R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 5 www.r-economy.ru Online ISSN 2412-0731 Original Paper © Zinovyeva, E.G., Balynskaya, N.R., Koptyakova, S.V., Akhmetzianova, O.O., 2020 doi 10.15826/recon.2020.6.1.001 Analysis of the residential mortgage market in the Ural Federal District E.G. Zinovyeva1 , N.R. Balynskaya1, S.V. Koptyakova1, O.O. Akhmetzianova2 1 Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia; e-mail: ekaterina_7707@mail.ru 2 University of Science and Technology of China, Hefei, Anhui, China ABSTRACT The relevance of this study stems from the fact that it analyzes the current situa- tion on the mortgage market in Russia: the influence of macro-economic factors causes a fall in collateral value, dramatic increase in mortgage default and poor performance of the Agency for Housing Mortgage Lending (AHML). The study is aimed at investigating the current state of residential mortgage lending on the re- gional level in Russia by focusing on the case of the Ural Federal District. The study considers the interests of all the participants of this market: individual borrowers, state authorities, financial and credit institutions engaged in mortgage lending. The study analyzes statistical data on the primary residential mortgage market in the Ural Federal District provided by the Central Bank of the Russian Federation, Federal State Statistics Service and the AHML. Results. Modern approaches to mortgage system evaluation are compared in order to identify and systematize the key criteria and statistical indicators characterizing the current state of this form of lending relationships. The analysis also brings to light the negative trends in mort- gage lending in the Ural Federal District. As a part of our further research, we are going to develop a procedure for evaluating the performance of a mortgage system. KEYWORDS residential mortgage, primary mortgage market, lenders, balanced autonomy model, single-tier and two-tiered models, mortgage institutions, lending market Анализ состояния рынка ипотечного жилищного кредитования в Уральском федеральном округе Е.Г. Зиновьева1 , Н.Р. Балынская1, С.В. Коптякова1, О.О. Ахметзянова2 1 Магнитогорский государственный технический университет им. Г.И. Носова, Магнитогорск, Россия; e-mail: ekaterina_7707@mail.ru 2 Научно-технический университет Китая, Хэфэй, Китай АННОТАЦИЯ Актуальность статьи обусловлена тем, что макроэкономическая ситуация последних лет оказала самое серьезное влияние на быстроразвивающуюся систему ипотечного кредитования в Российской Федерации, обнажив це- лый комплекс проблем – падение стоимости залога, резкий рост просро- ченной задолженности по выданным ипотечным кредитам, низкая эффек- тивность работы Агентства по ипотечному и жилищному кредитованию. Цель исследования – проанализировать состояние ипотечного жилищно- го кредитования в региональном разрезе на примере Уральского федераль- ного округа, учитывая интерес всех участников: населения, государства и финансово-кредитных институтов, имеющих в распоряжении временно свободные денежные средства и предоставляющие их во временное пользо- вание. Исследование базируется на аналитическом обзоре статистической информации, характеризующей первичный рынок ипотечного жилищного кредитования в Уральском федеральном округе. Информационно-эмпири- ческую базу исследования составили статистические материалы Централь- ного банка Российской Федерации, Федеральной службы государственной статистики, официальные отчетные данные Агентства по ипотечному и жилищному кредитованию. Результаты. По итогам сравнительного ана- лиза многообразия подходов к оценке эффективности функционирования системы ипотечного кредитования определены и систематизированы ос- новополагающие критерии и статистические показатели, характеризующие качество данной формы кредитных отношений; выявлены негативные тен- денции, характерные для системы ипотечного кредитования Уральского федерального округа. В  рамках дальнейшего исследования будет предло- жен алгоритм функционирования системы ипотечного кредитования. КЛЮЧЕВЫЕ СЛОВА ипотечное жилищное кредитование, первичный рынок ипотечного жилищного кредитования, кредитные организации, модель сбалансированной автономии, одноуровневая и двухуровневая модель, ипотечные институты, кредитный рынок FOR CITATION Zinovyeva, E.G., Balynskaya, N.R., Koptyakova, S.V., & Akhmetziano- va, O.O. (2020). Analysis of the residential mortgage market in