1. HOUSING RENTS in Wallonia: Modelling two different worlds Marko Kryvobokov ERES 2015, Istanbul, ITU, 26 June 2015

2. 1. Introduction 2. Literature review 3. Data 4. Model 5. Discussion

3. Wallonia : French-speaking Belgian region
1. Introduction
Wallonia : French-speaking Belgian region
One third of households are tenants (private and social)
One quarter of households in private rental sector

4. 1. Introduction Current situation in the private rental sector:
New rent: no regulation
Existing rent: annual indexation (nominal rent freeze)
Problems with data quality and availability
Proposals of the regional government:
Creation of the system of rent observation
New rent: more transparent estimation based on hedonic technique
Ex.: Mietspiegel (rent mirror): a system of reference grid and coefficients to calculate residential rent in a particular location, for particular building type, age and other attributes

5. 1. Introduction Pilot project: The first wave of rent observation
Two administrative districts (arrondissements) are covered
Proposal of the first hedonic model for residential rents
The aim of the study: creation of the hedonic model
with acceptable explanatory power
simple enough to serve as a reference grid
Methodology: ordinary least squares (OLS)

6. 2. Literature review Hedonic regression literature
Much less attention is paid to rents than to prices
Frew and Jud (2003): apartment values and rents are determined by the same attributes
Gallin (2008): long-run relationships between house prices and rents
Djurdjevic et al. (2008): OLS and two-level hedonic model of Swiss apartment rent market (about 12,000 obs.)
Löchl and Axhausen (2010): OLS, spatial lag and GWR models of asking residential rents in Zurich (>8,500 obs.)
Brunauer et al. (2010): mixed regression of apartment rents per square meter in Vienna (9,000 obs.)

7. 2. Literature review Belgian studies:
Vanneste et al. (2008): cartographic analysis of residential rents in Belgium
Vastmans and Vries (2013): search of a new base for rent indexation in Flanders
Bala et al. (2014): hedonic model of residential rents in Brussels (>330,000 obs.)
8 delineations of Brussels
spatial lag, spatial Durbin model, general spatial model
two-steps: first only structural attributes, then location
the positive effects of the closeness to agricultural areas and the lower accessibility to the main employment centres

8. Margin of error (at 95% confidence)
3. Data
The first wave of rent observation
Questionnaire: >50 questions about rents, charges, housing attributes, household characteristics and environment
2,206 face-to-face interviews among private tenants
Between Sep 2014 and Feb 2015
Arrondissement
Sample size
Sampling rate
Margin of error (at 95% confidence)
Charleroi
1,375
3,7%
2,6%
Nivelles
831
3,3%
3,4%

9. 3. Data
Average month rent in the arrondissements of Wallonia

10. 3. Data
Average month rent in the arrondissements of Wallonia

11. 3. Data Arrondissement Nivelles (Brabant Walloon):
northern part – rich suburbs of Brussels, university
southern part – mainly rural
Arrondissement Charleroi:
mainly urbanized area
the city of Charleroi (204,000 inhabitants)
chronical problems of poverty, unemployment, criminality and low quality of housing
Attribute
Nivelles
Charleroi
Population
384,000
428,000
Average number of persons in household
2.5
2.2
Annual net taxable income per person, €
18,475
13,472
Percentage of foreigners
9.1%
13.2%
Percentage of social housing
3.4%
10.2%

12. 3. Data
Arrondissement Nivelles

13. 3. Data
Arrondissement Charleroi

14. 3. Data
Sample distribution
Charleroi
Nivelles

15. 4. Model OLS specification:
Log-log (LnRent, Ln numerical variables, dummies)
Attribute specific to rental dwelling: duration of occupation
Internal and external attributes
Nivelles: model with 18 internal attributes only
obs. with standardized residuals > 2.5 are deleted
adj. R2=74.1%
Charleroi: model with 24 internal attributes only
adj. R2=50.3%
Submarkets vs. overall model (Schnare and Struyk, 1976) :
14,5% decrease in weighted standard error if submarkets

16. 4. Model
Location Value Response Surface (observed rent/predicted rent): Nivelles

17. 4. Model
Location Value Response Surface (observed rent/predicted rent): Charleroi

18. 4. Model Tested location attributes:
Distance variables (Ln and dummies for distance intervals):
- to Brussels
- to the Airport Brussels South - Charleroi
- to the University (UCL)
- to the railway station Charleroi South
Dummies for zones:
- type of residential complex (agglomeration, suburb, etc.)
- aggregations of municipalities
- aggregations of neighbourhoods
Statistical sectors’ data:
- population density
- number and percentage of foreigners, unemployed, rented dwelling

