Mortgage availability vs. availability of housing. We wanted the best, but it turned out...?

Автор: Basova Elena A.

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Статья в выпуске: 4 т.14, 2021 года.

Бесплатный доступ

The Russian government's decision to lower mortgage rates in the spring of 2020 was announced as a measure to support the population during the spread of coronavirus infection. However, is this decision optimal and strategically correct in terms of improving the availability of housing for Russian citizens in the future? In the context of rising real estate prices, this issue in domestic economic research is insufficiently addressed. The relevance of the research is additionally conditioned by the evaluation necessity of the “bubble” formation probability on the primary housing market due to the “double boom”. In this paper, we present the study results of availability of housing dynamics for the period from 2000 to 2020. We identified the regions with the lowest and the highest availability of housing. We have studied the current state of the mortgage lending market in Russia and constructed a multiple regression factor model for residential real estate prices on the new housing market. The scientific novelty of the presented research consists in the determination of the reasons of the rash growth of real estate prices in the spring of 2020 on the Russian housing market and their forecast for the medium term period. It is recognized that in modern conditions the key factor contributing to the increase in primary housing prices is the reduction in mortgage interest rates on loans. The decision to sharply reduce the interest rate has caused an increased demand for housing and the subsequent excessive growth of prices, which ultimately did not contribute to the availability of housing. We substantiate the absence of a “bubble” and the reasons for lower prices on the Russian residential real estate market in the short term. We have implemented the forecasting of primary housing prices through a combination of traditional economic tools and neural networks. The persistence of significant price levels on the Russian market of new buildings in the medium term can be explained by the negative trends in the development of key influencing factors and other macroeconomic indicators characterizing the socio-economic development of the country. We have proposed managerial measures aimed at expanding the availability of housing for Russian citizens.

Еще

Availability of housing, preferential mortgage lending, primary housing market, real estate price growth factors, neural network modeling

Короткий адрес: https://sciup.org/147235420

IDR: 147235420 | DOI: 10.15838/esc.2021.4.76.7

Список литературы Mortgage availability vs. availability of housing. We wanted the best, but it turned out...?

