Choosing the most effective one possible. Analysis of the best and most efficient use of the facility. Uses that are not the most effective

The influence of market behavior on the financial decision-making of individuals, companies, authorities dictates the concept of the most efficient use of real estate. Market factors determine the market value, so the demands of market forces on properties are essential to determine the most efficient use. Analysis of the most efficient use of the property involves holding detailed research of the market situation, characteristics of the assessed object, identification of the options demanded by the market, compatible with the parameters of the assessed object, calculation of the profitability of each option and the assessment of the value of real estate for each use case. Thus, the final conclusion about the most efficient use can only be made after calculating the cost.

The best and most efficient use of a real estate object is the option of using a vacant or built-up plot of land, which is legally possible and properly formalized, physically feasible, provided with appropriate financial resources and gives the maximum value. The optimal use of a plot of land is determined by the competing factors of the specific market to which the property being valued belongs, and is not the result of subjective speculation by the owner, developer or appraiser. Therefore, the analysis and selection of the most effective use is, in fact, an economic study of market factors that are significant for the evaluated object. Market factors used to formulate a conclusion about the most efficient use of real estate at the valuation date are considered in the general data set collected and analyzed to determine the value of the property. Therefore, the most efficient use can be qualified as the basis on which the market value is based.

Usually, the analysis of the most effective use is carried out according to several alternative options and includes the following areas: market analysis; analysis of the feasibility of the option; analysis of the most effective use. The listed areas of analysis imply a comprehensive study following questions: Market analysis involves identifying the demand for alternative use cases in order to study supply and demand, market capacity, dynamics of rental rates, etc. for each option. A feasibility study involves calculating the basic components of value: income stream and capitalization rates to determine the value, taking into account the variables of each legally sound and physically feasible option. The analysis of the most effective use implies the development of a detailed plan for the implementation of each option with consideration of specific market participants, the timing of the project, sources of funding for choosing the option that ensures the maximum productivity of the evaluated object.

Best use analysis criteria: legal admissibility (The appraiser considers particular restrictions, zoning regulations, building codes, regulations for the protection of historic buildings, as well as environmental legislation that may be specific types of potential uses. The choice of the most efficient use of real estate may be influenced by the existence of long-term leases); physical feasibility(The criteria for physical feasibility - size, shape, area, design, ground conditions and access roads to the site, and the risk of natural disasters (such as floods or earthquakes) - affect the intended use of the land); financial security(all uses that can provide positive income are considered financially feasible. If the use does not imply a regular income from the operation, then the analysis selects those options that create real estate at a cost equal to or greater than the cost of construction or refurbishment of the property for this new use. The appraiser must compare the capital gains or income from the use of the property with the capital expenditures incurred. If the income is only slightly lower or higher than the costs, such use is deemed to be financially impracticable.); maximum productivity(this is the highest value of the land plot as such, regardless of whether it is free (actually or conditionally) or built-up. The implementation of this criterion assumes, out of all legally permitted, physically realizable and providing a positive income value options, the choice of the type of use that provides the maximum cost the basis of real estate - a land plot The maximum productivity of a land plot is determined by correlating the amount of its income with the capitalization rate required by the market for this type of use). This sequence of the analysis procedure is due to the fact that the most effective use case, even with the necessary funding, is not feasible if it is legally prohibited or its physical implementation is impossible.

Appraisers use two techniques to analyze the best use of real estate.: the most efficient use of the site as undeveloped; the most efficient use of the site as built-up. Main reasons for determining the most efficient use of land as undeveloped: Allocation of the value of only the land plot in the real estate value. Using the comparable sales method for valuing built-up land. Calculating the loss in value due to external obsolescence. Assessment of the real value of land in the composition of the property, which is not optimal for this site. The analysis of the most efficient use of the built-up area is done for two reasons.: Identification of the type of use of the property that provides the highest total return on investment. Revealing on the market of real estate objects of the same purpose with a comparable level of efficiency of use.

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Choicemosteffectiveoptionuseobjectreal estate

Introduction

The analysis of the most effective use of a real estate object involves a detailed study of the market situation, characteristics of the property being evaluated, identification of options demanded by the market that are compatible with the parameters of the property being evaluated, calculation of the profitability of each option and an assessment of the value of real estate for each use case. The best and most efficient use of a real estate object is the option of using a vacant or built-up plot of land, which is legally possible and properly formalized, physically feasible, provided with appropriate financial resources and gives the maximum value.

The purpose of this work is to analyze the real estate object in terms of its best and most effective use as conditionally free, as well as with improvements. Land plot with cadastral number 78: 5516A: 2 and is located in St. Petersburg at the address: Prosveshcheniya prospect, house 40, letter A. For this, legally feasible options for the use of the land plot will be determined on the basis of the Land Use and Development Rules of St. Petersburg, and all the necessary calculations have also been carried out.

1 . Specificationsobject

This section of the work includes:

Quantitative and qualitative characteristics of the object, including information on property rights, encumbrances associated with the real estate object, on the physical properties of the real estate object, available information on wear and tear and obsolescence.

Quantitative and qualitative characteristics of the elements that make up the real estate object, which have specific features that affect the results of the project.

Information about the current use of the real estate object.

Other factors and characteristics related to the real estate object that significantly affect the technical and economic indicators of the project.

1.1 Descriptionlandplot

The description of the land plot is presented in the table below.

Table 1.1 - Description of the land plot

1.2 Description of improvements

A description of the improvements is presented in the table below.

Table 1.2 - Description of land improvements

1.3 Object history

In 1986, in the Vyborgsky district of St. Petersburg, on Prosveshcheniya Avenue, a new beautiful building appeared, built specifically for the first Children's Art School in the city. Bright, well-equipped classrooms, spacious corridors, a buffet, one exhibition and 2 concert halls. These beautiful premises house three departments - art, choreography and music. Such a creative community was new and immediately aroused keen interest not only among the residents of the microdistrict, but also among the entire city.

Figure:

In 1999, the Children's Art School was named after Georgy Sviridov, a remarkable Russian composer, whose life and work have been associated with our city for many years. A truly creative atmosphere, a high professional level of education have developed largely thanks to the founder and first director of the school, Honored Worker of Culture of Russia A.M. Serdyuk (1937-2001). Alexander Mikhailovich was a wonderful teacher and musician who perfectly mastered two instruments, organ and piano. His name was often found on the philharmonic posters not only in St. Petersburg, but also in other cities of Russia and abroad, it was such a leader who was able to create a team of excellent specialists in their field who love their profession and strive to captivate children into the world of music, dance, painting.

Since 2001, the school has been managed by the Honored Worker of Culture of the Russian Federation Natalia Mikhailovna Veledeeva. Natalia Mikhailovna has been working in the field of music education since 1966, in an administrative position since 1987. An experienced leader, N.M. Veledeeva enjoys great authority in the city, she is a member of the Board of Directors of Schools under the Committee on Culture.

The school has about 2000 children and more than 130 teachers. For great creative achievements, the best teachers were awarded honorary titles. "Honored Worker of Culture of the Russian Federation": N.M. Veledeeva, V.G. Efimov, M.V. Hwang, G.S. Leonova; "For achievements in culture": I.S. Sokolov, V.P. Kalshchikova; “For successes in the humanization of the St. Petersburg school”: N.M. Veledeeva, V.G. Efimov, M.V. Hwang, M.F. Pugachevsky, O. I. Kuznetsova, O.F. Perevertkina, M.V. Fursova, O. V. Gerasimova, Yu.B. Slivovsky. The school is proud of its talented students, the best of whom win the titles of laureates and diploma winners in many international competitions and festivals. Future professionals are awarded scholarships from the Committee for Culture of the Government of St. Petersburg, the Ministry of Culture of Russia, and the Philharmonic Society of St. Petersburg. The school collective proudly bears the name of the great Russian composer. On December 8, 2000, in connection with the 85th anniversary of GV Sviridov, the school museum "Sviridov in Leningrad" was solemnly opened.

Since 2000, the school has hosted the St. Petersburg Open St. Petersburg Festival of Children's Performing Arts named after G.V. Sviridov. In 2005, having become a member of the Association of Music Competitions of Russia, the festival acquired a new status: the International Competition for Youth Performing Arts named after G.V. Sviridov. All these years, the Honorary President of the Competition has been the composer's nephew Alexander Sergeevich Belonenko, and the permanent chairman of the jury has been the Honored Artist of Russia, professor of the St. Petersburg State Conservatory named after V.I. ON. Rimsky-Korsakov Oleg Yurievich Malov.

For many laureates, the victory at the Sviridov competition was a good start in their professional development.

For the youngest musicians (up to 10 years old), the School initiated the city festival "ISKORKI". Every two years, young soloists of all specialties compete in musical performance. Awarding and concert of laureates takes place in the Small Hall named after M.I. Glinka St. Petersburg Philharmonic. Today the school plays a significant role in the concert life of the city. Students of the music and choreographic departments constantly perform at large concert venues: in the Sheremetev Palace, the Big and Small Halls of the St. Petersburg Philharmonic, the Concert Hall of the Academic Capella, the Samoilovs' museum-apartment, the N.A. Rimsky-Korsakov. The guys take part in the music subscription of the House of Composers, the Zazerkalye theater, in the Kochneva House, in the F.I. Chaliapin, in the palaces of the Russian Museum.

One of the best music departments is piano. Such teachers as: G.S. Leonova, D.Yu. Bystrov, M.Yu. Kuzmina, O.F. Perevertkina, O. I. Radaeva, M.V. Hwangs successfully prepare their students for performances in competitions, city concerts and for admission to secondary schools. All departments of the school have former graduates who returned to their native walls after graduating from higher educational institutions. The school is famous for its creative teams, which perform not only in our city, but also abroad.

An exemplary collective brass band under the direction of the Honored Worker of Culture of the Russian Federation V.G. Efimov. - laureate of many international competitions and festivals - has been to Israel, Italy, Germany, France, Spain, Belgium, Greece. Choral group "Consonance" under the direction of M.V. Fursova is a laureate and a diploma winner of many international festivals: I prize of the competition-festival dedicated to the 90th anniversary of G.V. Sviridova (Moscow 2005), I degree diploma at the Open All-Russian Children's Choir Assembly "On the Banks of the Neva" (SP-b 2006), Gold medal of the international choir competition "Mundi Cantant" (Czech Republic, Olomouc 2007), I prize of the international competition named after G.V. Sviridova (SP-b 2007), Diploma of the II degree of the X St. Petersburg Festival of Children's Choirs "Classics and Modernity" (2007), 1st prize for 56 - Europees Musikfestival voor de Jeugd Neerpelt (Belgium 2008), Silver diploma at the 6th Festival e Concorso Corale Internazionale Jesolo Lido - VENEZIA (Italy 2008). The collective constantly travels with concerts to many cities of Russia and abroad: Poland, Hungary, Finland, France, Italy, Czech Republic, Belgium. The art department of the school employs 22 teachers. Among them: N.E. Belykh, V.P. Golovin, O. N. Pankratova, F.F. Rasulov, Yu.B. Slivovsky, M.S. Smirnov, N.P. Starova, M.V. Trofimova are members of the Union of Artists of the Russian Federation. The works of students of the art department have repeatedly won prizes at exhibitions in St. Petersburg, Russia, England, America, France, Germany, Finland, Poland, Czech Republic, Hungary.

