Analytical study of the actual cost of the completed overhaul of multi-apartment residential buildings in Russia

. This paper examines the distinctive features of the pricing system when planning and carrying out overhaul in multi-apartment residential buildings. The authors analyzed the data posted in public resources, after which they were optimized in terms of sufficiency for further research. Due to the qualitative heterogeneity of the data, a unified unit of measure was proposed and introduced for the present value of the overhaul works. To select data and determine their stable relationships in a series, calculations of absolute and relative indicators of variation were performed. The results of the study showed that the coefficient of variation of the average present costs of overhaul of apartment buildings within the framework of the totality of the constituent entities of the Russian Federation is equally homogeneous and heterogeneous for various types of work. It was found that for certain types of work, cost indicators have a stable average, while for others it is impossible to single out this indicator.


Introduction
The overhaul pricing system is part of the general market system of the Russian Federation and operates within the framework of existing market relations. At the same time, there are characteristic features of the overhaul sphere, which are the result of the uniqueness of the process of planning and implementing an overhaul. Such features include the involvement of a large number of subjects in the overhaul planning process. As a result, there is a problem of organizing, coordinating and controlling the activities of various organizations involved in the overhaul planning process, and the problems arising from it in the implementation of the final reliable accounting of the cost of overhaul of multi-apartment residential buildings. In addition, the sphere of overhaul of multi-apartment residential buildings is characterized by a variety of types of capital construction objects [1], which, in the absence of a unified pricing system for this variety, requires increased labor costs, since each object is considered as unique (even in cases where the object actually belongs to typical series of multi-apartment residential buildings).
The solution of the identified problems is a strategic direction in the development of the system of capital repairs of the housing stock. To improve the pricing system in the field of overhaul, it requires an in-depth analysis of both regulatory documentation and reporting documentation for completed projects for the overhaul of apartment buildings.

