Modeling dynamics of price changes in housing market in Kazan

Khabibrakhmanova, R.R.; Abramov, Ruslan A.

doi:10.2991/ttiess-17.2017.49

Cited by 1 publication

(1 citation statement)

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“…This paper presents the pricing in the housing market in Russia and highlights the pricing factors, which are divided by levels in relation to supply and demand. In [6][7][8][9][10], methods of neural network modeling were used to estimate the value of real estate.…”

Section: Introductionmentioning

confidence: 99%

Problems of modeling the valuation of residential properties

Bogdanova

Kamalova²,

Kravchenko

et al. 2020

Bus. Inform.

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The solution of the housing problem for many decades has been and remains one of the most important tasks facing the nation. The problem of modeling the value of residential properties is becoming more and more urgent, since a high-quality forecast makes it possible to reduce risks, both for government bodies and for realtors specializing in the purchase and sale of residential properties, as well as for ordinary citizens who buy or sell apartments. Predictive models allow us to get an adequate assessment of both the current and future situation on the residential property market, to identify trends in the cost of housing and the factors influencing these changes. This involves both the qualitative characteristics of the particular property, and the general condition and the dynamics of the real estate market. Russia is characterized by significant differences in the level of development of regions, therefore, by differences in trends of supply and demand prices for real estate. Valuation of residential properties at the regional level is particularly important, since all of the above determines the social and economic importance of this problem. This article presents a comprehensive model for estimating the value of residential properties in the secondary housing market of Moscow using decision tree methods and ordinal logistic regression. A predictive model of the level of housing comfort was built using the CRT decision tree method. The results of this forecast are used as input information for an ordinal logistic regression model for estimating the value of residential properties in the secondary market of Moscow. Testing the model on real data showed the high predictive ability of the model we generated.

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Section: Introductionmentioning

confidence: 99%