1. Urban and peri-urban residential rental markets: similar or different
Urban and peri-urban residential rental markets: similar or different? Marko Kryvobokov and Sébastien pradella ERES 2017, TU Delft, 29 June 2017

2. 1. Introduction 2. Literature review 3. Hypothesis 4. Data 5
1. Introduction 2. Literature review 3. Hypothesis 4. Data 5. OLS model 6. GWR model 7. Market basket value 8. Conclusion

3. 1. Introduction Economic theory of residential rent:
von Thünen: system of concentric rings, land rent decreases with distance to the centre
Alonso-Mills-Muth: bid-rent theory
Household location choice is a trade-off between housing surface and transportation cost to urban centre (where all the jobs are concentrated)
Better accessibility closer to the centre leads to higher demand there, which means higher rents and smaller dwelling surfaces

4. Economic theory of residential rent:
1. Introduction
Economic theory of residential rent:
Alonso: extensions relative to polycentric areas could be made
Muth (1967): other important determinants are the age of buildings and neighbourhoods, housing preferences, segregation
Straszheim (1973): the alternative attributes are the number of rooms, age of building, lot size, quality, etc.
Tiebout (1956): local amenities are more important than distance to the CBD and transportation costs

5. 1. Introduction
Factors encouraging polycentrism or centrifugal tendencies:
High rates of car ownership (e.g. Bertaud 2004)
Shrinking transportation costs (e.g. Glaeser and Kahn, 2003)
Development of shopping centres (e.g. Bish and Kirk, 1974)
Income and ethnic segregation, “white-flight” (e.g. Kain, 1968)
Harvey (2009): in the US, urban sprawl and the suburbanisation of jobs, improves the location opportunities
Charmes (2011): peri-urban areas as home-ownership preference for the French middle class (“clubbisation” of urban life)

6. 1. Introduction
Are residential rents in Wallonia higher in urban centres than in peri-urban areas?
Empirical study of residential rents in the region
Practical motivation of the study
25% of households are tenants in the private sector
Current situation: no regulation for new rents, annual indexation for existing rents
Tendency towards rent regulation
Pilot-projects of rent observation
Testing the regional reference grid for rents: the regression of internal attributes and location
How to incorporate location?

7. 2. Literature review
Since 1990s, the transformation of Belgian cities into urban regions is discussed in the literature (Van der Haegen et al., 1998)
Wallonia has a very high level of residential suburbanisation (e.g. Halleux et al. 2002).
While this sort of the spatial redistribution of population takes place, peri-urban dwellers continue to keep their relationships with agglomerations taking advantages of accessibility to employment, shops, education, health-care and other services (Barthe-Batsalle et al., 2002)
Evidence of a high appreciation of suburban areas in housing market in Belgium
Thomas and Vanneste (2007): “the suburbs of large cities are characterised by higher selling prices. We can mention particularly the municipalities of the Brussels periphery up to and including Namur”

8. 2. Literature review
Some evidence from the rental market Touati-Morel (2016): increased building rights in suburban Paris has led to increase in land rents Ingaramo and Sabatino (2011): some out-of-town zones show higher residential rents than their inner cities (the biggest metropolitan areas in Italy)

9. 2. Literature review
Hedonic regression literature: much less attention is paid to rents than to prices
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.)
Bala et al. (2014): hedonic model of residential rents in Brussels (>330,000 obs.)
8 delineations of Brussels
spatial models
the positive effects of the closeness to agricultural areas and the lower accessibility to the main employment centres

10. 3. Hypothesis
H1: The degree of urbanisation is a significant attribute determining residential rent H2: Residential rents in zones with different degrees of urbanisation are determined by different willingness-to-pay for housing attributes and location H3: Difference in the willingness-to-pay for location is actually increasing between zones with different degrees of urbanisation Methodologies: OLS and GWR regression

