Berna Keskin1 University of Sheffield, Department of Town and Regional Planning Alternative Approaches to Modelling Housing Market Segmentation: Evidence

Berna Keskin 1University 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


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.


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.


Housing Prices Per m² in Istanbul in 2000


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.


Market Wide Model (1st stage)

Model Summary

.780a .608 .605 .20030Model1

R R SquareAdjustedR Square

Std. Error ofthe Estimate

Predictors: (Constant), logarithm of the avarageincome, log of sqm, log_eqr1, log_trvtimejob,logarithm of tha age, log_livper_ist, garden, low_str,log_neigsat, site, continent, log_hhsize, log_schoolsat

a.

Coefficientsa

1.688 .155 10.911 .000

.003 .012 .004 .209 .834

-.015 .013 -.020 -1.150 .250

.025 .011 .038 2.209 .027

1.149 .030 .703 38.207 .000

.054 .011 .093 5.130 .000

.032 .038 .017 .848 .397

.159 .080 .035 1.977 .048

.004 .024 .003 .155 .877

.302 .053 .119 5.712 .000

-.062 .074 -.016 -.841 .401

-.122 .019 -.109 -6.364 .000

.086 .016 .097 5.399 .000

.170 .029 .115 5.797 .000

(Constant)

continent

garden

low_str

log of sqm

logarithm of tha age

log_schoolsat

log_neigsat

log_trvtimejob

log_livper_ist

log_hhsize

log_eqr1

site

logarithm of theavarage income

Model1

B Std. Error

UnstandardizedCoefficients

Beta

StandardizedCoefficients

t Sig.

Dependent Variable: Log of the housing pricea.

Hedonic modelling technique:• the price of housing unit as a dependent variable, and• the structural, locational


2nd Stage The Effects of the Segments Hedonic model: with spatial dummy variables as a proxy for segments

The need for the 2nd 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)


2nd 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

1st 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.

2nd SUBMARKET: Apartment blocks mostly constructed after 80’s (liberal economy), built-sell apartments and luxury sites.

3rd SUBMARKET: Apartment blocks and detached/attached houses in historical areas.

4th SUBMARKET: Apartments blocks mostly constructed in 2000’s, built-sell apartments and cooperatives.5th SUBMARKET: Squatter settlements, old summer houses (apartments)


2nd Stage The Effects of the Segments (a priori)

Model Summary

.823a .678 .674 .18190Model1



Predictors: (Constant), my 5th submarket, log_neigsat,log of sqm, log_trvtimejob, log_schoolsat, log_hhsize,log_eqr1, garden, low_str, my 4th submarket, site, my3rd submarket, continent, logarithm of tha age,logarithm of the avarage income, log_livper_ist, my 1stsubmarket

a.

ANOVAb

104.326 17 6.137 185.479 .000a

49.563 1498 .033

153.889 1515

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), my 5th submarket, log_neigsat, log of sqm, log_trvtimejob,log_schoolsat, log_hhsize, log_eqr1, garden, low_str, my 4th submarket, site, my3rd submarket, continent, logarithm of tha age, logarithm of the avarage income,log_livper_ist, my 1st submarket

a.

Dependent Variable: Log of the housing priceb.

Coefficientsa

2.575 .156 16.504 .000

1.054 .029 .645 36.552 .000

.020 .010 .034 2.030 .043

-.011 .011 -.017 -1.091 .275

.086 .015 .096 5.892 .000

.009 .012 .011 .694 .488

.127 .050 .050 2.548 .011

-.068 .068 -.018 -.992 .321

.007 .022 .005 .317 .752

-.040 .018 -.036 -2.188 .029

-.039 .011 -.061 -3.388 .001

.154 .073 .034 2.104 .036

-.043 .035 -.023 -1.242 .214

.067 .029 .046 2.327 .020

.102 .015 .138 6.769 .000

.050 .018 .049 2.742 .006

-.155 .014 -.214 -10.908 .000

-.138 .021 -.118 -6.438 .000

(Constant)

log of sqm


low_str

site

garden

log_livper_ist

log_hhsize

log_trvtimejob

log_eqr1

continent

log_neigsat

log_schoolsat

logarithm of theavarage income

1st submarket

3rd submarket

4th submarket

5th submarket

Model1

B Std. Error


Beta


t Sig.



