HOUSING AFFORDABILITY RANKING OF RUSSAIN REGIONS

The monitoring of regional housing market and housing affordability can be realized with certain rakings. For the moment there is no official approach to ranking. Anyway, the methodology for housing affordability estimation is rich enough to choose the proper way. This paper reviews some methods and applies them to the regional housing markets for better understanding of local processes and results.

Before evaluation of housing affordability it is necessary to indicate the money sources taking part in bargain: household savings and loans (credits). In general, the two sources are active, but the global financial crisis has almost switched off mortgage loans and households can rely on their own finance. Below we consider both cases.

1. Home buying with savings & mortgage loan.

Here we admit a normal economic situation when a household can take a mortgage loan for home buying. In this case the housing affordability rate is evaluated with the following formula:

       

                                                                   (1)

where H₁ - housing affordability index (%); R - monthly household's income; ε - maximum share of household's income that can be spent for loan payments; C - monthly loan payments; D - mortgage loan; 0,022*D/12 - monthly household's payments for loan service (health insurance, property insurance)[1].

Table 1 contains the ranking of the Russian regions by H₁ values in descending order for 2007. The H₁ rates refer to a household of 3 persons buying an apartment of 54 sq.m living space with a mortgage loan. In Russia 18 sq/m per capita is a minimum social standard of living space. The mortgage loan in rubles is granted by a bank, a AHML's partner, for 20 years and APR of 13,75%. The credit amount is 80% of real estate value. Monthly household's income is considered as double monthly average salary in regional economy. The limit of household's income spending on the mortgage loan is accepted at 50%[2]. The prices for apartments are dated 4Q 2007.

 

 

Table 1. Housing affordability ranking of the Russian regions in 2007[3].

Region	H1, %
Yamalo-Nenets Autonomous Okrug	139,8
Nenets Autonomous Okrug	137,8
Khanty-Mansi Autonomous Okrug-Yugra	120,6
Jewish Autonomous Oblast	111,0
Tyumen Oblast	108,6
Sakha (Yakutia) Republic	103,4
Kamchatka Krai	89,0
Republic of North Ossetia-Alania	79,2
Republic of Khakassia	71,8
Zabaykalsky Krai	71,2
Leningrad Oblast	70,4
Republic of Karelia	69,2
Republic of Kalmykia	69,0
Kemerovo Oblast	69,0
Amur Oblast	68,4
Tyva Republic	66,4
Komi Republic	65,4
Tomsk Oblast	64,6
Buryat Republic	64,2
Krasnoyarsk Krai	63,0
Novgorod Oblast	62,4
Khabarovsk Krai	62,2
Stavropol Krai	61,4
Saratov Oblast	61,0
Astrakhan Oblast	59,8
Arkhangelsk Oblast	59,4
Smolensk Oblast	58,4
Chelyabinsk Oblast	58,4
Republic of Tatarstan	57,4
Orenburg Oblast	56,4
Republic of Mordovia	55,6
Samara Oblast	55,2
Omsk Oblast	55,2
Oryol Oblast	55,0
Kursk Oblast	54,8
Republic of Adygea	54,6
Vologda Oblast	54,2
Kaliningrad Oblast	53,6
Primorsky Krai	53,2
Belgorod Oblast	52,4
Altai Republic	52,4
Bryansk Oblast	52,2
Rostov Oblast	52,0
Tula Oblast	51,8
Moscow Oblast	51,6
Voronezh Oblast	49,2
Lipetsk Oblast	46,6
Tambov Oblast	46,4
Irkutsk Oblast	45,6
Novosibirsk Oblast	45,6
Pskov Oblast	45,4
Republic of Bashkortostan	45,4
Volgograd Oblast	45,2
Kaluga Oblast	44,8
Ryazan Oblast	44,8
Vladimir Oblast	44,4
Ivanovo Oblast	44,4
Penza Oblast	44,0
Ulyanovsk Oblast	44,0
Mari El Republic	43,6
Chuvash Republic	43,2
Republic of Dagestan	42,8
Tver Oblast	42,4
Sverdlovsk Oblast	42,4
Yaroslavl Oblast	42,2
Krasnodar Krai	41,8
Udmurt Republic	41,6
Kirov Oblast	40,4
Kostroma Oblast	39,8
Perm Krai	39,0
Altai Krai	36,6
Saint-Petersburg	36,0
Moscow	34,2
Nizhny Novgorod Oblast	30,0

 

Based on the above ranking it is possible to make the following outcomes.

1) High affordability values are up to different types of regions. Firstly, it is common for oil-rich territories as (Yamalo-Nenets Autonomous Okrug, Nenets Autonomous Okrug, Khanty-Mansi Autonomous Okrug-Yugra). On the other hand, some economically depressed regions (for instance, Jewish Autonomous Oblast) unexpectedly have reached almost the same result. The paradox can be explained by useless of real estate there because the local population makes attempts to leave for better regions.

