nach oben

Journal of Geographical Systems

Erschienen in:

03.08.2017 | Original Article

Housing price prediction: parametric versus semi-parametric spatial hedonic models

verfasst von: José-María Montero, Román Mínguez, Gema Fernández-Avilés

Erschienen in: Journal of Geographical Systems | Ausgabe 1/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Vorheriger Artikel Pulling apart: new perspectives on the spatial dimensions of neighbourhood income disparities in Canadian cities

Nächster Artikel Modeling and simulating industrial land-use evolution in Shanghai, China

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Nonlinearity in the parameters also results in a nonlinear model, but this is not typical in hedonic house price models and it goes beyond the scope of this article.

That is, housing prices cannot be explained by a simple “global” model. In this vein, Brunsdon et al. (1996) argue that: (a) relationships can vary significantly over space and a “global” estimate of the relationships may obscure interesting geographical phenomena; and (b) variation over space can be sufficiently complex that it invalidates simple trend-fitting exercises.

We have removed the unitary column from X and included it explicitly in the model, in an additive term, in order to follow the same structure as in the parametric spatial variants of the model.

For the sake of consistency, we proceed in the case of SLM as with the other parametric models.

The empirical semivariogram is used to capture the structure and range of spatial autocorrelation.

If the edf value is very large, the spatial autocorrelation parameter might even be negative (as is the case when PSSD is included in a Durbin-type specification). This finding is in line with the results presented in Tables 1 and 4 of Bivand (2012).

Anselin L (1988) Spatial econometrics: methods and models. Kluwer, BostonCrossRef

Anselin L, Le Gallo J (2006) Interpolation of air quality measures in hedonic house price models: spatial aspects. Spat Econ Anal 1(1):31–52CrossRef

Anselin L, Lozano-Gracia N (2008) Errors in variables and spatial effects in hedonic house price models of ambient air quality. Empir Econ 34(1):5–34CrossRef

Anselin L, Syabri I, Kho Y (2004) GeoDa: an introduction to spatial data analysis. Geogr Anal 38(1):5–22CrossRef

Basile R, Durbán M, Mínguez R, Montero JM, Mur J (2014) Modelling regional economic dynamics: spatial dependence, spatial heterogeneity and nonlinearities. J Econ Dyn Control 48:229–245CrossRef

Bischoff O, Maennig W (2011) Rental housing market segmentation in Germany according to ownership. J Prop Res 28(2):133–149CrossRef

Bitter C, Mulligan GF, Dall’erba S (2007) Incorporating spatial variation in housing attribute prices: a comparison of geographically weighted regression and the spatial expansion method. J Geogr Syst 9(1):7–27CrossRef

Bivand RS (2012) After “Raising the Bar”: applied maximum likelihood estimation of families of models in spatial econometrics. Estadística Española 44(177):71–88

Bivand RS (2013) Spdep: Spatial dependence: Weighting schemes, statistics and models. R package version 0.5-56. http://CRAN.R-project.org/package=spdep

Bourassa SC, Hamelink F, Hoesli M, MacGregor B (1999) Defining housing submarkets. J Hous Econ 8(2):160–183CrossRef

Bourassa SC, Cantoni E, Hoesli M (2010) Predicting house prices with spatial dependence: a comparison of alternative Methods. J Real Estate Res 32(2):139–159

Brasington DM, Hite D (2005) Demand for environmental quality: a spatial hedonic analysis. Reg Sci Urban Eco 35(1):57–82CrossRef

Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: a method for exploring spatial nonstationarity. Geogr Anal 28(4):281–298CrossRef

Cebula RJ (2009) The hedonic pricing model applied to the housing market of the city of Savannah and its Savannah historic landmark district. Rev Reg Stud 39(1):9–22

Cellmer R (2014) The possibilities and limitations of geostatistical methods in real estate market analysis. Real Estate Manag Valuat 22(3):54–62CrossRef

