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The impact of house prices on banking stability in Vietnam: the moderating role of investor sentiment

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Abstract

This article aims to examine the moderating role of investor sentiment in the impact of house prices on banking stability over the period from 2017 to 2022 in Vietnam. The research tries to build a banking stability index by combining the principal components of an international rating system of financial institutions stability (CAMELS) through principal component analysis, while the average apartment price index in Hanoi and Ho Chi Minh City is used as a variable of house prices in Vietnam, and investor sentiment is measured using the Google search volume index. By using panel corrected standard errors, the research gives evidence of the positive impact of house prices on banking stability in Vietnam, and the moderating role of investor sentiment on this positive effect. Moreover, the research indicates the positive roles of bank efficiency, regulatory quality, and GDP growth for boosting banking stability, while the opposite impact can be seen in the case of bank concentration. In addition, there is no evidence of any influence of bank size on banking stability in Vietnam.

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Notes

  1. This paper translates this keyword into several Vietnamese-related terms such as “bất động sản,” “nhà ở,” “chung cư,” “khu đô thị,” “căn hộ,” among others. Then, an average of the Google trend index for those terms is taken in order to derive the single real estate market sentiment index (RSEN).

  2. This paper translates this keyword into several Vietnamese-related terms such as “giá nhà,” “giá chung cư,” “giá căn hộ” among others. Then, an average of the Google trend index for those terms is taken in order to derive the single real estate market sentiment index (RSEN).

  3. This paper translates this keyword into several Vietnamese-related terms such as “ngân hàng thương mại,” “tín dụng ngân hàng,” “ổn định ngân hàng,” “rủi ro ngân hàng” among others. Then, an average of the Google trend index for those terms is taken in order to derive the single real estate market sentiment index (RSEN).

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Author information

Authors and Affiliations

  1. Faculty of Finance and Banking, VNU University of Economics and Business, Hanoi, Vietnam

    Nguyen Thi Nhung, Nguyen Thi Thanh Huyen, Vo Hoai Anh, Nguyen Phuong Thao & Trinh Thao Van

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  1. Nguyen Thi Nhung
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Appendices

Appendices

Appendix 1: Weight calculation for different dimensions of Banking stability index (BSI)

Dimension

Components

Eigenvalues

Weights

1

2

3

2.283

1.543

1.008

Absolute

Percent

C

0.524

− 0.415

0.143

1.1966

− 0.6407

0.1441

0.69998

10.63%

A

0.403

− 0.015

− 0.271

0.9201

− 0.0235

− 0.2727

0.62400

9.48%

M

0.339

0.584

0.084

0.7743

0.9008

0.0848

1.75987

26.74%

E

0.344

0.617

0.014

0.7846

0.9522

0.0140

1.75077

26.60%

L

-0.134

0.074

0.922

− 0.3061

0.1140

0.9288

0.73666

11.19%

S

0.558

− 0.316

0.223

1.2742

− 0.4877

0.2245

1.01105

15.36%

Total

      

6.58232

100.00%

  1. Source: Authors.

Appendix 2: Apartment index of Hanoi and HCMC from Q1.2017 to Q4.2022

 

Apartment price volatility in both Hanoi and Ho Chi Minh City

API (Hanoi)

API (HCM)

HPI (Hanoi)

HPI (HCM)

HPI (AVR)