2) The low values of housing affordability have a double meaning as well. One part is identifying the territory with crisis and poor population (for an example, Kostroma Oblast). The other one concerns economically successful regions as Moscow and Saint-Petersburg where homes are in great demand that pushes housing bubbles.

Considering H₁ values by federal district (table 2), we find the following regularity: the lower population of the federal district the more housing affordability rate (55% correlation coefficient is significant at the 95% confidence interval).

Taking into account the migration flows from the Asian part of Russia to the central one, it is possible to conclude that the HAI values by federal districts reflect the population willingness to stay in their territories for a long time. Following the logic, the 6,5 million people in the Far East has the willingness to stay there in almost two times lower than 37,2 million in Central federal district.

Table 2. Housing affordability rates by federal district in 2007

Federal district	Population share, % (early 2007)	H1, %
Central	26,2	42,0
Northwestern	9,5	43,0
Volga	21,3	46,6
Southern	16,0	47,0
Siberian	13,8	55,4
Urals	8,6	67,0
Far Eastern	4,6	75,2
Russia	100,0	47,0

 

2. Savings as the only source for home buying.

The present financial crisis has made it difficult for households to take credits, mortgage loans and in home buying families can operate with their own savings. Hence, the method of housing affordability evaluation should be revised. In crisis situation housing affordability can be measured as a time period (in years) that a household require to save money enough for home buying. The formula below allows evaluating this character:

                                                                                           (2)

where H₂ - time period of money saving for home buying (years); S - home price; R - annual household's income; M -minimum level of annual household's subsistence. As in the previous case the household consists of 3 persons - employed parents and a child.

Technically, there is no difficulty to make certain calculations. The more precious result is in finding sensitivity of housing affordability to the financial crisis at least in 2008. There are may be a set of methods but we prefer the following percentage ratio:

                                                                          (3)

where  - time period of money saving for home buying in Q1 2008;  - time period of money saving for home buying in Q4 2008.

The k-values show the sensitivity level of regional housing market to the crisis. If k<0 the housing affordability goes down in spite of the crisis and the housing bubble makes a progress. When k>0 we say about housing bubble erosion and the market becomes more socially oriented.

Table 3 keeps the results of sensitivity estimation for a number of regional housing markets in Russia (the regions are arranged in descending order of k-values).

 

 

Table 3. Housing affordability in regional markets and their sensitivity to the financial crisis.

