• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2019.05a
• /
• pp.226-227
• /
• 2019

The basic type construction cost which is the base of the building cost estimation is being adjusted according to the price changes by utilizing the apartment construction cost index in order to flexibly operate it. In this study, we analyzed the change trends and characteristics of the housing cost index for the basic type building cost model project operated from September, 2012 to March, 2018. As a result, the increase in material costs is slight while the share of the labor cost increased in the construction cost due to the rise of labor unit price, leading to a perceived increase in sensitivity of labor costs. We should be careful to keep the sensitivity of the material cost and the labor cost to an appropriate level so that the index may not be distorted.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.44-48
• /
• 2015

Housing price is a key indicator of housing demand. Actual Transaction Price Index of Apartment (ATPIA) released by Korea Appraisal Board is useful to understand the current level of housing price, but it does not forecast future prices. Big data such as the frequency of internet search queries is more accessible and faster than ever. Forecasting future housing demand through big data will be very helpful in housing market. The objective of this study is to develop a forecasting model of ATPIA as a part of forecasting housing demand. For forecasting, a concept of time shift was applied in the model. As a result, the forecasting model with the time shift of 5 months shows the highest coefficient of determination, thus selected as the optimal model. The mean error rate is 2.95% which is a quite promising result.

• Journal of the Korea Institute of Building Construction
• /
• v.8 no.6
• /
• pp.65-74
• /
• 2008

The aim is to get comprehensive view point for the price of apartment. Apartment construction cost is the sun of land cost and building cost. Land price reflects the value of location where building stands. When the gap between price and affordability is narrow enough, effective demand promote apartment construction. The today's trends of rising price, which began in apartment housing, spreads to real estates market and finally overall consumer price. Problem is that price is decided only by supplier's interest. Equilibrium-pricing is common process in housing market. However it is important to review hedonic price and the factor of housing services and focused on the affordability of demanders. AHP analysis was used to study real needs and preference of demanders and dealt with 200 interviewees with brief checklists. We found that social factor is more important than building cost or site development. Especially location of apartment is most important to affect environment quality and accessibility to facilities.

• KIEAE Journal
• /
• v.7 no.3
• /
• pp.11-20
• /
• 2007

The purpose of this study is to analyse the effects of the eco-friendly factors on the apartment housing price rise and to suggest the desirable way of marketing strategy for apartment housing. For the analysis, the data of apartment sites in Seoul had been collected from September 2006 to February 2007. The data consisted of 95 apartment sites in Seoul. Data were analyzed with descriptives, crosstabs, and discriminant analysis by SPSS/PC for Window. Following result was obtained. The eco-friendly apartment housing price rate in Seoul was determined by eco-friendly landscape, green space rate, house unit size, installment sale price per pyeong, floor space index, distance from subway station when it was not considered the impact of building age, construction company's brand, and autonomous districts. Findings of this research can provide valuable information for marketing strategy of housing construction company.

• The Korean Journal of Applied Statistics
• /
• v.30 no.2
• /
• pp.271-280
• /
• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• Land and Housing Review
• /
• v.13 no.4
• /
• pp.45-53
• /
• 2022

This paper investigates empirically the lead-lag relation between the 'apartment price index' and 'Internet search volume'. This study uses Naver Trend Index as a proxy for Internet search volume. An increase in Internet search volume on the apartment price index indicates an increase in people's attention to an apartment. Different from previous studies exploring the relation between 'the released price index of the apartment' and 'Naver Trend Index', this study investigates the relation of the Naver Trend Index with 'the fundamental price component of an apartment' and 'the transitory price component of an apartment', respectively. The results of the Granger causality test reveal that there are bidirectional Granger causalities between the 'released price' and Naver Trend Index. In addition, the 'fundamental price component of an apartment' and Naver Trend Index have a feedback relation, while 'the transitory price component of an apartment' Granger causes the Naver Trend Index uni-directionally. The impulse response function analysis indicates that the shock of apartment prices increases Naver Trend Index in the first month. Overall, The close relationship between apartment prices and Naver Trend Index suggests that increases in the movement of apartment prices are positively associated with public attention on the apartment market.

• Journal of Cadastre & Land InformatiX
• /
• v.52 no.2
• /
• pp.211-231
• /
• 2022

This study attempted to identify the effects of macroeconomic variables such as the All Industry Production Index, Consumer Price Index, CD Interest Rate, and KOSPI on apartment lease prices divided into nationwide, Seoul, metropolitan, and region, and to present a methodological prediction model of apartment lease prices by region using Long Short Term Memory (LSTM). According to VAR analysis results, the nationwide apartment lease price index and consumer price index in Lag1 and 2 had a significant effect on the nationwide apartment lease price, and likewise, the Seoul apartment lease price index, the consumer price index, and the CD interest rate in Lag1 and 2 affect the apartment lease price in Seoul. In addition, it was confirmed that the wide-area apartment jeonse price index and the consumer price index had a significant effect on Lag1, and the local apartment jeonse price index and the consumer price index had a significant effect on Lag1. As a result of the establishment of the LSTM prediction model, the predictive power was the highest with RMSE 0.008, MAE 0.006, and R-Suared values of 0.999 for the local apartment lease price prediction model. In the future, it is expected that more meaningful results can be obtained by applying an advanced model based on deep learning, including major policy variables

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1361-1369
• /
• 2014

This research is for analyzing the change rate of housing rent price index produced by KAB (Korea Appraisal Board) in the monthly periodical, Survey on Housing Monthly Rent. The index is a very important and useful indicator to understand and diagnose the house rental market. However, the index is criticized in that it tends to decline when the price level of Jeonse (i.e., a typical type of dwellings in Korea, generally leased on a deposit basis for 1 or 2 years) is highly going up, which is inconsistent with the actual economic sentiment of tenants. We verify the reason why such phenomenon occurs and suggest a simple but novel method to analyze properly the change rate of the index. The main findings are as follows. The key factor to trigger the problem is the use of the conversion rate for Jeonse-to-monthly rent for constructing the rent price indexes. We separate the effect of the conversion rate out of the change rate of the index and quantify the adjusted real change rate showing an increase of the rent price level which is masked by the conversion rate before.

• Korea Real Estate Review
• /
• v.27 no.3
• /
• pp.71-86
• /
• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.463-470
• /
• 2016

In analysing a time series on the frequency domain, the spectral estimator (or periodogram) is a very useful statistic to identify the periods of a time series. However, the spectral estimator is very sensitive in nature to outliers, so that the spectral estimator in terms of M-estimation has been studied by some researchers. Pak (2001) proposed an empirical method to choose a tuning parameter for the Huber's M-estimating function. In this article, we try to implement Pak's estimation proposal in the spectral estimator. We use the Korean housing price index as an example data set for comparing various M-estimating results.