# Efficiency versus Fairness in the Management of Public Housing Assets in Palermo (Italy)

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## Abstract

**:**

## 1. Introduction

- -
- measuring the differential between the estimated prices and the ‘political prices’ that are fixed by the municipality according to the current laws, then assuming the ratio between the political and the estimated prices as an indicator of the overall efficiency of the public housing policy [34];
- -

## 2. Materials: Public Housing Policy in the Municipality of Palermo

- the additional subsidy to housing rent, for low-income households;
- the subsidy to involuntary arrears of tenants, implementing the Legislative Decree 102/2013;
- the subsidy for housing emergency.

#### 2.1. Public Housing Stock in Palermo

#### 2.2. The Transfer Programme of Public Housing

- being up-to-date with the payment of the rent and the condominium fees;
- no member of the family must own, use or usufruct a suitable housing for the family;
- to have had the final assignment of the dwelling for at least two years;
- being in possession of other legal requirements (that are set by Presidential Decree 30/12/1972 no. 1035).

## 3. Methods

- -
- reconsider the rules for the political pricing in order to grant homogeneous reductions of the real estate market estimates, for the purpose of the equalization between the current tenants (potential owners);
- -
- suggest possible variations in the political prices for those properties of which, due to any higher location characteristics, the market estimates are greater, and in respect of which the reduction is greater than the average.

- A real estate market survey aimed at providing a wide and articulated database intended to appraise the potential real estate market price of the properties to be transferred;
- The application of a multiple linear regression pattern, aimed at eliciting the marginal prices of the characteristics by which the properties to be transferred have been featured;
- The estimate of the properties to be transferred aimed at comparing the real estate potential market price to the political prices carried out by the municipality according to the current laws, in order to provide some critical item of the fairness of the municipal public housing management policy.

#### 3.1. Real Estate Market Survey: Characterization of the Sample

#### 3.2. The Multiple Regression Model

- a set of parameters that summarize the relationship between the dependent variable and the independent ones, under the assumptions that the former is the effect of the latter, and that in examining the influence of each independent variable on the dependent ones, the value of other independent variables are kept constant;
- a statistic for examining the significance of the parameters, and a probability value associated with each of these parameters;
- a value that summarizes the proportion of variance of the dependent variable that is generally explained by the independent variables.

- evaluation of the adequacy of the variables (measurement level, distribution, collinearity etc.);
- choice of the analytical strategy to insert the independent variables into an equation;
- interpretation of the solution;
- validation of the solution.

- The coefficient of determination ${R}^{2}$ which represents the proportion of variability of the $p$ explained by the independent variables, may measure the goodness of the proximity of the model to the original data; or ${R}^{2}adjusted$ which takes into account the number of explanatory variables included in the model and sample size;
- The $Ftest$, derived from the corresponding analysis of variance, which allows to decide if the variance induced by regression is statistically significant or may be assigned to the case to a predetermined level of confidence;
- The $ttest$ on the statistical significance of the individual variables;
- The variance inflation factor (VIF) is the ratio of variance in a model with multiple terms, divided by the variance of a model with one term alone. It quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate’s standard deviation) of an estimated regression coefficient is increased because of collinearity;
- The analysis of residuals, which may assess the appropriateness of the model by defining residual and examining residual plots. Residuals ${\epsilon}_{j}$ are the difference between the observed value of the dependent variable and the predicted value. The analysis of the residuals ${\epsilon}_{i}$: is a tool to verify the goodness of the regression model, i.e., if there are no violations in terms of linearity, additivity and homoschedasticity of particular variables.

- mean equal to zero: $E\left({\epsilon}_{i}\right)=0$; for each combination of values of the independent variables, the expected residual value must be equal to 0;
- homoschedasticity: $VAR\left({\epsilon}_{i}\right)={\sigma}^{2}$ for each $i$; the variance of the residuals must be constant for all combinations of the values of the independent variables;
- normality: the distributions of the values of ${\epsilon}_{i}$ for each combination of values of independent variability must be of normal form;
- absence of autocorrelation: $Cov\left({\epsilon}_{i},{\epsilon}_{i}\right)=0$, for each $i$ and $j$, with $i\ne j$; residues associated with different observations must not be correlated;
- the independent variables must not be correlated with the residuals: $Cov\left({\epsilon}_{i},{X}_{i}\right)=0$