The choreographic department employs a team of experienced teachers who have extensive experience in stage and pedagogical work and participate in international seminars on choreographic art: in the USA, France, Finland. Students of the department are trained according to the academic program. The department employs I.K. Novik is the leading director of the Zazerkalye St. Petersburg Children's Musical Theater. Under her leadership, interesting creative work is carried out, performances are carried out that combine the activities of the choreographic, music and art departments of the school. Established creative ties with the Vaganova Academy of Russian Dance. Students perform at the leading concert venues of the city: the Great Philharmonic Hall, the Academic Chapel, the Smolny Cathedral. One of the achievements of the department is the conquest by students of the titles of laureates and diploma winners at city and international festivals, including: "Dancing Olympus" (Berlin 2008), "Sparks" (SP-b 2009). Students' performances greatly decorate the holding of subscription and holiday concerts.

2 .Analysissurroundingwednesday

2.1 Analysislocationobject

Figure:

Development of the territory

The territory of the quarter in which the object under study is located is limited:

- Yesenin Street;

- Prospectus of Enlightenment;

-Ivana-Fomin street;

- Sirenev Boulevards.

The area under study is distinguished by an abundance of parks.

The largest of them are Sosnovka Park, with a well-groomed forest, footpaths, playgrounds and volleyball courts, the Forestry Academy Park, located in the area of \u200b\u200bMuzhestva Square.

The ecological state of the district's residential areas is generally good, but the degree of soil contamination in the micro-districts varies significantly. But the level of air pollution, in general, is higher than in the neighboring Primorsky district, where the air is actively ventilated from the bay. Also next to the object under study is the Kantemirovsky bridge, the territory of which is considered one of the noisiest places in the city.

Figure 2.1 - Local location of the object

Residential development

The composition of the housing stock of the Vyborgsky district practically repeats the chronology of the city's development. In the south - the old fund, in the central part - "stalinka" and "Khrushchev", in the north - mostly new houses. Among the main advantages of the Vyborgsky district is its developed infrastructure. The district ranks fourth in the ranking of St. Petersburg districts in terms of the availability of high-quality retail space. The area is known for shopping areas near the Udelnaya and Polytechnicheskaya metro stations, Svetlanovskiy and Shuvalovskiy shopping centers, huge shopping and leisure centers in the Ozerkov area and Prospekt Prosvescheniya metro station, O Kay hypermarkets, Lenta "And" Maksidom ". In the region there are business centers" Nobel "(Pirogovskaya embankment)," Aquatoria "(Vyborgskaya embankment)," Bekar "(B. Sampsonievsky prospect). It should be noted that most of the operating business centers are located on Vyborgskaya side.Vyborgsky district has a large number of objects (over 40), renting office space.Also, the district is reconstructing old buildings, design and construction of new structures for business centers.Vyborgsky district is successfully implementing the program of capital construction of multi-storey garages - parking lots.

general information

The Vyborgsky district (formed in 1718) is one of the largest in St. Petersburg today, moreover, it is one of the oldest: it got its name back in 1817. It was at this time that, by the decree of Peter I, the territory along the right bank of the Bolshaya Nevka and Neva, where the ancient road to Vyborg passed, began to be called the Vyborg side.

In the first years of the city's existence, the Vyborg side was considered a border line. In the first plans of the city, this territory was part of the Petersburg part, then it became an independent Finland, then Vyborg side.

Along the right bank of the Bolshaya Nevka, which was almost not flooded during floods, at the beginning of the 18th century, warehouses, barns, and hospitals began to be built. The embankment in this section was constantly expanding due to the filling of the bank and advanced into the river by almost 200 meters. Residential buildings appeared in 1711. The builders of the city, retired soldiers, and artisans settled here. This is how the settlements were formed: barrel, company, hospital, Sinyavinskaya. Here built-up areas alternated around the factories. In the first quarter of the 18th century, the Vyborg side was developed more slowly than other districts of St. Petersburg. In 1717 there were only 350 households on the Vyborg side (there were 1720 households on the Admiralty Island during the same period).

Various industrial enterprises appeared on the outskirts - sugar factories, tanneries, breweries; shipyard, barracks, prisons. Immediately on the embankments of the Neva, tsarist dignitaries are building dachas - G. Teplov, A. Bezborodko, Admiral A. Sinyavin and others.

The southern part of the district, adjacent to the Neva, became part of the city in the 18th century. A hospital settlement was formed here. The first military hospital in Russia (the Admiralty State Hospital) was founded by order of Peter I in 1715, next to it in 1717 a land military hospital was opened. The first architect of our city D. Trezzini built them on stilts, then M. Zemtsov rebuilt these buildings with two turrets at the corners and almost 300 meters long in stone. The look that we are seeing now was acquired by the Academy in the 60s of the last century. First, the buildings that overlook the Neva were rebuilt, then the rest. Since 1881, this complex became known as the Military Medical Academy. In the second half of the 19th century, an industrial area was formed along the banks of the Neva and Bolshaya Nevka. Here are the factories of A. Baranovsky, G. Lessner, L. Erickson, L. Nobel, as well as Sampsonievskaya, Nikolskaya and Vyborgskaya manufactories.

The central part of the modern district was previously called "Lesnoye". The exact boundaries of "Lesnoy" in a modern city are difficult to determine. Sergei Aleksandrovich Bezbakh (an outstanding local historian of the 1920s) noted in his book that “Lesnoy” refers to the area on the right side of the Vyborg highway (Engels ave.) Between Lanskaya street, Isakov lane. (now Manchester street), Staro-Pargolovsky prospect (now Maurice Torez ave.), Theological cemetery and the park of the Forest Institute. Now, according to the historian S. Glezerov: "The broad concept of" Forest "includes the nearby Malaya and Bolshaya Kushelevka, Grazhdanka and Sosnovka."

The land in the central and northern part of the modern Vyborg region used to belong to the tsarist nobles - Shuvalov, Levashov, Kushelev. Their surnames gave the name to the villages of Kushelevka, Levashovo, Shuvalovskoe. In the 19th century, the Forestry School moved to the Kushelevka area from Tsarskoe Selo, on the basis of which the Forestry Academy was later established. In the middle of the 19th century, the territory near the Forestry School began to be built up with dachas, but with the laying of a branch of the Finnish railway in the 70s, their construction moved to the north, to Shuvalovo and Ozerki. In the area of \u200b\u200bMuzhestva Square, all the main highways of Lesnoy are connected. In the 18th century they led to the Spasskaya manor. Then it was owned by an influential nobleman of the royal court of Catherine II and Paul I - Ivan Kushelev. Having bought part of the land from Count Bezborodko, he built a summer residence. At the beginning of the century, next to Kushelev's dacha, there was an English farm, the history of which dates back to the reign of Alexander I, when he allowed the English retired captain Davidson to arrange an exemplary agricultural farm, for which lands were acquired beyond the Vyborg side, including part of the Spasskaya manor. The farm did not become a role model, but brought only losses and in 1809 was returned to the treasury. Part of the farm's land was sold, except for two plots on which outbuildings were located. In 1811, the Forest Institute, which was then called "Forst Institute" (from the English "forest" - forest), which later gave the name of the entire area, was located there in 1811, transferred from Tsarskoye Selo. The institute gradually turned part of its lands into a forest park: instead of the old crooked roads to Spasskaya Manor and Murino, streets were built, pine trees were planted, nurseries, a botanical garden, and a greenhouse were set up.

Since the 30s of the XIX century, the institute, in need of money, began to sell part of its land to private individuals for the construction of summer cottages, and the area around it became a lively suburb. The land was quickly bought up by wealthy residents of St. Petersburg, who appreciated the attractiveness of these suburban areas. The resulting dacha area began to be called "dachas for the Forest Institute." Then, when the institute in 1837 became a military educational institution, which was called the Forest Corps, and the area began to be called the "Forest Corps". The name survived until the middle of the 19th century, later the word "corps" gradually fell out of use and the name "Lesnoy", and sometimes "Lesnoe", remained.

Over time, by the beginning of the XX century, the name "Lesnoy" covered a fairly large territory. When the Polytechnic Institute was opened here, the significance of Lesnoy especially increased. It has become a kind of St. Petersburg "Cambridge". The fate of "Lesnoy" after the revolution is typical for many metropolitan suburbs. “In the 1920s and 1930s, a simple public from among workers and Soviet employees lived here, and in the 60s the city came here,” writes S. Glezerov. Only a few buildings have survived from the old Lesnoy, among them - the wooden building of Bertling's dacha on Bolotnaya Street, famous for its revolutionary past, now it houses the Children's Historical Museum, Kotlov's mansion on Courage Square, the house of the Forest Institute Professor Dmitry Nikiforovich Kaigorodov near the Silver Pond, a mansion near the square Courage, which the old-timers call "Chaliapin's house." And today the park of the Forestry Academy (formerly the Forestry Institute), as before, is a favorite walking place for residents of the Vyborg district. The most ancient of the northern outskirts of the region is the village of Pargolovo. The first mention of him is found in the census salary book for Novgorod for 7008 (1500). Then the village was called Parkola. The Pargolovskaya manor, donated by Peter I to his daughter Elizaveta Petrovna, included the following villages: Suzdalskaya Sloboda (1st Pargolovo), Malaya Vologodskaya Sloboda (2nd Pargolov, in terms of area, the district occupies the 4th place after Kurortny, Pushkinsky and Primorsky districts. The district has existed since 1978, it borders on Primorsky, Kalininsky, Petrogradsky, Central.In the north, the district borders on Vsevolozhsky and Kurortny districts.In the south, its border runs along the Neva between Liteiny Bridge and Bolshaya Nevka, with the Central District Vyborgsky is connected by Liteiny Bridge, which thrown across the Bolshaya Neva.

The territory of the district stretched out in the direction from the Vyborgskaya embankment and expanded to the north. The area of \u200b\u200bthe territory is 11.5 thousand hectares. The population is about 412 thousand people. In terms of population, the district ranks third in the city after the Kalininsky and Nevsky districts.

Despite the fact that the Vyborgsky district is one of the leading places in the city in terms of saturation with industrial enterprises, it is one of the greenest territories of St. Petersburg. More than a third of the district is occupied by parks and squares. For example, the area of \u200b\u200bSosnovka Park is about 322 hectares. The territory of the park of the Forestry Academy is 65 hectares, green spaces in Levashovo - 346 hectares. By the amount of greenery per capita, the district ranks first in St. Petersburg.

The industrial zone along the Bolshaya Nevka still exists today. There are more than fifteen large enterprises on the territory, including "Engine", "Meson", "Uranus", "Petersburg Textile", "Sistema", "Krasnaya Nit", "Compressor", "Krasny Mayak", "Teplopribor", "Svetlana", the first confectionery plant "Azart" and others. In the post-Soviet period, most of the industrial production on the territory of the Vyborg side was closed. The idea arose to create a large business center in their place. The implementation of large investment projects - the automobile plants Nissan and Hyundai are under construction, the first stage of the Petmol dairy plant (Unimilk group of companies), the Capital-M business center on Matrosova Street, the Sterkh customs and logistics terminal in Pargolovo were opened.

In Soviet times, another industrial zone, Parnas, appeared in the north of the Vyborg district, where industrial construction continues today. It houses such large enterprises as Baltika Brewery OJSC, Parnas-M CJSC. Among them, in addition to those already mentioned above, are the Parnassian Experimental Plant of Metal Structures, an asphalt concrete plant, LPO Vibrator, Enterprise No. 2 Petrokhimopttorg, Pepsi-Cola, Wimm Bill Dann. The Northern customs post, warehouses and terminals are also located here. There is also a business center of the same name at Parnassus. Concrete garages of collective parking lots are lined up along the railway and Engels Avenue. Industrial zone "Parnas" continues to develop rapidly, mastering the territories around it, crawling over the Ring Road.

