Settlement-Preference Patterns and Characteristics of Households in Urban Renewal Area - Jinkyung LEE, Seungil LEE, Kyuil KIM -

Settlement-Preference Patterns and

Characteristics of Households in Urban

Renewal Area

Jinkyung LEE, Seungil LEE, Kyuil KIM

Institute of Urban Sciences, Univ. of

Seoul, Korea

Mar. 24, 2010

ICCSA 2010 INTERNATIONAL CONFERENCE, FUKUOKA, JAPAN

Contents

Housing Issues in Seoul

Research Questions

Method and Model

Data

Estimation Results

Findings and Discussion

2ICCSA 2010 INTERNATIONAL CONFERENCE, FUKUOKA, JAPAN

Housing Issues in Seoul


Decreasing housing stocks available for low

income groups by urban redevelopment

projects,

High rent, lack of affordability of low-income

families,

An excessive polarization: a gap of housing

affordability between income groups,

A localized imbalance between demand and

supply of housing.


What are preference factors of resettlement in

Urban Renewal Area (URA) ?

Identifying expected preference factors of URA

Is there the relationship between the elements

of the settlement pattern and some

household’s characteristics in URA ?

Examining characteristics among them

Research Questions

Method


The polytomous logistic regression model

(PLRM) is selected to test the attitude-need

recognition relationship in URA.

PLRM may be extended beyond the analysis

of dichotomous variables to the analysis of

categorical (nominal or ordinal) dependent

variables with more than two categories.

Model


For dependent variables with some number of categories M, this requires the

calculation of M-1 equations, one for each category relative to the reference

category, to describe the relationship between the dependent variable and the

independent variables. For each category of the dependent variable except the

reference category, we may write the equation 2 .

1,,2,1

),,,()(

21

2211

Mh

eXXXg khkhhh XbXbXba

kh

(2)

Model


Where the subscript k refers, as usual, to specific independent variables X and

the subscript h refers to specific values of the dependent variable Y. For the

reference category, g0(X1, X2, …, Xk) = 1. The probability that Y is equal to any

value h other than the excluded value h0 can be written by the equation 3

,1,,2,1

1

),,,(

1

1

)(

)(

21

2211

2211

Mh

e

e

XXXhYP

M

h

XbXbXba

XbXbXba

k

khkhhh

khkhhh

(3)

Model


For the five-category dependent variable, the PLRM in this study was expressed

with four log-linear functions as follows:

kjXXXpp

12121111051)/log( (5)

kiXXXpp

22221212052)/log( (6)

kiXXXpp

32321313053)/log( (7)

kiXXXpp

42421414054)/log( (8)

where,

pi = probability of event i for i =1,2,3,4,5

β1js, β2js, β3js, and β4js are parameters with 0 ≤ j ≤ m

Xks are independent variables with 1 ≤ k ≤ m

Variable of PLRM


Variables Categories

Dependent Settlement

Settlement 1: nearby of original site(S1, reference

category)

Settlement 2: same district (S2)

Settlement 3: within the Seoul (S3)

Settlement 4: without the Seoul (S4)

Settlement 5: others (S5)

Independent

Type of Urban Renewal

Project

Redevelopment(RD)

Reconstruction(RC=1)

Average Monthly

Household Income

1000 & under (I1)

1001-2000 (I2)

2001-3000 (I3)

3001-4000 (I4)

4001 & over (I5=1)

Job Location

Job Location 1: nearby of original site (J1=1)

Job Location 2: same district (J2)

Job Location 3: within the Seoul (J3)

Job Location 4: without the Seoul (J4)

Job Location 5: others (J5)

Data


The data was collected by the Seoul Government Residential

Improvement Committee (SGRIC) in 2008 to investigate dwelling

situations in URA and to develop policies improving their

problems.

The households data of this survey was selected by systematic

sampling among households living in 28 URA or owners of

houses there. A systematic sampling method was used to select

the representative samples.

The respondents were eligible respondents who were willing to

participate in face-to-face, in-home interviews. A total of 1,014 in-

home personal interviews were conducted.

Data: Sample profile


Contents Sample No. %

Total 1,014 100.0

Project type Redevelopment

Reconstruction

550

464

54.2

45.8

9 Zones of life

North-East 1

North-East 2

North-West

South-West 1

South-West 2

South-West 3

South-East 1

South-East 2

CBD

177

190

250

84

147

48

93

25

0

17.5

18.7

24.7

8.3

14.5

4.7

9.2

2.5

0

Occupancy type Owners

Tenants

499

515

49.2

50.8

Respondents

status

Household head

Household mate

Others

528

443

43

52.1

43.7

4.2

Gender Male

Female

379

635

37.4

62.6

Age

20-29

30-39

40-49

50-59

60 or older

41

158

248

216

351

4.0

15.6

24.5

21.3

34.6

Income

(U.S. $)

1000 & under

1001-2000

2001-3000

3001-4000

4001 & over

285

247

192

144

117

31.0

24.4

18.9

14.2

11.5

Dwelling

Period

2years & under

2∼ 4years

5∼ 9years

10∼ 19years

20years &over

84

181

223

206

320

8.3

17.9

22.0

20.3

31.6


PLRM relied on maximum likelihood procedures to estimate the

model coefficient. The model worked fairly well, as indicated by

the statistically significant model Chi-square and the McFadden

R2 of 0.060. PLRM had -2LL(0) of 419.570 and -2LL(k) of

357.809.

