A Coregionalized Model to Predict Housing Prices

Chica-Olmo, Jorge; Cano-Guervos, Rafael; Chica-Olmo, Mario

doi:10.1080/02723638.2013.778662

Cited by 26 publications

(20 citation statements)

References 42 publications

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“…Similar approaches have been employed by Small and Steimetz (2012), Helbich et al (2014), and Cohen and Coughlin (2008). 1 As another way to address spatial components, a geostatistical approach was used by Dubin (1992), Chica-Olmo (1995), Basu and Thibodeau (1998), Yoo andKyriakidis (2009), andChica-Olmo, Cano-Guervos, andChica-Olmo (2013).…”

Section: Hedonic Regression Modelingmentioning

confidence: 98%

The modifiable areal unit problem in hedonic house-price models

2015

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When geographically aggregated data are included in hedonic models, the resulting coefficients are biased by the spatial scale and spatial configuration of variable measurement. We explore the effects of this modifiable areal unit problem (MAUP) within the context of hedonic price models with an individual-level dependent variable. Specifically, we developed standard and spatial hedonic regression models in order to examine the effects of the MAUP on model fit and coefficient estimates. Our empirical analysis documents several significant scale and zoning effects in the hedonic modeling framework. First, neighborhood characteristics are clearly important in efforts to improve model fit-and they are more significant contributors in the standard model than in the spatial hedonic model. For aggregation scale, the model fit change of the standard model is relatively large, whereas the change is more modest for spatial models. The patterns of change in model fit for standard and spatial hedonic models clearly diverge from one another, implying the existence of a scale level showing a maximum functional range of the submarket on which scale dependencies are expected to have an impact. Regarding the zoning effect, the model fits for both standard and spatial hedonic models vary according to the submarket systems.

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Section: Hedonic Regression Modelingmentioning

confidence: 98%

The modifiable areal unit problem in hedonic house-price models

2015

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“…The difficulty involved in measuring spatially different variables means that these variables are omitted in the hedonic prices equation, thus resulting in the spatial dependence of the error term, since the microlocation characteristics [62] have not been specified in the model. Different methods can be used to eliminate omitted variable bias due to missing spatial variables [4] from the standpoint of both geostatistics [30,63] and spatial econometrics using SEM [20,64,65].…”

Section: Data and Case Studymentioning

confidence: 99%

“…The cokriging method has been used to spatially relate housing prices with different auxiliary variables [28], age or central heating [29], and quality of the area [30]. A recent application of this method to housing prices can be found in Kuntz and Helbich [31], who consider structural and neighborhood characteristics as auxiliary variables.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Housing Price Variations Using Spatio-Temporal Data

2019

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This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

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“…The possibilities offered by geostatistical methods in market analyses have been discussed by MARTINEZ et al (2000), CHICA-OLMO et al (2007, 2013, KULCZYCKI and LIGAS (2007), CICHOCIŃSKI (2009), MONTERO et al (2009), andCOLAKOVIC andVUCETIC (2012). Geostatistical methods create various possibilities in spatial analyses of transaction data, but they cannot always be used due to the adopted assumptions and the imperfect nature of the real estate market.…”

Section: Geostatistical Methodsmentioning

confidence: 99%

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

Cellmer

2014

Real Estate Management and Valuation

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In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution.This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.

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A Coregionalized Model to Predict Housing Prices

Cited by 26 publications

References 42 publications

The modifiable areal unit problem in hedonic house-price models

The modifiable areal unit problem in hedonic house-price models

Estimation of Housing Price Variations Using Spatio-Temporal Data

The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

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