# Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis

^{*}

## Abstract

**:**

## 1. Introduction

- Provisioning. These are all the goods that derive from ecosystems and that man uses to satisfy his needs. This category includes: food, deriving from organized systems such as agriculture, breeding and waterculture, and from wild sources; water, also a support service for the development of life; timber, used as a building material as well as fuel; fibers in general, both those obtained from agricultural systems and those produced by animals; the fuels; etc.;
- Regulating, that are benefits deriving from the regulation of ecosystem processes, such as climate regulation, natural risk management and waste treatment;
- Cultural, mainly characterized by intangibility, among whom are included cultural identity and diversity, values of cultural and landscape heritage, spiritual and inspirational services, entertainment and tourism;
- Supporting, means those that support and allow the supply of all other types of services, such as the formation of the soil and the nutrient cycle.

## 2. Aim of the Paper

^{2}, the work aims to estimate the contraction of real estate values that a polluting industrial activity causes on the territory. In other words, we intend to evaluate the reduction of mercantile appreciation for residential units around a production complex that causes negative externalities.

## 3. Essential Notions on Regression Analysis

_{1}, x

_{2}, …, x

_{n}. If the model assumes that the dependent variable is a linear combination of parameters, then we talk about linear regression [15,16,17].

- y
_{i}is the dependent variable; - x
_{pi}are the independent variables or regressors; - β
_{i}are the regression coefficients; - β
_{0}is the intercept and represents the point where the straight line crosses the ordinate axis; - ε
_{i}is the error.

- Y is a vector n × 1 of n observations of the dependent variable;
- X is a matrix n × (p + 1) of n observations of p + 1 regressors;
- ε is a vector n × 1 of n error terms;
- β is a vector (p + 1) × 1 of unknown regression coefficients.

_{0}and β

_{i}are known as estimates, and are obtained by the method of least squares. With the method of least squares we search for regression coefficients so that the estimated regression line is as close as possible to the observed data. With this method you assign to (β

_{0}, β

_{1}, β

_{2}, …, β

_{n}) those value b

_{0}, b

_{1}, b

_{2}, …, b

_{n}that make minimal the quantity:

_{i}are the values observed.

- Assigned x
_{1i}, x_{2i}, …, x_{pi}, the conditioned distribution of ε_{i}has average zero; - x
_{1i}, x_{2i}, …, x_{pi}, y_{i}, with i = 1, …, n are independent and identically distributed (i.i.d.); - x
_{1i}, x_{2i}, …, x_{pi}, y_{i}and ε_{i}have four moments; - Not perfect collinearity. When no regressor is linear combination of other regressors;
- Hypothesis of homoschedasticity;

^{2}. This coefficient is determined by breaking down the variance of the phenomenon into two components:

- Variance of regression;
- Variance of residuals.

_{i}explained and the greater the goodness of adaptation of the model used:

- ${\overline{y}}_{i}^{\text{}}$ represents the average of the values observed;
- ${y}_{i}$ represents the values observed;
- $\widehat{{y}_{i}}$ represents the expected theoretical values.

^{2}can be defined:

## 4. A Multiple Regression Model for Estimating Real Estate Values

_{1}, x

_{2}, …, x

_{7}are the seven identified characteristics:

- Living Surface (LSU) expressed in square meters;
- Number of Services (SERV) of the living unit;
- Conservation and Maintenance Level (CML) of the property. Usually this variable is appreciated by market operators according to a score rating scale, with 1 = poor, 3 = fair, 5 = good;
- Distance of the Apartment from the Ecosystem Service (DES), in meters;
- Floor Level (FLL) of the apartment;
- Panoramicity (PAN), according to the score rating scale usually used in the real estate agencies, 1 = bad, 3 = poor, 5 = fair, 7 = good, 9 = excellent;
- Lift Presence (LIFT), 0 = absent, 1 = present.

