Methodology of Assessing the Impact of Urban Development Value of the Territory on the Value

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Methodology of Assessing the Impact of Urban Development Value of the Territory on the Value of Residential Real Estate by Example of Kiev City, Ukraine1 I.M. Ciobanu1,a 1 – Candidate of engineering sciences, Kyiv, Ukraine a – jdoroshenko@ukr.net DOI 10.2412/mmse.16.23.38 provided by Seo4U.link

Keywords: residential real estate, urban development value, methodology, zonal coefficient of location, correlation connection, least squares method, economic and planning zoning.

ABSTRACT. The paper proposes a methodology for assessing the impact of the urban development value of the territory on the value of various types of residential real estate and it is proved that the urban development value of the territory, which is displayed through the value of the zonal coefficient of the location CL2 , is observed (laid) in the cost of housing and it is not the same for different types of multistory residential real estate objects.

Introduction. Ukraine is a country with a high level of urbanization (about 68% of the population lives in cities). Housing construction by area occupies the first place among other built-up land in cities, which plays an important role in the formation and development of cities. It occupies 15.3% of the built-up areas in the Balance Sheet of Kyiv City.

The urban value of the territory has a direct impact on the spatial distribution and density of residential property in the city plan and is a factor influencing the value of residential property. Therefore, we investigate the dependence between the cost of different types of residential development and the degree of urban development value of the territory, expressed by the magnitude of the zonal coefficient CL2 in order to determine the role of the land component in the structure of the value of residential real estate.

Scientific researches of Yu. Bocharova, O. Gutnova, V. Davydovych, M. Diomin, Ye. Kliushnichenko, O. Kudriavtsev, H. Lavryk, A. Ositniank, T. Panchenko, I. Prybitkova, B. Solucha, O. Khorhota, G.Filvarova, I.Fomin, T. Ustenko, Z. Yarhyna and others are devoted to the optimization of the functional use of territories and spatial organization of cities; they were conducted in conditions of universal ownership of land and real estate and the planned implementation of all city-planning measures. The issue of spatial organization of housing development was considered without taking into account the influence of legal categories and economic interest of land owners.

Based on the comparison method, according to which the value of land is measured by the proportion that it brings to the value of the property and increases with increasing degree of urban development value of the territory, to develop a methodology for assessing the impact of the urban development value of the territory on the cost of various types of residential real estate and prove that the urban development value of the territory, which is displayed through the value of the zonal coefficient C L2 is observed (laid) in the cost of residential development and it is not the same for different types of objects of the residential real estate, depending on their location.

The main material. Residential real estate most clearly reflects the urban construction value of the territory, because it provides social and consumer functions. Since commercial real estate objects can © 2017 The Authors. Published by Magnolithe GmbH. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

be presented on the market as a single business or as a common property, the difference between these indicators is almost impossible to analyze without statistical data. The presence of a business component is laid at the level of developer's profit in residential real estate.

The value of land can be defined as normative or expert. From the point of view of expert assessment, the market mechanisms operate only, and the final value already takes into account the entire set of features of the property itself and the features of its location.

Unlike expert evaluation of the normative monetary assessment, various factors affecting the regional, zonal and local levels are taken into account. Thus, the calculation of 1 m 2 of the land settlement is made on the basis of the base cost, depending on the level of development and arrangement of its territory, as well as its place in the national, regional and local systems of production and resettlement and is determined by the formula (1):

BCs 

Ex  Np  CL1 , Nc

(1)

where BCs – base cost of 1 m2 of settlements (UAH); Ex – expenses for the development and arrangement of the territory of the settlement; Np – norm of profit (6%); Nc – norm of capitalization(3%); The coefficient, which characterizes the functional use of the land, takes into account the relative profitability of the types of economic activity within its boundaries and for the residential land C f = 1.0. Taking into account that the objects of the unified functional use are analyzed in the work, the influence of the indicated coefficient is not taken into account at all. The coefficient that characterizes the dependence of rental income on the location of the locality in the national, regional and local systems of production and resettlement (CL1) is taken into account in the value of the base value. The coefficient, which reflects the features of the local location of the land plot and its environment (CL3), is not considered, as real estate objects are analyzed in a concentrated urban space with maximally approximate values. Proceeding from this, it is assumed that the unique coefficient of location that reflects the urban development value of the territory is the zonal coefficient CL2.

