IDEAS home Printed from https://ideas.repec.org/a/spr/anresc/v50y2013i2p603-621.html
   My bibliography  Save this article

Estimation of house prices in regions with small sample sizes

Author

Listed:
  • Luis Pereira
  • Pedro Coelho

Abstract

House price indexes had become important economic indicators worldwide, since movements in house prices have been closely correlated with the economic cycle. In order to compute these kind of indexes it is imperative to produce reliable estimates of the average transaction price of houses, not only at the macrolevel (e.g. national and state level), but also at the microlevel (e.g. district, municipalities or further disaggregate regional level). In Portugal, there is a rapidly growing demand of such microlevel statistics since the beginning of the recent financial and economic crisis. The Portuguese Statistical Office provides a range of invaluable data at national level; however, this data cannot be used directly to produce reliable regional-level estimates due to small sample sizes. In this paper we employ small area estimation techniques to produce design and model-based estimates of average transaction price of houses for Portuguese regions with small sample sizes. Our results show that the model-based estimates based on spatial and temporal models are more accurate than the traditional direct design-based estimates. The use of these techniques allows the production of information at disaggregated regional levels that would not be available under the traditional direct estimation approaches. Furthermore, it is even possible to produce reliable model-based estimates for geographical areas without sample. The estimates are expected to provide invaluable information to policy-analysts and decision-making. Copyright Springer-Verlag 2013

Suggested Citation

  • Luis Pereira & Pedro Coelho, 2013. "Estimation of house prices in regions with small sample sizes," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(2), pages 603-621, April.
    • Handle: RePEc:spr:anresc:v:50:y:2013:i:2:p:603-621
    DOI: 10.1007/s00168-012-0507-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00168-012-0507-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00168-012-0507-3?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. Gauri Sankar Datta & J. N. K. Rao & David Daniel Smith, 2005. "On measuring the variability of small area estimators under a basic area level model," Biometrika, Biometrika Trust, vol. 92(1), pages 183-196, March.
    2. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    3. Nathalie Girouard & Sveinbjörn Blöndal, 2001. "House Prices and Economic Activity," OECD Economics Department Working Papers 279, OECD Publishing.
    4. Isabel Molina & Nicola Salvati & Monica Pratesi, 2009. "Bootstrap for estimating the MSE of the Spatial EBLUP," Computational Statistics, Springer, vol. 24(3), pages 441-458, August.
    5. Monica Pratesi & Nicola Salvati, 2008. "Small area estimation: the EBLUP estimator based on spatially correlated random area effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(1), pages 113-141, 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. Eilers, Lea, 2017. "Is my rental price overestimated? A small area index for Germany," Ruhr Economic Papers 734, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.

    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. Caterina Giusti & Lucio Masserini & Monica Pratesi, 2017. "Local Comparisons of Small Area Estimates of Poverty: An Application Within the Tuscany Region in Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 131(1), pages 235-254, March.
    2. Anna Sikov & José Cerda-Hernandez, 2024. "Prediction in non-sampled areas under spatial small area models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1079-1116, September.
    3. Shonosuke Sugasawa & Tatsuya Kubokawa & J. N. K. Rao, 2018. "Small area estimation via unmatched sampling and linking models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 407-427, June.
    4. Miguel Boubeta & María José Lombardía & Domingo Morales, 2024. "Small area prediction of proportions and counts under a spatial Poisson mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1193-1215, September.
    5. Harm Jan Boonstra & Jan A. Van Den Brakel & Bart Buelens & Sabine Krieg & Marc Smeets, 2008. "Towards small area estimation at Statistics Netherlands," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 21-49.
    6. Kordos Jan, 2016. "Development of Small Area Estimation in Official Statistics," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 105-132, March.
    7. Jan Kordos, 2016. "Development Of Smallarea Estimation In Official Statistics," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 105-132, March.
    8. repec:csb:stintr:v:17:y:2016:i:1:p:105-132 is not listed on IDEAS
    9. Dian Handayani & Henk Folmer & Anang Kurnia & Khairil Anwar Notodiputro, 2018. "The spatial empirical Bayes predictor of the small area mean for a lognormal variable of interest and spatially correlated random effects," Empirical Economics, Springer, vol. 55(1), pages 147-167, August.
    10. N. Salvati & N. Tzavidis & M. Pratesi & R. Chambers, 2012. "Small area estimation via M-quantile geographically weighted regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 1-28, March.
    11. Lixia Diao & David D. Smith & Gauri Sankar Datta & Tapabrata Maiti & Jean D. Opsomer, 2014. "Accurate Confidence Interval Estimation of Small Area Parameters Under the Fay–Herriot Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 497-515, June.
    12. Tomasz Ża̧dło, 2015. "On longitudinal moving average model for prediction of subpopulation total," Statistical Papers, Springer, vol. 56(3), pages 749-771, August.
    13. Yoshimori, Masayo & Lahiri, Partha, 2014. "A new adjusted maximum likelihood method for the Fay–Herriot small area model," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 281-294.
    14. Baldermann, Claudia & Salvati, Nicola & Schmid, Timo, 2016. "Robust small area estimation under spatial non-stationarity," Discussion Papers 2016/5, Free University Berlin, School of Business & Economics.
    15. Angelo Moretti, 2023. "Estimation of small area proportions under a bivariate logistic mixed model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3663-3684, August.
    16. Katarzyna Reluga & María‐José Lombardía & Stefan Sperlich, 2023. "Simultaneous inference for linear mixed model parameters with an application to small area estimation," International Statistical Review, International Statistical Institute, vol. 91(2), pages 193-217, August.
    17. Chakraborty Adrijo & Datta Gauri Sankar & Mandal Abhyuday, 2016. "A Two-Component Normal Mixture Alternative to the Fay-Herriot Model," Statistics in Transition New Series, Statistics Poland, vol. 17(1), pages 67-90, March.
    18. Torabi, Mahmoud & Rao, J.N.K., 2014. "On small area estimation under a sub-area level model," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 36-55.
    19. repec:csb:stintr:v:17:y:2016:i:1:p:67-90 is not listed on IDEAS
    20. Szymkowiak Marcin & Młodak Andrzej & Wawrowski Łukasz, 2017. "Mapping Poverty at the Level of Subregions in Poland Using Indirect Estimation," Statistics in Transition New Series, Statistics Poland, vol. 18(4), pages 609-635, December.
    21. Malay Ghosh, 2020. "Small area estimation: its evolution in five decades," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 1-22, August.
    22. Ralf Münnich & Jan Burgard & Martin Vogt, 2013. "Small Area-Statistik: Methoden und Anwendungen," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 6(3), pages 149-191, March.

    More about this item

    Keywords

    C21; C89; R39;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • R39 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other

    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:50:y:2013:i:2:p:603-621. 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.