^{2}. In relation to the data collected and the phenomenon investigated, the link between the independent variables x

_{1}, x

_{2}, …, x

_{7}and the price P of the property is well explained through the function:

_{1}, β

_{2}, …, β

_{7}relative to the parameters represent the relationships that are established between the increase or decrease of the price P and the variation of the reference variable [44,45].

## 5. Case Study

^{2}and all forming part of multi-storey buildings with 3–4 floors above ground. The survey area around the foundry is homogeneous due to its extrinsic characteristics.

^{2}equal to 0.89 and a adjusted R

^{2}of 0.88, which express the wide acceptability of the results. The coefficients of the variables all have positive signs (LSU = 1.82; SERV = 2.18; CML = 1.56; DES = 0.08; FLL = 0.34; PAN = 2.78; LIFT = 9.75) and are statistically significant. Therefore, the coefficients identified allow to rewrite the regression function in the form:

9.75·LIFT

- The independent variables x
_{1}, x_{2}, …, x_{7}are deterministic and the independence of the s conditional distributions (with s = number of observations = 60) is verified because the sampled values were measured without error and are independent of each other; - The normality of the conditioned distributions and the linearity of the relationships among the variables are verified by the Q-Q plot method (Quantile-Quantile plot), according to which the observed (real) quantiles are compared with the expected quantiles in case the distribution is normal. Since the points are arranged along a straight line, it is possible to say that the distribution approximates the normal well. The reference graphic is in Figure 2;
- The hypothesis of homoschedasticity is verified by analyzing the residues. In fact, with the only exception of the FLL variable, however significant for estimation reasons, the graphs of the residues are presented approximately as points clouds that are arranged randomly within a horizontal band, as shown in the graphs in Figure 3;

_{0}and the alternative hypothesis H1 must be specified as follows:

- H
_{0}: β_{1}= β_{2}= β_{3}= β_{4}= β_{5}= β_{6}= β_{7}= 0 (there is no linear relationship between the dependent variable and the explanatory variables); - H
_{1}: β_{j}≠ 0 (there is a linear relationship between the dependent variable and at least one of the explanatory variables).

- Accept H
_{1}if F > F_{crit}, where F_{crit}is the critical value on the right tail of a distribution F with p and n − p − 1 degrees of freedom; - Otherwise accept H
_{0}.

_{crit}= 2.20. Since F = 62.192532 > F

_{crit}and furthermore F Significance = 0.00 < α, then it is possible to accept H

_{1}and conclude that there is a linear relationship between at least one explanatory variable and the price P. Therefore, the global significance test is successful.

_{i}, the t-test shows whether there is a significant linear relationship between the variable x

_{i}and P. Now the hypotheses are:

- H
_{0}: β_{i}= 0 (there is no linear relationship between the dependent variable P and the explanatory variable x_{i}); - H
_{1}: β_{i}≠ 0 (here is a linear relationship between x_{i}and P).

- Accept H
_{1}if t > t_{crit}, where t_{crit}is the critical value; - Otherwise accept H
_{0}.

- Since t stat > t
_{crit}with α = 0.05 (95%), the variables LSU, DES, PAN and LIFT have significant effects on the purchase price. So, taking into account the amount of the other variables, there is a linear relationship between each of the variables considered and the price P; - Since t stat > t
_{crit}with α = 0.5 (50%), the variable CML has effects on the price; - The variables SERV and FLL have no evident effects on the purchase price, because t stat < t
_{crit}for any level of α. However, these variables are taken into account in the analysis for extra-statistical considerations based on the importance from an estimative point of view.

## 6. Results and Discussions

^{2}. All the apartments all within a homogeneous zone, where the extrinsic (zonal) characteristics are the same.

^{2}, to which are added € 2180 for each bathroom over the first one.