The method of comparison or transfer was used to confirm this assumption. It consists of the principle of contribution according to which, the value of land is measured by the component (share) that it brings into the value of the property and increases as the degree of urban development value of the territory increases.

According to [1], the constituent components of the zonal value of the territories (CL2) are: 1) Transport and functional convenience; 2) The ecological quality of the territory; 3) Engineering and infrastructure support; 4) Social and urban-friendly attractiveness of the environment; 5) The level of social and economic development of the territory.

The research was conducted on the example of Kyiv City. According to [2], there are 741 economicplanning zones within the boundaries of Kyiv City, depending on the heterogeneity of the functional MMSE Journal. Open Access www.mmse.xyz

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

and planning qualities of the territory that affect the size of the rental income; the difference in accessibility at the level of engineering provision and improvement of the territory, development of the service sector of the population; the ecological quality of the territory and the attractiveness of the environment.

According to [3], there are seven planning zones: the Central, Southern, Urban Area of Vyshgorod, Western, Northern, Northern Left Bank and Eastern on the territory of the city.

The author studied the location of residential real estate on the territory of Kiev City. According to the results of research, it was found that housing development is dispersed in 432 economic and planning zones, of which 17 are residential buildings. The range of values for K м2 in Kiev City is from 0.5 to 6.95. It is established that despite one of the main principles of the allocation of economicplanning regions as a functional homogeneity. We also observe a significant number of areas of mixed functional use. It does not refer to small intersections in the general homogeneous functional space of other functions, but to the principle combination of functions.

Yu.F. Dehtiarenko, Yu.M. Palekha, Yu.M. Mantsevych, A.V. Tarnopolskyi [4] were performed zoning of the territory of Kyiv City on the distribution of the values of the zonal coefficient C L2 into 4 zones with the corresponding ranges: 0.1-0.99; 1-1.99; 2-3.49; 3.5-6.96, but such a grouping does not reflect with sufficient accuracy impact of the urban development value of the territory on residential real estate.

Based on studies of dispersal residential development of Kyiv City [5] and zoning of its territory according to the scale values of 0.5, it was established that zoning the territory of Kyiv City at the value of the coefficient CL2 > 3.0 is concentrated in the downtown. Therefore, in order to avoid significant fragmentation of the territory, it was proposed to perform zone zoning on the scale of value 1.0 within the values of the coefficient CL2 from 3.0 to 6.95.

Due to the above, the method proposed the entire territory of Kyiv City that are under residential development to merge into 8 zones of urban development value of the territory, with the following ranges of values of the zonal coefficient CL2 (Table 1, Fig. 1.):

Table 1. Zones of urban development value in Kiev, Ukraine for the residential buildings No zone 1 2 3 4

Value of the zonal coefficient С L2 6,00 - 6,95 5,0 – 5,99 4,0 - 4,99 3,0 – 3,99

Quantity of economicplanning zones

No zone

3 5 13 16

5 6 7 8

Value of the zonal coefficient С L2 2,0 - 2,99 1,5 - 1,99 1- 1,49 0,5 - 0,99

Quantity of economicplanning zones 60 140 113 41

Analyzing the dispersion of these zones, it should be noted that their greatest integrity is observed for the range of the value СL2 from 3.00 to 6.95, the smallest: from 0.50 to 2.99, respectively.

Further work also suggests that the urban development value of the territory displayed by the value of the zonal coefficient СL2, is observed (laid) in the cost of residential development. It is not the same for different types of objects of multi-storied residential real estate, depending on their location.

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

In order to identify this unevenness, the analysis of the cost of multistoried residential real estate objects of different periods of development (of different types) in the areas of different urban development values was performed.

Based on the sale data of residential real estate, a sample of 1,500 objects was created for the research.

