IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa13p1124.html
   My bibliography  Save this paper

Designing the housing market for 2030 - a foresight and econometric approach

Author

Listed:
  • João Marques
  • Miguel Viegas
  • Monique Borges
  • Eduardo Anselmo

Abstract

The paper presents a foresight analysis methodology and its empirical application in the context of housing market. This work was developed in the context of a wider research project, 'Drivers Of housiNg demand in Portuguese Urban sysTem' - DONUT, which analyses the Portuguese housing market. Decision making models were developed, combining technically informed subjectivity (foresight analysis) with more rigorous models (econometric models). The final outcome of this exercise is the estimation of housing characteristics and its hedonic prices in 2030, i.e., a picture of the housing market in 2030 is built. The importance of the housing sector requires a significant effort to understand its dynamics and analyze the main drivers of the housing market. The social and economic phenomena, as well as the heterogeneity of both housing (prices and features) and consumers are important elements not always included in the analytical models commonly used. The territory structure and the lack of information and transparency of the housing market mechanisms also influence its understanding. There is a variety of literature in the field of spatial economy that works as a theoretical basis for the estimation of hedonic housing prices. However, these analytical models fail on their inability to integrate the variability of exogenous factors. Forecasts on subject affects by high volatility and uncertainty require particular approaches such as foresight analysis which will be the main focus of this paper. In short, a methodology of foresight exercise is presented, discussing the combination of two techniques: scenario analysis and Delphi surveys. This methodology is supported on the assumption that it is possible: i) to discuss strategies in the context of great uncertainty; and ii) to identify trends and assess future evolution. In addition, the most relevant results of this exercise will be analyzed and presented, contributing to the definition of important guidelines for real estate agents. The work is structured in 4 parts: i) framework of the foresight exercise; ii) brief description of the exogenous housing market context (scenario analysis), iii) definition of the characteristics that describe the standard houses and their distribution in space, and iv) valuation of the standard houses previously identified.

Suggested Citation

  • João Marques & Miguel Viegas & Monique Borges & Eduardo Anselmo, 2013. "Designing the housing market for 2030 - a foresight and econometric approach," ERSA conference papers ersa13p1124, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa13p1124
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_01124.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joao Lourenço Marques & Eduardo Castro & Arnab Bhattacharjee & Paulo Batista, 2012. "SPATIAL HETEROGENEITY ACROSS SUBMARKETS: Housing submarket in an urban area of Portugal," ERSA conference papers ersa12p1111, European Regional Science Association.
    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. Eduardo Castro & João Marques & Paulo Batista & Monique Borges, 2014. "Integrated Decision Support System? DONUT-Prospect," ERSA conference papers ersa14p925, European Regional Science Association.

    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.

      More about this item

      JEL classification:

      • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
      • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

      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:wiw:wiwrsa:ersa13p1124. 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: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

      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.