Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

Won, Jaewoong; Lee, Jae-Su

doi:10.3390/su10010031

Cited by 21 publications

(20 citation statements)

References 24 publications

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“…Neighborhood environment is also a significant attribute for houses. In addition, residents prefer to live proximate to open spaces [17,18], commercial centers [38], primary and middle schools [39], and universities [40]. Building structure, which refers to the internal attributes of a house's structure, also play an important role in housing utility value.…”

Section: Selection Of Explanatory Variablesmentioning

confidence: 99%

Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

Zhang

Lin

2019

IJGI

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In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.

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Section: Selection Of Explanatory Variablesmentioning

confidence: 99%

Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

Zhang

Lin

2019

IJGI

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“…Won and Lee [15] paid attention to how the rents of the URHs were determined in Seoul using the spatial hedonic price model. They reported that factors such as net residential area and accessibilities to subway and public facilities exerted positive impacts on rents [32].…”

Section: Review Of Relevant Studiesmentioning

confidence: 99%

“…A number of studies have analyzed the characteristics and the effects of the small houses on their neighborhoods, or investigated the pricing mechanism and affordability and suggested policy implications [3,[6][7][8][9][10][11][12][13][14][15][16]. Also, earlier studies have focused on analyzing the residential characteristics and the residential satisfaction factors of single and two-person households as the primary consumers of small urban residences [13,[17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Locational and Neighborhood Environmental Factors on the Spatial Clustering Pattern of Small Urban Houses: A Case of Urban Residential Housing in Seoul

Shin

Lee

2019

Sustainability

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Small housing has gained prominence in the housing market due to substantial shifts of both population and household structures in Korea. The study aims to examine the spatial clustering pattern of urban residential houses (URHs) in terms of the spatial statistics, and analyze the impacts of both locational and neighborhood residential factors on the formation of the spatial clustering pattern in the Seoul metropolitan area. It is found that the URHs represent a significant spatial clustering pattern based on the census tracts. The hotspots are mostly found in the outskirts of Seoul, where the development of small houses has been concentrated for ten years. Also, both locational and neighborhood residential factors have significant effects on the hotspot formation of the URH. The hotspots are more likely to be found in areas having many available sites for development, high demand for habitation, and high expected profits from developing small residences. Another significant factor is access to public transit, amenities, and employment centers. Therefore, it is necessary to design a rent subsidy program for potential residents and to encourage the development of housing in nearby hotspots with relatively lower land values. It is also important to establish policies and strategies for planning the housing supply and managing the residential environment in low-rise residential areas.

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“…The externalities and spillover effect in housing markets have attracted growing scholarly interest [1][2][3][4][5][6][7]. Extensive studies have been reported for the housing markets in the developed world [8][9][10][11], while developing countries have also begun to pay attention [7,[12][13][14][15]. Meen [16] provides convincing economic explanations for the driving forces of spillover and summarizes four major mechanisms by which the housing price spillover can occur: migration, equity transfer, spatial arbitrage, and spatial patterns.…”

Section: Introductionmentioning

confidence: 99%

Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model

Zhang

Zheng

et al. 2020

IJGI

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This study analyzed the spillovers among intra-urban housing submarkets in Beijing, China. Intra-urban spillover imposes a methodological challenge for housing studies from the spatial and temporal perspectives. Unlike the inter-urban spillover, the range of every submarket is not naturally defined; therefore, it is impossible to evaluate the intra-urban spillover by standard time-series models. Instead, we formulated the spillover effect as a Markov chain procedure. The constrained clustering technique was applied to identify the submarkets as the hidden states of Markov chain and estimate the transition matrix. Using a day-by-day transaction dataset of second-hand apartments in Beijing during 2011–2017, we detected 16 submarkets/regions and the spillover effect among these regions. The highest transition probability appeared in the overlapped region of urban core and Tongzhou district. This observation reflects the impact of urban planning proposal initiated since early 2012. In addition to the policy consequences, we analyzed a variety of spillover “types” through regression analysis. The latter showed that the “ripple” form of spillover is not dominant at the intra-urban level. Other types, such as the spillover due to the existence of price depressed regions, play major roles. This observation reveals the complexity of intra-urban spillover dynamics and its distinct driving-force compared to the inter-urban spillover.

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Investigating How the Rents of Small Urban Houses are Determined: Using Spatial Hedonic Modeling for Urban Residential Housing in Seoul

Cited by 21 publications

References 24 publications

Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

The Impacts of Locational and Neighborhood Environmental Factors on the Spatial Clustering Pattern of Small Urban Houses: A Case of Urban Residential Housing in Seoul

Detecting Intra-Urban Housing Market Spillover through a Spatial Markov Chain Model

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