urban Economics. https://doi.org/10.1016/j.regsciurbeco.2011.03.006
• Alzami, F., Salam, A., Rizqa, I., Irawan, C., Andono, P. N., Aqmala, D. ve Sartika, M. (2024). Demand prediction for food and beverage SMEs using SARIMAX and weather data. Ingenierie des Systemes d'Information, 29(1), 293-300. https://doi.org/10.18280/isi.290129
• Awuah, K. G. B. ve Gyamfi-Yeboah, F. (2019). The Effect of Ground Rent and Unexpired Lease Term on Property Values in Ghana. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/ijhma-05-2018-0033
• Baghestani, H.ve Viriyavipart, A. (2019). Do Factors Influencing Consumer Home-Buying Attitudes Explain Output Growth? Journal of Economic Studies. https://doi.org/10.1108/jes-01-2018-0040
• Bao, T., Gong, J. P., Shu, X. ve Zhang, K. (2020). The Prediction of Dam Displacement Time Series Using STL, Extra-Trees, and Stacked LSTM Neural Network. Ieee Access. https://doi.org/10.1109/access.2020.2995592
• Beck, J. ve Saadatmand, Y. (2024). The impact of real estate agent and firm characteristics on sales prices under different market conditions and price segments. Business Economics, 59(1), 31–38. https://doi.org/10.1057/s11369-023-00340-4
• Besarria, C. d. N., Paes, N. L. ve Silva, M. (2018). Testing for Bubbles in Housing Markets: Some Evidence for Brazil. International Journal of Housing Markets and Analysis. https://doi.org/10.1108/ijhma-08-2017-0075
• Birkeland, K. B., D’Silva, A. D., Füss, R. ve Oust, A. (2021). International Real Estate Review. International Real Estate Review. https://doi.org/10.53383/100319
• Bulczak, G. (2021). Use of Google Trends to Predict the Real Estate Market: Evidence From the United Kingdom. International Real Estate Review. https://doi.org/10.53383/100332
• Cepni, O., Gupta, R. ve Tiwari, A. K. (2019a). The Role of Real Estate Uncertainty in Predicting US Home Sales Growth: Evidence From a Quantiles-Based Bayesian Model Averaging Approach. Applied Economics. https://doi.org/10.1080/00036846.2019.1654082
• Cepni, O., Gupta, R. ve Tiwari, A. K. (2019b). The Role of Real Estate Uncertainty in Predicting US Home Sales Growth: Evidence From a Quantiles-Based Bayesian Model Averaging Approach. Applied Economics. https://doi.org/10.1080/00036846.2019.1654082
• Çılgın, C. ve Gökçen, H. (2023). Machine Learning Methods for Prediction Real Estate Sales Prices in Turkey. RDLC. https://doi.org/10.7764/rdlc.22.1.163
• Guangliang, G., Zhifeng, B., Jie, C., K. A., Q., Timos, S., Fellow, IEEE ve Zhiang, W. (2019). Location-Centered House Price Prediction: A Multi-Task Learning Approach. https://doi.org/10.48550/arxiv.1901.01774
• Gill, K. K., Bhatt, K., Kaur, B. ve Sandhu, S. S. (2023). ARIMA approach for temperature and rainfall time series prediction in Punjab. Journal of Agrometeorology, 25(4), 571-576. https://doi.org/10.54386/jam.v25i4.2250
• Guijarro, F., (2021). A Mean-Variance Optimization Approach for Residential Real Estate Valuation. Real Estate Management and Valuation. https://doi.org/10.2478/remav-2021-0018
• Gupta, A. ve Newell, G. (2020). A Real Estate Portfolio Management Risk Assessment Framework for Nonlisted Real Estate Funds in India. Property Management. https://doi.org/10.1108/pm-04-2020-0023
• Hari, K.D.T. ve Adiputra, I.M P. (2024). Pengaruh Sales Growth, Net Profit Margin, Dan Price Earning Ratio Terhadap Return Saham: Studi pada Perusahaan Property dan Real Estate yang Terdaftar Di Bursa Efek Indonesia. JIMAT (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha, 15(01), 19-30.
