# Credit Risk Management of Property Investments through Multi-Criteria Indicators

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## Abstract

**:**

## 1. Introduction

- Mandatory minimum capital requirements, concerning the introduction of new rules for a more precise quantitative assessment of corporate risks and of the assets to be allocated and moreover tending to use—for prudential purposes—corporate risk management mechanisms;
- Prudential checks on capital adequacy, inherent both the supervision of the banks’ capital and the evaluation of the budget to be internally carried out by each individual intermediary;
- Market discipline, regarding the effective use of the disciplining power that is performed by the market, thanks to adequate disclosure transparency, in order to encourage safe and solid banking management practices.

## 2. Overview on the MCDA

- Full aggregation approach (or American school);
- Outranking approach (or French school);
- Goal, aspiration or reference level approach.

- Basic MCDA methods;
- Advanced MCDA methods;
- Fuzzy MCDA methods.

- Quantitative measurements;
- Qualitative initial measurements;
- Pairwise comparison of alternatives;
- Qualitative measurements not converted to quantitative variables.

- Analytic Hierarchy Process (AHP);
- Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE);
- Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

## 3. The Credit Risk Management and Assessment Models

^{+}model.

## 4. Aim of the Work

_{IPRE,risk}is defined through the reconstruction of the decision-making process carried out by the credit manager. In particular, these needs are simultaneously considered: (i) the creation of a platform that can be shared by the various subjects involved in the real estate financing; (ii) the definition of a model that is suitable for different cases, such as the granting of a new credit line, the debt restructuring, the management of the Unlikely To Pay (UTP) and the Non-Performing Loans (NPLs); (iii) the opportunity to avoid the formation of “black boxes” that are difficult to rebuild; (iv) the clear and transparent representation of the results that are achieved; (v) the simplification of the procedure, by avoiding the length of the bureaucracy and of the administrative justice; (vi) the advantage of the financial exposure of the credit institution; and (vii) the identification of a sustainable mortgage down payment for the debtor.

- The Decision Maker (DM), i.e., the credit manager who directly operates in the debt relief procedures;
- The Analyst Team, which applies the skills acquired on the MCDA and the consultancy for the Investment Management and the Advisory, Valuation, and Real Estate Services companies, support the DM in the decision-making process;
- The Stakeholders, i.e., the group of experts working in the real estate finance sector and faulty credit with underlying property, whose role is to determine the adequate importance of each criterion. In the present research, the panel of experts identified by the analyst team is composed of three subjects that know in depth the connection between property and credit: (a) the Head of mortgage and consumer credit issues in the Credit Department of the Italian Banking Association (ABI); (b) the CEO of a leading company specialized in the management, enhancement and disposal of NPLs with underlying real estate; and (c) the CFO of an important Investment Management Company that manages real estate funds for an Asset Under Management over 2 billion euros (SGR).

## 5. Criteria and Weights

- The financial strength, including market conditions, financial indicators, stress analysis, and predictability of cash flows;
- The activity’s features, concerning the location, design, and conditions of the properties under construction;
- The solidity of the sponsor or the promoter, relating to the financial capacity and the willingness to promote the property, the reputation and the previous experience with similar properties and the relationships with relevant experts in the sector;
- The guarantees package, regarding the nature of the privilege, the assignment of lease contracts, and the quality of insurance coverage.

where a, b = 1, 2, 3, 4, 5 are the five criteria studied. Then, in order to translate the verbal expressions used in the pairwise comparisons between the criteria made by the group of experts into mathematical language, the well-known Saaty scale is adopted (Saaty 2008). In order to facilitate the pairwise comparison in the input phase, an easy-to-read model is set up, consisting of bars and cursors whose scrolling is associated with a certain value; in this way, the comparisons allow to build the matrix of the criteria, which is capable of determining the weights of each criterion represented by a pie chart (see Figure 1).“With the aim of financing, refinancing and restructuring the debt that is derived from the investment properties, between the criterionaand the criterionbwhich of them is the most important?”

## 6. Normalization and Determination of the Best Alternative

- -
- distributive normalization$$p{n}_{i,j}=\frac{{p}_{i,j}}{\sqrt{{{\displaystyle \sum}}_{j=1\xf7m}{x}_{i,j}{}_{}^{2}}}$$
- -
- ideal normalization$$p{n}_{i,j}=\frac{{p}_{i,j}}{operator\left({x}_{i,j}\right)}$$

^{+}) and the negative ideal solution (A

^{−}) are determined, by collecting the best and the worst performances on each criterion and by assuming an absolute ideal and anti-ideal point. The identification of the positive ideal solution (A

^{+}) and the negative ideal solution (A

^{−}) collecting the best and the worst performances on each criterion leads to get:

## 7. Case Study

- First Scenario (Scenario n.1 of Table 8): is summarized by the wording “MIN t”—which stands for
**MIN**imization of**T**ime—and consists in the most practicable reduction Variation of Debt Repayment Period. - Second Scenario (Scenario n.2 of Table 8): is summarized by the wording “MED t-€”—which stands for
**MED**ium**T**ime and further debt in euros (EUR)—and is a compromise scenario between the one that is aimed at the minimization of Variation of Debt Repayment Period and the one that is aimed at the maximization of the Further Debt. - Third Scenario (Scenario n.3 of Table 8): is summarized by the wording “MAX €”—which stands for
**MAX**imization of the further debt in euros (EUR)—and represents the maximum possible Further Debt.