Public resources like the Internet portals on real estate agencies and periodicals were used as sources of information. Residential real estate is differentiated according to the types, which are based on the period of their construction: the 'Pre-revolutionary', the 'Pre-war', 'Stalin's' and 'Khrushchev’s' periods construction, Panel Construction and New Buildings in each of the 8 zones of urban construction value of the territory. The number of analogues for comparison is substantiated by the method of analogues in the expert monetary assessment of the real estate object. In particular, it is considered that a comparison of at least 5 analog objects is sufficient to establish the predictive estate prices.

Therefore, not less than 5 analogues were analyzed in zones with the greatest integrity of the range CL2 and 20 proposals for the sale of different types of housing were analyzed in zones with the least integrity. The peculiarity of the analysis was the selection of completely unified by area, the size of the objects of housing like apartments, according to the type of residential development, storey, etc.

Fig. 1. Economic planning zoning of the territory of Kiev City under residential buildings.

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

In addition, in order to identify the absolutely clear value of the urban development value of the territory under different types of residential buildings, all the houses in which the investigated apartments located, are located on plots of land the ownership of which is not formalized. The above analysis was performed using data from the automated system of the Software Complex ‘Cadastre’ of the Main Department of Land Resources of the Kyiv City State Administration (KCSA). The buildings are on the balance of the relevant housing maintenance offices, associations of co-owners of multi-apartment buildings (condominiums). In this case, the ownership of residential any buildings have not been acquired, and have not been issued, concerning that apartments were used for analysis, but also the land was not invented by the relevant housing-service offices. New buildings were seemed like exceptions, because they have built on designated land areas, since 2000.

Analysis of the statistical data of the value of various objects of residential real estate in the same area of urban development value has shown that the cost of 1m2 of residential real estate is the most expensive in the 'Pre-revolutionary' and the 'Pre-war' periods and 'Stalin's' period buildings in central areas. It was also interesting fact that the value of 'Khrushchev's' period, the 'Pre-war' and the 'Prerevolutionary' periods buildings in the indicated zones, is practically identical.

The problem of dispersal of housing becomes very relevant, with the rise of prices for residential real estate, approaching, and sometimes equals to the level of developed European countries.

Analysis of the dispersal of residential buildings, depending on the period of development, allowed revealing the interesting facts. In particular, the 'Pre-revolutionary' and the 'Pre-war' period buildings and buildings of 'Stalin's' period are concentrated in the central zones. There are not any objects of 'pane' construction in the years 1990-1995 practically, but somewhat smaller quantity of 'Khrushchev's' period buildings, but there are also 'impregnations' of new buildings (new housing development).

The method of least squares was applied for testing the hypothesis of the correlation of the zonal coefficient of the location CL2 with the cost of residential properties.

The correlation dependence is represented as: Mx(Y)=φ(x),

(2)

where φ(x) ≠ const.

The best estimate of the regression function in terms of the Least Squares Method is the selective regression curve of y for x. yx= φ˜(x, b0, b1, … bp),

(3)

where yx – is conventional selective medium variable y at a fixed value variable x= x; b0, b1, ... bp – are the parameters of the curve.

Statistical relationships between variables can be studied using correlation methods (establishing the relationship between two random variables and assessing its constraints) and regression (establishing the type of dependence between them) of analyzes.

Assumed that x is a coefficient of urban planning value of the territory CL2і;

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

y is the average price for n-type residential area in the relevant territory values of urban development CL2і.

Data on statistical dependence is conveniently set in the form of a correlation table (x i and yi are the middle of the corresponding intervals, ni and nj are the corresponding frequencies). If we depict the obtained dependence graphically by points on the coordinate plane, then we obtain the so-called correlation field (Fig. 2).

We can make assumptions about the existence of a linear correlation relationship between x and y, based on the form of the correlation field. Therefore, the regression equation will be built the formulas (4):

yx  b0  b1  x ,

(4)

For each value хі (і=1,2,…,l), that is, for each row of the correlation table we calculate group averages.

yx  b0  b1  x ,

(5)

We use the Least Squares Method, according to which unknown parameters are chosen in such a way that the sum of the squares of deviations of the empirical group average of the values (found by the regression equation) would be minimal for this purpose.  (b0  b1 x 

y)2  min

(6)

Considering this amount as a function b0 and b1, we differentiate it according to these parameters and equate the derivatives to zero (7):  b 0  b1

  2(b0  b1 x  y )  0,

(7)   2(b0  b1 x  y )  0.