The Chi-square was 61.760 with p-value of 0.005 in 36 df, which

indicated a very good overall model fit. To evaluate goodness of fit

of the model, SPSS computed two measures of the pseudo-

variance explained, Cox and Snell R2 of 0.109 and Negelkerke

R2 of 0.121.

Estimation Results

Estimation Results


Settlement a B S.E. Wald df Sig Exp(B)

S2

(vs.

S1)

Intercept

RD

RC

I1

I2

I3

I4

I5

J1

J2

J3

J4

J5

-2.403

0.405

0 b

0.167

-0.537

-0.122

0.330

0 b

0.830

0.024

0.807

0.957

0 b

0.573

0.301

.

0.514

0.447

0.440

0.439

.

0.478

1.152

0.497

0.523

.

17.570

1.808

.

0.106

1.442

0.077

0.566

.

3.011

0.000

2.637

3.348

.

1

1

0

1

1

1

1

0

1

1

1

1

0

0.000

0.179

.

0.745

0.230

0.781

0.452

.

0.083

0.984

0.104

0.067

.

1.499

.

1.182

0.525

0.885

1.391

.

2.293

1.024

2.242

2.604

.

S3

(vs.

S1)

Intercept

RD

RC

I1

I2

I3

I4

I5

J1

J2

J3

J4

J5

-1.709

-0.977

0 b

0.355

-0.175

-1.105

0.217

0 b

0.046

-18.626

-0.461

-0.873

0 b

0.710

0.431

.

0.714

0.662

0.897

0.754

.

0.580

0.000

0.679

0.893

.

5.794

5.131

.

0.247

0.070

1.517

0.083

.

0.006

.

0.462

0.957

.

1

1

0

1

1

1

1

0

1

1

1

1

0

0.016

0.023

.

0.619

0.791

0.218

0.773

.

0.936

.

0.497

0.328

.

0.376

.

1.426

0.839

0.331

1.243

.

1.047

8.147E-09

0.630

0.418

.

S4

(vs.

S1)

Intercept

RD

RC

I1

I2

I3

I4

I5

J1

J2

J3

J4

J5

-3.125

0.102

0 b

-0.616

0.281

-0.520

-1.131

0 b

0.358

-18.264

0.985

0.017

0 b

0.936

0.522

.

1.000

0.703

0.851

1.182

.

0.787

0.000

0.768

1.002

.

11.149

0.038

.

0.379

0.160

0.373

0.916

.

0.207

.

1.644

0.000

.

1

1

0

1

1

1

1

0

1

1

1

1

0

0.001

0.846

.

0.538

0.690

0.541

0.339

.

0.649

.

0.200

0.986

.

1.107

.

0.540

1.324

0.595

0.323

.

1.431

1.170E-08

2.678

1.018

.

S5

(vs.

S1)

Intercept

RD

RC

I1

I2

I3

I4

I5

J1

J2

J3

-1.071

-0.729

0 b

1.059

0.223

0.056

0.319

0 b

0.057

-19.347

0.127

0.446

0.233

.

0.433

0.408

0.432

0.463

.

0.341

0.000

0.351

5.781

9.760

.

5.968

0.298

0.017

0.473

.

0.028

.

0.131

1

1

0

1

1

1

1

0

1

1

1

0.016

0.002

.

0.015

0.585

0.897

0.492

.

0.868

.

0.717

0.482

.

15984047

8297216

8642131

10458066

.

1.058

3.959E-09

1.135

Findings


When household head’s job location is nearby of

original site, respondents are more likely to prefer

nearby. Especially, settlement-preference in same

district, household head’s job location is impact

factor.

Low income households and high income

households intend to prefer settlement within the

Seoul.

Discussion


Because householders in URA prefer to move to a

nearby village, the sudden increase in demand due to

redevelopment projects, can lead to an increase in the

house price for rent or buying.

Renewal development projects needs to be transferred

private initiatives to public initiatives, public sectors

should intervene directly and/or indirectly in the

projects to secure confidence and drive out absurdities.

Public sectors can expect a filtering effect as the

supply of diverse types of houses for original

householders including low income.

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Characteristics of Households in Urban

Renewal Area

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Settlement-Preference Patterns and Characteristics of Households in Urban Renewal Area - Jinkyung LEE, Seungil LEE, Kyuil KIM -