Socio-Economic and Spatial Characteristics of Wielkopolski National Park: Application of the Hedonic Pricing Method

Chwiałkowski, Cyprian; Zydroń, Adam

doi:10.3390/su13095001

Cited by 11 publications

(8 citation statements)

References 68 publications

(82 reference statements)

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“…The aesthetics are difficult to attribute to a specific sector they influence the most directly. However, based on the hedonic pricing experiences (Chwiałkowski & Zydroń, 2021;Sylla et al, 2019), we learn that aesthetic play an important role in the real estate businesses by influencing the price of housing estates. In the case study area, the aesthetics may also influence the artistic activities of small businesses which belong to the category of arts, entertainment, and recreation (Table 4).…”

Section: Results Of the Researchmentioning

confidence: 99%

Ecosystem services contributing to local economic sectors – conceptual framework of linking ecosystem services, benefits and economic sectors

Sylla

2023

EiS

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The paper introduces the principles of the System of Environmental-Economic Accounting-Ecosystem Accounting. The aim of the work is to present the application of the conceptual framework of linking ecosystem services, benefits and economic sectors. The analysis relates to years 2012 and 2018. The case study area represents five municipalities that cross borders with the Ślężański Landscape Park and its buffer zone in the Lower Silesia region in Poland. Results show that cropland-related ecosystem services contribute to 17% and 14 % percent of all companies operating in the case study area. The direct link between agricultural benefits and five economic sectors was established. The ecosystem extent account is created, and the land cover flows analysed. The applicability of ecosystem accounting to local governance is discussed.

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Section: Results Of the Researchmentioning

confidence: 99%

Ecosystem services contributing to local economic sectors – conceptual framework of linking ecosystem services, benefits and economic sectors

Sylla

2023

EiS

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“…Residents’ high horizontal view of green vegetation within minutes [ 60 ], 400 m [ 61 ], 160.9 m [ 62 ], 253 m [ 63 ] of the property were also reported to have positive effects on residential property values. Likewise, proximity to a national park had a positive effect on housing prices [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

Ewane,

Bajaj,

Velasquez-Camacho

et al. 2023

Heliyon

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“…The accurate estimation of the house prices in the market of real estates is of great importance for a large number of individual parties ranging from homeowners to policy-makers. Apart from the obvious supply and demand conditions, the house prices may be influenced by other factors that encompass, for example, the physical attributes of the dwellings (e.g., dwelling size, room count) [15,16], the marketing strategies employed during sale [17], environmental elements (like air quality and neighborhood safety) [18], and socioeconomic indicators including the presence of urban amenities in the area like parks and parking facilities [15,19,20] and statistical indicators like employment rate and population growth [21].…”

Section: House Prices Predictionmentioning

confidence: 99%

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

Karamanou,

Brimos,

Kalampokis

et al. 2024

Preprint

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In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. However, traditional predictive models have often overlooked the spatial interdependencies that play a vital role in shaping housing prices. This study applies Graph Neural Networks (GNNs) on Open Statistics Knowledge Graphs to model spatial dependencies and predict house prices across Scotland’s 2011 data zones. To this end, integrated statistical indicators are retrieved from the official Scottish Open Government Data portal. The three representative GNN algorithms employed - ChebNet, GCN, and GraphSAGE - demonstrate higher prediction accuracy than traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP). In addition, local and global explainability are employed to increase transparency and trust in the predictions made by the most accurate GNN - GraphSAGE. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainaibility results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both global surrogate model and the SHAP approach underscored the Comparative Illness Factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

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Socio-Economic and Spatial Characteristics of Wielkopolski National Park: Application of the Hedonic Pricing Method

Cited by 11 publications

References 68 publications

Ecosystem services contributing to local economic sectors – conceptual framework of linking ecosystem services, benefits and economic sectors

Ecosystem services contributing to local economic sectors – conceptual framework of linking ecosystem services, benefits and economic sectors

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

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