INFLATION RATE DETERMINATION IN INVESTMENT AND CONSTRUCTION SECTOR OF THE ECONOMY

The aim of the study: to show the complexity and heterogeneity of inflation processes, which  break down into inflation processes of sectoral economies, to conduct a comparative analysis of prices using two-dimensional distributions, to show the difference between the inflationary component in the investment and construction sector of the economy from inflation indicators, used in everyday real estate valuation practice, to propose a method for calculating the level of inflation that appraisers could use independently, based on current market data.

The system and comparative analysis, method of analogies, the content analysis were used as methods of the research. The main attention is paid to the use of multidimensional statistical analysis, the use of the logarithmically normal distribution model as distributions that adequately reflect the asymmetric empirical distribution of prices and convenient from a computational point of view, and the conclusion of the calculation formulas derived from the corresponding hypothesis. The latest data, published by Rosstat on the dynamics of prices for different groups of goods, as well as data from one of the construction companies of Saint Petersburg were used as statistical materials. The study is divided into the following stages: calculation of the inflation rate for the product group, based on the hypothesis of jointly logarithmically normal distribution of prices of the reference group of goods in the base year and in the year of comparison, as well as the hypothesis of logarithmically normal distribution of the weights of the consumer basket, comparison of the result with the official data, then a similar calculation of the inflation rate in the evaluation of objects in the investment and construction sector of the economy.

Results: the authors propose a method of determining the rate of inflation in the evaluation of market value income approach based on statistical analysis of the market data. The method allows setting the estimated inflation rate, acceptable for the construction industry and the tasks of assessing the real estate income approach.

Conclusion: In the evaluation of the real estate, it is necessary to study more thoroughly the sectoral inflation rates, since the forced use in the evaluation of the real estate data on inflation in the consumer sector is not always correct. General questions of the inflation rate estimation and deflator coefficients by the market data based on two-dimensional distributions of random variables are raised. Modern methods of big data processing, technical capabilities, a large number of software products on the market, the prospects of the digital economy pose the task of creating extensive and accessible to appraisers and analysts databases, equipped with analytical tools. The goal is difficult, because the carriers of reliable information (such as retail chains, for example) often avoid publicity. In this regard, there is a task to coordinate the efforts of the scientific community and business to solve common modern problems and challenges.

1. Metodicheskiye rekomendatsii po otsenke effektivnosti investitsionnykh proyektov: (Vtoraya redaktsiya) / M-vo ekon. RF, M-vo fin. RF, GK po str-vu, arkhit. i zhil. politike; ruk. avt. kol.: Kossov V. V., Livshits V. N., SHakhnazarov A. G. Moscow: Ekonomika, 2000. 421 p. (In Russ.)

2. Gribovskiy P. V. Otsenka dokhodnoy nedvizhimosti. Saint Petersburg: Piter, 2001. 336 p. (In Russ.)

3. Domadaran A. Investitsionnaya otsenka. Instrumenty i tekhnika otsenki lyubykh aktivov. Moscow: Al’pina Biznes Buks, 2004. 1342 p. (In Russ.)

4. Nikitskaya E.F. Investirovaniye v biznes: problemy otsenki v usloviyakh inflyatsii. Statistika i Ekonomika. 2012. No. 3. P. 56–61. (In Russ.)

5. Ozerov E.S., Pupentsova S.V. Upravleniye stoimost’yu i investitsionnym potentsialom nedvizhimosti. SPb.: Izd-vo Politekhn. un-ta, 2015. 600 p. (In Russ.)

6. Capova A.K. Sravnitel’nyy analiz pokazateley bazovoy inflyatsii dlya Rossii. Statistika i Ekonomika. 2016. No. 5. P. 63–71. (In Russ.)

