Housing bubbles in Russian regions
At the present time it is supposed that a housing bubble is the main reason of low-level housing affordability for an overwhelming majority of households in Russia. And the problem is that housing bubbles as market phenomenon are seemed to be difficult for analysis and resolving with administrative measures. So in this paper we take a focus on method testing housing markets for bubbles’ infection and its approbation on the Russian regions. What are the results?
1. Nature of housing bubbles. Reviewing some researches devoted to economic bubbles (housing bubble is one of the economic bubbles) showed that their heart is in accumulated error in economic agents' behavior [1; 5]. The error takes place in wrong valuation of property, i.e. market price is much more than real one. It is not actually to discuss whether this mistake is of rational nature or not. It is important that the market did not correct the mistake in time. Then the occurred mistake is reiterated by the great number of market agents and a bubble takes off. Bubble forming mechanism always has a reverse character because at the crucial moment the wrong market behavior seemed to be right.
Housing bubble is a multi-factor appearance. Normally there are two factor groups of housing bubbles: factors of demand and factors of supply [5]. The first ones are represented by different restrictions (juridical, corporate, natural) as well as sellers' expectations of profitability. The factors of supply usually refer to low-rate living space per capita, rising of households' income, liberalization of mortgage conditions, capital transferring to real estate market and making a real estate as an investment instrument, rush demand caused by some circumstances (for instance, on the eve of the President elections 2008 in Russia many people expected coming denomination and tried to invest their savings in real estate).
The analysis of market mistakes that launch the housing bubbles in Russia is out of the article purposes and it is a subject for special research. So we proceed to classification of housing bubbles. The criterion of housing bubble is that housing price growth rate becomes more than the housing purchasing power of salary (HPPS). The formula of the criterion is the following:
(1)
B- criterion of housing bubble means housing price (price per 1 sq. m, P) elasticity of HPPS (I). The parameter I shows the simplest housing affordability index, i.e. how many sq.m it is possible to buy for a monthly salary (HPPS):
(2)
W - an average monthly salary; the other characters are the same.
There may be a wish to replace the relative index I by the absolute value W. Of course, the replacement could simplify the formula (1), but in that case the model would have a risk of loosing its testing ability for housing bubbles. In fact, housing prices react to salary changing, however, the key ratio of consumer-focused housing market is not a monthly salary, but its housing purchasing power. So, our target is to compare the growth rates of housing price and HPPS.
Correspondently, the housing bubble will take place when one of the below conditions is realized (3)-(4):
(3)
(4)
Make some comments on the sets of inequalities (3) and (4). According to (3) housing market is infected by bubble due to the price growth rate is upper than the HPPS growth rate (figure 1). It is the housing bubble of type #1.
Figure 1. Forming of housing bubble #1
The condition (4) describes the housing bubble of type #2 when the price growth is accompanied by HPPS reduction (figure 2).
Figure 2. Forming of housing bubble #2
Despite the differences between (3) and (4) the housing bubbles #1 and #2 have the same reason - households income cannot catch up with housing prices. Taking into account the social effects, housing bubble #1 is less destructive than #2.
2. Price forming mechanisms and housing market typology. Above we had a view on the housing bubble criterion. But there is one more key factor of demand that is still out of our consideration. This is a living space per capita.
It could be supposed that the more number of housing sq.m per person make the weaker chances for housing bubble and vise versa. Correspondently, the formula for housing price elasticity of living space per capita is:
(5)
- housing price elasticity of living space per capita; 
- housing price; 
- living space per capita. The living space per capita is estimated as:
(6)
- housing stock of a territory (as a total floor area of all inhabitant dwellings); 
- population of the territory.
Calculating elasticity values of 
and 
, we come to a classification of housing markets. Figure 3 shows a coordinate frame where the abscissa axis is represented by elasticity H and the ordinate axis - elasticity B.
Figure 3. Types of housing markets
Thus, we get the HOB frame with 4 types of housing markets:
(7)
Now we proceed to find out the differences in market types.