The transport accessibility of the Vyborg District can be generally described as satisfactory.

Its central part is served by two metro stations - "Lesnaya" and "Vyborgskaya", the northern - by four stations: "Parnas", "Prospect Prosveshcheniya", "Ozerki", "Udelnaya". However, the territory of the region is too large, the existing metro stations are clearly insufficient. The main transport routes of the district, connecting its northern and southern parts, are: Bolshoi Sampsonievsky Avenue, Engels Avenue and Vyborgskoye Highway, as well as Lesnoy, Tikhoretsky Avenues and Kultury Avenue, which connect the northern and southern parts of the district. The western and eastern parts of the district are connected by Svetlanovsky, Lunacharsky, Prosvescheniya, Suzdalsky avenues. Owners of personal vehicles, leaving the area in the city center, are forced to stand idle in long traffic jams. The existing interchanges at the exit from the northern districts do not cope with the load, and at rush hour vehicles "stand" on Muzhestva Square, Svetlanovskaya Square and Lenin Square.

The immediate surroundings of the object - mainly manufacturing companies and residential buildings

· The level of traffic flow near the Assessment Object is high, and pedestrian activity is medium;

· Transport accessibility by public transport and road is characterized as good satisfactory.

3. Analysismarketcommercialreal estate

immovable commercial property

Commercial real estate survey. Commercial real estate traditionally includes the segments of office, retail, industrial, warehouse and hotel real estate. At the same time, retail real estate includes areas used for the placement of various services: catering, domestic, sports, entertainment, etc.

In accordance with the preliminary positioning of the appraisal object, this Report provides an overview of St. Petersburg commercial real estate in the following sectors:

Industrial and warehouse real estate;

Office real estate;

Land.

General situation. In the segment of commercial real estate, a progressive development is taking place: the share of unoccupied space is decreasing, and rental rates are growing in parallel with inflation. Analysts of all companies agree on this. Experts from various companies estimate the total supply of high-quality office space at about 2 million sq. M. The most meager estimates are in Knigh Frank St-Petersburg (1.65 million), while specialists from the management company Maris / CBRE (2.2 million) are more generous than others in this respect. All experts also agree that over the past the number of vacancies in the office segment decreased during the year (the current level of vacancies in companies is estimated at 11-13%). At the same time, the experts of Jones Lang LaSalle in St. Petersburg note that the office real estate market of St. Petersburg in 2011 remained a "tenant market", and the indicators of net absorption exceeded the volume of commissioning, as well as a decrease in the share of vacant space in 2012 from 19.7% to 13 ,5.

Stable demand for office premises, a decrease in the number of vacant premises led to an increase in rental rates. According to Maris analysts, class B offices have risen in price by 3-4%. In the operating class A office centers, rental rates have not changed significantly.

Experts estimate the current level of retail space occupancy in a similar way - at about 96% - which indicates a high degree of demand for this type of real estate in the city. Despite the modest share of vacant space, there is no rapid growth in rates in this segment either. Over the past year, retail places have risen in price in about the same way as offices - by 7-10%.

In 2012, there was almost no increase in the warehouse market (an increase of 1% of the available volume). Because of this, the occupancy rate of these areas increased significantly, developers urgently launched frozen projects. At the end of 2012, the capitalization rates in St. Petersburg are: for high-end office properties - 9.5-10%, for high-quality retail properties - 10-10.5%, for institutional quality assets in the warehouse real estate segment - 11-11 ,5%. In the regions, the minimum capitalization rates for high-end commercial real estate also underwent a slight downward correction.

Table 3-1 Minimum capitalization rates for quality commercial real estate in 2012

The development of commercial real estate segments in St. Petersburg is uneven. The most developed are the markets for office and retail real estate. The market for hotels and industrial and warehouse facilities lags far behind them.

Territorial distribution of commercial space. The territorial distribution of commercial space is highly dependent on their function. Office premises, both in business centers and built-in, as well as hotel facilities are mainly located in the central districts of St. Petersburg - Admiralteisky, Petrogradsky, Tsentralny. This is due to the proximity to business centers and tourist facilities.

Large shopping complexes are mainly located in residential areas (Primorsky, Vyborgsky, Moskovsky), which is determined by the presence of large land plots there and proximity to the bulk of consumers. Built-in retail premises are located mainly in the central districts (Admiralteysky, Vasileostrovsky, Petrogradsky, Central). High-quality industrial and warehouse complexes gravitate towards the main transport corridors of the city - the Seaport of St. Petersburg, the main northern corridor (Scandinavia highway), Moscow highway (M -10, direction to Moscow), Murmansk highway (M-18) and the western corridor (Tallinskoe highway, M-11).

According to GUION, the built-in industrial and warehouse premises for lease are mainly located in Vyborgsky, Nevsky, Krasnogvardeisky, Frunzensky and Kirovsky districts.

Market activity. According to analysts from the Center for Appraisal and Consulting of St. Petersburg, the market is experiencing some redistribution of forces in the investment segment and development - there is a "change of staff" due to the previous crisis. Since the second half of 2012, there has been a noticeable and rather sharp increase in the activity of buyers in the market at the expense of individuals and legal entities interested in acquiring relatively large commercial real estate objects (primarily buildings) for various purposes - retail, office, leisure and entertainment. This trend is largely due to the fact that Russia and, in particular, St. Petersburg in 2011 finally overcame the crisis and entered the real estate market at stable growth rates.

On the other hand, the world economy and financial markets continue to be in a fever due to the well-known events in the USA and Europe. This gives investors a certain amount of uncertainty about the future and rather serious inflationary expectations. In this situation, the clients got the motivation to purchase existing objects that have already been built and filled with tenants. Such real estate serves as an investment tool and insures itself against devaluing money. Indeed, according to the experience of 2008-2009, good quality properties filled with tenants, although they showed a slight decrease in the current indicators of occupancy and profitability, continued to generate cash flow.

In addition to investors, end buyers have become more active. This can be attributed to both the stabilization of the economy and the process of transferring the offices of a number of large companies, primarily the raw materials sector, to St. Petersburg. Very often, the strategy of such companies includes not leasing, but property ownership. They are aiming at acquiring modern high quality buildings in the best locations of the city and are willing to pay the appropriate price for them. In 2012, the real estate market was replenished with offers. The consequence of the crisis was that a number of owners, for one reason or another, put their objects up for sale. In addition, it was in 2012 that pledged objects of banks began to be sold. Banking structures have come to the critical point when it was necessary to make a decision regarding such assets. And often it was in favor of the sale, although sometimes at a significant discount. It should be noted that in this collateral mass, high-quality objects with a good location and level of construction are rare. Now buyers, who are closely monitoring the current situation, have the opportunity to enter into negotiations with banks and buy these collateral assets.

In general, the activity that began in the summer of 2012 has already led to some significant deals for the market. For example: the Jensen Group's acquisition of the Passage department store as a collateral asset, the sale of the Coliseum cinema, negotiating the sale of the Gallery shopping center and a number of completed or open transactions with well-known buildings in the city. This trend of the autumn-winter period will continue.

At the moment, there is a certain redistribution of forces in the investment segment and development. Some companies are increasing their influence, others are losing ground or leaving the market, and new players are also emerging.

If there are no global financial shocks in the future, then by the end of 2012 we will observe a fairly steady growth of the market - both rental and investment deals market.

Table 3-2. Return on investments in various segments of commercial real estate in St. Petersburg

Consumer market. In January-November 2012, compared to the same period last year, the retail trade turnover in the city increased by 8.3%, in the region - by 2.7%. Public catering turnover in the city increased by 2.4%, in the region - by 7.9%. In January-November of this year. the wholesale trade turnover in the city decreased by 10% compared to the same period last year, in the region increased by 1.7%.

Labor market. In October 2012, compared to October 2011, the number of jobs replaced in organizations (excluding small businesses) increased by 1.0% in St. Petersburg, and by 0.1% in the Leningrad Region. According to the data of the committees on labor and employment of the population of St. Petersburg and the Leningrad region, the number of officially registered unemployed from November 2011 to November 2012 decreased in the city by 22%, in the region - by 16% and amounted to 10.2 and 4.2 thousand at the end of November 2012, respectively . man. The level of registered unemployment at the end of November 2012 was 0.4% in St. Petersburg, 0.5% of the economically active population in the Leningrad Region, against 0.5% both in the city and in the region at the end of November 2011.

Construction and investment. In January-November 2012, the volume of investments in fixed assets in large organizations decreased in St. Petersburg as compared to January-November last year by 10.0% and amounted to 176.2 billion rubles, in the Leningrad region increased by 4.0% and amounted to 194.1 billion rubles. At first current year the volume of work on the type of activity "construction" in St. Petersburg also decreased compared to the corresponding period last year by 6.7% and amounted to 299.4 billion rubles, in the Leningrad region increased by 10.1% and amounted to 101.4 billion rubles.

The commissioning of residential buildings in January-November of this year, both in St. Petersburg and in the Leningrad Region, increased by 2.8% and 11.3%, respectively, compared to the corresponding period of last year, and amounted to 1,778.3 thousand sq. ... m of living space in St. Petersburg and 894.0 thousand sq. m. in the Leningrad region. In November of this year. in St. Petersburg, the production building of Ferromet-Invest LLC, the production and warehouse complex of Trader LLC, the production and technical center for the repair and sale of cars of Phoenix-Motors LLC, a warehouse complex with a parking lot of UNISTO CJSC were put into operation, warehouse of building materials CJSC "GRAN", 1 transformer substation, 3 gas stations, a preschool educational institution in the Pushkin district, a restaurant in the Kurortny district, a business center in the Moskovsky district, a social complex in the Kirovsky district and others.

In the Leningrad region, a greenhouse farm LLC "Vector", a selection and seed-growing center Forest Management of the Leningrad Region, a wood processing shop "Pallet-Plus", 2 boiler houses in Vyborg and Vsevolozhsky districts, 1 gas station, a preschool educational institution and a federal training center (Stage 1) in the Vsevolozhsk region and others.

4. Analysisthe bestandmosteffectiveuselandplotasconditionallyfree

The choice of the optimal use of the land.

An analysis in order to select the option of the best and most efficient use of land is usually carried out in two cases: when it is necessary to separately evaluate a plot of land, and also when choosing objects for comparative analysis. The concept of "best and most effective use" is defined as the probable use of the assessed object with maximum return, and the conditions of physical, legal, financial feasibility of such actions are indispensable. First, it is necessary to provide a list of functions that can be carried out on this piece of land without any restrictions. The most effective use is determined by the interaction of a number of factors:

Legal Feasibility: Considering uses that are permitted by zoning regulations, private initiative restrictions, historic zones and environmental laws

Physical Feasibility: Considering locally physically feasible uses.

Financial feasibility. Consideration of the fundamental possibility of providing financing for the project by attracting borrowed capital.

Maximum efficiency. Considering which economically viable use will generate the maximum net income or maximum present value

Analysis of the use of the site as conditionally free. Legal Feasibility. The main, auxiliary and conditionally permitted uses of land plots and capital construction objects on the territory of this zone, established in Appendix 3 to the Law of St. Petersburg "On the Rules of Land Use and Development of St. Petersburg" dated 16.02.2009, No. 29-10 (part II) are presented in the following table:

Table 4.1 - Types of permitted use of the research object

Objects of the types of use marked in paragraph 3 of this article with the sign (*) may be located only on land plots directly adjacent to the red lines of streets, roads, squares, driveways, embankments, boulevards, which are common areas, with the exception of intra-quarter driveways, in the absence legal norms prohibiting their placement.