Q1 2017

4.41389

4.52179

0.07%

− 0.24%

− 0.08%

Q2 2017

4.41498

4.5326

0.03%

0.24%

0.13%

Q3 2017

4.41818

4.53367

0.07%

0.02%

0.05%

Q4 2017

4.41894

4.54436

0.02%

0.24%

0.13%

Q1 2018

4.41947

4.5486

0.01%

0.09%

0.05%

Q2 2018

4.41976

4.55177

0.01%

0.07%

0.04%

Q3 2018

4.41996

4.55388

0.01%

0.05%

0.03%

Q4 2018

4.42046

4.55388

0.01%

0.00%

0.01%

Q1 2019

4.60956

4.55808

4.28%

0.09%

2.19%

Q2 2019

4.61192

4.55913

0.05%

0.02%

0.04%

Q3 2019

4.63813

4.56122

0.57%

0.05%

0.31%

Q4 2019

4.64113

4.56539

0.07%

0.09%

0.08%

Q1 2020

4.62514

4.56435

− 0.35%

− 0.02%

− 0.18%

Q2 2020

4.63022

4.57073

0.11%

0.14%

0.13%

Q3 2020

4.63262

4.57422

0.05%

0.08%

0.06%

Q4 2020

4.67069

4.59979

0.82%

0.56%

0.69%

Q1 2021

4.70025

4.60837

0.63%

0.19%

0.41%

Q2 2021

4.66945

4.61264

− 0.66%

0.09%

− 0.28%

Q3 2021

4.72155

4.61689

1.12%

0.09%

0.60%

Q4 2021

4.81002

4.66648

1.87%

1.07%

1.47%

Q1 2022

4.85403

4.67451

0.92%

0.17%

0.54%

Q2 2022

4.94021

4.7122

1.78%

0.81%

1.29%

Q3 2022

4.82875

4.71527

− 2.26%

0.07%

− 1.10%

Q4 2022

4.80035

4.73321

− 0.59%

0.38%

− 0.10%

  1. Source: Authors.

Appendix 3: Investor sentiment

 

HSEN

BSEN

RSEN

SVI

SENT

Q1 2017

31.0833

23.16667

18.5333

24.26111

3.188875

Q2 2017

34.25

24.75

19.5333

26.17778

3.264911

Q3 2017

41.3333

22.33333

20.5333

28.06667

3.334583

Q4 2017

35.5833

27.16667

21.0667

27.93889

3.33002

Q1 2018

36

24.25

21.1333

27.12778

3.300558

Q2 2018

35.5

28.58333

22.4

28.82778

3.361339

Q3 2018

41.5

26

23.1333

30.21111

3.40821

Q4 2018

39.5833

27.41667

22.7333

29.91111

3.39823

Q1 2019

40.1667

23.75

21.8667

28.59445

3.353212

Q2 2019

40.4167

27.66667

25.3333

31.13889

3.438458

Q3 2019

40.6667

28.5

24.0667

31.07778

3.436493

Q4 2019

34.9167

27.5

22.2667

28.22778

3.340307

Q1 2020

34.3333

24

22.0667

26.8

3.288402

Q2 2020

38.5833

33.08333

27.3333

33

3.496508

Q3 2020

37.75

32.41667

24.4

31.52222

3.450693

Q4 2020

31.75

30

23.6

28.45

3.348148

Q1 2021

31.5833

26.5

23.5333

27.20555

3.303421

Q2 2021

34.1667

32.66667

25.5333

30.78889

3.427154

Q3 2021

27.5

32.58333

20

26.69444

3.284455

Q4 2021

29.8333

33.83333

27.8667

30.51111

3.418091

Q1 2022

35.8333

37.58333

33.2667

35.56111

3.571253

Q2 2022

37.5

42.08333

31.2667

36.95

3.609566

Q3 2022

37.3333

42.58333

26.9333

35.61667

3.572814

Q4 2022

27.4167

47.08333

25.1333

33.21111

3.502885

  1. Source: Authors.

Appendix 4: Heteroscedasticity test

Fixed-effects (within) regression

Number of obs = 600

Group variable: banks

Number of groups = 25

R-sq:

Obs per group:

Within = 0.3312

Min = 24

Between = 0.2410

Avg = 24.0

Overall = 0.2574

Max = 24

corr(u_i. Xb) = − 0.3126

F(7.658) = 40.18

 

Prob > F = 0.00000

BSI

Coef

Std.Err

t

P > t

[95% Conf. Interval]

HPI

0.0740

0.5457

0.1400

0.8920

-0.9978

1.1459

SENT

0.0687

0.0493

1.3900

0.1640

− 0.0282

0.1656

INBEFF

− 0.2607

0.0205

− 12.7200

0.0000

− 0.3010

− 0.2205

SIZE

0.0817

0.0281

2.9100

0.0040

0.0266

0.1368

BCON

1.0454

1.3295

0.7900

0.4320

− 1.5659

3.6567

RQUA

0.1251

0.0554

2.2600

0.0240

0.0163

0.2339

GDP

0.8132

0.2100

3.8700

0.0000

0.4007

1.2258

_cons

− 2.3429

1.0013

− 2.3400

0.0200

− 4.3097

− 0.3762

sigma_u

0.12981728

 

sigma_e

0.08247045

 

rho

0.71246288

(fraction of variance due to u_i)

F test that all u_i = 0:F(24,558)

