Impact of Different Economic Areas on Yield Rates in the V4’s Capitals’ Office Markets

Nowak, Krzysztof Adam

Impact of Different Economic Areas on Yield Rates in the V4’s Capitals’ Office Markets

Central European Economic Journal

Volume 12 (2025): Issue 59 (January 2025)

By:

Krzysztof Adam Nowak

Open Access

|Oct 2025

Figures & Tables

The yield rates (left axis) and total office stock (right axis, in M/Sqm.) on the office markets in Bratislava, Budapest, Prague and Warsaw, Q1 2011 to Q4 2022.
Source: own elaboration on data provided by Colliers International Polska — The yield rates (left axis) and total office stock (right axis, in M/Sqm.) on the office markets in Bratislava, Budapest, Prague and Warsaw, Q1 2011 to Q4 2022. Source: own elaboration on data provided by Colliers International Polska

The cumulative effects of explanatory variables on yield rate in the office market in Bratislava
Source: own elaboration — The cumulative effects of explanatory variables on yield rate in the office market in Bratislava Source: own elaboration

The final ARDL models explaining the yield rate on each of the office markets_

BRATISLAVA – The V4 dataset			BUDAPEST – The EMU dataset			PRAGUE – The National dataset				WARSAW – The USA dataset
Independent variables	Coefficient	Std. Err.	Independent variables	Coefficient	Std. Err.	Independent variables	Coefficient	Std. Err.		Independent variables	Coefficient	Std. Err.
Intercept	−0.0009^***	0.0003	Adjustment variable			Intercept	0.0001		0.0005	Intercept	−0.0002	0.0002
DBRYL1	0.1824	0.1775	BUYL1	−0.1949^***	0.0362	DPRYL1	0.5501^***		0.1713	DWAYL1	0.2289	0.1621
DB10V4	−0.0182	0.1103	Long-run variables			DB10C	−0.0498		0.0598	DWAYL2	0.3239^*	0.1811
DB10V4L1	−0.2948^**	0.1003	B10E	0.8843^***	0.1105	DB10CL1	0.0558		0.0524	DB10U	−0.0552	0.0761
DB10V4L2	−0.1117	0.0814	HICPE	−0.5047^***	0.1480	DB10CL2	0.1241^**		0.0535	DB10UL1	0.1674^**	0.0749