And then we obtain a system of normal equations for determining linear regression parameters (8), after some simple transformations:

b0 n  b1 x   y, b0 x  b1 x   xy , 2

,

(8)

where n is a number of population units (given values of x and y). This is a system of normal equations of the least squares method for a linear function ( yx ).

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Dividing both sides of the two equations in n, we obtain (9):

b0  b1 x  y, b0 x  b 2 x 2  xy.

,

(9)

where the corresponding averages are determined by the formulas (10): m

 yn j

y

j 1

n

l

l

j

,x 

 xini i 1

n

m

i

, xy 

m

  xyn i 1

j

ij

x

2

i

j 1

,x  2

n

i 1

ni ,

n

(10)

Now we rewrite the regression equation as follows (11):

yx  y  byx ( x  x)

(11)

The regression coefficient shows how many units the variable y changes in average with an increment of x per unit.

The coefficients byx and 1/bxy determine the angle coefficients to the axis Ox of the corresponding regression lines that intersect at the point ( x, y ) .

Let's turn to the estimation of the correlation dependence density. However, you can see that byx depends on the unit of measurement of the variables. The value of r is an indicator of the density of the connection and it is called the sample correlation coefficient. If r>0 (byx>0, bxy>0), then the correlation dependence between the variables is called direct, if r<0 (byx<0, bxy<0) is inverse.

That is, the coefficient of correlation r for x and y is a geometric average of the regression coefficients r   byxbxy . We use the formula (12) for practical calculations:

r

n xy   x y

n x  ( x) n y 2

2

2

 ( xy ) 2



(12)

Determination coefficient (R2) shows the share of the changes (variation) of the resultant characteristic under the influence of the factor characteristic. It is calculated using the formula (13):

(y  y )  1 ( y)  y  n 2

R

2

j

2

j

2

(13)

j

j

It can vary from 0 to 1. If it is closer to 1, the tendency is established more adequate, and, accordingly, the connection of the chosen trend and the dynamic range are closer. Based on the value of the MMSE Journal. Open Access www.mmse.xyz

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

determination coefficient, in statistical practice, it is accepted to apply the following gradation of trend matching to the dynamic series: R2 = 0 means a lack of connection; R2 < 0.3 means weak connection; R2 = 0.3 to 0.6 means middle connection; R2 = from 0.7 to 0.9 means a high connection; R2 = from 0.9 to 1 shows that the selected trend fully corresponds to the dynamic range.

Fig. 2. Dependence of the value of residential real estate on the urban development value of the territory.

Based on this algorithm, there were designed and constructed regression equations for different types of housing and graphically represented the data for correlation analysis in the correlation field, i.e. points in the plane, each of which has coordinates (Fig.2).

The equation of a linear function is: - for the 'Pre-revolutionary' period buildings:

y = 2837,6 x + 9839,2; R² = 0,7469;

-for the 'Pre-war' period buildings:

y = 2601 x + 9435,1;

- for the 'Stalin's' period buildings:

y = 5313,7 x + 5969,1; R² = 0,9556;

- for the 'Khrushchev’s' period buildings:

y = 4168,6 x + 8181,2; R² = 0,732;

- for panel construction:

y = 1408 x + 16363; R² = 0,6386;

- for the new buildings:

y = 3821,5 x + 9665,2; R² = 0,7432.

R² = 0,662;

The conducted analysis allowed establishing that among the urban value of the territory (the values of the zonal coefficient location CL2) and the value of different types of housing there is a close correlation, which is in functional connection values of the average values of these characteristics.

