berna keskin & craig watkins1 university of sheffield, department of town and regional planning...

Berna Keskin & Craig Watkins 1University 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 Watkins 2University 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 errorcomputing the predictive accuracy


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.

Berna Keskin& Craig Watkins 4University 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 2007.

• 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.


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An example of the expert’s submarket identification


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The synthesis map of experts’ map

Berna Keskin 8University 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.


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Comparison of Models


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Comparison of Models

Basic Hedonic Model

P= f ( Fa, I, Lp, -Eq, S, A, Ls,)

Fa: Floor Area

S: SiteA: AgeLs: Low StoreyI: Income of the householdLp: Living Period in IstanbulEq: (-)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: SiteA: AgeC: ContinentI: Income of the householdLp: Living Period in IstanbulN: Neighbor satisfactionEq: (-)Earthquake DamageSm1: 1st submarket Sm3: 3rd submarketSm4: (-)4th submarketSm5: (-)5th submarket

Rsquare: 0.67

Hedonic Model with Cluster Submarket (PCA) Variables

P= f (Ls,I, Lp,N,Sm2, Sm3, Sm4,)

Ls: Low StoreyI: Income of the householdLp: Living Period in IstanbulN: Neighbor satisfactionS: SiteSm2: 2nd submarket Sm3: 3rd submarketSm4: 4th submarket

Rsquare: 0.61

Hedonic Model (experts’) submarket variables

P= f ( Fa, Ls, Lp, HS, Sm1, -Sm3, -Sm4, -Sm5

Fa: Floor AreaS: SiteLp: Living Period in IstanbulHs: Household sizeSm1: 1st submarket Sm3: (-)3rd submarketSm4: (-)4th submarketSm5: (-)5th submarket

Rsquare: 0.68


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RMSE Test


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Accuracy Test


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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.

berna keskin & craig watkins1 university of sheffield, department of town and regional planning...

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