DB10V4L3	0.2436^**	0.0872	GDPE	0.0004	0.0004	DB10CL3	−0.1933^***		0.0591	DHICPU	−0.0306^*	0.0172
DB10V4L4	−0.2822^**	0.0993	BURGR	−0.3462^***	0.0919	DM3C	0.0305		0.0284	DHICPUL1	−0.0617^***	0.0173
DHICPV4	−0.0693	0.0679	Short-run variables			DM3CL1	−0.0501^*		0.0269	DPLNUSD	0.0012	0.0013
DHICPV4L1	0.2485^***	0.0747	DBUYL1	−0.3789^***	0.1323	DHICPC	−0.0518^**		0.0189	DPLNUSDL1	0.0001	0.0014
DHICPV4L2	0.1277^*	0.0645	DBUYL2	−0.1405	0.1457	DHICPCL1	0.0155		0.0236	DPLNUSDL2	−0.0012	0.0015
DHICPV4L3	0.2561^***	0.0803	DBUYL3	−0.3115^*	0.1582	DHICPCL2	0.0534^**		0.0248	DPLNUSDL3	0.0034^**	0.0014
DHICPV4L4	0.2384^***	0.0585	DHICPE	0.0763^**	0.0363	DHICPCL3	0.0296		0.0275	DGDPU	0.0009^***	0.0002
DCETOPI	−0.0218^***	0.0041	DBURGR	0.0212^*	0.0124	DHICPCL4	0.0849^***		0.0272	DWAVSNA	7.80e-06^**	3.07e-06
DCETOPIL1	0.0053	0.0039	DBURGRL1	0.0223^**	0.0094	DGDPC	0.0001^**		0.0001	DWAVSNAL1	−3.50e-06	2.87e-06
DCETOPIL2	−0.0016	0.0030	Intercept	0.0098^***	0.0021	DPRVSNA	2.44e-06^*		1.23e-06	DWAVSNAL2	0.00001^***	2.80e-06
DCETOPIL3	0.0023	0.0031	-	-	-	DPRRGR	−0.0091		0.0119	DWARGR	0.0089	0.0079
DCETOPIL4	−0.0038	0.0025	-	-	-	DPRRGRL1	0.0249^*		0.0131	DWARGRL1	0.0044	0.0088
DGDPV4	0.0007^***	0.0002	-	-	-	-	-		-	DWARGRL2	0.0552^***	0.0127
DGDPV4L1	0.0006^**	0.0002	-	-	-	-	-		-	DWARGRL3	−0.0488^***	0.0142
DGDPV4L2	0.0005^**	0.0002	-	-	-	-	-		-	DWARGRL4	0.0274^***	0.0093
DGDPV4L3	0.0002	0.0001	-	-	-	-	-		-	-	-	-
DBRVSNA	−0.0001	0.0001	-	-	-	-	-		-	-	-	-
DBRVSNAL1	−0.0001	0.0001	-	-	-	-	-		-	-	-	-
DBRVSNAL2	0.0003^***	0.0001	-	-	-	-	-		-	-	-	-
DBRVSNAL3	0.0003^***	0.0001	-	-	-	-	-		-	-	-	-
DBRVSNAL4	−0.0001	0.0001	-	-	-	-	-		-	-	-	-
DBRRGR	−0.0195^*	0.0096	-	-	-	-	-		-	-	-	-
DBRRGRL1	−0.0182	0.0134	-	-	-	-	-		-	-	-	-
DBRRGRL2	−0.0315^**	0.0141	-	-	-	-	-		-	-	-	-
DBRRGRL3	−0.0512^***	0.0129	-	-	-	-	-		-	-	-	-
DBRRGRL4	−0.0500^***	0.0104	-	-	-	-	-		-	-	-	-
Adj. R²	0.7098		Adj. R²	0.5008		Adj. R²	0.6708			Adj. R²	0.5002

