# RECORD b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf
# TITLE: Sorting in an urban housing market—is there a response to demographic change?
# YEAR: 2021.0  VENUE: Review of Regional Research  DOI: 10.1007/s10037-021-00158-7
# AUTHORS: U. Neumann; Lisa Taruttis
# PAGES: 29  CANDIDATE TABLE PAGES (1-based): [12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]
# ABSTRACT: Using Dortmund as a case study we analyse whether rents and housing prices responded to local demographic change in a German city between 2007 and 2016. In a two-step analysis based on a spatial autoregressive hedonic pricing model and a discrete choice model of housing location we find that during the study period as a whole, higher local mortality induced a negative effect on apartment prices and rents. Yet, the neighbourhood effects of local ageing vary across sub-city districts. Most prominently, the study period was characterised by a strong and rising desire to purchase or rent housing in the vicinity of the city centre. Furthermore, prices for owner-occupied apartments and houses increased rapidly in the more well-off southern part of the city and particularly in a previously declining community, where a large-scale urban regeneration and environmental upgrading project has been implemented since 2011. The characteristics of households likely to move to this neighbourhood switched from low to high income.


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ORIGINAL PAPER
https://doi.org/10.1007/s10037-021-00158-7
Rev Reg Res (2022) 42:111–139
Sorting in an urban housing market—is there
a response to demographic change?
Uwe Neumann1
· Lisa Taruttis2
Accepted: 10 September 2021 / Published online: 7 October 2021
© The Author(s) 2021
Abstract Using Dortmund as a case study we analyse whether rents and housing
prices responded to local demographic change in a German city between 2007 and
2016. In a two-step analysis based on a spatial autoregressive hedonic pricing model
and a discrete choice model of housing location we ﬁnd that during the study period
as a whole, higher local mortality induced a negative effect on apartment prices and
rents. Yet, the neighbourhood effects of local ageing vary across sub-city districts.
Availability of data and material (data transparency) This study uses data from the following
sources:
1. dortmunderstatistik (2018); this data is available for download at: https://www.dortmund.de
2. RWI-GEO-GRID (2018) Socio-economic data on grid level, provided by Micromarketing-Systeme
und Consult GmbH (microm). Population by age and gender, https://doi.org/10.7807/microm:
einwGeAl:v5; Share of foreigners, https://doi.org/10.7807/microm:auslaender:V5
3. RWI-GEO-RED (2018) Real estate offers (purchase and rent) including object characteristics
gathered by the internet platform ImmobilienScout24. Apartments for rent, https://doi.org/10.7807/
immo:red:wm:suf:v1; Apartments for sale, https://doi.org/10.7807/immo:red:wk:v1; Houses for sale,
https://doi.org/10.7807/immo:red:hk:suf:v1
4. IT.NRW Information und Technik Nordrhein-Westfalen (Statistics North Rhine-Westphalia)
(2020) Bevölkerungsstand nach Altersjahren (90) – Gemeinden – Stichtag, Fortschreibung des
Bevölkerungsstandes, 31.12.2019. 12411–09iz; this data is available for download at https://www.
landesdatenbank.nrw.de
5. KOSTAT (2018) Municipal statistical data for sub-city districts, provided by KOSIS Association
KOSTAT, Bremen; this data is distributed to RWI under a contract and cannot be distributed further;
the data can be obtained from KOSTAT.
Code availability The computer code underlying the analysis was programmed using the Stata
statistical software package. The corresponding do-ﬁles are available from the authors upon request.
