# RECORD 03bfeba917085bc65a0ab641e5a03916b687b20f
# TITLE: Economic feasibility of wood-based structures—Improving urban carbon neutrality strategies
# YEAR: 2021.0  VENUE: nan  DOI: 10.1088/2634-4505/abfe05
# AUTHORS: I. Talvitie; J. Vimpari; S. Junnila
# PAGES: 10  CANDIDATE TABLE PAGES (1-based): [5, 7]
# ABSTRACT: Countries and cities alike have set carbon neutrality goals to the near future. Urban areas are experiencing an increased demand for new housing developments, and buildings are responsible for one third of all greenhouse gas emissions. The relative share of construction emissions is increasing as energy production is de-carbonizing. Wooden structures have showcased potential in substantially decreasing these emissions. However, for the construction industry to utilize environmentally friendly products, the cost efficiency needs to be determined. Prior research does not present conclusive results regarding the construction costs or value of wooden buildings. This study aims to determine the economic feasibility of wood-based structures in housing. We approach this by estimating the effect of wood on Finnish dwelling prices through hedonic regression analyses. Dwelling prices are analysed with transaction data from the Helsinki metropolitan area. It is provided by the Central Federation of Finnish Real Estate Agencies. The data represents occurred transactions between 1999 and 2018. The study discovers that a wooden structure has a positive price effect in the Helsinki metropolitan region suggesting an economically feasible opportunity to create low carbon urban housing. In the city of Helsinki, the effect is statistically significant (+8.85%) whereas no significance is found in either Espoo or Vantaa. The results suggest that in Helsinki, a direct financial benefit exists for both the city and private developers. For other cities, the study encourages to increase the consumption of wood due to its environmental properties. The study implies that wooden construction is an economically feasible solution in mitigating climate change. As this may provoke an increased demand for wood in construction, further research should be conducted to analyse its effects on the economy and the environment.


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LETTER • OPEN ACCESS
Economic feasibility of wood-based
structures—Improving urban carbon neutrality
strategies
To cite this article: Ilmari Talvitie et al 2021 Environ. Res.: Infrastruct. Sustain. 1 011002
 
View the article online for updates and enhancements.
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This content was downloaded from IP address 132.203.214.169 on 20/10/2026 at 20:13


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Environ. Res.: Infrastruct. Sustain. 1 (2021) 011002
Table 1. Summary statistics of regression variables.
Summary statistics
HMA all observations
HMA wooden observations
Helsinki all
Helsinki wood
Espoo all
Espoo wood
Vantaa all
Vantaa wood
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Mean
Std.dev
Housing characteristics
Price (€/m2)
3,988.40
1,419.87
3,718.56
932.11
4,271.92
1,604.94
3,730.37
989.22
3,800.47
1,139.35
3,442.91
740.99
3,489.81
990.37
3,844.41
954.48
Size (m2)
64.16
22.26
54.89
16.74
67.35
23.93
58.00
20.13
63.33
19.50
57.94
13.79
56.85
19.08
51.59
15.20
Built year
2,005.20
7.42
2,006.92
9.28
2,005.00
7.34
2,006.08
7.82
2,004.68
6.76
2,004.39
8.52
2,006.44
8.27
2,008.63
10.08
Rooms
1
0.09
0.28
0.14
0.35
0.07
0.26
0.18
0.38
