Multidimensional house price prediction with SOTA RNNs
Abstract
This paper introduces insights into the Turkish real estate market, which can be generalized globally. It primarily aims to find the best forecasting algorithms for the housing price index and compare their prediction performance over three, six, nine, and twelve months ahead by using recurrent neural networks (RNN) with a comparison of out-of-sample predicting power of econometrical models. For these purposes, we employ three RNN architectures in twenty-four settings, revealing that certain RNN architectures are the best predictors in forecasting the Turkish real housing price index. The RNN architectures outperform traditional econometric models; however, the more months forecasted, the lower the prediction power. The lagged values of the price-to-rent ratio, real rents, and the lagged USDTRY values contribute more than the other predictors in forecasting the real housing price index. The outcomes suggest that stocks, real estate investment trusts, and gold are neither complementary nor competing financial instruments since housing is an illiquid asset.
Keyword : housing price index prediction, recurrent neural networks, deep learning
How to Cite
Kütük, Y. (2024). Multidimensional house price prediction with SOTA RNNs. International Journal of Strategic Property Management, 28(6), 411–423. https://doi.org/10.3846/ijspm.2024.22661
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Brzezicka, J. (2022). The application of the simplified speculative frame method for monitoring the development of the housing market. Real Estate Management and Valuation, 30(1), 84–98. https://doi.org/10.2478/remav-2022-0008
Cagli, E. C. (2019). Explosive behavior in the real estate market of Turkey. Borsa Istanbul Review, 19(3), 258–263. https://doi.org/10.1016/j.bir.2018.10.002
Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299–362. https://doi.org/10.1353/eca.2004.0004
Chang, K.-L., Chen, N.-K., & Leung, C. K. Y. (2010). Monetary policy, term structure and asset return: Comparing REIT, housing and stock. The Journal of Real Estate Finance and Economics, 43(1–2), 221–257. https://doi.org/10.1007/s11146-010-9241-8
Chen, N.-K., & Cheng, H.-L. (2017). House price to income ratio and fundamentals: Evidence on long-horizon forecastability. Pacific Economic Review, 22(3), 293–311. https://doi.org/10.1111/1468-0106.12231
Cho, K., van Merriënboer, B., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv. https://doi.org/10.48550/arXiv.1406.1078
Coskun, Y. (2023). Housing affordability: An econometric framing for policy discussions. International Journal of Housing Markets and Analysis, 16(2), 374–407. https://doi.org/10.1108/IJHMA-01-2022-0015
Coskun, Y., & Jadevicius, A. (2017). Is there a housing bubble in Turkey? Real Estate Management and Valuation, 25(1), 48–73. https://doi.org/10.1515/remav-2017-0003
Coskun, Y., & Pitros, C. (2022). Is there a bubbly euphoria in the Turkish housing market? Journal of Housing and the Built Environment, 37, 2013–2032. https://doi.org/10.1007/s10901-022-09931-7
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Coskun, Y., Seven, U., Ertugrul, H. M., & Alp, A. (2020). Housing price dynamics and bubble risk: The case of Turkey. Housing Studies, 35(1), 50–86. https://doi.org/10.1080/02673037.2017.1363378
Dua, P., & Miller, S. M. (1996). Forecasting Connecticut home sales in a BVAR framework using coincident and leading indexes. The Journal of Real Estate Finance and Economics, 13(3), 219–235. https://doi.org/10.1007/bf00217392
Duran, H. E., & Özdoğan, H. (2020). Asymmetries across regional housing markets in Turkey. The Journal of Economic Asymmetries, 22, Article e00178. https://doi.org/10.1016/j.jeca.2020.e00178
Elíasson, L. (2017). Icelandic boom and bust: Immigration and the housing market. Housing Studies, 32(1), 35–59. https://doi.org/10.1080/02673037.2016.1171826
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813–836. https://doi.org/10.2307/2171846
Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211. https://doi.org/10.1207/s15516709cog1402_1
Engsted, T., & Pedersen, T. Q. (2015). Predicting returns and rent growth in the housing market using the rent-price ratio: Evidence from the OECD countries. Journal of International Money and Finance, 53, 257–275. https://doi.org/10.1016/j.jimonfin.2015.02.001
Erol, I. (2015). Türkiye’de konut balonu var mı? Konut sektörü kapitalizasyon oranları analizi. In E. Özçelik & E. Taymaz (Eds.), Türkiye Ekonomisinin Dünü, Bugünü Yarını, Yakup Kepenek’e ve Oktar Türel’e Armağan (pp. 323–344). İmge Kitabevi Yayınları.
