IDEAS home Printed from https://ideas.repec.org/p/inn/wpaper/2010-19.html
   My bibliography  Save this paper

Modeling House Prices using Multilevel Structured Additive Regression

Author

Listed:
  • Wolfgang Brunauer
  • Stefan Lang
  • Nikolaus Umlauf

Abstract

This paper analyzes house price data belonging to three hierarchical levels of spatial units. House selling prices with associated individual attributes (the elementary level-1) are grouped within municipalities (level-2), which form districts (level-3), which are themselves nested in counties (level-4). Additionally to individual attributes, explanatory covariates with possibly nonlinear effects are available on two of these spatial resolutions. We apply a multilevel version of structured additive regression (STAR) models to regress house prices on individual attributes and locational neighborhood characteristics in a four level hierarchical model. In multilevel STAR models the regression coefficients of a particular nonlinear term may themselves obey a regression model with structured additive predictor. The framework thus allows to incorporate nonlinear covariate effects and time trends, smooth spatial effects and complex interactions at every level of the hierarchy of the multilevel model. Moreover we are able to decompose the spatial heterogeneity effect and investigate its magnitude at different spatial resolutions allowing for improved predictive quality even in the case of unobserved spatial units. Statistical inference is fully Bayesian and based on highly efficient Markov chain Monte Carlo simulation techniques that take advantage of the hierarchical structure in the data.

Suggested Citation

  • Wolfgang Brunauer & Stefan Lang & Nikolaus Umlauf, 2010. "Modeling House Prices using Multilevel Structured Additive Regression," Working Papers 2010-19, Faculty of Economics and Statistics, Universität Innsbruck.
    • Handle: RePEc:inn:wpaper:2010-19
    as

    Download full text from publisher

    File URL: https://www2.uibk.ac.at/downloads/c4041030/wpaper/2010-19.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carlos Martins-Filho & Okmyung Bin, 2005. "Estimation of hedonic price functions via additive nonparametric regression," Empirical Economics, Springer, vol. 30(1), pages 93-114, January.
    2. Francesco Feri & Bernd Irlenbusch & Matthias Sutter, 2010. "Efficiency Gains from Team-Based Coordination—Large-Scale Experimental Evidence," American Economic Review, American Economic Association, vol. 100(4), pages 1892-1912, September.
    3. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2010. "Stochastic model specification search for Gaussian and partial non-Gaussian state space models," Journal of Econometrics, Elsevier, vol. 154(1), pages 85-100, January.
    4. Andrea Leiter & Gerald Pruckner, 2009. "Proportionality of Willingness to Pay to Small Changes in Risk: The Impact of Attitudinal Factors in Scope Tests," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 42(2), pages 169-186, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Cichulska Aneta & Cellmer Radosław, 2018. "Analysis of Prices in the Housing Market Using Mixed Models," Real Estate Management and Valuation, Sciendo, vol. 26(4), pages 102-111, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Gächter & Engelbert Theurl, 2010. "Health Status Convergence at the Local Level: Empirical Evidence from Austria," Working Papers 2010-23, Faculty of Economics and Statistics, Universität Innsbruck, revised Mar 2011.
    2. Octavio Fernández Amador & Josef Baumgartner & Jesús Crespo Cuaresma, 2010. "Milking the Prices: The Role of Asymmetries in the Price Transmission Mechanism for Milk Products in Austria," WIFO Working Papers 378, WIFO.
    3. David J. Cooper & Krista Saral & Marie Claire Villeval, 2021. "Why Join a Team?," Management Science, INFORMS, vol. 67(11), pages 6980-6997, November.
    4. Faralla, Valeria & Borà, Guido & Innocenti, Alessandro & Novarese, Marco, 2020. "Promises in group decision making," Research in Economics, Elsevier, vol. 74(1), pages 1-11.
    5. Thommes, Kirsten & Vyrastekova, Jana & Akkerman, Agnes, 2015. "Behavioral spillovers from freeriding in multilevel interactions," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 56(C), pages 78-87.
    6. Florian Huber & Tamás Krisztin & Philipp Piribauer, 2017. "Forecasting Global Equity Indices Using Large Bayesian Vars," Bulletin of Economic Research, Wiley Blackwell, vol. 69(3), pages 288-308, July.
    7. Stöckl, Thomas & Huber, Jürgen & Kirchler, Michael & Lindner, Florian, 2015. "Hot hand and gambler's fallacy in teams: Evidence from investment experiments," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 327-339.
    8. Liu, Wei-han, 2016. "A re-examination of maturity effect of energy futures price from the perspective of stochastic volatility," Energy Economics, Elsevier, vol. 56(C), pages 351-362.
    9. Dimitris Korobilis, 2021. "High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 493-504, March.
    10. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    11. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    12. Karlsson, Sune & Mazur, Stepan, 2020. "Flexible Fat-tailed Vector Autoregression," Working Papers 2020:5, Örebro University, School of Business.
    13. Alós-Ferrer, Carlos & Weidenholzer, Simon, 2014. "Imitation and the role of information in overcoming coordination failures," Games and Economic Behavior, Elsevier, vol. 87(C), pages 397-411.
    14. Castillo, Marco & Dickinson, David L., 2022. "Sleep restriction increases coordination failure," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 358-370.
    15. Karakostas, Alexandros & Kocher, Martin G. & Matzat, Dominik & Rau, Holger A. & Riewe, Gerhard, 2023. "The team allocator game: Allocation power in public goods games," Games and Economic Behavior, Elsevier, vol. 140(C), pages 73-87.
    16. Xie, Erhao, 2021. "Empirical properties and identification of adaptive learning models in behavioral game theory," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 798-821.
    17. Kevin Haninger & James K. Hammitt, 2011. "Diminishing Willingness to Pay per Quality‐Adjusted Life Year: Valuing Acute Foodborne Illness," Risk Analysis, John Wiley & Sons, vol. 31(9), pages 1363-1380, September.
    18. Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2020. "Bayesian Modelling of TVP-VARs Using Regression Trees," Working Papers 2308, University of Strathclyde Business School, Department of Economics, revised Aug 2023.
    19. Eric Eisenstat & Joshua C. C. Chan & Rodney W. Strachan, 2016. "Stochastic Model Specification Search for Time-Varying Parameter VARs," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1638-1665, December.
    20. Niko Hauzenberger & Michael Pfarrhofer, 2021. "Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(4), pages 1261-1291, October.

    More about this item

    Keywords

    Bayesian hierarchical models; hedonic pricing models; multilevel models; MCMC; P-splines;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inn:wpaper:2010-19. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Janette Walde (email available below). General contact details of provider: https://edirc.repec.org/data/fuibkat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.