IDEAS home Printed from https://ideas.repec.org/a/spr/anresc/v58y2017i1d10.1007_s00168-016-0801-6.html
   My bibliography  Save this article

A continuous spatio-temporal model for house prices in the USA

Author

Listed:
  • Márcio Poletti Laurini

    (FEARP-USP, CNPQ)

Abstract

We revisit the studies on the evolution of house prices in the USA using a spatio-temporal model estimated using a Bayesian method. This method introduces a new specification of an error correction model with random effects measured continuously in space. This model allows observing the deviations from the co-integration relationship in each analyzed location and a clearer interpretation of the house price dynamics between 1975 and 2011 for 381 metropolitan areas in the USA. The results indicate the presence of a housing price cycle, consistent with the patterns observed in the analyzed period.

Suggested Citation

  • Márcio Poletti Laurini, 2017. "A continuous spatio-temporal model for house prices in the USA," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(1), pages 235-269, January.
    • Handle: RePEc:spr:anresc:v:58:y:2017:i:1:d:10.1007_s00168-016-0801-6
    DOI: 10.1007/s00168-016-0801-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00168-016-0801-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00168-016-0801-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michela Cameletti & Finn Lindgren & Daniel Simpson & Håvard Rue, 2013. "Spatio-temporal modeling of particulate matter concentration through the SPDE approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(2), pages 109-131, April.
    2. Robert J Shiller, 2008. "Historic Turning Points in Real Estate," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 34(1), pages 1-13, Winter.
    3. MÁrcio Poletti Laurini & Luiz Koodi Hotta, 2014. "Forecasting the Term Structure of Interest Rates Using Integrated Nested Laplace Approximations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 214-230, April.
    4. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    5. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.
    6. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 45-90, February.
    7. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    8. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    9. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    10. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    11. Badi H. Baltagi & Jing Li, 2014. "Further Evidence On The Spatio‐Temporal Model Of House Prices In The United States," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(3), pages 515-522, April.
    12. Finn Lindgren & Håvard Rue & Johan Lindström, 2011. "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(4), pages 423-498, September.
    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. S. R. Johnson & S. E. Heaps & K. J. Wilson & D. J. Wilkinson, 2023. "A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), 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. Temple, Jonathan & Van de Sijpe, Nicolas, 2017. "Foreign aid and domestic absorption," Journal of International Economics, Elsevier, vol. 108(C), pages 431-443.
    2. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    3. J. B. Qian, 2016. "Estimation of Panel Model with Spatial Autoregressive Error and Common Factors," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 367-399, March.
    4. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    5. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    6. Arnab Bhattacharjee & Jan Ditzen & Sean Holly, 2022. "Spatial and Spatio-Temporal Error Correction, Networks and Common Correlated Effects," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, volume 43, pages 37-60, Emerald Group Publishing Limited.
    7. Nicoletta D’Angelo & Antonino Abbruzzo & Giada Adelfio, 2021. "Spatio-Temporal Spread Pattern of COVID-19 in Italy," Mathematics, MDPI, vol. 9(19), pages 1-14, October.
    8. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    9. Josep Lluís Carrion-i-Silvestre & Laura Surdeanu, 2016. "Productivity, Infrastructure and Human Capital in the Spanish Regions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(4), pages 365-391, October.
    10. Constantinescu, Mihnea & Lastauskas, Povilas, 2018. "The knotty interplay between credit and housing," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 241-266.
    11. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
    12. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.
    13. Halleck Vega, Solmaria & Elhorst, J. Paul, 2016. "A regional unemployment model simultaneously accounting for serial dynamics, spatial dependence and common factors," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 85-95.
    14. Wang, Craig & Furrer, Reinhard, 2021. "Combining heterogeneous spatial datasets with process-based spatial fusion models: A unifying framework," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    15. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    16. Bondo, Kristin J. & Rosenberry, Christopher S. & Stainbrook, David & Walter, W. David, 2024. "Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types," Ecological Modelling, Elsevier, vol. 493(C).
    17. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    18. I Gede Nyoman Mindra Jaya & Henk Folmer, 2024. "High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia," Mathematics, MDPI, vol. 12(18), pages 1-29, September.
    19. Johnson, Blair T. & Sisti, Anthony & Bernstein, Mary & Chen, Kun & Hennessy, Emily A. & Acabchuk, Rebecca L. & Matos, Michaela, 2021. "Community-level factors and incidence of gun violence in the United States, 2014–2017," Social Science & Medicine, Elsevier, vol. 280(C).
    20. Natalia Bailey & Sean Holly & M. Hashem Pesaran, 2016. "A Two‐Stage Approach to Spatio‐Temporal Analysis with Strong and Weak Cross‐Sectional Dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 249-280, January.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

    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:spr:anresc:v:58:y:2017:i:1:d:10.1007_s00168-016-0801-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.