^{2}, equipped with 2 bathrooms, with a 3-medium conservation level, located on the first floor, with a 5-medium panoramic view, served by a lift. Table 1 shows the maximum distance DES

_{max}(equal to 1,350 m) and the minimum distance DES

_{min}(50 m) that the ordinary house can have from the foundry. Therefore, by applying function (12), we obtain that the market value of the property drops from € 241,754 to € 137,953, with a contraction of 42.9%.

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Farber, S.C.; Costanza, R.; Wilson, M.A. Economic and ecological concepts for valuing ecosystem services. Ecol. Econ.
**2002**, 41, 375–392. [Google Scholar] [CrossRef] - UN. MEA Ecosystems and Human Well-being: Multiscale Assessment. Millennium Ecosystem Assessment Series; UN: Washington, DC, USA, 2006. [Google Scholar]
- Costanza, R.; d’Arge, R.; de Groot, R.; Farber, S.; Grasso, M.; Hannon, B.; Limburg, K.; Naeem, S.; O’Nell, R.V.; Paruelo, J.; et al. The value of the world’s ecosystem services and natural capital. Nature
**1997**, 387, 253–260. [Google Scholar] [CrossRef] - Daily, G.C.; Soderquist, T.; Aniyar, S.; Arrow, K.; Dasgupta, P.; Ehrlich, P.R.; Folke, C.; Jansson, A.M.; Jansson, B.O.; Kautsky, N.; et al. The value of nature and the nature of value. Science
**2000**, 289, 395–396. [Google Scholar] [CrossRef] [PubMed] [Green Version] - De Groot, R.S.; Wilson, M.A.; Boumans, R.M.J. A typology for the classification, description and valuation of ecosystem functions, goods and services. Special Issue: The Dynamics and Value of Ecosystem Services: Integrating Economic and Ecological Perspectives. Ecol. Econ.
**2002**, 41, 393–408. [Google Scholar] [CrossRef] [Green Version] - Nesticò, A.; Guarini, M.R.; Morano, P.; Sica, F. An Economic Analysis Algorithm for Urban Forestry Projects. Sustainability
**2019**, 11, 314. [Google Scholar] [CrossRef] [Green Version] - Nesticò, A.; Maselli, G. Sustainability indicators for the economic evaluation of tourism investments on islands. J. Clean. Prod.
**2020**, 248, 119217. [Google Scholar] [CrossRef] - Del Giudice, V. L’analisi di Regressione Multipla Nella Stima per Valori Tipici, Ce.S.E.T. Seminari. Aspetti Evolutivi Della Scienza Estimativa. Seminario in Onore di Ernesto Marenghi; Firenze University Press: Firenze, Italy, 1995. [Google Scholar]
- Schimmenti, E.; Asciuto, A.; Mandanici, S.; Viviano, P. L’utilizzo della regressione multipla nelle indagini estimative condotte in mercati fondiari attivi: Il caso studio di oliveti e vigneti in un territorio siciliano. Aestimum. Apprais. Rural. Econ.
**2012**, 60, 53–84. [Google Scholar] [CrossRef] - Isakson, H.R. Using Multiple Regression Analysis in Real Estate Appraisal. Apprais. J.
**2001**, 69, 424–430. [Google Scholar] - Zheng, S.; Cao, J.; Kahn, M.E.; Sun, C. Real Estate Valuation and Cross-Boundary Air Pollution Externalities: Evidence from Chinese Cities. J. Real Estate Financ. Econ.
