Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

DETERMINANTS OF THE RECENT ROMANIAN HOUSEHOLDS'

FINANCIAL BEHAVIOUR FOR HOUSING LOANS -

A TERRITORIAL ANALYSIS AT THE LEVEL OF NUTS 3 REGIONS

BABUCEA ANA-GABRIELA,

PROF. PHD., “CONSTANTIN BRÂNCUŞI” UNIVERSITY OF TÂRGU JIU, ROMANIA

e-mail: gabibabucea@gmail.com

Abstract: Recent studies on the evolution of the real estate market in Romania highlight a continuous increase of the stock of new

housing and in parallel an increase of the loan fund to the population for housing. In this context, the main purpose of

the article was to make an overview of the evolution of the housing fund in Romania, and of the households housing

loans, and to identify the differences and the similarities at the level of the NUTS3 administrative-territorial units,

respectively the counties in Romania, regarding the determinants on the housing loans for households. We used

specific statistical analysis methods for territorial analysis based on the data series of Romania's National Bank

(NBR), Romania's National Institute of Statistics (NIS), and National Commission for Prognosis (NCP), data series

available at the time of preparation of the study, for the entire set of variables considered determinants of the gaps in

this sector across the Romania's counties. Using cluster analysis were classified the Romanian counties, and then, were

identified the profile of each class of similar counties in order to the level of the households loans for the housing, and

identified the determinants. In this regard, the study confirms the existence of significant disparities at the level of

Romania's counties. The results can be used both in the analysis of territorial comparisons, but especially for political

decisions in Romania's territorial development.

Keywords: housing, private ownership, dwellings, housing loan, cluster analysis

Classification JEL: O16, O18, C38

1. INTRODUCTION

Housing is one of the basic needs of the population and access to it is an important factor in maintaining and

improving the quality of life as well as an essential component of society [1]. According to the National Institute of

Statistical data, Romania had at the end of the year 2016 a fund of about 9 million dwellings, increasing by 0.5 %, as

compared to the end of the year 2015, maintaining the ascendant trend in recent years. With about 20 million

inhabitants structured in 8.88 million dwellings occupied by 7.1 million households, there is a surplus of 1.7 million,

which are either uninhabited or rented.

Nevertheless, Aplopi, Iacoboaea, and Stănescu consider that "this balance is misleading since it hides a

mismatch of supply and demand for housing in terms of households and geographical distribution. A surplus of

available housing could be considered a favorable factor, which would facilitate mobility in a stable market

environment, but this is not the case of Romania, where most of the houses are unoccupied, represent holiday homes

(residences), are located in places where housing markets do not work, are substandard (in most cases) or unfinished

housing" [2, p.8]. On the other hand, at the Conference "Development Strategy of Romania" with the theme "Between

the Euro Zone and the Lion's Sign" organized by the Bursa press group in May 2017, Adrian Vasilescu, adviser to of

the National Bank of Romania (BNR) governor Mugur Isarescu, said "Romania currently has a deficit of about one

million houses".

It is significant to note that there is some particularity in the population/dwelling ratio: while the number of the

resident population in Romania decreases continuously, the dwellings stock increases. This can mean that there is a

population that has a dwelling, but want another one for children or simply sees in the housing acquisition a way to

protect the money and an investment for a future rent. Statistical data also confirm a revival of residential construction

in the last years in Romania. It is obvious the demand/”need” for the construction of residential buildings, the proof

evolution of the number of building authorizations at the territorial level. (See Table no.1)

Table no. 1 - Number of construction permits released for residential buildings, by development regions, in Romania

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

Year Nord-Est Sud-Est Sud-Muntenia Sud-Vest Oltenia Vest Nord-Vest Centru Bucuresti-Ilfov Total Romania

