The residential property price model for Greece

The residential property price model for Greece (work in progress)

Z. Bragoudakis and D. Sideris October 2016

1

The residential property price model for Greece

2

GREECE’s residential property price model    

Motivation Some Stylised facts Literature review The model

 The explanatory variables- the data  The methodology  The specification  Model properties  Forecasting ability  Policy implications and Conclusions

3

Motivation • •

To identify the determinants of house prices. To provide reliable forecasts of house prices. This is of importance as: – House price projections are used in Stress Testing Exercises implying a high level of public scrutiny of the outcomes. – Residential property accounts for around 82% of collateral requested by banks. – As a consequence house prices play an important role in the assessment of the health of bank balance sheets. – House price projections are part of the (B)MPE projections and their accuracy is important in the context of a complete projection narrative.

• Interesting to note that:  Houses have a double role, as consumption goods and as investment (Leung, 2004).  The construction sector (specially housing) had been a pillar of the Greek economy, strongly connected with many other sectors. It collapsed in the crisis.  Investing in the housing market has been for the Greek household a form of saving (see also Hardouvelis ,2009).

4

Causes of the housing crisis The market collapsed in the crisis. House prices fell by 42%. Transactions diminished heavily. Residential investment fell by 94% in the 2007-2015 period.

Causes of the housing crisis could be:     

The general adverse economic environment and the cumulative fall in GDP over 26%. The fall of the demand and the excess housing stock. Lack of liquidity in the Greek economy. Lack of positive prospects for the future of the housing market. The excessive tax burden of the private property, compared to the precrisis period.

5

Stylized facts The fall in housing investment 70000 60000 50000 40000 30000 20000 10000 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

IHR

GIR

IPR

ITR

Housing investment (IHR) fell the most (from 24,8bill. in 2007 to 1,5 bill. in 2015) compared to business investment (IPR) and government investment (GIR). 6

Stylized facts Housing investment as % of total investment

Housing investment as % of Real GDP 12.0%

100% 90%

10.0%

80% 70%

8.0%

60% GIR/ITR IPR/ITR IHR/ITR

50% 40% 30% 20%

6.0%

IHR/YER IPR/YER

4.0%

GIR/YER 2.0%

10% 2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1995

0.0%

0%

Housing investment (IHR) fell the most compared to business investment (IPR) and government investment (GIR) both as percentage of GDP (YER) [from 9.9% in 2007 to 0.8% in 2015] and of total investment (ITR)[from 40.2% in 2007 to 7%] in the crisis. 7

Stylized facts Home ownership rates in selected OECD countries in 2011

Idiosyncrasies in the economies. In Mediterranean countries, houses are not considered as assets to speculate. 8

Literature review •

Economic literature on modelling of house prices

•

A large number of studies have been reviewed covering the recent period (see among others the recent Gattini and Hiebert (2010), Case et al. (2013), Oikarinen (2012), Adams and Füss (2010), Kuethe and Pede (2011) and Gupta, Kabundi and Miller (2009)).

•

According to the studies, house prices may be determined by: – demand factors : household income, interest rates, financial wealth, demographic and labour market variables. – Supply factors: real construction costs , the housing stock. – Credit is considered to affect both supply and demand. – They also suggest that house prices may be driven by momentum (expectations) (accounted for by lags in house prices in most models).

•

Main findings are:

 The vast majority of empirical models adopt a dynamic approach (ECM, VECM), linking the evolution of house prices with their pre-selected fundamentals.  The fundamentals or determinants of house prices in the long run equilibrium relationship are chosen mainly on the basis of a demand equation, and vary from one market/country to the other reflecting both market and regulatory specificities.  Demand is a function of a number of variables such as past house prices, changes in expected house prices, household income and GDP, real mortgage rates, credit , financial wealth, demographic and labor market factors, the expected rate of return on housing, factors that facilitate or hinder access to the housing market such as financial innovation or institutional factors.

9

Literature review •

Economic literature on modelling house prices- the Greek case

Apergis and Rezitis (2003) provide evidence that house prices respond to the macroeconomic variables: interest rate, inflation, employment, money supply. Brissimis and Vlassopoulos (2007) examine the connection between mortgages and house prices in Greece. They do not find a long term causal relationship from mortgages to prices, but a short-run bi-directional relationship. Simigiannis and Hondroyiannis (2009) examine whether a bubble was present in the precrisis period by applying the user cost model. No evidence of bubble emerged. Merikas et al (2010) develop an equilibrium model for the Greek housing market and conclude that construction cost and the labour cost have a positive effect on house prices while interest rates and the non-construction investment have negative effects. Monokroussos and Thomakos (2014) find that the nominal housing prices are determined by real GDP, interest rates and inflation.

