Next Article in Journal
Performance of Remotely Sensed Soil Moisture for Temporal and Spatial Analysis of Rainfall over São Francisco River Basin, Brazil
Next Article in Special Issue
The Noise Properties and Velocities from a Time-Series of Estonian Permanent GNSS Stations
Previous Article in Journal
Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data
Previous Article in Special Issue
Geoscience Methods in Real Estate Market Analyses Subjectivity Decrease
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle

Estimating Residential Property Values on the Basis of Clustering and Geostatistics

Faculty of Civil Engineering and Geodesy, Military University of Technology, 00-908 Warsaw, Poland
Geosciences 2019, 9(3), 143;
Received: 7 February 2019 / Revised: 20 March 2019 / Accepted: 21 March 2019 / Published: 24 March 2019
(This article belongs to the Special Issue Geodesy and Geomatics Engineering)
PDF [2394 KB, uploaded 24 March 2019]
  |     |  


The article presents a two-stage model for estimating the value of residential property. The research is based on the application of a sequence of known methods in the process of developing property value maps. The market is divided into local submarkets using data mining, and, in particular, data clustering. This process takes into account only a property’s non-spatial (structural) attributes. This is the first stage of the model, which isolates local property markets where properties have similar structural attributes. To estimate the impact of the spatial factor (location) on property value, the second stage involves performing an interpolation for each cluster separately using ordinary kriging. In this stage, the model is based on Tobler’s first law of geography. The model results in property value maps, drawn up separately for each of the clusters. Experimental research carried out using the example of Siedlce, a city in eastern Poland, proves that the estimation error for a property’s value using the proposed method, evaluated using the mean absolute percentage error, does not exceed 10%. The model that has been developed is universal and can be used to estimate the value of land, property, and buildings. View Full-Text
Keywords: clustering; k-means; geostatistics; value map; spatial location; mass appraisal clustering; k-means; geostatistics; value map; spatial location; mass appraisal

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Calka, B. Estimating Residential Property Values on the Basis of Clustering and Geostatistics. Geosciences 2019, 9, 143.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top