I. Developing market. It characterized by that the housing price is directly proportional to HPPS (B>0) and the living space is in deficit here ( <!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman";} @page Section1 {size:612.0pt 792.0pt; margin:2.0cm 42.5pt 2.0cm 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> H>0). The case when parameters B and H are simultaneously of positive signs at the negative growth ratios of housing price and HPPS seems to be singular.
Especially we mark out the zone Ia. This segment shows the lower elasticity of P with respect to q and I. So, a housing market fitted for the zone may be qualified as free of housing bubble and of great demand for housing.
II. Developed market. At first, the market of this type has a surplus of housing (H<0) and the q-growth brings about the home price declining (
and 
). Such a situation can happen at a high-level of living space per capita. We do not consider the option when q goes down and P surges. Nowadays, this case is enough seldom and may be a force majeure consequence. For developed market it is normal the direct proportionality of P to I.
As for the size of living space per capita allowing the Russian housing market to get the "developed" status the polls testified the level of 36 sq.m per person [2]. It is 1.6 times lower than present value and corresponds to the same character of Ireland (35 sq.m in 2002), Finland (36,3 sq.m in 2002) and France (37,5 sq.m in 2002) [4]. Thus, potentially Russians prefer the European housing standards.
The zone IIa (figure 3) absorbs housing markets with no housing deficit and free of bubble (
). Here it is an important thing to note.
According to the typology, a housing bubble can occur in developing and developed housing market. This thesis can be approved by the practice. Nevertheless, the bubble social impact will be far unequal at the markets because the greater housing stock is able to relief negative price effects on households. In developing housing market many households without a private home have got a stress knowing that the bubble is delaying a family home buying. Bubble in developed market mostly just postpones the households' trade-in bargains for a dwelling of better quality.
III. Depressed market. The market depression is determined by that housing price is inversely proportional to raising values of q and I ( <!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-fareast-font-family:"Times New Roman";} @page Section1 {size:612.0pt 792.0pt; margin:2.0cm 42.5pt 2.0cm 3.0cm; mso-header-margin:36.0pt; mso-footer-margin:36.0pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> B<0,H<0). At first glance, it is a very good situation for home buying.
The depressed housing market like the developed one has no housing deficit. However, the nature of non-deficit is different. The housing stock in developed market just exceeds the households' needs but the housing surplus in depressed market is available due to the fact that the population has no wish to stay there anymore and leaves for a better region. Of course, housing bubble in depressed region may be out of sense, but theoretically the bubble #2 could take place (B<0). The zone IIIa (figure 3) includes the dying housing markets.
IV. Overheated market. For the analysis this type is the most interesting. Firstly, there is a housing deficit (H>0). Secondly, housing price is inversely proportional to HPPS (B<0).
Theoretically there could be 2 options when B<0: housing price is declaiming when HPPS rises and vice versa. Look through the first case. At least, it is strange when rising consumers' income makes home price go down in the context of housing deficit. This combination underlines the problem with demand-supply coordination and price-forming mechanism. It contradicts the market practice and, hence, may be eliminated.
The second option considers the reducing I and the increasing P. Thus, while housing deficit the booming misbalance between growth rates of P and I leads to a housing bubble #2. In comparison with housing bubble #1 the growing speed of bubble #2 potentially is much faster. The zone IVа (figure 3) indicates housing markets of poor housing deficit and low-expansion speed of bubble #2.
Using the above housing market typology we can determine the national priority purpose in creation of affordable housing market - all of the regional housing markets are to keep the quarter 2 but (no housing deficit, no housing bubble).
In the next section we will distinguish the regional housing markets of the Russian Federation by type and answer the question about a progress in housing policy achieving.
3. Testing of regional markets for housing bubbles. Traditionally, the Russian housing market is divided into two parts: a new-built housing market and the market covered "second-hand" housing. Based on the statistic data of 2003-06 we will calculate the market shares by market type.