- Value of the coefficients of determination equal to or greater than 0.95, or in some less restrictively are proposed values equal to 0.90 or, however, close to 0.90;
- The average error rate should not be higher than 10% and errors for each observation must not be greater than 15%, or the standard error should not be greater than 5% of the average price;
- The limit values of the t and F tests are based on the confidence interval that generally is equal to 0.95. An important condition in the regression analysis concerns the relation between the number of observations and the number of explanatory variables required.
- Practical criteria suggest that the relation between the number of observations and the number of variables should be $m>10\xb7n$, o $m=n$ + 30, or less restrictively $m>4\xb7n$ at least up to $m=10\xb7n$ in respect of the previous inequality, or even $m>5\xb7n$, where $m$ is the number of observations in the sample of property trades and $n$ is the number of the property features. In general, the choice of the criterion is related to the actual availability of the data.

#### 3.3. Appraisal and Benchmarking

## 4. Application and Results

#### 4.1. Real Estate Market Survey

#### 4.2. The Multiple Linear Regression Analysis and the Elicitation of the Predictors

- The coefficient of determination ${R}^{2}$, which represents the proportion of variability of $p/sq.m$ explained by the explanatory variables, and which represents a measure of the goodness of the proximity of the model to the original data in this case is 0.834, which can be considered an acceptable result;
- ${R}^{2}adjusted$ taking into account the number of explanatory variables $n=5$, i.e., the six real estate characteristics included in the model and the sample size $m=98$ has a value of 0.825, which can be considered acceptable;
- The $testF$ used to evaluate the statistical significance of the model as a whole is based on the relationship between the variance explained by the model and the residual variance; in this case the $p-valueobserved$ is less than the $theoreticalp-value$ $\left(P<0.05\right)$;
- The $ttest$ on the statistical significance of the individual predictors within the meaningful model for the five variables $\left(P<0.05\right)$, i.e., the real estate characteristics ${k}_{e1},$ ${k}_{e2}$, ${k}_{i}$, ${k}_{t}$, ${k}_{a2}$ have a significant influence on the formation of the $p/sq.m$;
- The VIF, that helps to quantifies the severity of multicollinearity in an ordinary least squares regression analysis, in this case is very low and there is not multicollinearity between the variables.
- The analysis of the eigenvalues to identify possible multicollinearity conditions identifies low values of the condition index, i.e., very low or lack of multicollinearity.
- The analysis of the residuals shows that the standardized residue approximates to the distribution to the normal one, i.e., $\mathit{E}\left({\epsilon}_{i}\right)=0$ for each combination of values of the independent variables, the expected residual value is equal to 0 and $VAR\left({\epsilon}_{i}\right)={\sigma}^{2}$ for each $i$ the variance of the residuals is constant for all combinations of the values of the independent variables (Figure 6 left);
- In the Normal P-P graph of standardized residual regression the points tend to be arranged even if with some approximation along a straight line (Figure 8b, right);

#### 4.3. Comparison between Political, Real Estate Market, and Observatory of the Real Estate Market (OMI) Prices

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 2.**Public housing on sale (

**a**) and public housing sold (

**b**) per district in Palermo (Italy) from January 2016 to May 2018.

**Figure 3.**Percentage of public housing sold per district from January 2016 to May 2018 in Palermo (Italy).

**Figure 6.**Relation between unit prices (y axis) and aggregate value quality index (x axis k*) of the real estate market sample analysed.

**Figure 7.**Probability density function for unit prices, overall quality score, and the six characteristics.

**Figure 8.**Distribution of the standardized residue (

**a**,

**left**); normal P-P graph of standardized residual regression (

**b**,

**right**).

**Figure 10.**Partial regression graph $p/sq.m$ - ${k}_{e1},$ ${k}_{e2}$, ${k}_{i}$, ${k}_{t}$, ${k}_{a2}$.

**Figure 11.**Comparison between political prices (black square), estimated market prices (red circle), and OMI prices (grey square) of the 32 dwellings (unitary price euros/sqm).

