In this research, it is aimed to find out the determinants of the
housing prices in Istanbul. A market-wide hedonic price model is
employed by taking into property characteristics, socio-economic
characteristics, neighbourhood quality characteristics and locational
characteristics. The dataset used for this hedonic model is composed of
two dataset. The data of property characteristics is provided from two
major real estate agent's websites and this data set contains 2,175
transactions of single-family homes sold in Istanbul in November 2006
and in April. The second dataset provides information about the
socio-economic and the neighbourhood quality characteristics. This
dataset is derived from a survey that was undertaken by Istanbul Greater
Municipality. The data of the locational characteristics such as travel
time to jobs, schools and shopping areas (or centres) are taken from the
second data set. The earthquake risk percentage measurement which is one
of the most important locational characteristics is taken into account
from predictions by the JICA (Japanese Agency for International
Cooperation) (IBB, 2007).
The results of the hedonic model suggest that the housing price is
determined by four types of characteristics: property, socio-economic,
neighbourhood quality and locational characteristics. Among the property
characteristics, living area being in a low storey building, being in a
secured site (with swimming pool and garage), are found to have a
positive impact on housing value. On the contrary to most studies on
housing prices, age has a counterintuitive sign. Such similar results
for Istanbul were found by Ozus et al. (2007) and Onder et al. (2004).
Among the socio-economic characteristics, the length of time the
inhabitants have lived in Istanbul, average income of the household and
neighbour satisfaction, as a variable in the behaviour characteristics,
have positive impacts on housing value. As expected, earthquake risk as
a locational variable with a negative impact.
The results of this study also display the demand side preferences
so that these can be used as a guide to improve the understanding within
the supply side and investors. In addition to supply side and investors,
policy makers and urban planners can use the results in order to analyze
housing market behaviour.
For further studies, a second model will be employed, which
includes neighbourhood dummy variables as a proxy for submarkets, and a
multi-level modelling framework as the main analytical tool. The results
of this can then be compared to those generated by two different forms
of the standard hedonic model. The comparative analysis focuses on the
estimated coefficients, significance and explanatory power of the
models. Furthermore, the spatial pattern of the residuals will be
explored and GIS techniques will be used to systematically examine the
weaknesses of the different modelling approaches.
ACKNOWLEDGEMENTS
I would like to thank two anonymous referees and Dr. Tom Kauko for
their helpful, constructive comments. I am grateful to my PhD supervisor
Dr. Craig Watkins who provided valuable suggestions that considerably
improved this paper and the research. Any errors are naturally my own
responsibility.
Received 9 January 2008; accepted 27 March 2008
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Berna KESKIN
Department of Town and Regional Planning, University of Sheffield,
Western Bank, Sheffield, S10 2TN, United Kingdom E-mail:
trp05bk@shef.ac.uk
SANTRAUKA
STAMBULO BUSTO RINKOS KAINY HEDONINE ANALIZE
Berna KESKIN
Siame darbe siekiama isnagrineti veiksnius, kurie daro itaka busto
kainoms Stambule. Pasitelkus hedonini kainu modeli, tyrinejami busto
kainas lemiantys veiksniai, atsizvelgiant i nekilnojamojo turto
charakteristikas, socialinius-ekonominius veiksnius, apylinkiu kokybes
bruozus ir vietos veiksnius. Rezultatai rodo, kad busto kainoms itaka
daro tokie veiksniai: gyvenamosios teritorijos dydis, pastato
aukstingumas, buvimas sklype ir pastato amzius. Be siu veiksniu, busto
kainas Stambule veikia ir laikas gyventas mieste, vidutines namu ukio
pajamos, patinkantys kaimynai bei zemes drebejimu rizika toje
teritorijoje. Siuloma atlikti tolesnius tyrimus, suformuojant antra
fiktyviuosius apylinkiu kintamuosius apimantj modeli, kuris bus taikomas
kaip subrinku pakaitalas, o naudojant daugialype modeliavimo struktura
bus siekiama isanalizuoti miesto busto subrinkos sistema.
Table 1. Descriptive statistics of housing units for Istanbul
transaction data N: 2175
Minimum Maximum
Price 34013.60 8,000,000
Age 0 150
Area 45 1920
Room 1 15
Total storey 1 27
Flat 0 1
Detached 0 1
Elevator 0 1
Garden 0 1
Balcony 0 1
Garage 0 1
Security 0 1
Swimming pool 0 1
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