Chica-Olmo J (1995) Spatial estimation of housing prices and locational rents. Urban Stud 32(8):1331–1344CrossRef

Chica-Olmo J (2007) Prediction of housing location price by a multivariate spatial method: cokriging. J Real Estate Res 29(1):95–114

Chica-Olmo J, Cano-Guervos R, Chica-Olmo M (2013) A coregionalized model to predict housing prices. Urban Geogr 34(3):395–412CrossRef

Cressie N (1993) Statistics for spatial data. Wiley, New York

Dubin RA (1998) Predicting house prices using multiple listings data. J Real Estate Financ 17(1):35–59CrossRef

Eilers PHC, Marx BD (1996) Flexible smoothing using B-splines and penalized likelihood (with comments and rejoinder). Stat Sci 11(2):89–121CrossRef

Elhorst JP (2014) Spatial econometrics. From cross-sectional data to spatial panels. Springer, Heidelberg

Elhorst JP, Freret S (2009) Evidence of political yardstick competition in France using a two-regime spatial Durbin model with fixed effects. J Reg Sci 49(5):931–951CrossRef

Fernández-Avilés G, Mínguez R, Montero JM (2012) Geostatistical air pollution indexes in spatial hedonic models: the case of Madrid, Spain. J Real Estate Res 34(2):243–274

Fletcher M, Gallimore P, Mangan J (2000) Heteroscedasticity in hedonic house price models. J Prop Res 17(2):93–108CrossRef

Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, New York

Gámez M, Montero JM, García N (2000) Kriging methodology for regional economic analysis. Int Adv Econ Res 6(3):438–450CrossRef

Goodman A, Thibodeau T (2003) Housing market segmentation and hedonic prediction accuracy. J Hous Econ 12(3):181–201CrossRef

Harville DA (1977) Maximum likelihood approaches to variance component estimation and related problems. J Am Stat Assoc 72(358):320–338CrossRef

Helbich M, Brunauer W, Hagenauer J, Leitner M (2013a) Data-driven regionalization of housing markets. Ann Assoc Am Geogr 103(4):871–889CrossRef

Helbich M, Brunauer W, Vaz E, Nijkamp P (2013b) Spatial heterogeneity in hedonic house price models: The case of Austria. Tinbergen Institute Discussion Paper TI 2013-171/VIII. Tinbergen Institute, Rotterdam

Jahanshiri E, Buyong T, Shariff ARM (2011) A review of mass valuation models. Pertanika J Sci Technol 19(S):23–30

Kuntza M, Helbichab M (2014) Geostatistical mapping of real estate prices: an empirical comparison of kriging and cokriging. Int J Geogr Inf Sci 28(9):1904–1921CrossRef

Lancaster KJ (1966) A new approach to consumer theory. J Polit Econ 74(2):132–157CrossRef

Lee DJ, Durbán M (2011) P-spline ANOVA-type interaction models for spatio-temporal smoothing. Stat Model 11(1):49–69CrossRef

LeSage P, Pace RK (2009) Introduction to spatial econometrics. Chapman & Hall/CRC Press, Boca RatonCrossRef

Lu B, Charlton M, Fotheringam AS (2014) Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data. Int J Geogr Inf Sci 28(4):660–681CrossRef

Manganelli B, Pontrandolfi P, Azzato A, Murgante B (2014) Using geographically weighted regression for housing market segmentation. Int J Bus Intell Data Min 9(2):146–159

Mark J, Goldberg M (1988) Multiple regression analysis and mass assessment: a review of the issues. Apprais J 56(1):89–109

Mason C, Quigley JM (2007) Non-parametric hedonic housing prices. Hous Stud 11(3):373–385CrossRef

McDonald J (1997) Fundamentals of urban economics. Prentice Hall, Upper Saddle River