11. 4. Data Wallonia (Belgium) : One of the three regions
3.6 millions inhabitants
262 municipalities
Classification of municipalities based on their degree of urbanisation (Van Hecke et al., 2009, based on 2001) :
Agglomerations
Suburbs (min 25% commuters to the agglomeration)
Commuting zones (min 15% commuters to the agglomeration)
Areas outside

12. 4. Data
Residential complexes

13. 4. Data 2 DBs: Housing quality survey 2012-2013 (940 obs.)
Tenant survey 2016 (875 obs.)
Geographic distribution
Survey
Agglomerations
Suburbs
Commuting zones
Outside
38.7%
15.8%
18.8%
26.7%
2016
41.5%
17.5%
18.3%
22.7%

14. 4. Data

15. 4. Data Month rent, € per square meter
Agglomerations
Suburbs
Commuting zones
Outside
TOTAL
7.33
7.47
6.48
6.61
7.00 N
369
149
188
234
940
2016
8.29
8.68
9.02
8.17
8.47
363
153
160
199
875
Increase
13.1%
16.2%
39.2%
23.6%
21.0%
No difference (at the 5%) between agglomerations and suburbs in
No difference between agglomerations and other zones in 2016
Increase in the Health Index ( ) is 4.9%
Increase in the Consumption Price Index ( ) is 4.0%

16. 4. Data Descriptive statistics (average values) 533.77 660.44 619.21
Variable
Agglomerations
Suburbs
Commuting zones
Outside
Rent, €
533.77
660.44
619.21
557.44
Dummy2016
0.50
0.51
0.46
DummyMoreThan9years
0.11
0.12
0.14
Living area, m²
76.3
91.0
75.3
84.9
DummyRowSemiDHouse
0.36
0.41
0.74
0.45
DummyBath0
0.01
0.16
0.02
DummyBath2+
0.04
0.10
0.05
DummyWC2+
0.30
0.26
DummyBefore1946
0.35
0.33
0.37
Dummy1971_1990
0.19
0.22
0.15
0.18
DummyAfter1990
0.13
0.27
0.20
DummyGarage
0.28
0.44
0.38
DummyThermostat
0.53
0.48
DummyHeatingNoControl
0.21
DummyKitchen
0.92
0.96
DummySingleGlazing
DistBrussels, km 81 66 67
111
DummyDistLuxembourg<60km
0.00
DummyDistMaastrichtAachen<30km
0.03
0.09

17. 5. OLS model Internal variables only, region Variable Estimate p-value
Constant
4.689
0.000
Dummy2016
0.131
DummyMoreThan9years
-0.180
Ln Living area, m²
0.329
DummyRowSemiDHouse
-0.059
DummyBath0
-0.177
0.002
DummyBath2+
0.163
DummyWC2+
0.110
DummyBefore1946
-0.036
0.025
Dummy1971_1990
0.056
0.004
DummyAfter1990
0.138
DummyGarage
0.067
DummyThermostat
0.072
DummyHeatingNoControl
-0.035
DummyKitchen
0.058
0.020
DummySingleGlazing
-0.056 N
1,815
Adj. R2
50.5%
Max VIF
1.59
Res. Moran’s I
0.127 (p=0.000)

18. 5. OLS model Location value response surface
Observed rent / predicted rent (with only internal variables)
Interpolation : IDW, 15 neighbours

