Estimating Residential Property Values on the Basis of Clustering and Geostatistics

Beata Calka

doi:10.3390/geosciences9030143

Abstract

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.

Highlights

Property value estimates are usually carried out to determine the value of individual or multiple properties
2), the result of agglomerative clustering, led to all the transactions being into five clusters
The k-means clustering was carried out iteratively, assuming that the preliminary divided into five clusters

Summary

Introduction

Property value estimates are usually carried out to determine the value of individual or multiple properties. The valuation of many properties at once, called mass appraisal, is carried out using statistical methods. Mass appraisal aims to determine a property’s market value for a general transaction or to draw up property value maps. Converting mass appraisals into algorithms requires the development of a model for estimating property value. These models are usually based on a comparative approach [1,2], which assumes that a property’s market value is determined by comparing it with other properties with known transaction (purchase and sale) prices and the factors differentiating these properties and affecting their value. All the information needed for the appraisal comes from the market and has a probabilistic character

Methods

Results

Discussion

Conclusion