Материал: Assessment of the situation on the regional housing market in Russia

(Caldera and Johansson, 2013) tested cross-country sample for presence of sustainable differences between groups of countries that were combined according to a certain principle (geographical, economic development level, etc.). They found that in countries with many available residential construction land lots and weaker construction regulation price elasticity of housing supply is relatively lower. presumable reasons of some kind of insensitivity of construction activity to housing price dynamics are historical (the period under observation is long enough and captures several Soviet Union years, Perestroika years and further recovery of the market). estate built in the Soviet Union was practically unified - within one region and between different regions there were almost no differences in construction style, so people had no choice but to live in standard apartments. It is also should be mentioned that many people that days lived in the halls of residence which were provided by government. the toughest part of the transitional period in Russian economy privately-owned companies started building up regions with constructions, which were distinguishable from Soviet style of housing construction in order to cover the free market share and fulfill appeared demand for better housing practically regardless price situation.that time the quality of construction became higher, buildings taller and placed with higher density in the most demanded parts of the regions due to the fact that market became competitive. And those people who could afford buying a new apartment created demand on primary market of real estate whereas those who could not afford a primary real estate could buy a flat on secondary market, which as a result pushed prices up. in turn encouraged more construction however by the time when price instead of market share became the matter regional market were already saturated to some extent and finding unsatisfied demand became the bigger problem. So after the period of construction boom prices also could be overshadowed by other factors such as for instance availability of suitable residential construction land lots, rising regulatory requirements and etc.other factors that were proved to be important drivers of housing construction are construction costs. Within the framework of the developed theoretical model they were assumed to be consisted of labor costs which were approximated with average wage level and capital costs which included expenses for materials, machinery, design, etc. Both of these type of costs affect negatively construction activity which complies with common sense and economic theory. So the hypothesis H3 can be confirmed.value of the coefficients shows that more influential factor is capital cost inflation because when it goes up by 1% the prices will drop by 0.35% whereas the increase of workers’ wage by 1% will diminish housing prices by only 0.085%. Despite the fact that wage is more statistically significant and rose more quickly compared to capital costs it should be noted that these costs account for more than 80% of the total cost of construction. Therefore even moderate inflation of their value can lead to considerable contraction of their operational marginality or to increase of the prices on primary real estate market and corresponding decrease of demand. should be noticed that as in the case of risk-aversion parameter that was included intodemand function the parameter alpha which reflects the marginal rate of substitution of capital costs by labor in total costs function is also unobservable. Due to the lack of company-level data on construction companies of each region about the structure of their expenditures this parameter was not estimated separately of the final coefficients of the structural model. This can be attributed to the shortcomings of the model.variables that affect housing construction activity indirectly (through their influence on housing prices) the most influential factor is housing stock per person. This variable has significant negative influence on construction activity when the indicator rises by 1% net size of dwelling fall by 0.38%. Therefore it could be concluded that when housing market is insufficiently saturated with living spaces construction companies build up more actively in order to gain market share and cover potential demand. And vise-a-versa when most areas suitable for residential construction had been already built up companies moderate their activity or move it to other regions where market is relatively free. influence of current consumption on the net size of dwelling is positive and significant: when current consumption grows by 1% construction activity increases by 0.15%. As was observed earlier current consumption and housing prices are directly related - when housing prices inflate households postpone housing consumption and chose other consumption opportunities. So far it could be concluded that the higher level of current consumption means higher housing prices,and current situation on the market in its turn promotes formation of positive expectation by construction companies and by this stimulates construction activity as well.of buying residential real estate relative housing returns negatively influence net construction. The indirect effect of this indicator is the most moderate among all of the demand-side variables: when this ratio increases by 1% the net size of dwelling drops by only 0.11%. And this result seems to be pretty straightforward because this variable reflects relative cost of buying for households not for construction companies. Due to the fact that indicator negatively influences housing prices it acts in opposite way compared to current consumption and facilitates the formation of negative expectations about future prices and depresses housing construction activity. things considered it could be concluded that all the demand-side and supply-side factors that were included in to the theoretical model of Russian regional housing markets some way participated in equilibrium formation process. Besides due to the fact that the estimated structural model as a whole appeared to be significant all the paths of influence of all indicators can be believed as reliable and therefore used not only for further research but also for studying the regulatory effects on the market. except discussion of direct and indirect effects of different micro and macro indicators on housing demand and supply one of the most interesting implication of this research is that the model predicts equilibrium states of the system, because all the coefficients were extracted out of interaction between prices estimated within demand function and amount of construction dependent on these prices. The analysis of residuals of each equation of the model will allow concluding about was the equilibrium on housing market in Russian regions persistent at each moment of time in past sixteen years. to the fact that the sample covers a really long period which includes at least two whole business cycles, two severe financial crises and a period of boom on housing market it is a question of special interest about the rationality of economic agents that moved prices that high or that low regarding their fundamentally justified level. It should be mentioned that by fundamental factors hereonly those factor included into the model were meant. the analysis of supply equation residuals were calculated as a difference between empirical values of net size of dwelling and those estimated within the model. The figure 9 below represents supply equation residuals.