 Uwe Neumann
uwe.neumann@rwi-essen.de
1
RWI—Leibniz Institute for Economic Research, Hohenzollernstr. 1–3, 45128 Essen, Germany
2
House of Energy Markets and Finance, University of Duisburg-Essen, Universitätsstr. 12, Essen,
Germany
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122
U. Neumann, L. Taruttis
Table 2 Selected housing market characteristics (mean advertising price in Euro m2)
2008
2015
Apartment/ Apartment/ House/
Apartment/ Apartment/ House/
Rent
Sale
Sale
Rent
Sale
Sale
Floorspace in m2
<50
5.34
984.00
–
6.68
1017.61
–
50–70
5.01
1101.22
–
6.11
1185.54
–
70–90
5.08
1301.43
–
5.95
1443.21
–
90–120
5.70
1451.05
–
6.76
1846.54
–
>120
5.83
1546.00
–
7.78
1789.15
–
Year of construction
<1950
5.18
1039.96
1318.86
6.16
866.43
1390.57
1950–1969
5.17
1075.74
1592.96
6.11
1083.10
1805.10
1970–1989
5.34
1202.78
1579.22
5.52
1303.89
1939.70
1990–1999
6.29
1612.56
1703.11
6.09
1767.56
2066.71
Since 2000
5.09
1465.22
2046.66
7.08
2036.45
2193.43
Residential community
City Centre
5.70
1424.95
1540.71
7.31
1819.54
2232.57
Nordstadt
4.83
797.81
858.14
5.82
686.05
763.52
DO-West
4.85
1095.35
1668.67
5.68
1085.24
1778.95
DO-North
4.71
1143.13
1576.20
5.53
1235.73
1723.59
DO-East
5.26
1262.20
1869.89
6.04
1436.93
1929.62
DO-South
5.78
1571.20
2062.03
6.51
1837.01
2379.88
Hörde
4.97
1055.90
1295.42
6.93
2162.84
2202.83
All
5.15
1237.07
1700.84
6.28
1440.09
1764.95
Duration of offer (days),
(mean)
22.8
68.8
47.4
27.2
49.3
78.4
Offers in relation to existing
stock of buildings (mean)a
0.18
0.13
0.11
0.23
0.07
0.09
Observations
12,927
3027
2500
12,856
2778
1740
Authors’ calculation based on RWI-GEO-RED (2018)
aoffers in relation to existing buildings: 2009 and 2015
(and no German) passport has increased in all Dortmund communities, particularly
since 2012.
The population with a foreign nationality represents only part of the migrant
population, which is commonly deﬁned as the population comprising the ﬁrst and
subsequent generations of immigrants, who came to Germany since the Second
World War. While immigration has put ageing to a temporary halt in Germany as
a whole, striking local variation reveals that ageing continues to proceed rapidly in
districts with a low migrant inﬂow. In 2015, over 30% of the population in Dortmund
East and South were older than 60, in Nordstadt only 15% (Table 1).
As in the City Centre and Nordstadt, population growth in Hörde combined with
a rising share of foreigners (from 17.8 to 20.5%) and a slight decrease in the share
of seniors. Ageing is most advanced in the Dortmund-East and South communities,
while families with children prevail more in the North and West.
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Sorting in an urban housing market—is there a response to demographic change?
123
During the study period, the share of immigrants increased most rapidly in the
outer districts (especially in Dortmund-North, East and South). It is, however, still
considerably lower in the southern part of Dortmund (Dortmund-East and South)
than in the Centre and North.
From 2008 to 2015 rents in Dortmund (in current prices) increased from 5.15
up to 6.28 C per m2 of residential ﬂoorspace (+22%) on average, prices for owner-
occupied apartments from 1237 to 1440 C per m2 (+16.4%) and for one- and two-
family homes from 1701 to 1765 (+3.7%) (Table 2).
As expected by land use theory, rents and prices for housing are highest in the
City Centre. They are lowest in the West and North communities, where popula-
tion density is comparatively low. Prices are higher in the South, however, where
density is lowest among all districts. In line with a well-known North-South divide
characterising the Ruhr area due to its speciﬁc settlement history (Wehling 2003),
intra-urban price differentials reveal that the ageing southern part of the city is
nevertheless preferred as location of residence.
Rents and prices in Hörde, where a large-scale urban regeneration programme
was carried out (see above), were below the Dortmund average in 2008, i.e., before
the upgrading came into effect. By 2015, prices had increased remarkably in this
community. Rents per m2 and prices for 1-/2-familiy homes now were second only
to those in the City Centre and apartments on offer for sale cost more than in any
other community. Rents in Hörde on average increased from 4.97 to 6.93 C per m2
(+39.4%), prices for apartments from 1056 to 2163 (+105%) and for houses from
1295 to 2203 (+70%).
5 Neighbourhood stratiﬁcation
5.1 Hedonic price functions for Dortmund
Estimation of hedonic price functions for rented and owner-occupied accommoda-
tion reveal considerable spatial autocorrelation among the dependent variable (as
denoted by the λ statistics in Table 3) and the residuals (cf. ρ statistics). The analysis
reveals a certain “size discount” on the rental market, as rents per m2 decrease in line
with an increase in total ﬂoorspace. No such “discount” characterises the markets
for accommodation for sale.