the Ural Federal District. R-economy, 6(1), 5–13. doi: 10.15826/recon.2020.6.1.001 ДЛЯ ЦИТИРОВАНИЯ Zinovyeva, E.G., Balynskaya, N.R., Koptyakova, S.V., & Akhmetziano- va, O.O. (2020). Analysis of the residential mortgage market in the Ural Federal District. R-economy, 6(1), 5–13. doi: 10.15826/recon.2020.6.1.001 http://doi.org/10.15826/recon.2020.6.1.001 http://10.15826/recon.2020.6.1.001 mailto:ekaterina_7707@mail.ru mailto:ekaterina_7707@mail.ru 6 www.r-economy.ru R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 Online ISSN 2412-0731 Introduction The recent macro-economic situation has had a considerable effect on the rapidly growing mortgage lending system in Russia, causing a fall in collateral value, a dramatic increase in mort- gage default and poor performance results of the Agency for Housing Mortgage Lending (AHML). Nevertheless, despite these negative conditions, the experience of developed countries shows that in a market economy, this form of credit relation- ships is one of the key instruments to handle so- cio-economic problems and ensure the affordabi- lity of housing. Mortgage market contributes to the develop- ment of a competitive economy, its stabilization and modernization, it also helps decrease infla- tion and social tension by proving people with housing and stimulating construction and other industries, by stabilizing the financial market and enhancing investment. Mortgage lending can differ from country to country due to differences in their socio-econo- mic development, financial and credit systems, legislation regulating mortgage relationships and the corresponding models of such relationships. In Denmark, most mortgages have been pro- vided by one of the major  mortgage banks for 150 years. In Germany, loans are offered not only by mortgage banks but also by building societies (Bausoarkassen)1. In the USA, although mort- gages are issued by commercial banks, savings- and-loan associations, and credit unions, it is the government sponsored enterprises (GSEs) estab- lished by the US Congress which hold a signifi- cant part of the national mortgage portfolio2. In the process of building a comprehensive residential mortgage system of its own, Russia can benefit from the experience of mortgage lending services accumulated by foreign banks. Compa- rative analysis of international models of mort- gage lending can provide us with insights about the model that may hold most promise for Rus- sia. In this article, we are also going to explore the current state of mortgage lending in Russian re- gions by focusing on the case of the Ural Federal District, in particular by looking at the interests of the key participants of the mortgage market: indi- 1 Getting a mortgage in Germany. Expatica site. Re- trieved from: http://www.expatica.com/de/housing/How-to- get-amortgage-in-Germany_740222.html (19.04.2017). 2 Compare Mortgage Options. U.S. Bank National Asso- ciation site. Retrieved from: https://www.usbank.com/home- loans/mortgage/compare-mortgage-options.aspx (19.04.2017). vidual borrowers, state authorities, financial and credit institutions engaged in mortgage lending. This general aim comprises the following specific objectives: we are going to consider the existing models of mortgage lending in different countries (USA, Canada, UK and Germany); in- vestigate the main challenges faced by the mort- gage lending system in Russia; and conduct a comprehensive analysis of the primary mortgage market and its efficiency by using the case of the Ural Federal District. As for the practical implications of this study, the described model of mortgage len- ding can be used by regional authorities in strategizing and decision-making in the sphere of socio-economic development of their respec- tive regions. Our findings can also be useful for devising ways of stimulating housing construc- tion through policy-making and legislation. Our research can be of interest to housing con- struction companies seeking to enhance their cooperation with the regional authorities. The results of this study can be used for rationaliza- tion of the use of funds in Russian regions and municipalities and their reallocation to address issues in the sphere of housing construction as a part of regional socio-economic policies. Conceptual and methodological framework The conceptual and methodological frame- work of this study is based on classical and con- temporary, theoretical