STOCHASTIC PRICING MODEL FOR THE REAL ESTATE MARKET: FORMATION OF LOG-NORMAL GENERAL POPULATION
https://doi.org/10.21686/2500-3925-2015-5-143-149
Abstract
We construct a stochastic model of real estate pricing. The method of the pricing construction is based on a sequential comparison of the supply prices. We proof that under standard assumptions imposed upon the comparison coefficients there exists an unique non-degenerated limit in distribution and this limit has the lognormal law of distribution. The accordance of empirical distributions of prices to thetheoretically obtained log-normal distribution we verify by numerous statistical data of real estate prices from Saint-Petersburg (Russia). For establishing this accordance we essentially apply the efficient and sensitive test of fit of Kolmogorov-Smirnov. Basing on “The Russian Federal Estimation Standard N2”, we conclude that the most probable price, i.e. mode of distribution, is correctly and uniquely defined under the log-normal approximation. Since the mean value of log-normal distribution exceeds the mode - most probable value, it follows that the prices valued by the mathematical expectation are systematically overstated.
Keywords
рыночная стои мость недвижимости,
стохастичес кая модель ценообразования,
геомет рическое броуновское движение,
мода логарифмически нормального закона распределения,
применение критерия согласия Колмогорова-Смирнова,
real estate market value,
stochastic model of pricing,
geometric Brownian motion,
mode of the logarithmi cally normal law distribution,
applications of the Kolmogorov-Smirnov test of fit
About the Authors
Oleg V. Rusakov
Saint-Petersburg State University
Russian Federation
Russian Federation
Michael B. Laskin
OOO “Invest-Stroy”
Russian Federation
Russian Federation
Olga I. Jaksumbaeva
Saint-Petersburg State University
Russian Federation
Russian Federation
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Review
For citations:
Rusakov O.V., Laskin M.B., Jaksumbaeva O.I. STOCHASTIC PRICING MODEL FOR THE REAL ESTATE MARKET: FORMATION OF LOG-NORMAL GENERAL POPULATION. Statistics and Economics. 2015;(5):116-127. (In Russ.) https://doi.org/10.21686/2500-3925-2015-5-143-149
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