**Table 1.**Public housing in Palermo (2017) [39].

Owner | Dwellings | Squatted Dwellings | |
---|---|---|---|

No. | No. | % | |

Municipality of Palermo | 4827 | 2580 | 53.4% |

Istituto Autonomo Case Popolari (IACP) | 19,208 | 2658 | 13.8% |

Total | 24,035 | 5238 | 21.8% |

Districts | Surface (sq.km) | Inhabitants | Population Density (inhab./sq.km) |
---|---|---|---|

I | 2.497 | 21,489 | 8606 |

II | 21.39 | 72,888 | 3408 |

III | 20.34 | 77,068 | 3789 |

IV | 26.16 | 112,158 | 4287 |

V | 17.53 | 120,885 | 6896 |

VI | 23.90 | 78,548 | 3287 |

VII | 32.95 | 74,330 | 2256 |

VIII | 15.32 | 127,794 | 8342 |

Cadastral Data | Cadastral Income | Political Prices (by Law) | |||||
---|---|---|---|---|---|---|---|

Zone | Category | Class | EUR/room | EUR/sqm | $\mathbf{Baseline}\text{}\mathit{P}\mathit{b}$ EUR/sqm | Price 1 EUR/sqm | Price 2 EUR/sqm |

1° | A3 | 4 | 43.38 | 2.53 | 253 | 202 | 182 |

6 | 59.39 | 3.46 | 346 | 277 | 249 | ||

A4 | 2 | 25.31 | 1.48 | 148 | 118 | 106 | |

4 | 35.12 | 2.05 | 205 | 164 | 147 | ||

2° | A2 | 5 | 54.23 | 3.16 | 316 | 253 | 228 |

6 | 64.56 | 3.76 | 376 | 301 | 271 | ||

7 | 77.47 | 4.52 | 452 | 361 | 325 | ||

A3 | 4 | 41.32 | 2.41 | 241 | 193 | 173 | |

5 | 48.55 | 2.83 | 283 | 226 | 204 | ||

6 | 56.81 | 3.31 | 331 | 265 | 239 | ||

7 | 67.14 | 3.91 | 391 | 313 | 282 | ||

A4 | 4 | 25.31 | 1.48 | 148 | 118 | 106 | |

5 | 29.44 | 1.72 | 172 | 137 | 124 | ||

6 | 34.60 | 2.02 | 202 | 161 | 145 | ||

7 | 40.80 | 2.38 | 238 | 190 | 171 | ||

3° | A2 | 3 | 74.89 | 4.37 | 437 | 349 | 314 |

4° | A2 | 7 | 64.56 | 3.76 | 376 | 301 | 271 |

A3 | 8 | 67.14 | 3.91 | 391 | 313 | 282 | |

A4 | 6 | 25.31 | 1.48 | 148 | 118 | 106 | |

7 | 29.44 | 1.72 | 172 | 137 | 124 | ||

8 | 34.60 | 2.02 | 202 | 161 | 145 | ||

5° | A2 | 3 | 44.41 | 2.59 | 259 | 207 | 186 |

4 | 51.65 | 3.01 | 301 | 241 | 217 | ||

5 | 61.98 | 3.61 | 361 | 289 | 260 | ||

6 | 72.30 | 4.22 | 422 | 337 | 304 | ||

7 | 85.20 | 4.97 | 497 | 398 | 358 | ||

A3 | 8 | 51.65 | 3.01 | 301 | 241 | 217 |

${k}_{e1}$ Location, urbanization and accessibility | 1. Location | 1. Settlement quality; 2. Mix of functions |

2. Urban facilities | 1. Public facilities and services | |

3. Accessibility | 1. Mobility from/to the area with private transportation; 2. Mobility from/to the area with public transportation; 3. Mobility within the neighbourhood | |

${k}_{e2}$ Neighbourhood characteristics | 1. Functional; 2. Symbolic characteristics | |

${k}_{i}$ Unit location within the building | 1. Panoramic quality and view; 2. Brightness; 3. Accessibility within the building | |

${k}_{t}$ Technological characteristics | 1. Building overall technological quality; 2. Unit finishes and windows quality; 3. Maintenance levels | |

${k}_{a1}$ Building architectural quality | 1. Overall building decoration | |

${k}_{a2}$ Unit architectural quality | 1. Size, functionality and distribution; 2. Additional surfaces; 3. Quality of finishes |

Id. | Floor | Size | Asking Prices | Characterization | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Rooms | Surface (sq.m) | Total (EUR) | Unit (rooms) (EUR/rooms) | Unit (surf.) (EUR/sq.m) | ke1 | ke2 | ki | kt | ka1 | ka2 | k* | ||