Region	, years	, years	, years	k, %
Republic of Dagestan	40,0	18,1	21,8	54,6
Novosibirsk Oblast	14,9	7,6	7,2	48,6
Altai Republic	34,3	19,6	14,7	42,8
Republic of Kalmykia	15,8	9,6	6,1	38,9
Kursk Oblast	16,8	10,4	6,5	38,4
Saratov Oblast	14,7	9,1	5,5	37,7
Pskov Oblast	22,2	13,9	8,3	37,3
Perm Krai	20,2	12,8	7,4	36,8
Tomsk Oblast	10,7	6,8	3,9	36,5
Kamchatka Krai	8,7	5,6	3,1	36,1
Stavropol Krai	18,1	11,7	6,4	35,5
Ivanovo Oblast	23,5	15,3	8,2	34,8
Zabaykalsky Krai	10,4	6,8	3,6	34,7
Kostroma Oblast	21,3	14,1	7,2	34,0
Penza Oblast	17,5	11,6	5,9	33,7
Rostov Oblast	19,6	13,1	6,5	33,2
Karachay-Cherkess Republic	11,1	7,4	3,6	32,8
Tver Oblast	22,6	15,3	7,3	32,3
Khabarovsk Krai	12,6	8,7	4,0	31,4
Kirov Oblast	24,1	16,6	7,5	31,3
Vladimir Oblast	22,4	15,5	6,9	30,6
Chuvash Republic	18,1	12,6	5,5	30,4
Tambov Oblast	17,5	12,3	5,1	29,3
Primorsky Krai	15,3	10,9	4,4	28,7
Republic of Bashkortostan	13,4	9,6	3,8	28,2
Krasnoyarsk Krai	10,9	7,9	3,0	27,2
Udmurt Republic	16,9	12,3	4,5	27,0
Altai Krai	30,8	22,5	8,2	26,8
Republic of Mordovia	22,4	16,4	6,0	26,8
Ulyanovsk Oblast	21,8	16,0	5,8	26,4
Arkhangelsk Oblast	11,0	8,1	2,9	26,4
Voronezh Oblast	15,8	11,7	4,1	25,9
Samara Oblast	17,1	12,8	4,3	25,1
Komi Republic	9,4	7,2	2,2	23,5
Saint-Petersburg	14,8	11,5	3,4	22,8
Tula Oblast	17,6	13,6	4,0	22,7
Tyva Republic	13,0	10,1	2,9	22,6
Republic of North Ossetia-Alania	13,6	10,5	3,1	22,6
Nizhny Novgorod Oblast	25,5	19,8	5,7	22,3
Bryansk Oblast	19,1	14,9	4,2	22,0
Ryazan Oblast	17,3	13,7	3,6	20,9
Astrakhan Oblast	14,8	11,9	2,9	19,8
Smolensk Oblast	15,5	12,5	3,1	19,7
Moscow	15,7	12,6	3,1	19,7
Republic of Tatarstan	10,1	8,1	2,0	19,7
Orenburg Oblast	13,5	10,9	2,6	19,3
Novgorod Oblast	12,8	10,4	2,4	18,6
Yaroslavl Oblast	16,0	13,1	2,9	18,1
Oryol Oblast	14,2	11,7	2,5	17,3
Mari El Republic	20,1	16,6	3,4	17,2
Kaliningrad Oblast	13,0	10,8	2,2	17,0
Moscow Oblast	11,5	9,5	1,9	16,9
Khanty-Mansi Autonomous Okrug-Yugra	4,4	3,7	0,7	15,5
Republic of Adygea	19,6	16,6	3,0	15,4
Vologda Oblast	11,3	9,6	1,7	15,2
Sakha (Yakutia) Republic	7,6	6,5	1,1	14,7
Sverdlovsk Oblast	10,0	8,6	1,4	14,0
Republic of Karelia	12,1	10,6	1,5	12,2
Kaluga Oblast	17,0	15,1	2,0	11,6
Amur Oblast	12,2	10,9	1,3	10,9
Belgorod Oblast	12,9	11,5	1,4	10,9
Tyumen Oblast	4,0	3,5	0,4	10,8
Lipetsk Oblast	12,4	11,2	1,2	10,0
Volgograd Oblast	18,6	17,0	1,7	8,9
Republic of Khakassia	10,4	9,5	0,9	8,7
Chelyabinsk Oblast	10,9	9,9	0,9	8,7
Leningrad Oblast	9,7	9,2	0,6	5,7
Yamalo-Nenets Autonomous Okrug	3,1	3,4	-0,3	-11,3
Nenets Autonomous Okrug	3,5	3,9	-0,4	-11,6
Kemerovo Oblast	7,7	10,7	-3,0	-38,4
Jewish Autonomous Oblast	7,8	16,9	-9,1	-117,0

 

The received results discover a huge regional differentiation.

1) Republic of Dagestan takes the first place in housing bubble volume and its bursting out in 2008: H₂ has decreased by 21,8 years or 54%. Considering the k-values Leningrad Oblast demonstrates the modest step to increasing of housing affordability.

2) There is a group of 4 regional housing markets that are fully unresponsive to the crisis challenge - housing affordability rates fall. It concerns Yamalo-Nenets Autonomous Okrug, Nenets Autonomous Okrug, Kemerovo Oblast, Jewish Autonomous Oblast (marked out with highlight).

3) As for the federal cities - Moscow and Saint-Petersburg - they are in the middle of the ranking table. It is possible to say that the cities are similar with the housing bubble scale and the market reaction to the difficult economic situation.

The table 4 contains the groups of regional housing markets according to their sensitivity to the crisis. 59 markets covered 75,8% people let the affordability level surge up by 10-40%. For the national housing market it is a positive movement when the crisis helps to crack down the bubble and favor households in home buying.

Table 4. Sensitivity rates of regional housing markets

k, %	Number of regions	Population share, %
< 0	4	2,5
0-10	5	6,7
10-20	21	30,5
20-30	19	26,0
30-40	19	19,3
40-50	2	2,0
> 50	1	1,9
Total	71	88,9

 

The final comment on the above ranking of regional housing markets is that the satisfied level of housing affordability is not enough reason for celebration a success. The survey findings underline the high degree of regional differentiation in their sensitivity to the common economic plight. It makes actual a selective regional policy.


[1] It is set by the Agency for housing mortgage lending OJSC (www.ahml.ru).

[2] In comparison in the USA the maximum ε-value is 25% (www.realtor.org).

[3] Due to the lack of statistic information some regions are not presented in the ranking: Murmansk Oblast, Republic of Ingushetia, Kabardino-Balkar Republic, Karachay-Cherkess Republic, Chechen Republic, Kurgan Oblast, Magadan Oblast, Sakhalin Oblast, Chukotka Autonomous Okrug.
[4]The research is done under the Grant of the President of the Russian Federation (project #MK-440.2008.6)

Gusev Alexander ,
28.09.2009. Views: 2204

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