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Project Management Risks in the Sphere of
Housing and Communal Services
Victoria Borkovskaya
*
Moscow State University of Civil Engineering, Yaroslavskoe shosse 26, Moscow, 129337, Russia
Abstract: This paper shows a project risk management system model
allowing enterprises to better identify risks in the sphere of housing and
communal services and to manage them throughout the life cycle of the
project. It shows the most popular methods of risk probability assessment
and tries to indicate the advantages of the robust approach over the
traditional methods. Modern development of project management as well
as the accumulated knowledge and experience in this field made it possible
to integrate project management knowledge into a single system model.
Within the framework of this model, standard and robust approaches are
applied and expanded for the tasks of project data analysis. The suggested
algorithms used to assess the parameters in statistical models allow to
obtain reliable estimates. In this study, the classification of risks was
determined by the degree of relevance. I conducted an analysis of
statistical data, such as requests for maintenance of housing stock of
different service lives. The frequency of failures in the work of housing
organizations was determined and the probability of accidents was
calculated. Taking into account these calculations, the housing stock was
graded according to the degree of admissibility of the risk of its
maintenance.
1 Introduction
This research is aimed at studying the methodological and organizational problems of
professional risk management in the sphere of housing and communal services. These are
defined as “a complex of economic sectors that ensure the functioning of residential
buildings that create safe, comfortable and comfortable living and the presence of people
(consumers) in them. It also includes social infrastructure facilities for servicing residents”
[1] In other words, this paper examines enterprises and systems within and across public
housing, public buildings and public utilities (e.g., enterprises providing electricity, water,
gas and other products and services).
The modern development of the Russian economy is becoming increasingly oriented
towards the introduction of the latest technology and technologies in all its spheres. At the
same time, the solution of the quality problem of any of the spheres of the economy directly
depends on the economic and social policy pursued by the state.
*
Corresponding author: BorkovskayaVG@mgsu.ru
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons
Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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Project risk management involves a set of methodology, methods, technical and
software tools used in the design and implementation of projects, in this case in the field of
housing and communal services, that facilitates the consideration and minimization of risks,
ie unique processes that are time-limited and resource-intensive.
A significant part of models and mechanisms for managing project risks are
optimization problems, which are, as a rule, complex and multiextremal.
In this process, the data itself largely determines the outcomes, and the problem of their
quality, i.e. correctness, interpretability, portability, control, redundancy, security, etc.,
come to the fore. This problem is intensified in the interstate communication, both at the
level of standardization specialists and at the level of specific commercial structures. That
is why this issue is given increased attention at the federal level.
2 Methodology
Risk in project management is often understood as a random event, which in case of its
implementation has a negative impact on the project (the case when this event has a
positive impact on the project is called an opportunity). This negative impact can be
manifested in the increase in the deadlines for the completion of work, and as a result, the
project completion deadline, the overestimation or underestimation of the cost of work and,
as a result, the budget excess or deficit, the quality of work, etc. We will mainly consider
temporary risks (exceeding the deadlines for performance of work) and cost risks
(exceeding the cost of work).
Risks are described by two main characteristics: The probability of occurrence of a risk,
since any project is by definition unique, it is not always possible or necessary to count on
the availability of statistical data. At best, you can use the notion of subjective probability,
which relies on expert assessments. Since expert assessments are generally rather crude, it
seems event and the consequences (damage) in its occurrence. A general characteristic is
the degree of influence (risk rank), which is the expected damage (the product of the
probability by the amount of damage). [2]
The quality of risk assessments is determined by both the set of actors going into the
formulas and the formulas themselves. [4] In recent times, high-quality risk assessments,
including estimates of probability, damage, and degree of influence, have become
widespread (especially in practice). This is because probabilistic estimates require statistics
or, in any case, a certain frequency of events. However natural to use qualitative estimates
of probability, damage, and degree of influence. The most common three-point scale of
probability and damage estimates: small risk (1), medium risk (2) and high risk (3). [2-4]
A small risk is deemed to have virtually no effect on the parameters of the project, and it
is usually not taken into account. We note that qualitative estimates hide their quantitative
counterparts. For example, the probability of a risk event from 0 to 0.05 can be attributed to
a small risk, from 0.05 to 0.3 to the average, and above 0.3 to a high risk. Similarly, if we
bear in mind temporary risks, deviations of about 5% of the duration of the project can be
attributed to small risks, from 5% to 30% to medium risks, and above 30% to high risks.