Fig.9. Residuals of supply equation.

could noticed that even though residuals are highly dispersed for different regions which one more time supports the idea that regions are highly heterogeneous in their economic development they majorly do not have any trend in time and seem to be quite stable. There are almost no significant deviations from average value for each region and all the values lie in the close neighborhood of zero. This result tells that the model quite precisely described variation of net construction variable and data fits theoretical models pretty well. So it could be concluded that supply for most regions was driven by fundamental factors even during periods of economic instability and even the period of construction boom was consistent with rational assumptions theory. equation residuals were calculated the same way as supply equation residuals as a difference between empirical market data and forecasted within the model data. The graph of their dynamics over time is presented below.

Fig.10. Residuals of supply equation

graph is much more interesting because residuals are volatile not only over different regions but also they are highly volatile in time. Taking into account deviations from zero there are obvious peaks and bottoms that reflect booms and bursts on housing market in Russia. first peak reflects the default of Russia in 1998 when there was hyperinflation and prices rocketed up very quickly but normalized within a year after that and even fall too much by the early 00’s. The drop in real income of citizens and weak demand contributed to the drawdown of prices. Considering the start of construction boom which created the situation of oversupply on the housing market prices fall unexpectedly low and some sort of anti-bubble existed. along with economic recovery in the country the housing market recovered too.The following years were a period of rapid growth in many industries including construction itself and related to it. Real income of households increased and their savings and particularly investment in housing increased as well. So housing market reached its equilibrium in mid-00’s but it persisted not long because the inflation of housing prices continued up to the crisis of 2008-2009. to the model and those fundamentals on which it is based there was a real estate bubble that time, because rapid growth of housing prices was not supported with the corresponding rise of real disposable income, drop of relative cost of buying additional housing or shortage of housing supply. Therefore it can be suggested that bubble was caused by irrational behavior of households that experienced some kind of money illusion. The link between money illusion and housing prices was established in the paper of (Shafir, Diamond and Tversky, 1997).market could not persist for a long time and eventually in 2008 it burst and prices experienced serious drawdown compared to their peak values. The bubble had been over by 2010 in most regions. Several of them, presumably regions with lowest level of income per person even experienced an anti-bubble again. After 2010 prices stayed at their equilibrium level according to the developed model of supply and demand on regional housing market. sum up, during the period in observation Russian regional real estate experienced several bubbles and even anti-bubbles which implies that price dynamics during that time could not be explained with those factors that were included in the model. The source of these bubbles is presumably the irrational behavior of households. and discussion

