Facing the Crisis: Housing Choices and
Housing Demand in Poland
Michal Gluszak
16th ERES Conference24-27 June, Stockholm
Agenda0
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1. Introduction1.1 Project summary1.2 Theoretical background , previous research and project rationale1.3 Research objectives, methodology and data sources
2. Tenure choice in Poland3. Preliminary analysis of housing demand
in Krakow4. Future research
Theoretical background1.2
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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)
Previous research1.2
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Foundations of discrete choice analysis of housing demand:» 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)
Project rationale1.2
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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
Project data and research outline1.3
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Tenure choice
Housing submarket choice/type, location/
Polish General Social Survey 1992 - 2008
Housing market in Poland 2007. Demand and buyers preferences
Repeated representative surveys conducted by Institute of Social Studies from 1992 to 2005
Representative survey on 1500 potential housebuyers in major polish cities (Warsow, Wrocław, Krakow, Tricity, Poznań) conducted by Millward Brown
RP
SP
1
2
Major housing markets in Poland1.2
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Krakow~ ,75mln
(1,08mln)
Warsaw ~1,65mln
(2,41mln)
Wroclaw~ ,63mln (,85
mln)
Poznan~ ,57mln (,83
mln)
TriCity~ ,75mln
(1,29mln)
∑~
4,35mln (6,46mln)
Tenure choices in Poland2.0
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Some preliminary results from revealed housing choices analysis are available
Basic information:» Data from Polish General Social Survey 2005» Econometric method: Multinominal logit model (MLN)
Dependent variable (tenure):» Ownership (1)» Rental (2)» Non-market rental (3)» Living with family (4)
Simple predictors
Predictors2.2
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Variable Description N Mean St. dev.
W Dummy (1 for villages) 1277 0,36
M100-Dummy (1 for towns with less than 100.000 citizens)
1277 0,32
M500-Dummy (1 for agglomerations with 100.001-500.000 citizens)
1277 0,19
M500+Dummy (1 for agglomerations with 500.000+ citizens)
1277 0,13
HOMPOP Number of household members 1277 3,58 1,666
AGE Household head age 1277 45,89 17,835
CONTDummy (1 for household which continue living in village/town/city where household head was born)
1277 0,58 0,493
INCOME Total household income (PLN/monthly) 1152 2041,952080,79
9
SAVE Dummy (1 if household is able to save) 1270 0,18 0,386
WORKADummy (1 for household with at least 2 working adults)
1277 0,73 0,446
Estimation results2.2
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Coef SE Wald Sig. Exp(B)
Rental Int. 0,843 1,045 0,650 0,420M100- 0,829 0,610 1,846 0,174 2,291M500- 1,634 0,588 7,722 0,005 5,125M500+ 2,047 0,637 10,336 0,001 7,745HOMPOP -0,396 0,171 5,377 0,020 0,673AGE -0,058 0,014 17,585 0,000 0,944CONT -0,248 0,422 0,346 0,557 0,780INCOME -0,001 0,000 6,760 0,009 0,999SAVE 0,145 0,534 0,074 0,786 1,156WORKA 0,006 0,454 0,000 0,989 1,006
Non-market rental
Int. -1,805 0,469 14,836 0,000M100- 2,471 0,254 94,880 0,000 11,838M500- 2,440 0,269 82,392 0,000 11,478M500+ 2,248 0,312 51,760 0,000 9,467HOMPOP 0,041 0,055 0,547 0,460 1,042AGE -0,010 0,005 3,826 0,050 0,990CONT -0,055 0,163 0,114 0,736 0,947INCOME 0,000 0,000 7,373 0,007 1,000SAVE -0,698 0,230 9,236 0,002 0,498WORKA -0,093 0,184 0,259 0,611 0,911
Living with family
Int. -0,095 0,509 0,035 0,852M100- -0,878 0,246 12,711 0,000 0,416M500- -0,996 0,320 9,697 0,002 0,369M500+ -1,327 0,423 9,844 0,002 0,265HOMPOP 0,067 0,063 1,138 0,286 1,069AGE -0,046 0,007 43,194 0,000 0,955CONT 0,678 0,226 8,988 0,003 1,970INCOME 0,000 0,000 0,000 0,995 1,000SAVE -0,154 0,258 0,356 0,550 0,857WORKA 0,356 0,255 1,943 0,163 1,427
*base category: ownership
Age and predicted tenure status2.3
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Source: author’s own using S-POST freeware http://www.indiana.edu/~jslsoc/spost.htm
Housing location choices in Krakow
KrakowIdiosyncratic case…
…but a good starting point, as the housing market behavior is similar to other major cities in Poland (and probably other CEE countries)
3.0
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Exploratory analysis
Districts in Krakowperceptual map…
→Some districts are quite similar, when buyers’ perceptions are concerned (possible substitutes)
3.1
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Model for location choice3.2
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Variable Description Type
REL-IMPRHouse quality improvement (PHV-FHV)/INCOME
Alternative specific
DISTANCE Distance from home to chosen district Alternative specific
AGE <26Dummy (1 for households with head aged <36)
Case specific
AGE 26-55Dummy (1 for households with head aged 35-55)
Case specific
AGE 55+ Dummy (1 households with head aged 56+) Case specific
INCOME Total household income (PLN/monthly) Case specific
HOUSE Dummy (1 if household wants to buy a house) Case specific
Alternatives:» Nowa Huta, Podgorze, Krowodrza, Center» Suburbs
Predictors:
CL model for district choice 1/23.2
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AS variables Coef. SE Sig
REL_IMPR .0619 .0243 0.011
DISTANCE -1.249 .115 0.000
Alternative-specific conditional logit
Number of cases = 271 (1355 obs.)Log likelihood = -284.55574 (7 iter.)Wald chi2(18) = 161.82Prob > chi2 = 0.0000
CL model for district choice 2/23.2
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CS variables Coef. SE Sig
podgorze
AGE 36-55 -.656 .639 0.305
AGE 56+ -.473 .863 0.584
INCOME .0004 .000 0.017
HOUSE .678 .639 0.289
krowodrza
AGE 36-55 -1.303 .525 0.013
AGE 56+ -.607 .659 0.357
INCOME .0003 .000 0.014
HOUSE .129 .555 0.816
center
AGE 36-55 -.493 .535 0.357
AGE 56+ -1.383 .804 0.086
INCOME .0003 .000 0.026
HOUSE -.164 .584 0.779
suburbia
AGE 36-55 -.961 .585 0.100
AGE 56+ -.163 .759 0.830
INCOME -.00008 .000 0.611
HOUSE 3.230 .582 0.000
*base category: nowa huta**consts not displayed
Thank you, questions and comments
welcomed!
Michal GluszakCracow University of [email protected]
16th ERES Conference24-27 June, Stockholm
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