These boundaries today are also determined by expert means and largely depend on the
terms of the project (rigidity of sanctions for failure to complete the project, for exceeding
the budget, etc.) As to the degree of influence, different methods are used here. The
simplest way is to determine the degree of influence, as the product of the risk assessment
for damage assessment (this technique is used in particular in the Savings Bank of Russia).
We get a six-point scale of the degree of influence 1, 2, 3, 4, 6, 9. Its disadvantage is that it
is uneven. Often a three-point scale of assessments of the degree of influence is used: small,
medium and high. In this case, to obtain estimates of the degree of influence, a convolution
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matrix of probability estimates and damage estimates is determined. An example of such a
matrix is shown below (Table 1).
Table 1. Matrix 1
3
2
3
3
2
2
2
2
1
1
1
2
У
В
1
2
3
You can see a certain priority ranking. Namely, the average damage, with a low
probability, experts consider to have a greater degree of influence (score 2) than a small
damage at an average probability (score 1).
If the probability estimate has a higher priority than the damage estimate, the matrix
will look like (Table 2)
Table 2. Matrix 2
3
2
2
3
2
1
2
3
1
1
1
2
У
В
1
2
3
Finally, if both estimates are equally priority, then the matrix will be symmetric (Table 3).
Table 3. Matrix 3.
3
2
3
3
2
2
2
3
1
1
2
2
У
В
1
2
3
The choice of the convolution matrix is of great importance, since the resulting impact
assessment is the basis for identifying the most significant risks and developing appropriate
response measures (risk reduction, risk transfer, risk acceptance or risk evasion). One of the
main measures of response is risk reduction that is, carrying out a number of activities that
reduce either the probability, or the damage, or both. Consider the task of reducing the
degree of risk to the required level with minimal costs.
Let there be n measures to reduce the risk, that is, to reduce the probability of either
damage, or both. Let us first consider the case when there is a set Q of n measures to reduce
the likelihood of occurrence of a risk event and a set of P of m mitigation measures. To
assess the effect of risk reduction measures, it is advisable to proceed to quantitative
estimates of probability and damage. Let pc and pb be the boundary values of the
probability. If the probability of a risk event lies in the half-open interval (0;pn], then this is
a small probability on a qualitative scale, if in the half-open interval (pn; pc], then the
average probability, and if in [pc;1), then high. Similarly, qн and qс Limit values of damage
Next, we denote ai - the effect from the i-th measure to mitigate the damage, ci - costs for
the i-th measure to reduce the probability, si - costs for the i-th measure to reduce the
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damage. We first consider the problem of reducing probability. Denote xi=1, if the event is
included in the risk management plan, xi=0, otherwise. Assuming that the effects are
summed, the total effect can be written as:
(1)
Let p be the existing probability level and p>pc, that is, the probability is high. To reduce
the probability to an average level, it is necessary to provide an effect of at least (p-pc), and
to a low level of effect no less than (po-pн).
Formulation of the problem. Define xi, i= minimizing.
(2)
Under restriction
(3)
By replacing the variables zi=1-xi, i= , the problem reduces to the classical knapsack
problem (see Appendix): to determine zi, i= , maximizing
(4)
Under restriction
(5)
The conclusion. As is known (see the appendix), the solution of the knapsack problem with
constraint A gives solutions for any smaller values, that is, for
that is, the minimum costs to ensure a low level risk.
3 Results and Discussion
The priority direction of reforming the sphere of housing and communal services is the
development of market relations in the housing and communal sector, which should be
accompanied by the search for methods of state (municipal) regulation, in particular,
through tariffs. The objective necessity of such regulation is connected with the
peculiarities of housing and communal services, first of all, their uninterrupted operation,
i.e. the impossibility of refusing to receive them for a long period, as well as the inability to
compensate for underserved services.
Influence on the nature of market relations in housing and communal services and the
features of the housing stock in Russia, in particular, is that, despite the large volumes of
privatized apartments, most of the residential houses remain municipal. Municipal property
is also a unified system for utilities, i.e. the relevant authorities are required to control the
use of municipal property. Previously listed features, as well as high social significance of
housing and communal services make it impossible to switch to completely free pricing.
Prices, as well as tariffs determined on their basis, should remain a means of implementing
a social and economic policy in the housing sector.