0.07
0.25
0.08
0.28
0.15
0.36
0.15
0.35
2
0.46
0.50
0.46
0.50
0.45
0.50
0.43
0.50
0.47
0.50
0.44
0.50
0.47
0.50
0.49
0.50
3
0.31
0.46
0.30
0.46
0.31
0.46
0.28
0.45
0.34
0.47
0.39
0.49
0.28
0.45
0.27
0.45
Condition
Excellent
0.01
0.09
0.04
0.18
0.01
0.10
—
—
0.01
0.08
0.01
0.12
0.01
0.11
0.07
0.25
Good
0.80
0.40
0.67
0.47
0.79
0.41
0.68
0.47
0.85
0.35
0.81
0.40
0.77
0.42
0.59
0.49
New
0.07
0.26
0.21
0.40
0.07
0.25
0.25
0.43
0.03
0.18
0.12
0.32
0.13
0.33
0.22
0.42
Decent
0.07
0.25
0.08
0.26
0.08
0.27
0.06
0.23
0.05
0.21
0.04
0.20
0.05
0.22
0.10
0.30
Poor
0.00
0.03
—
—
0.00
0.03
—
—
0.00
0.02
—
—
0.00
0.04
—
—
Other
0.05
0.22
0.02
0.13
0.05
0.22
0.01
0.11
0.06
0.24
0.02
0.14
0.03
0.18
0.02
0.14
New development (dummy)
0.42
0.49
0.33
0.47
0.43
0.49
0.33
0.47
0.39
0.49
0.15
0.36
0.44
0.50
0.42
0.49
Elevator (dummy)
0.76
0.43
0.13
0.34
0.78
0.41
0.32
0.47
0.75
0.43
0.14
0.35
0.72
0.45
0.02
0.13
Lot ownership
Owned
0.76
0.43
0.64
0.48
0.62
0.49
0.24
0.43
0.95
0.22
0.65
0.48
0.87
0.34
0.87
0.34
Rented
0.23
0.42
0.35
0.48
0.37
0.48
0.73
0.45
0.03
0.18
0.34
0.47
0.13
0.34
0.13
0.33
Other
0.01
0.10
0.01
0.12
0.01
0.10
0.03
0.17
0.01
0.12
0.01
0.12
0.01
0.07
0.01
0.08
Distance to CBD (km)
9.71
4.62
13.74
4.05
6.83
3.60
9.17
2.13
12.00
3.07
15.07
2.81
14.24
3.14
15.78
3.19
Sold during or after 2011
0.56
0.50
0.76
0.43
0.54
0.50
0.62
0.49
0.53
0.50
0.71
0.46
0.64
0.48
0.87
0.33
Neighbourhood characteristics
Population
517.41
336.68
315.65
222.81
620.57
373.45
507.34
264.40
422.73
262.96
287.83
118.05
370.52
203.51
216.34
152.56
Mean income per capita (€/year)
25,505.54
8,625.90
26,929.65
7,074.36
26,206.23
10,325.49
23,331.94
4,237.34
25,811.18
7,087.54
29,949.17
7,251.78
23,263.13
3,878.38
27,590.66
7,450.19
Number of buildings
12.85
8.46
23.84
12.56
12.48
8.01
20.17
10.18
13.24
8.41
26.77
15.10
13.33
9.56
24.59
12.02
Mean dwelling size (m2)
64.37
12.43
74.31
16.53
64.31
12.10
67.86
11.75
66.04
14.24
74.18
16.96
62.34
10.26
78.17
17.55
Share of university education
0.25
0.14
0.38
0.15
0.25
0.14
0.36
0.12
0.25
0.15
0.46
0.18
0.25
0.11
0.34
0.13
Share of families with children
0.17
0.10
0.17
0.16
0.17
0.09
0.14
0.10
0.17
0.11
0.16
0.17
0.16
0.12
0.20
0.17
Share of home-owners
0.11
0.18
0.20
0.30
0.11
0.17
0.09
0.14
0.10
0.17
0.23
0.32
0.13
0.20
0.25
0.34
Umemployment rate
0.40
0.29
0.36
0.35
0.40
0.28
0.30
0.25
0.43
0.30
0.41
0.36
0.36
0.30
0.38
0.39
Pensioner share
0.34
0.15
0.27
0.19
0.35
0.15
0.30
0.17
0.38
0.15
0.32
0.23
0.26
0.12
0.23
0.17
4


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Environ. Res.: Infrastruct. Sustain. 1 (2021) 011002
Figure 1. Occurred apartment building dwelling transactions in the HMA.
2.3. Wooden structures in the data
A wooden dwelling in this study is defined as a flat located in a building which is fully constructed of wood or
contains a wooden frame, e.g. buildings with a wooden frame but a brick facade are considered as a wooden
building in this study. Unfortunately, in most cases the data descriptions only detail the main building material
which can lead to incorrect material categorisations for dwellings. The data was processed carefully to avoid
this. The dwelling transaction data is presented in the Finnish language, thus, the following descriptions are
translated into English by the authors. These material descriptions convey the definitions of wooden apartment
building dwellings presented in the data;
(a) ‘Wood’ (obs. count 404)
(b) ‘Wood’ (obs. count 208)
(c) ‘CLT-frame with wooden façade’ (obs. count 11).
