Assessment of the situation on the regional housing market in Russia

Furthermore 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.

Due 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.

As 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).

There 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.

Besides, 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.

Also 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.

Finally 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.

The 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.

Furthermore, 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.

Due 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.

The list of references

Aoki, Kosuke, James Proudman, and Gertjan Vlieghe. 2004. “House Prices, Consumption, and Monetary Policy: A Financial Accelerator Approach.” Journal of Financial Intermediation 13 (4): 414-35.

Bajari, Patrick, Phoebe Chan, Dirk Krueger, and Daniel Miller. 2013. “A dynamic model of housing demand: estimation and policy implicatios” International Economic Review 54 (2): 409-442.

Ball, Michael. 1973. “Recent Empirical Work on the Determinants of Relative House Prices.” Urban Studies 10 (2): 213-33.

Beaulieu J. J. et al. Optimal Durable and Nondurable Consumption with Transactions Costs. - Board of Governors of the Federal Reserve System (US), 1993. - №. 93-12.

Bover, Olympia, John Muellbauer, and Anthony Murphy. 1989. “Housing, Wages and UK Labour Markets.” Oxford Bulletin of Economics and Statistics 51 (2): 97-136.

Caldera, Aida, and Еsa Johansson. 2013. “The Price Responsiveness of Housing Supply in OECD Countries.” Journal of Housing Economics 22 (3): 231-49.

Calomiris, Charles W., Stanley D. Longhofer, and William R. Miles. 2013. “The Foreclosure-House Price Nexus: A Panel VAR Model for U.S. States, 1981-2009: The Foreclosure-House Price Nexus.” Real Estate Economics 41 (4).

Capozza D., Helsley R., The fundamentals of land prices and urban growth, Journal of Urban Economics, Volume 26, Issue 3, 1989, Pages 295-306.

Case, Karl E., and Robert J. Shiller. 1988. The Efficiency of the Market for Single-Family Homes. National Bureau of Economic Research Cambridge, Mass., USA.

Catte, Pietro, Nathalie Girouard, Christophe Andrй, and Robert Price. 2004. “Housing Markets, Wealth and the Business Cycle.” OECD Economics Department Working Papers 394.

Cheshire P., Sheppard S. Estimating the demand for housing, land, and neighbourhood characteristics //Oxford Bulletin of Economics and Statistics. - 1998. - Т. 60. - №. 3. - С. 357-382.

Davis B. A., Heathcote C. R., O'Neill T. J. Estimating cohort health expectancies from cross?sectional surveys of disability //Statistics in Medicine. - 2001. - Т. 20. - №. 7. - С. 1097-1111.

Denise DiPasquale, William C. Wheaton, Housing Market Dynamics and the Future of Housing Prices, Journal of Urban Economics, Volume 35, Issue 1, 1994, Pages 1-27

Flavin, Marjorie. 2001. “Owner-Occupied Housing in the Presence of Adjustment Costs: Implications for Asset Pricing and Nondurable Consumption.” Manuscript, UCSD.

Fingleton B. Housing supply, housing demand, and affordability //Urban Studies. - 2008. - Т. 45. - №. 8. - С. 1545-1563.

Fuster, Andreas, and Basit Zafar. 2014. “The Sensitivity of Housing Demand to Financing Conditions: Evidence from a Survey.” FRB of New York Staff Report, no. 702

Gan, Li, and Qinghua Zhang. 2013. “Market Thickness and the Impact of Unemployment on Housing Market Outcomes.” National Bureau of Economic Research.

Ghent, Andra C., and Michael T. Owyang. 2010. “Is Housing the Business Cycle? Evidence from US Cities.” Journal of Urban Economics 67 (3): 336-51.

Glaeser, Edward L., Joseph Gyourko, and Albert Saiz. 2008. “Housing Supply and Housing Bubbles.” Journal of Urban Economics 64 (2): 198-217.

Goodman, Allen C. 1978. “Hedonic Prices, Price Indices and Housing Markets.” Journal of Urban Economics 5 (4): 471-84.

Green R. K., Malpezzi S., Mayo S. K. Metropolitan-specific estimates of the price elasticity of supply of housing, and their sources //The American Economic Review. - 2005. - Т. 95. - №. 2. - С. 334-339.

Grimes, Arthur, and Andrew Aitken. 2010. “Housing Supply, Land Costs and Price Adjustment.” Real Estate Economics 38 (2): 325-353.

Gyourko J., Saiz A. Construction costs and the supply of housing structure* //Journal of regional Science. - 2006. - Т. 46. - №. 4. - С. 661-680.

Hanushek E. A., Quigley J. M. What is the price elasticity of housing demand? //The Review of Economics and Statistics. - 1980. - С. 449-454.

Hirata, Hideaki, M. Ayhan Kose, Christopher Otrok, and Marco E. Terrones. 2012. “Global House Price Fluctuations: Synchronization and Determinants.” National Bureau of Economic Research.

Holly S., Jones N. House prices since the 1940s: cointegration, demography and asymmetries //Economic Modelling. - 1997. - Т. 14. - №. 4. - С. 549-565.

Hwang, Min, and John M. Quigley. 2006. “Economic Fundamentals in Local Housing Markets: Evidence from US Metropolitan Regions.” Journal of Regional Science 46 (3): 425-453.

Iacoviello, Matteo. 2004. “Consumption, House Prices, and Collateral Constraints: A Structural Econometric Analysis.” Journal of Housing Economics 13 (4).

Iacoviello, Matteo. 2005. “House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle.” American Economic Review, 739-764.