Descriptive statistics of time series employed in the final models

Time series	Yield rate	Yield rate	Yield rate	Yield rate	Government Bonds (10-yr)	Government Bonds (10-yr)	Government Bonds (10-yr)	Government Bonds (10-yr)	M3 aggregate	HICP inflation	HICP inflation	HICP inflation
Variable code	BRY	BUY	PRY	WAY	B10C	B10V4	B10E	B10U	M3C	HICPC	HICPV4	HICPE
Area	Bratislava	Budapest	Prague	Warsaw	Czechia	Visegrad group	EMU	USA	Czechia	Czechia	Visegrad group	EMU
Unit	%	%	%	%	%	%	%	%	Points (2015 = 100)	Rate of change	Rate of change	Rate of change
Min.	0.050	0.050	0.040	0.044	0.0025	0.0096	−0.0009	0.0065	79.60	−0.0079	−0.0079	−0.0069
Max.	0.075	0.078	0.068	0.065	0.0538	0.0630	0.0449	0.0383	175.46	0.0797	0.0534	0.0363
Mean	0.066	0.065	0.053	0.054	0.0200	0.0293	0.0169	0.0218	120.39	0.0082	0.0087	0.0050
S.D.	0.008	0.010	0.009	0.007	0.0127	0.0154	0.0127	0.0068	29.73	0.0144	0.0126	0.0075
Time series	HICP inflation	Stock index	Stock index	GDP	GDP	GDP	GDP	Exchange rate	Exchange rate	Vacant stock/Net Absorption^*	Vacant stock/Net Absorption^*	Vacant stock/Net Absorption^*
Variable code	HICPU	CETOPI	SNPI	GDPC	GDPV4	GDPE	GDPU	PLNUSD	USDEUR	BRVSNA	PRVSNA	WAVSNA
Area	USA	Central Europe	USA	Czechia	Visegrad group	EU	USA	Poland/USA	USA/EMU	Bratislava	Prague	Warsaw
Unit	Rate of change	Points	Points	Growth rate	Growth rate	Growth rate	Growth rate	PLN	USD	Sqm.	Sqm.	Sqm.
Min.	−0.0199	1,478.33	1,131.42	−8.736	−9.956	−11.395	−7.891	2.752	0.978	1.813	−622.36	−83.70
Max.	0.0374	2,515.03	4,766.18	7.376	8.875	12.140	7.759	4.953	1.452	18.607	185.28	523.58
Mean	0.0058	1,926.66	2,511.14	0.477	0.641	0.293	0.573	3.660	1.194	7.756	−1.85	15.34
S.D.	0.0119	241.73	945.58	1.872	2.041	2.507	1.685	0.440	0.110	3.946	96.92	78.20
Time series	Rent growth rate	Rent growth rate	Rent growth rate	Rent growth rate	-	-	-	-	-	-	-	-
Variable code	BRRGR	BURGR	PRRGR	WARGR	-	-	-	-	-	-	-	-
Area	Bratislava	Budapest	Prague	Warsaw	-	-	-	-	-	-	-	-
Unit	Growth rate	Growth rate	Growth rate	Growth rate	-	-	-	-	-	-	-	-
Min.	−0.070	−0.100	−0.035	−0.042	-	-	-	-	-	-	-	-
Max.	0.065	0.056	0.067	0.136	-	-	-	-	-	-	-	-
Mean	0.003	0.003	0.006	0.002	-	-	-	-	-	-	-	-
S.D.	0.023	0.023	0.018	0.028	-	-	-	-	-	-	-	-

Answers to the first two research questions

Research question	BRATISLAVA	BUDAPEST	PRAGUE	WARSAW
Which of the following economic areas’ sets of variables — i.e. the National, the V4, the EMU, or the USA — best explain fluctuations in the yield rates for the four office markets?	The V4	The EMU	The National	The USA
Which variables representing the consecutive groups of factors — i.e. monetary influence, capital market factors, economic situation, or local market factors — have a dominant impact on the yield rates?	The monetary factor – changes in the inflation rate (DHICPV4)	Long-term impact: The capital market factor – interest of government bonds (B10E); Short-term impact: The monetary factor – changes in the inflation rate (DHICPE)	The monetary factor – changes in the inflation rate (DHICPC)	The capital market factor – changes in interest from government bonds (DB10UL1)

Results of the White and the Breusch-Pagan heteroscedasticity tests on the final ARDL models

The White test for heteroscedasticity
BRATISLAVA		BUDAPEST		PRAGUE		WARSAW
chi2(41)	Prob > chi2	chi2(42)	Prob > chi2	chi2(41)	Prob > chi2	chi2(41)	Prob > chi2
42.00	0.4274^*	43.00	0.4282^*	42.00	0.4274^*	42.00	0.4274^*
The Breusch-Pagan test for heteroscedasticity
BRATISLAVA		BUDAPEST		PRAGUE		WARSAW
F(30, 11)	Prob > F	F(11, 31)	Prob > F	F(16, 25)	Prob > F	F(19, 22)	Prob > F
1.59	0.2112^*	0.39	0.9509^*	0.75	0.7238^*	0.67	0.8083^*

The final NARDL model explaining the yield rate on the office market in Bratislava with the USA dataset