Wood’s share of buildings is only 2.23%. The city of Helsinki contains 177 of these observations, the city of
Espoo 145, and the city of Vantaa 301. Figure 1 presents a map that conveys the occurred transactions of apart-
ment building dwellings, which have been constructed between 1990 and 2020 and sold between 1999–2018.
The green diamonds present wood and the white circles non-wooden observations. It is good to note that
the observations are located in multi-storey developments, therefore, each mark in the figure can represent
multiple observations.
3. Results
Table 2 presents regression results based on the model and methods showcased in section 2.1. The table details
the effect of wood, the explanatory power, sample size, and variable-groups used in each of the regression
analyses. The brackets following the variable group ‘postal codes’ indicates the amount of clusters used in the
regression analyses. Each regression uses the same base model, however, the resulting regression models differ
from each other due to the stepwise-method. The statistically significant variables are presented in table 3.
In table 2, the results for the HMA present that a wooden structure has a positive effect (ß-value = 0.017)
on dwelling prices in the entire region. However, based on a 95% confidence interval the effects is not statisti-
cally significant (p = 0.498). The explanatory power, adjusted R-squared, value is nearly 91%. Therefore, the
study does not consider adding additional variables. The regression analysis does not indicate any problems of
multicollinearity (VIF < 10).
To study the HMA in more detail, additional regression analyses were conducted for each city separately.
The reason behind the additional analyses is due to each city having their own urban developmentand housing
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Environ. Res.: Infrastruct. Sustain. 1 (2021) 011002
Table 2. Regression estimates for transaction prices. ∗p-value < 0.05, ∗∗p-value < 0.01, ∗∗∗p-value < 0.001.
(a)–(c) are presented in table 1. [Brackets] convey the amount of postal codes.
(1) HMA
(2) Helsinki
(3) Espoo
(4) Vantaa
Effect of wood
0.017
0.085∗∗∗
0.029
−0.011
Std. err.
[0.025]
[0.020]
[0.037]
[0.022]
R-squared
0.907
0.91
0.891
0.876
Adj R-squared
0.906
0.909
0.89
0.875
Sample size
27 954
14 867
7422
5665
Wooden observations
623
177
145
301
% of wooden obs.
2.23 %
1.19 %
1.95 %
5.31 %
Postal codes
Yes [137]
Yes [79]
Yes [33]
Yes [25]
(a) Housing characteristics
Yes
Yes
Yes
Yes
(b) Neighbourhood characteristics
Yes
Yes
Yes
Yes
(c) Distance to CBD
Yes
Yes
Yes
No
Table 3. Statistically significant variables.
(1) HMA
(2) Helsinki
(3) Espoo
(4) Vantaa
Housing characteristics
Size (log)
Age (log)
Rooms
1
2
3
Condition
Excellent
Good (control variable)
—
—
—
—
New
Decent
Poor
Other
New development (dummy)
Elevator (dummy)
Lot ownership
Owned
Rented (control variable)
—
—
—
—
Other
Distance to CBD (log)
Sold during or after 2011
Neighbourhood characteristics
Population (log)
Mean income per capita (log)
Number of buildings (log)
Mean dwelling size (log)
Share of university education
Share of families with children
Share of home-owners
Unemployment rate
Pensioner share
programmes. Therefore, individual analyses provide each city with detailed results in regard to future policy
making.
Table 2 presents that a wooden structure has a statistically significant positive effect (p-value = 0.000; ß-
value = 0.085) on dwelling prices in the capital city of Helsinki. The positive effect translates to a 8.87% price
premium for wooden dwellings. For example, considering a non-wooden apartment building which has a
market value of 200 000 €, the equivalent wooden dwelling will have a nearly 18 000 € higher price. Further-
more, the regression analysis’ adjusted R-squared value is nearly 91% and it is clustered by 79 postcodes. The
regression analysis does not indicate any problems of multicollinearity (VIF < 10).
