Pricing in the real estate market as a stochastic limit. Log Normal approximation

Oleg V. Rusakov, Olga I. Jaksumbaeva, Anastasiya A. Ivakina, Michael B. Laskin

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференции

1 цитирование (Scopus)

Выдержка

Abstract — We construct a stochastic model of real estate pricing. The method of the pricing construction is based on a sequential comparison of supply prices. We prove that under standard assumptions imposed upon the comparison coefficients there exists a unique non-degenerate limit in distribution and this limit has a Log Normal law of distribution. We verify agreement between empirical distributions of prices and theoretically obtained Log Normal distribution by numerous statistical data of real estate prices from Saint-Petersburg (Russia). To establish this accordance we essentially apply the efficient and sensitive test of fit of Kolmogorov-Smirnov. Basing on the world admitted standard of estimation prices in real estate market, we conclude that the most probable price, i.e. mode of distribution, is correctly and uniquely determined under the Log Normal approximation. Since the mean value of a Log Normal distribution exceeds the mode – most probable value, it follows that the prices valued by the mathem
Язык оригиналаанглийский
Название основной публикации2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы235-239
DOI
СостояниеОпубликовано - 2016
Опубликовано для внешнего пользованияДа

Отпечаток

Real estate market
Approximation
Pricing
Log normal distribution
Stochastic model
Empirical distribution
Russia

Цитировать

Rusakov, O. V., Jaksumbaeva, O. I., Ivakina, A. A., & Laskin, M. B. (2016). Pricing in the real estate market as a stochastic limit. Log Normal approximation. В 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) (стр. 235-239). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MCSI.2015.48
Rusakov, Oleg V. ; Jaksumbaeva, Olga I. ; Ivakina, Anastasiya A. ; Laskin, Michael B. / Pricing in the real estate market as a stochastic limit. Log Normal approximation. 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) . Institute of Electrical and Electronics Engineers Inc., 2016. стр. 235-239
@inproceedings{41394d20c4dd48aebe7cb854327491fe,
title = "Pricing in the real estate market as a stochastic limit. Log Normal approximation",
abstract = "Abstract — We construct a stochastic model of real estate pricing. The method of the pricing construction is based on a sequential comparison of supply prices. We prove that under standard assumptions imposed upon the comparison coefficients there exists a unique non-degenerate limit in distribution and this limit has a Log Normal law of distribution. We verify agreement between empirical distributions of prices and theoretically obtained Log Normal distribution by numerous statistical data of real estate prices from Saint-Petersburg (Russia). To establish this accordance we essentially apply the efficient and sensitive test of fit of Kolmogorov-Smirnov. Basing on the world admitted standard of estimation prices in real estate market, we conclude that the most probable price, i.e. mode of distribution, is correctly and uniquely determined under the Log Normal approximation. Since the mean value of a Log Normal distribution exceeds the mode – most probable value, it follows that the prices valued by the mathem",
keywords = "Keywords — real estate market value, stochastic model ofpricing, geometric Brownian motion, limit in probabily, Sharpeparameter, mode of the Log Normal law of distribution, applications of the Kolmogorov-Smirnov test of fit.",
author = "Rusakov, {Oleg V.} and Jaksumbaeva, {Olga I.} and Ivakina, {Anastasiya A.} and Laskin, {Michael B.}",
year = "2016",
doi = "10.1109/MCSI.2015.48",
language = "English",
pages = "235--239",
booktitle = "2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Rusakov, OV, Jaksumbaeva, OI, Ivakina, AA & Laskin, MB 2016, Pricing in the real estate market as a stochastic limit. Log Normal approximation. в 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) . Institute of Electrical and Electronics Engineers Inc., стр. 235-239. https://doi.org/10.1109/MCSI.2015.48

Pricing in the real estate market as a stochastic limit. Log Normal approximation. / Rusakov, Oleg V.; Jaksumbaeva, Olga I.; Ivakina, Anastasiya A.; Laskin, Michael B.

2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) . Institute of Electrical and Electronics Engineers Inc., 2016. стр. 235-239.

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференции

TY - GEN

T1 - Pricing in the real estate market as a stochastic limit. Log Normal approximation

AU - Rusakov, Oleg V.

AU - Jaksumbaeva, Olga I.

AU - Ivakina, Anastasiya A.

AU - Laskin, Michael B.

PY - 2016

Y1 - 2016

N2 - Abstract — We construct a stochastic model of real estate pricing. The method of the pricing construction is based on a sequential comparison of supply prices. We prove that under standard assumptions imposed upon the comparison coefficients there exists a unique non-degenerate limit in distribution and this limit has a Log Normal law of distribution. We verify agreement between empirical distributions of prices and theoretically obtained Log Normal distribution by numerous statistical data of real estate prices from Saint-Petersburg (Russia). To establish this accordance we essentially apply the efficient and sensitive test of fit of Kolmogorov-Smirnov. Basing on the world admitted standard of estimation prices in real estate market, we conclude that the most probable price, i.e. mode of distribution, is correctly and uniquely determined under the Log Normal approximation. Since the mean value of a Log Normal distribution exceeds the mode – most probable value, it follows that the prices valued by the mathem

AB - Abstract — We construct a stochastic model of real estate pricing. The method of the pricing construction is based on a sequential comparison of supply prices. We prove that under standard assumptions imposed upon the comparison coefficients there exists a unique non-degenerate limit in distribution and this limit has a Log Normal law of distribution. We verify agreement between empirical distributions of prices and theoretically obtained Log Normal distribution by numerous statistical data of real estate prices from Saint-Petersburg (Russia). To establish this accordance we essentially apply the efficient and sensitive test of fit of Kolmogorov-Smirnov. Basing on the world admitted standard of estimation prices in real estate market, we conclude that the most probable price, i.e. mode of distribution, is correctly and uniquely determined under the Log Normal approximation. Since the mean value of a Log Normal distribution exceeds the mode – most probable value, it follows that the prices valued by the mathem

KW - Keywords — real estate market value

KW - stochastic model ofpricing

KW - geometric Brownian motion

KW - limit in probabily

KW - Sharpeparameter

KW - mode of the Log Normal law of distribution

KW - applications of the Kolmogorov-Smirnov test of fit.

U2 - 10.1109/MCSI.2015.48

DO - 10.1109/MCSI.2015.48

M3 - Conference contribution

SP - 235

EP - 239

BT - 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Rusakov OV, Jaksumbaeva OI, Ivakina AA, Laskin MB. Pricing in the real estate market as a stochastic limit. Log Normal approximation. В 2015 Second International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) . Institute of Electrical and Electronics Engineers Inc. 2016. стр. 235-239 https://doi.org/10.1109/MCSI.2015.48