  1. Source: Authors’ extracted information from Stata 14.

Random-effects GLS regression

Number of obs = 600

Group variable: banks

Number of groups = 25

R-sq:

Obs per group:

Within = 0.3267

Min = 24

Between = 0.4192

Avg = 24,0

Overall = 0.3741

Max = 24

 

Wald Chi2(7) = 239.67

corr(u_i, X) = 0 (assumed)

Prob > F = 0.00000

BSI

Coef

Std.Err

t

P > t

[95% Conf. Interval]

HPI

0.08811

0.56159

0.16

0.875

− 1.012590

1.188812

SENT

0.08132

0.05007

1.62

0.104

− 0.016821

0.179462

BEFF

− 0.28167

0.02078

− 13.55

0.000

− 0.322407

− 0.240937

SIZE

0.02913

0.01277

2.28

0.023

0.004092

0.054166

BCON

− 0.82845

0.98884

− 0.84

0.402

− 2.766546

1.109645

RQUA

0.12408

0.05700

2.18

0.029

0.012360

0.235799

GDP

0.92972

0.21143

4.40

0.000

0.515316

1.344122

_cons

− 0.49547

0.50097

− 0.99

0.323

− 1.477342

0.486407

sigma_u

0.07013675

 

sigma_e

0.08247045

 

rho

0.41970459

(fraction of variance due to u_i)

  1. Source: Authors' extracted information from Stata 14.

Breusch and Pagan Lagrangian multiplier test for random effects

BSI[banks,t] = Xb + u[banks] + e[banks,t]

Estimated results:

 

Var

SD = sqrt(Var)

BSI

0.0283744

0.1684469

e

0.0068014

0.0824704

u

0.0049192

0.0701367

  1. Test: Var(u) = 0 chibar2(01) = 1688.64 Prob > chibar2 = 0.0000.
  2. Source: Authors’ extracted information from Stata 14.

Appendix 5: Autocorrelation test

Wooldridge test for autocorrelation in panel data

H0: no first-order autocorrelation

F(1, 24) = 0.036.

Prob > F = 0.8508.

Appendix 6: Cross-sectional dependence test

Residual cross section dependence test

 

Null hypothesis: No cross section dependence (correlation) in residuals

 

Equation: Untitled

 

Periods included: 24

 

Cross sections included: 25

 

Total panel observations: 600

 

Note: nonzero cross section means detected in data

 

Cross section means were removed during computation of correlations

 

Test

Statistic

d.f

Prob

Breusch–Pagan LM

7189,14,639

300

0.0000

Pesaran scaled LM

281,248,223

 

0.0000

Pesaran CD

84,7,887,791

 

0.0000

  1. Source: Authors’ extracted information from Eview 12.

Appendix 7: Robustness check: Alternative with hierarchical FLSC regression

Model

1

2

3

Independent variable

   

House price index (HPI)

0.195

− 0.195

31.38**

(0.40)

(− 0.23)

(2.03)

Moderator variables

   

SENT

 

0.0309

0.158***

 

(0.41)

(2.99)

SENT*HPI (HSENT)

  

(− 2.00)

  

(− 1,65)

Control variables

   

Bank efficiency (INBEFF)

− 0.430***

− 0.492***

− 0.425***

(− 17.46)

(− 18.24)

(− 17.12)

Bank size (SIZE)

0.00234

0.00123

0.00194

(0.36)

(0.23)

(0.31)

Bank concentration (BCON)

− 0.185

− 1265

1446

(− 0.17)

(− 0.95)

(1.22)

Regulatory quality (RQUA)

0.204**

0.167*

0.165*

(2.40)

(1.95)

(1.95)

GDP growth rate (GPD)

1.217***

1.292***

0.888***

(4.92)

(4.10)

(3.32)

Constant

0.693***

0.730*

0.0329

(2.79)

(1.86)

(0.10)

Number of observations

600

600

600

R-Squared

   

Prob > chi

0

0

0

  1. The signs *, **, and *** represent significance at 10%, 5%, and 1% level of significance, and values within parenthesis represent standard errors.
  2. Source: Authors.

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Nhung, N.T., Huyen, N.T.T., Anh, V.H. et al. The impact of house prices on banking stability in Vietnam: the moderating role of investor sentiment. J Bank Regul (2024). https://doi.org/10.1057/s41261-024-00252-z

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