The minimum area of \u200b\u200bland plots.

It is allowed not less than the amount of the area occupied by an existing or located on its territory capital construction object, and required in accordance with these Rules of the area of \u200b\u200bgreen areas, the area for the placement of parking spaces, driveways and other required in accordance with these Rules and technical regulations of auxiliary facilities intended for its maintenance and operation.

Territory utilization factor.

The utilization rate of the territory is defined as the ratio of the maximum total area of \u200b\u200bapartments that can be located on the territory of the land plot to the area of \u200b\u200bthe land plot. The following limiting maximum values \u200b\u200bof the coefficient of the use of ffff of the territory are established: - for plots of mid-rise and multi-storey residential buildings up to 9 floors - 1.7; - for plots of multi-storey residential buildings of 9 floors and above - 2.3.

Minimum indents of buildings, structures, structures from the boundaries of land plots.

The minimum indents from the boundaries of land plots of walls of buildings, structures, structures without windows: at a distance that provides normative insolation and illumination at a height of 6 meters or more at any point, along the boundaries of land plots conjugated and separated by common areas or along the boundaries of territories on which land plots are not formed; in the case of adjoining territories (land plots) located within the boundaries of territorial zones, the town planning regulations of which do not establish the types of permitted use, for which it is necessary to ensure normative insolation and illumination, a minimum offset from the boundaries of the sites that do not coincide with the red lines is allowed, 0 meters.

Minimum indents from the boundaries of land plots of walls of buildings, structures, structures with windows: at a distance that provides standard insolation and illumination at a height of 6 meters or more at any point, along the boundaries of adjacent land plots, along the boundaries of land plots separated by common areas, or along the borders of territories on which land plots have not been formed, but not less than 10 meters;

In the case of adjoining territories (land plots) located within the boundaries of territorial zones, the town-planning regulations of which do not establish the types of permitted use, for which it is necessary to ensure normative insolation and illumination, a minimum offset from the boundaries of the sites that do not coincide with the red lines is allowed, 3 meters.

The minimum indents from the boundaries of land plots of walls of buildings, structures, structures along the boundaries of land plots coinciding with the red lines of streets and driveways are established: for residential buildings with apartments on the first floors and educational and upbringing institutions facing the main streets - 6 meters; for residential buildings with apartments on the ground floors and educational and training institutions facing other streets and public thoroughfares - 3 meters; for other buildings - 0 meters.

Maximum protrusions beyond the red line of parts of buildings, structures, structures.

In accordance with Article 7 of Part II of these Rules: allowed: for balconies, bay windows, canopies - no more than 3 meters and above 3.5 meters from the ground level.

The maximum height of buildings, structures, structures on the territory of land plots.

The maximum height of buildings, structures, structures on the territory of a land plot located in built-up areas is not more than 30% higher than the average height of existing buildings in the quarter, namely, 30 meters for the development of built-up areas is not established), unless another value is indicated on the diagram of the boundaries of the town planning regulations in terms of the maximum height of buildings, structures and structures. In this case, if the restrictions related to the same territory coincide, the minimum limiting parameters apply, with the exception of the cases of local dominant placement.

The minimum share of green areas.

Table4-2. The minimum percentage of green areas for plots with different uses.

When forming land plots of multi-apartment residential buildings, parts of the adjoining open green spaces (up to 30% of their area) required by these Rules can be allocated for unification into independent land plots of public squares (gardens)

The minimum number of parking spaces.

The minimum number of parking spaces for storing individual vehicles on the territory of land plots is in accordance with Article 10 of Part II of the Rules: The minimum number of parking spaces for storing individual vehicles on the territory of land plots of polyclinics (type of use code 10910; 10840; 10950) - 1 parking space for 20 beds, as well as 1 parking space for 5 employees.

Minimum number of places at loading and unloading yards

The minimum number of places at loading and unloading yards on the territory of land plots - in accordance with Article 11 of Part II of the Rules: determined from the calculation: one place for objects with a total area of \u200b\u200b100 square meters up to 1,500 square meters and plus one place for every additional 1,500 square meters of the total area of \u200b\u200bobjects - for trade facilities (code 105), public catering facilities (code 106), industrial facilities (code 121), for enterprises for primary processing, packaging of agricultural products and maintenance of agricultural production (repair, storage) (code 131.2); one place for objects with a total area of \u200b\u200b100 square meters to 1250 square meters and plus one place for every additional 1250 square meters of the total area of \u200b\u200bobjects - for warehouse objects (code 122).

Minimum storage space

1. The minimum number of places on loading and unloading yards on the territory of land plots is determined on the basis of: one place for objects with a total area of \u200b\u200b100 square meters to 1500 square meters and plus one place for every additional 1500 square meters of the total area of \u200b\u200bobjects - for trade objects ( code 105), public catering facilities (code 106), industrial facilities (code 121), for enterprises for primary processing, packaging of agricultural products and maintenance of agricultural production (repair, storage) (code 131.2); one place for objects with a total area of \u200b\u200b100 square meters to 1250 square meters and plus one place for every additional 1250 square meters of total area for warehouses (code 122).

2. The area of \u200b\u200bparking spaces for storage (technological sludge) of freight vehicles is determined at the rate of 95 square meters per vehicle (including passages); when parking lots are adjacent to the carriageway of streets and driveways and the longitudinal arrangement of cars - 70 square meters per car.

Maximum height of fences of residential land plots

The maximum height of fencing of residential land plots is in accordance with article 13 of Part II of these Rules:: along high-speed highways - 2.5 meters; along streets and driveways - 1.8 meters; between adjacent building plots - 1.8 meters without agreement with adjacent land users. More than 1.8 meters - as agreed with adjacent land users. For residential areas, the height of 1.8 meters may be exceeded, provided that this does not violate the volumetric-spatial characteristics of the surrounding buildings and landscape, the norms of insolation and natural illumination.

Maximum hazard class

The maximum hazard class (according to the sanitary classification) of capital construction objects located on the territory of the zone is V (except for bus stations and intracity transport facilities). In the rules of land use and development of St. Petersburg, there are more than 100 types of permitted use of land plots. However, real estate professionals name significantly fewer segments of the land market. The characteristics of the plots that determine their other difference are recognized as pricing factors that affect the price of an object within a similar group, segment.

Segments allocated by the market for their functional purpose include land plots intended for use for:

Implementation of low-class dacha villages and gardening;

Residential low-rise buildings (town houses and cottages);

Residential high-rise buildings;

Hotel development;

Production and warehouse function;

Parking lots;

Open parking lots and parking lots;

Sports facilities;

Since we are talking about segmentation by the market, the land plots intended for construction with socially oriented objects and communal facilities belong to the group of investment-unattractive objects. For the purposes of analyzing the option of the best and most efficient use of the property by a typical investor, further segmentation of this group is meaningless.

The rules for land use and development of St. Petersburg also indicate options for using land plots that are limited in circulation or completely withdrawn from circulation. When choosing the option of the best and most efficient use of a property by a typical investor, such land plots are not analyzed. The types of functional use of the investigated land plot presented in the rules of land use and development of St. Petersburg can be grouped as follows:

Table 4-3 - Types of permitted functional use of the investigated land plot

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International engineering is an activity for the provision of a range of services of an industrial, commercial and scientific and technical nature, provided by both specialized engineering and consulting firms and industrial, construction and other companies. Engineering can be defined as a set of intellectual activities with the ultimate goal of obtaining the best results from capital investments or other costs associated with the implementation of projects for various purposes, due to the most rational selection and effective use of material, labor, technological and financial resources in their unity and interconnection , as well as methods of organization and management, based on advanced scientific and technical achievements and taking into account the specific conditions and factors of project implementation.

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Competition manages the selfish motives of enterprises and resource suppliers in such a way as to stimulate public interest in the efficient use of scarce resources. It is in the interests of society to use scarce resources at the lowest cost, that is, by the most effective methods. It is personal gain, encouraged and guided by a competitive market system, that stimulates an appropriate response to recognized changes in the needs of society. Enterprises seeking to get higher profits, on the one hand, and resource suppliers seeking more monetary rewards for them, on the other, agree among themselves to implement the very changes in the distribution of resources, and therefore in the structure of products, which society requires time. Competition controls or directs the self-interest motive in such a way that it automatically and involuntarily contributes to the best interests of society. Ultimately, the invisible hand concept is that firms maximize profits only if the social product gains public acceptance.

The theory of FSA also assumes the use at the creative stage of methods of collective creativity aimed at generating new ideas and proposals (some of them will be discussed below). All ideas put forward at this stage are systematized according to the analyzed functions. As a result of collective discussion, several options for new technical solutions for the implementation of functions are developed. In a number of cases, on the basis of the most effective proposals, a theoretical version of the product is built that best implements a set of functions. All other options are compared with it. For this comparison, a positive-negative matrix is \u200b\u200bused (Table 8.2).

The high complexity of calculations associated with the implementation of both principles leads to the need to use methods of mathematical modeling. Two ways are possible here. One way is to select the best design options using a general economic and mathematical model that simultaneously takes into account all the requirements of the systems approach. However, the large dimension and complexity of such a model make it practically unrealizable. Therefore, the most correct is another way, which consists in calculating the comparative economic efficiency using the system of models. In this regard, the work proposes a multilevel system of interrelated economic and mathematical models. Each level of this system is tied to certain stages of technical training.

The most difficult and in need of a high-quality formulation of work is the level of the self-supporting unit of the workstation of the accountant, at which the primary accounting is formed. We will not dwell on the need for the reliability of primary accounting at an industrial enterprise. We only point out that the basis for the implementation of the requirements of internal cost accounting as the main method of managing the activities of structural divisions of associations and enterprises is the correct organization of primary accounting. It is at the stage of collecting and registering initial data on business operations, that is, at the grassroots level - the self-supporting unit, that the best conditions should be created for obtaining truthful accounting information, deviations from the established standards and their culprits for the consistent implementation of the principles of effective functioning should be identified.

To assess research and development work that have no analogues, the selective method of forecasting and prospective planning (mass media) is also used. It is based on a systematic approach, as a result of which the solutions of particular problems, for example, the improvement of individual parameters of machines, are performed in such a way as to contribute to the best possible achievement of the general goal.This method uses elements of a number of well-known expert-evaluation systems, such as Patern , Delphi, network planning and management, and economic and mathematical methods. The SMP allows, in particular, to choose from several possible options for solving the problem, the optimal one, taking into account the probability of its implementation, costs, efficiency, development time, and to establish the most effective areas of its application. The selective method of forecasting and control includes a number of stages, the most important of which are the following:

We now turn to the question of how to conduct research based on

Holders of the patent RU 2543315:

The invention relates to computer technology, can be implemented on modern high-speed computers and used, for example, in the selection effective options in search, recommendation systems, decision support systems, Internet networks, systems for automatic classification of data packets, and in other related areas. The implementation of the claimed invention may include storing information on physical media, magnetic disks, network storage of information, processing it on a computer and providing the resulting set of effective options to the end user in any form available to him.

Before presenting the invention, for convenience and unambiguous understanding, it is advisable to give the decoding and definitions of the designations and / or terms used below.