_{IPRE,risk}index obtained are: in the “MED t-€” scenario the highest for two models out of four (see models 3 and 4), for the remaining perform better respectively the “MIN-t” scenario (Model 1) and the “MAX-€” (Model 2). For these reasons appears preferable the “MED t-€” scenario which is recommended by the DM as the best compromise solution. Therefore the “MED t-€” alternative represents a compromise solution between the “MAX €” scenario, which is characterized by the containment of the debt relief at the expense of the variation of the debt repayment and the financial covenants, and the “MIN t” one, which is characterized by the reduction of the variation of the debt repayment, but by a high debt relief with the improvements in the financial covenants compared to the other scenarios. The result obtained does not depend on the positive and the negative ideal solution that is used for the comparison with the alternatives; therefore, this guarantees the reliability of the ${I}_{IPRE,risk}$ proposed.

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

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Study | MCDA Technique Applied and Goal |
---|---|

Doumpos and Zopounidis (2004) | Applied to a real-world case involving the credit risk assessment issue and the comparison with the major well-known classification techniques, the PROMETHEE techniques were employed for the pairwise comparisons and to develop a suitable index for alternatives classification. |

Tomić-Plazibat et al. (2006) | PROMETHEE method for the final ranking of 500 Croatian firms and the AHP to determine the significance of the eleven criteria between the profitability, the liquidity and the solvency of the firms. |

Wu et al. (2012) | Evaluation of six urban commercial bank credit risk in China by applying AHP for group decision-making model and revised TOPSIS model. |

Ferreira et al. (2014) | Employing an AHP based methodology in the credit scoring system employed by one of the major banks in Portugal, this study proposes a methodological framework in order to adjust trade-offs among criteria considered and provide decision makers with a more transparent mortgage risk evaluation system. |

Ferreira and Santos (2016) | The results that derive from the application of the AHP, Delphi Method, and Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) in the trade-off readjustments operations during the credit risk analysis of the mortgage loans are compared in order to verify the advantages and limits of each of them. |

Wu et al. (2016) | An extended TOPSIS technique called R-TOPSIS is used with the grey relational degree to assess credit risk of 11 Chinese public banks and the AHP is applied in order to determine the weights of indexes considered in the group decision making process. |

Khalili and Khalilpour (2016) | The customers are classified according to the main collected indicators for credit decision based on the AHP and then their rank is performed by the TOPSIS method. |

Bahabadi and Mohammadi (2016) | The Refah bank credit customers are ranked through the use of AHP and TOPSIS: the first one in order to determine the weights of each indicator considered, and the second one for ranking the results. |

de Lima Silva et al. (2018) | The PROMETHEE application with the use of linear programming for parameter inference is aimed to calibrate the decisions and avoid the subjectivity incorporated by the mostly used rating systems with reference to the sovereign credit risk issues. |

Shen et al. (2018) | The credit risk of a financial enterprise’s potential strategic partners is evaluated in order to identify the most suitable one among a set of them by adopting a fuzzy TOPSIS new method. |

Yang et al. (2019) | Development of a hybrid multi-criteria technique combining grey relational analysis (GRA), the Decision-Making Trial and Evaluation Laboratory technique (DEMATEL), analytic network process (ANP), and the TOPSIS for quantifying data and, thereby, to establish a reasonable green credit evaluation mechanism for banks. |

Mehdiyev (2019) | Fuzzy TOPSIS technique based on fuzzy sets in determining the multiple criteria selection issues for the credit scoring process. |

Froelich and Hajek (2020) | Improvement of multi-criteria group decision-making technique for bank credit risk assessment combined with traditional TOPSIS approach to ranking alternatives |

Gaganis et al. (2020) | A Stochastic Multiobjective Acceptability Analysis -Fuzzy-FlowSort model, the first variant of PROMETHEE-based sorting methods, is employed in order to manage the credit risks of 55 European banks. |

Hassanzadeh and Valmohammadi (2021) | The assessment and rank of the Tehran stock market’s banks is performed by combining the fuzzy AHP, for calculating the weights of each criteria considered, with the TOPSIS techniques to rank the banks. The results support the decisions of both banks owners and investors to achieve their goals. |

Phase | Description | Subject | Analysis |
---|---|---|---|

(i) | definition of the goal | Analyst team and DM | Brainstorming |

(ii) | construction of the matrix of the criteria | Analyst team and DM | Brainstorming & AHP |

(iii) | determination of the local weights of the criteria | Stakeholders | AHP |

(iv) | normalization of the weights and construction of the alternatives’ matrix | DM, Stakeholders and Analyst team | PROMETHEE & TOPSIS |