B01 | 5 | 5 | 110 | 165,000 | 33,000 | 1500 | 3.0 | 3.0 | 5.0 | 4.1 | 2.0 | 3.5 | 3.6 |

B02 | 3 | 3.5 | 85 | 105,000 | 30,000 | 1235 | 3.1 | 2.0 | 1.2 | 3.5 | 3.5 | 2.6 | 2.7 |

B03 | 2 | 4 | 98 | 145,000 | 36,250 | 1480 | 3.6 | 3.5 | 4.9 | 1.6 | 4.0 | 2.2 | 3.0 |

B04 | 1 | 3.8 | 90 | 90,000 | 24,000 | 1000 | 2.3 | 1.5 | 3.8 | 2.0 | 1.5 | 3.0 | 2.3 |

B05 | r | 2.3 | 37 | 45,000 | 20,000 | 1216 | 3.9 | 4.5 | 3.8 | 3.0 | 1.0 | 1.8 | 3.3 |

B06 | r | 2.5 | 52.8 | 25,000 | 10,000 | 474 | 1.6 | 1.0 | 2.4 | 1.0 | 1.0 | 1.0 | 1.3 |

B07 | r | 3.5 | 87.5 | 139,000 | 39,714 | 1589 | 4.0 | 2.5 | 3.1 | 2.7 | 1.5 | 3.2 | 2.9 |

B08 | 7 | 3.8 | 110.0 | 107,000 | 28,533 | 973 | 2.4 | 2.0 | 4.6 | 2.8 | 2.5 | 2.6 | 2.8 |

B09 | 1 | 4.8 | 120.0 | 215,000 | 45,263 | 1792 | 4.0 | 3.5 | 4.4 | 4.3 | 3.0 | 4.8 | 4.1 |

B10 | 3 | 4.8 | 100.0 | 95,000 | 20,000 | 950 | 2.5 | 1.5 | 1.5 | 2.8 | 2.0 | 1.9 | 2.2 |

B11 | r | 3.5 | 85.0 | 75,000 | 21,429 | 882 | 2.6 | 1.0 | 2.4 | 1.0 | 1.0 | 1.0 | 1.5 |

