Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model

Yamagata, Yoshiki; Murakami, Daisuke; Yoshida, Takahiro; Seya, Hajime; Kuroda, Sho

doi:10.1016/j.landurbplan.2016.02.008

Cited by 52 publications

(36 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The explanatory variables of housing prices have been widely discussed in the housing literature. Bangura, Lee and Al-Masum discussed the ability of market fundamentals in explaining housing prices from the macroeconomic perspective [15][16][17], while Trojanek and Yamagata examined the importance of housing attributes in explaining housing prices from the microeconomic perspective [18,19]. In recent years, a great deal of research has studied the impacts of environmental elements on housing price.…”

Section: Introductionmentioning

confidence: 99%

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

Chen

Yao

Zhu

et al. 2020

IJGI

View full text Add to dashboard Cite

Diverse urban environmental elements provide health and amenity value for residents. People are willing to pay a premium for a better environment. Thus, it is essential to assess the benefits and values of these environmental elements. However, limited by the interpretability of the machine learning model, existing studies cannot fully excavate the complex nonlinear relationships between housing prices and environmental elements, as well as the spatial variations of impacts of urban environmental elements on housing prices. This study explored the impacts of urban environmental elements on residential housing prices based on multisource data in Shanghai. A SHapley Additive exPlanations (SHAP) method was introduced to explain the impacts of urban environmental elements on housing prices. By combining the ensemble learning model and SHAP, the contributions of environmental characteristics derived from street view data and remote sensing data were computed and mapped. The experimental results show that all the urban environmental characteristics account for 16 percent of housing prices in Shanghai. The relationships between housing prices and two green characteristics (green view index from street view data and urban green coverage rate from remote sensing) are both nonlinear. Shanghai's homebuyers are willing to pay a premium for green only when the green view index or urban green coverage rate are of higher value. However, there are significant differences between the impacts of the green view index and urban green coverage rate on housing prices. The sky view index has a negative influence on housing prices, which is probably because the high-density and high-rise residential area often has better living facilities. Residents in Shanghai are willing to pay a premium for high urban water coverage. The case of Shanghai shows that the proposed framework is practical and efficient. This framework is believed to provide a tool to inform the decisions of housing buyers, property developers and policies concerning land-selling and buying, property development and urban environment improvement.

show abstract

Section: Introductionmentioning

confidence: 99%

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

Chen

Yao

Zhu

et al. 2020

IJGI

View full text Add to dashboard Cite

show abstract

“…In the urbanscape, what urban dwellers value (as reflected via hedonic price analysis) is not the visibility per se, but the visual quality associated with the structure, colour, environmental image, etc. (Yamagata et al, ). For residents of RRC, close contact with polluted urban streams may have reinforced their perception that such watercourses could be a source of discomfort and health hazard (Procopiuck & Rosa, ).…”

Section: Resultsmentioning

confidence: 99%

Impacts of urban stream pollution: A comparative spatial hedonic study of high‐rise residential buildings in Guangzhou, south China

Chen

2018

Geographical Journal

View full text Add to dashboard Cite

Urban streams are scarce natural elements in compact cities, and suffer from pollution in the course of city expansion and densification, especially in developing nations like China. They may offer the amenity of a riverscape view, but also the dis-amenity associated with water pollution. Yet whether and how polluted urban streams affect high-rise residential property values remain under-investigated.Based on a total of 315 transaction records of apartment sales in two residential precincts located near two typical urban streams in Guangzhou, south China, this study attempts to assess the impacts of urban river pollution, in terms of view and proximity, at a neighbourhood level. A novel "cube contiguity", consisting of a three-dimensional spatial weighting matrix, is developed to incorporate the effect of property height in hedonic price models. Comparison of the spatial hedonic analysis of the two selected residential precincts reveals that views of heavily polluted urban streams fail to command a premium, and the impacts of proximity to urban streams vary considerably in high-rise, compact urban contexts, when micro-level landscape variations are carefully represented in hedonic modelling.This could be associated with the hydro-morphological features and riverine landscape, as well as with homebuyers' subjective perception of river pollution.Assessing the impacts of urban stream pollution could provide a basis for understanding the negative externalities of urban river pollution, establishing priorities for restoring polluted urban streams, and planning the provision of multi-functional green-blue spaces with respect to homebuyers' demand for environmental amenities in mid-and high-rise housing markets in both developed and developing nations. K E Y W O R D Scube contiguity, Guangzhou, high-rise residential building, spatial hedonic model, urban river pollution

show abstract

“…Cities are not homogeneous two-dimensional (2D) systems as we implicitly assumed; rather, urban physical form is normally heterogeneous and has 3-4 essential dimensions determined by physical characteristics, such as topography, 3D urban street canyons, and microclimate (see, [33][34][35]). Residents and their behavioral patterns are also heterogeneous.…”

Section: Discussionmentioning

confidence: 99%

Electricity Self-Sufficient Community Clustering for Energy Resilience

et al. 2016

Self Cite

View full text Add to dashboard Cite

Local electricity generation and sharing has been given considerable attention recently for its disaster resilience and other reasons. However, the process of designing local sharing communities (or local grids) is still unclear. Thus, this study empirically compares algorithms for electricity sharing community clustering in terms of self-sufficiency, sharing cost, and stability. The comparison is performed for all 12 months of a typical year in Yokohama, Japan. The analysis results indicate that, while each individual algorithm has some advantages, an exhaustive algorithm provides clusters that are highly self-sufficient. The exhaustive algorithm further demonstrates that a clustering result optimized for one month is available across many months without losing self-sufficiency. In fact, the clusters achieve complete self-sufficiency for five months in spring and autumn, when electricity demands are lower.

show abstract

Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model

Cited by 52 publications

References 56 publications

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

Impacts of urban stream pollution: A comparative spatial hedonic study of high‐rise residential buildings in Guangzhou, south China

Electricity Self-Sufficient Community Clustering for Energy Resilience

Contact Info

Product

Resources

About