Regarding the characteristics of neighbourhood populations, we ﬁnd that across
the complete survey period the price per m2 for apartments on offer for sale was
reduced by around 4% and rents by 1% in case ceteris paribus mortality in the
surrounding postal code district (Figs. 1 and 2) was 1% (1 death per 100 inhabitants)
higher. This may appear surprising, since prices are relatively high in the ageing
south of Dortmund. However, prices increased much more and are highest in the
“younger” City Centre and Hörde (see above). Rents and prices per m2 for apartments
were slightly lower (–0.2% and 0.6% respectively) in line with a 1% increase in the
share of foreign nationals among the residents at the level of postal codes. Whereas
local ageing corresponds to housing environments lacking the attributes of central
city quarters preferred by the younger population, apparently in Dortmund very high
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124
U. Neumann, L. Taruttis
Table 3 Estimates of hedonic price function, 2007–2016, GS2SLS
(1)
(2)
(3)
Rent
Purchase: Apartments
Purchase: 1-/2-Family Homes
Housing characteristics
Age categories
Yes
Yes
Yes
Age
–0.005***
–0.008***
–0.005***
(0.001)
(0.001)
(0.001)
Age2
0.000***
0.000***
0.000***
(0.000)
(0.000)
(0.000)
Floor space (log)
–0.106***
0.097***
0.671***
(0.008)
(0.014)
(0.013)
No of rooms
0.014***
0.007
–0.014***
(0.003)
(0.004)
(0.002)
Multi-family housing
–0.008***
–0.018***
–
Floor level
(0.001)
(0.002)
–
Multi-family housing:
further characteristics
Yes
Yes
–
Good state of house
0.071***
0.083***
0.038***
(0.004)
(0.006)
(0.006)
Bad state of house
–0.023***
–0.105***
–0.085***
(0.004)
(0.006)
(0.005)
Further characteristics
of housing standard
Yes
Yes
Yes
Duration of offer
0.000***
–0.000
0.000
(0.000)
(0.000)
(0.000)
Annual district characteristics at 5 digits postcode level
Population (log)
0.009
0.033**
0.059***
(0.007)
(0.017)
(0.014)
Mortality (deaths per
100 inhabitants)
–0.013**
–0.036***
0.006
(0.006)
(0.011)
(0.012)
Foreigners (in %)
–0.002***
–0.006***
–0.002
(0.000)
(0.001)
(0.002)
Residential communities: time effects (reference category 2007)
Year community ﬁxed
effects 2007–2010
Yes
Yes
Yes
2011_City Centre
0.061***
0.103***
0.276
(0.014)
(0.020)
(0.072)
2011_Nordstadt
0.003
–0.251***
–0.176**
(0.015)
(0.060)
(0.084)
2011_DO-West
–0.076***
–0.041***
–0.077
(0.012)
(0.015)
(0.064)
2011_DO-North
0.012
–0.058**
–0.116*
(0.011)
(0.027)
(0.063)
2011_DO-East
0.035***
0.007
–0.021
(0.010)
(0.013)
(0.066)
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Sorting in an urban housing market—is there a response to demographic change?
