Alternative Approaches to Modelling Housing Market Segmentation Evidence

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 Berna Keskin University of Sheffield, Department of Town and Regional Planning 1

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. 1. 2. 3. Berna Keskin Objectives: To examine the best way to conceptualize the structure of owner occupied housing market To identify the strengths and the weaknesses of the segmented model structures To examine relationship between locations and housing prices Approach: A standard hedonic model (market-wide model) A segmented model (using segmentation dummies in market-wide model) A multi-level model which includes segments and their interactions with each other and other spatial influences. University of Sheffield, Department of Town and Regional Planning 2

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. Berna Keskin University of Sheffield, Department of Town and Regional Planning 3

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

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

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

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) Berna Keskin University of Sheffield, Department of Town and Regional Planning 7

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) Berna Keskin University of Sheffield, Department of Town and Regional Planning 8

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

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. Berna Keskin University of Sheffield, Department of Town and Regional Planning 10

2 nd Stage. The 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 Berna Keskin University of Sheffield, Department of Town and Regional Planning 11

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

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 Berna Keskin Hedonic Model with a priori Submarket Variables P= f ( Fa, I, Lp, -Eq, S, A, C, N, Sm 1, Sm 3, -Sm 4, -Sm 5) 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 Sm 1: 1 st submarket Sm 3: 3 rd submarket Sm 4: (-)4 th submarket Sm 5: (-)5 th submarket Rsquare: 0. 67 Hedonic Model (experts’) submarket variables Hedonic Model with Cluster Submarket Variables P= f ( Fa, Ls, Lp, HS, A, Sm 1, -Sm 3, -Sm 4, Sm 5 Fa: Floor Area S: Site A: Age Lp: Living Period in Istanbul Hs: Household size Sm 1: 1 st submarket Sm 3: 3 rd submarket Sm 4: (-)4 th submarket Sm 5: (-)5 th submarket P= f ( Fa, I, Lp, Eq, S, A, C, Sc, Sm 4, Sm 5, Sm 7, -Sm 8) Rsquare: 0. 68 University of Sheffield, Department of Town and Regional Planning Fa: Floor Area S: Site A: Age I: Income of the household Lp: Living Period in Istanbul Sc: School satisfaction Eq: (-)Earthquake Damage Sm 4: 4 th submarket Sm 5: 5 th submarket Sm 7: 7 th submarket Sm 8: (-)8 th submarket Rsquare: 0. 64 13

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 Berna Keskin University of Sheffield, Department of Town and Regional Planning 14

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) Berna Keskin University of Sheffield, Department of Town and Regional Planning 15

Multi-level modelling (comparison) 2 level model Estimated Variance Standard Error Intra class correlation Submarket (a priori) 0. 0961 0. 03467 0. 23 Housing Unit 0. 1785 0. 90941 0. 77 2 level model Estimated Variance Standard Error Intra class correlation Submarket (experts’) 0. 1266 0. 045476 0. 34 Housing Unit 0. 17462 0. 9719 0. 66 2 level model Estimated Variance Standard Error Intra class correlation Submarket (cluster) 0. 132033 0. 037396 0. 34 Housing Unit 0. 1820813 2. 762246 0. 66 Berna Keskin University of Sheffield, Department of Town and Regional Planning 16

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

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

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

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

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

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. Berna Keskin University of Sheffield, Department of Town and Regional Planning 22