Geostatistical space-time mapping of house prices using Bayesian maximum entropy

Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variab...

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Bibliographic Details
Published in:International journal of geographical information science : IJGIS 2016-12, Vol.30 (12), p.2339-2354
Main Authors: Hayunga, Darren K., Kolovos, Alexander
Format: Article
Language:English
Subjects:
Bayesian analysis
Bayesian maximum entropy
Construction
Entropy
Geostatistics
Housing
Housing prices
kriging
Mapping
Mathematical models
Maximum entropy
Real estate
Small scale
Spacetime
Spatial analysis
Spatiotemporal
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Summary:Mapping spatial processes at a small scale is a challenge when observed data are not abundant. The article examines the residential housing market in Fort Worth, Texas, and builds price indices at the inter- and intra-neighborhood levels. To accomplish our objectives, we initially model price variability in the joint space-time continuum. We then use geostatistics to predict and map monthly housing prices across the area of interest over a period of 4 years. For this analysis, we introduce the Bayesian maximum entropy (BME) method into real estate research. We use BME because it rigorously integrates uncertain or secondary soft data, which are needed to build the price indices. The soft data in our analysis are property tax values, which are plentiful, publicly available, and highly correlated with transaction prices. The results demonstrate how the use of the soft data provides the ability to map house prices within a small areal unit such as a subdivision or neighborhood.
ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2016.1165820