A search engine is a computer program designed to search for information on the Internet. The search is performed on the basis of an arbitrary text query generated by the user. Search results are presented to the user sorted in accordance with a certain characteristic of relevance to the query. Examples of search engines are Bing, Google, Yahoo, Yandex.

A recommendation system is a computer program that selects from the entire set of presented alternatives (options) those that may be most interesting to a specific user, based on a number of characteristics, for example, a query entered by the user in a search engine. It should be noted that in most cases, recommender systems present the result either as a set of recommended options, or as a ranking of all or part of the presented options. Thus, the methods of processing and transforming information within the framework of recommender systems work in related areas, such as, for example, the problem of assessing the efficiency of enterprises, etc.

The principle of superposition (in this context, in contrast to the well-known principle of superposition in physics) consists in the sequential exclusion of variants from the original set using procedures that may be different at each stage of exclusion. An example of procedures is given in Appendix 1. At the first stage, the elimination is made from the entire initial set of options, at the second stage, the input set is the effective options identified at the first stage, and so on.

Effective ("good") elements (options) are those elements that are the best, most preferable, most useful according to the given parameters for solving specific problems in which it is necessary to rank options, and to meet the information needs of users (people, specialists, agents ).

Ineffective ("bad") elements (options) are those elements that certainly never (under no circumstances) can be used to solve specific problems, since there are more preferable options for their solution.

The value of efficiency, with the help of which the rules for selection and ranking of options are constructed, is set by an expert.

Most search engines have data storage and processing facilities that contain such performance (relevance) estimates for large representative sets of queries and search results for these queries. In such tools, a query and search results for it (variants) are represented by their sets of criteria and an assessment of the relevance of search results, set by experts.

There are various formal criteria for assessing the relevance of a search element to a search query, given constructively (for example, the frequency of the use of a word in the text or the TF-IDF criterion, which is the frequency of the use of query words in the text, taking into account the degree of importance of each word). Note that such formal criteria are more likely algorithms by which existing search engines actually perform a search than independent criteria that evaluate the results of this search. The scores calculated by such formal criteria may still be very different from the relevance scores given by experts.

At the moment, there are three main methods by which the selection and ranking of options occurs.

There is a known method for selecting and ranking options, which consists in the fact that each option is assigned an absolute assessment of the degree of "importance", using values \u200b\u200bfor several criteria. The most common way is to build a regression.

In addition, the McRank classification method can be used to rank options, the essence of which is to calculate the so-called "expected relevance" for each request-document pair as a function of the probability of belonging to the relevance classes obtained as a result of the classification. As a result of calculating the "expected relevance", the ranking of the "request-document" pair within each query occurs in descending order of the "expected relevance" (L. Ping, C.J.S. Burgess, K. By - McRank: Learning ranking using multivariate analysis and gradient acceleration enumeration. NIPS. Curran Associates. 2007-).

There is a known method for choosing alternatives, which consists in pairwise comparison of two options in order to identify the best of them. On the basis of the formation of such relations, an order is built with the help of which the options are selected.

An example of a known method is the support vector machine, which consists in translating the original vectors into a space of a higher dimension and searching for separating hyperplanes with a maximum gap in this space (K. Cortes, Vapnik V.N., Support vector machine, "Machine Learning" magazine, 20, 1995 - [C. Cortes, Vapnik VN; "Support-Vector Networks", Machine Learning, 20, 1995]), as well as other methods such as:

RankNet (Microsoft Bing search engine, C.J.S. Burgess, T. Shakde, et al. Teaching Ranking Using Gradient Descent, ISML, 2005: 89-96 -), the essence of which is to use a "neural network" and a probabilistic cost function for ranking search results,

RankBoost (J. Freund, R. Jere, R. E. Chapae and J. Singer. An efficient search acceleration algorithm for combined preferences, Journal of Machine Learning Research, 4: 933-969, 2003 -), which is based on the procedure sequential construction of a composition of machine learning algorithms for classifying pairs of documents.

FRank (M. Tsai, T.-Y. Liu, et al. Frank: A Ranking Method with Fidelity Loss Function, SIGIR 2007 - [M. Tsai, T.-Y. Liu, et al. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007]), which is a modification of the RankNet method, but instead of entropy values, the function of distribution accuracy is used as a cost function, and others.

There is a known method for choosing alternatives, which consists in a list comparison of options. In this case, the filtering of the entire set of alternatives is performed according to the specified rules.

Examples of this method include:

1. A method for constructing trees, minimizing the penalty function ListNet, in which a probability space is introduced on a set of permutations. The entropy function on the entered space is used as a loss function. (Zhe Cao, Tao Kin, Tai-Yan Liu, Ming-Feng Tsai and Hang Li. Teaching Ranking: From Pairwise to List Approach, 2007-),

2. The method of list comparison of RankCosine variants, which uses a loss function based on the similarity of the cosine of the angle between the ranked list and the original list of the training sample, to rank the search results (T. Keene, H.-D. Zhang, M.-F. Tsai, D.-S. Wang, T.-Ya. Leeu, H. Lee: Request-Dependent Loss Functions for Information Retrieval. Inf. Process. Manage. 44 (2): 838-855, 2008 - [T. Qin, X.-D. Zhang, M.-F. Tsai, D.-S. Wang, T.-Y. Liu, H. Li: Query-level loss functions for information retrieval. Inf. Process. Manage. 44 (2): 838-855, 2008)],

3. The AdaRank ranking method, in which the AdaBoost machine learning algorithm is used to construct the ranking function, which constructs a linear combination of classifiers to improve the ranking model. (Yu Hu, X. Lee. AdaRank: a brute force algorithm for information retrieval. SIGIR 2007 -),

4. The SoftRank ranking method, the essence of which is direct optimization of nonsmooth ranking metrics, (Mike Taylor, John Guyver, Steven Robertson, Tom Minka. SoftRank: Optimization of nonsmooth metrics, 2008 -) and others.

All these methods show a fairly high accuracy in their highly specialized fields.

The disadvantages of the known selection methods are:

The use of complex selection procedures when working with large amounts of data, which leads to a significant increase in computational complexity;

Low accuracy in the selection and ranking of options using a large number of criteria or / and with a large number of options.

As a rule, on large amounts of data, the decision tree search method is used. It consists in building a sequence of threshold procedures with the help of which options are selected.

The disadvantage of the decision tree search method is the low reliability of the results, since the choice of threshold procedures as a method for selecting and ranking options is not always justified (effective). In addition, not one criterion, but a whole group (their combination) can be used simultaneously to select or rank options, which is not taken into account in the decision tree search method. Often, it is impossible to select or rank the entire list of options according to any one criterion (several criteria). In this regard, in order to rank variants with high accuracy, it is necessary to construct a large number of such trees, and the results of their work must be aggregated.

Known methods are according to RF patent No. 2435212 "Collecting data on user behavior in web search to increase search relevance", RF patent No. 2443015 "Ranking functions using a modified Bayesian query classifier with incremental update", according to RF patent No. 2367997 "Advanced systems and methods of ranking documents based on structurally interrelated information ", which consist in collecting additional information, namely in using a Bayesian classifier, collecting information about user behavior, information about the structural relationships of documents, which is used to select and rank options. The disadvantage of the known methods is the complication of existing methods of selection and ranking of options by adding new criteria.

The closest in technical essence and the achieved result is a method for calculating a temporary weight for a search result, which consists in identifying a user event corresponding to a search result, and the user event has an event start time, an event end time and an event duration; determining the current time; and determining the time weight for this search result based on the temporal proximity of the current time to the user event. The method assumes that the time weight changes over time, increases exponentially as the current time approaches the start time of the event, is constant over the duration of the event, peaks at a point in time during the duration of the event, and decreases exponentially when the current time is removed. from the end time of the event. The method is designed to search for information on the Internet using a temporary weight to rank search results. (RF patent No. 2435213, IPC G06F 17/30, publ. 27.11.2011).

The disadvantage of the known method, as well as similar existing technologies for searching on demand on the Internet, is that they, as a rule, use "rough" algorithms for selection and ranking, i.e. algorithms with linear computational complexity O (n), where n is the number of options. As a rule, this complexity is achieved by the fact that the developed selection and ranking rules are simplified (more precisely, they are roughened) in order to provide an acceptable level of complexity. In this case, the result achieved with such methods is of lower quality.

The technical problem to be solved by the claimed invention is to create a new method for better selection and ranking of effective options, providing a high selection rate and high accuracy of results.

The technical problem posed is solved by the fact that according to the proposed invention, the method for selecting and ranking effective options according to the first embodiment consists in the fact that the criteria for assessing the relevance of a variant to a search query are preliminarily formed and a finite number of options or a set of procedures for selecting and ranking options and the sequence of their execution for selecting the options evaluated as the most effective, evaluating each of the options according to their relevance to the search query criteria, on the basis of which the options are ranked by assigning a rank to each of them from the condition of meeting the greatest number of criteria in descending order; the selection and ranking of the options are sequentially carried out by the superposition method in at least two stages, if the number of options in the remaining group of options corresponds to a predetermined finite number of options for selection or all specified selection procedures are used, the selection of options and their ranking is stopped and options from the selected group are evaluated as the most effective, if the number of options in the remaining group of options does not correspond to a predetermined finite number of options for selection, the selection of options and their ranking continues, while the selection of options, their ranking and exclusion are carried out until the specified number of options is reached or until all specified selection procedures will not be used and the selected group of options is assessed as the most effective.

The claimed method according to the first embodiment is characterized by the following additional essential features:

At the second and subsequent stages, criteria for evaluating the search query are formed, on the basis of which the options are ranked and the options are selected from the remaining array processed at the previous stage by the superposition method using methods whose computational complexity is not less than quadratic O (n 2) and exclude the next group of options with a lower rank.

The technical problem posed is solved by the fact that according to the proposed invention, the method for selecting and ranking effective options according to the second embodiment consists in the fact that the criteria for assessing the relevance of the option to the search query are preliminarily formed and a finite number of options for selection are set, evaluated as the most effective, each of variants by relevance to the search query criteria, on the basis of which the variants are ranked by assigning a rank to each of them from the condition of meeting the greatest number of criteria in descending order; the selection and ranking of options are sequentially carried out by the superposition method in at least two stages, if the number of options in the remaining group of options corresponds to a predetermined finite number of options for selection, the selection of options and their ranking is stopped and options from the selected group are estimated as the most effective if the number options in the remaining group of options does not correspond to the predetermined finite number of options for selection, the selection of options and their ranking continues, while the selection of options, their ranking and exclusion are carried out until a specified number of options is reached, the selected group of options is assessed as the most effective ...

The claimed method according to the second embodiment is characterized by the following additional essential features:

At the first stage, the selection of options is carried out in the presence of a large number of them by the superposition method using the selection and ranking methods characterized by linear computational complexity O (n), and the group of options that have the lowest rank are excluded;

At the second and subsequent stages, criteria for evaluating the search query are formed, on the basis of which the options are ranked and the options are selected from the remaining array processed at the previous stage by the superposition method using methods whose computational complexity is not less than quadratic O (n 2) and exclude the following group of options with a lower rank;

the method additionally defines a set of procedures for selecting and ranking options and the sequence of their implementation.

The technical problem posed is solved by the fact that according to the proposed invention, the method for selecting and ranking effective options according to the third embodiment consists in the fact that the criteria for assessing the relevance of a variant to a search query are preliminarily formed and a set of procedures for selecting and ranking options and the sequence of their execution for selecting options are set, evaluated as the most effective, evaluate each of the options according to their relevance to the search query criteria, on the basis of which the options are ranked by assigning a rank to each of them from the condition of meeting the greatest number of criteria in descending order; the selection and ranking of variants are sequentially carried out by the superposition method, at least in two stages, the selection of variants, their ranking and elimination are carried out until all the given selection procedures are used and the selected group of variants is assessed as the most effective.