**2013**, 48, 398–414. [Google Scholar] [CrossRef] - Liebelt, V.; Bartke, S.; Schwarz, N. Urban Green Spaces and Housing Prices: An Alternative Perspective. Sustainability
**2019**, 11, 3707. [Google Scholar] [CrossRef] [Green Version] - Xu, L.; You, H.; Li, D.; Yu, K. Urban green spaces, their spatial pattern, and ecosystem service value: The case of Beijing. Habitat Int.
**2016**, 56, 84–95. [Google Scholar] [CrossRef] - Isaac, D.; O’Leary, J. Property Valuation Principles, 2nd ed.; Palgrave MacMillan: London, UK, 2012. [Google Scholar]
- Kruskal, W.H.; Tanur, J.M. Linear Hypotheses. International Encyclopedia of Statistics; Free Press, Collier Macmillan: London, UK, 1978. [Google Scholar]
- Lindley, D.V. Regression and Correlation Analysis. In Series and Statistics; Eatwell, J., Milgate, M., Newman, P., Eds.; Time Palgrave MacMillan: London, UK, 1990. [Google Scholar]
- Birkes, D.; Dodge, Y. Alternative Methods of Regression; Yohn Wiley Sons: Hoboken, NJ, USA, 2011. [Google Scholar]
- Morano, P. L’analisi di Regressione per le Valutazioni di Ordine Estimativo; Celid: Torino, Italy, 2002. [Google Scholar]
- Acciani, C. La Regressione Lineare Multipla Nelle Valutazioni Immobiliari; Edagricole: Bologna, Italy, 1996. [Google Scholar]
- Bencardino, M.; Nesticò, A. Demographic Changes and Real Estate Values. A Quantitative Model for Analyzing the Urban-Rural Linkages. Sustainability
**2017**, 9, 536. [Google Scholar] [CrossRef] [Green Version] - De Luca, A. Le Applicazioni dei Metodi Statistici alle Analisi di Mercato. Manuale di Ricerche per il Marketing; FrancoAngeli: Milano, Italy, 2006. [Google Scholar]
- Damodar, N. Gujarati Essentials of Econometrics; McGraw-Hill Education: New York, NY, USA, 2009. [Google Scholar]
- Stock, J.H.; Watson, M.W. Introduzione All’econometria; Pearson: Torino, Italy, 2005. [Google Scholar]
- Gorla, M.S. Elementi di Statistica Applicata; Maggioli Editore: Rimini, Italy, 2011. [Google Scholar]
- Sen, A.; Srivastava, M. Regression Analysis: Theory, Methods and Applications; Springer: New York, NY, USA, 1990. [Google Scholar]
- Faraway, J. Linear Models with R; CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
- Dobson, A.J.; Barnett, A.G. An Introduction to Generalized Linear Models, 3rd ed.; CRC Press: New York, NY, USA, 2008. [Google Scholar]
- Levine, M.D.; Krehbiel, T.C.; Berenson, M.L. Statistica; Apogeo editore: Milano, Italy, 2006. [Google Scholar]
- Morano, P. Un modello di regressione in presenza di outlier per l’analisi del mercato immobiliare. Estimo E Territ.
**2001**, 10, 19–35. [Google Scholar] - Micelli, E. Qualità e Valori Immobiliari; Edagricole: Bologna, Italy, 1998. [Google Scholar]
- Agenzia del Territorio Manuale Operativo delle Stime Immobiliari; Agenzia del Territorio: Milano, Italy, 2012.
- De Mare, G.; Nesticò, A.; Tajani, F. The Rational Quantification of Social Housing. An Operative Research Model. ICCSA