19. 5. OLS model Internal and location variables, region Variable Region
Region without
Brabant Walloon
Constant
5.252 (0.000)
5.275 (0.000)
5.121 (0.000)
Dummy2016
0.135 (0.000)
0.127 (0.000)
0.128 (0.000)
DummyMoreThan9years
(0.000)
(0.000)
(0.000)
Ln Living area, m²
0.324 (0.000)
0.323 (0.000)
0.326 (0.000)
DummyRowSemiDHouse
(0.000)
(0.000)
DummyBath0
(0.003)
(0.003)
DummyBath2+
0.131 (0.000)
0.132 (0.000)
0.130 (0.000)
DummyWC2+
0.108 (0.000)
0.104 (0.000)
DummyBefore1946
(0.013)
(0.015)
(0.010)
Dummy1971_1990
0.050 (0.007)
0.046 (0.021)
DummyAfter1990
0.120 (0.000)
0.122 (0.000)
0.116 (0.000)
DummyGarage
0.065 (0.000)
0.066 (0.000)
DummyThermostat
0.063 (0.000)
0.062 (0.000)
0.064 (0.000)
DummyHeatingNoControl
(0.022)
(0.039)
DummyKitchen
0.056 (0.021)
0.058 (0.018)
0.050 (0.050)
DummySingleGlazing
(0.003)
(0.003)
(0.006)
Ln DistBrussels, km
(0.000)
(0.000)
(0.000)
DummyDistLuxembourg<60km
0.297 (0.000)
0.298 (0.000)
0.277 (0.000)
DummyDistMaastrichtAachen<30km
0.056 (0.053)
0.057 (0.051)
0.049 (0.097)
DummySuburb
0.043 (0.011) -
DummySuburb_x_Dummy2016
0.050 (0.033)
0.066 (0.017) N
1,815
1,589
Adj. R2
53.5%
53.4%
49.3%
Max VIF
1.60
1.57
Res. Moran’s I …
0.073 (p=0.000)
0.065 (p=0.002)

20. 5. OLS model
Internal and location variables, submodels for residential complexes
Variable
Agglomerations
Suburbs
Commuting zones
Outside
Constant
5.327 (0.000)
4.875 (0.000)
5.408 (0.000)
5.153 (0.000)
Dummy2016
0.103 (0.000)
0.119 (0.000)
0.183 (0.000)
0.156 (0.000)
DummyMoreThan9years
(0.000)
(0.000)
(0.000)
(0.004)
Ln Living area, m²
0.315 (0.000)
0.404 (0.000)
0.272 (0.000)
0.304 (0.000)
DummyRowSemiDHouse
(0.022)
(0.052)
(0.048) NS
DummyBath0
(0.016)
(0.000)
DummyBath2+
0.215 (0.000)
0.108 (0.067)
DummyWC2+
0.061 (0.042)
0.112 (0.007)
0.119 (0.002)
0.136 (0.000)
DummyBefore1946
(0.006)
(0.020)
Dummy1971_1990
0.048 (0.080)
0.121 (0.007)
DummyAfter1990
0.127 (0.000)
0.096 (0.053)
0.165 (0.000)
DummyGarage
0.094 (0.000)
0.086 (0.003)
DummyThermostat
0.069 (0.002)
0.080 (0.038)
0.070 (0.037)
0.052 (0.093)
DummyHeatingNoControl
(0.005)
DummyKitchen
0.079 (0.030)
DummySingleGlazing
(0.002)
Ln DistBrussels, km
(0.000)
(0.000)
(0.001)
(0.034)
DummyDistLuxembourg<60km -
0.281 (0.000)
DummyDistMaastrichtAachen<30km
0.097 (0.038) N
732
302
348
433
Adj. R2
51.2%
58.3%
60.9%
44.0%
Max VIF
1.49
2.20
1.86
1.70
Res. Moran’s I
0.085 (p=0.003)
0.104 (p=0.029)
0.026 (p=0.550)
0.076 (p=0.002)