125
Table 3 (Continued)
(1)
(2)
(3)
Rent
Purchase: Apartments
Purchase: 1-/2-Family Homes
2011_DO-South
0.073***
0.033**
0.053
(0.013)
(0.016)
(0.065)
2011_Hörde
–0.010
–0.032
0.008
(0.016)
(0.029)
(0.073)
Year community ﬁxed
effects 2012–2014
Yes
Yes
Yes
2015_City Centre
0.221***
0.234***
0.429***
(0.011)
(0.030)
(0.069)
2015_Nordstadt
0.146***
–0.020
0.057
(0.016)
(0.060)
(0.094)
2015_DO-West
0.132***
0.003
0.038
(0.010)
(0.022)
(0.064)
2015_DO-North
0.157***
0.050*
–0.003
(0.011)
(0.028)
(0.064)
2015_DO-East
0.147***
0.120***
0.087
(0.010)
(0.018)
(0.065)
2015_DO-South
0.166***
0.133***
0.216***
(0.012)
(0.020)
(0.065)
2015_Hörde
0.154***
0.151***
0.152**
(0.017)
(0.044)
(0.069)
2016_City Centre
0.240***
0.371***
–
(0.012)
(0.026)
–
2016_Nordstadt
0.181***
0.077
–
(0.020)
(0.063)
–
2016_DO-West
0.143***
0.075***
–
(0.011)
(0.019)
–
2016_DO-North
0.170***
0.099***
–
(0.013)
(0.029)
–
2016_DO-East
0.144***
0.159***
–
(0.012)
(0.018)
–
2016_DO-South
0.156***
0.227***
–
(0.013)
(0.019)
–
2016_Hörde
0.187***
0.267***
–
(0.019)
(0.037)
–
Further char. of plot
No
No
Yes
Year ﬁxed effects
2008–2016
Yes
Yes
Yes
Constant
0.008
1.437***
4.707***
(0.137)
(0.529)
(0.462)
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126
U. Neumann, L. Taruttis
Table 3 (Continued)
(1)
(2)
(3)
Rent
Purchase: Apartments
Purchase: 1-/2-Family Homes
Observations
16,004
15,786
25,347
λ
1.271***
0.743***
0.244***
(0.062)
(0.068)
(0.036)
ρ
1.176***
1.392***
1.447***
(0.073)
(0.173)
(0.186)
Authors’ calculation based on RWI-GEO-RED (2018) (excluding observations representing the annual top
5% of offer durations per category (rent, apartments/houses for sale)), KOSTAT (2018) and dortmunder-
statistik (2018). Sample of 1-/2-family homes comprises period 2008–2015 (reference category 2008).
Accommodation on offer for rent represented by a 30% random sample. Standard errors allowing for het-
eroscedasticity in parentheses.
***/**/*= signiﬁcant at 0.01/0.05/0.1-level
λ: spatial autocorrelation among dependent variable; ρ: spatial autocorrelation in error term; dependent
variable: (logarithmised) price/rent in Euro/m2 dwelling space (current prices, monthly observations, lat-
est observation per object); age categories: pre-1905, 1905–1930, 1930–1944, 1944–1960, 1960–1970,
1970–1980, 1980–1990, 1990–2000, reference category: post-2000; further characteristics of dwellings
in multi-family housing: cellar, lift, garden, balcony, ﬁtted kitchen; further characteristics of 1-/2-family-
homes: size of plot in m2, terraced housing, building of new house is projected
shares of residents with a migrant background do not correspond to the most desired
features of urbanism.
By 2015 and 2016 rents and prices had increased in all communities. Between
2007 and 2015, apartments for sale had experienced the strongest increase in prices
per m2 in the City Centre (+23%) and in Hörde (+15%). In 2016 the upswing
accelerated even more, amounting to a 37% increase compared to 2007 in the
City Centre and to a 27% increase in Hörde. Prices increased considerably also in
Dortmund South (+22%) and East (+16%), but below 10% in the North and West
(Table 3 and Fig. 1).
It is very likely that prices in parts of Hörde had been affected by the anticipated
improvements even before new housing at the Phoenix-See entered the market. Yet,
the main increase in prices took place after 2011.
Rents increased most strongly in the City Centre also (+24%), followed by Hörde
(+18%), yet differentials across the Dortmund residential communities in the in-
crease of rents were less distinct than the increase in prices for apartments on offer
for sale. Increases in rents per m2 were lowest in the East and West communities,
but still amounted to 14% in these communities (Table 3 and Fig. 2).
Between 2008 and 2015, prices per m2 of residential ﬂoorspace increased even
more concerning houses on offer for sale in the City Centre (+43%) than for apart-
ments. Prices per m2 for houses increased signiﬁcantly also in the South (+22%)
and in Hörde (+15%).
5.2 Hedonic price functions for Dortmund residential communities
In the hedonic pricing estimations for Dortmund as a whole, neighbourhood-level
price effects with respect to demographic characteristics measured at the level of
postcode zones were found to be signiﬁcant concerning rented accommodation and
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Sorting in an urban housing market—is there a response to demographic change?
127
Fig. 1 Price change 2007–2016
per unit (Euro per m2 in cur-
rent prices) for apartments on
offer for sale in Dortmund res-
idential communities (in %);
hedonic price function (Table 3);
***/**= regression coefﬁcients,
signiﬁcant at 0.01/0.05-level,
spatial autoregressive model.