The sampling for the new housing market is represented by 62 regions accumulated 90% population, 91% housing stock and 95% new-build housing. As for the "second-hand" housing market the sampling takes 71 regions with 95% population and 96% housing stock. Thus, the samplings are enough representational. The appendix (table А and table B) contains the sheets of regional housing markets with their types changing from year to year[1].
To show how it works, we pay closer attention to new-built housing markets of Moscow and Saint-Petersburg. Correspondently with B and H values the housing market positions are dotted in the relevant coordinate frames (figure 4 and figure 5). The figure 4 displays the Moscow market place stably in IV type. At the same time the Saint-Petersburg market was more dynamic with long-distance moving between I and IV types (figure 5).
Figure 4. The elasticity values in the HOB-frame for new-built housing market of Moscow
Figure 5. The elasticity values in the HOB-frame for new-built housing market of Saint-Petersburg
The table 1 and table 2 contain the type structure for new-built housing and "second-hand" housing markets.
Table 1. Regional new-built housing distribution by market type
Market type
|
Number of regions
|
Market share, %
|
2003
|
2004
|
2005
|
2006
|
2003
|
2004
|
2005
|
2006
|
I
|
39
|
30
|
40
|
11
|
60,7
|
47,6
|
68,4
|
10,2
|
II
|
-
|
-
|
-
|
1
|
-
|
-
|
-
|
0,1
|
III
|
1
|
1
|
1
|
1
|
1,4
|
2,1
|
0,2
|
0,4
|
IV
|
22
|
31
|
21
|
49
|
33,4
|
46,4
|
26,8
|
84,5
|
Table 2. Regional housing stock distribution by market type
Market type
|
Number of regions
|
Market share, %
|
2003
|
2004
|
2005
|
2006
|
2003
|
2004
|
2005
|
2006
|
I
|
44
|
24
|
46
|
15
|
64,7
|
35,7
|
59,3
|
14,5
|
II
|
-
|
1
|
-
|
-
|
-
|
0,02
|
-
|
-
|
III
|
3
|
2
|
1
|
1
|
4,9
|
1,0
|
0,02
|
0,3
|
IV
|
24
|
44
|
24
|
55
|
26,1
|
59,2
|
36,6
|
80,9
|
Figure 6. The shares of new-built and second-hand housing markets of type I and type IV
4. Bubbles' takeover of regional housing markets. In this section we proceed to housing bubbles' scale analysis. Table 3 and table 4 image the figures about new-built and second-hand housing markets' shares contaminated by bubble #1 and #2.
Table 3. Housing bubbles in regional new-built housing markets
Year
|
Housing bubble #1
|
Housing bubble #2
|
Number of regions
|
Market share infected by bubble, %
|
Number of regions
|
Market share infected by bubble, %
|
2003
|
27
|
46,2
|
23
|
34,8
|
2004
|
25
|
41,9
|
32
|
48,5
|
2005
|
29
|
60,0
|
22
|
27,0
|
2006
|
8
|
6,3
|
50
|
84,9
|
Table 4. Housing bubbles in regional second-hand housing markets
Year
|
Housing bubble #1
|
Housing bubble #2
|
Number of regions
|
Market share infected by bubble, %
|
Number of regions
|
Market share infected by bubble, %
|
2003
|
28
|
45,1
|
27
|
31,0
|
2004
|
21
|
34,0
|
46
|
60,1
|
2005
|
34
|
47,7
|
25
|
36,6
|
2006
|
11
|
11,5
|
56
|
81,3
|
The data analysis makes it clear that in 2006 the national housing market (new-built and second-hand) was more than 80% spoilt with the worse bubble #2. Totally, over 90% housing market were injured with bubbles. That underlines the misbalance market price forming mechanisms and it is possible to suppose that this is the eave of a market collapse.
