TIME SERIES AND NEURAL NETWORK ALGORITHMS IN REAL ESTATE VALUATION

This article deals with the problem of forecasting prices for real estate in the long and medium term for management decisions. The real estate market is one of the most dynamic areas of the Russian economy. Rapidly changing factors and price dynamics require a thorough study of new advanced methods using innovative technologies. Forecasting is an integral part of the mass valuation of real estate, it is impossible to plan future expenses or to build economic development plans. The price situation described by the average prices in the residential real estate market is a fundamental object for evaluation and forecasting in the study of the residential real estate market. Based on average prices, prices are managed in the residential real estate market. These indicators are considered when forecasting the market price of real estate, which is important in the development of the subjects of the real estate market auxiliary techniques for the selection of strategic actions for the development and improvement of the housing sector. Mass valuation of real estate as a complex system requires not only the definition of the parameters characterizing the price of real estate, and the identification of dependencies that link these parameters, but also the construction of a forecast of real estate prices in the future. Market conditions are constantly changing, and time has a direct impact on all market processes and decision-making. Seasonal calibration of prices for real estate objects is executed. The idea of using artificial neural networks that meet the modern requirements of real estate valuation is analyzed and proposed. A mathematical model based on harmonic series (Fourier series) and a neural network model are constructed and analyzed. A comparative analysis of the growth trends in the value of real estate.

1. Demina D. S. Comparison of time series forecasting results based on the trend model and autoregressive analysis//in the book: radio Electronics, electrical engineering and energy abstracts of the twenty-third International scientific and technical conference of students and postgraduates. In 3 volumes. 2017. P. 249.

2. Ivanov V. V., Kryanev A.V., Sevastianov L. A., Udumyan D. K. Forecasting time series using metric analysis//In book: Information and telecommunication technologies and mathematical simulation of hi-tech systems all-Russian conference with international participation. 2017. P. 286- 287.

3. Letova M. S. Additive time series model / / E-Scio. 2017. No. 8 (11). Pp. 5- 11.

4. Muhametrahimova E. R. Prediction of market value of the property by applying artificial neural networks//journal of modern research. 2017. No. 4- 1 (7). Pp. 68-73.

5. Markaryan D. M., Ledovskaya N. B. Multivariate static analysis of the time series / / in the collection: scientific discoveries 2017 Proceedings of the XXII International scientific and practical conference. 2017. P. 120-121.

6. E. honey, Honey V. V. Mathematical model describing trends in the primary real estate market//Actual problems of Economics, sociology and law. 2017. No. 2. P. 67-70.

7. Moskalenko M. A. time series Analysis. basics / / in the collection: the Interaction of the financial and real sector of the economy in the context of the formation of the knowledge economy collection of articles of the International scientific and practical conference. 2017. P. 131-136.

8. Sidorova N. P. Demina D. S. forecasting Methods based on time series analysis//information technology Bulletin. 2017. Vol.13. No. 3. P. 118-126.

9. Robkin M. Y., Avakian A. V. Kohonen Neural network and fuzzy neural network in data mining//In the book: Improvement of methodology of knowledge in the development of science collection of articles on the results of International scientific-practical conference: in 2 hours 2017. Pp. 36-39.

10. Yarashev S. A., Averkin A. N. Neuro-fuzzy methods for time series prediction//In the book: System analysis and information technology (SAIT - 2017) proceedings of the Seventh International conference. 2017. P. 588-591.