the spatial estimation of appartment rents: a brief introduction

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harald@schernthanner.de 1/10

The spatial estimation of appartment rents: A brief

introduction

Harald Schernthanner

harald@schernthanner.de

My research Spatial analysis and visualization of appartment rents, to optimize the spatial view of real estate portals. Machine learning, Geographic

weighted regression and Kriging to improve spatial rental price estimation.

Application of different R packages: raster / spded / gstat / RandomForest

harald@schernthanner.de 3/10

Publishing average appartment rents is nonsense from a researchers point of view.

harald@schernthanner.de

Rental price modelling done by real estate portals is non spatial. Analysis and maps base on descriptive statistics and/or hedonic analysis.

harald@schernthanner.de

Hedonic regression = Non spatial state of the art

Equation estimates based on "intrinsic" values:

Room number Space in m² Kitchen

harald@schernthanner.de

Spatial Interpolation Gitta Info (2015) Unknown points

are predicted by surrounding points.

Maps of temperature or height are common applications.

harald@schernthanner.de

First Law of Geography - basic principle behind interpolation

"Everything is related to everything else, but near things are more related than distant things."

Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2): 234-240.

harald@schernthanner.de

State of the art - median rental price

harald@schernthanner.de

State of the art - quiete coarse

harald@schernthanner.de

Trend surface for 70m² flats 05/15

14 € / m² → Why no legend?

harald@schernthanner.de

State of the art - overview?

harald@schernthanner.de

Trend surface - Residential areas

harald@schernthanner.de

Rental estimation per building block

harald@schernthanner.de

Rental estimation as thematic building height

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