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Original article УДК 330.332:69 (510)
DOI: http://dx.doi.org/10.21285/2227-2917-2018-1 -23-27
REAL ESTATE INVESTMENT DECISION SUPPORT SYSTEM
© S.A. Astafiev8, Guo Yib
aBaikal State University,
11, Lenin St., Irkutsk, 664003, Russian Federation bBeijing Blue-Home Import&Export Co., LTD, 21, Gongtibeilu St, Beijing, 10020, China
ABSTRACT. PURPOSE. Nowadays in China traditional systems of making decisions in the sphere of real estate investments do not need the further development. However, there is necessity to develop modern system of support in making decisions in the sphere of investments into real estate. METHODS. The system is based on a recently developed decision support system for new technology, a new model of the real estate decision support system. Data warehouse, on-line analytical processing (OLAP), data mining is a decision support system development in emerging technologies; these technologies will be introduced to the real estate decision support system. RESULTS AND THEIR DISCUSSIONS. In this work, we carried out the analysis of the new technologies used to optimize the real estate investment decision support system. OLAP data warehouse can analyze large amounts of data, from which extract useful information to play a supporting role in decision-making. Data mining is the discovery of knowledge arising from the concept, the data mining techniques to the analysis of the data warehouse, data warehouse can be effectively excavated from the valuable things, which is conducive to supporting decision-making. CONCLUSIONS. "Grey model" of forecasting and the Markov chain prediction model are two commonly used models in the system analysis. In addition to the standard models of decision-making in the field of real estate investment, it is proposed to modernize the Markov chain model taking into account the uncertainty factor and apply it in the implementation of urban development plans. Keywords: real estate decision support system, data warehouse, online analytical pprocessing,, data mining, real estate, efficiency of investments, urban development
Article info. Received January 29, 2018; accepted for publication February 10, 2018; available online March 29, 2018.
For citation: Astafiev S.A., Guo Yi. Real estate investment decision support system. Izvestiya vuzov. Investitsii. Stroitel'stvo. Nedvizhimost[Proceedings of Universities. Investment. Construction. Real estate], 2018, vol. 8, no. 1, pp. 23-27. (In Russian). DOI: 10.21285/2227-2917-2018-1-23-27
СИСТЕМА ПОДДЕРЖКИ ПРИНЯТИЯ ИНВЕСТИЦИОННЫХ РЕШЕНИЙ В СФЕРЕ НЕДВИЖИМОСТИ
С.А. Астафьев, Го И
Байкальский государственный университет,
664003, Российская Федерация, г. Иркутск, ул. Ленина, 11
Пекинская экспортно-импортная компания с ограниченной ответственностью «Синий-Дом», 10020, Китайская Народная Республика, г. Пекин, ул. Гондивэйлу, 21
РЕЗЮМЕ. ЦЕЛЬ. В настоящее время в Китае традиционные системы принятия решений в сфере инвестиций в недвижимость уже не нуждаются в дополнительном развитии. Однако есть потребность развитии современных систем поддержки принятия решений в сфере инвестиций в недвижимость. МЕТОДЫ. Система опирается на недавно разработанную систему поддержки принятия решений по новой технологии, новую модель системы поддержки принятия решений по недвижимости. Хранилище данных, онлайн-аналитическая обработка (OLAP), интеллектуальный анализ данных - это разработка системы поддержки принятия решений в новых технологиях; эти технологии будут внедрены в решение системы поддержки принятия решений по недвижимости. РЕЗУЛЬТАТЫ И ИХ ОБСУЖДЕНИЕ. В представленной работе проведен анализ новой технологии, используемой для оптимизации системы поддержки принятия инвестиционных решений в сфере недвижимости. Хранилище данных OLAP может анализировать большие объемы данных, из которых извлекается полезная информация, чтобы играть вспомогательную роль в принятии решений. Интеллектуальный анализ данных - это обнаружение знаний, возникающих из концепции, методы интеллектуального анализа данных для анализа хранилища данных, что способствует поддержке принятия решений. ВЫВОДЫ. «Серая модель» прогнозирования (GM) и Марковские цепи являются двумя часто используемыми моделями в системном анализе. Помимо стандартных моделей принятия решений в сфере инвестиций в недвижимости,
aSergey A. Astafiev, Doctor of Economic Sciences, Associate Professor, Head of the Department of Economy and Management of Investments and Real Estate, e-mail: [email protected] bGuo Yi., Manager, e-mail: [email protected]
ISSN 2227-2917 (print) Известия вузов. Инвестиции. Строительство. Недвижимость Том 8, № 1 2018
ISSN 2500-154X (online) Proceedings of Universities. Investment. Construction. Real estate Vol. 8, No. 1 2018
предлагается модернизировать модель «цепи Маркова» с учетом наличия фактора неопределенности и применить ее при реализации градостроительньк планов по развитию городов.
Ключевые слова: система поддержки принятия решений в сфере недвижимости, хранилище данных, онлайн-аналитическая обработка, интеллектуальный анализ данных, недвижимость, эффективность инвестиционных вложений, градостроительство.