and applied research works on mortgage lending written by Russian and in- ternational scholars. It also relies on the main le- gal acts regulating lending relationships in general and mortgage lending in particular. At the centre of our study is the category ‘model of mortgage lending’, which corresponds to specific aspects of mortgage lending in diffe- rent countries determined by their socio-eco- nomic development, financial and credit systems, and laws governing mortgage lending. It should be noted that the model of mortgage lending is usually understood as a set of characteristics and relationships within the system of mortgage len- ding in a specific country [1]. In international practice, there are two ba- sic models of attracting funds to the sphere of mortgage lending: these are single-tier (European countries) and two-tiered (USA, UK and Cana- da) models [2]. In both models funds are attrac- ted by means of refinancing of mortgage markets http://doi.org/10.15826/recon.2020.6.1.001 http://www.expatica.com/de/housing/How-to-get-amortgage-in-Germany_740222.html http://www.expatica.com/de/housing/How-to-get-amortgage-in-Germany_740222.html https://www.usbank.com/homeloans/mortgage/compare-mortgage-options.aspx https://www.usbank.com/homeloans/mortgage/compare-mortgage-options.aspx R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 7 www.r-economy.ru Online ISSN 2412-0731 through assignment of home loans by secondary market operators and securitization3. For all the local variations, mortgage lending tends to follow some general patterns, which can be described as three basic models: contractual-savings, se- condary market model (or American model) and mortgage bank model [3; 4]. As the experience of developed countries shows, rational state policy in the sphere of mort- gage lending may ensure the transformation of this sphere into a self-financing sector, capable of providing stable development of a housing mar- ket. Mortgage serves as a catalyst of the real estate market and interconnected spheres since growth in effective demand for housing stimulates con- struction, manufacturing of building materials and equipment as well as innovation in architec- ture. It also contributes to growth of retail indus- try and enhances employment rates [5]. In developed countries, the mortgage mech- anism of housing acquisition is prioritized by the state socio-economic policy due to its efficien- cy: it helps attract considerable investment to the real sector of economy by encouraging hou- sing construction. Moreover, affordable mort- gages help the state meet the housing needs of its citizens. A long history of mortgage lending has resulted in the appearance of three classical models [6; 7]: truncated-open, balanced-autono- mous and expanded-open. Let us consider them in more detail: 1. The balanced autonomous model (con- tractual savings system) is a model of mortgage lending based on the loan and savings principle similar to private building societies such as Ger- man Bausparkasse, French Livret Epargne Loge- ment, and American savings and loans. The total portfolio of credit resources is formed not from the funds attracted on the open capital market but from the savings of future borrowers following the same principle as mutual funds [8]. In this mo- del, lenders are not only mortgage banks, but also specialized savings banks such as building soci- eties and savings banks [9]. An essential element of this model is the housing contractual savings, exceedingly wide spread in Germany, France, and Australia. Recently, they have also come in use in the new EU member states such as the Czech 3 Want A Mortgage? Forbes site. Retrieved from: https:// www.forbes.com/sites/nickclements/2016/09/30/want-a- mortgagethe-credit-score-used-by-mortgage-companies-will- surprise-you (19.04.2017). Republic, Croatia, Slovakia and Hungary4. Apart from Europe, this model is also applied in Angola, Indonesia, Morocco, Thailand, Tunisia, and Chile. We believe, however, that the balanced autono- mous model is not well-suited for Russia for the fol- lowing reasons: first, it limits the amount of attract- ed funds to the savings of contributors interested in obtaining credits for buying or building homes and does not include savings and resources of oth- er economic entities; and second, inflation makes it impossible to set acceptable mortgage rates [10]. 