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Environ. Res.: Infrastruct. Sustain. 1 (2021) 011002
Table 4. Alternative regression approach—gradually adding variables.
(1) Helsinki
(2) Helsinki
(3) Helsinki
(4) Helsinki
(5) Helsinki
Wood
−0, 259∗∗
−0, 167∗
0.043
0, 041∗
0, 085∗∗∗
Std. err.
[0, 079]
[0, 07]
[0, 024]
[0, 020]
[0, 020]
R-squared
0.003
0.574
0.85
0.894
0.91
Adj R-squared
0.003
0.574
0.85
0.894
0.909
N
14 888
14 888
14 888
14 867
14 867
(a) Age & size
Yes
Yes
Yes
Yes
(b) Housing characteristics
Yes
Yes
Yes
(c) CBD Euclidean distance
Yes
Yes
Yes
(d) Neighbourhood characteristics
Yes
Yes
(e) Postal codes
Yes [79]
In Espoo a wooden structure similarly has a positive effect on dwelling prices. However, the effect is not
statistically significant based on a 95% confidence interval (p-value = 0.443). The adjusted R-squared value
is 89% and the model is clustered by 33 postcodes. The regression analysis does not indicate any problems of
multicollinearity (VIF < 10).
The effect of a wooden structure, on the other hand, is negative in the city of Vantaa. However, the effect is
not statistically significant based on a 95% confidence interval (p-value = 0.613). For Vantaa, the explanatory
power of the model is 87.5%, and it is clustered by 25 postcodes. However, the variable ‘distance to centre’ was
excluded from the analysis for Vantaa since it induced high problems of multicollinearity. The study excludes
the variable from the analysis since it decreases the VIF levels considerably and does not alter the regression
results significantly.
In addition to the full regression models, this study presents an alternative approach. Table 4 presents the
alternative manner to analyse the effect of wood on dwelling prices in Helsinki. The figure details an approach
that gradually adds explanatory variables into the regression model. A similar approach is presented in Fuerst
et al (2016). The first model (1) uses the coefficient wood as the only explanatory variable. The second model
adds age and size as additional variables since they have the most effect on the explanatory power of the model.
This is followed by the remaining housing and neighbourhood characteristics. The final model (5) is the same
as presented in table 2. Interestingly, table 4 conveys that when wood is used as the only explanatory variable it
has a statistically significant negative effect. However, by adding more hedonic variables into the equation the
effect of wood at first neutralizes and eventually culminates into a statistically significant positive effect. Each
regression analysis is clustered by 79 postal codes.
4. Discussion and conclusion
The share of global population living in urban areas is increasing rapidly, which stimulates an increased
demand for new urban housing and subsequent GHG emissions. At the same time, cities across the globe have
set carbon neutrality as near future goals. Since carbon neutrality goals are approaching fast, and wood has the
potential to decrease emissions, this study was set to evaluate the economic feasibility of wood construction in
urban settings.
The study found a positive price impact for wooden apartments in the Helsinki metropolitan region. The
positive price premium of 8.87% for wooden dwellings presents a direct financial benefit for the city and private
developers in the capital city of Helsinki. Based on the results, this study suggests the city of Helsinki should
increase the consumption of wood in construction. In addition to the financial benefit, research has showcased
the capabilities of wooden structures in mitigating climate change (Amiri et al 2020 and Buchanan and Levine
1999). These environmental benefits would directly assist in achieving the carbon neutrality goals set by the
city.
For Espoo and Vantaa respectively, the results present that a wooden structure does not affect dwelling
prices significantly in respect to non-wooden materials. Even without a direct financial benefit the study argues
for the two cities to consider wooden construction as a tool to offset emissions. The European Green Deal
(2019) states that to reach a climate neutral future it requires actions such as investing in environmentally-
friendly technologies. Based on prior research (Amiri et al 2020, Buchanan and Levine 1999 and ˇsvajlenka
et al 2017) this study considers wooden construction to be such a technology that can reduce urban emissions.
Therefore, the study proposes that Espoo and Vantaa consider wooden construction materials to be lifted on a
higher pedestal when compared to non-wooden materials, e.g. concrete, due to the material’s environmental
capabilities. Especially with the cities attempting to achieve carbon neutrality by 2030.
7