• International conference on construction engineering and project management
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• 2013.01a
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• pp.28-35
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• 2013

The construction industry has introduced the Leadership in Energy and Environmental Design (LEED) rating system to promote objective evaluations of the sustainability of buildings. Three important values to consider when implementing sustainability are the associated environmental, social, and economic impacts. Recently, researchers have begun to investigate the real estate value of LEED certified buildings in terms of the rental cost, occupancy rate, cost per unit area, and resale value in order to better understand the economic benefits of the LEED rating system. However, the economic benefits also encompass economic effects such as the impact of LEED certified buildings on neighborhood real estate values surrounding the certified buildings. This research examines whether the enhanced real estate value of LEED certified buildings in New York City extends to surrounding commercial buildings, utilizing spatial analysis via a Geographic Information System (GIS) and the hedonic pricing method to derive meaningful economic relationships. The results provide practical insights into the economic effect of LEED certified buildings that will be of interest to city officials and planners, as well as the owners, developers, investors and other stakeholders of surrounding buildings.

• Spatial Information Research
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• v.10 no.2
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• pp.233-246
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• 2002

The purpose of this paper is to quantitatively measure the effect of location in evaluating the land value through the implementation of GIS coupled with spatial statistical analysis. We assumed that the hedonic price model, which was commonly used in modelling the land value, could not explain the spatial factor effectively. In order to add the spatial factor, the analysis of the spatial autocorrelation was used. The present project used 54 standard land price samples from 1421 parcel land values and applied Kriging to predict stochastically the unsampled values on the basis of spatial autocorrelation between location of vector data. This study confirms that the spatial variogram analysis has an advantage of predicting spatial dependence process and revealing the positive premium and the negative penality on location factor objectively.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Journal of Korean Society of Rural Planning
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• v.30 no.2
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• pp.1-11
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• 2024

Farmland transaction prices are affected by various factors such as politics, society, and the economy. The purpose of this study is to identify multiple factors that affect the farmland transaction price due to changes in the actual transaction price of farmland by farmland unit from 2016 to 2020. There are several previous studies analyzed the determinants of farmland transaction prices by considering spatial dependency. However, in the case of land transactions where the time and space of the transaction affect simultaneously, if only spatial dependence is considered, there is a limitation in that it cannot reflect spatial dependence that occurs over time. In order to solve these limitations, To address these limitations, this study builds a spatio-temporal autoregressive model that simultaneously considers spatial and temporal dependencies using farmland transactions in Jinju City as an example. As a result of the analysis, it was confirmed that there was significant spatio-temporal dependence in farmland transactions within the previous 30 days. This means that if the previous farmland transaction was carried out at a high price, it has a spatio-temporal spillover effect that indirectly affects the increase in the price of other nearby farmland transactions. The study also found that various location attributes and socioeconomic attributes have a significant impact on farmland transaction prices. The spatio-temporal autoregressive model of farmland prices constructed in this study can be used to improve the prediction accuracy of farmland prices in the farmland transaction market in the future, and it is expected to be useful in drawing policy implications for stabilizing farmland prices

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.1
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• pp.43-51
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• 2012

The OLS(ordinary least squares) method is widely used in hedonic housing models. One of the assumptions of the OLS is an independent and uniform distribution of the disturbance term. This assumption can be violated when the spatial autocorrelation exists, which in turn leads to undesirable estimate results. An alterative to this, spatial econometric models have been introduced in housing price studies. This paper describes the comparisons between OLS and spatial econometric models using housing transaction prices of Busan, Korea. Owing to the approaches reflecting spatial autocorrelation, the spatial econometric models showed some superiority to the traditional OLS in terms of log likelihood and sigma square(${\sigma}^2$). Among the spatial models, the SAR(Spatial Autoregressive Models) seemed more appropriate than the SAC(General Spatial Models) and the SEM(Spatial Errors Models) for Busan housing markets. We can make sure the spatial effects on housing prices, and the reconstruction plans have strong impacts on the transaction prices. Selecting a suitable spatial model will play an important role in the housing policy of the government.