10

The model – the explanatory variables Explanatory variables examined for the specification of the model: Demand variables  disposable household income  GDP  real mortgage rate  financial wealth  demographic and labour market factors (population, employment)  taxes Supply variables  Construction cost  Permits Credit Expectations formation  Sentiment indicators (ESI, ASE)  Past realizations of house prices

11

The model – the methodology

 Error Correction Model: House prices adjust to a long-run equilibrium relationship as dictated by theory, but with some delay due to market imperfections, slow adjustment of behaviour, etc.  The specification of the ECM is based on the General-to-Specific methodology (Hendry approach).  The ECM can be used to determine over/undervaluation of house prices as a deviation to the values implied by the long-run equation.  It can be used for forecasting purposes.  It can be easily communicated.  It will be incorporated in the BoG model of the Greek economy to be used for policy scenarios.

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The model-the data Nominal House Prices(HPI) and Real House Prices ( HPI_PCD)

Real GDP and Real GDP per capita 64,000

7,200

60,000

6,800

56,000

6,400

52,000

6,000

48,000

5,600

44,000

5,200

120 110 100 90 80 70 60 50 40 30

40,000 96

98

00

02

04

06

HPI

08

10

12

14

4,800 1998

2000

2002

2004

HPI_PCD

2006

2008

2010

2012

2014

Real GDP per capita (rhs) Real GDP (lhs)

CREDIT

RELAT IVE_ESI 1.15

90,000

1.10

80,000

1.05

70,000

1.00

60,000

0.95

50,000

0.90

40,000 0.85

30,000 0.80

20,000 0.75 99

10,000 00 01

02

03

04

05

06

07

08

09

10

11

12

13

14

00

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

13

15

The data CONSTRUCTION_COST

Nominal House Prices(HPI) and Real House Prices ( HPI_PCD) 105 120

100

110 100

95

90

90

80 70

85

60

80

50 40

75

30 96

98

00

02

04 HPI

06

08

10

12

14

70 00

HPI_PCD

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

Decomposition of construction cost ( 60.8 % cost_mat , 39.2% cost_lab)

Housing Interest Rates (HLIR)

104

16

102

14

100 98

12

96

10

94 92

8 90

6

88 II

III IV

2010

4

I

II

III IV

2011

I

II

III IV

2012

I

II

III IV

2013

I

II

III IV

2014

I

II

III IV

2015

CONSTR_COST COST_LABOUR COST_MATERIAL

2 1998

2000

2002

2004

2006

2008

2010

2012

2014

14

The data PROPERTY_TAXES

Nominal House Prices(HPI) and Real House Prices ( HPI_PCD) 3,500

120

3,000

110 100

2,500

90

2,000 80 70

1,500

60

1,000

50

500 40

0

30 96

98

00

02

04 HPI

06

08

10

12

98

14

00

02

04

06

08

10

12

14

16

HPI_PCD

Tax Revenues on Property as % of Nominal GDP 2.0

1.6

1.2

0.8

0.4

0.0 94

96

98

00

02

04

06

08

10

12

14

16

15

The model – the final specification • • • • • • • • • • • • • • • •

Variables used in the long-run model: real house prices (hpi), private consumption deflator (pcd), real GDP(rgdp), taxes/nominal GDP, construction cost(Constr _ cost), real interest rates( rhlir), dummy for the economic crisis, Greek economic sentiment indicator(esi_gr)/Euro-zone economic sentiment indicator(esi_ez). Variables used in the dynamic model: real house prices, real GDP, credit, dummy for the economic crisis. Estimation Sample: 2000Q2 – 2015Q4. Forecasting Evaluation Sample: 2012Q1 – 2015Q4. 16

The model specification: The Long–run relationship 5.0

Dependent Variable: LOG(HPI/PCD) Method: Least Squares Sample (adjusted): 2000Q1 2015Q4 Included observations: 64 after adjustments Variable C LOG(RGDP/POP) LOG(CONSTR_COST) LOG(TAXES/YEN) DUM08 RHLIR ESI_GR/ESI_EZ R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic

Coefficient -2.15 0.65 1.11 -0.07 -0.11 -0.01 0.19 0.95 0.94 0.04 0.10 116.74 178.83

4.8 4.6

t-Statistic Prob. -2.79 3.69 5.48 -4.52 -3.95 -1.90 2.43 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter.