2). The truncated-open model (traditional, single-tier) is limited to the primary market of mortgage lending where lenders receive mort- gage bonds from their clients and use them as se- curity to attract external investment. This model is typical of Western Europe (the UK, France, Denmark, and Spain), Eastern Europe (Bulgaria, Poland and Hungary), Israel, Australia and some Latin American countries. In EU countries, however, mortgage rates vary significantly (the difference can be more than two times) [11; 12]). In Spain, the terms and conditions of a mortgage credit are the most liberal in Europe: a buyer can borrow up to 100% of the property’s value for up to 35 years, with the mortgage origination fee of 1.5% of the property value. However, if a buyer decides to use a construction mortgage, they could save up to a half of the origination fee [13]. In France, a typical mort- gage allows a buyer to borrow up to 80% of the prop- erty’s value for up to 25 years. French banks’ lending standards are generally more conservative than in some other European countries and the recent cuts to subsidized interest-free loans for home purchasing have changed the situation as banks started to raise their mortgage lending standards [14]. In the UK, the loan to value ratio is up to 70% while the mort- gage rates continue falling and at the moment are at the level of 3.14%. The minimum down payment is about 15% of the property’s value [15]. Although the truncated-open (traditional, single-tier) model of mortgage lending is a proto- type of the market model of lending relationships, we believe that it would not be a good fit to the Russian national context. A key characteristic of the truncated-open model is the direct dependence of mortgage rates on the general state and stability of the country’s economy, which also affects mortgage banking 4 Top 5 countries with the lowest mortgage rates. Tranio site. Retrieved from: https://tranio.com/switzerland,japan,fin- land,germany,luxembourg/analytics/top_5_countries_with_ the_lowest_mortgag e_rates_5108/ (19.04.2017). http://doi.org/10.15826/recon.2020.6.1.001 https://www.forbes.com/sites/nickclements/2016/09/30/want-a-mortgagethe-credit-score-used-by-mortgag https://www.forbes.com/sites/nickclements/2016/09/30/want-a-mortgagethe-credit-score-used-by-mortgag https://www.forbes.com/sites/nickclements/2016/09/30/want-a-mortgagethe-credit-score-used-by-mortgag https://www.forbes.com/sites/nickclements/2016/09/30/want-a-mortgagethe-credit-score-used-by-mortgag https://tranio.com/switzerland,japan,finland,germany,luxembourg/analytics/top_5_countries_with_the_l https://tranio.com/switzerland,japan,finland,germany,luxembourg/analytics/top_5_countries_with_the_l https://tranio.com/switzerland,japan,finland,germany,luxembourg/analytics/top_5_countries_with_the_l 8 www.r-economy.ru R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 Online ISSN 2412-0731 activities in specific favourable and unfavourable periods. In practice, within this model, there are no universal standard parameters of mortgages, instead such parameters as the costs and terms of home loans are usually set by each individual mortgage bank depending on specific conditions. While the scope of mortgage service is limi- ted in Russia, the mortgage rates tend to be higher and the mortgage terms are shorter than in its Eu- ropean counterparts. 3. The expanded open model (model of the secondary mortgage market, two-tiered) is also commonly referred to as the ‘American mortgage model’ because it is the most popular in the US5 [16]. The primary mortgage market is where bor- rowers can obtain home loans directly from pri- mary lenders while the secondary market deals with sales of securities or bonds collateralized by the value of mortgage loans. The expanded open model means that a person with a certain annual income purchases a move-in ready home paying in cash only an in- significant part of its value (10–20%) while the rest is borrowed from a specialized mortgage bank with the borrower’s property (either already owned or being purchased) used as a collateral (at the interest rate of 7–9%). As a rule, it takes about 15–30  years to repay such mortgage loans depending on the borrower’s annual income and the mortgage type. Within this model, the primary lender can refinance the issued mortgages either by selling them directly to investors or specialized institu- tions of the secondary market (secondary mar- ket operators) or