Erol, I., & Unal, U. (2015). Role of construction sector in economic growth: New evidence from Turkey (MPRA Paper No. 68263). Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/68263/1/MPRA_paper_68263.pdf
Garber, P. M. (2000). Famous first bubbles: The fundamentals of early manias. The MIT Press. https://doi.org/10.7551/mitpress/2958.001.0001
Girouard, N., Kennedy, M., van den Noord, P., & André, C. (2006). Recent house price developments: The role of fundamentals (OECD Economics Department Working Papers No. 475). OECD Publishing. https://doi.org/10.1787/864035447847
Glindro, E. T., Subhanij, T., Szeto, J., & Zhu, H. (2011). Determinants of house prices in nine Asia-Pacific economies. International Journal of Central Banking, 7(3), 163–204. https://www.ijcb.org/journal/ijcb11q3a6.pdf
Goodhart, C., & Hofmann, B. (2008). House prices, money, credit, and the macroeconomy. Oxford Review of Economic Policy, 24(1), 180–205. https://doi.org/10.1093/oxrep/grn009
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. https://doi.org/10.1109/TNNLS.2016.2582924
Guo, J., Chiang, S., Liu, M., Yang, C.-C., & Guo, K. (2020). Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance? International Journal of Strategic Property Management, 24(5), 300–312. https://doi.org/10.3846/ijspm.2020.12742
Gyourko, J., & Saiz, A. (2006). Construction costs and the supply of housing structure. Journal of Regional Science, 46(4), 661–680. https://doi.org/10.1111/j.1467-9787.2006.00472.x
Hill, R. J., & Trojanek, R. (2022). An evaluation of competing methods for constructing house price indexes: The case of Warsaw. Land Use Policy, 120, Article 106226. https://doi.org/10.1016/j.landusepol.2022.106226
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hong, J., Choi, H., & Kim, W. (2020). A house price valuation based on the random forest approach: The mass appraisal of residential property in South Korea. International Journal of Strategic Property Management, 24(3), 140–152. https://doi.org/10.3846/ijspm.2020.11544
International Monetary Fund. (2006). Financial soundness indicators: Compilation guide. International Monetary Fund, Monetary and Financial Systems and Statistics Departments.
Iskenderoglu, O., & Akdag, S. (2019). Türkiye’de reel konut fiyatlarında balonların varlığı üzerine uygulamalı bir analiz. Business and Economics Research Journal, 10(5), 1085–1093. https://doi.org/10.20409/berj.2019.223
Jadevicius, A., & Huston, S. (2015). ARIMA modeling 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
Kalczynski, P., & Zerom, D. (2015). Price forecast valuation for the NYISO electricity market. Kybernetes, 44(4), 490–504. https://doi.org/10.1108/K-08-2014-0174
Kolli, C. S., & Tatavarthi, U. D. (2020). Fraud detection in bank transactions with a wrapper model and Harris water optimization-based deep recurrent neural network. Kybernetes, 50(6), 1731–1750. https://doi.org/10.1108/K-04-2020-0239
Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y
MacKinnon, J. G. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11(6), 601–618. 3.0.co;2-t> https://doi.org/10.1002/(sici)1099-1255(199611)11:6<601::aid-jae417>3.0.co;2-t
Malpezzi, S., & Maclennan, D. (2001). The long-run price elasticity of supply of new residential construction in the United States and the United Kingdom. Journal of Housing Economics, 10(3), 278–306. https://doi.org/10.1006/jhec.2001.0288
Masters, D., & Luschi, C. (2018). Revisiting small batch training for deep neural networks. arXiv. https://doi.org/10.48550/arXiv.1804.07612
Milunovich, G. (2020). Forecasting Australia’s real house price index: A comparison of time series and machine learning methods. Journal of Forecasting, 39(7), 1098–1118. https://doi.org/10.1002/for.2678
Ng, S., & Perron, P. (2001). Lag length selection and the construction of unit root tests with good size and power. Econometrica, 69(6), 1519–1554. https://doi.org/10.1111/1468-0262.00256
Özgüler, İ. C., Büyükkara, Z. G., & Küçüközmen, C. C. (2023). Discovering the fundamentals of the Turkish housing market: A price convergence framework. International Journal of Housing Markets and Analysis, 16(1), 116–145. https://doi.org/10.1108/IJHMA-09-2021-0103
Phan, T. D. (2018). Housing price prediction using machine learning algorithms: The case of Melbourne City, Australia. In Proceedings of the 2018 International Conference on Machine Learning and Data Engineering (ICMLDE) (pp. 1–5). IEEE. https://doi.org/10.1109/iCMLDE.2018.00017
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Robstad, Ø. (2018). House prices, credit, and the effect of monetary policy in Norway: Evidence from structural VAR models. Empirical Economics, 54(2), 461–483. https://doi.org/10.1007/s00181-016-1222-1
Sharma, M., & Shekhawat, H. S. (2021). Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm. Kybernetes, 50(12), 3331–3366. https://doi.org/10.1108/K-09-2020-0563
Smith, L. N. (2017). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 464–472). IEEE. https://doi.org/10.1109/WACV.2017.58
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