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Facing the Crisis: Housing Choices and Housing Demand in Poland

Facing the Crisis: Housing Choices and Housing Demand in Poland. Michal Gluszak. 16 th ERES Conference 24-27 June, Stockholm. 0. Agenda. Introduction 1.1 Project summary 1.2 Theoretical background , previous research and project rationale

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Facing the Crisis: Housing Choices and Housing Demand in Poland

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  1. Facing the Crisis: Housing Choices and Housing Demand in Poland MichalGluszak 16th ERES Conference 24-27 June, Stockholm

  2. 0 Agenda • Introduction 1.1 Project summary 1.2Theoreticalbackground , previousresearch and projectrationale 1.3Researchobjectives, methodology and data sources • Tenurechoicein Poland • Preliminaryanalysis of housingdemandinKrakow • Futureresearch 16th ERES Conference, Stockholm

  3. 1.2 Theoreticalbackground • Lancaster and Rosen approach to consumer choice theory for differenciated goods • A paradigm in empirical demand and price studies • Random utility and theory of discrete choice (developed by McFadden) • Practical and intuitive approach to analyze housing demand • A method to incorporate bounded rationality (suggested by Anderson, de Palma and Thisse, 1992) 16th ERES Conference, Stockholm

  4. 1.2 Previousresearch • Foundations of discretechoiceanalysis of housingdemand: • Quigley (1977), McFadden (1978) • Numerous studies using discrete choice theory as a method of housing demand analysis, since late 70-ties of the last century: • Longley (1984); Quigley (1985), • Anas and Arnott (1991);Earnhart (1998), • Tu (2001), Gibb;Meen and MacKay (2000). • Bourassa and Hoesli (2007) 16th ERES Conference, Stockholm

  5. 1.2 Project rationale • Knowledge gap: • Little effort has been made to understand the nature of demand on emerging markets (CEE countries), after system transformation • Few studies on recent market developments, and their consequences at microlevel • Potential applications: • Better understanding of urban development patterns • Prediction of housing submarket and intra-city price dynamics • Simulation of housing policies effects 16th ERES Conference, Stockholm

  6. 1.3 Project data and researchoutline 1 Repeatedrepresentativesurveysconducted by Institute of SocialStudiesfrom 1992 to 2005 Polish General Social Survey 1992 - 2008 Tenure choice RP SP Representative survey on 1500 potential housebuyers in major polish cities (Warsow, Wrocław, Krakow, Tricity, Poznań) conducted by Millward Brown 2 Housing submarket choice /type, location/ Housing market in Poland 2007. Demand and buyers preferences 16th ERES Conference, Stockholm

  7. 1.2 Major housingmarketsin Poland TriCity ~ ,75mln (1,29mln) Poznan ~ ,57mln (,83 mln) Warsaw ~1,65mln (2,41mln) Wroclaw ~ ,63mln (,85 mln) Krakow ~ ,75mln (1,08mln) ∑ ~ 4,35mln (6,46mln) 16th ERES Conference, Stockholm

  8. 2.0 Tenurechoicesin Poland • Some preliminary results from revealed housing choices analysis are available • Basic information: • Data from Polish General Social Survey 2005 • Econometric method: Multinominallogit model (MLN) • Dependent variable (tenure): • Ownership (1) • Rental (2) • Non-market rental (3) • Living with family (4) • Simple predictors 16th ERES Conference, Stockholm

  9. 2.2 Predictors 16th ERES Conference, Stockholm

  10. 2.2 Estimationresults *base category: ownership 16th ERES Conference, Stockholm

  11. 2.3 Age and predictedtenure status Source: author’s own using S-POST freeware http://www.indiana.edu/~jslsoc/spost.htm 16th ERES Conference, Stockholm

  12. 3.0 HousinglocationchoicesinKrakow Krakow Idiosyncratic case… …but a good starting point, as the housing market behavior is similar to other major cities in Poland (and probably other CEE countries) 16th ERES Conference, Stockholm

  13. 3.1 Exploratoryanalysis Districts in Krakow perceptual map… Some districts are quite similar, when buyers’ perceptions are concerned (possible substitutes) 16th ERES Conference, Stockholm

  14. 3.2 Model for locationchoice • Alternatives: • Nowa Huta, Podgorze, Krowodrza, Center • Suburbs • Predictors: 16th ERES Conference, Stockholm

  15. 3.2 CL model for districtchoice 1/2 Alternative-specific conditional logit Number of cases = 271 (1355 obs.) Log likelihood = -284.55574 (7 iter.) Wald chi2(18) = 161.82 Prob > chi2 = 0.0000 16th ERES Conference, Stockholm

  16. 3.2 CL model for districtchoice 2/2 *base category: nowa huta**consts not displayed 16th ERES Conference, Stockholm

  17. Thank you, questions and comments welcomed! MichalGluszak CracowUniversity of Economics gluszakm@uek.krakow.pl 16th ERES Conference 24-27 June, Stockholm

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