1. Berna Keskin1 University of Sheffield, Department of Town and Regional Planning Alternative Approaches to Modelling Housing Market Segmentation: Evidence from Istanbul Berna Keskin (Ph.D Candidate) Town and Regional Department The University Of Sheffield Primary Supervisor: Prof. Craig Watkins Secondary Supervisor: Dr. Cath Jackson

2. Berna Keskin2 University of Sheffield, Department of Town and Regional Planning Introduction: Aim & Objectives Aim: The content of this research is to understand the spatial distribution of housing prices. The main aim of my research is to compare the effectiveness of different models of house prices that captures segmented price difference in Istanbul. Objectives: 1.To examine the best way to conceptualize the structure of owner occupied housing market 2.To identify the strengths and the weaknesses of the segmented model structures 3.To examine relationship between locations and housing prices Approach: 1.A standard hedonic model (market-wide model) 2.A segmented model (using segmentation dummies in market-wide model) 3.A multi-level model which includes segments and their interactions with each other and other spatial influences.

3. Berna Keskin3 University of Sheffield, Department of Town and Regional Planning Motivation of the Study  Segmented Market structure — Housing market in Istanbul are highly segmented — There are significant price differences, in different parts of the market for homes with the same physical features and locational attributes  Population :10,033,478. — Istanbul population/Turkey : 14.78 % in 2000 (TUIK,2006), surpasses the population of 22 EU countries (Eurostat). — 2,550,000 households and 3,391,752 housing units  The problems: — high increase rate in population, — the gap in the incomes — lack of enough amounts of residential plots. — land rent and speculation.

4. Berna Keskin4 University of Sheffield, Department of Town and Regional Planning Housing Prices Per m² in Istanbul in 2000

5. Berna Keskin5 University of Sheffield, Department of Town and Regional Planning Data Variables Property Characteristics Socio-economic Characteristics Neighbourhood Characteristics Locational Characteristics 1.Housing Type 2. Rooms 3. Floor Area 4. Elevator 5. Garden 6. Balcony 7. Storey 8. Site 9. Age 1.Income 2.Household size 3.Living period in the neighbourhood 4.Living period in Istanbul Satisfaction from: 1.School 2.Health service 3.Cultural facilities 4.Playground 5.Neighbour 6.Neighbourhood quality 1.Earthquake risk 2.Continent 3.Travel time to shopping centres 4.Travel time to jobs and schools * Italic variables are excluded due to multicollinearity.

6. Berna Keskin6 University of Sheffield, Department of Town and Regional Planning Market Wide Model (1 st stage) Hedonic modelling technique: the price of housing unit as a dependent variable, and the structural, locational

7. Berna Keskin7 University of Sheffield, Department of Town and Regional Planning 2 nd Stage The Effects of the Segments  Hedonic model: with spatial dummy variables as a proxy for segments  The need for the 2 nd stage : effectiveness of market-wide model. So: Segmentation is added into the hedonic model as a dummy variable. Segmentation is determined in 3 ways: 1.A priori identification (5 submarket) 2.Experts’ identification (5 submarket) 3.Cluster Analysis (12 submarket)

8. Berna Keskin8 University of Sheffield, Department of Town and Regional Planning 2 nd Stage The Effects of the Segments (A priori) A priori : segmentations which are considered to be the most `probable`. Five segmentations were chosen by taking account of : — Housing prices — Housing types — Location — Size — Age — Income — Living period — Neighborhood quality 1 st SUBMARKET: Waterside house (along bosphorus, literally called as “yali”), gated communities, residences, low storey apartments by the shore, detached houses close to the city centers. 2 nd SUBMARKET : Apartment blocks mostly constructed after 80’s (liberal economy), built-sell apartments and luxury sites. 3 rd SUBMARKET : Apartment blocks and detached/attached houses in historical areas. 4 th SUBMARKET: Apartments blocks mostly constructed in 2000’s, built-sell apartments and cooperatives. 5 th SUBMARKET: Squatter settlements, old summer houses (apartments)

9. Berna Keskin9 University of Sheffield, Department of Town and Regional Planning 2 nd Stage The Effects of the Segments (a priori)

10. Berna Keskin10 University of Sheffield, Department of Town and Regional Planning 2 nd Stage The Effects of the Segments (Experts’ identification) — segmentations which are determined by experts. — 10 interviews were done with real estate managers. — 7 maps were drawn by experts and 5 submarkets were identified mainly focusing on the housing prices.

