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

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

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

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

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)

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.)

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

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%

3. Data Average month rent in the arrondissements of Wallonia

3. Data Average month rent in the arrondissements of Wallonia

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%

3. Data Arrondissement Nivelles

3. Data Arrondissement Charleroi

3. Data Sample distribution Charleroi Nivelles

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

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

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

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

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 1946-1970 0.055 Dummy constructed 1991-2000 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

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 1946-1970 0.055 Dummy constructed 1991-2000 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

4. Model Predicted rent Observed rent

4. Model Predicted rent Observed rent

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%

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?

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)?

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.)

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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

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