# RECORD 78349a30196c9ee010a5b6087b25ddd2eb8a9b3d # TITLE: Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy) # YEAR: 2021.0 VENUE: Buildings DOI: 10.3390/buildings11050213 # AUTHORS: P. Morano; F. Tajani; F. Di Liddo; Michele Darò # PAGES: 23 CANDIDATE TABLE PAGES (1-based): [15, 16] # 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. ===== PAGE 1 (score 8, numeric-paren 0, table-words 0, var-words 8) ===== Buildings 2021, 11, 213. https://doi.org/10.3390/buildings11050213 www.mdpi.com/journal/buildings Article Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy) Pierluigi Morano 1, Francesco Tajani 2, Felicia Di Liddo 1,* and Michele Darò 3 1 Department of Civil, Environmental, Land, Building Engineering and Chemistry (DICATECh), Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy; pierluigi.morano@poliba.it 2 Department of Architecture and Design, Sapienza University of Rome, Via Flaminia 359, 00196 Rome, Italy; francesco.tajani@uniroma1.it 3 Mi.Da—Sound Design, Via Porro 9, 10064 Pinerolo, Italy; michele.daro@awn.it * Correspondence: felicia.diliddo@poliba.it 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. Keywords: noise pollution; environmental factors; property prices; genetic algorithm; residential market 1. Introduction Noise is one of the most significant environmental risks to health. It continues to be a growing question of concern discussed by policy decision-makers. Road traffic, public transportation systems, workplaces, industrial machinery, loud music and electronic equipment have a relevant impact on people’s well-being. The correlations between some pathologies and the continuous exposure to the physical agent of noise are known; in the context of health protection in the workplace, for example, there have been specific laws and procedures that regulate the exposu1re of workers to noise, identifying limits of level and exposure time, and in some cases, suitable personal protective equipment to protect hearing. In the context of traffic noise, the Environmental Noise Directive of the European Union [1] is focused on the noise generated by this source and defines the assessment threshold; currently, at least 100 million EU people are affected by traffic noise. Especially in urban areas, there are also additional sources of noise that weigh on the population, such as anthropic noise in crowded places and the so-called “nightlife” noise. This Citation: Morano, P.; Tajani, F.; Di Liddo, F.; Darò, M. Economic Evaluation of the Indoor Environmental Quality of Buildings: The Noise Pollution Effects on Housing Prices in the City of Bari (Italy). Buildings 2021, 11, 213. https://doi.org/10.3390/ buildings11050213 Academic Editors: Alessandro Cannavale, Francesco Martellotta and Francesco Fiorito Received: 5 May 2021 Accepted: 16 May 2021 Published: 19 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/). ===== PAGE 15 (score 15, numeric-paren 0, table-words 4, var-words 3) ===== Buildings 2021, 11, 213 15 of 23 The implementation of the EPR-MOGA technique was carried out four times (one for each study sample) including the same initial conditions (maximum number of additive terms, range candidate exponents) in order to obtain a model for each municipal OMI area of the city of Bari and to compare the outputs. For each elaboration of EPR-MOGA, several models were generated. Table 2 shows the four equations selected (a model for the OMI central area, one for the semi-central area, one for the peripheral area and, finally, one for the suburban area). For each of them, the statistical accuracy of the outputs in terms of COD is indicated in Table 2. Table 2. Models generated by EPR-MOGA implementation for each municipal OMI areas of the city of Bari. Municipal OMI Area Model COD (%) Central − 2.1364 ∙ Dl0.5 + 2.0268 ∙ Sc ∙ Dl0.5 ∙ Ld + 3.2642 ∙ A ∙ Ds + + 1.555 ∙ L0.5 + 4.4836 ∙ S0.5 − 6.9297 ∙ S0.5 ∙ L0.5 ∙ Ds0.5 ∙ Ld0.5 + 9.4094 86.73 Semi-central + 33.7673 ∙ Sc0.5 ∙ Dl2 ∙ Ds0.5 ∙ Qn2 + 0.34205 ∙ P0.5 ∙ Sf0.5 − + 28.409 ∙ P0.5 ∙ Dl2 ∙ Dv0.5 ∙ Qn2 ∙ Ld0.5 + 2.0779 ∙ L0.5 − + 2.0641 ∙ L ∙ Ld0.5 + 0.56217 ∙ B0.5 ∙ A2 ∙ Sc2 ∙ T0.5 + 3.6085 ∙ S0.5 − + 4.9704 ∙ S2 ∙ B ∙ Sc ∙ Dl0.5 ∙ Dt0.5 + 9.2473 83.15 Peripheral + 2.2214 ∙ Dv0.5 ∙ Ds ∙ Qn + 0.54754 ∙ Sc0.5 ∙ Sa0.5 + 0.83222 ∙ P0.5 ∙ ∙ Ds + 0.18301 ∙ A0.5 + 4.5316 ∙ L0.5 ∙ Sf0.5 ∙ Dt0.5 ∙ Qn ∙ Ld2 − + 2.5508 ∙ L0.5 ∙ P0.5 ∙ Sf0.5 ∙ Dt + 5.2405 ∙ S0.5 − 2.0766 ∙ S ∙ Ld + 8.6718 83.14 Suburban + 1.6114 ∙ Dl0.5 + 2.1545 ∙ Dl ∙ Dv + 1.8803 ∙ Sc ∙ Dv + 0.19514 ∙ ∙ A0.5 + 8.3442 ∙ S0.5 − 5.2521 ∙ S + 7.5065 81.71 It should be noted that each model is characterized by a high statistical accuracy level (COD = +86.73% for the OMI central area model, +83.15% for the semi-central one, +83.14% for the OMI peripheral area equation and +81.71% for the model related to the suburban area) and includes a large number of influential factors (seven for the central area study sample, fourteen for the semi-central area sample, twelve for the peripheral one, five for the suburban area study sample). Furthermore, for all models the algebraic structure of the equations, i.e., the complexity of the terms that compose them, does not allow an immediate interpretation of the functional relationships among the variables. In