Since, the regression coefficient (b1, which denote the byx) shows how many units on average will change the variable y with increasing variable x per unit. It follows from this that due to increment of

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

urban values of the area per unit, the cost of a particular type of real estate will increase to a corresponding value byx. This statement can be specified by formula (14):

CL 2  1  Vr(CL 2 )  byx

(14)

where Vr(CL2) - the value of residential real estate Equation (14) will be as follows: - for the 'Pre-revolutionary' period buildings: CL2 + 1 = VrevR(CL2)+ 2837,6; -for the 'Pre-war' period buildings: CL2 + 1 = Vwar R(CL2)+ 2601; - for the 'Stalin's' period buildings: CL2+ 1 = VstalR(CL2)+ 5313,7; - for the 'Khrushchev’s' period buildings: CL2+ 1 = VKhrushR(CL2)+ 4168,6; - for panel construction: CL2 + 1 = VpanR(CL2)+ 1408; - for the new buildings: CL2+ 1 = VnewR(CL2)+ 3821,5.

If the regression equation and correlation fields had been analyzed, then the clear linear dependence appeared between the cost of different types of residential development and the degree of urban development value of the territory, as evidenced by the value of R2, which is the determination coefficient and reflects the density index of connection. Since, in all cases, R2 > 0 varies from 0.6386 to 0.9556 and, accordingly, byx> 0 , the correlation dependence between the variables is sufficiently large and is called direct. So, the hypothesis had been proven.

Hence the regression equation can be written as a straight line equation у = kx+b, the coefficient k is called the angular coefficient of the straight line.

The angular coefficient with the precision to the sign is equal to the tangent of the acute angle formed by the straight line with the abscissa (or is equal to the tangent of the angle between the direct and positive direction of the axis Ox). The value of the angle between the straight lines characterizes the tightness of the connection between the random variables: if the angle is smaller, the connection is closer.

We calculate the value of the angular coefficient for different types of housing using the following formula:

k  tg £  (c/d) k1 = 0,3255; k2 = 0,1340; k3 = 0,4550; k4 = 0,3398; k5 = 0,2089; k6 = 0,3098; where k1 – the value of the angular coefficient for the 'Pre-revolutionary' period buildings; k2 – the value of the angular coefficient for the 'Pre-war' period buildings; k3 – the value of the angular coefficient for the 'Stalin's' period buildings; k4 – the value of the angular coefficient for the 'Khrushchev’s' period buildings; k5 – the value of the angular coefficient for panel construction; k6 – the value of the angular coefficient for the new buildings.

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(15)

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Therefore, the value of the angular coefficient of the linear equation for different types of housing varies from 0.13 to 0.46, which actually reflects the difference in the quality of housing within the same type of development, taking into account physical and moral deterioration, the quality of building materials, etc.

Summary. Based on the results of research, conducted by us, the methodology for assessing impact of the urban development of the territory on the cost of various types of residential real estate had been developed.

Based on this of this methodology, there were established: - The dependence of the value of residential real estate on the urban development value of the territory is different for different types of development (buildings); - The indicator of the integral coefficient of value of different types of residential development varies directly in proportion to the growth of urban development value of the territory; - Between the urban development value of the territory and the cost of housing there is a close correlation, which is reflected by the determination coefficient that varies from 0.6 to 0.9, which characterizes different types of housing; - Therefore, the value of the angular coefficient of the linear equation for different types of housing varies from 0.13 to 0.46, which actually reflects the difference in the quality of housing within the same type of development, taking into account physical and moral deterioration, the quality of building materials, etc.

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[3] On the approval of the General Plan of Kiev and its planning projects for the suburban until 2020 (2002). Decision of Kiev сity сouncil 2002 № 370/1804. Available at: http://kmr.ligazakon.ua/SITE2/l_docki2.nsf/alldocWWW/56E1D135DED9D0B0C22573C00053FC A6?OpenDocument [4] Dehtiarenko Yu.F., Mantsevich Yu.M., Palekha Y.M., Tarnopolskyi A.V., 2008. Impact of monetary valuation on the land market in Kyiv: state, problems and prospects of disintegration .

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59-68.

[5] Doroshenko I.M., 2012. Regularities of formation and development of residential real estate market in Ukraine. Abstract of Cand. Sci. (Tech.) dissertation, 05.24.04, KNUCA, Kyiv, Ukraine – p.160 [in Ukrainian].

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