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Fig. 1. Analytical framework of this study employed to define the dimensionality of consumer housing value (mod. from Property Value Formation model of Kauko 2004, 2006b) (characteristics excluded from the empirical assessments of this study are illustrated with light grey).

Table 1. Categories from the analytical framework (bolded) and the survey variables on Statement 14 employed in the empirical analysis on consumer housing value dimensions in Denmark, Finland, Norway, and Sweden.
PROPERTY VALUE Survey statements related to the analytical framework on property value formation (the variables excluded in the analysis are in parentheses)
Physical characteristics of the house “For the house or apartment in itself, indicate the importance of…
14 C2 … the amount of natural light indoors”
14 C3 … functional floorplan”
14 C6 … design and visual appeal of the building (architecture)”
(14 C4 … newly built”)
(14 C5 … recently renovated”)
“For environmental and sustainability aspects related to the building you live, indicate the importance of…
14 D1 …the building consists mainly of renewable materials (construction, interior, exterior)”
14 D2 …the building has a low carbon footprint in construction”
14 D3 …the building has a low carbon footprint in use”
14 D5 …recyclability at end-of-lifetime of building”
(14 D4 …the building is well insulated and use little energy for heating or air-conditioning”)
“For construction and material attributes related to the building you live, indicate the importance of…
14 E1 …solidity and durability”
14 E2 …maintenance (frequencies and costs)”
14 E6 …materials used in load-bearing construction (non-visible materials)”
14 E7 …indoor visible materials (floors, walls and ceilings)”
14 E8 …outdoor visible materials (outdoor cladding)”
(14 E3 …fire safety/vulnerability to fire”)
(14 E4 …insulation regarding sound”)
(14 E5 …healthy indoor environment (e.g. air quality)”)
Locational quality
Basic characteristics 		“For the location and neighborhood of your home, indicate the importance of…
Distances and accessibility 14 B3 …short distance to day-care “
14 B4 …short distance to schools”
14 B6 …short distance to family or friends”
Social factors 14 B8 …located in an attractive community with a good reputation“
Services 14 B2 …short distance to city center (shops and other services)”
14 B5 …short distance to leisure facilities (sports parks/training center/pool etc.)”
Physical
environment 		14 B1 …nice view from the area”
14 B7 …short distance to recreational areas: parks/forests/water”
*14 C1 …nice view from the house or apartment”
*In the original survey questionnaire this was classified as “Physical attribute of the house”, but since it relates more to the environment than to physical attributes of the house, it was re-categorized into the “Physical environment”.
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Fig. 2. Procedure of defining respondents with prejudices against the usage of wood as a structural material in building.

Table 2. Frequencies for responses for each questionnaire statement (percentages in parentheses) for Denmark, Finland, Norway, and Sweden, at levels 1–9 (where 10 stands for “Don’t know”) (n = 2191). The frequencies with highest percentages by statements are bolded.
STATEMENT 1 2 3 4 5 6 7 8 9 10
14 C2 … the amount of natural light indoors” 25 14 42 96 268 437 576 343 381 9
(1.1) (0.6) (1.9) (4.4) (12.2) (19.9) (26.3) (15.7) (17.4) (0.4)
14 C3 … functional floor plan” 19 7 28 63 175 337 600 439 502 21
(0.9) (0.3) (1.3) (2.9) (8.0) (15.4) (27.4) (20.0) (22.9) (1.0)
14 C6 … design and visual appeal of the building (architecture)” 100 63 163 207 388 455 410 211 181 13
(4.6) (2.9) (7.4) (9.4) (17.7) (20.8) (18.7) (9.6) (8.3) (0.6)
14 D1 …the building consists mainly of renewable materials (construction. interior. exterior)” 141 70 157 207 399 373 323 164 149 208
(6.4) (3.2) (7.2) (9.4) (18.2) (17.0) (14.7) (7.5) (6.8) (9.5)
14 D2 …the building has a low carbon footprint in construction” 169 71 142 177 405 334 307 177 146 263
(7.7) (3.2) (6.5) (8.1) (18.5) (15.2) (14.0) (8.1) (6.7) (12.0)