2014 7041 4287 5689 2665 3616 5191 3004 6179 37672

2015 7665 4868 6066 2677 3562 5438 2837 5999 39112

2016 7231 4280 5632 2680 4030 6174 3152 5474 38653

2017* 5600 3087 3825 1914 2801 3899 2428 4051 27605

* Only the first 8 months of the year

Source: Based on data from NIS http://www.insse.ro/cms/en/content/construction-permits-released-buildings-18

Starting with 2012, the number of private-funded dwellings completed annually reverted to an upward trend,

since 2009, under the impact of the economic and financial crisis, their number has continuously diminished (See

Figure 1). Note that, since 2014, the number of residential buildings in the urban area exceeds for the first time those in

the rural area, which reinforces the idea that the developers have relaunched in the construction of residential buildings

in the urban area with the same intensity as in 2008 (See Figure 1 – b.).

a) b)

Source: Based on NIS Tempo Online database http://statistici.insse.ro/shop/index.jsp?page=tempo3&lang=en&ind=LOC101A

Figure no. 1. – a) Evolution of the Romania's dwellings stock, by urban/rural area, b) Evolution of the number of

finished dwellings during the current year, by urban/rural area, for the period 2007-2016

Recently, the National Bank of Romania has signaled the rhythm of household housing lending which

accelerated the price of housing, given that in Romania, the house price index (HPI), which measures inflation in the

residential property market, is continuously increasing, higher than in the EU, starting with the year 2016, according to

Eurostat.

Table no. 2. - House price index (2015 = 100) - quarterly data

Geo 2015/Q1 2015/Q2 2015/Q3 2015/Q4 2016/Q1 2016/Q2 2016/Q3 2016/Q4 2017/Q1

EU 98,06 99,7 100,94 101,3 102,05 103,66 105,14 105,91 106,61

RO 101,01 99,76 98,87 100,36 103,65 106,59 105,86 107,7 108,96

Source: Eurostat, http://ec.europa.eu/eurostat/en/web/products-datasets/-/PRC_HPI_Q

One question may be:”What is the source of financing these constructions?” For population, there are at least

two apparent two sources of funding the housing acquisition. One of them is from their own savings deposits in banks.

In the years after the economic crisis, most Romanians as the almost all Europeans have increased continuously their

saving in banks based on different reasons, especially because of the pension systems [3]. Such that, starting with the

year 2014 the level of term deposits was higher than their loans [4, 5]. Therefore, in the conditions in which the interest

rates paid by banks have decreased to the historical minimums, the households have turned towards lending again.

However, the main source for housing purchase/constructions remains the lending by banks, especially through

programs for young people who are purchasing the first home. Most of the interested population has enough money for

applicate to the advantageous lending of the programs such as the "Prima Casă" started by the government during the

crisis or the "Casa mea", an alternative from the banking sector, and should make the housing acquisition of more

attractive, even in the face of the risks associated with long-term loans.

Moreover,”banks concentrated on offering consumer loans and loans for house purchase as the household’s

sector had a potential for growth” [6, p.108]. In this context, real estate developers, who have learned from the real

estate crisis in the year 2008, are selling their dwellings from the project stage, so built them on the buyers' money.

Studying the evolution of the real estate market in Romania can be a highlight that the continuous increase in the stock

of new housing correlates with an increase in the level of housing loans granted by banks to the population.

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

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a) Deposits and loans of households b) Loans of households by type

Source: Based on datasets from RNB, Loans to households, http://www.bnro.ro/Loans-to-households-6374.aspx and Deposits of households

http://www.bnro.ro/Household-deposits-6377.aspx

Figure no. 2. – Evolution of the loans to households; and deposits of households, for the period from January 2007 to

August 2017, at the end of the month (Lei, Thousand)

In addition, even if the house prices are continuously rising, are below as there was in the year 2008, the

mortgage costs have fallen sharply while the average wage in the economy is higher, and for a long time. It should note

the credit demand for housing in lei increase because of the stability of the national currency against the Euro.

