• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.06a
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• pp.204-205
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• 2020

Construction Standard Unit Price is the unit price calculated based on the market price for work items in construction projects that have already been conducted. It is used as basic data for calculating the budget price of public construction projects. In the Construction Standard Unit Price Book implemented in the second half of 2020, there are 1,810 types of unit prices. Since 2017, 100-150 construction standard unit prices have been revised semiannually (on January 1 and July 1 of each year) through Construction Site Surveys. Other work items have been set based on the rate of inflation during the corresponding period. Later in 2020, this procedure was changed, with on-site survey period extended to one year to strengthen the construction standard unit price investigation. The revisions previous announced during the second half of the year were changed only to reflect the price inflation rates. With such changes in the revisions to construction standard unit prices, one important issue that was raised: The timing of announcing the revisions during the second half of the year (reflecting the price inflation rates). The market unit wage, which is the unit price standard of labor cost that takes up a large part of the construction cost, is announced in January and September. The figures announced in September is reflected on the construction standard unit price four months later in January, but the market unit wage announced in January is reflected only six months after in July, which causes a timing issue. As such, the current study analyzed problems rising from the changed timing of the announcements of the construction standard unit price during the second half of the year, in addition to analyzing their impact on public construction projects.

• East Asian Journal of Business Economics (EAJBE)
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• v.4 no.4
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• pp.1-7
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• 2016

This paper has studied the impacts of the exchange rate, government debt as a percent of GDP and other relevant macroeconomic variables on aggregate output in the Philippines. A simultaneous-equation model consisting of aggregate demand and short-run aggregate supply is applied. The dummy variable technique is employed to detect whether the slope and intercept of the real effective exchange rate may have changed. Real depreciation during 1998.Q1 - 2006.Q3, real appreciation during 2006.Q4 - 2016.Q1, a lower domestic debt as a percent of GDP, a lower real interest rate, a higher stock price or a higher lagged real oil price would raise aggregate output. Recent trends of real peso appreciation, declining domestic debt as a percent of GDP, lower real interest rates, and rising stock prices are in line with the empirical results and would promote economic growth. The authorities may need to continue to pursue fiscal prudence and maintain a stronger peso as the positive effect of real appreciation dominates its negative effect in recent years.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.4
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• pp.663-680
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• 2010

This paper aims at analysing Regional Patterns of Farmland Price Changes for the Farmland Reverse Mortgage System. Farmland Reverse Mortgage(FRM) is a system in which the aged farmers in the rural areas receive certain amount of money monthly through the liquidation of their own farmlands for the life time. Farmland price affects the farmland annuity considerably. In the future, if the farmland price goes down than the price when the borrower joined FRM, the borrower can get profits from the pension. Based on the results, the farmland price of Kyeonggi-do is strongly related to economic growth rates(index of industrial product). while that of Gyeongsangbuk-do is weakly related to economic variables including economic growth rates. Therefore, the expectation of farmland value rising rate will be higher in Kyeonggi-do than in Gyeongsangbuk-do. Thus the number of borrowers who want to join FRM in Gyeongsangbuk-do will be more than those in Kyeonggi-do.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.274-283
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• 2022

Korea is facing a number difficulties arising from rising housing prices. As 'housing' takes the lion's share in personal assets, many difficulties are expected to arise from fluctuating housing prices. The purpose of this study is creating housing price prediction model to prevent such risks and induce reasonable real estate purchases. This study made many attempts for understanding real estate instability and creating appropriate housing price prediction model. This study predicted and validated housing prices by using the LSTM technique - a type of Artificial Intelligence deep learning technology. LSTM is a network in which cell state and hidden state are recursively calculated in a structure which added cell state, which is conveyor belt role, to the existing RNN's hidden state. The real sale prices of apartments in autonomous districts ranging from January 2006 to December 2019 were collected through the Ministry of Land, Infrastructure, and Transport's real sale price open system and basic apartment and commercial district information were collected through the Public Data Portal and the Seoul Metropolitan City Data. The collected real sale price data were scaled based on monthly average sale price and a total of 168 data were organized by preprocessing respective data based on address. In order to predict prices, the LSTM implementation process was conducted by setting training period as 29 months (April 2015 to August 2017), validation period as 13 months (September 2017 to September 2018), and test period as 13 months (December 2018 to December 2019) according to time series data set. As a result of this study for predicting 'prices', there have been the following results. Firstly, this study obtained 76 percent of prediction similarity. We tried to design a prediction model of real estate transaction price with the LSTM Model based on AI and Bigdata. The final prediction model was created by collecting time series data, which identified the fact that 76 percent model can be made. This validated that predicting rate of return through the LSTM method can gain reliability.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4D
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• pp.523-531
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• 2008