7. Marques C. Neves P., Sarmento L. Evaluating core inflation indicators. Economic modelling. 2003. No. 20. P. 765–775.

8. Mayer B. Venkatu G. Trimmed-Mean Inflation Statistics: Just Hit the One in the Middle. Federal Reserve Bank of Atlanta. 2014. No. 3. P. 27.

9. Ozerov E.S., Pupentsova S.V. Preodoleniye nedostatkov normativno-metodicheskogo obespecheniya otsenochnoy deyatel’nosti. Voprosy otsenki. 2016. No. 2 (84). P. 13–19. (In Russ.)

10. Samuel’son P.A., Nordkhauz V.D. Ekonomika. Moscow, 1999. (In Russ.)

11. Ozerov E.S., Pupentsova S.V. Modelirovaniye protsessa tsenoobrazovaniya v sdelkakh s kommercheskoy nedvizhimost’yu. Imushchestvennyye otnosheniya v Rossiyskoy Federatsii. 2015. No. 12 (171). P. 29–37. (In Russ.)

12. TSeny v Rossii 2016. Federal’naya sluzhba gosudarstvennoy statistiki. URL: http://www.gks.ru/ bgd/regl/b16_17/Main.htm (accessed: 12.01.2017) (In Russ.)

13. Statistika na ofitsial’nom sayte TSentral’nogo banka RF URL: http://www.cbr.ru/statistics/ (accessed: 12.01.2017) (In Russ.)

14. Analiticheskiye obzory na ofitsial’nom sayte Ministerstva ekonomicheskogo razvitiya RF. URL: http://economy.gov.ru/minec/activity/sections/macro/monitoring (accessed: 12.01.2017) (In Russ.)

15. Indeksy tsen v stroitel’stve. Mezhregional’nyy informatsionno-analiticheskiy byulleten’. Moscow: OOO «KO-INVEST», 2016. No. 94. P. 174. (In Russ.)

16. Indeksy izmeneniya smetnoy stoimosti na 4 kvartal 2015 god. Minstroy Rossii. Pis’mo No. 40538-ES/05 ot 14.12.2015 g. URL: https://smetnoedelo.ru/indeksy/indeksy-izmeneniya-smetnoy-stoimosti-na-4-kvartal-2015-god.html (accessed: 12.01.2017) (In Russ.)

17. Indeksy izmeneniya smetnoy stoimosti na 4 kvartal 2014 god. Minstroy Rossii. Pis’mo No. 25374-YUR/08 ot 13.11.2014 URL: https://smetnoedelo.ru/indeksy/indeksy-izmeneniya-smetnoy-stoimosti-na-4-kvartal-2014-god.html (accessed: 12.01.2017) (In Russ.)

18. Sostavleniye smet v stroitel’stve na osnove smetno-normativnoy bazy 2001 goda. Ed. P.V. Goryachkina. Moscow: izd-vo RTS•ES, 2003. 560 p. (In Russ.)

19. Didkovskiy V.M. Novyye razrabotki po sisteme indeksov tsen v stroitel’stva. Indeksy tsen v stroitel’stve. 2005. No. 51. P. 7–18. URL: http://www.coinvest.ru/doc/doc.php?ID=133 (In Russ.)

20. Rusakov O.V., Laskin M.B., Dzhaksumbayeva O.I., Stabrovskaya K.YU. Opredeleniye skidki na torg po statisticheskim dannym. Vestnik grazhdanskikh inzhenerov. 2016. No. 2. (In Russ.)

21. Laskin M.B. Korrektirovka rynochnoy stoimosti po tsenoobrazuyushchemu faktoru «ploshchad’ ob”yekta». Imushchestvennyye otnosheniya v Rossiyskoy Federatsii. 2017. No. 8(191). P. 86–99. (In Russ.)

22. Rusakov O.V., Laskin M.B., Jaksumbaeva O.I. Pricing in the real estate market as a stochastic limit. Log normal approximation. International Journal of Mathematical Models and Methods in Applied Sciences. 2016. Vol. 10. P. 229–236.