IDEAS home Printed from https://ideas.repec.org/a/spr/astaws/v14y2020i2d10.1007_s11943-020-00272-x.html
   My bibliography  Save this article

Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik
[Collection of data for rent indexes: Overview and discussion from a statistical perspective]

Author

Listed:
  • Göran Kauermann
  • Michael Windmann

    (Ludwig-Maximilians-Universität München)

  • Ralf Münnich

Abstract

Zusammenfassung Der Artikel diskutiert die verschiedenen Methoden bei der Datenerhebung von Mietspiegeln. Es werden Vor- und Nachteile der in der Praxis zu findenden Methoden diskutiert und aus dem statistischen Blickwinkel beleuchtet. Dabei gehen wir den drei Fragen nach: Wer wird befragt? Wie wird befragt? Wie erfolgt die Stichprobenziehung? Neben statistischen Aspekten werden die Mietspiegel der 30 größten Städte als Beispiel herangezogen, um aufzuzeigen, dass die angewandte Methodik in der Praxis sehr heterogen ist.

Suggested Citation

  • Göran Kauermann & Michael Windmann & Ralf Münnich, 2020. "Datenerhebung bei Mietspiegeln: Überblick und Einordnung aus Sicht der Statistik [Collection of data for rent indexes: Overview and discussion from a statistical perspective]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(2), pages 145-162, July.
    • Handle: RePEc:spr:astaws:v:14:y:2020:i:2:d:10.1007_s11943-020-00272-x
    DOI: 10.1007/s11943-020-00272-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11943-020-00272-x
    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/s11943-020-00272-x?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. Mary E. Thompson, 2019. "Combining Data from New and Traditional Sources in Population Surveys," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 79-89, May.
    2. Rao, J. N. K. & Wu, Changbao, 2010. "Pseudo–Empirical Likelihood Inference for Multiple Frame Surveys," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1494-1503.
    3. Florian Meinfelder, 2014. "Multiple Imputation: an attempt to retell the evolutionary process," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(4), pages 249-267, November.
    4. Jean-Claude Deville & Yves Tille, 2004. "Efficient balanced sampling: The cube method," Biometrika, Biometrika Trust, vol. 91(4), pages 893-912, December.
    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. Timo Schmid & Markus Zwick, 2020. "Vorwort der Herausgeber," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(2), pages 117-120, July.

    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. Tomasz Bąk, 2021. "Spatial sampling methods modified by model use," Statistics in Transition New Series, Polish Statistical Association, vol. 22(2), pages 143-154, June.
    2. Matei Alina, 2021. "Book Review," Journal of Official Statistics, Sciendo, vol. 37(4), pages 1079-1081, December.
    3. Louis Geiler & Séverine Affeldt & Mohamed Nadif, 2022. "A survey on machine learning methods for churn prediction," Post-Print hal-03824873, HAL.
    4. Louis‐Paul Rivest & Serigne Abib Gaye, 2023. "Using Survey Sampling Algorithms For Exact Inference in Logistic Regression," International Statistical Review, International Statistical Institute, vol. 91(1), pages 18-34, April.
    5. Lennart Bondesson, 2010. "Conditional and Restricted Pareto Sampling: Two New Methods for Unequal Probability Sampling," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 514-530, September.
    6. Raphaël Jauslin & Bardia Panahbehagh & Yves Tillé, 2022. "Sequential spatially balanced sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 33(8), December.
    7. Tillé, Yves & Favre, Anne-Catherine, 2005. "Optimal allocation in balanced sampling," Statistics & Probability Letters, Elsevier, vol. 74(1), pages 31-37, August.
    8. Yves Tillé, 2016. "The legacy of Corrado Gini in survey sampling and inequality theory," METRON, Springer;Sapienza Università di Roma, vol. 74(2), pages 167-176, August.
    9. G. Alleva & G. Arbia & P. D. Falorsi & V. Nardelli & A. Zuliani, 2023. "Optimal two-stage spatial sampling design for estimating critical parameters of SARS-CoV-2 epidemic: Efficiency versus feasibility," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 983-999, September.
    10. Tobias Schoch & André Müller, 2020. "Treatment of sample under-representation and skewed heavy-tailed distributions in survey-based microsimulation: An analysis of redistribution effects in compulsory health care insurance in Switzerland," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 267-304, December.
    11. Antonio Arcos & María del Mar Rueda & Manuel Trujillo & David Molina, 2015. "Review of Estimation Methods for Landline and Cell Phone Surveys," Sociological Methods & Research, , vol. 44(3), pages 458-485, August.
    12. Stefan Zins & Jan Pablo Burgard, 2020. "Planning Domain Sizes in Cluster Sampling," Research Papers in Economics 2020-06, University of Trier, Department of Economics.
    13. Alfio Marazzi & Yves Tillé, 2017. "Using past experience to optimize audit sampling design," Review of Quantitative Finance and Accounting, Springer, vol. 49(2), pages 435-462, August.
    14. Jelke Wibbeke & Payam Teimourzadeh Baboli & Sebastian Rohjans, 2022. "Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.
    15. Ralf Münnich & Siegfried Gabler & Christian Bruch & Jan Pablo Burgard & Tobias Enderle & Jan-Philipp Kolb & Thomas Zimmermann, 2015. "Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 269-304, December.
    16. J. N. K. Rao, 2021. "On Making Valid Inferences by Integrating Data from Surveys and Other Sources," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 242-272, May.
    17. Rivest, Louis-Paul & Ebouele, Sergio Ewane, 2020. "Sampling a two dimensional matrix," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    18. Ralf Thomas Münnich, 2016. "Vorwort des Herausgebers," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(4), pages 197-203, December.
    19. Lin, X. Sheldon & Yang, Shuai, 2020. "Fast and efficient nested simulation for large variable annuity portfolios: A surrogate modeling approach," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 85-103.
    20. Amin, Adnan & Shah, Babar & Khattak, Asad Masood & Lopes Moreira, Fernando Joaquim & Ali, Gohar & Rocha, Alvaro & Anwar, Sajid, 2019. "Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods," International Journal of Information Management, Elsevier, vol. 46(C), pages 304-319.

    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:astaws:v:14:y:2020:i:2:d:10.1007_s11943-020-00272-x. 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.