This study tried to find differences between housing lotting prices and sale prices owing to new multi-family housing price regulation. As the results of this study, they are as follows; First, this study shows housing market in Busan has a preferences of new housing which has a new housing form differing from the existing housing form. For example, the mixed-use apartment with higher stories shows steeper incline than the apartments with the existing forms. Second, the new housing prices are affected by the information that affect the price of the old existing housing. They are rates of green area of an apartment complex, the number of household, accessibility to downtown Busan and etc.. They are also confirmed factors that affect a rise of used-housing price in other studies. Third, brand value of apartments affects new housing prices. For example, if the major construction companies build the new apartment, it shows a rising trend than any other housing. Therefore, the local construction companies are expected to be put on a disadvantage places than major construction companies. Fourth, the lotting prices are the most important cause that lead to rise the new housing prices. Accordingly, the present lotting prices are expected that upward tendency the purchasing prices of the new housing will not continue, because the lotting prices have risen since the government removed lotting price regulations and exceeded the level of used-housing prices. And it denote that importance of housing sub-market which indicates rates of old existing housing market rising, frist preference Gu, second preference Gu, rate of multi-family housing.

• Journal of Korean Society of Forest Science
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• v.59 no.1
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• pp.9-14
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• 1983

The study was performed to decide the returns or investment efficiency of the fuel forest established project and to examine its economic value as compared the benefit with the cost occurring from that established by I.B.R.D loan. The data got from the surveying plot and other things connected with the project were applied to the measures of benefit-cost ratio and internal rate of return (IRR). The following are the results from the economic analysis of the fuel forest created by the loan per hectare 1) In case of converting the fuelwood value from the fuel forest into briquet price, the benefit-cost ratio is 1.18 at the 6 percent discount rate and the IRR is appraised to 12.2 percent 2) In the sensitivity analysis estimated by the rising rate of rural wages 27% the yearly mean, the B/C ratio is 1.07 at the 6 percent discount rate and the IRR 9.2 percent. 3) In the sensitivity analysis estimated by the rising rate of briquet price, 26% the yearly mean, the B/C ratio is 1.34 at the 6 percent discount rate and the IRR is appraised high to 15.7 percent 4) In the event of including indirect effects to the direct in the project, the economic effect could be increased just a little more.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.170-177
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• 2017

The price of land in Jeju is reaching a new high every day and this phenomenon not only causes real difficulties for the purchase of real estate by local residents, but also results in psychological deprivation. Therefore, this study analyzes the factors causing the increase of the land price in Jeju, in order to examine the measures required to stabilize the land price which is continuously rising. As a result of this study, we developed a land price prediction model including seven variables, including the 'inflation rate', 'interest rate', and 'population'. According to the model, land prices in Jeju are expected to rise steadily, and it is predicted that in 2020 the price will increase to 170% of that in 2015 and will triple by 2025. Based on the results of this study, this study suggested policy alternatives, such as 'Establishing a tourism policy for managing the number of tourists' and 'increasing the approval standards for development activities'. The two policies proposed in this study can be implemented as a regional initiative, which may be less effective than the changes in the national system, but it is meaningful that the efforts to stabilize the land price will continue at the regional level.

• Land and Housing Review
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• v.2 no.4
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• pp.367-377
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• 2011

This paper investigates the determination factors' variation of real estate price after sub-prime financial crisis, in korea, using a VAR model. The model includes land price, housing price, housing rent (Jensei) price, which time period is from 2000:1Q to 2011:2Q and uses interest rate, real GDP, consumer price index, KOSPI, the number of housing construction, the amount of land sales and practices to impulse response and variance decomposition analysis. Data cover two sub-periods and divided by 2008:3Q that occurred the sub-prime crisis; one is a period of 2000:1Q to 2008:3Q, the other is based a period of 2000:1Q to 2011:2Q. As a result, Comparing sub-prime crisis before and after, land price come out that the influence of real GDP is expanding, but current interest rate's variation is weaken due to the stagnation of current economic status and housing construction market. Housing price is few influenced to interest rate and real GDP, but it is influenced its own variation or Jensei price's variation. According to the Jensei price's rapidly increasing in nowadays, housing price might be increasing a rising possibility. Jensei price is also weaken the influence of all economic index, housing price, comparing before sub-prime financial crisis and it is influenced its own variation the same housing price. As you know, real estate price is weakened market basic value factors such as, interest rate, real GDP, because it is influenced exogenous economic factors such as population structural changes. Economic participators, economic officials, consumer, construction supplyers need to access an accurate observation about current real estate market and economic status.

• Environmental and Resource Economics Review
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• v.24 no.4
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• pp.727-745
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• 2015

This paper analyzes the firms' optimization behavior in response to rising demand for non-priced renewable energy in the manufacturing industries by using an input distance function. The annual estimates of the shadow price of renewable energy is derived and the trend of its shadow price over time is analyzed. The degree of substitution of renewable energy for fossil-fuels is examined. The input-based Malmquist productivity index, defined as a composite of the technical efficiency and technical change measures, is measured. The contribution of renewable energy input growth to the Malmquist index is analyzed. Empirical results indicate that the shadow price of renewable energy declined at an average annual rate of 17% over the period 1992-2012. Substitutability between renewable energy and fossil-fuels was limited. On average, a 1% increase in renewable energy would decrease Malmquist index by 0.04% per year.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.