However, it is well-known that, the Romania's economic development is characterized by increasing inequalities within

the country, being the country with the highest social inequalities in the EU. After the economic crisis, the regional

polarization of the economy has also resulted in significant manifestations of internal labor mobility in Romania, as

highlighted by the study of the World Bank "Cities-magnet, Migration and commuting in Romania" which identifies

the most attractive cities of Romania: București, Cluj-Napoca, Timișoara and Brașov [7]. In this context, the main

purpose of the article is to determine whether the housing loans by the population is a national phenomenon or a

phenomenon focuses on certain counties, thus there are regional disparities because, on different reasons caused by the

regional life conditions, the financial behavior of the population is different. It also aims at identifying the determinants

that generated this financial behavior of the population. The study aims to define a current territorial profile of

Romania, at the level of the 41 counties, NUTS 3 regions, including Bucuresti, Romania's capital, concerning the

housing loans.

Source: https://commons.wikimedia.org/w/index.php?curid=4738779

Figure no. 3. - Romania's administrative regions NUTS3 – counties

The following working hypotheses have been shaped:

1. There are disparities at the territorial level in Romania regarding the volume of housing loans accessed by the

population;

2. There is a correlation between the distribution of the population housing loans and the counties with capitals that

are the most attractive cities of Romania considering the living conditions, named ”Cities-Magnet”.

2. DATASETS OVERVIEW

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

The analysis, based on open data from Romania's National Bank - Financial and Monetary - online Statistics,

regards the main indicators of households' credits for housing acquisitions, at the county level, at the end of August

2017, both in Lei (Million):

IND_1 - Credit to households - lending for house purchase in Lei, and

IND_2 - Credit to households - lending for house purchase in foreign exchange, EUR, and other currencies.

The datasets for the main indicators of the territorial distribution of the credit in Lei and EUR or other

currencies are presented in Table no. 3. In the same table, there is the algorithm for calculating territorial indices and

rates of the gap, and concentration coefficient Struck (Cs) for Romanian NUTS 3 regions, indicators that describe the

considered territorial series.

Was applied the specific indicators system to describing the phenomenon at the territorial level. On the one

hand, for describing the differences between the counties, but on the other hand for identification the typical and

significant aspect of the phenomena, the uniformity of the territorial distribution, as well as their uneven dispersion.

To quantify the degree of uniformity of distributions at the territorial level, statistical methodology holds

specific indicators. A spatial distribution coefficient named the concentration coefficient in space is used. The

variability of the inferior limit of the Gini coefficient, as compared with the n products analysed, determines a relative

difficulty in using it in temporal comparisons. To eliminate that disadvantage, a number of correction procedures were

propose in order to deal with the Gini coefficient. Such a procedure was proposed by R. Struck [8]. The coefficient

named Gini-Struck is a corrected form of Gini coefficient:

Characterization the intensity of variables considered was accomplished by calling the concentration

coefficient Struck (Cs) whose value is between 0 - corresponding to spatial Series perfectly uniform and 1 - for major

concentration, in other words, disparities.

It should be noted that the dimension of credit to households - lending for house purchase in Lei is higher than

purchase in foreign exchange, EUR, and other currencies, and a very large difference between București (including

Ilfov County) in front of the all other regions, for both of variables. Same different aspects regard counties as Cluj,

Timiș, Constanța, and Brașov. (See Figure no. 4 and Table no. 3.)

Source: Own with SPSS v.20.0, based on data from http://www.bnro.ro/Loans-and-Deposits-by-County-3211.aspx

Figure no. 4. - Credit to households for house purchase in Lei and in foreign exchange, in August 2017, by counties

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

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Table no. 3. – Structure of the credit to households - lending for house purchase in Lei and in foreign exchange, EUR, and other currencies, by counties, the algorithm for

calculating territorial indices and rates of gap by the București-Ilfov Region, and coefficient of concentration among the Romanian counties in August 2017