The development of market relations in the housing and utilities sector will lead to an
increase in the uncertainty that always occurs when moving to the market. The
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concretization of the risk category with regard to the activities of housing and communal
enterprises allows us to conclude that the risk is the uncertainty associated with the cost of
maintenance costs of housing stock, or the likelihood of an unfavorable outcome (in this
case, not only for the enterprise, but also for its end users - residents ) in connection with
the maintenance of housing. It has now become clear that reducing the uncertainty, and
hence the risks in the housing and communal sector, is a task that has reached a nationwide
scale, on the basis of which the socio-economic security of the country depends. The
solution of this problem must be linked both to the improvement of the housing stock
condition and to the reduction of uncertainty within the industry, primarily by improving
the methodology for the formation of tariffs, i.e., to increase the validity of determining the
costs of specific utilities. In addition, in the sphere of housing and communal services, there
are also objective sources of sectoral risks that may not be present in other countries - e.g.,
in the RF, there is a single network of water, heat and power supply often with significantly
worn-out networks within the municipality. Therefore, failures in any one link in the
housing and utilities sector, even without direct access to the population, can lead to
considerable damage, even to people's lives and health (for example, accidents at a thermo
plant and power station in winter). In this regard, it is necessary to note the difficulties in
managing risks directly at enterprises engaged in housing services, which in many ways are
an intermediate link between resource-producing monopoly enterprises dictating their terms
to these enterprises, and often municipal authorities. [5-7]
In addition, risks in the housing and communal services need to be considered in the
regional aspect, when there is an increase in many municipalities due to budget deficit and
low real incomes, which are the main sources of financing for the housing and communal
services sector. The probability of underfunding, i.e. the financial risks of housing and
communal enterprises in these conditions are increasing many times. Therefore, the
resolution of issues of intergovernmental fiscal relations, as well as the implementation of
large-scale socio-economic programs, are the most important directions for reducing
regional risks in housing and communal services. [8-9]
Separately, it is necessary to note the high legal risks caused by historical features of the
object of servicing the municipal housing stock, which is represented mostly by multi-store
apartment houses. These houses are the property of the municipality, which partially leases
them, as well as private property of citizens who have privatized their apartments. Lack of
appropriate legal registration of relations in connection with the maintenance of housing
stock and inconsistencies in the housing and civil codes, for instance, make many court
proceedings difficult due to non-payment of utility bills, use of public places, etc. [10-12]
In order to classify risks by the degree of admissibility, I analyzed statistical data -
applications for maintenance of housing stock of different service lives and the frequency
of disruptions in the work of housing organizations, to calculate the probability of
accidents. Taking into account these calculations, the housing stock was graded according
to the degree of admissibility of the risk of its content (Table 4).
Calculations for the housing stock of Moscow showed that in percentage terms this fund
is distributed as follows: risk-free zone - 41.5%, zone of acceptable risk - 36.7%, critical
risk zone - 11.4% and zone of catastrophic risk – 10.4%. [13]
Due to are latively predictable 80-90% of the collection of the rent from the population
of public housing provides the company relatively stable, albeit relatively low income.
However, as barriers to the development of commercial activities are removed, municipal
unitary enterprises will begin to provide additional paid services already at market prices.
Accordingly the commercial risks of this activity will be somewhat higher than in the
provision of the basic range of services. Many entrepreneurs already have an adequate
understanding of the problem of commercial risks in housing and communal services, so
the interest in this area, not yet divided by the private business, is growing every day.
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Table 4. Determination of zones of admissibility of housing stock risk in connection with its
maintenance.
Risk-free
zone
Area
of permissible risk
Zone
critical risk
Zone
catastrophic risk
Costs insignificant,
profit earning
New housing fund of
high-quality
construction taking
into account the
warranty period
Depreciation - 0-20%
Repair work is mainly
of a current nature All
costs are included in
the price
Housing stock with a
wear amount of 21-
40%
Significant volumes of
capital work
Emergence of accidents,
work to eliminate them
Loss of resources, losses
Housing stock with wear
and tear 41-60%
Work in emergency
mode, a large number
of accidents Huge
losses, irreversible
process of deteriorating
housing conditions
Unsuitable for
residence Housing
stock with deterioration
of 61% or more
Separately, it is necessary to say something about high production risks. The production
risk with respect to the housing and utilities sector is characterized as a danger of potential
loss of resources or shortfall in income, as well as the appearance of additional losses in
comparison with the option designed for rational use of resources. [14-15]
The activities of utilities are affected by all of the risks considered, but the degree of
their impact is different. Experts differentiated the degree of influence of these risk factors
on the activities of enterprises (Fig. 1).