BRATISLAVA – The USA dataset
Independent variables		Coefficient			Std. Err.
Constant		0.1399^**			0.0331
BRYL1		−1.7601^**			0.4249
HICPUL1⁺		−0.4858^*			0.1881
HICPUL1⁻		−0.0364			0.1276
USDEUR⁺		0.2266^**			0.0749
USDEUR⁻		−0.0114			0.0199
SNPIL1⁺		−0.0923^***			0.0166
SNPIL1⁻		−0.0133			0.0136
GDPUL1⁺		−0.0014			0.0016
GDPUL1⁻		−0.0045^*			0.0020
BRVSNAL1⁺		0.0021^**			0.0007
BRVSNAL1⁻		0.0008^*			0.0003
BRRGRL1⁺		−0.2651^*			0.1008
BRRGRL1⁻		−0.0368			0.0485
ΔBRYL1		0.6558			0.3742
ΔBRYL2		−0.2981			0.1903
ΔHICPU⁺		−0.2202^*			0.0833
ΔHICPUL1⁺		0.1738			0.1169
ΔHICPU⁻		−0.0677			0.0795
ΔHICPUL1⁻		−0.0044^***			0.0713
ΔUSDEUR⁺		0.0980^*			0.0387
ΔUSDEURL1⁺		−0.0932			0.0541
ΔUSDEUR⁻		−0.0405			0.0272
ΔUSDEURL1⁻		−0.0375^*			0.0151
ΔSNPI⁺		−0.0599^**			0.0172
ΔSNPIL1⁺		−0.0160			0.0182
ΔSNPI⁻		0.0013			0.0124
ΔSNPIL1⁻		0.0201			0.0171
ΔGDPU⁺		−0.0002			0.0009
ΔGDPUL1⁺		0.0007			0.0008
ΔGDPU⁻		−0.0007			0.0004
ΔGDPUL1⁻		0.0020^**			0.0006
ΔBRVSNA⁺		0.0010^**			0.0004
ΔBRVSNAL1⁺		−0.0004			0.0003
ΔBRVSNA⁻		0.00001			0.0004
ΔBRVSNAL1⁻		−0.0005^*			0.0002
ΔBRRGR⁺		−0.1524^*			0.0602
ΔBRRGRL1⁺		0.0296			0.0265
ΔBRRGR⁻		0.0343			0.0280
ΔBRRGRL1⁻		0.0276			0.0235
Adj. R²		0.6686
Tests		Stat.			p-value
Portmanteau test (chi²)		19.06^*			0.5178
Breusch-Pagan test (chi²)		0.4469^*			0.5038
Ramsey RESET test (F)		47.98^*			0.1056
Jarque-Bera test (chi²)		1.426^*			0.4901
Bounds cointegration test
p levels of critical values		10%		5%		1%
Lower and upper bounds^		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-statistics	3.5008^*	2.12	3.23	2.45	3.61	3.15	4.43
t-statistics	−4.1429^*	−2.57	−4.04	−2.86	−4.38	−3.43	−4.99

Results of the Jarque-Bera and the Shapiro-Wilk normality tests for residuals of the final ARDL models

The Jarque-Bera normality test
BRATISLAVA		BUDAPEST		PRAGUE		WARSAW
Chi(2)	Prob > chi2	Chi(2)	Prob > chi2	Chi(2)	Prob > chi2	Chi(2)	Prob > chi2
1.024	0.5994^*	1.587	0.4523^*	1.775	0.4117^*	3.104	0.2118^*
The Shapiro-Wilk normality test
BRATISLAVA		BUDAPEST		PRAGUE		WARSAW
z	Prob > z	z	Prob > z	z	Prob > z	z	Prob > z
0.831	0.20287^*	0.666	0.25276^*	0.750	0.22665^*	0.987	0.16187^*

Results of the Bounds cointegration test (Case 3)