2nd 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.

Source | SS df MS Number of obs = 1628-------------+------------------------------ F( 15, 1612) = 229.48 Model | 114.688101 15 7.64587342 Prob > F = 0.0000 Residual | 53.709122 1612 .033318314 R-squared = 0.6811-------------+------------------------------ Adj R-squared = 0.6781 Total | 168.397223 1627 .103501674 Root MSE = .18253

------------------------------------------------------------------------------ LOGprice | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- continent | -.0007762 .0108061 -0.07 0.943 -.0219718 .0204193 garden | .0025735 .0116877 0.22 0.826 -.0203512 .0254982 low_str | .0218501 .0099633 2.19 0.028 .0023077 .0413925 LOGsqm | 1.081056 .0262159 41.24 0.000 1.029635 1.132477 LOGage | -.0228293 .0096516 -2.37 0.018 -.0417603 -.0038982log_school~t | -.052207 .0305338 -1.71 0.087 -.1120972 .0076831 log_neigsat | .0735292 .0722884 1.02 0.309 -.06826 .2153184log_livper~t | .1253313 .0411161 3.05 0.002 .0446846 .2059781 log_hhsize | -.1478915 .0618915 -2.39 0.017 -.2692878 -.0264952 log_eqr1 | .0014807 .0191644 0.08 0.938 -.0361091 .0390705 LOGincome | .0251674 .0261492 0.96 0.336 -.0261227 .0764574 exp_1 | .1122694 .0162255 6.92 0.000 .0804442 .1440946 exp_3 | -.128223 .0138803 -9.24 0.000 -.1554483 -.1009977 exp_4 | -.2137861 .0146333 -14.61 0.000 -.2424884 -.1850838 exp_5 | -.1967963 .0281816 -6.98 0.000 -.2520727 -.1415199 _cons | 2.768837 .1410366 19.63 0.000 2.492203 3.045472


2nd 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


2nd Stage The Effects of the Submarkets (Cluster Analysis)

Model Summary

.801a .641 .636 .19270Model1



Predictors: (Constant), clusters 12th submarket, log_schoolsat, clusters 11th submarket, log_neigsat, site,low_str, clusters 4thsubmarket, log_trvtimejob,clusters 6th submarket, log_eqr1, garden, clusters2nd submarket, clusters 5thsubmarket, clusters 9thsubmarket, logarithm of tha age, log of sqm, clusters8th submarket, clusters 3rd submarket, clusters7thsubmarket, continent, log_livper_ist, logarithm ofthe avarage income

a.

ANOVAb

98.661 22 4.485 120.769 .000a

55.218 1487 .037

153.878 1509

Regression

Residual

Total

Model1

Sum ofSquares df Mean Square F Sig.

Predictors: (Constant), clusters 12th submarket, log_schoolsat, clusters 11thsubmarket, log_neigsat, site, low_str, clusters 4thsubmarket, log_trvtimejob,clusters 6th submarket, log_eqr1, garden, clusters 2nd submarket, clusters5thsubmarket, clusters 9th submarket, logarithm of tha age, log of sqm, clusters8th submarket, clusters 3rd submarket, clusters 7thsubmarket, continent, log_livper_ist, logarithm of the avarage income

a.

Dependent Variable: Log of the housing priceb.

Coefficientsa

1.379 .177 7.770 .000

1.110 .030 .678 37.479 .000

.033 .010 .057 3.204 .001

.009 .011 .013 .785 .433

.074 .016 .083 4.652 .000

-.017 .013 -.023 -1.342 .180

.239 .053 .094 4.515 .000

.046 .026 .033 1.773 .076

-.099 .021 -.089 -4.626 .000

-.007 .012 -.011 -.589 .556

.088 .084 .019 1.049 .294

.159 .044 .083 3.587 .000

.290 .036 .195 8.104 .000

-.026 .103 -.004 -.257 .797

.034 .024 .030 1.380 .168

.553 .086 .109 6.432 .000

.066 .033 .038 2.004 .045

.020 .016 .027 1.253 .211

.222 .026 .167 8.682 .000

-.116 .049 -.046 -2.377 .018

.002 .027 .001 .066 .947

.147 .115 .021 1.281 .200

.251 .139 .029 1.803 .072

(Constant)

log of sqm


low_str

site

garden

log_livper_ist

log_trvtimejob

log_eqr1

continent

log_neigsat

log_schoolsat

logarithm of the avarageincome

clusters 2nd submarket

clusters 3rd submarket

clusters 4thsubmarket


clusters 6th submarket






Model1

B Std. Error


Beta


t Sig.