Purpose
Real estate investment is not only related to the development of the national economy, but also involves the construction, finance, business, municipal construction, energy, transportation and other important sectors. The real estate market changes quickly, the huge investment, high risk, to complete such a decision requires taking into account the main market makers, cost of materials, municipal construction and many other factors, and making comprehensive judgments. This complex decisionmaking has been difficult to properly make the experience alone. Real estate is a very comprehensive strong systems engineering, related to the state, collective and individual interests, the ups and downs of the national economy, its prosperity and a reflection of low economic development. Therefore, the real estate industry can help policy-makers that estimate influence of various factors on the cost of real estate, according to scientific decision-making methods, tools supporting decision makers to make decisions.
Methods
Decision support system is a computer-based system to help decision makers to interact directly with the system through the use of unstructured data and decisionmaking model. To solve the problem through decision support systems, real estate developers to develop the project can be a variety of situations to have a more in-depth understanding of the factors that can be integrated in all aspects of investment projects to make a reasonable judge, thereby reducing the real estate investment uncertainty, to make the investment more accurately.
1) Real estate investment decision support system's basic functionality
The system is divided into the real estate market research and forecasting, economic evaluation, risk analysis and feasibility report generation, and other four modules. Through the real estate market re-
search and forecasting module, users can easily understand the status of the real estate market conditions and the economy, and to future developments in the real estate market to make a rough prediction. Through economic evaluation it is possible to make the accurate assessment of investment projects to determine the profitability of this project. Risk analysis module of the investment projects as a general risk analysis, the user through the various investment programs of economic evaluation and risk trade-off, can make more accurate judgments. Carrying out the feasibility study on the project can provide assessment of its efficiency according to the researcher's tasks.
2) The basic structure The factors influencing decisionmaking at investment into the real estate are changeable in combination with the fact that investment into the real estate in itself is highly risky. All this does the traditional systems of decision-making incapable to satisfy requirements at adoption of investment decisions in the sphere of real estate. In turn modern methods of decision-making can effectively solve these problems. Therefore, the system draws a recently developed decision support system for new technology, a new model of the real estate decision support system. Data warehouse, On-Line Analytical Processing (OLAP), data mining is a decision support system development in emerging technologies, these technologies will be introduced to the real estate decision support system, decision support systems can enhance the function. The structure of the system shown in Figure 1. Among them, the data warehouse is to support the needs of decision-making in the database developed on the basis of a new technology. Data warehouse can be used for a large number of transaction data in the database to clean up, extraction and conversion, according to the main decision-making need to re-organize. Data warehouse in a variety of decision problems can be adapted to the
ISSN 2227-2917 (print) ISSN 2500-154X (online)
Известия вузов. Инвестиции. Строительство. Недвижимость Том 8, № 1 2018 Proceedings of Universities. Investment. Construction. Real estate Vol. 8, No. 1 2018
diversity of requirements. OLAP data warehouse can analyze large amounts of data, from which extract useful information to play a supporting role in decision-making. Data mining is the discovery of knowledge arising from the concept, the data mining techniques to the analysis of the data warehouse can be effectively excavated from the valuable things, which is conducive to supporting decision-making.
Traditional decision support system is the use of databases, multi-model humancomputer interaction organic combination, support scientific decision makers to achieve a comprehensive integrated system. Since the formation of decision support technology in the world has been widely used, but the decision support Development also encountered some problems, the problems are the following:
1) Decision support system (DSS) database can only be used for general processing of raw data and summary, and decision support involves a lot of historical data and semi-structured problems. The traditional database management system is difficult to solve complex semi-structured problems, cannot meet the needs of DSS;
2) DSS to integrate data, but in reality the data are often decentralized and mostly distributed in the heterogeneous data platform, data integration is not easy;
3) The issues involved in decisionmaking itself, the dynamic and complex nature of the situation should be different for different treatment methods, and model analysis capabilities provided by the library is limited, the results obtained are often unsatisfactory;
4) Decision support system requires data, models, knowledge and interface integration. The ability to decision-making on the basis of data with the insignificant volume of information (in the view of its absence) at the existing technology of management of databases is weak. Data warehouse, OLAP and data mining techniques, decision support system to inject new vitality is conducive to solving the above conventional decision support system problems for
decision support for the development of a new way.
1) Data warehouse (DW) technology
Information systems, there are two
types of data: operational data and decision support type of data. The former is generated by the daily transaction processing, which is processed to the former (clean up and integration) formed. At present, there are decisions to support the theory of community-based data system, known as data warehouses. When the need for policymaking departments to provide timely, accurate, detailed and reliable risk information, mass data storage and processing has become the most important issue.
2) The online analytical processing (OLAP)
OLAP is a decision analysis tool, which is a particular problem for online data access and data analysis resulting from a technology that can analyze the requirements of stuff. It is fast and flexible way to query large amounts of data processing complex, and intuitive, easy to understand in the form of the query results to various decision makers, resulting in highly summarized information.
