Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application

Ćetković, Jasmina; Lakić, Slobodan; Lazarevska, Marijana; Žarković, Miloš; Vujošević, Saša; Cvijović, Jelena; Gogić, Mladen

doi:10.1155/2018/1472957

Cited by 35 publications

(28 citation statements)

References 23 publications

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“…The principle of NN works on the assumption that the basic process of interconnections between variables determines the difficulty of correlation among dependant and independent variables [21]. Based on research, NN tools are encouraged to be used as an analytical tool that increases the efficiency of accuracy in various prediction models [22]. In this paper, an NN model is created to predict CCI for future projects with the use of a few selected variables.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

Velumani

Nampoothiri

2021

IRASE

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The Construction Industry Development Council (CIDC) of India has been calculating and publishing the Construction Cost Index (CCI), monthly, since 1998. Construction cost variations interrogate different kinds of projects such as roads, power plants, buildings, industrial structures, railways and bridges. The success rate of completion of construction project is diminished due to the lack of prediction knowledge in CCI. Predicting CCI in greater accuracy is quite difficult for contractor and academicians. The following factors are influenced higher in CCI such as population, unemployment rate, consumer price index (CPI), long term interest rate, domestic credit growth, Gross Domestic Product (GDP) and money supply (M4). CCI can be used to forecast the construction cost. The relevant resource data was collected across the nation between 2003 and 2018. As outcome-based, non-econometric tools such as smoothing techniques, artificial neural network (ANN) and support vector machines (SVMs) have produced a better outcome. Among these, smoothing techniques have given the notable low error and high accuracy. This accuracy has measured by Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The major objective of this research is to help the cost estimator to avoid underestimation and overestimation.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

Velumani

Nampoothiri

2021

IRASE

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“…Global financial crises exposed the risks related to real estate booms. Researches indicate that the real estate market could become the reason of vulnerability or even crisis (Cetkovic et al, 2018). It is stressed that crises related to weaknesses in residential property markets were quite common throughout history (Bengtsson, Grothe, & Lepers, 2018).…”

Section: Main Aspects Of Investing In Real Estatementioning

confidence: 99%

“…The rapid development and expansion of China's real estate market have been an element of the country's dramatic economic growth (Wu, Gyourko, & Deng, 2015). Urbanisation processes in China have been very rapid during the last few decades creating an increase in property prices in major cities (Chen, Wang, & Liu, 2015). The government carefully monitors operations in the real estate sector.…”

Section: Chinamentioning

confidence: 99%

Evaluation of investing in real estate in EU and non-EU countries based on MCDM

Erdogan

Naumcik

2019

Modern Building Materials, Structures and Techniques (MBMST)

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Investment in real estate is a zoning issue as the real estate market is closely related to economic development and trends in real estate market are considered to be indicators of trends in the whole economy of the country. The goal of this paper is to analyse the main aspects and considerations when investing in real estate, evaluate investment in real estate situation in different EU and non-EU countries and introduce MCDM methods that could be used for selecting a state for investment in real estate. It is identified that when investing in real estate various political, social, economic, environmental and other factors have to be taken into consideration. Analysed examples of EU (Lithuania, Romania, UK) and non-EU (Turkey, China, Russia) countries show different risks and opportunities for investments in real estate. MCDM methods are applicable to evaluate which countries are most attractive for investment in real estate. Described TOPSIS and ARAS methods could be used for assessing states as alternatives when selecting where to invest

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“…e authors of many recent publications, for example [1], emphasize that artificial neural networks (ANN) as complex nonlinear systems can take into account an unlimited number of external factors and dynamic interactions. Due to this, an ANN allows for solving multiple complex real problems that could not be solved by other methods.…”

Section: Introductionmentioning

confidence: 99%

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

Yasnitsky

Alekseev

2021

Complexity

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In the modern scientific literature, there are many reports about the successful application of neural network technologies for solving complex applied problems, in particular, for modeling the urban real estate market. There are neural network models that can perform mass assessment of real estate objects taking into account their construction and operational characteristics. However, these models are static because they do not take into account the changing economic situation over time. Therefore, they quickly become outdated and need frequent updates. In addition, if they are designed for a specific city, they are not suitable for other cities. On the other hand, there are several dynamic models taking into account the overall state of the economy and designed to predict and study the overall price situation in real estate markets. Such dynamic models are not intended for mass real estate appraisals. The aim of this article is to develop a methodology and create a complex model that has the properties of both static and dynamic models. Moreover, our comprehensive model should be suitable for evaluating real estate in many cities at once. This aim is achieved since our model is based on a neural network trained on examples considering both construction and operational characteristics, as well as geographical and environmental characteristics, along with time-changing macroeconomic parameters that describe the economic state of a specific region, country, and the world. A set of examples for training and testing the neural network were formed on the basis of statistical data of real estate markets in a number of Russian cities for the period from 2006 to 2020. Thus, many examples included the data relating to the periods of the economic calm for Russia, along with the periods of crisis, recovery, and growth of the Russian and global economy. Due to this, the model remains relevant with the changes of the international economic situation and it takes into account the specifics of regions. The model proved to be suitable for solving the following tasks: industrial economic analysis, company strategic and operational management, analytical and consulting support of investment, and construction activities of professional market participants. The model can also be used by government agencies authorized to conduct public cadastral assessment for calculating property taxes.

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Assessment of the Real Estate Market Value in the European Market by Artificial Neural Networks Application

Cited by 35 publications

References 23 publications

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

Volatility forecast of CIDC Construction Cost Index using smoothing techniques and machine learning

Evaluation of investing in real estate in EU and non-EU countries based on MCDM

The Complex Neural Network Model for Mass Appraisal and Scenario Forecasting of the Urban Real Estate Market Value That Adapts Itself to Space and Time

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