(Authors’ illustration. Map data:
GeoBasis-DE/BKG, Google.
Data sources: RWI-GEO-RED
(2018), KOSTAT (2018) and
dortmunderstatistik (2018); lines
display the boundaries of 5-digit
postal code zones)
Dortmund-West:
+7.5***
Dortmund-North:
+9.9**
Nordstadt: +7.7
City Centre: 
+37.1***
Dortmund-East: +15.9***
Dortmund-South: +22.7***
Hörde: +26.7***
Fig. 2 Price change 2007–2016
per unit (Euro per m2 in current
prices) for rented accommo-
dation in Dortmund residential
communities (in %); hedo-
nic price function (Table 3);
***/**= regression coefﬁcients,
signiﬁcant at 0.01/0.05-level,
spatial autoregressive model.
(Authors’ illustration. Map data:
GeoBasis-DE/BKG, Google.
Data sources: RWI-GEO-RED
(2018), KOSTAT (2018) and
dortmunderstatistik (2018); lines
display the boundaries of 5-digit
postal code zones)
Dortmund-West:
+14.3***
Dortmund-North:
+17.0***
Nordstadt: +18.1***
City Centre: 
+24.0***
Dortmund-East: +14.0***
Dortmund-South: +15.6***
Hörde: +18.7***
apartments and houses for sale. It is not surprising that across the complete study
period prices were higher in more densely populated districts, since apparently prices
per m2 are highest and increased most considerably in the City Centre. Further, prices
and rents were found to be reduced in line with a higher local mortality and (slightly)
by a higher share of foreigners among the local population.
As expected, hedonic price estimations for separate residential communities con-
ﬁrm that considerable spatial autocorrelation needs to be accounted for in the mea-
surement of housing prices. Regarding demographic characteristics measured at the
smaller 1km2-grid scale for each housing category, the analysis suggests that local
ageing affects prices for houses on offer for sale in a different way than prices for
apartments and rents (Table 4). In the estimations for Dortmund as a whole the coef-
ﬁcients assigned to district-level mortality were signiﬁcant and negative among rents
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128
U. Neumann, L. Taruttis
Table 4 Estimates of hedonic price function for residential districts, 2007–2016, GS2SLS
(1)
(2)
(3)
Rent
Purchase: Apartments
1-/2-Family Homes
Annual district characteristics at 1km grid level
Community 1 (City Centre)
Population (log)
Yes
Yes
Yes
Population aged >60
–0.003
0.000
0.027***
(in %)
(0.003)
(0.006)
(0.008)
Foreigners (in %)
–0.003**
–0.009***
–0.002
(0.001)
(0.003)
(0.005)
New stocka
0.185
0.209***
0.451
(0.159)
(0.061)
(0.644)
Observations
1839
2118
670
λ
0.944***
0.585***
0.506***
ρ
0.874***
0.973***
1.079***
Community 2 (Nordstadt)
Population (log)
Yes
Yes
Yes
Population aged >60
0.001
0.003
0.011
(in %)
(0.004)
(0.014)
(0.023)
Foreigners (in %)
–0.000
–0.002
–0.004
(0.002)
(0.005)
(0.008)
New stocka
–0.360
0.158
0.292
(0.254)
(0.182)
(0.204)
Observations
1149
820
404
λ
1.063***
0.674***
0.186
ρ
0.627***
0.389**
0.923***
Community 3 (Dortmund West)
Population (log)
Yes
Yes
Yes
Population aged >60
0.006***
0.000
0.003
(in %)
(0.002)
(0.002)
(0.002)
Foreigners (in %)
0.003***
–0.003
–0.002
(0.001)
(0.003)
(0.002)
New stocka
–0.631***
0.047
0.156***
(0.094)
(0.118)
(0.058)
Observations
3997
2767
5279
λ
0.390***
0.552***
0.407***
ρ
0.939***
0.976***
1.366***
Community 4 (Dortmund North)
Population (log)
Yes
Yes
Yes
Population aged >60
0.003
0.009**
0.002
(in %)
(0.002)
(0.004)
(0.003)
Foreigners (in %)
–0.001
0.001
–0.003
(0.001)
(0.002)
(0.002)
New stocka
–0.091
0.176
0.073
(0.074)
(0.167)
(0.055)
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Sorting in an urban housing market—is there a response to demographic change?