14 D3 …the building has a low carbon footprint in use” 129 64 128 156 364 349 375 212 216 198
(5.9) (2.9) (5.8) (7.1) (16.6) (15.9) (17.1) (9.7) (9.9) (9.0)
14 D5 …recyclability at end-of-lifetime of building” 209 80 160 170 336 328 315 184 162 247
(9.5) (3.7) (7.3) (7.8) (15.3) (15.0) (14.4) (8.4) (7.4) (11.3)
14 E1 …solidity and durability” 6 4 14 30 135 297 511 506 651 37
(0.3) (0.2) (0.6) (1.4) (6.2) (13.6) (23.3) (23.1) (29.7) (1.7)
14 E2 …maintenance (frequencies and costs)” 10 5 23 59 221 333 585 434 473 48
(0.5) (0.2) (1.0) (2.7) (10.1) (15.2) (26.7) (19.8) (21.6) (2.2)
14 E6 …materials used in load-bearing construction (non-visible materials)” 60 32 76 121 333 360 441 292 321 155
(2.7) (1.5) (3.5) (5.5) (15.2) (16.4) (20.1) (13.3) (14.7) (7.1)
14 E7 …indoor visible materials (floors. walls and ceilings)” 24 11 43 74 266 383 545 429 367 49
(1.1) (0.5) (2.0) (3.4) (12.1) (17.5) (24.9) (19.6) (16.8) (2.2)
14 E8 …outdoor visible materials (outdoor cladding)” 64 28 65 130 311 407 509 345 263 69
(2.9) (1.3) (3.0) (5.9) (14.2) (18.6) (23.2) (15.7) (12.0) (3.1)
14 B3 …short distance to day-care “ 748 153 152 127 232 208 227 142 162 40
(34.1) (7.0) (6.9) (5.8) (10.6) (9.5) (10.4) (6.5) (7.4) (1.8)
14 B4 …short distance to schools” 663 147 160 141 223 248 272 151 152 34
(30.3) (6.7) (7.3) (6.4) (10.2) (11.3) (12.4) (6.9) (6.9) (1.6)
14 B6 …short distance to family or friends” 154 69 201 175 435 365 384 199 196 13
(7.0) (3.1) (9.2) (8.0) (19.9) (16.7) (17.5) (9.1) (8.9) (0.6)
14 B8 …located in an attractive community with a good reputation“ 120 51 104 137 326 382 470 297 297 7
(5.5) (2.3) (4.7) (6.3) (14.9) (17.4) (21.5) (13.6) (13.6) (0.3)
14 B2 …short distance to city center (shops and other services)” 99 60 133 148 296 355 488 298 309 5
(4.5) (2.7) (6.1) (6.8) (13.5) (16.2) (22.3) (13.6) (14.1) (0.2)
14 B5 …short distance to leisure facilities (sports parks/training center/pool etc.)” 241 119 214 262 396 364 290 151 143 11
(11.0) (5.4) (9.8) (12.0) (18.1) (16.6) (13.2) (6.9) (6.5) (0.5)
14 B1 …nice view from the area” 62 42 111 159 346 377 486 289 311 8
(2.8) (1.9) (5.1) (7.3) (15.8) (17.2) (22.2) (13.2) (14.2) (0.4)
14 B7 …short distance to recreational areas: parks/forests/water” 54 25 80 96 263 355 539 331 441 7
(2.5) (1.1) (3.7) (4.4) (12.0) (16.2) (24.6) (15.1) (20.1) (0.3)
14 C1 …nice view from the house or apartment” 70 45 128 189 359 427 422 286 263 2
(3.2) (2.1) (5.8) (8.6) (16.4) (19.5) (19.3) (13.1) (12.0) (0.1)
Table 3. Original variables employed in the final exploratory factor analysis models by countries.
Property value Survey statement Denmark Finland Norway Sweden
Physical
characteristics
of the house 		… the amount of natural light indoors” X
… functional floor plan” X X X X
… design and visual appeal of the building” X X X
…the building consists mainly of renewable materials” X X X X
…the building has a low carbon footprint in construction” X X X X
…the building has a low carbon footprint in use” X X X X
…recyclability at end-of-lifetime of building” X X X X
…solidity and durability” X X X X
…maintenance” X X X X
…materials used in load-bearing construction” X X X X
…indoor visible materials” X X X X
…outdoor visible materials” X X X X
Locational
quality 		…short distance to day-care “
…short distance to schools”
…short distance to family or friends” X
…located in an attractive community with a good reputation“ X X X X
…short distance to city center” X X X X
…short distance to leisure facilities” X X X X
…nice view from the area” X X X X
…short distance to recreational areas: parks/forests/water” X
…nice view from the house or apartment” X X X X
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Fig. 3. Simplified illustration of the country-wise exploratory factor solutions for consumer housing value dimensions in Denmark, Finland, Norway and Sweden. View larger in new window/tab.

Table 4. The number of respondents living in multi-storey houses and other types of houses, and the amount and proportion of prejudiced in both categories. The χ2-test p-value shows the statistical significances in the number of prejudiced between housing types.