Counties

Credit in Lei – IND_1

gi gi2

Credit in foreign exchange, EUR, and other

currencies – IND_2 gi gi

2

Total Credit – IND_3

gi gi2

Lei Million Territorial

indices Rates of gap % Lei Million

Territorial

indices Rates of gap % Lei Million

Territorial

indices Rates of gap %

Alba 316,5 0,022 -97,77 0,0089 0,0001 264,9 0,022 -97,81 0,0095 0,0001 581,4 0,022 -97,79 0,0092 0,0001

Arad 560,4 0,039 -96,05 0,0158 0,0002 395,5 0,033 -96,73 0,0142 0,0002 955,9 0,036 -96,37 0,0151 0,0002

Argeş 762,8 0,054 -94,63 0,0215 0,0005 575,1 0,047 -95,25 0,0206 0,0004 1337,9 0,051 -94,91 0,0211 0,0004

Bacău 484,5 0,034 -96,59 0,0137 0,0002 387,1 0,032 -96,80 0,0139 0,0002 871,6 0,033 -96,69 0,0138 0,0002

Bihor 728,2 0,051 -94,87 0,0205 0,0004 619 0,051 -94,89 0,0222 0,0005 1347,2 0,051 -94,88 0,0213 0,0005

Bistriţa-Năsăud 189,8 0,013 -98,66 0,0054 0,0000 186,5 0,015 -98,46 0,0067 0,0000 376,3 0,014 -98,57 0,0059 0,0000

Botoşani 195,1 0,014 -98,63 0,0055 0,0000 149,6 0,012 -98,76 0,0054 0,0000 344,7 0,013 -98,69 0,0054 0,0000

Braşov 1.357,50 0,096 -90,43 0,0383 0,0015 1.025,20 0,085 -91,53 0,0368 0,0014 2382,7 0,091 -90,94 0,0376 0,0014

Brăila 287,8 0,020 -97,97 0,0081 0,0001 188,4 0,016 -98,44 0,0068 0,0000 476,2 0,018 -98,19 0,0075 0,0001

Buzău 340,1 0,024 -97,60 0,0096 0,0001 234,4 0,019 -98,06 0,0084 0,0001 574,5 0,022 -97,82 0,0091 0,0001

Caraş-Severin 138,6 0,010 -99,02 0,0039 0,0000 94,0 0,008 -99,22 0,0034 0,0000 232,6 0,009 -99,12 0,0037 0,0000

Călăraşi 141,4 0,010 -99,00 0,0040 0,0000 99,0 0,008 -99,18 0,0036 0,0000 240,4 0,009 -99,09 0,0038 0,0000

Cluj 2.465,20 0,174 -82,63 0,0695 0,0048 1.566,60 0,129 -87,06 0,0562 0,0032 4031,8 0,153 -84,67 0,0636 0,0041

Constanţa 1.574,10 0,111 -88,91 0,0444 0,0020 1.329,30 0,110 -89,02 0,0477 0,0023 2903,4 0,110 -88,96 0,0458 0,0021

Covasna 128,1 0,009 -99,10 0,0036 0,0000 62,7 0,005 -99,48 0,0022 0,0000 190,8 0,007 -99,27 0,0030 0,0000

Dâmboviţa 228,9 0,016 -98,39 0,0065 0,0000 159,5 0,013 -98,68 0,0057 0,0000 388,4 0,015 -98,52 0,0061 0,0000

Dolj 831,5 0,059 -94,14 0,0234 0,0005 663,6 0,055 -94,52 0,0238 0,0006 1495,1 0,057 -94,32 0,0236 0,0006

Galaţi 583,8 0,041 -95,89 0,0165 0,0003 516,6 0,043 -95,73 0,0185 0,0003 1100,4 0,042 -95,82 0,0174 0,0003

Giurgiu 97,7 0,007 -99,31 0,0028 0,0000 69,3 0,006 -99,43 0,0025 0,0000 167 0,006 -99,37 0,0026 0,0000