**2012**, 7334, 27–43. [Google Scholar] [CrossRef] - Epley, D.R.; Burns, W. The Correct use of Confidence Intervals and Regression Analysis in Determining the Value of Residential Homes. Real Estate Econ.
**1978**, 6, 70–85. [Google Scholar] [CrossRef] - Waltl, S.R. Variation across Price Segments and Locations: A Comprehensive Quantile Regression Analysis of the Sydney Housing Market. Real Estate Econ.
**2016**, 47, 723–756. [Google Scholar] [CrossRef] - Kim, H.; Hung, K.; Park, S.Y. Determinants of Housing Prices in Hong Kong: A Box-Cox Quantile Regression Approach. J. Real Estate Finan. Econ.
**2015**, 50, 270–287. [Google Scholar] [CrossRef] - Zietz, J.; Zietz, E.N.; Sirmans, G.S. Determinants of House Prices: A Quantile Regression Approach. J. Real Estate Financ. Econ.
**2008**, 37, 317–333. [Google Scholar] [CrossRef] - Rosen, S. Hedonic prices and implicit markets: Product differentiation in pure competition. J. Political Econ.
**1974**, 82, 34–55. [Google Scholar] [CrossRef] - Witte, A.D.; Sumka, H.J.; Erekson, H. An Estimate of a Structural Hedonic Price Model of the Housing Market: An Application of Rosen’s Theory of Implicit Markets. Econometrica
**1979**, 47, 1151–1173. [Google Scholar] [CrossRef] [Green Version] - Smith, T.R. Multiple regression and the appraisal of single residential properties. Apprais. J.
**1971**, 39, 277–284. [Google Scholar] - Smith, B.A. Measuring the Value of Urban Amenities. J. Urban Econ.
**1978**, 5, 370–387. [Google Scholar] [CrossRef] - Cohen, J.P.; Coughlin, C.C.; Clapp, J.M. Local Polynomial Regressions versus OLS for Generating Location Value Estimates. J. Real Estate Finan. Econ.
**2017**, 54, 365–385. [Google Scholar] [CrossRef] - Simonotti, M. La Stima Immobiliare; UTET Libreria: Milano, Italy, 1997. [Google Scholar]
- Lindley, D.V. Regression and correlation analysis. In New Palgrave: A Dictionary of Economics; Mcmillan: New York, NY, USA, 1987. [Google Scholar]
- Simonotti, M. Un’applicazione Dell’analisi di Regressione Multipla Nella Stima di Appartamenti Genio Rurale; Edagricole: Bologna, Italy, 1991. [Google Scholar]
- Del Giudice, V. L’analisi di regressione multipla nella stima per valori tipici. Aestimum
**2009**, 15, 119–128. [Google Scholar] [CrossRef] - Pavlov, A.D. Space-Varying Regression Coefficients: A Semi-parametric Approach Applied to Real Estate Markets. Real Estate Econ.
**2003**, 28, 249–283. [Google Scholar] [CrossRef] - Lai, T.-Y.; Wang, K. Comparing the Accuracy of the Minimum-Variance Grid Method to Multiple Regression in Appraised Value Estimates. Real Estate Econ.
**1996**, 24, 531–549. [Google Scholar] [CrossRef] - Antoniucci, V.; Marella, G. Small town resilience: Housing market crisis and urban density in Italy. Land Use Policy
**2016**, 59, 580–588. [Google Scholar] [CrossRef] - Antoniucci, V.; Marella, G. Is social polarization related to urban density? Evidence from the Italian housing market. Landsc. Urban Plan.
**2018**, 177, 340–349. [Google Scholar] [CrossRef] - Dolores, L.; Macchiaroli, M.; De Mare, G. Soglie monetarie per la vendita pubblica di spazi pubblicitari nel contratto di sponsorizzazione culturale. LaborEst
**2019**, 19, 5–9. [Google Scholar] [CrossRef] - Nesticò, A.; Maselli, G. Declining discount rate estimate in the long-term economic evaluation of environmental projects. J. Environ. Account. Manag.
**2020**, 8, 93–110. [Google Scholar] [CrossRef] - Ross, S.M. Introduzione alla statistica. Maggioli Editore: Rimini, Italy, 2014. [Google Scholar]