• Land and Housing Review
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• v.15 no.2
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• pp.89-106
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• 2024

Apartments constitute 64% of the housing type composition, representing the highest proportion among housing types. This proportion has been increasing annually. Given this trend, apartment prices are likely to have a significant impact on the national economy and people's livelihoods. This study examines the impact of the recent development of Seodaegu Station on the surrounding apartment market, with a specific focus on the effects of the educational environment. To this end, we conduct empirical analysis employing a hedonic price model and spatial autocorrelation analysis, based on actual transaction price data from the Ministry of Land, Infrastructure, and Transport. The study revealed three key findings: first, the development of Seodaegu Station positively impacted apartment prices. Second, this positive effect increases with the proximity to Seodaegu Station. Third, the enhancement of the educational environment nearby the Seodaegu Station development also positively influenced apartment prices. This study aims to serve as baseline research output for the public management of future metropolitan transportation facility development projects and for predicting apartment price trends.

• Environmental and Resource Economics Review
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• v.18 no.2
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• pp.311-344
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• 2009

The hypothesis that the common market equilibria for housing attributes are attained within distinctive submarkets was tested. Markets for housing attributes with greater supply flexibility, i.e., structural variables, were found to be closer to their common equilibria than markets with less supply flexibility, i.e., neighborhood and distance variables. In addition, submarkets with greater mobility were found to achieve common market equilibria for more housing attributes with greater supply flexibility, but not for housing attributes with lower degree of supply flexibility. Results suggest supply flexibility and occupier mobility are both necessary conditions for achieving common market equilibria for housing attributes.

• Journal of the Korean Geographical Society
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• v.39 no.6 s.105
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• pp.907-921
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• 2004

Lots of characteristics such as dwelling, neighborhood, and accessibility characteristics affect to the housevalue. Many researches have been done to identify values of each characteristic using hedonic technique. However, there is a limit to identify interaction of each characteristic and variation of each characteristic among the accessibility context. This paper has implemented the Expansion Method research paradigm to model the housevalue determination process in the city of Seoul. The findings of this paper have revealed the presence of contextual variations in the housevalue determination process. The initial model for housevalue reveals that as $F_1$ increases (i.e., larger the number of rooms/bathrooms, larger parking space) and/or $F_2$ increases (i.e., higher owner occupied housing units, higher apartment housing units) and/or $F_3$ increases, (i.e., higher the ratio of higher than college graduated households, 8 school zone, older housing units) the estimated housevalue increases. However, the above relationships drift across their respective contexts. The houses which have negative $F_1$ value, the housevalue does not fluctuate according to the distance to the city center or subcenters. However, the houses which have positive $F_1$ value, the closer to the subcenters or shorter to the river, the higher the estimated housevalues. On the other hand, in areas far from the subcenters, the estimated housevalues does not fluctuate much according to the corresponding $F_2$ level. In areas close to the subcenters, the estimated housevalues vary tremendously according to the $F_2$ value. In the residual analysis, it is revealed that large apartment which are located in Kangnam, IchongDong, MokDong are underestimated. This paper has contributed to our understanding of the housevalue determination process by providing an alternative conceptualization to the traditional approach.

• Management & Information Systems Review
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• v.37 no.1
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• pp.155-175
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• 2018

Lots of previous researches on determinants of apartment prices in Korea consider spatial dependence while few studies regard endogeneity of spatial lag by adding a spatial lag to an OLS regression. Thus, this study intends to include this spatial lag in its analysis of determinants of apartment price in Busan by using a two-stage quantile regression. The empirical results are : the coefficient of spatial lag variable is more than 0.5 and is statistically significant at 1% level. From this result we can confirm that the effect of the price of nearby apartment on that of another apartment is very big. We also find that apartment buyers prefer larger size, height in both the total floors and living floor, south-facing living room with a ocean view, and proximity to metros, high school and coast. Unlike our expectation, however, mountain view is less favored than building view, which we can guess is because apartments with mountain views are mostly located in the low-priced apartment area where some of their living rooms face north. Quantile regression also explains the effect of hedonic characteristics on apartment price better than OLS estimation. For instance, the effect of south facing living room variable on the price is twice larger in high-price apartments than in low-price counterparts. And the effect of vicinity to the coast or the ocean is ten times bigger in high priced apartments.