Gorj 230,1 0,016 -98,38 0,0065 0,0000 109,5 0,009 -99,10 0,0039 0,0000 339,6 0,013 -98,71 0,0054 0,0000

Harghita 88,7 0,006 -99,37 0,0025 0,0000 75,0 0,006 -99,38 0,0027 0,0000 163,7 0,006 -99,38 0,0026 0,0000

Hunedoara 273,3 0,019 -98,07 0,0077 0,0001 201,2 0,017 -98,34 0,0072 0,0001 474,5 0,018 -98,20 0,0075 0,0001

Ialomiţa 151,2 0,011 -98,93 0,0043 0,0000 100,3 0,008 -99,17 0,0036 0,0000 251,5 0,010 -99,04 0,0040 0,0000

Iaşi 1.605,70 0,113 -88,69 0,0453 0,0020 1.216,90 0,101 -89,95 0,0437 0,0019 2822,6 0,107 -89,27 0,0446 0,0020

Maramureş 320,1 0,023 -97,74 0,0090 0,0001 278,8 0,023 -97,70 0,0100 0,0001 598,9 0,023 -97,72 0,0095 0,0001

Mehedinţi 147,6 0,010 -98,96 0,0042 0,0000 152,4 0,013 -98,74 0,0055 0,0000 300 0,011 -98,86 0,0047 0,0000

Mureş 531,9 0,037 -96,25 0,0150 0,0002 405,2 0,033 -96,65 0,0145 0,0002 937,1 0,036 -96,44 0,0148 0,0002

Neamţ 255,5 0,018 -98,20 0,0072 0,0001 233,7 0,019 -98,07 0,0084 0,0001 489,2 0,019 -98,14 0,0077 0,0001

Olt 241,8 0,017 -98,30 0,0068 0,0000 136,8 0,011 -98,87 0,0049 0,0000 378,6 0,014 -98,56 0,0060 0,0000

Prahova 822,7 0,058 -94,20 0,0232 0,0005 510,7 0,042 -95,78 0,0183 0,0003 1333,4 0,051 -94,93 0,0210 0,0004

Satu Mare 288,4 0,020 -97,97 0,0081 0,0001 227,8 0,019 -98,12 0,0082 0,0001 516,2 0,020 -98,04 0,0081 0,0001

Sălaj 163,6 0,012 -98,85 0,0046 0,0000 151,6 0,013 -98,75 0,0054 0,0000 315,2 0,012 -98,80 0,0050 0,0000

Sibiu 1.006,70 0,071 -92,91 0,0284 0,0008 721,8 0,060 -94,04 0,0259 0,0007 1728,5 0,066 -93,43 0,0273 0,0007

Suceava 339,3 0,024 -97,61 0,0096 0,0001 299,6 0,025 -97,53 0,0107 0,0001 638,9 0,024 -97,57 0,0101 0,0001

Teleorman 170,3 0,012 -98,80 0,0048 0,0000 117,7 0,010 -99,03 0,0042 0,0000 288 0,011 -98,90 0,0045 0,0000

Timiş 2.239,40 0,158 -84,22 0,0631 0,0040 1.532,00 0,127 -87,35 0,0550 0,0030 3771,4 0,143 -85,66 0,0595 0,0035

Tulcea 238,7 0,017 -98,32 0,0067 0,0000 156,8 0,013 -98,71 0,0056 0,0000 395,5 0,015 -98,50 0,0062 0,0000

Vaslui 220,2 0,016 -98,45 0,0062 0,0000 151,6 0,013 -98,75 0,0054 0,0000 371,8 0,014 -98,59 0,0059 0,0000

Vâlcea 307,1 0,022 -97,84 0,0087 0,0001 249,1 0,021 -97,94 0,0089 0,0001 556,2 0,021 -97,89 0,0088 0,0001

Vrancea 225,4 0,016 -98,41 0,0064 0,0000 152,9 0,013 -98,74 0,0055 0,0000 378,3 0,014 -98,56 0,0060 0,0000