(v) | computation of the global weights | Analyst team | TOPSIS |

(vi) | aggregation of the weights and identification of the best alternative | Analyst team | TOPSIS |

n. | Criteria | Description | Formula |
---|---|---|---|

1 | Interest Cover Ratio (ICR) | It measures the debtor’s safety margin to pay interest on their debt over a specified period. The ratio is calculated by dividing the debtor’s EBIT by the interest expense of the same in the period considered. The lower the ratio, the more the debtor has a reduced safety margin. | $ICR=\frac{EBIT}{\mathrm{Interest}\mathrm{Expense}}$ |

2 | Debt Service Cover Ratio (DSCR) | It represents the property’s ability to produce sufficient cash flow to cover debt payments (including the lease). The higher the ratio, the easier it will be to get the loan. | $DSCR=\frac{EBIT}{\mathrm{Total}\mathrm{Debt}\mathrm{Service}}$ |

3 | Loan to Value Ratio (LTV) | It consists in determining the ratio between the mortgage amount (MA) and the value of the property considered (APV). In general, the lower the ratio the greater the possibility that the loan will be granted. | $LTV=\frac{MA}{APV}$ |

4 | Debt relief (s) | It expresses the amount of debt reduction by considering the initial and the further one | $s=1-\frac{\mathrm{Initial}\mathrm{debt}}{\mathrm{Further}\mathrm{debt}}$ |

5 | Variation of Debt Repayment Period ($\Delta t)$ | It refers to the number of years that differentiate the payment period of the further debt and that of the initial debt | $\Delta t={t}_{further}-{t}_{initial}$ |

n. | Criteria | ABI | NPLs | SGR | Mean |
---|---|---|---|---|---|

1 | $ICR$ | 8% | 4% | 13% | 8% |

2 | $DSCR$ | 45% | 9% | 35% | 30% |

3 | $LTV$ | 17% | 36% | 7% | 20% |

4 | $s$ | 20% | 44% | 42% | 35% |

5 | $\Delta t$ | 10% | 8% | 3% | 7% |

CR | 9.3% | 8.3% | 9.3% | 9.0% |

n. | Criteria | u.m. | Operator | Range | Preference Threshold | Veto Threshold |
---|---|---|---|---|---|---|

1 | $ICR$ | % | MAX | NA | NA | >170% |

2 | $DSCR$ | % | MAX | NA | NA | >100% |

3 | $LTV$ | % | MIN | 20–90% | <20% | >90% |

4 | $s$ | % | MIN | NA | NA | >40% |

5 | $\Delta t$ | years | MIN | 0—15 | <0 | >15 years |

Normalization Type | A^{+}/A^{−}: Best and Worst Performance | A^{+}/A^{−}: Absolute Ideal and Anti-Ideal Point |
---|---|---|

Distributive | Model 1 | Model 2 |

Ideal | Model 3 | Model 4 |

ID | Main Use | Gross Area (m²) | Gross Building Area (m²) | Annual Rent (EUR) |
---|---|---|---|---|

1 | Hotel and restaurant | ~8700 | ~13,200 | ~600,000 |

2 | Office and retail | ~1200 | ~1500 | ~350,000 |

3 | Residential areas | N.A. | ~3600 | N.A. |

Total Gross Area (m^{2}) | ~9900 | ~18,300 | ~950,000 |

n | Scenario | Further Debt (EUR) | $\mathit{s}$ | ${\mathit{t}}_{\mathit{f}\mathit{u}\mathit{r}\mathit{t}\mathit{h}\mathit{e}\mathit{r}}\left(\mathbf{Years}\right)$ | $\mathbf{\Delta}\mathit{t}\left(\mathbf{Years}\right)$ | $\mathit{I}\mathit{C}\mathit{R}$ | $\mathit{D}\mathit{S}\mathit{C}\mathit{R}$ | $\mathit{L}\mathit{T}\mathit{V}$ |
---|---|---|---|---|---|---|---|---|

1 | MIN t | 5,500,000 | 35% | 10 | 5 | 571% | 100% | 58% |

2 | MED t-€ | 6,500,000 | 24% | 15 | 10 | 560% | 104% | 62% |

3 | MAX € | 8,000,000 | 6% | 20 | 15 | 467% | 106% | 72% |

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## Share and Cite

**MDPI and ACS Style**

Locurcio, M.; Tajani, F.; Morano, P.; Anelli, D.; Manganelli, B.
Credit Risk Management of Property Investments through Multi-Criteria Indicators. *Risks* **2021**, *9*, 106.
https://doi.org/10.3390/risks9060106

**AMA Style**

Locurcio M, Tajani F, Morano P, Anelli D, Manganelli B.
Credit Risk Management of Property Investments through Multi-Criteria Indicators. *Risks*. 2021; 9(6):106.
https://doi.org/10.3390/risks9060106

**Chicago/Turabian Style**

Locurcio, Marco, Francesco Tajani, Pierluigi Morano, Debora Anelli, and Benedetto Manganelli.
2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators" *Risks* 9, no. 6: 106.
https://doi.org/10.3390/risks9060106