Scenic landscapes, visual accessibility and premium values in a single family housing market: A spatial hedonic approach

2017 ◽  
Vol 46 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Jay Mittal ◽  
Sweta Byahut
Keyword(s):  
Real Estate ◽  
Regression Model ◽  
Geographically Weighted Regression ◽  
Local Governments ◽  
Mean Value ◽  
Weighted Regression ◽  
Single Family ◽  
Land Protection ◽  
Geographically Weighted Regression Model ◽  
Visibility Index

This article uses a hedonic modelling approach to assess the implicit willingness to pay for the visual accessibility of voluntarily protected, privately owned, scenic lands based on single family houses. These lands are perpetually protected to preserve natural, historic, and scenic characteristics. The capitalized house premium was captured using a visual accessibility variable, which was a combined weighted measure of ‘view’ and ‘proximity,’ referred to here as the Gravity Inspired Visibility Index. Both global ( adjusted R 2 = 0.52, AICc = 29,828) and geographically weighted regression models ( adjusted R 2 = 0.59, AICc = 29,729) estimated the price effect but the geographically weighted regression model outperformed the global model. The results from the geographically weighted regression model indicated an average 3.4% price premium on the mean value of homes in the study area. The paper offers a useful framework for evaluating the effect of land protection for planning and real estate purposes. It also offers useful insights for conservation agencies, local governments, professional planners, and real estate professionals for prioritizing land sites with scenic views.

scholarly journals Impact of a Vicinity of Airport on the Prices of Single-Family Houses with the Use of Geospatial Analysis

2019 ◽  
Vol 8 (11) ◽  
pp. 471 ◽  
Author(s):  
Cellmer ◽  
Bełej ◽  
Konowalczuk
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Multiple Regression Model ◽  
Geographical Information ◽  
Weighted Regression ◽  
Single Family ◽  
Geographically Weighted Regression Model ◽  
Gis Tools ◽  
Spatial Autoregressive ◽  
Real Estate Prices

This article analyses the adverse impact of Chopin Airport in Warsaw on the prices of single-family houses located within the aircraft noise impact zone. The specific feature of the largest airport in Poland is its location within the city limits and the resulting direct surroundings of both multi- and single-family housing developments. Not only is the nuisance due to the proximity of the airport resulting from the actual exposure to an excessive noise level but also from legal restrictions associated with the Limited Use Area (LUA). The study used statistical modeling by applying a classic multiple regression model, spatial autoregressive model and geographically weighted regression model. Moreover, Geographical Information System (GIS) tools and geostatic modeling were used to visualise the results. The modeling results clearly show the significant impact of the neighborhood nuisance and the related spatial distribution of real estate prices. In addition, the geographically weighted regression model indicates that the proximity to an airport adversely affects the rate of price changes over time.

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

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