by exchanging them for mort- gage-backed securities [17; 18]. The expanded open model is more suitable for the Russian context since, unlike the contractual savings system, it does not require much time for the accumulation of natural persons’ funds in the initial period [19]. This means that the expanded open model can be implemented much faster and on a massive scale. Our analysis of different models of mortgage lending has led us to the conclusion that the ex- panded open model is optimal for Russia (Fi- gure  1) and that it will help make housing more affordable and accessible for Russian citizens. Since this model is open and oriented towards obtaining resources from the free market, it is quite 5 Homebuying Step by Step. Canada Mortgage and Hous- ing Corporation site. Retrieved from: https://www.cmhcschl. gc.ca/en/co/buho/step-by-step/index.cfm (19.04.2017). susceptible to changes in the financial and credit market. The stability of the system, however, is en- sured by the government through legal, financial and licencing regulation. The government can also give guarantee and insurance against risks, provide tax preferences and offer targeted subsidies. It should be noted that the structure of the current legal framework for mortgage lending in Russia is oriented towards building a secondary mortgage market, that is, it relies on the expanded open model. This becomes obvious if we look at Chapters 3 and 8 of the Federal Law ‘On Mort- gage (Pledges of Immovable Property)’, describing the mortgagee’s rights to the obligation secured by mortgage, assignment of rights under mortgage agreement, transfer and pledge of encumbrance. The two-tiered scheme of mortgage lending in Russia was officially adopted in the ‘Concept of the Development of the Residential Mortgage System’6, which launched the implementation of a consistent government policy aimed to ensure the rights of lenders and investors on the mort- gage market and at the same time to make hous- ing and mortgages more affordable and accessible for creditworthy citizens. The two-tiered model of mortgage lending underpinning the Russian residential mortgage system holds considerable potential for state reg- ulation of this market and, consequently, the real estate market, securities market and macro-eco- nomic regulation in general [1]. Results and discussion The main focus of this study is the financial, economic, institutional and legal relationships in the residential mortgage market in Russia. At the key stage of this study we analyzed the available statistical data on the primary mortgage market in Russian regions [20; 21]. The data we used at this and the following stages of analysis were pro- vided by the Central Bank of the Russian Federa- tion, Federal State Statistics Service (Rosstat) and AHML in 2014–2018. 6 The Decree of the Government of the Russian Fede- ration of 11.01.2000 № 28 (amended as of 08.05.2002) ‘On the Measures for the Development of the Residential Mort- gage System in the Russian Federation’ (together with the ‘Concept of the Development of the Residential Mortgage System in the Russian Federation’ and the ‘Plan of Prepara- tion of the Drafts of Regulatory Acts for the Development of the Residential Mortgage System in the Russian Federation’). Retrieved from: http://www.consultant.ru/cons/cgi/online. cgi?req=doc&base=LAW&n=36649&fld=134&dst=10000 00001,0&rnd=0.8203381367685081#049319826734995775 (02.03.2020). http://doi.org/10.15826/recon.2020.6.1.001 https://www.cmhcschl.gc.ca/en/co/buho/step-by-step/index.cfm https://www.cmhcschl.gc.ca/en/co/buho/step-by-step/index.cfm http://www.consultant.ru/cons/cgi/online.cgi?req=doc&base=LAW&n=36649&fld=134&dst=1000000001,0&rnd=0 http://www.consultant.ru/cons/cgi/online.cgi?req=doc&base=LAW&n=36649&fld=134&dst=1000000001,0&rnd=0 http://www.consultant.ru/cons/cgi/online.cgi?req=doc&base=LAW&n=36649&fld=134&dst=1000000001,0&rnd=0 R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 9 www.r-economy.ru Online ISSN 2412-0731 If we look at the statistics on the number of lenders on the primary mortgage market in the Ural Federal District, we see the following picture: in the given period, the Central Bank’s policy led to a decline in the number of lenders, including mortgage lenders; falling national currency value on the global market; and reduced demand for banking products (see Table 1). Table 1 Number of lending institutions on the