Fig. 1. The degree of influence of risk factors in utilities.
This author believes that it is necessary to move to a more systematic system for
recording and dividing the risks of accidents in the housing sector. To do this, it is
necessary to assess at the stage of price determination and contract conclusion what is the
degree of production risk in servicing this housing stock and what financial compensation,
for the most part from the municipality, the contractor will have to undertake for servicing
it. The number of these accidents is inversely related to the compliance with the terms of
the current and major repairs. By investing additional funds, managers can increase the
lifespan of the building and significantly reduce the accident rate, so that the probability of
accidents will decrease. [16] Currently, with housing stock having varying degrees of
depreciation, the main efforts of housing and utility organizations are directed to
maintaining the housing in in a normal but sometimes (often?) dilapidated state, and, of
course, a minimum of work is carried out on houses with a service life of 10-15 years. This
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period of depreciation, in our opinion, requires very close attention when the life cycle of
the building and its construction and maintenance costs are being calculated.
Currently, the costs of emergency recovery work are not well planned. At many
enterprises the costs for this type of work are not included at all in the composition of costs,
i.e. are not taken into account when forming tariffs. Liquidation of the consequences of
accidents is carried out at the expense of funds intended for current and capital repairs,
which distorts the structure of planned costs and leads to a decrease in the reliability of
services. Scheduled preventive maintenance frequently gives way to emergency repair work
today. In order to increase the justification of expenses for housing and communal services,
we believe that it is necessary to take into account the risk that relates not so much to the
object of influence (housing stock) as to the type of activity, i.e. to the maintenance and
maintenance of this facility. At the same time, the condition of the servicing object - the
residential building - is directly related to the growing uncertainty in the process of its
servicing. In assessing the production risk, we are primarily interested in the fate of the
whole object and the measure of danger and the degree of potential damage in the work of a
housing-operating enterprise as a result of a failure, i.e. accidents, as well as the likelihood
of harm to both physical and moral character of the final consumer of housing and
communal services - the inhabitants. At the same time, we are interested not only in the
principle of compensation for losses, but in the emergence of the possibility of preventing
damage that may result from an accident. Based on the studies carried out, an algorithm for
recording emergency recovery work was developed for the economic justification of
expenses for the implementation of these activities as part of the cost of housing
maintenance services. Calculated in this way the amount of funds for emergency work can
be realistically estimated. This could be fixed in the form of an insurance fund. Separately,
it should be noted that for housing stock belonging to critical and catastrophic risk zones,
responsibility should be distributed between enterprises and the municipality, which must
compensate for the increased costs due to the worn condition of houses and other factors, in
order to prevent price discrimination of those living in this fund. Enterprises under this
scheme of work can be set up to identify internal and external factors that affect the amount
of risk, to assess and determine the economic feasibility of investing funds in improving
repair and maintenance technologies and materials, and in conducting preventive
maintenance work. These measures will save money for the liquidation of accidents, and
for the municipality this will mean improving the state of urban residential real estate and
reducing the use of budget funds to eliminate the consequences of emergencies. [17-20]
4 Conclusions
In conclusion, the application of improved enterprise risk management practices can
alleviate current problems in the construction and management of housing, public buildings
and utilities, both as individual, discrete entitites as well as across municipalities and states.
Presently, the development of housing and communal services is hampered by an
unfavorable ratio of risk-return factors. While the high degree of risk that exists in these
enterprises is typically planned to be compensated by higher profitability, this is almost
impossible in practice because of the great social burden that subsequent and unplanned
higher prices to the consumers would require. Therefore, in order to improve the ratio of
profitability to risk, to increase the investment attractiveness of housing and other public
enterprises, it is necessary for the enterprises as well as the state and the municipality to
appropriately and thoroughly assess and manage the risks of their activities in the planning,
construction and management stages.
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I want to thank Professor Richard L. Roe of Georgetown University Law Center for his editorial
assistance with this paper.
References
1. Borkovskaya V.G. Economic cooperation in housing maintenance organizations.
Dissertation. Moscow 2002.