regional housing market

All things considered quite satisfying results were obtained - the model of equilibrium on Russian regional housing market was constructed based on economically justified assumptions and was proved overall significant. Therefore it could be concluded that the aim of the study stated in the very beginning was reached. Besides, not only overall model appeared to be relevant but each equation and both demand-side factors and supply-side factors used for modeling equilibrium states are significant as well.was proved on empirical data covering a long period of time that housing prices are heavily dependent on the amount of living spaces available at the market, other alternatives of consumption or savings and historical performance of residential real estate as an asset class relative to the cost of buying it such as mortgage expenses, depreciation and alternative rate of return on financial assets.the initial indicator of housing demand - housing stock per person - was an endogenous variable presumably connected to the availability of spare residential land lots. It was instrumented with indicators of regional and national business cycle, whereas these variables were excluded out of the initial model estimation because the insignificant direct impact on housing prices. Anyway it was proved that they participate in housing price determination through their connection to housing construction activity.result one more time supports the idea presented by(Leamer, 2007) that housing market is highly connected with overall economic situation. Therefore it should be noted that in order to avoid the problem of endogeniety the additional indicators of residential land market need to be included into the modeling or a different than housing stock per person indicator should be used. activity in each region in its turn mainly orients on expected housing prices that were assumed to be based on observed current prices. A little bit less influential both by statistical significance and the absolute value of the regression coefficient are cost components: labor-related and capital-related. But at the same time the power of capital goods inflation influence is much higher compared to wage inflation. It can be explained by the fact that expenses of the construction companies on materials, machinery, design, etc. amount up to 80% in overall construction costs and even moderate inflation of these costs can have significant impact on marginality of the business. This result particularly supports the estimates conducted by (Gyorko and Saiz, 2006) who studied the influence of cost composition on housing supply. comparison of fundamentally justified equilibrium housing prices forecasted within the model and observed prices that existed on the market allowed drawing a conclusion that housing prices periodically sharply deviated from the equilibrium state. Peaks of these deviations match not only with crisis events such as 1998 or 2008 years when pricesdropped significantly but also considerable upward deviation can be observed between 2005 and 2007 when there was a period of rapid economic growth in Russia.to the fact that these leap of housing prices was not implied by economic fundamentals it could be suggested that it was caused by households’ irrationality and overly optimistic expectations that lead to the housing bubble and the ensuing burst. The similar situation could be observed on the US housing market during the pre-crisis period and according to the conclusions of Robert Shiller presented in his book “Irrational exuberance” (2000) one of the main reasons of that was irrational behavior of American households. a separate important implication the relevance of the used method of data processing can be outlined. Structural model estimation allowed not only concluding about the factors that drive housing prices but also determining the path through which households’ and companies’ decisions influence equilibrium states on the using market of Russian regions. This paper fills the gap in the research field not only because it was implemented using structural estimation approach instead of reduced-form approach but also due to the fact that developing Russian market was studied whereas this method of analysis is usually used for studying developed markets (mainly the USA). are a few limitations of this research that need to be discussed. First of all, one of the core assumptions used for demand function modeling was that individuals maximize their utility function which was based on consumption CAPM model. This model can be challenged by certain number of economists that criticize the whole concept of this model or its particular assumptions. , the strong assumption about homogeneity of all the households was made. Within the framework of this model all the households are rational and have same wealth and utility function which in reality can be not that way. As in papers for instance (Iacoviello and Neri, 2008) the individuals can be divided into patient and impatient and their interaction on loan market might define interest rates in the model and participate in housing equilibrium determination process. among limitation the assumption of competitive structure on residential real estate construction market should be mentioned. It is implied in the model that construction companies have the same total cost function and they are price-takers on both housing and resources markets. This condition was used for simplification of the calculations, but in reality the industry structure can be different in each region. The construction companies also may compete not only within one region but also on cross-regional market and this kind of interaction was also omitted.it should be noticed that the lack of individual-level and company-level data did not allowed the estimation of such unobservable variables as risk-aversion parameter (denoted in the model as delta) and marginal rate of substitution of capital by labor (denoted in the model as alpha). These parameters in the estimated model were incorporated into assessed coefficients of corresponding variables.following suggestions for further research in the field can be outlined. First of all a straightforward approach of endogeniety elimination can be suggested - simply to include some variables that were considered as omitted in this research. Among them could be the amount of spare land appropriate for residential construction, an average price of square meter of this land, probably other qualitative characteristics of constriction in particular connected to air quality, neighborhood, etc. Also the influence of strictness of construction regulation and some measures of bureaucratic difficulties can be taken into account. , as was mentioned earlier instead of competitive structure of construction industry other forms of competition can be modeled and more realistic and comprehensive picture of housing price driver can be obtained. to the fact that according of the model estimators there were a serious deviations of housing prices from equilibrium the whole separate research can be devotedto understanding this phenomenon. Besides, one also could try to measure theconvergence speed towards equilibrium with help of error correction model.

