Retail accessibility and proximity effects on housing prices in Seoul, Korea: A retail type and housing submarket approach

Jang, Myungjun; Kang, Chang-Deok

doi:10.1016/j.habitatint.2015.07.004

Cited by 91 publications

(49 citation statements)

References 25 publications

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“…First, we did not consider the service capacity of amenities and variations of distance decay in the accessibility assessment; and the spatial separation to amenities can be network-based [62,73] or follows a power law [12], thus mediating the correlation [13]. Second, in the future, researchers should focus on communities located in a smaller urban sector rather than an entire city to minimize the impact of geographical heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

“…The vital role that public transit plays can also be extended to suburban regions, where the housing units located within the transit-oriented development zones are priced much higher than the units further away [29]. In addition, property value increases due to other retail amenities, such as convenience stores, supermarkets, and drugstores that cater to nearby residents [11,12,16]. In this regard, exploring the effect of price determinants, especially accessibility to public transit and retail amenities, is a fundamental step towards understanding the Chinese housing market.…”

Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

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Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

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Abstract:The housing market in Chinese metropolises have become inflated significantly over the last decade. In addition to an economic upturn and housing policies that have potentially fueled the real estate bubble, factors that have contributed to the spatial heterogeneity of housing prices can be dictated by the amenity value in the proximity of communities, such as accessibility to business centers and transportation hubs. In the past, scholars have employed the hedonic pricing model to quantify the amenity value in relation to structural, locational, and environmental variables. These studies, however, are limited by two methodological obstacles that are relatively difficult to overcome. The first pertains to difficulty of data collection in regions where geospatial datasets are strictly controlled and limited. The second refers to the spatial autocorrelation effect inherent in the hedonic analysis. Using Beijing, China as a case study, we addressed these two issues by (1) collecting residential housing and urban amenity data in terms of Points of Interest (POIs) through web-crawling on open access platforms; and (2) eliminating the spatial autocorrelation effect using the Eigenvector Spatial Filtering (ESF) method. The results showed that the effects of nearby amenities on housing prices are mixed. In other words, while proximity to certain amenities, such as convenient parking, was positively correlated with housing prices, other amenity variables, such as supermarkets, showed negative correlations. This mixed finding is further discussed in relation to community planning strategies in Beijing. This paper provides an example of employing open access datasets to analyze the determinants of housing prices. Results derived from the model can offer insights into the reasons for housing segmentation in Chinese cities, eventually helping to formulate effective urban planning strategies and equitable housing policies.

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Section: Discussionmentioning

confidence: 99%

Section: Housing Development and Data Issues In Chinamentioning

confidence: 99%

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Ying

Chen

et al. 2017

IJGI

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“…Jang and Kang [20] focused their attention on the retail sector and investigated spatial accessibility and proximity effects on housing prices. The multilevel hedonic price model was used to isolate differential effects of accessibility by retail type in housing price submarkets by managing housing attributes, location and transportation characteristics, and neighborhood land-use features.…”

Section: Urban Vibrancymentioning

confidence: 99%

“…In real estate research, the importance of market segmentation for property price prediction is assumed to explain property prices across space. Therefore, the integration of approaches for modeling spatial effects has been fundamental [20,33].…”

Section: Housing Prices and Spatial Analysesmentioning

confidence: 99%

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

2020

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Urban vibrancy is defined and measured differently in the literature. Originally, it was described as the number of people in and around streets or neighborhoods. Now, it is commonly associated with activity intensity, the diversity of land-use configurations, and the accessibility of a place. The aim of this paper is to study urban vibrancy, its relationship with neighborhood services, and the real estate market. Firstly, it is used a set of neighborhood service variables, and a Principal Component Analysis is performed in order to create a Neighborhood Services Index (NeSI) that is able to identify the most and least vibrant urban areas of a city. Secondly, the influence of urban vibrancy on the listing prices of existing housing is analyzed by performing spatial analyses. To achieve this, the presence of spatial autocorrelation is investigated and spatial clusters are identified. Therefore, spatial autoregressive models are applied to manage spatial effects and to identify the variables that significantly influence the process of housing price determination. The results confirm that housing prices are spatially autocorrelated and highlight that housing prices and NeSI are statistically associated with each other. The identification of the urban areas characterized by different levels of vibrancy and housing prices can effectively support the revision of the urban development plan and its regulatory act, as well as strategic urban policies and actions. Such data analyses support a deep knowledge of the current status quo, which is necessary to drive important changes to develop more efficient, sustainable, and competitive cities.

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“…The retail and services are an important factor behind urban development, and generally accessibility to services and shops increase residential satisfaction and decrease household overall commuting costs and time (Glaeser, Kolko, & Saiz, 2001). Despite potential negative externalities most studies found that the accessibility of convenience stores as well as public services in most cases has a positive impact on house prices (Chiang, Peng, & Chang, 2015;Jang & Kang, 2015;Koster & Rouwendal, 2012;Song & Sohn, 2007).…”

Section: Externalities and Real Estate Valuementioning

confidence: 99%

Externalities and House Prices: A Stated Preferences Approach

Głuszak¹

2018

EBER

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Retail accessibility and proximity effects on housing prices in Seoul, Korea: A retail type and housing submarket approach

Cited by 91 publications

References 25 publications

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Exploring Determinants of Housing Prices in Beijing: An Enhanced Hedonic Regression with Open Access POI Data

Urban Vibrancy: An Emerging Factor that Spatially Influences the Real Estate Market

Externalities and House Prices: A Stated Preferences Approach

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