Prague’s housing market

This page summarized a project of Prague’s housing market analysis. The purpose of this is to shed a light on a following questions: How does the availability to public transport affects the price of residential land in the city of Prague? Are there any significant spatial clustering patterns? The project tries to analyse to which extent each public transport mode affects the residential land price. The analysis was conducted with QGIS and R softwares with Opendata given by Prague’s Institute of Planning and Development.

1. Introduction
2.  Problem Statement
3.  Data Collection
4.  Data Processing – Price Map Dataset filtering
5.  Data Processing – Public Transport Stops Dataset filtering
6.  Data Processing – Calculating Distances
7.  Data Analysis:
   •  Summary statistics
   •  Residential land price spatial distribution
   •  Area
   •  Nearest metro station spatial distribution
   •  Nearest tram stop spatial distribution
   •  Nearest bus stop spatial distribution
   •  Nearest train station spatial distribution
8. Modelling
   • Investigating Spatial Autocorrelation
     • Global Autocorrelation
     • Local Autocorrelation
       • Spatial distribution of moran statistics for price
       • Spatial distribution of moran statistics for area
       • Spatial distribution of moran statistics for nearest bus stop distance
       • Spatial distribution of moran statistics for nearest metro station distance
       • Spatial distribution of moran statistics for nearest tram stop distance
       • Spatial distribution of moran statistics for nearest train station distance
     • Geographically Weighted Regression
       • Fitting Ordinary Least Squares Model
       • Fitting GWR model
       • Analysing Spatial distributions of fitted model coefficients
9. Conclusion

The source code is acessible on Projects GITHUB Repository