.12 4.4

0.01 0.00 0.00 0.00 0.00 0.06 0.02

.08 4.2 .04 4.0 .00 -.04

4.523 0.175 -3.429 -3.193 -3.336

-.08 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 Residual

Actual

Fitted

10

Series: Residuals Sample 2000Q1 2015Q4 Observations 64

8

The ADF tests provide evidence in favour of stationarity for the residuals of the l-r relation. Hence, they can be used as error correction term

6

4

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

8.78e-18 -0.006662 0.105241 -0.070919 0.039355 0.632496 2.856204

Jarque-Bera Probability

4.322356 0.115189

2

0 -0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

0.10

17

Robustness check of the long-run parameters 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 2008

2009

2010

2011

2012

CUSUM of Squares

3

2.0

2

1.5

1

2013

2014

2015

5% Significance

2.0 1.5

1.0 1.0

0 0.5 -1

0.5 0.0

-2 -3

-0.5

-4

-1.0 2009

2010

2011

2012

2013

2014

2015

0.0

-0.5 2009

2010

Recursive C(1) Estimates ± 2 S.E.

2011

2012

2013

2014

2015

2009

2010


2012

2013

2014

2015

2014

2015


.4

.05

.010

.3

.00

.005

.2

2011

-.05

.000

-.10

-.005

-.15

-.010

-.20

-.015

.1 .0 -.1

-.25

-.2

-.020

-.30 2009

2010

2011

2012

2013

2014

2015


-.025 2009

2010

2011

2012

2013


2014

2015

2009

2010

2011

2012

2013


.6 .4

.2

.0

-.2

-.4 2009

2010

2011

2012

2013


2014

2015

18

The model specification: the short-run dynamics .06

Dependent Variable: DLOG(HPI/PCD) Method: Least Squares Sample (adjusted): 2001Q2 2015Q4 Included observations: 59 after adjustments Variable

.04 .02 .00 .04

Coefficient Std. Error t-Statistic

Prob.

-.02

.02

-.04

.00

-.06

-.02

C DLOG(RGDP/POP) DLOG(CREDIT) ECT(-1)

-0.014 0.411 0.375 -0.107

0.002 0.129 0.057 0.046

-5.598 3.190 6.529 -2.336

0.000 0.002 0.000 0.023

-.04 -.06 01

02

03

04

05

06

07

Residual

08

09 Actual

10

11

12

13

14

15

Fitted

14

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.649 0.630 0.013 0.010 172.688 33.961 0.000

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

-0.004 0.022 -5.718 -5.577 -5.663 1.884

Series: Residuals Sample 2001Q2 2015Q4 Observations 59

12 10 8 6 4 2

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

2.08e-18 0.001864 0.031829 -0.041067 0.013072 -0.255812 3.691698

Jarque-Bera Probability

1.819670 0.402591

0 -0.04

-0.03

-0.02

-0.01

0.00

0.01

0.02

0.03

19

Robustness check of the short-run parameters .100 .075 .050 .025 .000 -.025 -.050 -.075 -.100 02

03

04

05

06

07

08

09

10

12

13

14

15

± 2 S.E.

Recursive Residuals 2.0

11

15

1.5

10

1.0 5 0.5 0 0.0 -5

-0.5 -1.0

-10 02

03

04

05

06

07

08

09

10

11

12

13

14

15

02

03

04

05

06


07

08

09

10

11

12

13

14

15

13

14

15


10

4

5

2

0

0

-5

-2

-10

-4

-15

-6

-20

-8 02

03

04

05

06

07

08

09

10

11

12


13

14

15

02

03

04

05

06

07

08

09

10

11

12


20

The model- economic implications In the long run: LOG(HPI/PCD) = -2.15 + 0.65*LOG(RGDP/POP) + 1.1*LOG(CONSTR_COST) - 0.009*(RHLIR) 0.07*LOG(TAXES/YEN) - 0.10*DUM08 + 0.19*ESI_GR/ESI_EZ

 real house prices are influenced positively mainly by real GDP per capita and construction cost; the real interest rate on mortgages has a limited negative effect. The results confirm standard economic theory.  Taxation and the crisis period exert negative impact, whereas sentiment in Greece compared to that of the EZ has a positive influence. These results indicate specific characteristics of the Greek market during the recent period. The elasticities take reasonable size: For example an increase of 1% of GDP/capita would lead to an increase of 0.65 in house prices.