BUCUREŞTI

(Including ILFOV

County)

14.191,8 1 0 0,4001 0,1601 12.108,4 1 0 0,4344 0,1887 26300,20 1 0 0,4152 0,1724

Total 35471,5 - - 1 0,17919 27876,1 - - 1 0,204885 63347,6 - - 1 0,19012848

Source: Own base on data from http://www.bnro.ro/Loans-and-Deposits-by-County-3211.aspx

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The values of coefficients of concentration Gini-Struck, higher than 0.4, indicate an excessive concentration

of credits to households lending for house purchase for all type, in lei, in foreign exchange, or total.

Table no. 4. - Coefficients of concentration Gini-Struck, August 2017

Credits to households lending for house purchase Coefficient of concentration Gini-Struck

- in Lei 0,398334

- in foreign exchange, EUR, and other currencies, 0,430125

- Total, in Lei, and in foreign exchange, EUR, and other currencies 0,412167

Source: Own calculations

Therefore, all those confirm the first hypothesis: There are disparities at the territorial level in Romania

regarding the volume of housing loans accessed by the population.

Considering the nature of data, the statistical method used in this research is cluster analysis, also known as

segmentation or taxonomy analysis. Cluster analysis aims to identify a set of homogeneous groups by grouping

elements to minimize variation within groups and maximize variation between groups. In the study firstly performed a

hierarchical method to define the number of clusters for the Romania's NUTS3 regions, excluding București-Ilfov

region because of the large distance between Ilfov (including București – capital of Romania), and the other counties.

In this regard were used Ward method, which is an agglomerative clustering algorithm. In addition, Ward’s is the only

one among the agglomerative clustering methods based on a classical sum-of-squares criterion, producing groups that

minimize within-group dispersion at each binary fusion [9]. Therefore, it starts out with 40 clusters of size 1 (counties)

and continues until all the regions are included into one cluster. To minimize the Sum of Squares (SS) of any two

(hypothetical) clusters that can form on each step is the aim of this method because it is a measure of homogeneity of

the cluster. We used Squared Euclidian distance measure to define the distances between the counties. Then, the k-

means procedure actually forms the clusters.

3. RESULTS AND DISCUSSIONS

Cluster procedure performed in SPSS v. 20.0. The number of clusters results by using the Ward method and

K-means for final clustering. In addition, the cluster procedures identify, both, four clusters (described in Table 5) as

the figures below show:

a) b)

Figure no. 5. – SPSS Output – Cluster Analysis procedure using Ward method: a) Dendrograme from Ward method,

and b) Line chart of coefficients for numer of cluster identification

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The results of final clustering using K-means procedure indicate 4 clusters present in the table below:

Table no. 5. – Cluster membership using K-means cluster

Cluster County Number of members

1 Iași, Constanța, Brașov 3

2 Arad, Argeş, Bacău, Bihor, Dolj, Galați, Mureș, Prahova, Sibiu 9

3 Cluj, Timiș 2

4

Alba, Bistrița Năsăud, Botoșani, Brăila, Buzău, Caraș-Severin, Călărași, Covasna, Dâmbovița,

Giurgiu, Gorj, Harghita, Hunedoara, Isalomița, Mureși, Mehedinți, Neamț, Olt, Satu Mare,

Sălaj, Suceava, Teleorman, Tulcea, Vaslui, Vâlcea, Vrancea

26

Looking to the distances between final cluster centers from the figure below, or on the dendrogram, can be

noted that the members of the clusters no. 3 (Cluj, Timiș), and no. 1 (Iași, Constanța, and Brașov) are more appropriate

than are the members of the other two clusters, but in the same time very distances from those.