129
Table 4 (Continued)
(1)
(2)
(3)
Rent
Purchase: Apartments
1-/2-Family Homes
Observations
1935
687
3321
λ
0.869***
0.669***
0.176*
ρ
0.660***
0.311**
1.107***
Community 5 (Dortmund East)
Population (log)
Yes
Yes
Yes
Population aged >60
0.001
–0.000
0.003***
(in %)
(0.001)
(0.002)
(0.001)
Foreigners (in %)
–0.002*
–0.006**
–0.000
(0.001)
(0.003)
(0.001)
New stocka
–0.354***
0.025
0.009
(0.94)
(0.096)
(0.020)
Observations
3205
3288
6806
λ
0.973***
1.064***
0.171**
ρ
0.849***
0.526***
1.314***
Community 6 (Dortmund South)
Population (log)
Yes
Yes
Yes
Population aged >60
0.001
0.003
0.007***
(in %)
(0.001)
(0.003)
(0.002)
Foreigners (in %)
–0.000
–0.003
0.000
(0.001)
(0.003)
(0.002)
New stocka
0.060
–0.083
0.050
(0.125)
(0.076)
(0.076)
Observations
1594
2433
4957
λ
0.701***
0.575***
0.437***
ρ
0.688***
0.973***
1.433***
Community 7 (Hörde)
Population (log)
Yes
Yes
Yes
Population aged >60
–0.003
–0.012
0.018
(in %)
(0.006)
(0.013)
(0.016)
Foreigners (in %)
–0.004
–0.014
0.000
(0.006)
(0.009)
(0.008)
and apartments for sale, but insigniﬁcant for houses (see above). In the separate es-
timations for prices per m2 in one- or two-family houses in residential communities
the coefﬁcients assigned to the share of the population aged over 60 are signiﬁcant
and positive in three communities (City Centre, East and South) and insigniﬁcant in
the other communities.
Within the ageing East and South communities, neighbourhoods with a high share
of seniors obviously represent surroundings deemed attractive for one- and two-
family homes. Regarding apartments for sale, a high share of the population aged
over 60 is assigned a positive price effect in Dortmund North, while the respective
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130
U. Neumann, L. Taruttis
Table 4 (Continued)
(1)
(2)
(3)
Rent
Purchase: Apartments
1-/2-Family Homes
New offersa
–0.143
–0.377
–0.796
(0.226)
(0.278)
(0.574)
Observations
626
646
475
λ
0.760***
0.518***
0.182
ρ
0.017
0.740***
0.918***
Housing characteristics
Yes
Yes
Yes
Year ﬁxed effects
Yes
Yes
Yes
Constants
Yes
Yes
Yes
Authors’ calculation based on RWI-GEO-RED (2018) (excluding observations representing the annual
top 5% of offer durations per category (rent, apartments/houses for sale)) and RWI-GEO-GRID (2018).
Sample of 1-/2-family homes comprises period 2008–2015 (reference category 2008). Accommodation on
offer for rent is represented by a 30% random sample. Standard errors allowing for heteroscedasticity in
parentheses
***/**/*= signiﬁcant at 0.01/0.05/0.1-level
Dependent variable: (logarithmised) price/rent in Euro/m2 dwelling space (current prices, monthly obser-
vations, latest observation per object); housing characteristics cf. Table 3
aAnnual offerings in relation to the existing stock of residential buildings
coefﬁcients turn out insigniﬁcant in all other districts. Rents are affected (positively)
only in Dortmund West (Table 4).
Rents and prices for apartments were affected (slightly) negatively by higher local
shares of foreigners in the City Centre and Dortmund East, but rents were affected
(slightly) positively by a higher share of foreigners among the local neighbourhoods
of Dortmund West. Whereas local ageing and migrant agglomeration at the level
of larger districts (representing around 20,000 inhabitants on average) tend to be
regarded as disadvantageous, at the level of 1km2 grids both characteristics may
affect local housing environments in the opposite direction. A larger share of senior
citizens, for example, may be attributed to a safe neighbourhood at the very local
level. Within a suburban district, on the other hand, a comparatively higher share of
migrants may correspond to more central locations where prices are higher.