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Appendix

of research papers based on reduced-form models

(Hirata et al. 2012)

“Global House Price Fluctuations: Synchronization and Determinants”

IMF Working paperSample: 18 advanced OECD (Organization for Economic Cooperation and) countries;period: quarterly series from January 1971 to March 2011Housing prices determinants: GDP, equity prices, credit, short- and long-term interest rates

(Igan and Loungani 2012)

“Global Housing Cycles” Working paper Sample: 22 advanced countries;period: different for each country (quarterly data)Housing prices determinants: lagged affordability; Income per capita growth rate; working-age population growth rate; equity market growth rate; credit growth rate; short-term interest rate; long-term interest rate

(Vandenbussche, Vogel, and Detragiache 2012)

“Macroprudential Policies and Housing Prices-A New Database and Empirical Evidence for Central, Eastern, and South-Eastern Europe”

IMF Working paperSample: 16 CESEE countries (including Russia);period: different for each country but generally beginning from 2000 (quarterly data).Housing prices determinants: GDP per capita, Domestic real interest rate; Foreign real effective interest rate; Working population data; Macroprudential policy measures (for Russia only liquidity measures such as reserve requirements rate on fc and lc deposits and reserve requirements base): Fixed-effect OLS regressionRussia has an almost flat curve of macroprudential policy indicator constructed by authors, so this factor was not proved to be important, however for majority of other countries the changes of macropolicy led to shocks on housing markets.

(Calomiris, Longhofer, and Miles 2013)

“The foreclosure-house price nexus: a panel VAR model for U.S. states, 1981-2009” Real Estate Economics. - 2013. - Т. 41. - №. 4. - С. 709-746.Sample: all the states of the USA

Time period: 1989-2009 (quarterly data)Housing prices determinants: growth of home prices, foreclosure rate; growth rates of employment, single-family permits, existing home sales,

(Krainer and Wilcox 2013)

“Evidence and Implications of Regime Shifts: Time‐Varying Effects of the United States and Japanese Economies on House Prices in Hawaii” Real Estate Economics. - 2013. - Т. 41. - №. 3. - С. 449-480.Sample: Real House Price Indexes in Hawaii, in the USA and in Japan

Time period: 1976-2008 (annual data)Housing prices determinants: demand factors such as relative housing prices (US/ Hawaii and Japan/Hawaii), Stock prices, Net Worth, GDP, Net Worth*High income share

(Fuster and Zafar 2014)

“The Sensitivity of Housing Demand to Financing Conditions: Evidence from a Survey” FRB of New York Staff Report. - 2014. - №. 702.Sample: 1211 household heads in the USA

Time period: 2014 (monthly data)Housing prices determinants: change of down payment, non-housing wealth shock and change of mortgage rate

Method: OLS (panel regression)of mortgage conditions (such as decrease of down payment) and external increase of income positively influence constructed by authors indicator “willingness to pay” (WTP). This effect is higher for households with income lower than the median in the sample. However the influence of particularly mortgage rate is moderate.