1# RECORD 78349a30196c9ee010a5b6087b25ddd2eb8a9b3d2# TITLE: Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy)3# YEAR: 2021.0 VENUE: Buildings DOI: 10.3390/buildings110502134# AUTHORS: P. Morano; F. Tajani; F. Di Liddo; Michele Darò5# PAGES: 23 CANDIDATE TABLE PAGES (1-based): [15, 16]6# ABSTRACT: Among environmental factors, noise represents one of the most relevant determinants on human health and on the urban quality level and, consequently, on real estate values. Thus, the noise pollution issue plays a significant role in public urban policies aimed at increasing the acoustic comfort level and creating more sustainable and comfortable cities. The real estate market is highly sensitive to noise factor and the residential prices can be strongly influenced by a high acoustic pollution rate. The present research aims to analyze the functional relationships between noise pollution and selling prices in four municipal areas of the city of Bari (Southern Italy). For each area, a study sample constituted by two hundred residential properties sold in 2017–2019 was detected for the identification of the main influential factors on prices and the investigation of the contribution of noise on them. The implementation of an econometric technique was used to obtain four different models (one for each municipal area of the city of Bari) able to explain the specific impact of noise pollution level on selling prices. From the comparison of the results obtained for each area, the outputs confirm the expected phenomena in terms of a decrease of noise component influence on residential prices from the central area to the peripheral. For the suburban area of the city of Bari, the model obtained does not include the noise pollution factor, showing a lower (scarce) importance of the environmental factor among the buyer and seller bargaining phases.789===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) =====1011 12 13 14 15 16Buildings 2021, 11, 213. https://doi.org/10.3390/buildings11050213 17www.mdpi.com/journal/buildings 18Article 19Economic Evaluation of the Indoor Environmental Quality 20of Buildings: The Noise Pollution Effects on Housing Prices 21in the City of Bari (Italy) 22Pierluigi Morano 1, Francesco Tajani 2, Felicia Di Liddo 1,* and Michele Darò 3 231 Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), 24Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy; pierluigi.morano@poliba.it 252 Department of Architecture and Design, Sapienza University of Rome, Via Flaminia 359, 00196 Rome, Italy; 26francesco.tajani@uniroma1.it 273 Mi.Da—Sound Design, Via Porro 9, 10064 Pinerolo, Italy; michele.daro@awn.it 28* Correspondence: felicia.diliddo@poliba.it 29Abstract: Among environmental factors, noise represents one of the most relevant determinants on 30human health and on the urban quality level and, consequently, on real estate values. Thus, the 31noise pollution issue plays a significant role in public urban policies aimed at increasing the acoustic 32comfort level and creating more sustainable and comfortable cities. The real estate market is highly 33sensitive to noise factor and the residential prices can be strongly influenced by a high acoustic 34pollution rate. The present research aims to analyze the functional relationships between noise 35pollution and selling prices in four municipal areas of the city of Bari (Southern Italy). For each area, 36a study sample constituted by two hundred residential properties sold in 2017–2019 was detected 37for the identification of the main influential factors on prices and the investigation of the 38contribution of noise on them. The implementation of an econometric technique was used to obtain 39four different models (one for each municipal area of the city of Bari) able to explain the specific 40impact of noise pollution level on selling prices. From the comparison of the results obtained for 41each area, the outputs confirm the expected phenomena in terms of a decrease of noise component 42influence on residential prices from the central area to the peripheral. For the suburban area of the 43city of Bari, the model obtained does not include the noise pollution factor, showing a lower (scarce) 44importance of the environmental factor among the buyer and seller bargaining phases. 45Keywords: noise pollution; environmental factors; property prices; genetic algorithm; 46residential market 47 481. Introduction 49Noise is one of the most significant environmental risks to health. It continues to be a 50growing question of concern discussed by policy decision-makers. Road traffic, public 51transportation systems, workplaces, industrial machinery, loud music and electronic 52equipment have a relevant impact on people’s well-being. The correlations between some 53pathologies and the continuous exposure to the physical agent of noise are known; in the 54context of health protection in the workplace, for example, there have been specific laws 55and procedures that regulate the exposu1re of workers to noise, identifying limits of level 56and exposure time, and in some cases, suitable personal protective equipment to protect 57hearing. In the context of traffic noise, the Environmental Noise Directive of the European 58Union [1] is focused on the noise generated by this source and defines the assessment 59threshold; currently, at least 100 million EU people are affected by traffic noise. 