11. Berna Keskin11 University of Sheffield, Department of Town and Regional Planning 2 nd StageThe Effects of the Submarkets (Cluster Analysis) Cluster Analysis is done in order to group the neighborhoods into submarkets. 12 clusters are displayed by the programme by taking account of these variables: — Housing prices — Floor area — Age — Rooms — Income of households — Living period in Istanbul — Neighborhood quality — Travel time to jobs, school, shops — Transportation satisfaction — Earthquake Risk

12. Berna Keskin12 University of Sheffield, Department of Town and Regional Planning 2 nd Stage The Effects of the Submarkets (Cluster Analysis)

13. Berna Keskin13 University of Sheffield, Department of Town and Regional Planning Comparison of Models Basic Hedonic Model P= f ( Fa, I, Lp, -Eq, S, A, Ls, N) Fa: Floor Area S: Site A: Age Ls: Low Storey I: Income of the household Lp: Living Period in Istanbul N: Neighbor satisfaction Eq: (-)Earthquake Damage Rsquare: 0.60 Hedonic Model with a priori Submarket Variables P= f ( Fa, I, Lp, -Eq, S, A, C, N,Sm1, Sm3, -Sm4, -Sm5) Fa: Floor Area S: Site A: Age C: Continent I: Income of the household Lp: Living Period in Istanbul N: Neighbor satisfaction Eq: (-)Earthquake Damage Sm1: 1 st submarket Sm3: 3 rd submarket Sm4: (-)4 th submarket Sm5: (-)5 th submarket Rsquare: 0.67 Hedonic Model with Cluster Submarket Variables P= f ( Fa, I, Lp, Eq, S, A, C, Sc,Sm4, Sm5, Sm7, -Sm8) Fa: Floor Area S: Site A: Age I: Income of the household Lp: Living Period in Istanbul Sc: School satisfaction Eq: (-)Earthquake Damage Sm4: 4 th submarket Sm5: 5 th submarket Sm7: 7 th submarket Sm8: (-)8 th submarket Rsquare: 0.64 Hedonic Model (experts’) submarket variables P= f ( Fa, Ls, Lp, HS, A,Sm1, -Sm3, -Sm4, - Sm5 Fa: Floor Area S: Site A: Age Lp: Living Period in Istanbul Hs: Household size Sm1: 1st submarket Sm3: 3rd submarket Sm4: (-)4th submarket Sm5: (-)5th submarket Rsquare: 0.68

14. Berna Keskin14 University of Sheffield, Department of Town and Regional Planning Multi-level modelling multilevel modeling: how the individual level (micro level) outcomes are affected by the individual level variables and group level (macro level or contextual level) variables. multi-level modelling provides assessing variation in housing prices at several levels simultaneously

15. Berna Keskin15 University of Sheffield, Department of Town and Regional Planning Contextual Level of Multi-level Modelling Segmentation is added into the multi-level model as level 2 Segmentation (Level 2-macro level-contextual level) is determined in 3 ways: 1.A priori identification (5 submarket) 2.Experts’ identification (5 submarket) 3.Cluster Analysis (12 submarket)

16. Berna Keskin16 University of Sheffield, Department of Town and Regional Planning Multi-level modelling (comparison) 2 level modelEstimated Variance Standard ErrorIntra class correlation Submarket (experts’)0.12660.0454760.34 Housing Unit0.174620.97190.66 2 level modelEstimated Variance Standard ErrorIntra class correlation Submarket (a priori)0.09610.034670.23 Housing Unit0.17850.909410.77 2 level modelEstimated Variance Standard ErrorIntra class correlation Submarket (cluster)0.1320330.0373960.34 Housing Unit0.18208132.7622460.66

17. Berna Keskin17 University of Sheffield, Department of Town and Regional Planning Multi-level modelling (a priori)

18. Berna Keskin18 University of Sheffield, Department of Town and Regional Planning Multi-level modelling (experts’)

19. Berna Keskin19 University of Sheffield, Department of Town and Regional Planning Multi-level modelling (cluster analysis)

20. Berna Keskin20 University of Sheffield, Department of Town and Regional Planning Effectiveness of models

21. Berna Keskin21 University of Sheffield, Department of Town and Regional Planning Effectiveness of Models

22. Berna Keskin22 University of Sheffield, Department of Town and Regional Planning Conclusions  “Housing submarkets matter” in explaining the structure of the urban housing market system.  From the three-stage methodology : different models have different effectiveness. However the submarket aggregation plays an important role in the improvement of the models.  “Models were performing better with the expert identified submarket dummies are employed”. Experts have a better, realistic and more detailed information about submarkets rather than a priori or statistical tools.  To overcome the problems of hedonic models, multi-level modelling approach may be a solution. Multi-level modelling can be an alternative method to capture and model the housing system.