B12 | 2 | 1.8 | 40.0 | 42,000 | 24,000 | 1050 | 1.6 | 1.0 | 1.7 | 2.8 | 2.0 | 1.7 | 2.0 |

B13 | 1 | 2.5 | 34.0 | 25,000 | 10,000 | 735 | 3.1 | 3.5 | 3.1 | 1.2 | 2.0 | 1.0 | 2.3 |

B14 | 3 | 5.3 | 115.0 | 220,000 | 41,905 | 1913 | 2.3 | 4.0 | 4.9 | 3.8 | 3.5 | 3.7 | 3.7 |

B15 | 9 | 5.0 | 181.8 | 180,000 | 36,000 | 990 | 2.1 | 2.0 | 4.2 | 3.0 | 3.0 | 4.0 | 2.9 |

B16 | 5 | 4.5 | 110.0 | 155,000 | 34,444 | 1409 | 3.5 | 2.5 | 4.4 | 3.8 | 3.0 | 3.3 | 3.5 |

B17 | 6 | 6.0 | 150.0 | 190,000 | 31,667 | 1267 | 2.8 | 2.5 | 4.3 | 4.7 | 5.0 | 4.3 | 3.9 |

B18 | 6 | 2.8 | 75.0 | 77,000 | 28,000 | 1027 | 1.9 | 1.5 | 4.0 | 2.4 | 1.5 | 1.8 | 2.3 |

B19 | 1 | 3.8 | 120.0 | 170,000 | 45,333 | 1417 | 3.1 | 3.0 | 2.3 | 4.7 | 5.0 | 5.0 | 3.8 |

B20 | 1 | 2.5 | 55.0 | 45,000 | 18,000 | 818 | 2.5 | 1.0 | 1.5 | 2.7 | 1.5 | 2.0 | 2.0 |

B21 | 4 | 4.8 | 112.0 | 65,000 | 13,684 | 580 | 2.9 | 2.0 | 1.6 | 1.2 | 3.0 | 3.0 | 2.0 |

B22 | 4 | 5.8 | 110.0 | 225,000 | 39,130 | 2045 | 3.5 | 4.0 | 4.7 | 3.8 | 3.5 | 5.0 | 4.0 |

B23 | 6 | 2.5 | 50.0 | 65,000 | 26,000 | 1300 | 2.8 | 4.0 | 4.6 | 3.1 | 4.0 | 2.5 | 3.5 |

B24 | 9 | 3.8 | 90.0 | 175,000 | 46,667 | 1944 | 3.5 | 3.5 | 4.6 | 3.9 | 3.0 | 3.5 | 3.8 |

B25 | 2 | 5.0 | 100.0 | 68,000 | 13,600 | 680 | 2.8 | 1.0 | 1.0 | 1.2 | 1.0 | 1.3 | 1.4 |

**Table 6.**Political, estimated and Observatory of the Real Estate Market (OMI) prices of the 32 dwellings (Districts V and VI).

Location | Political Price (P) | Characterization | Estimated Market Price (E) | OMI Zone | OMI Price (O) | Ratio (P/E) *100 | Ratio (P/O) *100 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

id | District | Street/Square | EUR/sq.m | ke1 | ke2 | ki | kt | ka2 | EUR/sq.m | id | EUR/sq.m | % | % |

1 | V | Petralie (IACP-L. 745) | 133 | 1.0 | 1.0 | 1.5 | 1.5 | 2.0 | 502 | E19 | 800 | 26 | 17 |

2 | V | Erice (IACP-L. 745) | 163 | 1.0 | 1.5 | 3.5 | 2.0 | 2.0 | 738 | E19 | 800 | 22 | 20 |

3 | VI | Carreca | 139 | 1.0 | 2.0 | 4.0 | 2.0 | 3.0 | 875 | E19 | 800 | 16 | 17 |

4 | V | Erice (IACP-L. 745) | 182 | 1.0 | 1.5 | 2.0 | 2.0 | 2.0 | 625 | E19 | 800 | 29 | 23 |

5 | VI | Florio | 163 | 4.0 | 2.0 | 3.5 | 2.5 | 2.0 | 1113 | E15 | 880 | 15 | 19 |

6 | V | Erice (IACP-L. 745) | 229 | 1.0 | 1.5 | 1.5 | 2.0 | 2.0 | 601 | E19 | 800 | 38 | 29 |

7 | V | Brancato | 180 | 4.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1140 | B3 | 880 | 16 | 20 |

8 | VI | Calandrucci | 139 | 2.5 | 1.5 | 1.5 | 2.0 | 2.0 | 763 | E19 | 800 | 18 | 17 |

9 | VI | Centorbe | 139 | 2.5 | 2.0 | 1.5 | 2.0 | 2.0 | 800 | E19 | 800 | 17 | 17 |

10 | VI | Centorbe | 139 | 2.5 | 2.0 | 2.5 | 2.0 | 2.0 | 849 | E19 | 800 | 16 | 17 |

11 | VI | Paladini | 139 | 3.0 | 2.5 | 3.0 | 2.0 | 2.5 | 989 | E19 | 800 | 14 | 17 |

12 | VI | Ragusa | 139 | 2.5 | 2.5 | 1.5 | 2.5 | 2.5 | 911 | E19 | 800 | 15 | 17 |

13 | VI | Ragusa | 139 | 2.5 | 2.5 | 3.0 | 2.5 | 2.5 | 984 | E19 | 800 | 14 | 17 |

14 | VI | Rossi | 155 | 2.5 | 2.5 | 1.5 | 2.5 | 2.0 | 891 | E19 | 800 | 17 | 19 |

15 | VI | Rossi | 155 | 2.5 | 2.5 | 2.0 | 2.5 | 2.0 | 916 | E19 | 800 | 17 | 19 |

16 | V | Nicosia (IACP-L. 745) | 161 | 1.0 | 1.5 | 1.5 | 2.0 | 2.0 | 601 | E19 | 800 | 27 | 20 |

17 | V | Nicosia (IACP-L. 745) | 149 | 1.0 | 1.5 | 1.5 | 2.0 | 2.0 | 601 | E19 | 800 | 25 | 19 |

18 | VI | Paladini | 139 | 3.0 | 2.5 | 1.0 | 2.0 | 2.5 | 892 | E19 | 800 | 16 | 17 |

