Facing the Crisis: Housing Choices and Housing Demand in Poland

Facing the Crisis: Housing Choices and

Housing Demand in Poland

Michal Gluszak

16th ERES Conference24-27 June, Stockholm

Agenda0

16th ERES Conference, Stockholm 2

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


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


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


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


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


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


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


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


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


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


Exploratory analysis

Districts in Krakowperceptual map…

→Some districts are quite similar, when buyers’ perceptions are concerned (possible substitutes)

3.1


Model for location choice3.2


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


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


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

Facing the Crisis: Housing Choices and Housing Demand in Poland

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