Figure no. 6. – SPSS Outputs of K-means Cluster Analysis procedure

In the absence of București-Ilfov region, the counties of Cluj and Timiș, on the one hand, followed by Iași,

Constanța, and Brasov, on the other hand, are the counties with the highest loans for dwellings at the level of August

2017, with a large distance from the other counties. In the condition that the four clusters were formed by classifying

counties into homogeneous groups, the ANOVA tests indicate significance level for both considered variables, so each

other contribute to the separation of the clusters. (See figure no. 7)

Figure no. 7. – SPSS Output of K-means Cluster Analysis procedure - Test of variance

According to the World Bank study, on the next 5 years: "of those who consider moving, a disproportionate

share are young people - the average age for people who consider moving is 35 years" [5, p.112 ], we find that this

phenomenon is already showing increasing trend starting with the year 2012, as can see in Figure no. 9

Source: Own based on NIS TEMPO_POP301A

Figure no. 8. – Evolution of the settling of domiciles by age group

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

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In addition, ”by-and-large, people that consider moving have a higher educational attainment, have higher

incomes, and are to a larger degree employed in the private sector than the country average" [5, p.112 ], is a result of an

existing success phenomena. Analyzing the internal mobility in Romania for the last ten years, noted that in the year

2007 and 2008 București and Cluj County were the first preference in residence changes.

Starting with 2012 is shaping a large group of other counties in population destination preference: Iași, Brasov,

Cluj, and Timiș, even if București remains the favourite destination. It is worth noting that Ilfov County or Constanța is

not among these. (See Figure No. 9)

Source: Own based on NIS data Tempo_POP304B

Figure no. 9. – Settling of the residence, by counties

On the other hand, if we are talking about domicile changes, alongside București, in the group of the

preferable counties, Iași, Brasov, Cluj, and Timiș, we find starting with 2012 and Ilfov County. It is worth mentioning

that Constanța County is not among them. (See Figure 10)

Given that, București and the capitals of the counties Cluj, Timiș, Iași, and Brașov are the largest cities in

Romania. Much more, there are important university centers. It is appreciated that a large part of the loans for

purchasing dwellings is for the population that established residence in recent years in these cities, as alternatives to the

high level of rent for housing. In addition, the high level of housing prices in București made it much of the people who

decided to settle in București to focus on buying a home in Ilfov County, much cheaper.

Source: Own based on NIS data Tempo_POP301B

Figure no. 10. – Settling of the domicile, by counties

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„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

If we appreciate that București, the capital of the country, also owns the housing fund with the highest seismic

risk, and therefore a part of the credit extended to the population is aimed at replacing them, there is a premise that

much of the loans were accessed by the resident population that has changed its domicile in București or in Ilfov

County. As for Iași County, with the capital city of Iași, starting in 2012 on the second place in the preference of

settling of domicile, we appreciate the influence of a large number of citizens of the Republic of Moldova, who

obtained the Romanian citizenship and not necessarily buy dwellings, all the more with credit from the bank. In fact,

Iași County is only in the group no. 3 after București on the one hand and, Cluj and Timiș on the other in cluster

analysis regarding loans for housing. Constanța County has a peculiarity, too. It is not found in the top of the "cities -

magnet" with Bucuresti, Cluj, Timișoara, and Brasov counties, but the clustering analysis of the housing loans of the

population places it alongside Brasov and Iași counties in group no. 3. We appreciate that these credits were accessed

especially for dwellings of the category "holiday house" given its geographical position and the current trend of the real

estate developers to build seaside residential complexes.

All those means that the second hypothesis is confirmed: There is a correlation between the distribution of the

population housing loans and the counties with capitals that are the most attractive cities of Romania considering the

living conditions, named ”Cities-Magnet”, respectively București-Ilfov, Timișoara, Cluj and Brașov, but for Iași and

Constanța there are other peculiar reasons for housing loans.