fact, each term of the equations is a combination of different variables that occur several times in the same model. Therefore, the functional correlations of the i-th independent explanatory variable with the variation of the selling prices were explicated by means of a simplified approach that considers the other variable values equal to the average values of the starting database (value 1 or 0 if the factor is a dummy variable). Moreover, the approach provides the analysis of the changes in value of the assessed variations of selling prices in correspondence of each i-th variable in the admissible range of its corresponding sample values. In particular, for each municipal OMI area Tables 3, 4, 5 and 6 show the functional relationships typology (direct or negative) found for each explanatory variable selected by the four models. For each variable, the marginal influence on selling prices in terms of average percentage variation obtained in correspondence of the passage from a level to the next one is reported. From the analysis of functional correlations between influential factors selected by the models and market prices, the empirical evidence of the coefficient signs is verified for all municipal OMI areas of the city of Bari. Thus, for each explanatory variable the consistency of the links with the expected phenomena is verified. ===== PAGE 16 (score 20, numeric-paren 0, table-words 3, var-words 12) ===== Buildings 2021, 11, 213 16 of 23 Table 3. Total selling price average percentage variation for the variables chosen by the model for the OMI central area of the city of Bari. Variable Denomination Variable Acronym Functional Correlation Average Percentage Variation (%) Distance from the waterfront Dl negative −3.56 Maintenance conditions Sc direct 20.22 Sound level in the street where the property is located Ld negative −3.31 Lift A direct 30.95 Distance from the nearest railway station Ds negative −3.01 Floor level L direct 2.67 Size S direct 22.80 Table 4. Total selling price average percentage variation for the variables chosen by the model for the OMI semi-central area of the city of Bari. Variable Denomination Variable Acronym Functional Correlation Average Percentage Variation (%) Maintenance conditions Sc direct 19.49 Distance from the waterfront Dl direct 0.63 Distance from the nearest railway station Ds direct 2.48 Perceived environmental quality level Qn direct 0.52 Presence of parking P direct 4.3 Maintenance conditions of the building facades adjacent to the property Sf direct 9.01 Distance from the nearest public green space Dv negative −1.14 Sound level in the street where the property is located Ld negative −3.46 Floor level L parabolic 0.77 Bathroom number B direct 0.09 Lift A direct 8.85 Road private and public vehicular traffic level T direct 3.08 Size S direct 19.91 Distance from the nearest highway Dt negative −1.77 Table 5. Total selling price average percentage variation for the variables chosen by the model for the OMI peripheral area of the city of Bari. Variable Denomination Variables Acronym Functional Correlation Average Percentage Variation (%) Distance from the nearest public green space Dv direct 3.73 Distance from the nearest railway station Ds direct 7.11 Perceived environmental quality level Qn direct 4.73 ===== PAGE 17 (score 14, numeric-paren 0, table-words 1, var-words 11) ===== Buildings 2021, 11, 213 17 of 23 Maintenance conditions Sc direct 8.69 Maintenance conditions of the public spaces adjacent to the property Sa direct 11.26 Presence of the parking P direct 3.59 Lift A direct 16.72 Floor level L direct 1.38 Maintenance conditions of the building facades adjacent to the property Sf direct 2.52 Distance from the nearest highway Dt parabolic −2.85 Sound level in the street where the property is located Ld negative −2.47 Size S direct 21.57 Table 6. Total selling prices average percentage variation for the variables chosen by the model for the OMI suburban area of the city of Bari. Variable Denomination Variables Acronym Functional Correlation Average Percentage Variation (%) Distance from the waterfront Dl direct 5.52 Distance from the nearest public green space Dv direct 1.2 Maintenance conditions Sc direct 17.74 Lift A direct 17.73 Size S direct 0.88 It should be pointed out that for the suburban area the variable Ld was not selected by the model as an influencing factor on selling prices. Therefore, the following considerations related to the noise pollution incidence on property prices exclusively refer to the remaining three OMI areas (central, semi-central, peripheral) of the city of Bari. Thus, with reference to the sound level detected in the street where the property is located (Ld), the graphs in Figures 7, 8 and 9 show a negative correlation between the unit selling prices and the Ld label for the central, semi-central and peripheral municipal areas. Furthermore, in each Figure the percentage variations of the total selling prices for the noise pollution class rating for the three OMI areas of the city of Bari are reported. Figure 7. Functional relationship between the Ld label and the prices and percentage variations for the noise pollution class rating for the OMI central area of the city of Bari.