• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.879-896
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• 2013

The assessed land values and housing prices have been widely utilized as a basic information for the land and house trades and for evaluating governmental and local taxes. However, there exists a price difference in actual markets between the assessment level and assessed land values or housing prices. This paper emphasizes the spatial mismatch between the assessed land values and housing market prices and particularly addresses the following two aspects by focusing on spatial effects of the modifiable areal units, which would substantially affect the estimation of the assessed land values and housing prices. First, we examine the spatial distributions of the assessed land values and housing market prices, and the gap between those prices, on the basis of the aggregated spatial units(i.e., aggregation districts). Second, we explore the scale effect of the MAUP(modifiable areal unit problem) generally embedded in estimating the prices of the sampled standard lands and houses, and calibrating the correction index for the land values and housing prices for the individuals. For the application, we analysed the land values and housing prices in Seoul utilizing GIS and statistical software. As a result, some spatial clusters that the housing market prices are significantly higher than the assessed land values were identified at a finer geographic level. Also, it was empirically revealed that the statistical results from the regression of regional variables on the assessed land values for the individuals are significantly affected by the aggregation levels of the spatial units.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.43-49
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• 2020

The purpose of this study was to analyze factors affecting the housing market by setting household loans and M2, which are liquidity indicators, and the industrial production index reflecting economic fluctuations, as variables, and to determine the effect of expected housing prices. An empirical analysis was conducted based on the data from January 2005 to May 2020, and the HP filter was applied to the real house price as the expected house price variable. As a result of the analysis, it was found that real household loans, real M2, and so on, had an effect on house prices, and expectations for past house prices and house prices increased the house prices in the present period. These results show that even though the liquidity expansion is aimed at revitalizing the economy, it can affect housing prices as well.

• Journal of the Economic Geographical Society of Korea
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• v.16 no.4
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• pp.631-646
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• 2013

This paper empirically explores the pattern of causality between bank lending and housing prices in Korea over a period of the early 1990s to the end of 2000s by employing a long term cointegration and short-term time series regression analysis. Although the contemporaneous correlation between bank lending and housing prices is large, the analysis shows that the intense interaction between credit growth and bank lending to household arises from a growth in banking lending responding to an increase in housing prices. In addition, the regulatory change such as the introduction of financial constraints on bank loans such as LTV and DTI in the early and mid-2000s has played a significant role in stabilizing financial and real estate markets.

• Journal of KIBIM
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• v.10 no.3
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• pp.54-62
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• 2020

There is growing consensus among planners and policymakers that brownfield remediation has positive impacts on neighborhoods in terms of housing prices, public health, and environmental quality. However, there is a limited understanding of how brownfield redevelopments spatially affect neighborhood housing turnover and stability. This paper addresses the spatial impacts of brownfield redevelopments on neighboring housing turnover in Cuyahoga County, Ohio. This study examines housing turnover before and after the remediation of brownfield sites countywide and in housing submarkets stratified by household income. Based on housing sales data between 1996 and 2007, the extended Cox Hazard model with the difference-in-difference approach is employed to clarify the causal relationships between brownfield redevelopments and neighboring housing turnover. Additionally, along with the results of the previous study examining impacts of brownfield remediation on nearby housing prices, this paper estimates the change of neighborhood stability due to brownfield redevelopments based on both attributes of housing prices and turnovers.

• 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.

• International Area Studies Review
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• v.14 no.3
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• pp.246-263
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• 2010

This study examines how the long-run relations between housing price and inflation in the United Sates have changed since the year of 2000. Johansen co-integration test, estimation of long-run equilibrium equation, and Granger causality tests are conducted, based on the VECM. Data covers the period from the first quarter of 1975 to the second quarter of 2010. I adopt the recursive estimation method in which the final period of the estimation is expanded by one quarter, starting from the first quarter of 2000. The empirical results are as follows: (1) In spite of the sharp increase of housing price, the long-run relationship of house prices and inflation has been remained stable until 2007, showing that house prices are a stable inflation hedge in the long run. (2) The housing price plunge since 1997 does not seem to be related to the restore of the long-run relationship between housing prices and inflation. (3) Granger causality test results support the hypothesis that inflation granger-causes housing prices with 10% significance level, but reject the hypothesis that housing price granger-causes inflation.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.27-43
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• 2020

Purpose - This study explores the impact of the real estate industry on related industries for the perspective of Chinese steel companies. Design/methodology/approach - The impact of housing prices on the 41 listed steel companies' performance was analyzed by using the panel data model. We used two kinds of housing price indexes that are set in the panel data models to estimate the range of the real estate market, driving the performance growth of steel listed companies. Moreover, the net profit of steel companies is used as the dependent variable. To test the stability of the model, ROA used as a dependent variable for the robustness test. Also, to avoid the time trend of housing prices, this paper selects the growth rate of housing prices as the primary research variable. After Fisher-type testings, there is no unit root problem in both independent and dependent variables. Findings - The results indicated that the rise in the housing price has a positive influence on the steel company performance. When the housing price increases by 1%, the net profit of steel enterprises will increase by 5 to 20 million yuan. Research implications or Originality - In this paper, empirical data at the micro-level and panel model are used to quantify China's real estate industry's driving effect on the iron and steel industry, providing evidence from the microdata level. It helps us to understand further the status and role of China's real estate industry in the economic structure.

• Journal of Cadastre & Land InformatiX
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• v.45 no.2
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• pp.203-216
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• 2015

This study focuses on the relationship between housing sale prices and Jeonse prices, amid a recent surge of Jeonse price and Jeonse-to-housing sale price ratio. There are many studies about the relationship between house prices and Jeonse, but they couldn't fully explain what makes them spike up. In addition to this relationship, this paper deals with the difference of Jeonse system on regions and price levels. Using Granger causality and Spearman's Correlation Coefficient, the outcome is drawn. As the result, the expected rate for housing sale prices effects on the Jeonse-to-housing sale price ratio. The higher on sale price, the lower the Jeonse-to-housing sale price ratio regarding the region difference.

• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

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