In the short run: DLOG(HPI/PCD) = -0.01 + 0.41*DLOG(RGDP/POP) + 0.37*DLOG(CREDIT) - 0.11*ECT(-1)

 House prices growth depends on the growth in real GDP/capita and in credit. The Error Correction Term ensures adjustment to equilibrium.

21

Out of sample performance over 2012q1-2015q4 At levels

At annual growth rates 20

110

15

100 10

RW 90

5 RW 0

AR(2)

80

AR(2)

-5

ECM

70

ECM

-10 -15

60 02 03

04

05

06

07

08

HPIF_RW HPIF_AR2

09

10

11

12

13

HPIF_ECM HPI

14

15

02

03

04

05

06

07

08

09

10

11

12

13

14

15

Year % Change HPIF_RW Year % Change HPIF_ECM Year % Change HPIF_AR2 Year % Change HPI 22

An evaluation of the model’s forecasting ability “a horse race” Can our model beat AR(2) and RW model? (the standard exercise in forecasting) The pseudo real-time forecasting exercise is implemented as follows:  First, we estimate the price model for the period 2000q1-2011q4, leaving a period of four years (16 observations) for the assessment of the forecast performance.  Second, a dynamic 12-step ahead forecast is then drawn from the model. The procedure is repeated, adding an extra observation each time, estimating the model anew and executing the 12-period ahead dynamic forecast. The procedure is sequentially repeated until the entire sample of observations has been exhausted.  Third, the respective RMSE is calculated by comparing the forecast figures with the actual values.  Fourth, we calculate the relative RMSE of ECM against to AR(2) and RW. 23

Forecasting Evaluation Results 1 quarter ahead

4 quarters ahead

12 quarters ahead

1 quarter ahead

4 quarters ahead

12 quarters ahead

RMSE t+1 RMSE t+4 RMSE t+12 RMSE t+1 Compared to AR(2) Compared to AR(2) Compared to AR(2) Compared to RW

RMSE t+4 Compared to RW

RMSE t+12 Compared to RW

Iterations

2012q1-2014q4 2012q2-2015q1 2012q3-2015q2 2012q4-2015q3 2013q1-2015q4

Average

1.076 1.787 0.658 1.540 0.040

0.610 1.065 0.734 1.079 0.945

0.549 1.271 0.846 1.213 1.087

0.332 0.041 0.440 0.050 0.002

0.152 0.074 0.108 0.043 0.063

0.162 0.119 0.209 0.242 0.119

1.020

0.887

0.993

0.173

0.088

0.170

 The choice and the specification of the Error Correction Model used is deemed to be adequate, since it outperforms the Random Walk in every forecasting horizon (1, 4, and 12 quarters ahead).  It also outperforms the AR(2) model in 4 and 12 quarters ahead, which makes it appropriate for use for the purposes of the BMPE projections. 24

Conclusions • A house price model for the Greek economy has been estimated. • According to it, house prices are determined by: real GDP/capita, the interest rate and construction cost (as expected by standard economic theory) sentiment, credit conditions and taxation (factors specific to the Greek case) • The model has been shown to be statistically robust and to have satisfactory forecasting ability. • The model can be used in: medium term forecasting and policy simulations. 25

There is an old dictum in Greece saying “No one lost his money buying land”. Thank you for your attention! 26

Background slides

27

Overview of replies of the Questionnaire •

Large majorities of NCBs use models to forecast house prices • Mainly OLS (DE, NL, FI) , ECM , and VECM (Chart 1)

• Explanatory variables:  Fundamentals of demand and credit (all NCBs included a measure of income as a key driver of housing demand : Real or nominal disposable income or real GDP  in some cases housing supply, confidence, unemployment/employment, tax • Main use of models is for forecasting (Chart 2) Chart 1 Type of models used by NCBs to forecast residential property prices

Chart 2 Main use of the house price forecasting models across countries

(number of replies, more than one reply possible)

(percentage of total replies)

Note: Other includes: dynamic factor model, BVAR, structural system of equations, quantitative tools which are part of a broader macro econometric model.