19. Arrondissement Nivelles Arrondissement Charleroi
4. Model
Variable
Arrondissement Nivelles
Arrondissement Charleroi
Constant
5.319
5.474
Ln Duration of occupation
-0.022
-0.044
Ln Living surface
0.263
0.109
Ln Nb bedrooms
0.159
0.451
Dummy Detached house
0.080
0.060*
Dummy Standard apartment -
0.033
Dummy Apartment studio
-0.099
0.104
Ln Nb bathrooms
0.667
0.271*
Ln Nb WC
0.402
0.158
Dummy garage
0.085
Dummy constructed before 1919
-0.046
Dummy constructed
0.055
Dummy constructed
0.052
Dummy constructed after 2000
0.120
Dummy constructed before 1946
-0.071
Dummy constructed after 1990
0.035
Dummy >120m2 constr. before 1946
-0.234
-0.042
Ln Nb floors
0.045
Dummy Kitchen furnished
0.054
Dummy Kitchen fully equipped
0.025*
0.037
Dummy Private garden
0.038
Dummy Double or triple glazing
0.065
Dummy Elevator
0.068
Dummy Heating central common
0.100
Dummy Heating stove
-0.054
Dummy Heating gas convector
-0.051
Dummy Heating fuel oil fireplace
-0.792
-0.124
Dummy Water heater instantaneous
0.030
0.032
Ln Distance to Brussels
-0.136
Dummy Nivelle south
-0.068
Dummy Distance to University <1km
0.146*
Dummy Suburb
0.062
Dummy Distance to Station Charleroi South <3km
Ln % unemployed
-0.070 N
713
1,293
Adj. R2
76.2%
52.1%
Max VIF
3.18
2.86
Res. Moran’s I
0.008 (p=0.133)
0.031 (p=0.000)
* – significant at the 10% level

20. Arrondissement Nivelles Arrondissement Charleroi
4. Model
Variable
Arrondissement Nivelles
Arrondissement Charleroi
Constant
5.319
5.474
Ln Duration of occupation
-0.022
-0.044
Ln Living surface
0.263
0.109
Ln Nb bedrooms
0.159
0.451
Dummy Detached house
0.080
0.060*
Dummy Standard apartment -
0.033
Dummy Apartment studio
-0.099
0.104
Ln Nb bathrooms
0.667
0.271*
Ln Nb WC
0.402
0.158
Dummy garage
0.085
Dummy constructed before 1919
-0.046
Dummy constructed
0.055
Dummy constructed
0.052
Dummy constructed after 2000
0.120
Dummy constructed before 1946
-0.071
Dummy constructed after 1990
0.035
Dummy >120m2 constr. before 1946
-0.234
-0.042
Ln Nb floors
0.045
Dummy Kitchen furnished
0.054
Dummy Kitchen fully equipped
0.025*
0.037
Dummy Private garden
0.038
Dummy Double or triple glazing
0.065
Dummy Elevator
0.068
Dummy Heating central common
0.100
Dummy Heating stove
-0.054
Dummy Heating gas convector
-0.051
Dummy Heating fuel oil fireplace
-0.792
-0.124
Dummy Water heater instantaneous
0.030
0.032
Ln Distance to Brussels
-0.136
Dummy Nivelle south
-0.068
Dummy Distance to University <1km
0.146*
Dummy Suburb
0.062
Dummy Distance to Station Charleroi South <3km
Ln % unemployed
-0.070 N
713
1,293
Adj. R2
76.2%
52.1%
Max VIF
3.18
2.86
Res. Moran’s I
0.008 (p=0.133)
0.031 (p=0.000)
* – significant at the 10% level

21. 4. Model
Predicted rent
Observed rent

22. 4. Model
Predicted rent
Observed rent

23. Residuals as percentages of rent Duration of occupation, years
4. Model
Residuals
Nivelles
Charleroi
Residuals as percentages of rent
Duration of occupation, years
Written contract
Nivelles
Charleroi
<-50% 11 8
90.0%
32.0%
-50% to -26% 6
68.2%
-25% to -1% 5
97.5%
76.2%
0% to 25% 4
98.5%
78.9%
26% to 50%
100.0%
72.7%

24. 5. Discussion Charleroi : The steps of rents divisible by 50 € exist
(300 €, 350 €, 400 €, …, 750 €)
The same rent can be paid for very different housing quality
Shorter average duration of occupation, no written contract in 78% cases of extremely low negative residuals
GWR: no increase in the model’s explanatory power
How to explain:
Very high observed rents?
Very low observed rents?

25. 5. Discussion Possible explanation of high observed rents:
Three ‘slumlords’ hypotheses (Lind and Blomé, 2012):
Households do not pay themselves?
Households get some other value (illegal activities, illegal subletting)?
Households have no choice?
Additional explanations:
A barrier for tenants with unstable/insufficient income?
No access to information for new tenants?
Possible explanation of low observed rents:
Non-market relations between landlords and tenants (relatives, ethnic community support, etc.)?
In problematic and aggressive social environment, landlords want to avoid new potentially problematic tenants and therefore appreciate their existing ‘good’ tenants and freeze rents for them (not applying indexation)?

26. 5. Discussion Perspective:
Market value definition: “… parties had each acted knowledgeably, prudently, and without compulsion”
The rental market with strong informal component is difficult to formalize with a regression equation
Perspective:
To study the socioeconomic profile of tenants
To study the legal issues (written and registered contract)
To study the application of rent indexation
Qualitative research (informal relationships, etc.)

27. Thank you for your attention !

28. Arrondissement Nivelles Arrondissement Charleroi
3. Data
Short descriptive statistics (average values)
Attribute
Arrondissement Nivelles
Arrondissement Charleroi
Month rent (no charges), €
735
508
Month rent per m2, €
9.26
6.44
Duration of occupation, months 61 63
Dummy apartment
0.64
0.47
Living surface, m2 92 94
Nb bathrooms
1.06
1.00
Nb toilets
1.21
1.10
Dummy garage
0.28
0.15
Dummy constructed before 1946
0.18
0.61
Dummy constructed after 1990
0.42
0.11

29. Données