Comparison of Models

Basic Hedonic Model

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

Fa: Floor Area

S: SiteA: AgeLs: Low StoreyI: Income of the householdLp: Living Period in IstanbulN: Neighbor satisfactionEq: (-)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 Variables

P= f ( Fa, I, Lp, Eq, S, A, C, Sc,Sm4, Sm5, Sm7, -Sm8)

Fa: Floor Area

S: SiteA: AgeI: Income of the householdLp: Living Period in IstanbulSc: School satisfactionEq: (-)Earthquake DamageSm4: 4th submarket Sm5: 5th submarketSm7: 7th submarketSm8: (-)8th submarket

Rsquare: 0.64

Hedonic Model (experts’) submarket variables

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

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

Rsquare: 0.68


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


Contextual Level of Multi-level ModellingSegmentation 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)


Multi-level modelling (comparison)

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



Submarket (a priori) 0.0961 0.03467 0.23

Housing Unit 0.1785 0.90941 0.77



Submarket (cluster) 0.132033 0.037396 0.34

Housing Unit 0.1820813 2.762246 0.66


Multi-level modelling (a priori)Mixed-effects REML regression Number of obs = 1695 ----------------------------------------------------------- | No. of Observations per Group Group Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ expert | 5 60 339.0 670 housing_unit | 1695 1 1.0 1 ----------------------------------------------------------- Wald chi2(8) = 2040.63 Log restricted-likelihood = 402.48575 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ LOGprice | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------------

HOUSING UNIT LEVEL low_str | .0232196 .0099345 2.34 0.019 .0037484 .0426907 LOGsqm | 1.036151 .0258163 40.14 0.000 .9855525 1.08675 LOGage | .0016356 .0095016 0.17 0.863 -.0169873 .0202584 site | .0555968 .0141667 3.92 0.000 .0278306 .083363

SUBMARKET LEVEL LOGincome | .0474035 .023615 2.01 0.045 .0011191 .093688 log_neigsat | .0807594 .0664319 1.22 0.224 -.0494448 .2109636 log_livper~t | .1739966 .0361864 4.81 0.000 .1030725 .2449206 log_eqr1 | -.0101088 .0183265 -0.55 0.581 -.0460281 .0258106 _cons | 2.505805 .1252099 20.01 0.000 2.260398 2.751212 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] expert: Identity | sd(_cons) | .1266766 .0454765 .0626784 .2560206 housing_unit: Identity | sd(_cons) | .1746259 .9714991 3.21e-06 9497.532 -----------------------------+------------------------------------------------ sd(Residual) | .067224 2.523634 7.46e-34 6.06e+30 ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(2) = 290.49 Prob > chi2 = 0.0000 Note: LR test is conservative and provided only for reference


Multi-level modelling (experts’)Mixed-effects REML regression Number of obs = 1695 Group Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ Submarket| 5 128 339.0 474 Housing unit | 1695 1 1.0 1 ----------------------------------------------------------- Wald chi2(8) = 1864.11 Log restricted-likelihood = 367.42966 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ LOGprice | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+----------------------------------------------------------------

HOUSING UNIT LEVEL low_str | .0016644 .010295 0.16 0.872 -.0185134 .0218423 LOGsqm | 1.03863 .0270467 38.40 0.000 .9856194 1.091641 LOGage | .0282012 .0093544 3.01 0.003 .0098669 .0465356 site | .0875709 .0142355 6.15 0.000 .0596698 .1154719