Anyway, each year the expected crisis receives a note of postponement thanks to the market instability when the bubbles' market shares jumped up and down (table 3 and table 4). In case of housing stabilization, the blown bubble #2 sized of 80% of the total housing market will unavoidably burst out with the followed social indignations and economic losses.
It is difficult to unambiguously affirm about the sources of housing bubbles #1 and #2, but, in our opinion, the housing bubble #1 is born by the great consumer demand and the #2 - by the wave of investment demand. According to this hypothesis the Russian housing market does not have an end consumer orientation. It is rather a market of investment capitals focused on bullish of home prices. In 2006 the estimated total housing market capitalization was 410% GDP in comparison with 300% in 2003 and 250% in 2000.
The tables 1-2 data allow making the following conclusions:
1. In 2003-06 for both kinds of housing market it was a tendency to dominate the market types I and IV. The II and III types were met very seldom. It says that, at first, the national housing market was not depressed, but, on the other hand, there were no regions with developed housing market at all.
Formally, in 2006 the Republic of Adygea had got the developed market of new-built housing (table 1). However, when scrutinized closely, the housing price growth was accompanied with decreasing of living space per capita due to the cut of housing stock. Such a situation was not typical for developed market. The same figure was up to the second-hand housing market of Taimyr autonomous region in 2004 (table 2). So, the mentioned regions suffered from housing price growth due to the housing stock reduction.
Regarding the depressed new-built housing markets that status labeled the following regions: Orenburg Oblast (2003), Samara Oblast (2004), Republic of Karelia (2005), Buryat Republic (2006) (table 1). Second-hand housing markets of type II and III see in the appendix (table A and table B).
2. Comparing the types of housing markets of a region it is possible to find that in most cases they are the same. As for their frequency of changing some regions demonstrate a certain flexibility (usually, from I to IV and back). As well there are some territories where the housing markets stuck to one of the types. For instance, during 2003-06 the new-built housing market of Mordovia, the second-hand housing markets of Kaliningrad oblast, Republic of North Ossetia belonged to type I.
Worth noting that in 2003-06 among all the Russian regions only Oryol Oblast kept the new-built and second-hand housing markets of type I. Besides, in 2003-06 the constant supporters of type IV became the new-built housing market of Kursk Oblast, Komi Republic and the second-hand housing market of Amur Oblast (table A and table B, appendix).
3. In 2003-06 there was a great disorientation of the regional housing markets - they quickly changed the type moving to "overheated market". As a result, the share of developing housing markets was reduced in 6 times: from 60,7% to 10,2%. The share of new-built housing markets referred to "overheated" surged 2.5 times: from 33,4% to 84,5% (table 1). The same story could be observed at the second-hand housing markets when the developing part of the market was dramatically squeezed at housing bubbles blooming (table 2).
4. The detailed research of the market shares of type I and IV (table 1 and table 2) gives a conclusion about short-term (annual) cyclical fluctuations of new-built and second-hand housing markets towards pricing overheating and social negative affects. The annual fluctuation amplitude increased from 13% to 58% (figure 6).
5. Conclusion.
* The main survey outcome is the classification of regional housing markets in accordance with the demand factors' impacts on housing prices. There has been determined the housing markets with bubbles, bubble scales.
* In our opinion, the survey would be enriched by implementing of HAI (Housing Affordability Index) instead of HPPS but that was impossible due to the lack of statistic information.
* The special attention could be paid to working out of a regional map of housing market types and its analysis. The map would show the territories occupied with housing bubbles and could serve as monitoring instrument of the national housing project and regional housing programs fulfillment.
References
1. Soros, G. (2003) The Alchemy of Finance. Wiley.
2. «Argumenty & facty», #34, August 22-28, 2007.
3. www.gks.ru - Federal State Statistics Service.
4. www.urban-planet.org - web-site of Alexander Gusev.
5. www.wikipedia.org - the Free Encyclopedia Wikipedia.
Appendix
Table А. Types of new-built housing market by region
Table B. Types of "second-hand" housing market by region
index
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