Mareeva S.V. Non-monetary inequalities and their impact on the economic situation of the population. In: Gorshkov M.K., Tikhonova N.E. (Eds.). Rossiiskoe obschestvo i vyzovy vremeni. Kniga 3 [Russian Society and Challenges of the Time. Book Three]. Moscow: Ves Mir, 2016. Pp. 247–265 (in Russian).
Basova E.A. Theoretical-methodological bases of researching the notion of “non-monetary inequality”. Aktual’nye problemy ekonomiki i prava=Actual Problems of Economics and Law, 2020, vol. 14, no. 3, pp. 415–427. DOI: http://dx.doi.org/10.21202/1993-047X.14.2020.3.415-427 (in Russian).
Aganbegyan A.G. O prioritetakh sotsial’noi politiki [About Priorities of Social Policy]. Moscow: Izd. Delo, 2020. 512 p.
Petty V., Smith A., Ricardo D., Keynes J., Freeman M. Klassika ekonomicheskoi mysli: sochineniya [Classics of Economic Thought: Essays]. Moscow: EKSMO-Press, 2000. 896 p.
Ricardo D. Nachala politicheskoi ekonomii i nalogovogo oblozheniya. Izbrannoe [On the Principles of Political Economy and Taxation. Selected Works]. Moscow: Eksmo, 2007. 960 p.
Marshall A. Printsipy ekonomicheskoi nauki [Principles of Economics]. Moscow: Progress Publishers, 1993. Vol. 1. 415 p.
Lin Y. Application of nonparametric regression in house price forecast. Advances in Social Sciences, January, 2020, vol. 09(08), pp. 1123–1131. DOI:10.12677/ASS.2020.98156
Gao P., Zhang R., Yang X. The application of stock index price prediction with neural network. Mathematical and Computational Applications, 2020, vol. 25(3), pp. 53–69. DOI:10.3390/mca25030053
Yasnitskii L.N., Yasnitskii V.L. Development and application of the complex neural network model for mass appraisal and forecasting of the urban real estate in the case study of the real estate markets of Yekaterinburg and Perm. Imushchestvennye otnosheniya v Rossiiskoi Federatsii=Property Relations in the Russian Federation, 2017, no. 3, pp. 68–84 (in Russian).
Yilmaz B., Kestel S. A stochastic approach to model housing markets: The US housing market case. Numerical Algebra, 2018, vol. 8(4), pp. 481–492. DOI:10.3934/naco.2018030
Netunaev E.B. The effectiveness of monetary policy as a tool to counter financial bubbles. Vestnik Instituta Ekonomiki Rossiyskoy akademii nauk=The Bulletin of the Institute of Economics of the Russian Academy of Sciences, 2018, no. 3, pp. 134–150 (in Russian).
Glaeser E., Gottlieb J.D., Tobio K. Housing booms and city centers. American Economic Review, 2012, vol. 102(3), pp. 127–133. DOI:10.1257/aer.102.3.127
Aoki K., Proudman J., Gertjan V. House prices, consumption, and monetary policy: a financial accelerator approach. Journal of Financial Intermediation, 2004, vol. 13, pp. 414–435.
Glaeser E.L., Gyourko J., Saiz A. Housing supply and housing bubbles. Journal of Urban Economics, 2008, vol. 64, pp. 198–217.
Caraiani P., Luiky M.-A., Wesselbaumz D. Credit policy and asset price bubbles. Journal of Macroeconomics, 2020, vol. 65, pp. 103–229. DOI:10.1016/j.jmacro.2020.103229
Dokko J., Doyle B.M., Kiley M.T., Kim J. Monetary policy and the global housing bubble. Economic Policy, 2011, vol. 26(66), pp. 237–287. DOI:10.1111/j.1468-0327.2011.00262.x
Curry B., Morgan P.Н., Silver M. Neural networks and non-linear statistical methods: An application to the modelling of price-quality relationships. Computers and Operations Research, 2002, vol. 29(8), pp. 951–969. DOI:10.1016/S0305-0548(00)00096-4
Clapp J. M., Giaccotto C. Evaluating house price forecasts. Journal of Real Estate Research, 2002, vol. 24(1), pp. 1–26.
Dua P., Miller S., Smyth D. Using leading indicators to forecast U.S. home sales in a Bayesian vector autoregressive framework. Journal of Real Estate Finance and Economics, 1999, no. 18(2), pp. 191–205.
Maqsood Z., Akhtar R., Latif H. Housing price forecastability by using factor analysis. Pyrex Journal of Business and Finance Management Research, 2019, vol. 5 (3), pp. 25–31.
Chen H., Wang S., Li Z., Xia J., Wei J. The housing price forecasting model based on improved GM model. Computer Science and Application, 2018, vol. 08(12), pp.1804–1812. DOI:10.12677/CSA.2018.812200
Daiitbegov D.M. Komp'yuternye tekhnologii analiza dannykh v ekonometrike: monografiya [Computer Technologies of Data Analysis in Econometrics: Monograph]. Moscow: Vuzovskii uchebnik: INFRA-M, 2018. 587 p.
Mariel P., Hoyos D., Meyerhoff J. Econometric modelling: basics. In: Environmental Valuation with Discrete Choice Experiments, Guidance on Design, Implementation and Data Analysis. 2020. Pp. 61–81. DOI:10.1007/978-3-030-62669-3_5
Balatsky E.V., Yurevich M.A. Application of neural networks for forecasting inflation: New opportunities. Vestnik URFU=The Bulletin of Ural Federal University. Economics and Management, 2018, no. 5, pp. 823–838 (in Russian).
Ketova K.V., Vavilova D.D. Neural network forecasting algorithm as a tool for assessing human capital trends of the socio-economic system. Ekonomicheskie i sotsial'nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 2020, vol. 13, no. 6, pp. 117–133. DOI: 10.15838/esc.2020.6.72.7 (in Russian).
Serrano W. The random neural network in price predictions. In: Artificial Intelligence Applications and Innovations. 2020. Pp. 303–314. DOI:10.1007/978-3-030-49161-1_26
Linh M. The hedonic pricing model applied to the housing market. International Journal of Economics and Business Administration, 2020, VIII (Special Issue 1), pp. 416–428. DOI:10.35808/ijeba/526
Gupta N., Nigam S. Crude oil price prediction using artificial neural network. Procedia Computer Science, 2020, vol. 170, pp. 642–647. DOI:10.1016/j.procs.2020.03.136
Khalafallah A. Neural network based model for predicting housing market performance. Tsinghua Science & Technology, 2008, no. 13, pp. 325–328. DOI:10.1016/S1007-0214(08)70169-X
Khaikin S. Neironnye seti: polnyi kurs [Neural Networks: a Full Course]. Moscow: Williams, 2006, 1104 p.
Kubishin E.S., Sedlov A.P., Soboleva I.V. Poverty in Russia: Measurement methodology and international comparisons. Vestnik Instituta Ekonomiki Rossiyskoy akademii nauk=The Bulletin of the Institute of Economics of the Russian Academy of Sciences, 2021, no. 1, pp. 56–70. DOI: 10.24412/2073-6487-2021-1-56-70 (in Russian).
Varlamova T.P. Mortgage lending system in Russia: establishment and prospects for development. Vestnik SGSEU=Vestnik of Saratov State Socio-Economic University, 2018, no. 5(74), pp. 136–139 (in Russian).
L’vov D.S. Russia: The framework of reality and the contours of the future. Ekonomicheskie i sotsial'nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 2007, no. 37, pp. 27–33 (in Russian).

Еще

Статья научная