Very important reasons that support the decision for housing loans by minimizing the risks associated with

these credits return are the economic conditions in these counties. Considering the recent prognosis of the Romania's

National Commission for Prognosis regarding the main economic and social indicators for the period up to the year

2020, we can note, comparing with the national level, that București-Ilfov has the best performance for all indicators. In

the same time, the other counties clustered with higher level of housing loans, respectively: Timiș, Cluj, and after

Constanța, Brașov and Iași have significant values for main indicators: Unemployment rate at the end of the year (%),

Monthly net average earning (lei/employee), or Gross domestic product value, by counties and regions (Lei, Million),

current prices, too. (See Table no. 6)

Table no. 6 – Prognosis of the main economic and social indicators in territorial profile up to 2020

Main Indicators Year Total Romania's

economy

București-

Ilfov Timiș Cluj Brasov Iași Constanța

Monthly net

average earning -

Lei/employee

2015 1859 2645 2060 2025 1783 1798 1735

2016 2047 2880 2259 2241 1977 1982 1894

2017 2274 3139 2501 2504 2194 2246 2108

2018 2476 3425 2742 2754 2399 2440 2281

2019 2674 3706 2961 2980 2585 2663 2464

2020 2864 3969 3181 3198 2763 2852 2639

Unemployment rate

at the end of the

year - %

2015 5 1,8 1,3 2,3 3,8 4,5 3,6

2016 4,8 1,6 1,3 2,3 3,7 4,5 3,5

2017 4,3 1,5 1,3 2,2 3,5 4,2 3,4

2018 4 1,4 1,2 2,1 3,4 4,1 3,2

2019 3,7 1,4 1,2 2,1 3,4 4 3,1

2020 3,4 1,3 1,2 2,1 3,3 3,8 3,0

Gross domestic

product value, by

counties and

regions - Lei

million, current

prices

2015 711103 194554 31579 32252 23747 21906 34957

2016 761474 209505 33928 34911 25440 23607 37641

2017 816544 224348 36294 37322 27284 25437 40221

2018 879462 241078 39045 40134 29416 27491 43269

2019 946740 258933 41963 43158 31705 29719 46483

2020 1014013 276512 44861 46221 34019 32000 49656

Source: Romania's NCP http://www.cnp.ro/user/repository/prognoze/prognoza_profil_teritorial_mai_2017.pdf

It should note that București and Ilfov County are well ahead of national indicators, while Cluj and Timiș are

above average. Iași, Brasov, and Constanța oscillate around national average values. In perspective of the year 2020,

those counties remain with better performance than total Romania's economy; practically continue to be attractive from

the point of view of working conditions, allowing at least theoretical, and reimbursement of credits.

CONCLUSIONS

At territorial level, the analysis of households' credit for housing acquisitions revealed major differences

between București (including Ilfov County) and the other counties, in time that another class of counties seems to

depart from the rest of the counties: Cluj, Timiș, Iași, Constanța, and Brașov. The explanation of the dominant and

favorable position of the București-Ilfov region, followed by other 5 counties identified, consists in their higher

economic potential in the income of the population, but also in their attractiveness for labour force, given the recent

World Bank report "Cities-Magnet, Migration and commuting in Romania" [10]. The report identifies a segment of the

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Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Special Issue, volume I/2017

„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

young population, people with 35 years average of age, with some educational background and wages higher than

average in the national economy, born in other region or city, but having good jobs after graduation in these cities

named ”City-Magnet”, but have not own houses.

They seem to be an important segment of the population eligible for real estate loans, preferring the rate of

credit to a comparable rent. Therefore, by presenting some of the current specific aspects of household's housing

loaning in Romania at the regional level, as for Constanța and Iași counties, this paper is a contribution to the drawing

up of some future research directions, in order to deepen some of the above-mentioned aspects.

The empirical results of this research define profiles of NUTS3 regions and highlighting factors that can

influence the regional development. The study also highlights the fact that the persistence of significant spatial

disparities in Romania, even in financial behavior of the households for housing loans, but even if is based on the

economic potential, in Romania there is a strong culture of residential property among the population.

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