The claimed method but the third embodiment is characterized by the following additional essential features:

At the first stage, options are selected if there are a large number of them by the superposition method using selection and ranking methods characterized by linear computational complexity O (n), and the group of options that have the lowest rank is excluded

At the second and subsequent stages, criteria for evaluating the search query are formed, on the basis of which the options are ranked and the options are selected from the remaining array processed at the previous stage by the superposition method using methods whose computational complexity is not less than the quadratic 0 (n2) and exclude the next group of options with lower rank.

Additionally, a finite number of selection options are specified, which are evaluated as the most effective;

To select the most effective group of options, additional selection and ranking methods and the sequence of their execution are set, and the selection and ranking are repeated.

The technical result, the achievement of which is ensured by the implementation of the entire claimed set of essential features of the method, consists in increasing the speed and accuracy (reliability) of the selection of effective options in search, recommendation systems due to the possibility, using the principle of superposition, to regulate the complexity of procedures for identifying effective options.

The essence of the invention is illustrated by Fig. 1, which shows a flow diagram for the implementation of the proposed method, where:

1 - initial set of options (many different options);

2 - the procedure for eliminating ineffective objects at the first stage using approximate methods;

3 - a set of options left after the first stage of selection;

4 - exclusion of ineffective options using exclusion procedures;

5 - consistent application of procedures for the elimination of ineffective objects using approximate methods;

6 - a subset of options that does not contain ineffective options;

7 - the operation of ranking the group of options obtained at step 6 using both approximate and exact methods;

8 - the operation of assigning all ineffective options to the lowest rank and adding these options to the final list after the ranked options;

9 - providing the final ordered group of options to the end consumer;

10 shows a group of ineffective options cut off using a sequential superposition of elimination procedures.

The proposed method is based on the superposition method, which consists in the sequential elimination of previous options using some procedures, which may be different at each stage of elimination.

The inventive method is carried out as follows (figure 1).

A large set of options 1 exists or is being formed, which may include ineffective options.

The term "large set of options (search elements)" is considered within the framework of the concept of "Big Data", which appeared in connection with the development of information technologies and includes approaches to processing huge volumes of heterogeneous information.

A large set of options (search elements) within the framework of this concept is understood as a structured or unstructured dataset of a huge volume and significant variety.

In order to exclude ineffective options and select the most effective options, the criteria for assessing the relevance of a variant (search element) to a search query are preliminarily formed and, if necessary, a finite number of options (search elements) are set for selection, assessed as the most effective (as the most appropriate to the criteria for assessing relevance search query). Next, each of the options (search elements) is assessed according to their relevance to the search query criteria, on the basis of which the options (search elements) are ranked by assigning a rank to each of them from the condition of meeting the largest number of criteria in descending order. The selection and ranking of variants (search elements) is carried out sequentially by the superposition method in at least two stages.

The method can be defined differently - a set of used selection and ranking methods and the sequence of their application can be specified.

At the first stage, options are selected from a large number (item 2 of Fig. 1) by the superposition method using selection and ranking methods characterized by linear computational complexity O (n).

For this operation, well-known methods with linear computational complexity O (n) can be used, such as, for example, the relative majority rule, Borda's rule, the rule of overthreshold choice, and others. The most complete list of selection rules is given in Appendix 1.

As a result, two groups of variants are formed: a group of variants 10 having the lowest rank, and a group of variants 3 to be further analyzed.

The group of variants 10, which have the lowest rank, are excluded (item 4 of Fig. 1).

At the next stage, the criteria for evaluating the search query are formed, on the basis of which the options are ranked. The selection of options from the remaining processed array is carried out according to the superposition method (item 5 of Fig. 1) using methods whose computational complexity is not less than the quadratic O (n 2).

For this operation, well-known methods can be used, the computational complexity of which is at least quadratic O (n 2), such as, for example, the minimal nondominated set, Richelson's rule, or rules based on the construction of a majority or tournament matrix (see Appendix 1).

The selection of options and their ranking is stopped and the options (search elements) from the selected group are evaluated as the most effective or promising when all the used (specified) methods of selection and ranking are fulfilled, or if the number of options in the remaining group of options corresponds to a predetermined finite number of options (search elements) for selection. The selection of options and their ranking can be carried out repeatedly by specifying additional methods of selection and ranking, as well as the sequence of their execution.

Otherwise, the selection and ranking continue to be carried out as described above (items 7 and 8 of Fig. 1). That is, the group of options 6 is ranked using the ranking operations 7, if necessary, you can add (item 8 of Fig. 1) options to it from the group of ineffective options 10. The selection of options (search elements), their ranking and elimination are carried out (item 9 of FIG. .1) until the specified number of options (search elements) is reached or until all the used (specified) methods of selection and ranking are fulfilled, and the selected group of options 9 (search elements) is assessed as the most effective (promising). Thus, there is a selection and ranking of effective options, their ranking and provision of these options to the final consumer.

The superpositional approach is used when it is impossible to unambiguously determine by one criterion which options are effective and which are not. A distinctive feature of the method is the ability to identify, in the presence of a large number of criteria from a large number of options, those options that are effective, as well as the ability to adjust the computational complexity of the proposed method. The inventive method allows you to go from complex mechanisms for identifying effective options to composite ones, which are a combination or superposition of simpler procedures. The results of the previous stages of selection and ranking are processed in the following stages of the method.

In addition, in the claimed method for ranking options, not one criterion can be simultaneously used, but a whole group of criteria (their combination), which is not taken into account, for example, in the well-known decision tree search method, which uses the simplest threshold procedures, the choice of which is not always justified.

In contrast to the known methods, the superposition method is quite flexible and allows you to vary the number of stages in the selection method.

The superposition approach excludes the possibility of losing effective options in the case of using approximate methods. After the sequential composition of the exclusion stages, the remaining options are selected and ranked. All ineffective options that were excluded before the ranking procedure will have the lowest (worst) rank and will be selected (offered) for solving problems in the last place.

Approximate methods, which are used to reduce the number of options at high speed, mean selection and ranking rules with linear computational complexity O (n). Such rules (methods) should use (read) the values \u200b\u200bof the parameters of each option (alternative) only such a number of times k that does not depend on the number of options (alternatives) n, and is significantly less than n. In the fastest (ideal) case for the rule with complexity O (n), each option is used only once. The rule has the ability to determine whether a variant is effective or not, based only on the data of this one variant, without comparing it with each of the other variants. For example, for the rule of discarding ineffective options, with values \u200b\u200b"below average" for some parameter (for which the values \u200b\u200bare, the higher the better), it is required to count the value of each option only 2 times: the first time to calculate the average, and the second time to determine if this option is higher or lower than the average. This rule applies to rules with linear computational complexity O (n).

Thus, the use of selection and ranking methods characterized by linear computational complexity O (n) provides a significant increase in the speed of selection of effective options in search and recommendation systems.

However, initially, it is rather difficult to use subtle (exact) methods for the entire range of options due to the large number of options. When using approximate procedures for cutting off ineffective options, the number of different options decreases, which ultimately leads to the possibility of using more subtle methods for selecting and ranking the remaining options.

Thin (exact) methods that are used when there are a small number of options are selection and ranking rules, the computational complexity of which depends solely on the number of times each option is used. There are rules that use pairwise "distances" between options (alternatives), in special scales. Such rules must enumerate all other options for each option, i.e. perform (n times n) actions, the computational complexity is quadratic. There are also rules for comparing each option with all sorts of other options in order to more accurately determine the position of this option in relation to the rest. The computational complexity of such rules is even higher. We can say that rules with complexity, starting with a quadratic O (n 2), cannot be applied on the full set of options (in the millions) when solving the problem of search and ranking on the Internet, and in similar problems in other areas of activity, since computing the complexity of these rules strongly depends on the number of options available in the set.

Thus, the use of methods, the computational complexity of which is not lower than the quadratic O (n 2), provides a significant increase in the accuracy (reliability) of the selection of effective options in search and recommendation systems.

The advantage of the method is that it becomes possible to regulate the computational complexity of the procedure for identifying effective options. This means that if the application of some procedures required huge computational resources on a large amount of data, then after the successive elimination of variants, the same procedures on the remaining subset can work rather quickly. In other words, by setting a certain limit on the amount of computational resources used to execute the method, it is possible to establish the number of stages that can be used to cut off obviously ineffective options using fast approximate methods, after which one can use rather laborious procedures that reveal effective options with sufficiently high precision. This is the control of the computational complexity of the method.

The inventive method can also be applied in the task of teaching ranking, that is, the task of choosing options with previously known estimates of their usefulness by criteria. The method allows to form, according to a previously known degree of utility (efficiency) of some options, rules for their selection and ranking (a set of used selection and ranking methods, as well as the sequence of their application), in accordance with which the selection and ranking of other options can be made, about the degree of utility ( efficiency) of which nothing is known.

The inventive method can be carried out using known hardware and software. The implementation of the proposed method includes:

1. Collection and storage of data /

2. Data processing, selection and ranking of options.

3. Providing results to the user.

Collection and storage of data. At this stage, the collection and storage of the necessary information about the existing options takes place. Information about options can be collected from existing data sources, for example, from various existing information systems, websites, web services, other data servers, computer files, i.e. from all sources that store information about variants in a format suitable for further processing. Data collection can be performed using existing software that extracts data from external sources (for example, ETL systems or tools for collecting the content of web pages on the Internet), or implemented on a computer using any programming language, in particular, a programming language C, C ++, C #, Java, Python, PHP and many more. Information storage can be carried out both on a server or a group of servers using existing platforms that store data, and on any storage medium from which it is possible to further read the available information. Also, information storage can be carried out directly in the computer's RAM in the case when there is no need to permanently store information.

Data processing, including, according to the claimed method, the selection and ranking of options using approximate and accurate methods, is implemented using a computer, which makes ranking options and identifying the most effective of them. The stage of data processing can be performed both on the server and on the user's computer itself.

After completing the stage of data processing, the results obtained are provided to the end user in any format suitable for him. The results of the execution can be stored on the server, other media from which it is possible to read it further, or can be presented to the user's computer screen directly using a web browser or any other software tool with which information is viewed.

Examples of implementation of the method.

The problem of finding relevant pages on the Internet with selection and ranking based on the principle of superposition

The task of finding relevant pages on the Internet, ranking based on the idea of \u200b\u200bsuperposition can be implemented as follows. First, by quick (approximate) methods, obviously irrelevant pages are excluded. These irrelevant pages can be, for example, those pages that do not belong to the given topic, contain spam, viruses, advertisements, undesirable content for the user, phishing (Internet fraud) and others. Then, on the remaining significantly smaller set of pages, finer (accurate) ranking methods are applied, which, however, require more computational resources (slow). The irrelevant pages mentioned above can never be relevant to the user's request, which means that their use in more time-consuming methods is redundant and simply unnecessary. IN this example superposition of some set of fast but approximate methods (used to cut off only the most irrelevant pages) and some set of exact methods (used for the final ranking of a small number of alternatives) gives a gain in speed and accuracy (relevance) of the final ranking. In particular, there is no need for detailed ranking for irrelevant pages, it is enough to assign them all the same rank (the last place in the ranking).