N. | P [€ × 1000] | LSU [m^{2}] | SERV [n.] | CML | DES [m] | FLL | PAN | LIFT |
---|---|---|---|---|---|---|---|---|

1 | 240 | 115 | 2 | 3 | 730 | 1 | 5 | 0 |

2 | 205 | 100 | 1 | 5 | 740 | 1 | 1 | 1 |

3 | 235 | 125 | 2 | 1 | 585 | 3 | 3 | 1 |

4 | 210 | 120 | 2 | 3 | 585 | 2 | 3 | 0 |

5 | 165 | 90 | 1 | 5 | 785 | 3 | 1 | 0 |

6 | 215 | 120 | 1 | 3 | 425 | 1 | 3 | 1 |

7 | 220 | 120 | 2 | 3 | 430 | 3 | 5 | 1 |

8 | 135 | 80 | 1 | 5 | 495 | 2 | 5 | 1 |

9 | 180 | 90 | 2 | 5 | 570 | 1 | 7 | 0 |

10 | 185 | 90 | 2 | 5 | 570 | 1 | 5 | 1 |

11 | 210 | 125 | 2 | 3 | 800 | 1 | 5 | 1 |

12 | 270 | 125 | 2 | 5 | 810 | 2 | 7 | 0 |

13 | 215 | 120 | 2 | 3 | 810 | 2 | 1 | 0 |

14 | 195 | 85 | 1 | 5 | 900 | 2 | 5 | 1 |

15 | 205 | 85 | 2 | 3 | 950 | 1 | 7 | 1 |

16 | 235 | 100 | 2 | 5 | 1000 | 1 | 7 | 1 |

17 | 170 | 80 | 2 | 5 | 950 | 2 | 5 | 1 |

18 | 220 | 95 | 1 | 5 | 950 | 3 | 7 | 0 |

19 | 265 | 110 | 2 | 3 | 950 | 1 | 5 | 1 |

20 | 105 | 83 | 2 | 5 | 200 | 1 | 5 | 0 |

21 | 120 | 95 | 2 | 5 | 200 | 2 | 7 | 1 |

22 | 105 | 73 | 1 | 3 | 580 | 3 | 3 | 0 |

23 | 140 | 65 | 1 | 3 | 580 | 2 | 5 | 1 |

24 | 135 | 80 | 1 | 3 | 970 | 1 | 3 | 0 |

25 | 180 | 85 | 1 | 3 | 980 | 2 | 3 | 1 |

26 | 130 | 80 | 2 | 3 | 400 | 2 | 3 | 0 |

27 | 130 | 100 | 1 | 1 | 430 | 3 | 3 | 0 |

28 | 120 | 90 | 2 | 3 | 200 | 4 | 5 | 1 |

29 | 165 | 100 | 2 | 5 | 490 | 2 | 5 | 0 |

30 | 205 | 120 | 1 | 3 | 530 | 1 | 3 | 1 |

31 | 150 | 80 | 2 | 5 | 560 | 3 | 1 | 0 |

32 | 185 | 90 | 2 | 5 | 570 | 1 | 5 | 1 |

33 | 200 | 95 | 1 | 3 | 800 | 1 | 3 | 0 |

34 | 230 | 95 | 2 | 5 | 830 | 5 | 5 | 1 |

35 | 175 | 83 | 1 | 1 | 810 | 1 | 1 | 1 |

36 | 140 | 80 | 2 | 5 | 200 | 3 | 7 | 1 |

37 | 145 | 80 | 1 | 1 | 580 | 4 | 7 | 1 |

38 | 180 | 80 | 2 | 5 | 950 | 2 | 5 | 0 |

39 | 215 | 95 | 1 | 5 | 950 | 3 | 7 | 1 |

40 | 240 | 110 | 2 | 3 | 950 | 1 | 5 | 1 |

41 | 120 | 83 | 2 | 5 | 200 | 1 | 5 | 1 |

42 | 135 | 95 | 2 | 5 | 200 | 2 | 7 | 0 |

43 | 125 | 75 | 1 | 1 | 580 | 3 | 3 | 0 |

44 | 120 | 65 | 1 | 3 | 580 | 2 | 5 | 0 |

45 | 170 | 80 | 1 | 3 | 970 | 1 | 3 | 1 |

46 | 180 | 85 | 1 | 3 | 980 | 2 | 3 | 0 |

47 | 135 | 80 | 2 | 3 | 400 | 2 | 3 | 0 |

48 | 210 | 120 | 1 | 3 | 425 | 1 | 3 | 0 |

49 | 215 | 120 | 2 | 3 | 430 | 3 | 5 | 1 |

50 | 145 | 80 | 1 | 5 | 495 | 2 | 5 | 0 |