Country # of respondents in
multi-storey and other types of houses 		# of prejudiced in
multi-storey and other types of houses 		% of prejudiced in
multi-storey and other types of houses 		χ2-test p-value
Denmark 99/472 43/212 43%/45% 0.788
Finland 198/375 55/44 39%/13% 0.000***
Norway 99/391 49/45 49%/12% 0.000***
Sweden 201/356 73/64 36%/18% 0.000***
*suggestive evidence on statistical significance, **moderate evidence on statistical significance, ***very strong evidence on statistical significance.
Table 5. Logistic regression model on the linkages between the dimensions of CMVE and prejudices against building with wood among Danish consumers.
Predictor factor β SE β Wald’s χ2 df p eβ
Intercept –0.218 0.085 6.568 1 0.010** 0.804
Apartment layout and maintenance 0.144 0.102 1.978 1 0.160 1.155
Building materials 0.001 0.106 0.000 1 0.989 1.001
Life-cycle ecological sustainability –0.126 0.092 1.865 1 0.172 0.882
Pleasant architecture and aesthetic milieu –0.224 0.090 6.145 1 0.013** 0.800
Urban life in a good neighborhood 0.201 0.108 3.460 1 0.063* 1.223
*suggestive evidence on statistical significance, **moderate evidence on statistical significance, ***very strong evidence on statistical significance χ2 = sig. 0.022; Cox & Snell R2 = 0.023; and Nagelkerke R2 = 0.031; Predictive accuracy = 55.3%.
Table 6. Logistic regression model on the linkages between the dimensions of CMVE and prejudices against building with wood among Finnish consumers.
Predictor factor β SE β Wald’s χ2 df p eβ
Intercept –1.731 0.127 186.494 1 0.000*** 0.177
Apartment layout, maintenance and building materials 0.052 0.133 0.153 1 0.696 1.053
Life-cycle ecological sustainability –0.507 0.120 17.700 1 0.000*** 0.602
Connection with nature –0.350 0.129 7.339 1 0.007*** 0.705
Urban life in a good neighborhood with closeness to family and friends 0.589 0.160 12.958 1 0.000*** 1.801
*suggestive evidence on statistical significance, **moderate evidence on statistical significance, ***very strong evidence on statistical significance χ2 = sig. 0.000; Cox & Snell R2 = 0.063; and Nagelkerke R2 = 0.105; Predictive accuracy = 82.9%.
Table 7. Logistic regression model on the linkages between the dimensions of CMVE and prejudices against building with wood among Norwegian consumers.
Predictor factor β SE β Wald’s χ2 df p eβ
Intercept –1.468 0.118 155.614 1 0.000*** 0.230
Apartment layout, maintenance and building materials 0.007 0.131 0.003 1 0.959 1.007
Life-cycle ecological sustainability –0.085 0.124 0.466 1 0.495 0.919
Aesthetic milieu –0.229 0.114 4.018 1 0.045** 0.796
Urban life in a good neighborhood with pleasant architecture 0.259 0.152 4.018 1 0.089* 1.295
*suggestive evidence on statistical significance, **moderate evidence on statistical significance, ***very strong evidence on statistical significance χ2 = sig. 0.078; Cox & Snell R2 = 0.015; and Nagelkerke R2 = 0.023; Predictive accuracy = 80.8%.
Table 8. Logistic regression model on the linkages between the dimensions of CMVE and prejudices against building with wood among Swedish consumers.
Predictor factor β SE β Wald’s χ2 df p eβ
Intercept –1.163 0.102 129.872 1 0.000*** 0.313
Apartment layout, maintenance and building materials 0.069 0.113 0.370 1 0.543 1.071
Life-cycle ecological sustainability –0.113 0.105 1.144 1 0.285 0.893
Pleasant architecture and aesthetic milieu –0.335 0.136 5.853 1 0.002*** 0.715
Urban life in a good neighborhood 0.329 0.136 5.853 1 0.016** 1.390
*suggestive evidence on statistical significance, **moderate evidence on statistical significance, ***very strong evidence on statistical significance χ2 = sig. 0.002; Cox & Snell R2 = 0.029; and Nagelkerke R2 = 0.044 Predictive accuracy = 75.9%.
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Fig. 4. Simplified illustration of the impacts of consumer housing values on the likelihood of prejudices (red increase, green decrease, grey no evidence against building with wood). For Life-cycle ecological sustainability, only in Finland statistical evidence on the decrease of likelihood for prejudices was received. View larger in new window/tab.