Table 1 shows the simplest example of using two methods for choosing options - the Pareto rule and a four-step selection method based on the idea of \u200b\u200bsuperposition. In the task, it is necessary to determine which of the options are most relevant (suitable) to the entered user request. Each variant is evaluated according to three criteria: the number of words from the request in the head of the document, the number of words from the request in the entire document, the Boolean model (the presence of all the words of the request in the document). In the example, the selection is made from 29 options.

If the usual Pareto rule is used, then the relevant documents will be documents No. 2, 5, 6. When using the Pareto rule, each option must be compared with all other options, ie. each of the 29 options must be matched against each other. This means that the more options are in the sample, the greater the computational complexity of this rule, which leads to the need to use simpler (approximate) selection rules.

However, the Pareto rule can be applied if we use a method for selecting and ranking effective options based on the idea of \u200b\u200bsuperposition. Table 1 shows a four-step selection method, which consists in sequentially applying three suprathreshold rules, after which the Pareto rule is applied.

At the first stage, all variants (documents) are excluded, the title of which does not contain a single word from the request. Thus, the number of options is reduced from 29 to 8.

At the second stage, there is an exclusion of those variants for which not a single word from the query was found in the main part of the document. Then the number of options is reduced from 8 to 6. After that, only those documents are selected that contain all the words from the query. As a result, the number of options is reduced to 4. After that, the Pareto rule is applied for the remaining options, and the final choice includes only 3 options (documents) - No. 2, 5, 6.

In this example, the results of both methods are the same. However, the computational complexity of the Pareto rule is much higher. Therefore, if the number of options is small, the choice of the selection and ranking method is not fundamental (it does not matter). However, in conditions when the number of variants reaches several million, it is necessary to use the second method based on the idea of \u200b\u200bsuperposition, since it allows you to combine simple and complex selection rules, which reduces the computational complexity of the method.

In a number of models, in order to present the most interesting and demanded offer, participants in social networks need to segment user groups according to their common interests or according to the intensity of information exchange between them. In this case, for example, the cutoff according to the rule "no more than one contact in the last year" (for a certain set of goods and services) allows you to immediately narrow down the number of options within the group to a level acceptable for more complex algorithms. Of course, having more than one contact per year does not imply common interests of users, i.e. obviously ineffective variants of grouping (segmentation) of social network participants according to their interests are cut off, with a simultaneous and sharp decrease in the group size.

Thus, the presented method allows selection and ranking of options with high accuracy, especially in the presence of a large number of options characterized by a large set of indicators, since it allows a combination of approximate and accurate procedures.

The claimed method can be used in the selection of effective options in search, recommendation systems, decision support systems, Internet networks, systems for automatic classification of data packets and in other related areas.

In addition, the invention can be used to solve the problem of teaching ranking, that is, the problem of choosing options with previously known estimates of their usefulness by criteria, for example, when assessing the efficiency of enterprises, retail outlets and other objects in related areas.

Appendix 1. List of selection rules given in the work of FT Aleskerov, E. Kurbanov "On the degree of manipulability of collective selection rules", Automation and Remote Control, 1998, No. 10, 134-146.

1. Plurality rule

The choice includes alternatives that are best for the largest number of criteria, i.e.

those. means the number of criteria for which the alternative a is at least qth place in their ordering. Thus, if q \u003d 1, then a is the best alternative for criterion i; if q \u003d 2, then a - either the first or the second best alternative, etc. The number q will be called the level of the procedure.

those. alternatives are selected that are among the q best for the maximum number of criteria.

This selection rule has linear computational complexity; for ranking, the computational complexity of the rule depends on the value of q. For q<

3. Threshold rule

Let ν 1 (x) be the number of criteria for which the alternative x is the worst in their ordering, ν 2 (x) is the number of criteria for which the alternative x is the second worst, and so on, ν m (x) is the number of criteria, for which alternative x is best. The alternatives are then ordered lexicographically. They say that alternative x V - dominates alternative y if ν 1 (x)< ν 1 (y) или, если существует k≤m такое, что ν i (x)= ν i (y), i=1, …, k-1, и ν k (x)< k (y). Другими словами, в первую очередь сравниваются количества последних мест в упорядочениях для каждой альтернативы, в случае, когда они равны, идет сравнение количества предпоследних мест, и так далее. Выбором являются альтернативы, недоминируемые по V.

This selection and ranking rule has linear computational complexity.

4. Borda rule

Each alternative x∈A is associated with a number r i (x, P →) equal to the cardinality of the set of alternatives worse than x in the criterion P i ∈ P →, that is, r i (x, P →) \u003d | L i (x) | \u003d | (b ∈ A: x P i b) | ... The sum of these values \u200b\u200bfor i∈N is called the Borda rank for the alternative x,

Choice includes alternatives with maximum rank

5. Black's procedure

If there is a Condorcet winner, it is declared a collective choice, otherwise the Borda rule is used.

6. Coombs' procedure.

The option that is considered the worst by the maximum number of voters is excluded. Then the profile is narrowed down to a new set X and the procedure continues until there are only non-excludable variants. Note here the difference between Coombs' Rule and the Voice Transmission System. The Coombs Rule strikes out the worst options, while the transmission system strikes out the best options for the minimum number of voters.

This selection and ranking rule has linear computational complexity.

7. Hara procedure

For each alternative, the number of first places in the orderings by criteria is counted. Then the alternatives with the smallest number of first places drop out of the voting. The procedure is repeated until the choice remains non-empty.

This selection and ranking rule has linear computational complexity.

8. Reverse majority rule

The selection includes alternatives that are worst for the smallest number of criteria.

This selection and ranking rule has linear computational complexity.

9. Copland's first rule

For each alternative, two indicators are calculated: the sum of the number of alternatives that are worse than the specified for each criterion, and the sum of the numbers of alternatives that are better than the specified for each criterion. The collective choice includes alternatives with the greatest difference between these two indicators.

This selection rule has a linear computational complexity; for ranking, the computational complexity of the rule strongly depends on the input data and is quadratic in the worst case.

10. Reverse Borda procedure (with the transfer of votes)

For each alternative, the Borda rank is calculated. Then the alternative with the lowest rank is eliminated. Borda ranks are recalculated for a set of alternatives without a dropped alternative. The procedure is repeated until the selection is not empty.

For the choice, the computational complexity of the rule is quadratic at worst. For ranking, the computational complexity of the rule is not less than quadratic.

11. Nanson's rule

The rank of the Borda is calculated for all options. Then the average Borda score is calculated and only those x variants for which the Borda score is below average are excluded. Then the set X \u003d A \\ (x) is constructed, and the procedure is applied to the narrowed profile / X. The procedure continues until there are only non-excludable options. For the choice, the computational complexity of the rule is quadratic at worst. For ranking, the computational complexity of the rule is not less than quadratic.

12. Minimum dominant set

The set of alternatives Q is dominant if and only if any alternative from Q dominates any alternative outside Q by the majority relation.

A dominant set is minimal if none of its own subsets is dominant. The collective choice is the minimum dominant set, if there is one, or their union, if there are several. This rule for selection and ranking has a computational complexity of at least quadratic.

13. Minimum non-dominated set

The set of alternatives Q is undominated if and only if there is no alternative outside Q that dominates any alternative from the set Q.

An undominated set is minimal if none of its proper subsets is undominated. The collective choice is the minimum non-dominated set, if there is one, or their union, if there are several. This rule for selection and ranking has a computational complexity of at least quadratic.

14. Minimum weakly stable set

The set of alternatives Q is weakly stable if and only if the existence of an alternative y outside Q dominating an alternative x from Q implies the existence of an alternative z from Q such that z dominates Y-

A weakly stable set is minimal if none of its proper subsets is weakly stable. The collective choice is the minimum weakly stable set, if there is one, or their union, if there are several.

This rule for selection and ranking has a computational complexity of at least quadratic.

75. Fishburne Rule

Let us construct a new binary relation y, in which x dominates y if and only if the upper contour of the alternative x is a proper subset of the upper contour of the alternative y.

The collective choice will be a set of alternatives that are not dominated by y.

This rule for selection and ranking has a computational complexity of at least quadratic.

16. Uncovered set I.

We construct a new binary relation 5, in which x dominates y if and only if the lower contour of the alternative y is a proper subset of the lower contour of the alternative x.

The collective choice will be a set of alternatives not dominated by the relation 8.

This rule for selection and ranking has a computational complexity of at least quadratic.

17. Uncovered set II

Alternative x B - alternative y dominates if x is dominated by y by majority ratio and the upper contour of alternative x is a subset of the upper contour of alternative y. The collective choice includes alternatives that are not dominated by relation B. This rule for selection and ranking has a computational complexity of at least quadratic.

18. Richelson's rule

A new binary relation σ is constructed in which x dominates y if and only if

The bottom contour y is a subset of the bottom contour x

Top path x is a subset of top path y

In one of the two above cases, the entry occurs as a "proper subset"

The collective choice includes non-dominated software alternatives.

This rule for selection and ranking has a computational complexity of at least quadratic.

19. Copland's first rule

The collective choice includes alternatives with the maximum power difference between the lower circuit and the upper circuit.

This rule for selection and ranking has a computational complexity of at least quadratic.

20. Copland's second rule

The collective choice includes alternatives with the maximum power of the lower circuit.

This rule for selection and ranking has a computational complexity of at least quadratic.

21. Copland's third rule

The collective choice includes alternatives with a minimum upper circuit power.

This rule for selection and ranking has a computational complexity of at least quadratic.

22. Two-step plurality rule

First, the simple majority rule is used (i.e., the option that receives more than 50% of the votes - the first places - in the ordering of voters is chosen) If such an option is found, then the procedure stops. If there is no such option, then two options are selected that have received more votes than the other options (if there are more than two, then the two with the lowest numbers are taken). Then, assuming that the opinions of voters regarding these options (if the others are deleted) do not change, we again apply the rule of simple majority / votes - already on a two-element set.

Since individual opinions are presented in the form of linear orders, there is always (with an odd number of voters) a single winning option.

This selection and ranking rule has linear computational complexity.

First, the simple majority rule is used (i.e. the option that receives more than 50% of the votes is chosen). If such an option is found, then the procedure stops. If there is no such option, then option x with the minimum number of votes is deleted from the list.

Then the procedure is again applied to the set X \u003d A \\ (x and the profile / X This rule for selection and ranking has linear computational complexity.

24. Young's procedure

If there is a Condorcet winner for the profile, it is selected and the procedure stops there. If there is no such option, then all sorts of coalitions are considered, in which there are partial Condorcet winners. Next, the function u (x) is defined as the cardinality of the maximum coalition in which x is the winner of the Condorcet.

Then the options with the maximum ux value are selected:

This rule for selection and ranking has a computational complexity of at least quadratic.

25. Simpson's procedure (maximin procedure)

We construct a matrix S + such that ∀ a, b∈X, S + \u003d (n (a, b)), where

n (a, b) \u003d card (i∈N | aP i b), n (a, a) \u003d + ∞.

This rule for selection and ranking has a computational complexity of at least quadratic.

26. Minimax procedure

Let us construct a matrix S such that ∀ a, b∈X, S + \u003d (n (a, b)), n (a, a) \u003d - ∞.

Collective choice is defined as

This rule for selection and ranking has a computational complexity of at least quadratic.

