HOCHSCHULE
FRESENIUS UNIVERSITY OF APPLIED SCIENCES
Presentation

Recreational
Accessibility
& Housing Prices

A research presentation on how proximity to recreational areas and the availability of nearby recreational spaces may relate to housing prices in Düsseldorf.

1. Research Project

Urban green spaces and recreational amenities are increasingly recognized as important determinants of quality of life, residential attractiveness and urban housing markets (Kolbe & Wüstemann, 2015; Ramírez-Juidías et al., 2022).

1.1

Research Question

How do proximity to recreational areas and the availability of nearby recreational spaces relate to housing prices in Düsseldorf?

1.2

Research Objective

To examine whether recreational accessibility, measured through proximity and availability indicators, is associated with housing prices in Düsseldorf.

Why It Matters

Urban Planning

Understanding recreational accessibility can support evidence-based planning decisions.

Why It Matters

Housing Market Analysis

Environmental amenities may influence residential attractiveness and market prices.

Why It Matters

Quality of Life

Parks and recreational areas contribute to health, well-being and urban livability.

2. Research Logic

The project follows the logic of research methodology: research question, objective, literature review, research gap and methodology.

Research Question
Research Objective
Literature Review
Research Gap
Methodology

3. Literature Review

The literature suggests that housing prices are influenced not only by structural characteristics but also by locational and environmental amenities.

3.1

Housing Prices

Housing prices reflect a bundle of structural, locational and environmental characteristics.

Key Sources
Rosen (1974)
Basu & Thibodeau (1998)
Geoghegan et al. (1997)

3.2

Green Spaces

Green spaces generate environmental, social and economic value in housing markets.

Key Sources
Kolbe & Wüstemann (2015)
Ramírez-Juidías et al. (2022)
Lee & Li (2009)

3.3

Accessibility

Accessibility measures influence residential attractiveness and housing market outcomes.

Key Sources
Wittowsky et al. (2020)
Helbich et al. (2014)
Apparicio et al.

3.4

GIS Applications

GIS and geospatial analysis provide new opportunities for accessibility measurement.

Key Sources
Wei et al. (2022)
OpenStreetMap Foundation
Lovelace et al. (2024)

4. Research Gap

Existing research has examined green spaces and housing prices, but several gaps remain for this specific research project.

✓ What We Know

  • Green spaces affect housing prices
  • Accessibility matters
  • GIS can measure accessibility

✗ What We Don't Know

  • Düsseldorf
  • Distance + availability together
  • OSM + R workflow

5. Theoretical Framework

The study is based on Hedonic Pricing Theory, which explains housing prices as the combined value of property characteristics.

Hedonic Pricing Theory

Property Price
=
Structural Attributes + Locational Attributes + Environmental Attributes
5.1

Environmental Amenity

Recreational accessibility is treated as an environmental amenity within the hedonic pricing framework.

5.2

Expected Relationship

Better access to recreational areas may be reflected in higher housing prices per square meter.

6. Conceptual Model

Recreational accessibility is conceptualized through two measurable dimensions: proximity and availability.

Distance to Recreation

meters

Areas within 500m

count

Recreational Accessibility

Housing Prices

€/m²

7. Methodology

The study adopts a quantitative research design and combines housing market data with geospatial information.

7.1

Study Area

Düsseldorf, Germany.

7.2

Housing Data

ImmobilienScout24 and available Düsseldorf open-data sources.

7.3

Spatial Data

OpenStreetMap data for parks, forests, meadows and recreation grounds.

7.4

Tools

R, GIS, spatial analysis and thematic mapping.

7.5

Unit of Analysis

Residential properties located within Düsseldorf.

7.6

Research Design

Quantitative, secondary-data-based and geospatial.

8. Analytical Workflow

The workflow translates the research question into measurable spatial indicators and statistical analysis.

Housing Data
OpenStreetMap
Accessibility Indicators
Spatial Analysis
Regression Analysis
Interpretation

9. Variables

Variable Type Measurement
Housing Price Dependent Variable €/m²
Distance to Recreational Area Independent Variable Meters
Number of Recreational Areas Independent Variable Count within 500m
Dependent

Housing Price

Measured as euros per square meter.

Independent

Distance to Recreation

Measured in meters from each residential property.

Independent

Areas within 500m

Measured as count of recreational areas within walking distance.

10. Expected Contribution

Academic

Düsseldorf Evidence

Provides evidence for a city that is less represented in existing studies.

Methodological

OSM + R Workflow

Demonstrates how open spatial data and reproducible R workflows can be used.

Practical

Urban Planning

May support evidence-based discussion about recreational infrastructure.

11. Next Steps

Data Collection
Geospatial Processing
Accessibility Calculation
Regression Analysis
Interpretation

12. References

Rosen (1974); Geoghegan et al. (1997); Basu & Thibodeau (1998); Lee & Li (2009); Kolbe & Wüstemann (2015); Helbich et al. (2014); Wittowsky et al. (2020); Ramírez-Juidías et al. (2022); Wei et al. (2022); Lovelace et al. (2024); OpenStreetMap Foundation.

13. Thank You

Questions?

This research project investigates whether recreational accessibility is associated with housing prices in Düsseldorf.