BRATISLAVA – the V4 dataset
p levels of critical values		10%		5%		1%
Lower and upper bounds		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-statistics	2.364	2.284	3.728	2.760	4.412	3.914	6.059
t-statistics	−0.491	−2.427	−3.896	−2.805	−4.358	−3.575	−5.304
BUDAPEST – the EMU dataset
p levels of critical values		10%		5%		1%
Lower and upper bounds		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-statistics	9.379^***	2.598	3.883	3.150	4.606	4.461	6.308
t-statistics	−5.382^***	−2.508	−3.619	−2.864	−4.032	−3.587	−4.866
PRAGUE – the NATIONAL dataset
p levels of critical values		10%		5%		1%
Lower and upper bounds		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-statistics	7.606	2.328	3.658	2.794	4.299	3.910	5.821
t-statistics	−1.813	−2.493	−3.973	−2.854	−4.413	−3.591	−5.306
WARSAW – the USA dataset
p levels of critical values		10%		5%		1%
Lower and upper bounds		I(0)	I(1)	I(0)	I(1)	I(0)	I(1)
F-statistics	6.283	2.291	3.717	2.765	4.393	3.913	6.020
t-statistics	−0.519	−2.438	−3.909	−2.813	−4.367	−3.578	−5.304

The independent variables in ARDL and NARDL models

Types of variables according to the Gordon growth model	Variables characteristics	Economic areas’ sets of variables
Types of variables according to the Gordon growth model	Variables characteristics	The National	The V4	The EMU	The USA
Risk-free interest rates	Government bonds interest (10-yr)	SK, HU, CZ, PL	Average in V4 countries	EMU	USA
Monetary (risk premium)	M3 monetary aggregate	EMU, HU, CZ, PL	-	EMU	USA
	HICP inflation rate	SK, HU, CZ, PL	Average in V4 countries	EMU	USA
	Exchange rates	-	-	National currency/EUR	National currency/USD
Alternative investments (risk premium)	Stock indices	SAX, BUX, PX, WIG20	CETOP	DAX	S&P 500
Country specific (risk premium)	GDP growth rate qoq	SK, HU, CZ, PL	Average in V4 countries	EMU	USA
Office market characteristics (risk premium)	Vacancy/net absorption^*	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw
Cash flow growth	Rent growth rate	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw	Bratislava, Budapest, Prague, Warsaw

Results of long-run and short-run asymmetry, long-run positive and negative effects, and the Wald test

Independent variables	Long-run asymmetry	Short-run asymmetry	Wald test long-run asymmetry	Wald test short-run asymmetry
Independent variables	F-stat	F-stat	F-stat	F-stat
HICPU	4.746^*	0.0112	3.99	0.01
USDEUR	11.12^**	2.271	7.02^*	2.27
SNPI	35.51^***	8.562^**	16.12^**	8.56^**
GDPU	89.53^***	1.215	18.36^**	1.22
BRVSNA	7.605^*	2.903	7.42^*	2.90
BRRGR	11.24^**	10.26^**	6.01^*	10.26^**
Long-run asymmetry positive and negative effects
Independent variables	Long-run effect⁺		Long-run effect⁻
Independent variables	Coefficient	F-stat	Coefficient	F-stat
HICPU	−0.276^**	11.65	0.021	0.0838
USDEUR	0.129^**	17.64	0.006	0.3333
SNPI	−0.052^***	34.37	0.008	0.8072
GDPU	−0.001	0.7935	0.003^*	6.595
BRVSNA	0.001^**	13.4	−0.000^**	8.726
BRRGR	−0.151^**	10.66	0.021	0.5401

Results of the Breusch-Godfrey autocorrelation test on the final ARDL models

Number of lags	BRATISLAVA		BUDAPEST		PRAGUE		WARSAW
Number of lags	chi2	Prob > chi2	chi2	Prob > chi2	chi2	Prob > chi2	chi2	Prob > chi2
1	0.097	0.7557^*	0.317	0.5733^*	0.441	0.5067^*	0.845	0.3581^*
2	0.389	0.8233^*	0.321	0.8516^*	0.691	0.7077^*	1.840	0.3985^*
3	1.693	0.6386^*	0.409	0.9384^*	2.460	0.4825^*	2.943	0.4004^*
4	1.836	0.7658^*	1.294	0.8624^*	2.562	0.6335^*	5.481	0.2414^*