3) Data mining (DM) technology
Data mining can be called knowledge
discovery in databases, data is extracted from a large number of reliable, innovative and effective and can be understood in the high-level process model is the database technology, artificial intelligence, neural networks, machine learning and other fields interdisciplinary. Data mining is a process that is based on large databases and from which it extracts unknown information. The aim of DM is to assist in the analysis, decision-makers to find the relationship between the data and that the elements are ignored. Commonly used data mining techniques and algorithms are decision trees, neural networks, the genetic algorithm, fuzzy mathematics, statistical analysis, visualization and so on. Data warehouse, OLAP, data mining as three separate information processing occurs, but they are to solve the problem of decision-support analysis developed.
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Известия вузов. Инвестиции. Строительство. Недвижимость Том 8, № 1 2018 Proceedings of Universities. Investment. Construction. Real estate Vol. 8, No. 1 2018
Fig. 1. A real estate investment decision support system Рис. 1. Система поддержки принятия инвестиционных решений в сфере недвижимости
Practical Application and Analysis of Real Estate Decision System:
1) Market research and forecasting Market research is a real estate investment in a very important element, often the success or failure of investment relations. In this system, market research and forecasting module includes state of the economy, urban economy, the consolidation of the city, the city weather conditions, urban development plan, the housing situation of urban land, supply of real estate market conditions, competitors and competitive real estate conditions and other relevant circumstances of the project itself. In addition, the large amount of historical data for forecasting the real estate market to provide convenient conditions.
2) Economic evaluation Economic evaluation is a real estate
investment decision-making in the essential content. Construction project economic evaluation is an integral part of project feasibility study and an important part of the project, an important means of scientific decision-making. The purpose is to calculate the economic evaluation of the project benefits and costs, the proposed project's financial viability and economic feasibility analysis and discussion, to make comprehensive economic evaluation of the project provide the basis of scientific decision-making.
3) Risk analysis
Although real estate investments are with high returns, there are also high risks, its risk analysis are necessary. It is necessary to properly evaluate the risk of real estate investors, the right to guide the development of decision-making, reduce blindness and errors in decision-making. Because of the fact that real estate investment have a lot risk factors, it is necessary to use models with fuzzy logics for their estimation.
4) Feasibility report generation
Drawing up a report with a feasible
study of investing in real estate when implementing the Decision Support System is based on filling in template forms, which allows the user to avoid filling them manually. Decision-makers in forecasting investments in real estate, through the four methods presented, can improve the quality of decisionmaking.
Results and their discussions
In the course of research and analysis of different models of forecasting real estate investments, the task of building an accurate forecast of the development of the city of Tsysi was set. According to the rating of one hundred cities, the city of Tsysi takes 11th place, the competitiveness of the 17th place, in the rating of ten cities Zhejiang province 2nd place. At the end of 2003, under the leadership of the city administration, the Bu-
Известия вузов. Инвестиции. Строительство. Недвижимость Том 8, № 1 2018 ISSN 2227-2917 (print)
Proceedings of Universities. Investment. Construction. Real estate Vol. 8, No. 1 2018 ISSN 2500-154X (online)
reau of national land resources, as the main Executive organization, conducted a new round of land-use planning and change planning. The Bureau of national land resources uses GIS software to conduct research and forecast the need for land resources for urban development. With the help of GIS technologies, changes of different types of land were obtained during the previous round of planning and the direction of land development for the construction of residential and non-residential facilities in the city of Tsysi was presented. However, taking into account the influence of a significant
number of different factors on the spatial development of any city, as well as their probabilistic nature, we have set the task of improving the quality of forecasting the city development on the basis of the modernization of the «Grey» Model and the construction of the so-called "Grey Markov's Model", i.e. the model working with incomplete data. The results of the model application allowed us to confirm our hypothesis that the upgraded gray Markov model gives better forecasts of the spatial development of the city than the approaches currently used in China.
Comparison of results of forecasting Сравнение результатов прогнозирования
Table 1
Таблица 1
Year On the fact of land use for the construction of the city, km2 «Grey» Model Relative mistakes Grey Markov's Model Relative mistakes
2006 19403.98 19124,61 -279 19418,37 14.39
2016 24788.72 20360,23 -4428.49 24566,96 221.76
Main conclusions
From results it is visible that the value of model of a gray Markov chain has predicted the need for the earth for development of the city closer to real value, than value of forecasting on «Grey» Model. For city building the model of a «gray» Markov chain determines requirement in 2006 by growth of need for the earth - 19418,37 hm2, in 2016 24566.96 in comparison with the
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Contribution
Astafiev S.A., Guo Yi. have equal author's rights. Asta-fiev S.A. bears the responsibility for plagiarism.
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Conflict of interests
The authors declare no conflict of interests regarding the publication of this article.
ISSN 2227-2917 (print) Известия вузов. Инвестиции. Строительство. Недвижимость Том 8, № 1 2018
ISSN 2500-154X (online) Proceedings of Universities. Investment. Construction. Real estate Vol. 8, No. 1 2018
