A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

2020 ◽  
pp. 476-500
Author(s):  
Miraç Eren ◽  
Ali Kemal Çelik ◽  
İbrahim Huseyni
Keyword(s):  
Genetic Algorithm ◽  
Growth And Development ◽  
Consumer Price Index ◽  
Housing Demand ◽  
Grey Model ◽  
Demand Forecast ◽  
Factors Affecting ◽  
Urbanization Rate ◽  
Housing Investments ◽  
Demand Forecasts

Housing sector is commonly considered as a very strong economic industry in terms of both its contribution to creating employment and its impact on other associated sectors. By means of its featured characteristics, the sector also plays an important role on economic growth and development of emerging countries. In this respect, any evidence that determines factors affecting housing investments and future demand behavior may be remarkably valuable for monitoring possible future excess supply and deficits. This chapter attempts to determine factors affecting housing demand in Turkey during a sample period of 2003-2011 using a genetic algorithm-based multivariate grey model. Housing demand forecasts are also employed until the year 2020. Results reveal that several factors including M2 money supply, consumer price index and urbanization rate have an impact on housing demand. According to housing demand forecasts, a significant housing demand increase is expected in Turkey.

A Genetic Algorithm-Based Multivariate Grey Model in Housing Demand Forecast in Turkey

2016 ◽  
pp. 42-65
Author(s):  
Miraç Eren ◽  
Ali Kemal Çelik ◽  
İbrahim Huseyni
Keyword(s):  
Genetic Algorithm ◽  
Growth And Development ◽  
Consumer Price Index ◽  
Housing Demand ◽  
Grey Model ◽  
Demand Forecast ◽  
Factors Affecting ◽  
Urbanization Rate ◽  
Housing Investments ◽  
Demand Forecasts

Housing sector is commonly considered as a very strong economic industry in terms of both its contribution to creating employment and its impact on other associated sectors. By means of its featured characteristics, the sector also plays an important role on economic growth and development of emerging countries. In this respect, any evidence that determines factors affecting housing investments and future demand behavior may be remarkably valuable for monitoring possible future excess supply and deficits. This chapter attempts to determine factors affecting housing demand in Turkey during a sample period of 2003-2011 using a genetic algorithm-based multivariate grey model. Housing demand forecasts are also employed until the year 2020. Results reveal that several factors including M2 money supply, consumer price index and urbanization rate have an impact on housing demand. According to housing demand forecasts, a significant housing demand increase is expected in Turkey.

Tax debt as a threat to the economic security of the Russian Far East

2020 ◽  
Vol 13 (2) ◽  
pp. 216-242
Author(s):  
V.A. Yakimova ◽  
A.A. Orekhova
Keyword(s):  
Russian Far East ◽  
Consumer Price Index ◽  
Far East ◽  
Federal District ◽  
Economic Security ◽  
Factors Affecting ◽  
Correlation And Regression Analysis ◽  
Debt Collection ◽  
Far Eastern ◽  
The Russian Far East

Subject. The article addresses the tax liabilities of taxpayers registered in the subjects of the Far Eastern Federal District, which should be paid to the consolidated budget of the Russian Federation, as well as the factors of the said debt growth. Objectives. Our aim is to assess the level of tax debt of regions of the Russian Far East and identify the correlation between the factors and the amount of tax debt. Methods. The study rests on methods of analysis, generalization, grouping, systematization, and the correlation and regression analysis. Results. We analyzed the level of tax debt for the entire Far Eastern Federal District and by region, identified factors affecting the growth of tax debt therein. The paper assesses the structure of tax debt by type of taxes and activity of debtors. The unveiled factors may help control changes in the size of tax debt in the Russian Far East and develop effective measures to improve the debt collection. Conclusions. The study shows that there is an increase in the tax debt in the regions of the Russian Far East, in the VAT in particular. The factor analysis revealed that the volume of sales of wholesale enterprises, investment in fixed capital, the consumer price index have the largest impact on the amount of tax debt.

