Capturing locational effects: application of the K-means clustering algorithm

This study proposes a hedonic pricing model to efficiently capture the values of locations without assuming a specific functional form or the factors affecting it. The K-means clustering algorithm serves as a subdivider for allocating indicators into samples according to their locational similarity....

Full description

Saved in:
Bibliographic Details
Published in:The Annals of regional science 2024-06, Vol.73 (1), p.265-289
Main Authors: Ryu, Doojin, Hong, Jengei, Jo, Hyunjae
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Cluster analysis
Clustering
Economics
Economics and Finance
Environmental Economics
Geography
Landscape/Regional and Urban Planning
Microeconomics
Original Paper
Regional/Spatial Science
Shadow prices
Taxation
Vector quantization
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study proposes a hedonic pricing model to efficiently capture the values of locations without assuming a specific functional form or the factors affecting it. The K-means clustering algorithm serves as a subdivider for allocating indicators into samples according to their locational similarity. The advantage of this approach is that it allows the value of a location to be measured using only its latitude and longitude. We examine the predictive accuracy of the model in an out-of-sample context based on apartment transaction data for Seoul, the capital of Korea. Our results show that the predictive power of the proposed model is significantly higher than that of conventional models.
ISSN:0570-1864
1432-0592
DOI:10.1007/s00168-024-01263-4