Mínguez R, Montero JM, Fernández-Avilés G (2013) Measuring the impact of pollution on property prices in Madrid: objective versus subjective pollution indicators in spatial models. J Geogr Syst 15(2):169–191CrossRef

Montero JM, Larraz B (2010) Estimating housing prices: a proposal with spatially correlated data. Int Adv Econ Res 16(1):39–51CrossRef

Montero JM, Larraz B (2011) Interpolation methods for geographical data: housing and commercial establishment markets. J Real Estate Res 33(2):233–244

Montero JM, Larraz B, Páez A (2009) Estimating commercial property prices: an application of cokriging with housing prices as ancillary information. J Geogr Syst 11(4):407–425CrossRef

Montero JM, Fernández-Avilés G, Mínguez R (2011) Spatial hedonic pricing models for testing the adequacy of acoustic areas in Madrid, Spain. Invest Reg J Reg Res 21:157–181

Montero JM, Mínguez R, Durbán M (2012) SAR models with nonparametric spatial trends. A P-spline approach. Estadística Española 54(177):89–111

Montero JM, Fernández-Avilés G, Mateu J (2015) Spatial and spatio-temporal geostatistical modeling and kriging. Wiley, ChichesterCrossRef

Neill HR, Hassenzahl DM, Assane DD (2007) Estimating the effect of air quality: spatial versus traditional hedonic price models. South Econ J 73:1088–1111

Osland L (2010) An application of spatial econometrics in relation to hedonic house price modeling. J Real Estate Res 32(3):289–320

Pace RK (1993) Nonparametric methods with applications to hedonic models. J Real Estate Finance Econ 7(3):185–204CrossRef

Pace RK (1998) Appraisal using generalized additive models. J Real Estate Res 15(1):77–99. http://aresjournals.org/doi/pdf/10.5555/rees.15.1.m2g7602885041757

Pavlov A (2000) Space varying regression coefficients: a semi-parametric approach applied to real estate markets. Real Estate Econ 28(2):249–283CrossRef

R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org

Rodriguez-Alvarez MX, Lee DJ, Kneib T, Durbán M, Eilers P (2015) Fast smoothing parameter separation in multidimensional generalized P-splines: the SAP algorithm. Stat Comput 25(5):941–957CrossRef

Small KA, Steimetz S (2006) Spatial hedonics and the willingness to pay for residential amenities. Working Paper 050631, University of California–Irvine, Department of Economics, Irvine

Straszheim M (1974) Hedonic estimation of housing market prices: a further comment. Rev Econ Stat 56(3):404–406CrossRef

von Graevenitz K, Panduro TE (2015) An alternative to the standard spatial econometric approaches in hedonic house price models. Land Econ 91(2):386–409CrossRef

Wood SN (2006a) An introduction to generalized additive models with R. CRC Press, Boca Raton

Wood SN (2006b) Low-Rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025–1036CrossRef

Wood SN, Augustin NH (2002) GAMs with integrated model selection using penalized regression splines and applications to environmental modelling. Ecol Model 157(2–3):157–177CrossRef

Yu DL (2006) Spatially varying development mechanisms in the Greater Beijing area: a geographically weighted regression investigation. Ann Reg Sci 40(1):173–190CrossRef

Titel: Housing price prediction: parametric versus semi-parametric spatial hedonic models
verfasst von: José-María Montero
Román Mínguez
Gema Fernández-Avilés
Publikationsdatum: 03.08.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Geographical Systems / Ausgabe 1/2018
Print ISSN: 1435-5930
Elektronische ISSN: 1435-5949
DOI: https://doi.org/10.1007/s10109-017-0257-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2018

Modeling and simulating industrial land-use evolution in Shanghai, China

A novel algorithm for fully automated mapping of geospatial ontologies

Erratum to: Housing price prediction: parametric versus semi-parametric spatial hedonic models

Pulling apart: new perspectives on the spatial dimensions of neighbourhood income disparities in Canadian cities