Iacoviello, Matteo. 2010. Housing in DSGE Models: Findings and New Directions. Springer

Iacoviello, Matteo M., and Stefano Neri. 2008. “Housing Market Spillovers: Evidence from an Estimated DSGE Model.” National Bank of Belgium Working Paper, no. 145.

Igan, Deniz, and Prakash Loungani. 2012. “Global Housing Cycles.”

Ihlanfeldt, Keith, and Tom Mayock. 2014. “Housing Bubbles and Busts: The Role of Supply Elasticity.” Land Economics 90 (1): 79-99.

Ilmanen A. Expected returns on major asset classes. - 2012.

Kenny G. Modelling the demand and supply sides of the housing market: evidence from Ireland //Economic Modelling. - 1999. - Т. 16. - №. 3. - С. 389-409.

Kishor, N. Kundan, and Hardik A. Marfatia. 2016. “The Dynamic Relationship Between Housing Prices and the Macroeconomy: Evidence from OECD Countries.” The Journal of Real Estate Finance and Economics, January.

Krainer, John, and James A. Wilcox. 2013. “Evidence and Implications of Regime Shifts: Time-Varying Effects of the United States and Japanese Economies on House Prices in Hawaii: Regime Shifts in Real Estate Markets.” Real Estate Economics 41 (3).

Leamer, Edward E. 2007. “Housing Is the Business Cycle.” National Bureau of Economic Research.

Malpezzi, Stephen, and others. 2003. “Hedonic Pricing Models: A Selective and Applied Review.” Section in Housing Economics and Public Policy: Essays in Honor of Duncan Maclennan.

Mankiw N. G., Shapiro M. D. Risk and return: Consumption versus market beta. - 1984.

Mayo S. K. Theory and estimation in the economics of housing demand //Journal of Urban Economics. - 1981. - Т. 10. - №. 1. - С. 95-116.

Paciorek A., Supply constraints and housing market dynamics, Journal of Urban Economics, Volume 77, September 2013, Pages 11-26

Pain N., Westaway P. Modelling structural change in the UK housing market: a comparison of alternative house price models //Economic Modelling. - 1997. - Т. 14. - №. 4. - С. 587-610.

Poterba, James M. 1984. “Tax Subsidies to Owner-Occupied Housing: An Asset-Market Approach.” The Quarterly Journal of Economics 99 (4): 729.

Poterba J. M., Weil D. N., Shiller R. House price dynamics: the role of tax policy and demography //Brookings Papers on Economic Activity. - 1991. - Т. 1991. - №. 2. - С. 143-203.

Rosen, Sherwin, and Robert H. Topel. 1986. A Time-Series Model of Housing Investment in the US. National Bureau of Economic Research Cambridge, Mass., USA.

Shafir E., Diamond P., Tversky A. Money illusion //The Quarterly Journal of Economics. - 1997. - С. 341-374.

Schiller R. J. Irrational exuberance //Princeton UP. - 2000.

Tsatsaronis, Kostas, and Haibin Zhu. 2004. “What Drives Housing Price Dynamics: Cross-Country Evidence.” BIS Quarterly Review, March.

Vandenbussche, Mr Jerome, Ms Ursula Vogel, and Ms Enrica Detragiache. 2012. Macroprudential Policies and Housing Prices A New Database and Empirical Evidence for Central, Eastern, and Southeastern Europe. 12-303. International Monetary Fund.

Wagner, Wolf. 2008. “The Homogenization of the Financial System and Financial Crises.” Journal of Financial Intermediation 17 (3)

Yunus, Nafeesa, and Peggy E. Swanson. 2013. “A Closer Look at the U.S. Housing Market: Modeling Relationships among Regions: A Closer Look at the U.S. Housing Market.” Real Estate Economics 41 (3)



Appendix

Review of research papers based on reduced-form models

Article attributes	Sample	Variables	Results
(Hirata et al. 2012) “Global House Price Fluctuations: Synchronization and Determinants” IMF Working paper	Sample: 18 advanced OECD (Organization for Economic Cooperation and Development) countries; Time period: quarterly series from January 1971 to March 2011	Housing prices determinants: GDP, equity prices, credit, short- and long-term interest rates Method: Factor Augmented Vector Autoregression (FAVAR)	Authors found the evidence of strong linear relationship between housing prices and credit conditions, but no evidence of intertemporal interaction between housing prices and business cycle, equity market movements and interest rates.
(Igan and Loungani 2012) “Global Housing Cycles” IMF Working paper	Sample: 22 advanced countries; Time 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 Method: Pooled OLS regression	Housing affordability negatively affects real estate return for more than eighty percent of observed regions. Besides change in personal disposable income was proved as a significant factor of pushing prices up. There is also a positive relation between house price changes and population growth.
(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 paper	Sample: 16 CESEE countries (including Russia); Time 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) Method: Fixed-effect OLS regression	Russia 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. It was proved that after shock prices are tend to converge towards equilibrium rather fast. Moreover there was determined an intertemporal dependency structure of housing prices. Estimates for lagged changes in per capita GDP and interest rates, changes in working-age population are not significant
(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, Method: Panel Vector Autoregression (PVAR)	Foreclosure and housing prices are highly correlated with each other. This dependence results from the fact that housing is collateral for the mortgage and housing price shocks disturb credit market and these conditions in turn affect prices. Foreclosures negatively impact home prices. But the negative impact of prices on foreclosures is larger. The variance decompositions show that prices explain 16% of the variation in, while foreclosures explain only 5% of the variation in prices.
(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 Method: Constant-coefficient model VS Time-Varying coefficient model	The time-varying coefficient model appeared to be significantly better than constant-coefficient model, so the regime shift existed. Relative house prices, Net Worth, GDP and Net Worth*High income share appeared to be significant for housing price index determination.
(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)	Easing 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.

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