60Especially in urban areas, there are also additional sources of noise that weigh on the 61population, such as anthropic noise in crowded places and the so-called “nightlife” noise. This 62Citation: Morano, P.; Tajani, F.; 63Di Liddo, F.; Darò, M. Economic 64Evaluation of the Indoor 65Environmental Quality of Buildings: 66The Noise Pollution Effects on 67Housing Prices in the City of Bari 68(Italy). Buildings 2021, 11, 213. 69https://doi.org/10.3390/ 70buildings11050213 71Academic Editors: Alessandro 72Cannavale, Francesco Martellotta 73and Francesco Fiorito 74Received: 5 May 2021 75Accepted: 16 May 2021 76Published: 19 May 2021 77Publisher’s 78Note: 79MDPI 80stays 81neutral with regard to jurisdictional 82claims 83in published maps and 84institutional affiliations. 85 86Copyright: © 2021 by the authors. 87Licensee MDPI, Basel, Switzerland. 88This article is an open access article 89distributed under the terms and 90conditions of the Creative Commons 91Attribution 92(CC 93BY) 94license 95(http://creativecommons.org/licenses96/by/4.0/). 979899===== PAGE 15 (score 15, numeric-paren 0, table-words 4, var-words 3) =====100101Buildings 2021, 11, 213 10215 of 23 103 104The implementation of the EPR-MOGA technique was carried out four times (one for 105each study sample) including the same initial conditions (maximum number of additive 106terms, range candidate exponents) in order to obtain a model for each municipal OMI area 107of the city of Bari and to compare the outputs. 108For each elaboration of EPR-MOGA, several models were generated. Table 2 shows 109the four equations selected (a model for the OMI central area, one for the semi-central 110area, one for the peripheral area and, finally, one for the suburban area). For each of them, 111the statistical accuracy of the outputs in terms of COD is indicated in Table 2. 112Table 2. Models generated by EPR-MOGA implementation for each municipal OMI areas of the 113city of Bari. 114Municipal OMI Area 115Model 116COD (%) 117Central 118− 2.1364 ∙ Dl0.5 + 2.0268 ∙ Sc ∙ Dl0.5 ∙ Ld + 3.2642 ∙ A ∙ Ds + 119+ 1.555 ∙ L0.5 + 4.4836 ∙ S0.5 − 6.9297 ∙ S0.5 ∙ L0.5 ∙ Ds0.5 ∙ Ld0.5 + 9.4094 12086.73 121Semi-central 122+ 33.7673 ∙ Sc0.5 ∙ Dl2 ∙ Ds0.5 ∙ Qn2 + 0.34205 ∙ P0.5 ∙ Sf0.5 − 123+ 28.409 ∙ P0.5 ∙ Dl2 ∙ Dv0.5 ∙ Qn2 ∙ Ld0.5 + 2.0779 ∙ L0.5 − 124+ 2.0641 ∙ L ∙ Ld0.5 + 0.56217 ∙ B0.5 ∙ A2 ∙ Sc2 ∙ T0.5 + 3.6085 ∙ S0.5 − 125+ 4.9704 ∙ S2 ∙ B ∙ Sc ∙ Dl0.5 ∙ Dt0.5 + 9.2473 12683.15 127Peripheral 128+ 2.2214 ∙ Dv0.5 ∙ Ds ∙ Qn + 0.54754 ∙ Sc0.5 ∙ Sa0.5 + 0.83222 ∙ P0.5 ∙ 129∙ Ds + 0.18301 ∙ A0.5 + 4.5316 ∙ L0.5 ∙ Sf0.5 ∙ Dt0.5 ∙ Qn ∙ Ld2 − 130+ 2.5508 ∙ L0.5 ∙ P0.5 ∙ Sf0.5 ∙ Dt + 5.2405 ∙ S0.5 − 2.0766 ∙ S ∙ Ld + 8.6718 13183.14 132Suburban 133+ 1.6114 ∙ Dl0.5 + 2.1545 ∙ Dl ∙ Dv + 1.8803 ∙ Sc ∙ Dv + 0.19514 ∙ 134∙ A0.5 + 8.3442 ∙ S0.5 − 5.2521 ∙ S + 7.5065 13581.71 136It should be noted that each model is characterized by a high statistical accuracy level 137(COD = +86.73% for the OMI central area model, +83.15% for the semi-central one, +83.14% 138for the OMI peripheral area equation and +81.71% for the model related to the suburban 139area) and includes a large number of influential factors (seven for the central area study 140sample, fourteen for the semi-central area sample, twelve for the peripheral one, five for 141the suburban area study sample). 142Furthermore, for all models the algebraic structure of the equations, i.e., the 143complexity of the terms that compose them, does not allow an immediate interpretation 144of the functional relationships among the variables. In fact, each term of the equations is 145a combination of different variables that occur several times in the same model. 146Therefore, the functional correlations of the i-th independent explanatory variable 147with the variation of the selling prices were explicated by means of a simplified approach 148that considers the other variable values equal to the average values of the starting 149database (value 1 or 0 if the factor is a dummy variable). Moreover, the approach provides 150the analysis of the changes in value of the assessed variations of selling prices in 151correspondence of each i-th variable in the admissible range of its corresponding sample 152values. 