mortgage market of the Ural Federal District in 2014–2018 Year Number of lenders, total including: mortgage lenders mortgage investors mortgage refinance lenders lenders attract- ing secondary market investors 2014 35 34 9 1 6 2015 32 31 8 0 3 2016 29 28 8 1 1 2017 26 26 9 4 3 2018 23 23 10 8 3 Compiled by the authors based on the AHML data. Re- trieved from: https://cbr.ru/statistics/pdko/Mortgage/ (Ac- cessed: 02.03.2020) In 2014–2018, 11 lenders left the primary mortgage market, that is, the number of partici- pants fell by 32.3%, from 35 to 23. According to Figure 2, Sverdlovsk region had the maximum number of lenders on the mortgage market in 2014 (15 participants). In 2016, this re- gion accounted for the largest share (45%) in the total number of lenders in the Ural Federal Dis- trict. In 2014–2018, Kurgan region had the smal- lest number of lenders on the mortgage market. 100 80 60 50 20 0 2014 2015 2016 2017 2018 Kurgan region Tyumen region Sverdlovsk region Chelyabinsk region Figure 2. Structure of the mortgage market in the Ural Federal District in 2014–2018 Compiled by the authors based on the AHML data. Retrieved from: https://cbr.ru/statistics/pdko/Mortgage/ (Accessed: 02.03.2020) The regional distribution pattern of issued mortgages in the given period remained prac- tically the same. Borrowers in Tyumen region accounted for the largest volume of issued mort- gages and, accordingly, the highest percentage in the overall volume of operations on the mortgage Insurance company Valuation company Borrower Property seller Commercial bank Regional operator AHML Investor Property as mortgage security Health insurance, title insurance, and homeowners insurance Market valuation Purchase and sale transaction Mortgage Real estate purchase agreement Sale of loans Mortgage sale Re�nancing of the regional operator Mortgage re�nancing Placement of mortgage-backed securities Guarantees for mortgage-backed securitiesNational government Real estate agent Figure 1. Expanded open model (secondary, 2-tiered) of mortgage lending in Russia [3] http://doi.org/10.15826/recon.2020.6.1.001 https://cbr.ru/statistics/pdko/Mortgage/ https://cbr.ru/statistics/pdko/Mortgage/ 10 www.r-economy.ru R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 Online ISSN 2412-0731 market in 2014–2018. The lowest figures were supplied by Kurgan region and the Yamalo-Ne- nets Autonomous District (Figure 3). 2014 2015 2016 2017 2018 Kurgan region Tyumen region Sverdlovsk region Chelyabinsk regionincl. Yamalo-Nenets AR incl. Khanty-Mansiysk AR Figure 3. Volume of mortgage loans given to natural persons in 2014–2018 in the Ural Federal District Compiled by the authors based on the AHML data. Retrieved from: https://cbr.ru/statistics/pdko/Mortgage/ (Accessed: 02.03.2020) The dynamics of the volume of mortgage lending shown in Figure 3 determined the dy- namics of mortgage debt, which grew steadily in 2014–2018. As of the end of 2018, the amount of mortgage debt in roubles increased by 18,743 rou- bles (by 117.5%) in comparison with 2014 and reached 125,792 roubles. This situation to a significant extent was determined by per capita income in the region (Table 2). In the Ural Federal District, per capita in- come increased from 28.7 thousand roubles a month in 2013 to 34.9 thousand roubles a month in 2018. According to Rosstat, the Yamalo-Ne- netsk Autonomous Region ranked first among other Ural regions in 2018 in terms of per capita income and its dynamics (79.3 thousand roubles a month) while the poorest region, lagging behind the rest, is Kurgan region with per capita income of 20.3 thousand roubles a month. Table 2 Per capita income in the Ural Federal District, rbs Regions 2013 2014 2015 2016 2017 2018 Ural Federal District 28719 29997 32794 32907 33643 34955 Kurgan region 17076 18315 20310 20175 20660 20334 Sverdlovsk region 30459 31538 34113 34718 35210 36735 Tyumen region 36167 37783 41893 42657 44241 46124 Khanty-Mansiysk Autonomous Region – Yugra 39882 40811 46221 46934 48834 50717 Yamalo-Nenets Autonomous Region 58829 62020 67624 72358 76027 79398 Tyumen region without autonomous regions (Khanty-Mansiysk and Yamalo-Nenets) 23169 25142 27448 27044 27672 29162 Chelyabinsk region 21971 23070 24654 23657 23719 24386 Compiled by the authors on the basis of Rosstat data. Retrieved from: https://www.gks.ru/free_doc/new_site/population/urov/ urov_11sub.htm (Accessed: 02.03.2020) Table 3 Weighted average