51 | 225 | 90 | 2 | 3 | 1120 | 4 | 5 | 1 |

52 | 225 | 100 | 2 | 5 | 1300 | 2 | 5 | 0 |

53 | 255 | 120 | 1 | 3 | 1250 | 1 | 3 | 1 |

54 | 180 | 80 | 2 | 5 | 1350 | 3 | 1 | 0 |

55 | 215 | 90 | 2 | 5 | 1270 | 1 | 5 | 1 |

56 | 250 | 120 | 2 | 3 | 1220 | 2 | 3 | 0 |

57 | 255 | 120 | 1 | 3 | 1100 | 1 | 3 | 1 |

58 | 170 | 120 | 2 | 1 | 80 | 1 | 1 | 1 |

59 | 90 | 75 | 1 | 1 | 80 | 3 | 1 | 1 |

60 | 125 | 100 | 2 | 1 | 50 | 1 | 1 | 0 |

LSU | SERV | CML | DES | FLL | PAN | LIFT | |
---|---|---|---|---|---|---|---|

LSU | 1 | ||||||

SERV | 0.24 | 1 | |||||

CML | −0.17 | 0.25 | 1 | ||||

DES | 0.08 | −0.10 | 0.20 | 1 | |||

FLL | −0.22 | 0.01 | −0.04 | −0.07 | 1 | ||

PAN | −0.07 | 0.26 | 0.45 | 0.02 | 0.09 | 1 | |

LIFT | 0.11 | −0.02 | −0.07 | 0.02 | −0.03 | 0.19 | 1 |

Output | ||||||
---|---|---|---|---|---|---|

Multiple R | 0.95 | |||||

R^{2} | 0.89 | |||||

Adjusted R^{2} | 0.88 | |||||

Standard Error | 16.07 | |||||

Observations | 60 | |||||

df | SS | MS | F | SignificanceF | ||

Regression | 7 | 112,426 | 16,061 | 62 | 0 | |

Residual | 52 | 13,429 | 258 | |||

Total | 59 | 125,855 | ||||

Coefficients | Standard Error | t Stat | Significance Value | Lower95% | Upper 95% | |

Intercepts | −72.60 | 16.24 | −4.47 | 0.00 | −105.18 | −40.02 |

LSU | 1.82 | 0.14 | 13.30 | 0.00 | 1.54 | 2.09 |

SERV | 2.18 | 4.74 | 0.46 | 0.65 | −7.32 | 11.69 |

CML | 1.56 | 1.86 | 0.84 | 0.40 | −2.17 | 5.29 |

DES | 0.08 | 0.01 | 12.06 | 0.00 | 0.07 | 0.09 |

FLL | 0.34 | 2.20 | 0.15 | 0.88 | −4.08 | 4.75 |

PAN | 2.78 | 1.31 | 2.12 | 0.04 | 0.15 | 5.42 |

LIFT | 9.75 | 4.35 | 2.24 | 0.03 | 1.02 | 18.48 |

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**MDPI and ACS Style**

Nesticò, A.; La Marca, M.
Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis. *Sustainability* **2020**, *12*, 6304.
https://doi.org/10.3390/su12166304

**AMA Style**

Nesticò A, La Marca M.
Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis. *Sustainability*. 2020; 12(16):6304.
https://doi.org/10.3390/su12166304

**Chicago/Turabian Style**

Nesticò, Antonio, and Marianna La Marca.
2020. "Urban Real Estate Values and Ecosystem Disservices: An Estimate Model Based on Regression Analysis" *Sustainability* 12, no. 16: 6304.
https://doi.org/10.3390/su12166304