19 | VI | Scaglione | 164 | 1.5 | 2.5 | 3.5 | 1.5 | 1.5 | 752 | E19 | 800 | 22 | 20 |

20 | VI | Michelangelo | 139 | 3.0 | 2.5 | 3.5 | 2.5 | 2.0 | 1043 | E19 | 800 | 13 | 17 |

21 | VI | Alibrandi | 150 | 3.3 | 1.0 | 2.0 | 1.5 | 1.5 | 754 | E19 | 800 | 20 | 19 |

22 | VI | Calandrucci | 118 | 2.5 | 1.5 | 1.5 | 2.0 | 2.0 | 763 | E19 | 800 | 15 | 15 |

23 | VI | Michelangelo | 139 | 3.0 | 2.5 | 3.5 | 3.0 | 2.5 | 1119 | E19 | 800 | 12 | 17 |

24 | VI | Paladini | 150 | 2.5 | 1.5 | 2.5 | 2.0 | 2.0 | 811 | E19 | 800 | 18 | 19 |

25 | VI | Paladini | 139 | 2.5 | 1.5 | 2.0 | 2.0 | 2.0 | 787 | E19 | 800 | 18 | 17 |

26 | VI | Zumbo | 139 | 2.5 | 2.0 | 1.5 | 1.5 | 2.5 | 771 | E19 | 800 | 18 | 17 |

27 | V | Agostino | 158 | 3.5 | 2.5 | 2.5 | 3.0 | 3.0 | 1148 | C5 | 880 | 14 | 18 |

28 | V | L’Emiro | 163 | 3.5 | 2.5 | 3.0 | 3.0 | 3.0 | 1172 | C5 | 880 | 14 | 19 |

29 | V | Casalini | 145 | 2.0 | 4.0 | 1.5 | 3.0 | 3.0 | 1051 | E20 | 860 | 14 | 17 |

30 | VI | Zumbo | 139 | 2.5 | 2.0 | 1.0 | 1.5 | 2.5 | 747 | E19 | 800 | 19 | 17 |

31 | V | Agostino | 169 | 3.5 | 2.5 | 3.0 | 3.0 | 3.0 | 1172 | C5 | 880 | 14 | 19 |

32 | VI | Paladini | 139 | 3.0 | 2.5 | 3.5 | 2.5 | 2.0 | 1043 | E20 | 860 | 13 | 16 |

Statistical | Prices | Ratio | Differentials | ||||
---|---|---|---|---|---|---|---|

Index | Political (P) | OMI (O) | Estimated (E) | P/E | P/O | E–P | O–P |

EUR/sq.m | EUR/sq.m | EUR/sq.m | No. | No. | EUR/sq.m | EUR/sq.m | |

Min | 118 | 800 | 502 | 0.12 | 0.15 | 370 | 571 |

Average | 151 | 816 | 873 | 0.18 | 0.19 | 722 | 665 |

Median | 142 | 800 | 862 | 0.16 | 0.17 | 723 | 661 |

Max | 229 | 880 | 1172 | 0.38 | 0.29 | 1009 | 722 |

SD | 20 | 31 | 188 | 0.06 | 0.02 | 191 | 33 |

RSD% | 13% | 4% | 22% | 31% | 13% | 26% | 5% |

Differentials between Prices | ||
---|---|---|

Estimated–Political E–P EUR | OMI–Political O–P EUR | |

Total | 2,661,757 | 2,400,460 |

© 2019 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/).

## Share and Cite

**MDPI and ACS Style**

Napoli, G.; Giuffrida, S.; Trovato, M.R.
Efficiency versus Fairness in the Management of Public Housing Assets in Palermo (Italy). *Sustainability* **2019**, *11*, 1199.
https://doi.org/10.3390/su11041199

**AMA Style**

Napoli G, Giuffrida S, Trovato MR.
Efficiency versus Fairness in the Management of Public Housing Assets in Palermo (Italy). *Sustainability*. 2019; 11(4):1199.
https://doi.org/10.3390/su11041199

**Chicago/Turabian Style**

Napoli, Grazia, Salvatore Giuffrida, and Maria Rosa Trovato.
2019. "Efficiency versus Fairness in the Management of Public Housing Assets in Palermo (Italy)" *Sustainability* 11, no. 4: 1199.
https://doi.org/10.3390/su11041199