Assessment Of The Value Of Land Sites Under Oil-And-Gas Production Facilities

Graphoanalytical analysis of the state of the cadastral valuation of land in the mining zone based on reliable information.

Additional substantiation of the methodology for assessing the cadastral value of land sites used for mining.

Identification of information sources of cadastral evaluation

The most reasonable source of information on assessing both the market and cadastral value of real estate is the data of the Federal State Registration, Cadastre and Cartography Service (Rosreestr). The State Cadastral Assessment Data Fund can be accessed at https://rosreestr.ru/wps/portal/cc_ib_svedFDGKO. Then it is possible to select the desired report on the assessment of the cadastral value of land sites in a particular region. In this case, the cadastral value of land sites as part of industrial and other special purpose lands of the Khanty-Mansiysk Autonomous Okrug is considered.

6.2. Graphical-analytic analysis of the state of cadastral evaluation of land in mining zone

According to (Svitelskaya, 2016, 2017a; Postanovleniye Pravitel'stva Khanty-mansiyskogo avtonomnogo okruga – Yugry. 2014) land sites are divided into seven groups, each of which includes land sites for allocation of the following respectively:

• the first – ground-based objects of space infrastructure, energy, air transport;

• the second - objects of capital construction of industry;

• the third – objects of road transport;

• the fourth – facilities for the development of minerals, including transport and urban infrastructure;

• the fifth – communication facilities, transport – rail and road, pipeline, water – zones with special conditions of use;

• sixth – defense facilities.

Table 01 shows the cadastral value of land sites in the industrial and other special purpose lands of the Khanty-Mansiysk Autonomous Okrug. Regarding the cadastral value of these land sites, this article will determine the cadastral value of land sites intended for mining of minerals. The industry lands in this table are highlighted in group 2, and for mining in the form of a group 4.

Figure 01 shows the geographical distribution of the cadastral value for all groups of land sites in a mapping environment Bing (Yarmolenko, Putintseva, & Pisetskaya, 2019).

The excess of the cadastral value of group 4 over group 2 on average amounts to 122.01 / 110.23 = 1.107 or 10.7 percent in the subject of the Russian Federation. It is associated with risk to take such a ratio as the basis in calculation of the cadastral value relative to industrial land without additional justification because the maximum value of the cadastral value for industrial land is 156.55 (Nizhnevartovsk municipal district) and more than the same value of 143.59 for land sites occupied under mining. Here 156.55/143.59=1.090, which equals 9.0%. Therefore, additional studies are needed to justify the methodology for assessing the cadastral value of land sites used for mining, relative to industrial land.

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6.3. Additional substantiation of the methodology for assessing the cadastral value of land sites used for mining

We will take the basic provisions of the income method for assessing the value of real estate as the basis of the research (Gribovskiy, 2001).

The value of the land of the second group – land sites occupied by industrial facilities – can be written as:

$V\_2=I\_2/i\_2$ , (1)

where V_2 – the value of the land of the second group,

I_2 – profit received from the land site of the second group,

i_2 – rate of return from the land of the second group.

For a site of the fourth group – land sites occupied for mining – we can write:

$V\_4=I\_4/i\_4$ , (2)

where V_4, I_4, i_4 – the same values assigned to the land site of the fourth group.

Based on expressions (1) and (2) we can find

$V\_4=V\_2I\_4/i\_4i\_2/I\_2$ (3)

Profit values in (3) will be determined on the basis of real data for 2017 taken from (Sorokin, 2017). Tables 02 and 03 show the values of revenue and net profit for enterprises in the oil and gas brunch and industry, respectively, at the end of 2017 for this.

In order to compare the data in tables 02 and 03 , the volumes of revenue and net profit of industrial enterprises will be reduced to data on enterprises in the oil and gas industry. We define the correction factor as the quotient of dividing the average revenue of oil and gas enterprises by the average revenue of industrial enterprises (1 740 343 617/119 746 313 = 14.53) for this. By multiplying this correction factor by the average value of net profit of 33,907,330 industrial enterprises, we get the reduced average value of net profit of these industries equal to 33,907,330 * 14.53 = 492,795,175.8. Then we can find the relationship.

$\frac{I_4}{I_2}=\frac{134749546}{492795175.8}=0.27$, where $I_4$ is given in table 02 as the corresponding average.

Since net profit is calculated as the difference between profit and its 20 percent value, the replacement of profit ratios by the ratio of net profits is justified here.

We now can calculate the ratio $i_2{i}_4$. According to (Sorokin, 2017), the profit margin $i_4$ in the mining sectors, we assume equal to 19.2%, and in the industries $i_2$– equal to 12.5%.

The we have $\frac{i_2}{i_4}=\frac{12.5}{19.2}$ =0.65 and in accordance with (3):

$V_{\mathrm{4}}=V_{\mathrm{2}}\bullet \mathrm{0.27}\bullet \mathrm{0,65}=V_{\mathrm{2}}\bullet \mathrm{0,18}$.

Now we will consider the same situation at the beginning of the crisis period, that is, at the end of 2013. Similar data are given in Tables 04 and 05 .

The correction factor as the quotient of dividing the average revenue of oil and gas enterprises by the average revenue of industrial enterprises according to Tables 04 and 05 is 1,382,581,468 / 71,870,919 = 19.237. By multiplying this correction factor by the average value of the net profit of 11,402,552 industrial enterprises, we obtain the reduced average value of the net profit of these sectors equal to:

11,402,552 * 19.237 = 219350991.9.

Then we can find the ratio $\frac{I_4}{I_2}=\frac{205172950}{\mathrm{219350991,9}}=0.94$, where $I_4$ is given in table 04 as the corresponding average.

Accepting the rate of return for the period from the end of 2013 to the end of 2017 constant for the above sectors of the economy, we assume that $\frac{i_2}{i_4}=\frac{12.5}{19.2}$ =0.65 and in accordance with (3):

$$V_{\mathrm{4}}=V_{\mathrm{2}}\bullet \mathrm{0.94}\bullet \mathrm{0,65}=V_{\mathrm{2}}\bullet \mathrm{0.61}.$$