Berna Keskin & Craig Watkins1 University of Sheffield, Department of Town and Regional Planning Exploring the Case for Expert-Defined Housing Submarket Boundaries Berna Keskin and Craig Watkins University of Sheffield

Berna Keskin& Craig Watkins2 University of Sheffield, Department of Town and Regional Planning Introduction: Aim & Objectives Aim: to explore the merits of expert-defined submarket boundaries when compared with submarkets constructed using other statistical methods. Objectives: how should analysts seek to construct submarkets if they are operating in a market where the quality and availability of housing transactions datasets is limited? Approach: 1.The use of prior knowledge; Principal components analysis (PCA) combined with cluster analysis and definitions based on the views of (expert) real estate professionals. The performance of these approaches is compared in terms of their impact on the accuracy of hedonic price estimates. 2.measuring the impact on standard error computing the predictive accuracy

Berna Keskin & Craig Watkins3 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 : % 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& Craig Watkins4 University of Sheffield, Department of Town and Regional Planning Submarket Delineation (A priori- PCA&Cluster Analysis-Experts’) A priori : segmentations which are considered to be the most `probable`. (5 submarket) Principal components analysis (PCA) combined with (K means) cluster analysis, (5 submarket) Consultation with real estate agents and valuers working in the Istanbul market. eight semi-structured interviews conducted in November spatial submarket boundaries on a 1/200,000 scale map the interviewees drew between five and seven submarkets, even though no guidance was provided and no restrictions were set.

Berna Keskin & Craig Watkins5 University of Sheffield, Department of Town and Regional Planning An example of the expert’s submarket identification

Berna Keskin & Craig Watkins6 University of Sheffield, Department of Town and Regional Planning The synthesis map of experts’ map

Berna Keskin & Craig Watkins7 University of Sheffield, Department of Town and Regional Planning The synthesis map of experts’ map

Berna Keskin8 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.

Berna Keskin & Craig Watkins9 University of Sheffield, Department of Town and Regional Planning Comparison of Models

Berna Keskin & Craig Watkins10 University of Sheffield, Department of Town and Regional Planning Comparison of Models Basic Hedonic Model P= f ( Fa, I, Lp, -Eq, S, A, Ls,) Fa: Floor Area S: Site A: Age Ls: Low Storey I: Income of the household Lp: Living Period in Istanbul 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 (PCA) Variables P= f (Ls,I, Lp,N,Sm2, Sm3, Sm4,) Ls: Low Storey I: Income of the household Lp: Living Period in Istanbul N: Neighbor satisfaction S: Site Sm2: 2 nd submarket Sm3: 3 rd submarket Sm4: 4 th submarket Rsquare: 0.61 Hedonic Model (experts’) submarket variables P= f ( Fa, Ls, Lp, HS, Sm1, -Sm3, -Sm4, - Sm5 Fa: Floor Area S: Site Lp: Living Period in Istanbul Hs: Household size Sm1: 1st submarket Sm3: (-)3rd submarket Sm4: (-)4th submarket Sm5: (-)5th submarket Rsquare: 0.68

Berna Keskin & Craig Watkins11 University of Sheffield, Department of Town and Regional Planning RMSE Test

Berna Keskin & Craig Watkins12 University of Sheffield, Department of Town and Regional Planning Accuracy Test

Berna Keskin & Craig Watkins13 University of Sheffield, Department of Town and Regional Planning Conclusions The specification based on prior knowledge led to the greatest reduction in standard error (at more than 20%). The expert-defined formulation reduced the standard error by just over 15% The predictive accuracy test showed that the expert-defined submarket formulation produced the largest proportionate decrease. It also generated the largest proportion of estimates within ten and twenty per cent of the actual value with more than 20% and 40% in the respective bands. The results do not provide comprehensive evidence that expert-defined submarkets are superior to specifications based on alternative methods. The expert-defined model does, however, perform well in terms of predictive : the submarkets constructed are a reasonable approximation of the ‘true’ submarket structure. These findings suggest that the methods used here to consult expert and construct a consensus view might offer a reasonable solution to analysts operating in markets where data availability is limited.