In the City Centre and in the East and West communities, housing prices appar-
ently were affected to a signiﬁcant extent by the volume of housing units entering
the market in a speciﬁc year. In the Centre, average prices for apartments were even
higher in case a larger number of apartments were on offer for sale in the respec-
tive neighbourhood. Rents were affected in the East and West communities, where
a large local volume of rental offers coincided with a price reduction.
5.3 Community-level variation in location choice probabilities
The third step of our analysis examines the likelihood of households to have chosen
accommodation in one of the seven Dortmund residential communities in each year
during the period from 2009 to 2016.
In 2009, the mean sizes of households likely to move into rented accommo-
dation were smaller than in Dortmund-West (which was the base category) in all
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Sorting in an urban housing market—is there a response to demographic change?
131
Table 5 Location choice probabilities, by Dortmund residential community (Multinomial logit estimation, selected results for 2009 and 2015/2016)
City Centre
Nordstadt
DO-West
DO-North
DO-East
DO-South
Hörde
2009
Rent
Est. household size
–1.106***
–0.458***
–
1.074***
0.023
–0.459***
–0.693***
(log)
(0.060)
(0.072)
–
(0.071)
(0.058)
(0.067)
(0.110)
Total rent/pp (log)
1.164***
1.144***
–
–0.892***
–0.070
0.398***
0.803***
(0.062)
(0.070)
–
(0.069)
(0.059)
(0.069)
(0.103)
a0h;t
–0.396***
–0.910***
–
–0.718***
–0.037
–0.663***
–1.817***
(0.036)
(0.043)
–
(0.041)
(0.032)
(0.038)
(0.060)
Observations
9524
Pseudo-R2
0.046
Apartments for sale
Est. household size
–
2.038***
0.634***
2.136***
0.621***
0.302***
0.195
(log)
–
(0.102)
(0.089)
(0.132)
(0.091)
(0.087)
(0.150)
Total price/pp (log)
–
–2.685***
–0.820***
–1.871***
–0.706***
–0.031
–0.589***
–
(0.115)
(0.102)
(0.137)
(0.103)
(0.090)
(0.176)
a0h;t
–
–0.667***
–0.002
–1.513***
–0.055
–0.501***
–1.605***
–
(0.070)
(0.055)
(0.096)
(0.056)
(0.063)
(0.096)
Observations
3656
Pseudo-R2
0.082
Houses for sale
Est. household size
0.589***
1.841***
–
0.671***
0.149
–0.116***
1.066***
(log)
(0.131)
(0.160)
–
(0.098)
(0.093)
(0.098)
(0.154)
Total price/pp (log)
–0.137
–0.967***
–
–0.911***
–0.677***
–0.195***
–0.683***
(0.137)
(0.164)
–
(0.098)
(0.086)
(0.089)
(0.172)
a0h;t
–1.767***
–3.617***
–
–0.674***
–0.007
–0.155**
–2.608***
(0.117)
(0.273)
–
(0.080)
(0.063)
(0.065)
(0.172)
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132
U. Neumann, L. Taruttis
Table 5 (Continued)
City Centre
Nordstadt
DO-West
DO-North
DO-East
DO-South
Hörde
Observations
2114
Pseudo-R2
0.057
2016
Rent
Est. household size
–1.955***
0.868***
–
1555***
–0.790***
–0.609***
–0.619***
(log)
(0.055)
(0.0965)
–
(0.072)
(0.048)
(0.053)
(0.062)
Total rent/pp (log)
3.005***
–0.212***
–
–1.550***
1.333***
1.034***
1.864***
(0.064)
(0.085)
–
(0.082)
(0.055)
(0.060)
(0.069)
a0h;t
–0.570***
–1.631***
–
–1.892***
–0.044
–0.405***
–0.983***
(0.041)
(0.060)
–
(0.067)
(0.033)
(0.0036)
(0.044)
Observations
11,143
Pseudo-R2
0.144
Apartments for sale
Est. household size
–0.417***
2.509***
0.921***
2.352***
–
–0.041
–0.206
(log)
(0.115)
(0.142)
(0.103)
(0.152)
–
(0.113)
(0.140)
Total price/pp (log)
1.080***
–4.092***
–1.370***
–2.705***
–
0.490***
2.009***
(0.133)
(0.193)
(0.124)
(0.186)
–
(0.129)
(0.173)
a0h;t
–1.114***
–2.349***
–0.252***
–1.942***
–
–0.866***
–2.249***
(0.089)
(0.145)
(0.064)
(0.125)
–
(0.079)
(0.151)
Observations
2512
Pseudo-R2
0.196
Houses for sale (2015)
Est. household size
–0.255
3.102***
–
0.274***
–0.593***
–0.668***
–0.509**
(log)
(0.186)
(0.394)
–
(0.092)
(0.100)
(0.110)
(0.226)
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Sorting in an urban housing market—is there a response to demographic change?