153In particular, for each municipal OMI area Tables 3, 4, 5 and 6 show the functional 154relationships typology (direct or negative) found for each explanatory variable selected by 155the four models. For each variable, the marginal influence on selling prices in terms of 156average percentage variation obtained in correspondence of the passage from a level to the 157next one is reported. From the analysis of functional correlations between influential factors 158selected by the models and market prices, the empirical evidence of the coefficient signs is 159verified for all municipal OMI areas of the city of Bari. Thus, for each explanatory variable 160the consistency of the links with the expected phenomena is verified. 161 162 163164165===== PAGE 16 (score 20, numeric-paren 0, table-words 3, var-words 12) =====166167Buildings 2021, 11, 213 16816 of 23 169 170Table 3. Total selling price average percentage variation for the variables chosen by the model for 171the OMI central area of the city of Bari. 172Variable 173Denomination 174Variable 175Acronym 176Functional 177Correlation 178Average Percentage 179Variation (%) 180Distance from the waterfront 181Dl 182negative 183−3.56 184Maintenance conditions 185Sc 186direct 18720.22 188Sound level in the street where 189the property is located 190Ld 191negative 192−3.31 193Lift 194A 195direct 19630.95 197Distance from the nearest railway 198station 199Ds 200negative 201−3.01 202Floor level 203L 204direct 2052.67 206Size 207S 208direct 20922.80 210Table 4. Total selling price average percentage variation for the variables chosen by the model for 211the OMI semi-central area of the city of Bari. 212Variable 213Denomination 214Variable 215Acronym 216Functional 217Correlation 218Average Percentage 219Variation (%) 220Maintenance conditions 221Sc 222direct 22319.49 224Distance from the waterfront 225Dl 226direct 2270.63 228Distance from the nearest railway 229station 230Ds 231direct 2322.48 233Perceived environmental quality 234level 235Qn 236direct 2370.52 238Presence of parking 239P 240direct 2414.3 242Maintenance conditions of the 243building facades adjacent to the 244property 245Sf 246direct 2479.01 248Distance from the nearest public 249green space 250Dv 251negative 252−1.14 253Sound level in the street where the 254property is located 255Ld 256negative 257−3.46 258Floor level 259L 260parabolic 2610.77 262Bathroom number 263B 264direct 2650.09 266Lift 267A 268direct 2698.85 270Road private and public vehicular 271traffic level 272T 273direct 2743.08 275Size 276S 277direct 27819.91 279Distance from the nearest highway 280Dt 281negative 282−1.77 283Table 5. Total selling price average percentage variation for the variables chosen by the model for 284the OMI peripheral area of the city of Bari. 285Variable 286Denomination 287Variables 288Acronym 289Functional 290Correlation 291Average Percentage 292Variation (%) 293Distance from the nearest public 294green space 295Dv 296direct 2973.73 298Distance from the nearest railway 299station 300Ds 301direct 3027.11 303Perceived environmental quality 304level 305Qn 306direct 3074.73 308309310===== PAGE 17 (score 14, numeric-paren 0, table-words 1, var-words 11) =====311312Buildings 2021, 11, 213 31317 of 23 314 315Maintenance conditions 316Sc 317direct 3188.69 319Maintenance conditions of the 320public spaces adjacent to the 321property 322Sa 323direct 32411.26 325Presence of the parking 326P 327direct 3283.59 329Lift 330A 331direct 33216.72 333Floor level 334L 335direct 3361.38 337Maintenance conditions of the 338building facades adjacent to the 339property 340Sf 341direct 3422.52 343Distance from the nearest highway 344Dt 345parabolic 346−2.85 347Sound level in the street where the 348property is located 349Ld 350negative 351−2.47 352Size 353S 354direct 35521.57 356Table 6. Total selling prices average percentage variation for the variables chosen by the model for 357the OMI suburban area of the city of Bari. 358Variable 359Denomination 360Variables 361Acronym 362Functional 363Correlation 364Average Percentage 365Variation (%) 366Distance from the waterfront 367Dl 368direct 3695.52 370Distance from the nearest 371public green space 372Dv 373direct 3741.2 375Maintenance conditions 376Sc 377direct 37817.74 379Lift 380A 381direct 38217.73 383Size 384S 385direct 3860.88 387It should be pointed out that for the suburban area the variable Ld was not selected 388by the model as an influencing factor on selling prices. Therefore, the following 389considerations related to the noise pollution incidence on property prices exclusively refer 390to the remaining three OMI areas (central, semi-central, peripheral) of the city of Bari. 391Thus, with reference to the sound level detected in the street where the property is 392located (Ld), the graphs in Figures 7, 8 and 9 show a negative correlation between the unit 393selling prices and the Ld label for the central, semi-central and peripheral municipal areas. 394Furthermore, in each Figure the percentage variations of the total selling prices for the 395noise pollution class rating for the three OMI areas of the city of Bari are reported. 396 397Figure 7. Functional relationship between the Ld label and the prices and percentage variations for the noise pollution 398class rating for the OMI central area of the city of Bari. 399