GRAVITATIONAL MODELS IN THE MORTGAGE MARKET OF RUSSIA

Research purpose. One of the ways to improve the living conditions of Russians is the development of the mortgage market. The peculiarity of the mortgage market in Russia is that credit institutions, located in the Central Federal District provide more than 90% of the volume of mortgage loans in rubles, and resident individuals - borrowers from other federal districts, receive most of these loans. Thus, the Central Federal District is the center of attraction of borrowers and determines the main trends in the mortgage market. In addition, if still to consider that migration flows have the developed tendency in a section of federal districts, then there is a question of a possibility to create the gravitational model, the obtained mortgage loans borrowers of the explored federal districts in the center of attraction, this is the research purpose.

Materials and methods. The study was conducted, based on official state statistics - ROSSTAT and the Bank of Russia, using correlation regression analysis and adaptive prediction methods. Analysis of migration indicators by federal districts revealed the features that led to the construction of three gravitational models.

Results. The first gravitational model is suitable for borrowers of four federal districts:  Northwestern, Southern, Far Eastern and North-Caucasian regions. The second gravitational model is under the first type and is typical for the Volga Federal District, which has its own specifics. The third model, which determines the volume of mortgage loans in rubles, obtained by borrowers of the Siberian and Ural Federal Districts, for these districts the gravitational constant of the models turned out to be almost the same that is approximately equal to 11, which indicates the same situation, both on the mortgage market and migration sentiments. In the paper, based on the constructed gravitational models of the mortgage market of the Central Federal District, a forecast of the volume of mortgages was given received in both the context of federal districts and the entire Russian mortgage market for 2018, which required the construction of forecast models for migration indicators. The construction of gravitational models was carried out based on indicators, taken in dynamics up to 2016 inclusive. Therefore, comparing the forecast with its implementation in 2017, the quality of the forecast of the constructed models was evaluated, which showed good results.

Conclusion. In Central Federal District, more than a half of credit institutions - 52% are concentrated. They issue the main volume of all mortgage-housing loans in the country - 94%. The constructed gravitational models, considering the migration processes, give an answer to the question of the volumes of development of the primary mortgage market, and therefore allow assessing the risks and opportunities for the development of the secondary mortgage market.

1. Anikin A. V. Amerikanskaya politicheskaya ekonomiya posle Franklina. YUnost’ nauki: ZHizn’ i idei mysliteley-ekonomistov do Marksa. 2nd ed. Moscow: Politizdat, 1975. 384 P. (In Russ.)

2. Isard, Walter. Location Theory and Trade Theory: Short-Run Analysis. Quarterly Journal of Economics. 1954. 68 (2).

3. SHumilov A.V. Otsenivaniye gravitatsionnykh modeley mezhdunarodnoy torgovli: obzor osnovnykh podkhodov. Ekonomicheskiy zhurnal VSH•E. 2017. Vol. 21. No. 2. P. 224–250. (In Russ.)

4. Anderson J.E., van Wincoop E. Gravity with gravitas: a solution to the border puzzle. American Economic Review. 2003. Vol. 93. No. 1. P. 170–192

5. Mezhregional’nyye mezhotraslevyye modeli mirovoy ekonomiki. Ed. A.G. Granberga i S.M. Men’shikova. Novosibirsk: Nauka, 1983. 273 p. (In Russ.)

6. YUnusov M.A. Adaptatsiya gravitatsionnykh metodov k issledovaniyu effektivnosti predprinimatel’skikh setey. Upravleniye ekonomicheskimi sistemami: elektronnyy nauchnyy zhurnal. 2011. No.3. URL: http://www.uecs.ru (In Russ.)

7. TSentral’nyy bank Rossiyskoy Federatsii URL: http://www.cbr.ru/statistics/?PrtId=ipoteka (accessed: 15.04.2018) (In Russ.)

8. Huff, DL, A Probabilistic Analysis of Shopping Center Trade Areas. Land Economics 1963. 39. P. 81–90

9. Converse P.D. New Laws of Retail Gravitation. Journal of Marketing. 1949. Oct. P. 379–384.

10. KHavinson M.YU, Kulakov M.P. Kontseptsiya dinamicheskoy gravitatsionnoy modeli migratsii naseleniya. Moscow: Regional’nyye problemy. 2016. Vol. 19. No. 4. P. 12–19. (In Russ.)

11. Nguyen-Hoang P., McPeak J. Leaving or staying: Inter-provincial migration in Vietnam. Asian and Pacific Migration Journal. 2010. 19 (4). P. 473–500.

12. Trofimova N.A., Razumovskaya V.A. Modifitsirovannaya gravitatsionnaya model’ trudovoy migratsii. Analiz i modelirovaniye ekonomicheskikh protsessov. Sbornik statey ed. V.Z. Belen’kogo, Iss. 8. Moscow: TSEMI RAN, 2010. 131 P. (In Russ.)

13. Kel’nik A.V. Regulirovaniye vnutrenney migratsii naseleniya v aspekte razvitiya Minsk: Belarus. navuka, 2012. 161 p. (In Russ.)

14. Ravenstein E. The Laws of Migration. Journal of the Statistical Society of London. 1885. Vol. 48. No. 2. P. 167–235.

15. Limonov L.E. Regional’naya ekonomika i prostranstvennoye razvitiye. Moscow: Izdatel’stvo YUrayt. 2015. Vol. 1. P. 133–137. (In Russ.)

16. Federal’noy sluzhby gosudarstvennoy statistiki (Rosstat) URL: http://www.gks.ru (accessed: 15.04.2018) (In Russ.)

17. Karmanov M.V., Smelov P.A., Egorova E.A., Zolotareva O.A., Kuchmayeva O.V. Metodologicheskiye voprosy ekonomiko-statisticheskogo analiza sotsial’no-demograficheskikh grupp naseleniya. Monografiya. Moscow: MESI, 2010(In Russ.)

18. KHayyashi F. Ekonometrika. Moscow: Izdatel’skiy dom «Delo» RANKH i GS, 2017. 728 p. (In Russ.)

19. Lukashin YU. P. Adaptivnyye metody kratkosrochnogo prognozirovaniya vremennykh ryadov. Moscow: Finansy i statistika, 2003. 416 p. (In Russ.)

20. Prognoz sotsial’no-ekonomicheskogo raz- vitiya Rossiyskoy Federatsii na 2017 god i na planovyy period 2018 i 2019 godov URL: http://economy.gov.ru/wps/wcm/connect/9dd9931d-3960-454c-a8db-ec6fc1ab4bfc/prognoz_2017_2019.pdf?MOD=AJPERES&CACHEID=9dd9931d-3960-454c-a8db-ec6fc1ab4bfc (accessed: 15.04.2018) (In Russ.)