period and rate of mortgages in roubles given to natural persons in 2014–2018 in the Ural Federal District Region 2014 2015 2016 2017 2018 Weighted average mort- gage period, months Weighted average rate, % Weighted average mort- gage period, months Weighted average rate, % Weighted average mort- gage period, months Weighted average rate, % Weighted average mort- gage period, months Weighted average rate, % Weighted average mort- gage period, months Weighted average rate, % Ural Federal District 187.3 12.39 189.8 12.41 183.4 13.42 186.4 12.73 187.3 10.65 Kurgan region 215.0 11.61 205.5 12.26 199.9 13.19 195.3 12.60 194.3 10.58 Sverdlovsk region 180.2 12.48 184.4 12.46 182.0 13.53 186.2 12.71 189.0 10.66 Tyumen region 200.9 12.31 203.6 12.33 192.2 13.35 197.3 12.71 194.7 10.62 including Khan- ty-Mansiysk Autonomous Region – Yugra 220.1 12.31 203.5 12.33 188.5 13.30 190.5 12.83 190.4 10.67 including Yama- lo-Nenets Autono- mous Region 217.0 11.83 211.3 11.91 193.5 13.52 196.3 12.58 194.7 10.50 Chelyabinsk region 159.6 12.68 163.8 12.55 156.7 13.60 159.9 12.83 165.6 10.75 Compiled by the author based on Rosstat data. Retrieved from: https://cbr.ru/statistics/pdko/Mortgage/ (Accessed: 02.03.2020) http://doi.org/10.15826/recon.2020.6.1.001 https://cbr.ru/statistics/pdko/Mortgage/ https://www.gks.ru/free_doc/new_site/population/urov/urov_11sub.htm https://www.gks.ru/free_doc/new_site/population/urov/urov_11sub.htm https://cbr.ru/statistics/pdko/Mortgage/ R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 11 www.r-economy.ru Online ISSN 2412-0731 In the given period, the weighted average rate in roubles for the Ural Federal District fell by 1.74 percentage points and the weighted average mort- gage period in 2014–2018 remained virtually un- changed – 187.3 months (15.6 years) (Table 3). In the given period, the weighted average rate decreased in all the regions – in Kurgan re- gion, by 1.03%; in Sverdlovsk region, by 1.82%; in Tyumen region, by 1.69%; and in Chelyabinsk region, by 1.93%. As for the mortgage period, in some regions it became longer (Sverdlovsk re- gion, by 7.2 months and in Chelyabinsk region, by 6 months) while in Kurgan and Tyumen regions, it, on the contrary, shortened – by 20.7 months and 6.2 months respectively. Conclusion The study of theoretical principles and aspects of mortgage lending has led us to the following conclusions. Our analysis of the problems and prospects of the Russian mortgage system centred around the model of mortgage lending [1]. Analysis of the three key models (balanced autonomous model or contractual savings system; truncated open mo- del or traditional single-tier model; and expanded open model or secondary mortgage model, two- tiered), characteristic of international practices of mortgage lending [22; 23], in their relation to the Russian context has brought to light the follo- wing priorities in the development of the national system of lending relationships: first, it is essential to safeguard the interests of both lenders and bor- rowers; second, to enhance affordability of mort- gages for average consumers and assign a priority role to mortgage lenders in the credit sector; and, finally, to enhance state regulation of mortgage lending relationships [24; 25]. The comprehensive analysis of the primary mortgage market in the Ural Federal District has revealed the following problems. First, in the given period, the number of par- ticipants of this market fell dramatically, in par- ticular the number of lenders (by 32.3%). At the end of the period, there were 23 participants. It is expected that this negative trend will have nega- tive long-term repercussions such as a decline in competition in the banking sector and tiering of the banking system, reduced number of market niches where the right lenders could be found for investment projects (borrowers) of different risk levels. Second, in 2014–2018, there was a steady growth of mortgage debt, which increased by 18,743 roubles (or 117.5%). Another trend was the ageing of mortgage debt due to poor asset ma- nagement, unstable financial and economic situa- tion of borrowers caused by the changing macro- and microeconomic conditions in the country. Third, in the given period, there was a drop in the mortgage rates from 12.41% in 2014 to 10.65% in 2018, which significantly affected the real estate prices and made housing more attractive in terms of investment opportunities. Overall, our