Note: Forecast (DE, ES, FR, CY, LV, LT, MT, PT, SK and FI); Forecast/Valuation (GR, LU, DK); Forecast/Scenario Analysis (BE, EE, IE, NL); Forecast/Valuation/Scenario Analysis (IT, BG)

28

1 quarter ahead

4 quarters ahead

12 quarters ahead

1 quarter ahead

4 quarters ahead

12 quarters ahead

Ratio ECM Ratio ECM Ratio ECM RMSE/ RMSE RMSE/ RMSE/ RMSE AR(2) RMSE AR(2) AR(2)

Ratio ECM RMSE/ RMSE RW

Ratio ECM Ratio ECM RMSE/ RMSE/ RMSE RMSE RW RW

Iterations 2012q1-2014q4 2012q2-2015q1 2012q3-2015q2 2012q4-2015q3 2013q1-2015q4

1.08 1.79 0.66 1.54 0.04

0.61 1.07 0.73 1.08 0.94

0.55 1.27 0.85 1.21 1.09

0.33 0.04 0.44 0.05 0.00

0.15 0.07 0.11 0.04 0.06

0.16 0.12 0.21 0.24 0.12

Average

1.02

0.89

0.99

0.17

0.09

0.17

BVAR_2 (HPI/PCD, RGDP) BVAR_3 (HPI/PCD, RGDP,HLIR/PCD,)

1.04 1.14

1.00 1.11

0.98 1.01

0.43 0.52

0.53 0.62

0.75 0.79

BVAR_4 (HPI/PCD, RGDP,HLIR/PCD,CREDIT)

0.98

0.78

0.66

0.43

0.43

0.51

29

Quandt-Andrews unknown breakpoint test Null Hypothesis: No breakpoints within 15% trimmed data Varying regressors: All equation variables Equation Sample: 2000Q1 2015Q4 Statistic

Value

Prob.

Maximum LR F-statistic (2008Q1) Maximum Wald F-statistic (2008Q1)

35.180 175.898

0.000 0.000

Exp LR F-statistic Exp Wald F-statistic

14.392 84.336

0.000 0.000

Ave LR F-statistic Ave Wald F-statistic

16.303 81.515

0.000 0.000

Note: probabilities calculated using Hansen's (1997) method

30

Issues for discussion-proposal Proposal: • This new model will be part of the Bank of Greece toolkit for policy scenarios and forecasting for the residential property prices and it will also be used in the bank stress test exercise framework.

31

LINKS

abbreviati on

T O SOURCE

RGDP (REAL GDP PRICES 2010, EXPENDITURE SIDE)_SA

Y:\quart nat accounts\DATA\BOG\PROVISIONAL\SA.DATA\QaccESA10_SA _LEVELS.xls

Nominal Disposable Income

http://www.statistics.gr/el/statistics//publication/SEL95/-

ndi

IHR Housing Investment (REAL Housing investment PRICES 2010, EXPENDITURE SIDE)_SA

Y:\quart nat accounts\DATA\BOG\PROVISIONAL\SA.DATA\QaccESA10_SA _LEVELS.xls

ihr

PCD (CHANGE PRIVATE CONSUMPTION DEFLATOR, PRICES 2010 =100)_SA

Y:\quart nat accounts\DATA\BOG\PROVISIONAL\SA.DATA\QaccESA10_SA_CHANGES.xls

pcd

PCD (CHANGE PRIVATE CONSUMPTION DEFLATOR, PRICES 2010 =100)_SA

Y:\quart nat accounts\DATA\BOG\PROVISIONAL\SA.DATA\QaccESA10_SA_CHANGES.xls

rgdp

POP (TOTAL POPULATION )

http://ec.europa.eu/eurostat/web/products-datasets//lfsq_pganws

pop

POP2549 (TOTAL POPULATION 25-49)


pop2549 levels

POP2549/POP (POPULATION 25-49)


pop2549

COST _LAB (Labour cost index in construction NSA 2010=100)

http://www.statistics.gr/el/statistics//publication/DKT09/-

cost_lab

COST _MAT 2010=100)


cost_mat

Construction Costs Index ( = 60.8*cost_mat+39.2*cost_lab) (NSA 2010=100)


constr_co st

Permits (PRIVATE BUILDING ACTIVITY ( according to construction permits)

ELSTAT, Bank of Greece

(Material Costs Index, NSA

permits

Housing capital Stock

HPI (HOUSE PRICE INDEX 2007=100 ) NSA

HKS

http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY =129.RPP.Q.GR.N.TF.00.3.00

HPI (HOUSE PRICE INDEX 2010=100 ) NSA

hpi

#DIV/0!

hpi2010 http://sdw.ecb.int/browseTable.d o?node=2120778&REF_AREA= 168&ICP_ITEM=&SERIES_KEY =122.ICP.M.GR.N.000000.4.INX