Results of the ADF stationarity test of time series employed in the final ARDL and NARDL models

BRATISLAVA – the V4 dataset (ARDL)
Variable		BRY	B10V4	HICPV4	CETOPI	GDPV4	BRVSNA	BRRGR
Levels	Test statistic	−0.864	−2.281	−2.486	−3.246^**	−5.985^***	−2.417	−4.611^***
First differences	Test statistic	−5.010^***	−2.848^*	−7.630^***	−5.566^***	-	−6.661^***	-
BUDAPEST – the EMU dataset (ARDL)
Variable		BUY	B10E	HICPE	GDPE	BURGR	-	-
Levels	Test statistic	−1.141	−2.078	−2.664^*	−6.690^***	−3.588^**	-	-
First differences	Test statistic	−3.131^**	−3.683^***	−6.960^***	-	−6.284^***	-	-
PRAGUE – the NATIONAL dataset (ARDL)
Variable		PRY	B10C	M3C	HICPC	GDPC	PRVSNA	PRRGR
Levels	Test statistic	−1.481	−0.897	0.360	−3.926	−5.883^***	−4.564^***	−1.996
First differences	Test statistic	−2.988^**	−3.565^**	−4.499^***	−6.677^***	-	-	−7.200^***
WARSAW – the USA dataset (ARDL)
Variable		WAY	B10U	HICPU	PLNUSD	GDPU	WAVSNA	WARGR
Levels	Test statistic	−1.018	−2.165	−4.566^***	−1.096	−6.499^***	−4.524^***	−3.513
First differences	Test statistic	−2.524^**	−3.285^**	-	−5.656^***	-	-	−6.799^***
BRATISLAVA – the USA dataset (NARDL)
Variable		BRY	HICPU	USDEUR	SNPI	GDPU	BRVSNA	BRRGR
Levels	Test statistic	−4.566^***	−4.566^***	−1.864	−1.631	−6.499^***	−2.417	−4.611^***
First differences	Test statistic	-	-	−4.358^***	−5.273^***	-	−6.661^***	-

DOI: https://doi.org/10.2478/ceej-2025-0016 | Journal eISSN: 2543-6821 | Journal ISSN: 2544-9001

Journal RSS Feed

Language: English

Page range: 260 - 283

Submitted on: May 25, 2025

Accepted on: Sep 15, 2025

Published on: Oct 31, 2025

Published by: Faculty of Economic Sciences, University of Warsaw

In partnership with: Paradigm Publishing Services

Publication frequency: 1 times per year

Keywords:

yield rate,

capitalization rate,

office market,

the Autoregressive Distributed Lag model (ARDL),

the Nonlinear Autoregressive Distributed Lag model (NARDL)

Related subjects:

Business and economics,

Political economics,

Economic theory, systems and structures,

Microeconomics,

Macroeconomics,

Economic policy

© 2025 Krzysztof Adam Nowak, published by Faculty of Economic Sciences, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.

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Impact of Different Economic Areas on Yield Rates in the V4’s Capitals’ Office Markets

Figures & Tables

Figure 1.

Figure 2.

The final ARDL models explaining the yield rate on each of the office markets_

Descriptive statistics of time series employed in the final models

Answers to the first two research questions

Results of the White and the Breusch-Pagan heteroscedasticity tests on the final ARDL models

The final NARDL model explaining the yield rate on the office market in Bratislava with the USA dataset

Results of the Jarque-Bera and the Shapiro-Wilk normality tests for residuals of the final ARDL models

Results of the Bounds cointegration test (Case 3)

The independent variables in ARDL and NARDL models

Results of long-run and short-run asymmetry, long-run positive and negative effects, and the Wald test

Results of the Breusch-Godfrey autocorrelation test on the final ARDL models

Results of the ADF stationarity test of time series employed in the final ARDL and NARDL models

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