Materials and methods
To process a large amount of data, it is required to develop an information system with a given algorithm for processing the initial data [2].
The first stage of the study is the analysis of the cost of overhaul work (services) performed in 3 constituent entities of the Russian Federation from each federal district.
The purpose of the analysis is to analyze the list and scope of work (services) approved in the constituent entities of the Russian Federation for the overhaul of common property in apartment buildings. The main tasks are: 1. Development and formation of an information system for processing the estimate documentation provided for analysis; 2. Filling the information system with the initial data necessary for the analysis.
To perform a comprehensive analysis of the estimate documentation, an information system was proposed [3], developed taking into account the most significant data for the analysis, contained in open sources, including those provided by the Housing and Communal Services Reform Assistance Fund.
As initial data for the analysis, data from open sources were used, which, in turn, contain the following information about apartment buildings: address, general information about the houses, information on the formation of the capital repair fund, information on the financing of capital repairs, date of updating information, cost works (services) in accordance with the concluded contracts, the volume of works (services) for capital repairs in accordance with the units of measurement.
Open data on completed and planned overhauls contain fairly objective information about the types of work carried out as part of the overhaul, but at the same time, depending on the constituent entity of the Russian Federation, there is heterogeneity of the information provided in these sources.
To achieve the goals set for the researchers, the array of initial data was optimized. Initial data optimization was carried out on the basis of the principle of sampling the necessary and sufficient data to achieve the goal, without loss of quality [4]. As a result of optimization, the following data were selected as the initial data to be processed using the proposed information system: 1. Constituent entity of the Russian Federation; 2. Address; 3. Type of work; 4. Year of work in accordance with the short-term plan for the implementation of the regional program; 5. Cost of work (services) adopted by the acts; 6. Unit of measurement; 7. Scope of work; 8. Floors; 9. Type of roofing; 10. Material of wall structures; 11. Total area of an apartment building. It should be noted that the heterogeneity of the initial data for various constituent entity of the Russian Federation is caused by different degrees of enlargement of the types of work and units of measurement. So, for hotel regions, the types of work described in paragraph 166 of the "Housing Code of the Russian Federation" were used, and for others, the consolidation was carried out in arbitrary form.
Taking into account the fact that the initial data for various subjects of the Russian Federation contain information on the types of work with varying degrees of enlargement, different units of measurement are used to display the amount of work performed on major repairs, the researchers were faced with the task of bringing the calculated indicators of the overhaul cost to unified units of measurement.
As a unified unit of measure for the overhaul of multi-apartment residential buildings, it was decided to use "rubles per m 2 of the total area of an apartment building", which, in turn, will make it possible to maximize the use of data obtained from open sources, as well as form an aggregated base cost of overhaul [5].
To bring the cost indicators contained in the source data to a unified unit of measurement, it is necessary to make a following calculation for each apartment building using the formula: Where C ocost of major repairs per m 2 of the total area of an apartment building; C t icost of the i-th type of work on major repairs in an apartment building; S tottotal area of an apartment building. At the stage of preparing the initial data, indicators were laid that distribute a large array of data on multi-apartment residential buildings into several types according to design features. These indicators include: number of floors, type of roofing, material of wall structures [6].
The division of multi-apartment residential buildings into types according to design features allows the most objective assessment of the cost of the work being carried out, since it takes into account the need to make various organizational and technological decisions in the production of the same type of work for structures belonging to different groups [7][8][9].
To assess the stable relationships between the resulting indicators of the cost of work per m 2 of the total area of an apartment building in various regions of the Russian Federation, calculations of absolute and relative indicators of variation were made [10]. The numerical characteristics of the variational series were obtained as a result of processing the information system developed as part of the study, which are also statistical characteristics or estimates. For calculations, the previously given summary characteristics of variational series are sufficient: average cost indicators by type of work [11,12].
The study identified the following parameters: The arithmetic mean, also called the sample mean. The arithmetic mean characterizes the values of the feature around which observations are concentrated, i.e., central distribution trend.
Where xivalues of the studied trait (options); nstatistical aggregate. Median (Me) -a measure of the central tendency, which lies in the fact that it is not affected by a change in the extreme members of the variation series, if any of them, less than the median, remains less than it, and any, greater than the median, continues to be greater than it. The median is preferable to the arithmetic mean for a series in which the extreme variants, in comparison with the rest, turned out to be excessively large or small; Mode (Mo) -a measure of the central tendency, which lies in the fact that it also does not change when the extreme members of the series change, i.e., has a certain resistance to trait variation.
The range of variation is the difference between the maximum and minimum values of the attribute of the primary series. The range of variation is most useful when a quick and general view of variability is needed when comparing large numbers of samples [13].
Dispersion -characterizes the measure of spread around its mean value (measure of dispersion, i.e., deviation from the mean).
Standard deviation.
The coefficient of variation is a measure of the relative scatter of aggregate values: it shows what proportion of the average value of this quantity is its average scatter. Aggregates with a coefficient of variation V > 30-35% are considered to be heterogeneous.
Mean linear deviation -calculated in order to take into account the differences of all units of the population under study.
Where fsample of studied parameters. The quartile range includes the median and 50% of the observations that reflect the central trend of the trait, excluding the smallest and largest values.
Quartiles are the values of a feature in a ranked distribution series, chosen in such a way that 25% of the aggregate units will be less than Q1, 25% will be between Q1 and Q2, 25% between Q2 and Q3. The remaining 25% are superior to Q3. Linear coefficient of variation or Relative linear deviation characterizes the share of the average value of the sign of absolute deviations from the average value.
Oscillation coefficient reflects the relative fluctuation of the extreme values of the attribute around the average. The skewness and kurtosis are characteristics of the form. The most accurate and common indicator of asymmetry is the moment skewness.
If the skewness is zero, then the distribution is symmetrical. If the distribution is asymmetric, one of the frequency polygon branches has a gentler slope than the other. If the skewness is right-sided, then the following inequality is true: xB > Me > Mo, which means that higher values of the trait appear predominantly in the distribution. If the skewness is lefthanded, then the following inequality holds: xB < Me < Mo, which means that lower values are more common in the distribution. The greater the value of the skewness, the more asymmetric the distribution (up to 0.25 -the skewness is insignificant; from 0.25 to 0.5moderate; over 0.5 -significant) [14].
Kurtosis is an indicator of the steepness (pointiness) of the variational series compared to the normal distribution.
Most often, kurtosis is estimated using the indicator: If the kurtosis is positive, then the polygon of the variational series has a steeper top. This indicates the accumulation of attribute values in the central zone of the distribution series, i.e., about the predominant appearance in the data of values close to the average value. If the kurtosis is negative, then the polygon has a flatter peak compared to the normal curve [15]. This means that the values of the attribute are not concentrated in the central part of the series, but rather evenly scattered over the entire range from the minimum to the maximum value. The greater the absolute value of the kurtosis, the more significantly the distribution differs from the normal one [16].
To assess the significance of the kurtosis, statistics Ex /s Ex are calculated, where s Ex -standard error of the kurtosis coefficient.
As a result of calculating the parameters presented above in price levels for 2021 for the average present costs of major repairs within individual constituent entities of the Russian Federation, obtained as a result of processing the information system formed as part of the study, results were obtained at three qualitative levels: • High -(+1); • Average -(0); • Absent (-1). The stable relationships obtained as a result of the analysis between the indicators of the average present value of the overhaul of multi-apartment residential buildings are presented in Tables 1 -6.   1557.45 (0) 9 fl.
type floor Present value of work per unit of the total area of an apartment building, rub/m 2 All types 6-8 fl.

Conclusions
As a result of the study, a number of mathematical characteristics were obtained, with the help of which we can conclude that the coefficient of variation of the average present costs of overhaul of multi-apartment residential buildings within the framework of the totality of the constituent entities of the Russian Federation is equally homogeneous and heterogeneous for various types of work, which suggests that between some costs of identical types of major repairs carried out within the considered subjects of the Russian Federation, stable values of the average cost are observed, while for the other part of the average cost value it is impossible to determine. At the same time, the degree of homogeneity of the average reduced costs of work on the overhaul of multi-apartment residential buildings within the framework of individual constituent entities of the Russian Federation is much higher. For the part of the works where the coefficient of variation of the average present costs of overhaul is > 70%, it is necessary to carry out further studies, including an assessment of the relationships, taking into account more detailed types of work on capital repairs of multiapartment residential buildings.