Application Research of Neural Expert System on Tree Growth Model

2014 ◽  
Vol 513-517 ◽  
pp. 3728-3731
Author(s):  
Wen Qing Zhang
Keyword(s):  
Expert System ◽  
Tree Growth ◽  
Growth And Development ◽  
Production Management ◽  
Environmental Control ◽  
Growth Parameters ◽  
Growth Models ◽  
Growth Management ◽  
Management Decision ◽  
Factors Affecting

In order to simulate growth and development process of tree, then provide services for production management and scientific research, all kinds of tree growth models are constructed. The paper firstly considers a variety of factors affecting the growth and development of tree, then studies artificial intelligence knowledge such as neural network and expert system, uses the neural expert system to solve the acquisition and management of tree growth parameters, and design and develop tree growth management and expert system based on growth models, the models combine morphogenesis model of tree and knowledge model to provide comprehensive environmental control and management decision-making. Practice has indicated that the growth models of tree can reflect the growth of trees under different physiological and ecological conditions, and visual effect is very good.

scholarly journals Analysis of Factors Influencing Effective Utilization Coefficient of Irrigation Water in the Manas River Basin

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 189
Author(s):  
Lili Yang ◽  
Tong Heng ◽  
Guang Yang ◽  
Xinchen Gu ◽  
Jiaxin Wang ◽  
...  
Keyword(s):  
Irrigation Water ◽  
Large Scale ◽  
Grey Model ◽  
Analytic Hierarchy ◽  
Effective Utilization ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Utilization Coefficient ◽  
Using Data ◽  
Effective Use

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

scholarly journals Factors Affecting the Performance of Kuwait Stock Market

2016 ◽  
Vol 9 (5) ◽  
pp. 23
Author(s):  
Ebrahim Merza ◽  
Sayed-Abbas Almusawi
Keyword(s):  
Interest Rate ◽  
Real Estate ◽  
Stock Market ◽  
Banking Sector ◽  
Stock Exchange ◽  
Consumer Price Index ◽  
Gold Price ◽  
Oil Price ◽  
Factors Affecting ◽  
Insurance Sector

<p>This paper aims at finding the effective factors that influence three sectors in Kuwait stock exchange market (KSE) in addition to the whole stock market. The three sectors are banking, real estate and insurance sectors. The paper measures KSE performance through the average share prices calculated on a quarterly basis starting from 2005 until first quarter of 2015. It is found that each sector behaves differently towards macroeconomic variables. The most important determinants for the KSE overall market performance were found to be gold price and the deposits rate. Individually, the banking sector is influenced by consumer price index, interest rate on loans, oil price and gold price. The insurance sector is influenced by money supply, residential real estate price and oil price. The real estate sector is influenced by the exchange rate with respect to US dollars, interest rate on loans, oil price and gold price.</p>

Assessing Low-Cost Housing Demand in Melaka: PLS-SEM Approach

2013 ◽  
Vol 838-841 ◽  
pp. 3156-3162 ◽  
Author(s):  
Noor Yasmin Zainun ◽  
Nadzirah Roslan ◽  
Aftab Hameed Memon
Keyword(s):  
Low Income ◽  
Goodness Of Fit ◽  
Housing Stock ◽  
Low Cost ◽  
Great Influence ◽  
Economic Factor ◽  
Housing Demand ◽  
Factors Affecting ◽  
Low Cost Housing ◽  
The Government

Housing is one of the basic needs of human. Population in Malaysia is increasing and expected to reach up to 35 million in year 2020. This phenomenon creates high demand for housing. To tackle the squatter problems, the government introduces low-cost housing. Low cost house is known as the government house, where the price is cheaper but still comfortable. Although there are many of low-cost housing projects have been completed to cope with the need of the citizen especially for low-income group. However, census report reveled that these is huge demand of low-cost housing. This demand might be because of various factors which are very essential to identify in order to meet the required demand of low cost houses. Hence, this study is carried out to assesse the demand of low cost housing in Melaka, determine the significant factors affecting demand of low-cost housing, and establish PLS-SEM model for assessing factors affecting low-cost housing demand. In this study, data are collected by distributing questionnaire in Melaka state. The collected data from survey was analyzed using statistical software SPSS and presented in graphs and chart. Further, factors affecting low cost housing demand in Melaka were modeled with the SmartPLS v2.0. The model shows the relationship between low cost housing demand and its indicators. The finding of the study showed that most significant indicators affecting the demand of low-cost housing in Melaka are the economic factors which include housing stock, inflation rate and Gross Domestic Products (GDP). The Goodness of Fit showed that the model has substantial explaining power for the assessing factors affecting low cost housing demand in Melaka which the values is 0.481. This means that the economic factor has a great influence on the low-cost housing demand in Melaka.

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