21. 6. GWR model
Internal variables, extractions from GWR for residential complexes
Variable
Agglomerations
Suburbs
Commuting zones
Outside
Constant
4.628
4.658
4.693
4.604
Dummy2016
0.119
0.133
0.140
DummyMoreThan9years
-0.134
-0.221
-0.198
-0.164
Ln Living area, m²
0.342
0.350
0.331
0.352
DummyRowSemiDHouse
-0.082
-0.049
-0.058
-0.051
DummyBath0
-0.014
-0.008
-0.062
-0.159
DummyBath2+
0.258
0.159
0.154
0.124
DummyWC2+
0.042
0.070
0.106
0.102
DummyBefore1946
-0.052
-0.120
-0.034
-0.041
Dummy1971_1990
0.075
0.066
0.067
0.039
DummyAfter1990
0.094
0.122
0.112
DummyGarage
0.073
0.082
0.074
0.051
DummyThermostat
0.048
0.046
0.060
0.043
DummyHeatingNoControl
-0.017
-0.031
-0.044
-0.059
DummyKitchen
0.062
0.078
0.064
DummySingleGlazing
-0.039
-0.070
-0.069
-0.075 N
732
302
348
433
Inverse distance squared, Euclidian distance, row-standardised, adoptive Gaussian kernel type
R²adj = 57.0%, Moran’s I = (p=0.002)

22. 7. Market basket value Typical dwelling
Variable
Row house
Detached house
Apartment
Living area, m² 94
108 68
DummyBath0
DummyBath2+
DummyWC2+
DummyBefore1946 1
Dummy1971_1990
DummyAfter1990
DummyGarage
DummyThermostat
DummyHeatingNoControl
DummyKitchen
DummySingleGlazing
Estimation of market basket value for a typical dwelling

23. Standard error of a predicted value Confidence interval (5%)
7. Market basket value
Difference in market basket value of a typical dwelling OLS
Row house
Detached house
Apartment
Standard error of a predicted value
Confidence interval (5%)
2016
Suburbs / Agglomerations
+3.8%
+5.5%
0.0%
+1.7%
+3.4%
+5.1%
6.0%
11.8%
Commuting zones / Agglomerations
+1.9%
+10.5%
+2.4%
+10.9%
-0.5%
+7.8%
5.3%
10.4%
Outside / Agglomerations
+6.3%
+12.4%
-2.8%
+2.5%
-7.0%
-1.7%
GWR
Row house
Detached house
Apartment
2016
Suburbs / Agglomerations
+14.2%
+14.3%
+11.4%
+9.4%
+9.5%
Commuting zones / Agglomerations
+7.3%
+8.9%
+6.0%
+7.6%
+7.2%
+8.8%
Outside / Agglomerations
+6.7%
+9.1%
+0.9%
+3.0%
+3.6%
+5.8%
GWR, no Brabant Walloon
Row house
Detached house
Apartment
2016
Suburbs / Agglomerations
+3.2%
+3.7%
+3.8%
+3.5%
+4.2%
Commuting zones / Agglomerations
+5.3%
+7.1%
+5.5%
+7.2%
Outside / Agglomerations
+7.8%
+10.1%
+1.9%
+4.7%
+6.8%

24. 8. Conclusion
Rents per square meter are broadly similar in zones with different degrees of urbanisation
Regional OLS: location in suburbs increases rent by 4.3% and by 5% for the newer data (H1 is true for suburbs). The influence of Brabant Walloon (Brussels suburbs) is not crucial. Outside Brabant Walloon, location in suburbs increases rent by 6.6%.
Submodels OLS for residential complexes: the willingness-to-pay for housing attributes and location are significantly different (H2 is true). The rent of a typical dwelling outside agglomerations can be 5%-12% higher than in agglomerations according to the newer data.
GWR provides much stronger difference: the rent of a typical dwelling outside agglomerations is always higher than in agglomerations. The estimated increase is 9%-14% in suburbs, 6%-9% in commuter zones and 1%-9% outside residential complexes. Excluding Brabant Walloon, the increase is weaker, but still significant: 3%-4% in suburbs, 4%-7% in commuter zones and 2%-10% outside residential complexes. Stronger increase for the newer data is confirmed (H3 is true).
Model fit is often weak, but the positive impact of locations outside agglomerations is significant. The degree of urbanisation can be incorporated into the regional reference grid for rents.

25. Thank you for attention!

26. Location value response surface (DB 2012-2013)