analysis of the financial, econo- mic, institutional and legal relations on the pri- mary mortgage market in the Ural Federal Dis- trict has demonstrated that at its current stage, the research in this sphere lacks consistency, parti- cularly in the evaluation of the mortgage system’s efficiency on the regional level. In our further studies we intend to bridge the existing gaps in research literature by developing a methodology for systematizing the criteria and indicators of the mortgage system’s efficiency. The procedure will include the following stages: creating a ranking system based on the mortgage system’s efficiency criteria and the corresponding set of sta- tistical indicators; analysis of the influence that spe- cific statistical indicators have on the system; and, finally, developing an integral indicator for evalua- tion of the system’s efficiency on the regional level. References 1. Zinovieva, E.G., Vasilieva, A.G., & Usmanova, E.G. (2018). Contemporary issues and trends in the development of the Russian national mortgage system. St Petersburg: Info-Da. (In Russ.) 2. Minz, V.M. (2012). Models of mortgage lending and prospects of their application in Russia. Bankovskoye delo, 6, 30–34. (In Russ.) 3. Pavlova, I.V. (2016). Foreign experience of mortgage lending and its applicability in Russia. Bankovskoye delo, 4, 13–19. (In Russ.) 4. Merkulov, V.V. (2013). International experience of mortgage lending and prospects of its use in Russia. St Petersburg: Piter. (In Russ.) 5. 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Available at: https://elibrary.ru/item.asp?id=36334806. http://doi.org/10.15826/recon.2020.6.1.001 https://ssrn.com/abstract=2005895 https://ssrn.com/abstract=2005895 http://doi.org/10.1016/j.jhe.2005.07.003 http://doi.org/10.1111/jmcb.12620 http://doi.org/10.1016/j.ijindorg.2003.07.002 http://doi.org/10.1016/j.regsciurbeco.2013.12.004 http://doi.org/10.1016/j.regsciurbeco.2013.12.004 http://doi.org/10.1007/s11146-018-9683-y http://doi.org/10.1016/j.econmod.2012.08.004 http://doi.org/10.1016/j.econmod.2012.08.004 http://doi.org/10.1016/j.jhe.2014.01.002 http://doi.org/10.1016/j.jhe.2014.01.002 http://doi.org/10.1016/j.jimonfin.2018.06.013 http://doi.org/10.1016/j.jhe.2009.10.003 http://doi.org/10.1016/j.jhe.2009.10.003 http://doi.org/10.1016/j.ecosys.2016.08.001 http://doi.org/10.1080/13563467.2018.1545755 https://elibrary.ru/item.asp?id=36334806 R-ECONOMY, 2020, 6(1), 5–13 doi: 10.15826/recon.2020.6.1.001 13 www.r-economy.ru Online ISSN 2412-0731 Information about the authors Ekaterina G. Zinovyeva – Associate Professor, Nosov Magnitogorsk State Technical University (38 Lenina av., Magnitogorsk, 455000, Russia); e-mail: ekaterina_7707@mail.ru Natalya R. Balynskaya – Associate Professor, Nosov Magnitogorsk State Technical University (38 Lenina av., Magnitogorsk, 455000, Russia); e-mail: balynskaya@list.ru Svetlana V. Koptyakova – Associate Professor, Nosov Magnitogorsk State Technical University (38 Lenina av., Magnitogorsk, 455000, Russia); e-mail: svetlana.cop@yandex.ru Oksana O. Akhmetzianova – Researcher, University of Science and Technology of China (96, JinZhai Road Baohe District, Hefei, Anhui, 230026, China); e-mail: oksanochka-star@mail.ru ARTICLE INFO: received December 7, 2019; accepted March 05, 2020 Информация об авторах Зиновьева Екатерина Георгиевна – доцент, Магнитогорский государственный технический университет им. Г.И. Носова (455000, Россия, Магнитогорск, пр. Ленина, 38); e-mail: ekaterina_7707@mail.ru Балынская Наталья Ринатовна – доцент, Магнитогорский государственный технический университет им. Г.И. Носова (455000, Россия, Магнитогорск, пр. Ленина, 38); e-mail: balynskaya@list.ru Коптякова Светлана Владимировна – доцент, Магнитогорский государственный технический университет им. Г.И. Носова (455000, Россия, Магнитогорск, пр. Ленина, 38); e-mail: svetlana.cop@yandex.ru Ахметзянова Оксана Олеговна – исследователь, Научно-технический университет Ки- тая, Хэфэй, Китай (230026, КНР, Хэфэй, ул. Цзиньчжай, 96); e-mail: oksanochka-star@mail.ru ИНФОРМАЦИЯ О СТАТЬЕ: дата поступления 7 декабря 2019 г.; дата принятия к печати 5 марта 2020 г. http://doi.org/10.15826/recon.2020.6.1.001 mailto:ekaterina_7707@mail.ru mailto:balynskaya@list.ru mailto:svetlana.cop@yandex.ru mailto:oksanochka-star@mail.ru mailto:ekaterina_7707@mail.ru mailto:balynskaya@list.ru mailto:svetlana.cop@yandex.ru mailto:oksanochka-star@mail.ru