133
Table 5 (Continued)
City Centre
Nordstadt
DO-West
DO-North
DO-East
DO-South
Hörde
Total price/pp (log)
1.764***
–0.816***
–
–0.437***
0.767***
1.161***
1.262***
(0.181)
(0.256)
–
(0.105)
(0.103)
(0.113)
(0.219)
a0h;t
–2.600***
–6.213***
–
–0.343***
–0.054
–0.422***
–2.477***
(0.197)
(0.757)
–
(0.080)
(0.072)
(0.081)
(0.187)
Observations
1651
Pseudo-R2
0.107
Authors’ calculation based on RWI-GEO-RED (2018), excluding observations representing the annual top 5% of offer durations per category (rent, apartments/houses for
sale). Robust standard errors in parentheses
***/**/*= signiﬁcant at 0.01/0.05/0.1-level
est. household size= ﬂoorspace of dwelling in m2, divided by the average ﬂoorspace per person in 2013 in corresponding residential community (Stadt Dortmund 2017);
total rent/pp: total rent, divided by the average monthly purchasing power per household at the level of 1km grids; total price/pp: total price, divided by the average annual
purchasing power per household; a0h;t Dannual choice probability among households with mean characteristics
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134
U. Neumann, L. Taruttis
districts except for Dortmund North and East. Total rents in relation to household
income were higher in all districts than in Dortmund-West, except for North and
East (Table 5).
In general, considering housing on offer for rent or sale mean and household-
speciﬁc probabilities characterising the likelihood to choose among six out of seven
Dortmund residential communities remained relatively stable over the study period,
e.g., one- and two-person-households being likely to move to the City Centre and
low-income households prone to choose Nordstadt. A considerable change in the
choice probabilities relating to household characteristics, however, took place with
respect to Hörde. This change corresponds to a considerable upswing in prices for
apartments and houses on offer for sale, which is likely to result at least partly
from the environmental upgrading due to the Emscher Conversion and Phoenix-
See development. In 2009, households moving into purchased accommodation in
Hörde were likely to earn a comparatively low income (as denoted by negative
coefﬁcients assigned to the total price of the apartment or house in relation to local
income). In 2016, they were likely to be well-off and—in case they were moving
into apartments—to pay higher total prices even than households moving to the City
Centre (Table 5).
For households with mean characteristics (as denoted by the coefﬁcients for
a0h,t), however, it remained improbable to choose accommodation for rent or sale in
Hörde. Regarding rented accommodation and one- or two-family homes for sale, the
Dortmund West community represented mean preferences most accurately in 2009
and 2015/16. With respect to owner-occupied apartments, mean preferences shifted
from the City Centre (2009) to the Dortmund East community. Whereas total prices
in relation to local income differed only insigniﬁcantly between the Centre and East
in 2009, in 2016 total prices for apartments in the Centre had become considerably
(i.e. over one standard deviation, Table 5) higher than in the East.
While both the Centre and Hörde experienced an upswing of local housing prices,
Nordstadt was affected by a continuing downturn, speciﬁcally concerning owner-
occupied apartments. While the size of households likely to move in remained
comparatively large, total prices remained considerably below those in the other
districts and the gap between Nordstadt and the other districts in this respect has
widened. In 2009, Nordstadt prices in relation to local income were 2.69 standard
deviations below those in the reference category (which was the City Centre), in
2016 they were over 4 standard deviations below the reference category (Dortmund
East).
6 Conclusions
We analyse whether housing prices and location choice probabilities in Dortmund
over the period from 2007 to 2016 indicate a response to local demographic change
at the level of different neighbourhood scales. It is difﬁcult to isolate the effects of
local ageing from other determinants of residential sorting in empirical analysis. We
therefore apply different methods and incorporate neighbourhood-level statistics at
varying spatial scales in order to evaluate the robustness of our results.
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