HICP (2005=100) NSA


HICP (2010=100) NSA


hicp2010

HICP (2015=100) NSA


hicp2015

Mortage Interest Rate (monthly data, quarterly using average)

http://sdw.ecb.europa.eu/quickview.do?SERIES_KEY =124.MIR.M.GR.B.A2C.AM.R.A.2250.EUR.N

Housing Loans inerest rate (Floating rate or up to 1 year rate fixation) Στεγαστικά δάνεια (επιτόκιο κυμαινόμενο ή σταθερό έω ς 1 έτος)

http://www.bankofgreece.gr/Pages/en/Statistics/rates _markets/deposits.aspx

Housing loans (Volume of new bank loans vis-àvis euro area residents ) - (millions of EUR, not seasonally adjusted)

http://www.bankofgreece.gr/Pages/en/Statistics/rates _markets/deposits.aspx

hicp2005

mir http://www.bankofgreece.gr/Pag es/el/Statistics/rates_markets/d eposits.aspx

hlir

hlv

ESI_GR

IOBE

esigr

ESI_EA

IOBE

esiea

32

33

2010: FAP – New tax to replace ETAK (introduced in 2007) Law 3842/23.4.2010 Higher exemption value (400,000€), coefficient progressively reaching 1%-2% of property value Introduced in 2010Q2 (but was actually never claimed by the state due to IT issues) 2011: EETHDE – New tax on built properties with electricity supply Law 4021/3.10.2011 Coefficients varying from 3-16 € per square meter, multiplied by 1 - 1.25 depending on the age of the construction Introduced in 2011Q4 FAP – pre-existing tax, but now with lower exemption value (200,000€) and increased coefficients Valid from 2011Q1 but the bills were sent from 2013Q2 for both years 2011 and 2012 2013: EETA – Tax replacing EETHDE with approximately the same structure Law 4152/09.05.2013 Introduced in 2013Q2 2014: ENFIA –Tax replacing EETA (EETHDE) and previous taxes on property. Refers to all kind of property included land Law 4223/31.12.2013 – Law 4286/19.09.2014 Coefficients depending on administrative zone values (antikeimenikes), varying from 2-13 €/sq.m. , multiplied by 1 - 1.25 depending on the age of the construction. A different scale is used for land. Introduced in 2014Q1

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Capital tax on property – Review Taxation on property was first introduced in Greece in 1975 in the form of a tax on capital. It was withdrawn in 1980 and a low taxation (2%-4%) on rental income was imposed instead. In 1993 the previous taxation regime was again abolished and a new fixed tax along with an additional tax on income was introduced. In 1997 a tax (FMAP) of 0.3%-0.8% was imposed on properties (or portfolios) of significant value held by a single person or company. Then in 2007 a new tax (0.1%) on property was again introduced (ETAK) for all properties to replace all previous taxes and unify then in one single tax (applied in 2008 Q1). Property values less than 100,000€ for single persons and 200,000€ for families were exempt from ETAK. Nevertheless, several of the earlier taxes never ceased to apply. In 2010 (applied in 2010 Q3) a new law introduces a new tax (FAP) with a higher exempt value but with a coefficient progressively reaching 1% - 2% for property value exceeding 400,000€. In 2011 tax exempt value drops to 200,000€ and the coefficient for the lower categories doubles. In 2011 Q3 an additional capital tax was imposed on all built properties (EETHDE) paid through the electricity bills. Finally, in 2014 Q1 a new unified tax including both EETHDE and FAP is introduced which applies to all kinds of property (such as building plots, plots outside the urban plan, agricultural property, buildings that are under construction, etc.).

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Compare to other studies Monokroussos and Thomakos(2014)

Brissimis and Vlassopoulos(2006)

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• European Systemic Risk Board (ESRB) • Single Supervisory Mechanism - ECB Banking Supervision (SSM)

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GREECE’s residential property price forecasting model Methodological issues: A literature review indicates:  Generally, the long-term fundamentals of house prices are chosen on the basis of a demand equation, and less often also on the basis of supply-side factors. ( See for example, Muellbauer, J. (2012) and Murphy, A. (2004)).  Structural characteristics such as housing related taxes and subsidies are also found to play a role.  The approaches adopted in our model (also from others NCBs and the ECB) tend to follow the “mainstream” literature with its key determinants – income , financing costs and demographic characteristics – often complemented by some proxies of housing supply, credit and in some cases dummy variables which capture country-specific structural variables (such as tax changes, etc). 39

Stylized facts

Housing loans and housing prices

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