2. Borkovskaya V.G. Complex models of active control systems at the modern developing
enterprises. Advanced Materials Research (Volumes 945-949). Chapter 22:
Manufacturing Management and Engineering Management. June 2014. Pages 3012-
3015. DOI: 10.4028/www.scientific.net/AMR.945-949.3012.
3. Burkova I.V. The method of network programming in problems of nonlinear
optimization. I.V. Burkova // Automation and telemechanics. - 2009.-No. 10.-P.15-21.
4. Burkov V.N., Novikov D.A., Shchepkin A.V. (2015) Simulation Models for Control
Mechanisms in Ecological-Economic Systems. In: Control Mechanisms for Ecological-
Economic Systems. Studies in Systems, Decision and Control, vol 10. Springer, Cham.
5. Korol, E.A., Kagan P.B., Khorkina, Zh.A. Ways to improve the efficiency of housing
investment programs being implemented. Industrial and civil engineering. 2010. No.
10. P. 17-19.
6. Borkovskaya V.G. The concept of innovation for sustainable development in the
construction business and education. Applied Mechanics and Materials. (Volumes 475-
476). Chapter 15: Engineering Management. December 2013. Pages 1703-1706. DOI:
10.4028/www.scientific.net/AMM.475-476.1703.
7. E.A. Korol, S.V. Komissarov, P.B. Kagan, S.G. Arutyunov. The Solution of problems
of organizational-technological simulation of building processes. Industrial and civil
engineering, 3 (2011).
8. Boris Titarenko, Amir Hasnaoui, Roman Titarenko. Risk management system model
for construction projects. IOP Conference Series: Materials Science and Engineering,
Volume 365, Safety in Construction.
9. BorkovskayaV.G, Passmore D. Behavioral engineering model to identify risks of losses
in the construction industry. Advances in Economics, Business and Management
Research (France-Netherlands). Atlantis Press. In press.
10. E.A. Korol, A.A. Pleshivtsev, The technology of construction of low-rise buildings
from sandwich panels (2017).
11. Korol E.A., Kagan P.B. Komissarov S.V. Management of urban planning programs.
Town planning. 2010. No. 4. P. 57-60.
12. Borkovskaya V.G., Degaev E., Burkova I. Environmental economic model of risk
management and costs in the framework of the quality management system // MATEC
Web of Conf., 193 (2018) 05027. DOI:
https://doi.org/10.1051/matecconf/201819305027.
13. Annual report of a public corporation - the Housing and Utilities Reform Fund for
2017. On line: http://fondgkh.ru/workresult/cat/godovyie-otchetyi-fonda/
14. Leitch, M. "The Risk Management Gap", Board Leadership, vol. 2017, no. 154, pp. 1-
3. doi:10.1002/bl.30088.
15. Borkovskaya V, Passmore D. Application of Failure Mode and Effects Analysis in
Ecology in Russia. MATEC Web of Conf., 193 (2018) 05027. DOI:
https://doi.org/10.1051/matecconf/201819305026
16. BorkovskayaV.G., Bardenwerper W., Roe R. Interactive Teaching of Risk
Management in the Russian Construction Industry. IOP Conf. Series: Materials
Science and Engineering 365 (2018) 062030 doi:10.1088/1757-899X/365/6/062030.
8
MATEC Web of Conferences 251, 06025 (2018) https://doi.org/10.1051/matecconf/201825106025
IPICSE-2018
17. Borkovskaya V.G, Bardenwerper W, Roe R. Sustainability Risk Management: The
Case for Using Interactive Methodologies for Teaching, Training and Practice in
Environmental Engineering and Other Fields. Advances in Economics, Business and
Management Research (France-Netherlands). Atlantis Press. In press.
18. Degaev E.N. New classification of foaming agents for fire extinguishing //
MATEC Web of Conferences 193, 02032 (2018). DOI:
https://doi.org/10.1051/matecconf/201819302032.
19. Korol, E.A., Pugach, E.M., Kharkin, Yu.A. The influence of technological factors on
the formation of communication layers of a multilayer enclosing structure. Vestnik
MGSU.2014. 3
20. E. Korol, N. Shushunova, Green Roofs: Standardization and Quality Control of
Processes in Green Construction. MATEC Web of Conferences 106, 06014 (2017)
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MATEC Web of Conferences 251, 06025 (2018) https://doi.org/10.1051/matecconf/201825106025
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