Hybrid Geoinformatic-Statistical Analysis for Assessing Single-Family-Home Values: the Case of College Station, Texas

2004 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
A. Taibah ◽  
M. Sharkawy
Keyword(s):  
Statistical Analysis ◽  
Single Family ◽  
Family Home ◽  
College Station ◽  
Home Values

Measuring the Impact of Light Rail Systems on Single-Family Home Values: A Hedonic Approach with Geographic Information System Application

1998 ◽  
Vol 1617 (1) ◽  
pp. 38-43 ◽  
Author(s):  
Hong Chen ◽  
Anthony Rufolo ◽  
Kenneth J. Dueker
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Property Values ◽  
Light Rail ◽  
Single Family ◽  
Negative Effect ◽  
Family Home ◽  
Home Values ◽  
The Impact ◽  
Positive Effect

In theory, proximity to light rail transit (LRT) may have two different effects on residential property values. On the one hand, accessibility (proximity to LRT stations) may increase property values. On the other hand, nuisance effects (proximity to the LRT line and stations) may decrease property values. Existing empirical studies are inconclusive, and failure to separate the effects of accessibility from the nuisance effects may explain some of the ambiguity. An examination is presented of the impact of the light rail system (MAX) in Portland, Oregon, on single-family home values using distance to rail stations as a proxy for accessibility and distance to the line itself as a proxy for nuisance effects. Geographic information system techniques are employed to create spatial-related variables and merge data from various sources. The study results confirm the hypothesis that the light rail has both a positive effect (accessibility effect) and a negative effect (nuisance effect) on single-family home values. The positive effect dominates the negative effect, which implies a declining price gradient as one moves away from LRT stations for several hundred meters. Without controlling for the nuisance effect of the distance to the rail line, the estimated coefficients on distance from stations appear to be biased and would underestimate the accessibility effect. The finding of an independent nuisance effect suggests that previous hedonic models may have reached contradictory results because the nuisance effect differs with different types of rail or other local characteristics.

scholarly journals Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

2020 ◽  
Vol 17 (17) ◽  
pp. 6359 ◽  
Author(s):  
Quynh C. Nguyen ◽  
Yuru Huang ◽  
Abhinav Kumar ◽  
Haoshu Duan ◽  
Jessica M. Keralis ◽  
...  
Keyword(s):  
Computer Vision ◽  
Built Environment ◽  
Local Area ◽  
Physical Disorder ◽  
Single Family ◽  
Google Street View ◽  
Environment Effects ◽  
Street View ◽  
Sociodemographic Disparities ◽  
Family Home

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

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