27. Strong q-Pareto simple majority rule

Let f (P →; i; q) \u003d (X∈A- || card (D ↓ i (x)) ≤q) determine q + 1 options from the maximum and lower in the linear order P i. Let ℑ \u003d (I⊂N- || card (I) \u003d) (where the function [χ] means the smallest integer greater than or equal to x) be a family of coalitions of a simple majority. Let us introduce a function that chooses an option that is among the upper options for each voter in at least one coalition of a simple majority, and start with q \u003d 0. If there is no such option for q \u003d 0, then the choice over coalitions of the simple majority with q increased by one (that is, the weight q \u003d 1), etc., is re-examined, until the choice is empty. From this non-empty set, the option with the smallest number is selected, which is accepted as a collective choice.

This selection and ranking rule has exponential computational complexity.

28. Strong q-Pareto majority rule

This rule is similar to rule 26, with the addition that if several options are selected, then for each of them the number of coalitions that have chosen it is calculated. Then the options with the maximum value of this indicator are selected.

Options with the maximum value of this indicator are selected. This selection and ranking rule has exponential computational complexity.

29. Strongest q-Pareto simple majority rule

We introduce the function

C (A) \u003d ∩ I ∈ ℑ f (P →; I; q)

where f (P →; I; q) \u003d (χ ∈ A - | | card (∩ ↓ i (x)]) ≤ q), card (I) \u003d [n / 2] is a function that chooses a variant, Pareto optimal in every coalition of simple majority, and start with q \u003d 0. If there are no such elements, then the case q \u003d 1, q \u003d 2, etc. is considered, until the choice is empty. From this non-empty set, the option with the smallest number is selected, which is taken as a collective choice. This selection and ranking rule has exponential computational complexity.

30. Rule of suprathreshold selection

Let the criterion φ (x), φ: A → R 1, be given on the set A, and the threshold function V: 2 A → R 1 on the set 2 A, which assigns to each set Xe2A the threshold level V (X).

The suprathreshold selection rule is presented as the following expression:

n ¯ s t: y ∈ C (X) ⇔ (y ∈ X & ϕ (y) ≥ V (X)).

This selection rule has a linear computational complexity; for ranking, the computational complexity depends on the input data, in the worst case, no more than quadratic.

1. A method for selecting and ranking effective search results options, which consists in the fact that the criteria for assessing the relevance of a search result option to a search query are preliminarily formed and a finite number of search results options or a set of procedures for selecting and ranking search results options and the sequence of their execution for selecting result options are set the search, assessed as the most effective, evaluate each of the search results options according to their relevance to the search query criteria, on the basis of which the search results options are ranked by assigning a rank to each of them from the condition of meeting the largest number of criteria in descending order; the selection and ranking of search results variants are sequentially carried out by the superposition method in at least two stages, if the number of search results variants in the remaining group of search results variants corresponds to a predetermined finite number of search results variants for selection or all specified selection procedures are used, selection of search result variants and their ranking is stopped and the search result options from the selected group are evaluated as the most effective, if the number of search results options in the remaining group of search result options does not correspond to a predetermined finite number of search result options for selection, the selection of search results and their ranking continues, while selection of search results options, their ranking and exclusion are carried out until a specified number of search results options is reached or until all specified procedures are used in The selection and the selected group of options are rated as the most effective.

2. The method according to claim 1, characterized in that at the first stage the search results are selected in the presence of a large number of them by the superposition method using selection and ranking methods characterized by linear computational complexity O (n), and the group of search results is excluded that have the lowest rank.

3. The method according to claim 1, characterized in that at the second and subsequent stages, the criteria for evaluating the search query are formed, on the basis of which the variants of the search results are ranked and the variants of the search results are selected from the remaining array processed at the previous stage by the superposition method using the methods, the computational complexity of which is not less than quadratic O (n 2) and exclude the next group of search results with a lower rank.

4. A method for selecting and ranking effective variants of search results, which preliminarily formulates criteria for assessing the relevance of a variant of search results to a search query and sets a finite number of variants of search results for selection, assessed as the most effective, evaluates each of the variants of search results by relevance search query criteria, on the basis of which the search results are ranked by assigning a rank to each of them from the condition of meeting the greatest number of criteria in descending order; the selection and ranking of search results variants are sequentially carried out using the superposition method in at least two stages, if the number of search results in the remaining group of search results matches a predetermined finite number of search results for selection, the selection of search results and their ranking is stopped and the variants search results from the selected group are assessed as the most effective, if the number of search results in the remaining group of search results does not correspond to a predetermined finite number of search results for selection, the selection of search results and their ranking continues, while the selection of search results, their ranking and exclusion are carried out until a predetermined number of search results is reached, the selected group of search results is evaluated as the most effective.

5. The method according to claim 4, characterized in that at the first stage, the selection of search results options is carried out in the presence of a large number of them by the superposition method using selection and ranking methods characterized by linear computational complexity O (n), and a group of search results options is excluded that have the lowest rank.

6. The method according to claim 4, characterized in that at the second and subsequent stages, the criteria for evaluating the search query are formed, on the basis of which the variants of the search results are ranked and the variants of the search results are selected from the remaining array processed at the previous stage by the superposition method using the methods, the computational complexity of which is not less than quadratic O (n 2) and exclude the next group of search results with a lower rank.

7. The method according to claim 4, characterized in that a set of procedures for selecting and ranking options for search results and the sequence of their execution are additionally specified.

8. A method for selecting and ranking effective search results options, which consists in the fact that criteria for assessing the relevance of a search result option to a search query are preliminarily formed and a set of procedures for selecting and ranking search results options and the sequence of their execution for selecting search result options evaluated as the most effective evaluating each of the search results options according to their relevance to the search query criteria, on the basis of which the search results options are ranked by assigning a rank to each of them from the condition of meeting the greatest number of criteria in descending order; the selection and ranking of search results variants are sequentially carried out by the superposition method in at least two stages, the selection of search results, their ranking and exclusion are carried out until all the specified selection procedures are used and the selected group of search results is evaluated as the most effective.

9. The method according to claim 8, characterized in that at the first stage, the search results are selected in the presence of a large number of them by the superposition method using selection and ranking methods characterized by linear computational complexity O (n), and the group of search results is excluded that have the lowest rank.

10. The method according to claim 8, characterized in that at the second and subsequent stages, criteria for evaluating the search query are formed, on the basis of which the variants of the search results are ranked and the variants of the search results are selected from the remaining array processed at the previous stage by the superposition method using the methods, the computational complexity of which is not less than quadratic O (n 2) and exclude the next group of search results with a lower rank.

11. The method according to claim 8, characterized in that, additionally, a finite number of variants of the search results are specified for the selection, evaluated as the most effective.

12. The method according to claim 8, characterized in that in order to select the most effective group of search results options, additional selection and ranking methods and the sequence of their execution are set and the selection and ranking are repeated.

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The best and most efficient use (NINEI) of property is the most probable way of its exploitation, which is physically possible, legally permitted, economically sound, feasible from a financial point of view and leading to the maximum value of this property.

Real estate is property that can be used in more than one way. Since each method of using a property is associated with a certain value of its value, before the assessment, one method of use is selected, which is called the best and most effective.

The Best Use Statement reflects the Valuer's opinion regarding the best use of the property based on market analysis. The term “best and most efficient use” as used in this report means the use that, of all reasonably feasible, physically feasible, financially acceptable, properly secured and legally acceptable uses, results in the highest present value of land.

Best and most effective use analysis is performed by verifying that the considered use cases meet the following criteria:

Legislative Permit: Consideration of uses that are permitted by zoning ordinances, restrictions on private initiative, historic zones and environmental legislation.

Physical Feasibility: Considering locally physically feasible uses.

Financial Feasibility: Considering what physically feasible and legally permitted use will generate acceptable income for the site owner.

The criterion for financial feasibility is a positive return on invested capital, i.e. return equal to or greater than the cost of compensation for maintenance costs, financial obligations and the return of the capital itself.

Maximum efficiency: Considering which financially viable use will generate the maximum net income or maximum present value.

The environment, the topography of the land plot allows it to be used for the construction of buildings of various functionalities - administrative, warehouse, agricultural. At the same time, additional capital investments are not required that would compensate for the physical characteristics of the study area (size and shape, topographic and geographical features, engineering-geological and hydrogeological conditions, availability of transport and utilities).

There were no legal conditions limiting the nature of use - legislative, municipal acts and regulatory requirements. There is no information on the proposed changes to the regulations in the relevant part, which would significantly affect the nature of use. The attitude of the local population is generally favorable.

In order to correctly use the approaches to assessing the cost, as well as to give an answer to the question of the expediency of the further existence of the improvements available on the site, the determination of the best use of the improvements available on the site is carried out in two stages:

For the site as free;

For a site with existing improvements.

This analysis assumes that the land is undeveloped (or could be vacated by demolishing existing buildings). The valuation of the land plot under these conditions is necessary for the correct application of the cost valuation method.

Analysis of the possibilities of using the site as free

When determining the most effective use of a plot of land as free, the following are taken into account:

Intended purpose and permitted use;

Prevailing land use methods in the immediate vicinity of the assessed land plot;

Development prospects of the area in which the land plot is located;

Expected changes in the land and other real estate market;

Current use of the land.

However, the choice of the option for the most efficient use of land can be carried out among the options that are physically possible, economically justified, meet the requirements of the legislation, financially feasible, and as a result of which the estimated value of the value of the land plot will be maximum (the principle of the most efficient use).

If the value of the vacant plot exceeds the value of the property with improvements, the best and most efficient use is to use the land as vacant. However, in this case the situation is different. The building as a whole, as a single real estate object, certainly exceeds the value of the land plot. In addition, the appraised premises are located inside a building belonging to different owners, and therefore the owner of this premises is limited in his ability to independently use his share in the land plot. In connection with the above, the plot of land related to the subject of valuation cannot be considered as vacant. Therefore, the evaluator analyzed the possible options for using the site with the existing improvements.

Conclusion: The location of the land plot, as well as the entire infrastructure of the area, makes it economically efficient to use it for construction, commercial buildings.

Analysis of the use cases of the assessed object: Non-residential building

Use as production and storage facilities

The object of assessment is a non-residential building, which imposes restrictions on the possible options for its use. For example, it is unacceptable to use premises associated with the organization of production, the technological process of which, in one way or another, is associated with an increased level of noise, vibration, harmful emissions, fire hazard. The use of premises as a warehouse is completely unjustified in terms of financial efficiency. The income from the lease of warehouse premises is significantly lower than the corresponding income from the use of it as a retail or office space. Therefore, such use cases should be discarded.

Use of premises as a trade

Analysis of the real estate market shows that the cost of sale and rental rates for retail space are higher than for office space. Therefore, such use could be more efficient. At the same time, when choosing the option for the most effective use, it should be borne in mind that the use of the premises to accommodate a full-fledged store is associated with high costs due to the need for significant redevelopment and repair. The surrounding infrastructure of the location of the object of the assessment is industrial, administrative and large shopping facilities. Thus, taking into account that the subject of assessment is located, although in a busy place, but not well enough from the point of view of small retail trade, the financial viability of this option looks doubtful.

The use of premises as a commercial object - an office building.

Analysis of the real estate market shows that a commercial building is a much quieter investment object. The demand for commercial premises is growing following the economy. The reason for the fact that at present raw materials, financial, banking structures are actively investing in the commercial real estate market lies in the high level of profitability of office projects. With a high investment potential, Kemerovo is experiencing an acute shortage of high-quality commercial premises. In connection with the foregoing, the Appraiser believes that such use is the most effective.

Conclusion: Thus, the best and most efficient use of the evaluated object is to use the building as an office building.