SUBMARKET LEVEL LOGincome | .080302 .0241835 3.32 0.001 .0329031 .1277008 log_neigsat | .1492683 .0660211 2.26 0.024 .0198694 .2786673 log_livper~t | .2370063 .0364979 6.49 0.000 .1654717 .3085408 log_eqr1 | -.0754051 .017868 -4.22 0.000 -.1104257 -.0403845 _cons | 2.28664 .1209502 18.91 0.000 2.049582 2.523698 ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Submarket: Identity sd(_cons) | .0961835 .0346763 .0474486 .1949743 -----------------------------+------------------------------------------------ Housing_Unit: Identity | sd(_cons) | .1785125 .909417 8.23e-06 3872.974 -----------------------------+------------------------------------------------ sd(Residual) | .0683336 2.375723 1.74e-31 2.68e+28 ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(2) = 225.59 Prob > chi2 = 0.0000 Note: LR test is conservative and provided only for reference


Multi-level modelling (cluster analysis)Multi-level random coefficient estimation (submarket: cluster identification)

Mixed-effects REML regression Number of obs = 1540 Group variable: clusters Number of groups = 12

Obs per group: min = 2 avg = 128.3 max = 642

Wald chi2(8) = 1517.62 Log restricted-likelihood = 319.14103 Prob > chi2 = 0.0000

------------------------------------------------------------------------------ LOGprice | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- LOGincome | .2575627 .0408225 6.31 0.000 .1775521 .3375733 low_str | .0467662 .0235945 1.98 0.047 .0005219 .0930105 LOGsqm | 1.049377 .0289667 36.23 0.000 .9926028 1.10615 LOGage | .0474617 .0309843 1.53 0.126 -.0132663 .1081898 log_neigsat | .1153349 .0739143 1.56 0.119 -.0295344 .2602043 log_livper~t | .2732901 .0483016 5.66 0.000 .1786207 .3679596 log_eqr1 | -.0964436 .0371273 -2.60 0.009 -.1692118 -.0236754 site | .0818725 .0341488 2.40 0.017 .0149421 .1488029 _cons | 1.720028 .1650315 10.42 0.000 1.396572 2.043484 ------------------------------------------------------------------------------

------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ clusters: Independent | sd(LOGinc~e) | .0179455 .0338954 .0004428 .7272841 sd(low_str) | .0525382 .029998 .0171577 .1608755 sd(LOGsqm) | .0001794 .0528508 3.4e-255 9.4e+246 sd(LOGage) | .0831873 .0269248 .0441116 .1568778 sd(log_eqr1) | .0658807 .0453014 .0171178 .2535529 sd(site) | .0810882 .0283171 .0408985 .1607712 sd(_cons) | .1289985 .0584563 .0530711 .313553 -----------------------------+------------------------------------------------ sd(Residual) | .1893967 .003486 .1826861 .1963538 ------------------------------------------------------------------------------ LR test vs. linear regression: chi2(7) = 125.14 Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference


Effectiveness of models Comparison of Root Mean Square Error Test

Model RMSE Percent Reduction in

RMSE (%) Market-wide hedonic model 0.2003

Hedonic model with a priori submarket dummy

0.1819 9.19

Hedonic model with expert submarket dummy

0.1803 10

Hedonic model with cluster analysis submarket dummy

0.1927 3.8

Multi-level model identified by a priori submarket

0.1885 5.89

Multi-level model identified by expert submarket

0.1815 9.38

Multi-level model identified by cluster analysis

0.1893 5.49


Effectiveness of Models Comparison of predictive accuracy

Model % of cases predicted

within 20% accuracy

% of cases predicted

within 10% accuracy

Market-wide hedonic model 34.43 18.71

Hedonic model identified by a priori submarket

29.93 16.50

Hedonic model identified by expert submarket

41.79 21.83

Hedonic model identified by cluster analysis submarket

31.58 16.41

Multi-level model identified by a priori submarket

27.26 14.57

Multi-level model identified by expert submarket

41.83 21.83

Multi-level model identified by cluster analysis

33.01 17.19


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.

Documents

Berna Keskin1 University of Sheffield, Department of Town and Regional Planning Alternative Approaches to Modelling Housing Market Segmentation: Evidence