• Heinrich, M., Maurin, M. A., Schreck, T. ve Just, T. (2016). Characteristics of German Foreclosed Residential Assets, Their Real Values and Discounts. An Empirical Study. https://doi.org/10.15396/lares-16-heinrich_characteristics_artigo
• Hobijn, B., Franses, P. H. ve Ooms, M. (2004). Generalizations of the KPSS-test for Stationarity. Statistica Neerlandica. https://doi.org/10.1111/j.1467-9574.2004.00272.x
• Huang, R. ve Mao, S. (2022). Research on Precision Marketing of Real Estate Market Based on Data Mining. Scientific Programming. https://doi.org/10.1155/2022/8198568
• Kang, J., Lee, H., Jeong, S.-Y., Lee, H. C. ve Oh, K. J. (2020). Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence. Sustainability. https://doi.org/10.3390/su12072899
• Kobzan, S., Поморцева, О.E, Ivakhnenko, A. ve Tolsta, M. (2022). Features of Investment in the Real Estate Market. Municipal Economy of Cities. https://doi.org/10.33042/2522-1809-2022-3-170-214-222
• Kuru, M. ve Calis, G., (2021). Sale Price Classification Models for Real Estate Appraisal. RDLC. https://doi.org/10.7764/rdlc.20.3.440
• Lepetit, L. ve Strobel, F. (2013). Bank Insolvency Risk and Time-Varying Z-Score Measures. Journal of International Financial Markets Institutions and Money. https://doi.org/10.1016/j.intfin.2013.01.004
• Liu, G. (2022). Research on Prediction and Analysis of Real Estate Market Based on the Multiple Linear Regression Model. Scientific Programming. https://doi.org/10.1155/2022/5750354
• Ma, X., Zhang, Z., Han, Y. ve Yue, X.-G. (2019). Sustainable Policy Dynamics—A Study on the Recent “Bust” of Foreign Residential Real Estate Investment in Sydney. Sustainability. https://doi.org/10.3390/su11205856
• McGonigle, E. T., Killick, R. ve Nunes, M. A. (2022). Modelling Time-Varying First and Second-Order Structure of Time Series via Wavelets and Differencing. Electronic Journal of Statistics. https://doi.org/10.1214/22-ejs2044
• Museleku, E.K. (2022). Modelling Apartments Values in the Nairobi Metropolitan Area, Kenya. Property Management. https://doi.org/10.1108/pm-03-2022-0023
• Nasseri, A., Neyshabori, M. R. ve Fard, A. F. (2013). Time Series Analysis of Furrow Infiltration. Irrigation and Drainage. https://doi.org/10.1002/ird.1756
• Onyekwere, S. C., Dike, J. ve Eshun, B. A. (2021). The Impact of Oil Price Shocks on Macroeconomic Activity: Searching Evidence From Oil Exporting and Importing Countries Using Unstructured Vector Autoregressive (VAR) Model. Asian Bulletin of Energy Economics and Technology. https://doi.org/10.20448/journal.507.2021.61.1.29
• Pandey, A. V. ve Jain, A. (2017). Comparative Analysis of KNN Algorithm Using Various Normalization Techniques. International Journal of Computer Network and Information Security. https://doi.org/10.5815/ijcnis.2017.11.04
• Poon, J. (2014). Do Real Estate Courses Sufficiently Develop Graduates’ Employability Skills? Perspectives From Multiple Stakeholders. Education + Training. https://doi.org/10.1108/et-06-2013-0074
• Rusyana, A., Marzuki,N. ve Flancia, M. (2017). SARIMA model for forecasting foreign tourists at the Kualanamu International Airport. In Proceedings - 2016 12th International Conference on Mathematics, Statistics, and Their Applications, ICMSA 2016: In Conjunction with the 6th Annual International Conference of Syiah Kuala University (pp. 153-158). IEEE. https://doi.org/10.1109/ICMSA.2016.7954329
• Saha, E., Hazra, A. ve Banik, P. (2016). SARIMA modeling of the monthly average maximum and minimum temperatures in the eastern plateau region of India. Mausam, 67(4), 841-848.
• Sergoyan, H. T. ve Bezirganyan, G. V. (2022). Automated Real Estate Valuation With Machine Learning: a Case Study on Apartment Sales in Yerevan. Journal of Architectural and Engineering Research. https://doi.org/10.54338/27382656-2022.2-012
• Shulgan, R., Yanchuk, O., Pakharenko, O. ve Pryshchepa, A. M. (2021). Study on the Influence of Roadways on Land Plots According to the Results of Monetary Evaluation. Geomatics and Environmental Engineering. https://doi.org/10.7494/geom.2021.15.3.5
• Siddiqui, M. (2024). The impact of Digital marketing on real estate sales. SSRN. https://ssrn.com/abstract=4816652
• Ullah, F., Sepasgozar, S. M. E. ve Wang, C. (2018). A Systematic Review of Smart Real Estate Technology: Drivers Of, and Barriers To, the Use of Digital Disruptive Technologies and Online Platforms. Sustainability. https://doi.org/10.3390/su10093142
• Yan, N. (2019). Study on the Influence of Monetary Policy on Real Estate Price in China. Journal of Service Science and Management. https://doi.org/10.4236/jssm.2019.122011
• Yuan, X. (2023). A Review of Domestic and Foreign Research on Real Estate Financial Management. Proceedings of Business and Economic Studies.
• Yang, F., Wu, X., Zhang, M. ve Chang, X. (2023). Time series forecast of air travel demands with considering the influences of new routes. In 2023 9th International Conference on Computer and Communications, ICCC 2023 (pp. 2259-2263). IEEE. https://doi.org/10.1109/ICCC59590.2023.10507681
• Zhang, W., Chen, S.-F., Guo, D. ve Li, B. (2019). The Impact of Internet Real Estate Intermediary Platform on the Real Estate Market. https://doi.org/10.1145/3371238.3371259